diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..97765604ac8a074b9e8e623333bd668ed963d3c1
--- /dev/null
+++ b/README.md
@@ -0,0 +1,80 @@
+---
+tags:
+- espnet
+- audio
+- automatic-speech-recognition
+- speech-translation
+language: multilingual
+datasets:
+- owsm_v3.1
+license: cc-by-4.0
+---
+
+## OWSM: Open Whisper-style Speech Model
+
+[OWSM](https://arxiv.org/abs/2309.13876) is an Open Whisper-style Speech Model from [CMU WAVLab](https://www.wavlab.org/). It reproduces Whisper-style training using publicly available data and an open-source toolkit [ESPnet](https://github.com/espnet/espnet).
+
+Our demo is available [here](https://huggingface.co/spaces/pyf98/OWSM_v3_demo).
+
+OWSM v3.1 is an improved version of OWSM v3. It significantly outperforms OWSM v3 in almost all evaluation benchmarks.
+We do not include any new training data. Instead, we utilize a state-of-the-art speech encoder, [E-Branchformer](https://arxiv.org/abs/2210.00077).
+
+This is a base size model which has 101M parameters and is trained on 180k hours of public speech data.
+Specifically, it supports the following speech-to-text tasks:
+- Speech recognition
+- Any-to-any-language speech translation
+- Utterance-level alignment
+- Long-form transcription
+- Language identification
+
+
+### Citing OWSM, Branchformers and ESPnet
+
+```BibTex
+@article{peng2023owsm,
+  title={Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data},
+  author={Yifan Peng and Jinchuan Tian and Brian Yan and Dan Berrebbi and Xuankai Chang and Xinjian Li and Jiatong Shi and Siddhant Arora and William Chen and Roshan Sharma and Wangyou Zhang and Yui Sudo and Muhammad Shakeel and Jee-weon Jung and Soumi Maiti and Shinji Watanabe},
+  journal={arXiv preprint arXiv:2309.13876},
+  year={2023}
+}
+@inproceedings{peng23b_interspeech,
+  author={Yifan Peng and Kwangyoun Kim and Felix Wu and Brian Yan and Siddhant Arora and William Chen and Jiyang Tang and Suwon Shon and Prashant Sridhar and Shinji Watanabe},
+  title={{A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks}},
+  year=2023,
+  booktitle={Proc. INTERSPEECH 2023},
+  pages={2208--2212},
+  doi={10.21437/Interspeech.2023-1194}
+}
+@inproceedings{kim2023branchformer,
+  title={E-branchformer: Branchformer with enhanced merging for speech recognition},
+  author={Kim, Kwangyoun and Wu, Felix and Peng, Yifan and Pan, Jing and Sridhar, Prashant and Han, Kyu J and Watanabe, Shinji},
+  booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
+  pages={84--91},
+  year={2023},
+  organization={IEEE}
+}
+@InProceedings{pmlr-v162-peng22a,
+  title = 	 {Branchformer: Parallel {MLP}-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding},
+  author =       {Peng, Yifan and Dalmia, Siddharth and Lane, Ian and Watanabe, Shinji},
+  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
+  pages = 	 {17627--17643},
+  year = 	 {2022},
+  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
+  volume = 	 {162},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {17--23 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v162/peng22a/peng22a.pdf},
+  url = 	 {https://proceedings.mlr.press/v162/peng22a.html},
+  abstract = 	 {Conformer has proven to be effective in many speech processing tasks. It combines the benefits of extracting local dependencies using convolutions and global dependencies using self-attention. Inspired by this, we propose a more flexible, interpretable and customizable encoder alternative, Branchformer, with parallel branches for modeling various ranged dependencies in end-to-end speech processing. In each encoder layer, one branch employs self-attention or its variant to capture long-range dependencies, while the other branch utilizes an MLP module with convolutional gating (cgMLP) to extract local relationships. We conduct experiments on several speech recognition and spoken language understanding benchmarks. Results show that our model outperforms both Transformer and cgMLP. It also matches with or outperforms state-of-the-art results achieved by Conformer. Furthermore, we show various strategies to reduce computation thanks to the two-branch architecture, including the ability to have variable inference complexity in a single trained model. The weights learned for merging branches indicate how local and global dependencies are utilized in different layers, which benefits model designing.}
+}
+@inproceedings{watanabe2018espnet,
+  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
+  title={{ESPnet}: End-to-End Speech Processing Toolkit},
+  year={2018},
+  booktitle={Proceedings of Interspeech},
+  pages={2207--2211},
+  doi={10.21437/Interspeech.2018-1456},
+  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
+}
+```
diff --git a/data/token_list/bpe_unigram50000/bpe.model b/data/token_list/bpe_unigram50000/bpe.model
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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d6327da127e870bcb8c737dceb3bd47ccbce63da74ddb094f64afe313d68c8c
+size 1041297
diff --git a/exp/s2t_stats_raw_bpe50000/train/feats_stats.npz b/exp/s2t_stats_raw_bpe50000/train/feats_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ef4b5e465110edf32eec024cf2427eedd677f5733bb87d6b2131e6984a6e13f
+size 1402
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/RESULTS.md b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/RESULTS.md
new file mode 100644
index 0000000000000000000000000000000000000000..ee95e1ea7d72ebb9b32bc93fd5cc29b0c8d67706
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/RESULTS.md
@@ -0,0 +1,9 @@
+<!-- Generated by scripts/utils/show_asr_result.sh -->
+# RESULTS
+## Environments
+- date: `Thu Jan 18 13:29:51 CST 2024`
+- python version: `3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0]`
+- espnet version: `espnet 202308`
+- pytorch version: `pytorch 1.13.1`
+- Git hash: `e5c058e28bd8db071b19cb45688165b7013c0938`
+  - Commit date: `Tue Dec 26 21:55:42 2023 -0600`
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c6a3d53bfbf7429df0f5322b40f8bd8e2ed498b
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
@@ -0,0 +1,50257 @@
+config: conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml
+print_config: false
+log_level: INFO
+drop_last_iter: false
+dry_run: false
+iterator_type: sequence
+valid_iterator_type: null
+output_dir: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000
+ngpu: 1
+seed: 42
+num_workers: 8
+num_att_plot: 0
+dist_backend: nccl
+dist_init_method: file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_2e5d3388-5a62-4d9b-abf3-231f106e2517
+dist_world_size: 16
+dist_rank: 0
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: slurm
+multiprocessing_distributed: true
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 45
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - acc
+    - max
+-   - valid
+    - total_count
+    - max
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: true
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 15000
+batch_size: 256
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/s2t_stats_raw_bpe50000/splits12/speech_shape
+- exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe
+- exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe
+- exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe
+valid_shape_file:
+- exp/s2t_stats_raw_bpe50000/valid/speech_shape
+- exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe
+- exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe
+- exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe
+batch_type: unsorted
+valid_batch_type: null
+fold_length:
+- 80000
+- 150
+- 150
+- 150
+sort_in_batch: descending
+shuffle_within_batch: false
+sort_batch: descending
+multiple_iterator: true
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+chunk_excluded_key_prefixes: []
+train_data_path_and_name_and_type:
+-   - exp/s2t_stats_raw_bpe50000/splits12/wav.scp
+    - speech
+    - kaldi_ark
+-   - exp/s2t_stats_raw_bpe50000/splits12/text.prev
+    - text_prev
+    - text
+-   - exp/s2t_stats_raw_bpe50000/splits12/text.ctc
+    - text_ctc
+    - text
+-   - exp/s2t_stats_raw_bpe50000/splits12/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_v3/wav.scp
+    - speech
+    - kaldi_ark
+-   - dump/raw/dev_v3/text.prev
+    - text_prev
+    - text
+-   - dump/raw/dev_v3/text.ctc
+    - text_ctc
+    - text
+-   - dump/raw/dev_v3/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+exclude_weight_decay: false
+exclude_weight_decay_conf: {}
+optim: adamw
+optim_conf:
+    lr: 0.001
+    betas:
+    - 0.9
+    - 0.98
+    eps: 1.0e-06
+    weight_decay: 0.0
+scheduler: piecewiselinearwarmuplr
+scheduler_conf:
+    warmup_steps_list:
+    - 0
+    - 30000
+    - 60000
+    warmup_lr_list:
+    - 0.0
+    - 0.0001
+    - 0.001
+token_list:
+- <blank>
+- <unk>
+- <na>
+- <nospeech>
+- <abk>
+- <afr>
+- <amh>
+- <ara>
+- <asm>
+- <ast>
+- <aze>
+- <bak>
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+- <umb>
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+- <26.34>
+- <26.36>
+- <26.38>
+- <26.40>
+- <26.42>
+- <26.44>
+- <26.46>
+- <26.48>
+- <26.50>
+- <26.52>
+- <26.54>
+- <26.56>
+- <26.58>
+- <26.60>
+- <26.62>
+- <26.64>
+- <26.66>
+- <26.68>
+- <26.70>
+- <26.72>
+- <26.74>
+- <26.76>
+- <26.78>
+- <26.80>
+- <26.82>
+- <26.84>
+- <26.86>
+- <26.88>
+- <26.90>
+- <26.92>
+- <26.94>
+- <26.96>
+- <26.98>
+- <27.00>
+- <27.02>
+- <27.04>
+- <27.06>
+- <27.08>
+- <27.10>
+- <27.12>
+- <27.14>
+- <27.16>
+- <27.18>
+- <27.20>
+- <27.22>
+- <27.24>
+- <27.26>
+- <27.28>
+- <27.30>
+- <27.32>
+- <27.34>
+- <27.36>
+- <27.38>
+- <27.40>
+- <27.42>
+- <27.44>
+- <27.46>
+- <27.48>
+- <27.50>
+- <27.52>
+- <27.54>
+- <27.56>
+- <27.58>
+- <27.60>
+- <27.62>
+- <27.64>
+- <27.66>
+- <27.68>
+- <27.70>
+- <27.72>
+- <27.74>
+- <27.76>
+- <27.78>
+- <27.80>
+- <27.82>
+- <27.84>
+- <27.86>
+- <27.88>
+- <27.90>
+- <27.92>
+- <27.94>
+- <27.96>
+- <27.98>
+- <28.00>
+- <28.02>
+- <28.04>
+- <28.06>
+- <28.08>
+- <28.10>
+- <28.12>
+- <28.14>
+- <28.16>
+- <28.18>
+- <28.20>
+- <28.22>
+- <28.24>
+- <28.26>
+- <28.28>
+- <28.30>
+- <28.32>
+- <28.34>
+- <28.36>
+- <28.38>
+- <28.40>
+- <28.42>
+- <28.44>
+- <28.46>
+- <28.48>
+- <28.50>
+- <28.52>
+- <28.54>
+- <28.56>
+- <28.58>
+- <28.60>
+- <28.62>
+- <28.64>
+- <28.66>
+- <28.68>
+- <28.70>
+- <28.72>
+- <28.74>
+- <28.76>
+- <28.78>
+- <28.80>
+- <28.82>
+- <28.84>
+- <28.86>
+- <28.88>
+- <28.90>
+- <28.92>
+- <28.94>
+- <28.96>
+- <28.98>
+- <29.00>
+- <29.02>
+- <29.04>
+- <29.06>
+- <29.08>
+- <29.10>
+- <29.12>
+- <29.14>
+- <29.16>
+- <29.18>
+- <29.20>
+- <29.22>
+- <29.24>
+- <29.26>
+- <29.28>
+- <29.30>
+- <29.32>
+- <29.34>
+- <29.36>
+- <29.38>
+- <29.40>
+- <29.42>
+- <29.44>
+- <29.46>
+- <29.48>
+- <29.50>
+- <29.52>
+- <29.54>
+- <29.56>
+- <29.58>
+- <29.60>
+- <29.62>
+- <29.64>
+- <29.66>
+- <29.68>
+- <29.70>
+- <29.72>
+- <29.74>
+- <29.76>
+- <29.78>
+- <29.80>
+- <29.82>
+- <29.84>
+- <29.86>
+- <29.88>
+- <29.90>
+- <29.92>
+- <29.94>
+- <29.96>
+- <29.98>
+- <30.00>
+- ▁
+- ','
+- ▁the
+- .
+- s
+- ▁and
+- ▁of
+- ▁to
+- ▁a
+- 。
+- ▁in
+- ''''
+- ▁that
+- ▁i
+- ▁it
+- ▁was
+- en
+- e
+- t
+- ▁you
+- ▁he
+- ▁is
+- 的
+- '-'
+- ▁for
+- ▁de
+- ▁with
+- ▁be
+- n
+- d
+- ▁as
+- ▁his
+- ▁we
+- の
+- ▁on
+- 、
+- ▁die
+- ▁had
+- a
+- ▁but
+- ▁so
+- '?'
+- o
+- ▁not
+- ▁at
+- ▁have
+- ▁this
+- ▁und
+- er
+- ▁her
+- i
+- ▁they
+- m
+- ed
+- re
+- ▁she
+- ▁an
+- ▁by
+- ▁der
+- ing
+- ▁all
+- ▁are
+- ▁la
+- が
+- を
+- ▁from
+- ▁me
+- ▁which
+- ▁my
+- es
+- ▁one
+- は
+- ▁no
+- ▁there
+- 了
+- ▁him
+- ▁or
+- ▁were
+- に
+- y
+- ▁do
+- ▁what
+- r
+- ▁zu
+- ▁our
+- u
+- ▁if
+- ly
+- ▁ist
+- ▁would
+- ▁when
+- ▁their
+- ▁will
+- ▁said
+- で
+- ▁es
+- ▁like
+- ▁das
+- ▁Sie
+- ▁them
+- 在
+- ▁can
+- ▁who
+- ▁out
+- ▁know
+- te
+- ▁been
+- ▁ich
+- ▁about
+- ▁up
+- ▁I
+- '!'
+- ▁more
+- ▁man
+- ve
+- 我
+- ▁que
+- ▁un
+- 是
+- ▁sie
+- 和
+- ▁then
+- ▁your
+- ▁ein
+- ▁some
+- ▁den
+- а
+- ▁now
+- 你
+- ▁von
+- ▁nicht
+- ▁very
+- と
+- ▁has
+- ▁into
+- in
+- ▁time
+- ▁в
+- ar
+- ▁just
+- ▁не
+- ▁um
+- k
+- ▁could
+- ▁dass
+- ▁Und
+- 人
+- ▁think
+- ▁auf
+- al
+- de
+- ▁и
+- ▁war
+- ▁eine
+- ▁over
+- ▁mit
+- ▁well
+- も
+- 他
+- ▁see
+- ▁also
+- ▁wir
+- ▁other
+- ▁des
+- le
+- ▁how
+- ll
+- ▁than
+- 不
+- 啊
+- ▁these
+- ▁little
+- ▁sich
+- ▁"
+- ten
+- c
+- an
+- ▁us
+- 上
+- ▁l
+- ▁two
+- ▁any
+- 有
+- ▁don
+- ▁go
+- ▁did
+- ▁people
+- ▁only
+- е
+- na
+- ▁good
+- l
+- st
+- ▁where
+- ▁на
+- ▁el
+- ▁se
+- z
+- to
+- 中
+- ▁first
+- g
+- ta
+- ▁na
+- ▁di
+- ▁als
+- is
+- ▁come
+- ▁Ich
+- ▁much
+- ▁für
+- ne
+- ▁с
+- 我们
+- 大
+- ▁get
+- ▁here
+- ▁down
+- у
+- ▁du
+- ▁le
+- ▁что
+- から
+- ▁way
+- h
+- ▁y
+- ▁wie
+- ▁should
+- ▁before
+- ▁am
+- ▁made
+- ▁those
+- ▁after
+- 一
+- 'on'
+- ▁upon
+- ▁because
+- ▁back
+- ▁right
+- ▁haben
+- se
+- ▁great
+- 他们
+- ▁say
+- ▁going
+- です
+- 这个
+- ▁dem
+- и
+- 就
+- f
+- ▁по
+- ▁make
+- ra
+- 来
+- ▁The
+- م
+- ▁er
+- ▁its
+- ▁such
+- ▁ver
+- ▁may
+- w
+- ge
+- ▁even
+- la
+- ▁men
+- 说
+- ':'
+- ▁new
+- ▁im
+- 地
+- é
+- da
+- ch
+- ▁through
+- 呢
+- ▁long
+- な
+- me
+- ▁never
+- ▁most
+- ▁Es
+- as
+- il
+- p
+- ▁A
+- ▁must
+- '"'
+- ▁sind
+- ▁day
+- し
+- 一个
+- ▁really
+- ▁per
+- b
+- ▁came
+- ▁con
+- '1'
+- 这
+- man
+- ▁And
+- ▁life
+- ▁many
+- м
+- ▁old
+- est
+- я
+- j
+- ▁я
+- ▁yeah
+- 'no'
+- ro
+- ▁again
+- т
+- ▁things
+- ▁La
+- ▁hat
+- be
+- ma
+- ▁mu
+- ▁own
+- 她
+- den
+- ▁take
+- ci
+- ▁et
+- ▁aus
+- ▁being
+- ▁je
+- ga
+- os
+- 它
+- ni
+- we
+- ▁mr
+- ▁still
+- ir
+- ▁我
+- '2'
+- ▁last
+- か
+- 小
+- ▁too
+- ▁les
+- ▁might
+- 会
+- ▁work
+- us
+- ▁у
+- ce
+- や
+- ▁every
+- un
+- ▁We
+- 子
+- ▁ku
+- ▁al
+- 去
+- ▁за
+- ▁So
+- ы
+- at
+- ▁want
+- る
+- ی
+- ▁Die
+- 到
+- it
+- ▁went
+- ▁look
+- ▁über
+- х
+- ▁In
+- ti
+- ▁years
+- 对
+- ’
+- 吗
+- ▁это
+- ▁same
+- ▁three
+- ▁something
+- ▁da
+- do
+- li
+- ▁werden
+- ▁let
+- ▁thought
+- ▁himself
+- ▁ne
+- й
+- ▁ah
+- 被
+- ▁van
+- って
+- ▁got
+- ▁ge
+- д
+- ▁world
+- ね
+- い
+- ▁wenn
+- va
+- 好
+- ▁einen
+- 要
+- ▁thing
+- ▁while
+- ▁à
+- ▁part
+- 都
+- ه
+- с
+- 着
+- ▁year
+- el
+- ▁del
+- ▁oh
+- ▁away
+- ▁hand
+- ▁Er
+- em
+- et
+- ho
+- 下
+- た
+- ▁Das
+- ▁auch
+- ka
+- ▁ya
+- ter
+- ▁kind
+- ▁without
+- ▁place
+- ж
+- ▁put
+- 出
+- lo
+- ba
+- ment
+- ▁oder
+- ▁under
+- 没有
+- した
+- л
+- ▁вы
+- ▁una
+- ▁off
+- th
+- ▁то
+- ri
+- 里
+- ▁yet
+- ze
+- н
+- する
+- ▁diese
+- ▁another
+- ers
+- ▁why
+- je
+- ▁found
+- 从
+- 时
+- ko
+- 就是
+- sa
+- ng
+- ▁S
+- ▁shall
+- ▁aber
+- ▁sein
+- or
+- ▁а
+- 点
+- '3'
+- ur
+- ка
+- ▁tell
+- ▁god
+- お
+- ▁و
+- ▁Aber
+- ke
+- ness
+- 也
+- ▁far
+- able
+- ▁give
+- si
+- して
+- ▁though
+- ▁nothing
+- mo
+- ▁eyes
+- 手
+- ▁once
+- 家
+- x
+- ver
+- ▁always
+- ▁het
+- 日
+- ▁saw
+- ▁house
+- men
+- 用
+- di
+- ▁ha
+- この
+- ja
+- go
+- ▁wird
+- ▁ever
+- ▁een
+- ▁face
+- о
+- )
+- ▁vor
+- ▁你
+- 年
+- ▁love
+- am
+- 想
+- ung
+- ent
+- ▁uns
+- ▁home
+- he
+- на
+- ▁habe
+- ation
+- ▁head
+- ▁nach
+- ли
+- ▁nur
+- mi
+- ▁few
+- zi
+- ▁young
+- но
+- ن
+- て
+- ▁mean
+- ▁mich
+- ki
+- ▁better
+- ▁each
+- ▁einem
+- ▁find
+- ha
+- ▁si
+- ▁business
+- ш
+- ت
+- では
+- ▁left
+- ▁both
+- ton
+- ▁It
+- ▁v
+- der
+- v
+- ▁einer
+- 那
+- 为
+- wa
+- ▁night
+- ·
+- 过
+- ▁took
+- ▁(
+- ▁können
+- ▁mind
+- ▁—
+- ya
+- ▁father
+- ▁mir
+- ▁moment
+- ▁done
+- 得
+- ▁va
+- ca
+- د
+- 水
+- ▁su
+- 」
+- ▁against
+- ▁need
+- ry
+- 吧
+- 的人
+- ▁end
+- ▁yes
+- sch
+- ▁lot
+- ity
+- という
+- um
+- ▁course
+- co
+- ▁dat
+- ▁half
+- ▁between
+- 高
+- ▁Wir
+- ▁من
+- ant
+- り
+- ▁next
+- ste
+- 当
+- ▁noch
+- ▁pro
+- ▁He
+- 多
+- 做
+- ▁E
+- gen
+- 本
+- 这些
+- 事
+- でも
+- ▁می
+- ▁door
+- к
+- ▁called
+- ted
+- son
+- ▁looked
+- za
+- ▁ja
+- ul
+- 前
+- tu
+- ▁ko
+- ▁wurde
+- ye
+- ス
+- ،
+- 什么
+- ▁por
+- ▁kann
+- ▁los
+- ▁asked
+- ش
+- ▁seen
+- ▁как
+- 回
+- ла
+- 「
+- ▁дробь
+- ▁улица
+- 心
+- ▁side
+- ▁told
+- ▁does
+- ら
+- '4'
+- 者
+- lu
+- ▁son
+- ▁having
+- 生
+- ▁water
+- 分
+- ol
+- 三
+- ▁sehr
+- ▁quite
+- ▁hatte
+- 看
+- ▁El
+- ▁heard
+- ▁whole
+- ▁heart
+- ▁believe
+- '".'
+- ▁te
+- ▁met
+- ч
+- 打
+- 与
+- ▁name
+- ▁uh
+- 的时候
+- ▁ni
+- 而
+- ▁mother
+- ▁در
+- ▁ال
+- vo
+- 把
+- ▁bei
+- land
+- 能
+- ▁ob
+- ▁knew
+- ▁о
+- ▁op
+- ▁ma
+- ▁different
+- ▁around
+- ▁best
+- ▁به
+- ▁call
+- 可以
+- ▁mi
+- ▁enough
+- ▁il
+- ▁second
+- ▁para
+- く
+- 行
+- 自己
+- ▁par
+- 性
+- ie
+- ▁point
+- ▁seemed
+- ю
+- ます
+- 后
+- ▁ba
+- ▁set
+- ▁four
+- 所
+- ました
+- ة
+- ▁pas
+- ▁mrs
+- so
+- には
+- ▁woman
+- ia
+- ven
+- ▁room
+- ▁да
+- 山
+- ting
+- その
+- ・
+- bo
+- S
+- ▁five
+- ▁che
+- ion
+- 이
+- ▁high
+- 走
+- ▁所以
+- ▁sure
+- ▁但是
+- ▁durch
+- 还
+- '5'
+- ▁hundred
+- ▁country
+- ▁light
+- ▁sir
+- та
+- ▁use
+- ▁hier
+- ▁anything
+- ▁sa
+- г
+- ▁к
+- ▁actually
+- 现在
+- ad
+- by
+- ▁bir
+- ku
+- ▁он
+- ▁quarter
+- ▁wa
+- ▁po
+- ó
+- ▁при
+- ▁almost
+- ▁про
+- ▁days
+- ▁от
+- ▁他
+- 天
+- ken
+- ▁help
+- 最
+- ▁care
+- ▁sort
+- ر
+- 您
+- ▁dieser
+- ▁Der
+- po
+- 特
+- id
+- 新
+- ▁L
+- ▁ab
+- ですね
+- ▁gibt
+- ▁soon
+- ler
+- 国
+- ▁com
+- ▁з
+- ▁Be
+- 我的
+- ▁white
+- ▁alle
+- ▁dann
+- du
+- ling
+- ▁small
+- 力
+- ي
+- less
+- ▁together
+- ▁fact
+- im
+- ▁since
+- ▁money
+- ak
+- ▁мы
+- 很
+- à
+- 个
+- 你的
+- ▁qui
+- ▁doing
+- さん
+- hi
+- 更
+- ▁until
+- 给
+- ine
+- 斯
+- ▁talk
+- ▁question
+- ▁nor
+- 老
+- き
+- ty
+- ▁twenty
+- ▁didn
+- ▁used
+- ik
+- vi
+- ▁bit
+- ▁hard
+- ▁miss
+- zu
+- ▁big
+- ▁full
+- ▁Le
+- ▁Re
+- ▁yn
+- ▁De
+- ist
+- よ
+- 非常
+- au
+- ▁gave
+- 不是
+- ▁morning
+- さ
+- з
+- ▁mehr
+- ов
+- 将
+- ку
+- ▁keep
+- pa
+- ut
+- 可能
+- ▁lord
+- ▁rest
+- ▁number
+- ▁An
+- que
+- ▁real
+- 가
+- ▁words
+- ▁began
+- and
+- ки
+- 呀
+- ▁ik
+- ▁immer
+- ▁however
+- ▁hands
+- ▁open
+- ▁king
+- 头
+- ▁bin
+- ▁ب
+- A
+- ▁lo
+- bi
+- ▁looking
+- ▁раз
+- ▁pe
+- led
+- ل
+- ▁па
+- ▁c
+- п
+- ig
+- р
+- ▁turned
+- ▁feel
+- ▁En
+- 那个
+- ▁waren
+- 可
+- lar
+- ic
+- ▁і
+- ▁felt
+- ▁так
+- 比
+- ▁zum
+- ▁bu
+- ها
+- 再
+- ▁في
+- ▁wirklich
+- 像
+- в
+- 外
+- ▁etwas
+- 开始
+- 已经
+- 长
+- ちょっと
+- ▁poor
+- ▁pour
+- ▁D
+- ▁power
+- 或
+- 名
+- ▁word
+- ▁among
+- gi
+- 度
+- ▁myself
+- ▁children
+- ▁during
+- ا
+- ▁whom
+- ▁ka
+- しました
+- ▁这
+- ▁lady
+- ▁large
+- ▁matter
+- ▁death
+- ▁Ver
+- ▁Ja
+- ▁vi
+- pe
+- ▁true
+- '6'
+- ▁certain
+- ate
+- ом
+- kan
+- nt
+- ▁state
+- ▁pa
+- 并
+- ь
+- ▁till
+- ▁که
+- ▁dans
+- ▁person
+- 月
+- 金
+- ▁present
+- ▁general
+- les
+- ▁tun
+- ▁O
+- tion
+- ă
+- 向
+- ▁boy
+- 因为
+- ▁live
+- ▁case
+- ▁est
+- ▁brought
+- 以
+- ▁black
+- 成
+- 又
+- 你们
+- ek
+- ▁order
+- ▁dis
+- oj
+- ▁rather
+- mu
+- だ
+- ▁Se
+- 太
+- ▁voice
+- ▁w
+- ▁все
+- ▁taken
+- zo
+- 道
+- ▁Menschen
+- 先
+- jo
+- ть
+- ▁Wenn
+- 让
+- ▁perhaps
+- ▁given
+- ない
+- 内
+- ▁hear
+- ▁already
+- ju
+- 所以
+- ся
+- ▁une
+- dy
+- ▁important
+- ▁girl
+- ▁human
+- ▁show
+- 发
+- ▁вот
+- 这种
+- ▁ihre
+- ▁را
+- ▁C
+- wi
+- ▁hij
+- á
+- ▁ihr
+- ▁B
+- ▁qu
+- ▁ihn
+- ▁K
+- ▁company
+- lan
+- ▁meine
+- ▁strong
+- ren
+- ▁thousand
+- the
+- った
+- га
+- ▁seine
+- す
+- ▁air
+- ▁coming
+- ▁But
+- 面
+- ▁friend
+- ▁--
+- ben
+- ▁within
+- ▁round
+- 海
+- 方
+- ▁Li
+- ▁change
+- ва
+- ji
+- ard
+- ▁six
+- み
+- 二
+- ai
+- ▁often
+- ▁everything
+- ▁sea
+- ع
+- 没
+- ▁along
+- nie
+- ▁sent
+- ▁Ein
+- ▁whether
+- 他的
+- '7'
+- ▁wissen
+- 无
+- う
+- 这是
+- とか
+- 尔
+- per
+- ▁estas
+- 但
+- ▁но
+- ieren
+- ▁This
+- ▁car
+- ive
+- ▁themselves
+- ب
+- 车
+- 口
+- ▁forward
+- 入
+- 学
+- ▁co
+- ▁N
+- ado
+- ▁Na
+- 西
+- 何
+- ▁others
+- ▁za
+- í
+- つ
+- го
+- ▁我们
+- ▁idea
+- 开
+- ▁keur
+- س
+- ▁across
+- ▁dear
+- ▁stood
+- ▁school
+- ▁那
+- ▁understand
+- ▁sagte
+- ▁making
+- ▁dan
+- ck
+- bu
+- ▁family
+- ▁fire
+- ▁nature
+- ▁near
+- ▁из
+- 于
+- ем
+- もう
+- ▁times
+- да
+- ▁herself
+- ▁public
+- ▁niet
+- ▁b
+- ▁Zeit
+- ▁read
+- ▁cannot
+- ▁pre
+- ▁hope
+- 花
+- ▁turn
+- ▁city
+- ル
+- ▁این
+- cu
+- ▁würde
+- è
+- ▁است
+- ▁zijn
+- ger
+- 死
+- ▁از
+- sta
+- ны
+- ▁viel
+- ▁li
+- ner
+- ▁women
+- ▁gone
+- ▁sagen
+- ster
+- ▁non
+- ▁sur
+- 听
+- ▁然后
+- fe
+- ▁ser
+- ▁means
+- ▁thus
+- いい
+- ▁tu
+- ▁child
+- ▁least
+- ق
+- ▁sehen
+- ▁T
+- ▁nu
+- 化
+- ▁pretty
+- aba
+- ▁ki
+- ты
+- ▁sense
+- ▁says
+- ada
+- ▁أ
+- fa
+- 工作
+- ので
+- ▁reason
+- 这样
+- ▁einfach
+- ▁keine
+- ▁behind
+- 日本
+- ▁この
+- ▁wife
+- ▁Il
+- T
+- E
+- いた
+- che
+- ▁voor
+- ▁short
+- ▁bi
+- 一些
+- ▁body
+- ▁indeed
+- 先生
+- ▁ce
+- ▁feet
+- wo
+- 一样
+- '8'
+- ▁M
+- 就像
+- ▁wo
+- 还是
+- 德
+- نا
+- ▁до
+- ▁Ma
+- ▁sat
+- ▁continue
+- ま
+- ts
+- ▁unter
+- age
+- q
+- ▁ask
+- 儿
+- ▁U
+- ▁close
+- ить
+- ம்
+- ▁several
+- 放
+- ▁won
+- ▁om
+- ية
+- ▁whose
+- не
+- ▁market
+- 之
+- ▁red
+- ▁possible
+- C
+- up
+- 你知道
+- ▁amb
+- イ
+- わ
+- 部
+- ▁leave
+- tes
+- など
+- 는
+- として
+- ▁less
+- ile
+- ク
+- ▁become
+- ля
+- ▁later
+- 重
+- 拉
+- ان
+- 吃
+- ▁known
+- ▁Un
+- ف
+- ▁growth
+- zen
+- ä
+- P
+- ft
+- ▁Ge
+- ▁friends
+- و
+- lich
+- ▁either
+- ny
+- gu
+- ▁story
+- ▁probably
+- 体
+- ous
+- ana
+- ▁ihm
+- ー
+- ▁Sch
+- ▁able
+- ▁thou
+- ▁else
+- ▁wieder
+- io
+- 白
+- ц
+- 跟
+- 万
+- ▁run
+- 光
+- 位
+- ▁diesem
+- ▁bad
+- б
+- ▁alone
+- 但是
+- ات
+- ▁そして
+- ▁gut
+- ز
+- ▁start
+- 法
+- ▁past
+- 路
+- ▁itself
+- iert
+- aj
+- ▁în
+- š
+- ▁Art
+- ле
+- ▁jetzt
+- I
+- ran
+- і
+- ▁therefore
+- ア
+- ▁free
+- ▁sometimes
+- ▁passed
+- ў
+- ▁speak
+- mos
+- 数
+- 问题
+- yo
+- ▁book
+- ▁line
+- ▁andere
+- ▁mal
+- chen
+- ми
+- ▁Bu
+- ▁above
+- ▁became
+- ▁Al
+- bar
+- 我们的
+- ▁wanted
+- des
+- ال
+- way
+- 利
+- era
+- ك
+- ▁bo
+- ▁Je
+- ▁cried
+- '9'
+- ban
+- ▁today
+- tro
+- ▁au
+- 今
+- 고
+- ▁getting
+- 别
+- ▁okay
+- ley
+- ▁kaj
+- ham
+- 马
+- ん
+- fer
+- sten
+- 지
+- ▁saying
+- ▁No
+- ▁bring
+- ▁zwei
+- 四
+- ated
+- 需要
+- ▁p
+- 田
+- ح
+- ▁weil
+- ▁las
+- ▁z
+- mer
+- gel
+- ▁és
+- 世界
+- '10'
+- ber
+- 孩子
+- んだ
+- ет
+- end
+- 都是
+- ▁ta
+- ▁그
+- だった
+- 王
+- リ
+- ▁form
+- ▁dead
+- ▁hour
+- ▁future
+- ма
+- ▁bis
+- ▁machen
+- 物
+- پ
+- лі
+- ▁earth
+- ▁zur
+- 自
+- nd
+- ful
+- カ
+- ▁là
+- ▁government
+- ▁Do
+- 钱
+- ▁،
+- му
+- nu
+- ▁play
+- ▁remember
+- ▁land
+- ▁bed
+- ▁vous
+- ere
+- ▁plus
+- 知道
+- 两
+- 带
+- 真
+- ▁fast
+- ish
+- ning
+- 快
+- 身
+- ▁fell
+- ▁dark
+- ▁held
+- 时间
+- rs
+- 定
+- им
+- ▁As
+- ▁started
+- ▁lost
+- op
+- 主
+- ▁hi
+- ▁como
+- 爱
+- ▁ت
+- ▁answer
+- ▁Was
+- 一下
+- ▁bar
+- ▁один
+- tor
+- します
+- gan
+- ▁cost
+- ная
+- ▁fa
+- nya
+- ang
+- ▁сто
+- ой
+- ▁early
+- ari
+- min
+- ご
+- ▁müssen
+- su
+- ▁ground
+- lin
+- ▁answered
+- ▁ago
+- red
+- ▁anderen
+- ▁lay
+- 明
+- ▁Vi
+- ▁ў
+- ▁Ba
+- con
+- ▁stand
+- 正
+- ▁但
+- 安
+- 知
+- 加
+- ▁под
+- 合
+- ▁selbst
+- 谁
+- 起
+- han
+- ▁они
+- ▁em
+- ра
+- ▁Ha
+- ç
+- ']'
+- tt
+- vu
+- 五
+- ▁truth
+- ▁Ka
+- 美
+- ▁mar
+- ot
+- ド
+- ▁мне
+- 看到
+- ▁alles
+- 流
+- ▁denke
+- ▁try
+- ac
+- iz
+- ait
+- 人们
+- ker
+- cy
+- ru
+- ▁clear
+- ко
+- 同
+- ▁Leute
+- ▁working
+- 克
+- ▁Ne
+- ▁front
+- ▁towards
+- ▁replied
+- ト
+- port
+- ▁taking
+- ▁Ihnen
+- ▁viele
+- 火
+- 然后
+- ▁же
+- 怎么
+- her
+- え
+- র
+- ▁further
+- 等
+- 那么
+- day
+- 的话
+- ин
+- 叫
+- ▁Dinge
+- ▁fine
+- ▁ad
+- 市
+- வ
+- ▁kon
+- ▁ну
+- ам
+- 只是
+- om
+- ▁zurück
+- ▁trying
+- ."
+- tan
+- ism
+- ▁law
+- O
+- ▁town
+- ▁было
+- ▁для
+- 感
+- ▁fear
+- ff
+- ▁Da
+- 门
+- lle
+- ez
+- B
+- 全
+- 气
+- これ
+- にも
+- ▁его
+- ini
+- ern
+- まで
+- ire
+- 目
+- ▁ты
+- 真的
+- ラ
+- ;
+- ▁thank
+- ▁со
+- ▁table
+- are
+- ▁ye
+- ؟
+- ▁Y
+- 使用
+- 中国
+- ▁есть
+- ▁third
+- ▁church
+- ▁dieses
+- ▁expect
+- ú
+- 作
+- います
+- ex
+- ▁Du
+- ▁art
+- 平
+- ▁Sa
+- ▁diesen
+- ный
+- ▁sun
+- ▁doubt
+- ▁бы
+- ▁return
+- qui
+- ▁system
+- ▁pay
+- ap
+- ▁sin
+- ▁era
+- ▁[
+- ü
+- ance
+- のは
+- ▁plan
+- 도
+- め
+- ▁maybe
+- ▁soul
+- ர்
+- コ
+- 野
+- ▁dr
+- ita
+- tre
+- ina
+- ло
+- ▁muss
+- ▁الم
+- sen
+- 通
+- ▁aan
+- 请
+- ▁sound
+- ring
+- ▁거
+- ند
+- one
+- ▁Wie
+- 南
+- ▁beautiful
+- ▁seems
+- 見
+- ах
+- ▁week
+- ▁brother
+- ▁два
+- ▁با
+- ▁kept
+- sha
+- 時
+- elle
+- あ
+- ary
+- gar
+- ▁certainly
+- ▁continued
+- ▁comes
+- ▁P
+- ▁top
+- ▁position
+- ча
+- 어
+- '20'
+- sto
+- ▁там
+- ит
+- ▁gu
+- ▁нас
+- K
+- ▁suddenly
+- ▁seven
+- ▁thinking
+- ▁А
+- している
+- 에
+- ab
+- ▁wish
+- ▁talking
+- ▁ready
+- ▁Mi
+- ▁master
+- F
+- ▁service
+- 自己的
+- ন
+- ▁evening
+- しています
+- ▁view
+- ▁cap
+- ▁thy
+- 那些
+- ▁nie
+- ک
+- ▁ما
+- sel
+- ▁subject
+- 通过
+- ▁Per
+- '...'
+- 应该
+- ▁Ta
+- ▁eines
+- ▁cold
+- ure
+- és
+- pi
+- ▁dé
+- 文
+- ▁longer
+- ▁letter
+- ▁cause
+- ▁Ro
+- ▁happened
+- kin
+- 生活
+- ды
+- ▁deep
+- ▁nous
+- ▁если
+- dan
+- ▁common
+- ding
+- 立
+- 使
+- ▁happy
+- ▁post
+- van
+- ▁zij
+- bel
+- でした
+- ン
+- tar
+- ▁дом
+- ▁ca
+- んで
+- ▁low
+- ▁arm
+- 区
+- ▁gi
+- ▁cu
+- 动
+- ▁pass
+- ти
+- ни
+- то
+- ble
+- ▁interest
+- ▁dit
+- 格
+- ah
+- ▁schon
+- cia
+- ix
+- 这么
+- ê
+- igen
+- ▁ihnen
+- 只
+- ▁john
+- 米
+- une
+- ▁Ihre
+- ▁manner
+- ▁living
+- ies
+- ▁To
+- 它们
+- 声
+- து
+- ama
+- ▁natural
+- ある
+- 送
+- いて
+- ▁hold
+- ▁thirty
+- ▁eight
+- ▁「
+- ▁б
+- ▁Welt
+- mp
+- 研究
+- ▁spirit
+- fi
+- ▁months
+- された
+- ▁river
+- ▁அ
+- ين
+- ▁unsere
+- 出来
+- ▁husband
+- ▁watch
+- G
+- ক
+- ▁capital
+- ite
+- sh
+- 店
+- 不会
+- mal
+- ris
+- 问
+- ▁road
+- 意
+- 住
+- ▁rose
+- ▁fi
+- los
+- ▁guess
+- val
+- 总
+- ▁Ab
+- 的是
+- ▁seeing
+- ▁support
+- 喜欢
+- ня
+- ной
+- ▁result
+- ub
+- ▁Ni
+- 有一个
+- 我想
+- 原
+- ▁You
+- ▁value
+- во
+- ط
+- ▁deal
+- ▁meet
+- ▁results
+- 号
+- ▁wurden
+- 長
+- bra
+- 今天
+- tic
+- 木
+- 不能
+- ▁street
+- 次
+- 希望
+- ▁vol
+- 情
+- 给我
+- ▁green
+- ар
+- ▁denn
+- ▁social
+- ▁hours
+- 或者
+- 科
+- ▁returned
+- ▁cut
+- ил
+- ▁experience
+- vis
+- ian
+- ون
+- len
+- ▁sub
+- ungen
+- ▁strange
+- ud
+- 元
+- ▁werde
+- 戦
+- кі
+- いる
+- ая
+- 是的
+- ▁whatever
+- ▁einige
+- ▁sight
+- 之前
+- ▁mein
+- 公司
+- 半
+- 的事情
+- ▁drei
+- 言
+- ▁doesn
+- 公
+- ischen
+- car
+- ▁その
+- ▁Aus
+- ей
+- те
+- 也是
+- 是一个
+- ▁să
+- ▁fair
+- ▁У
+- ▁terms
+- ▁food
+- 気
+- ▁minutes
+- ef
+- 的东西
+- ▁seiner
+- ▁아
+- 能够
+- ль
+- خ
+- ▁ex
+- ons
+- sche
+- ▁Te
+- ▁court
+- ▁process
+- ナ
+- tel
+- ▁account
+- 发现
+- 该
+- 品
+- 林
+- ▁received
+- ▁blood
+- ▁меня
+- ных
+- ence
+- ag
+- D
+- ▁followed
+- ▁Ra
+- ▁ho
+- ▁Mo
+- 干
+- ▁pri
+- ロ
+- 张
+- がある
+- ò
+- ▁feeling
+- ну
+- ici
+- ▁Co
+- 州
+- 觉得
+- ▁particular
+- las
+- ல்
+- マ
+- ▁Di
+- ▁makes
+- ні
+- lie
+- ▁はい
+- ▁yo
+- ▁due
+- 他们的
+- ▁move
+- ▁یک
+- ▁english
+- 第
+- line
+- ā
+- 女
+- ▁أن
+- ▁mais
+- ▁seem
+- 讲
+- 代
+- 八
+- ▁Vor
+- ende
+- ▁damit
+- ما
+- 线
+- ▁act
+- ▁beyond
+- ria
+- れ
+- nen
+- ▁captain
+- zer
+- ▁single
+- ▁hair
+- ▁об
+- té
+- ▁arms
+- ▁spoke
+- ▁They
+- tra
+- ▁suppose
+- ▁có
+- tas
+- க்
+- ர
+- ▁self
+- ▁Also
+- ▁information
+- ▁data
+- ▁simple
+- وا
+- ста
+- 之后
+- ша
+- ▁wild
+- ▁horse
+- ige
+- ▁R
+- 还有
+- 一点
+- M
+- king
+- ▁tried
+- ▁sleep
+- č
+- 相
+- ▁mine
+- 을
+- 石
+- ée
+- 社会
+- ndo
+- 色
+- _
+- mes
+- 量
+- 人が
+- 神
+- ▁wrong
+- 大家
+- ▁effect
+- ▁wind
+- ▁history
+- ▁sit
+- ▁yourself
+- ور
+- 才
+- 集
+- ▁eye
+- 球
+- 理
+- ▁walk
+- ▁fifty
+- ブ
+- ▁states
+- 军
+- cer
+- ▁foot
+- ▁Um
+- 们
+- レ
+- hu
+- cht
+- ▁очень
+- 台
+- だけ
+- じ
+- 形
+- ▁inter
+- ▁Les
+- ▁problem
+- gri
+- ▁although
+- ▁fer
+- ▁maar
+- こ
+- 空
+- ме
+- 不要
+- ▁gold
+- ische
+- N
+- 找
+- ▁G
+- ▁mo
+- fu
+- ▁这是
+- ura
+- mente
+- ally
+- ▁F
+- 关于
+- 北
+- if
+- 很多
+- î
+- ▁space
+- ست
+- ▁когда
+- ▁period
+- なかった
+- ▁ei
+- 所有
+- ▁könnte
+- ています
+- んです
+- ▁glad
+- こと
+- タ
+- ▁weiß
+- 六
+- ▁example
+- ▁двадцать
+- ▁stop
+- ▁stuff
+- 私
+- ▁els
+- ron
+- ▁reached
+- 认为
+- ▁doctor
+- ę
+- 政府
+- 让我
+- ▁none
+- nde
+- ano
+- ම
+- ▁daughter
+- ▁send
+- ▁force
+- のか
+- use
+- 进
+- tter
+- ▁chance
+- ras
+- ▁attention
+- че
+- 见
+- ▁W
+- 买
+- ▁fall
+- ▁pot
+- 教
+- ম
+- room
+- ▁Man
+- ş
+- について
+- ▁она
+- ▁blue
+- ▁exactly
+- ale
+- 如果
+- ▁New
+- 东西
+- né
+- ▁dir
+- 時間
+- ▁Ku
+- ▁camp
+- ▁stay
+- wer
+- ▁party
+- ▁Si
+- ▁especially
+- ض
+- 完全
+- 川
+- wn
+- af
+- ando
+- fen
+- ди
+- ▁character
+- ▁dich
+- ▁except
+- вер
+- 布
+- ▁please
+- my
+- ок
+- ▁Pa
+- そう
+- ▁Als
+- ▁share
+- ▁hatten
+- ия
+- フ
+- 事情
+- ▁Leben
+- ▁higher
+- ▁tôi
+- 阿
+- 何か
+- ement
+- 受
+- になる
+- ▁mis
+- ▁outside
+- ▁following
+- ▁hu
+- ▁level
+- ▁Me
+- ные
+- ▁geht
+- 信
+- ру
+- ▁couple
+- ▁lui
+- ▁necessary
+- ok
+- ▁Auf
+- ele
+- 器
+- 给你
+- 卡
+- 味
+- やっぱり
+- 起来
+- це
+- 话
+- ▁instead
+- ▁và
+- ▁opportunity
+- ▁carried
+- ▁nun
+- eg
+- nk
+- ▁appeared
+- ▁toward
+- 食
+- 如何
+- ▁gab
+- cho
+- ▁respect
+- 风
+- ati
+- ▁south
+- ▁Teil
+- 美国
+- シ
+- ▁added
+- ž
+- ▁window
+- ▁如果
+- nda
+- 正在
+- ▁او
+- är
+- ன்
+- ö
+- ▁control
+- ating
+- もの
+- ▁england
+- ор
+- ф
+- за
+- ▁seinen
+- ▁пере
+- ▁nine
+- па
+- ▁questions
+- ই
+- 成为
+- 越
+- オ
+- ▁ought
+- 期
+- ▁más
+- ▁save
+- 九
+- 朝
+- del
+- ць
+- ▁Mu
+- ▁late
+- ▁lower
+- ▁нь
+- 民
+- ira
+- 以后
+- ▁lived
+- ▁health
+- ▁knowledge
+- ▁ke
+- ▁Lo
+- 写
+- ▁што
+- ns
+- ▁нет
+- ▁现在
+- res
+- са
+- ▁customers
+- ▁tra
+- он
+- 嘛
+- ▁dollars
+- ый
+- ё
+- ▁Mae
+- ▁Bo
+- ▁gehen
+- cha
+- ▁wat
+- ▁Po
+- र
+- 歌
+- ▁opened
+- ks
+- ار
+- 取
+- ▁Im
+- chi
+- ▁miles
+- ▁grand
+- eth
+- let
+- また
+- 少
+- ▁haar
+- ている
+- ▁pen
+- ▁wall
+- ach
+- க்க
+- ▁è
+- 学校
+- 选择
+- ▁comp
+- ▁eighteen
+- ▁step
+- ▁easy
+- tur
+- 哪
+- heit
+- ば
+- ▁quickly
+- don
+- ▁Mal
+- mar
+- ▁wait
+- ging
+- 书
+- っていう
+- ky
+- 回来
+- 员
+- eur
+- ба
+- ▁Pro
+- 城
+- ▁Bi
+- ien
+- ▁neither
+- ▁уже
+- ре
+- tal
+- ▁fellow
+- ise
+- ▁group
+- ▁product
+- lla
+- ▁Ri
+- ام
+- 人の
+- ▁building
+- ▁чтобы
+- ide
+- ▁因为
+- ▁goes
+- ▁north
+- ▁chi
+- ▁jeung
+- ▁Z
+- ▁vielleicht
+- ▁music
+- ு
+- 一种
+- ▁main
+- ▁три
+- una
+- ▁paper
+- 周
+- ри
+- ▁bright
+- ▁beginning
+- க
+- ным
+- out
+- ▁V
+- ры
+- tin
+- 几
+- ▁hall
+- L
+- ▁ningal
+- ▁wonder
+- ▁diwawancara
+- ▁forth
+- ▁olohok
+- ▁office
+- ▁christ
+- ▁sweet
+- ▁möchte
+- ミ
+- 後
+- ذ
+- ▁wollen
+- stre
+- stand
+- ▁ihren
+- ities
+- H
+- ned
+- yi
+- ▁var
+- ▁wäre
+- ▁figure
+- ▁mhm
+- ▁Ki
+- 为什么
+- ▁entre
+- ▁nearly
+- ▁ran
+- ▁dipoto
+- ன
+- ▁wartawan
+- ▁avec
+- ▁forty
+- 任何
+- 制
+- ▁пра
+- 十
+- kt
+- 在这里
+- ке
+- ▁team
+- ali
+- time
+- ▁mon
+- ida
+- 夜
+- 清
+- bre
+- 进行
+- ▁Mann
+- lor
+- med
+- 的一个
+- ▁ganz
+- ج
+- دا
+- 由
+- ▁bien
+- 机
+- 本当に
+- 拿
+- と思います
+- 近
+- ▁Con
+- 大学
+- ▁kam
+- ğ
+- ges
+- wy
+- ▁rich
+- 直
+- ийн
+- ▁interesting
+- 的な
+- ▁Mit
+- ду
+- ▁There
+- ▁local
+- ▁konnte
+- или
+- ▁sollte
+- 急
+- ▁Am
+- 波
+- 特别
+- ▁purpose
+- 其他
+- 倒
+- mon
+- ▁este
+- ▁lives
+- ல
+- ということです
+- ▁будет
+- ан
+- ▁daß
+- 的地方
+- ▁Ad
+- ту
+- ville
+- ▁key
+- kel
+- pan
+- ص
+- 巴
+- ▁một
+- 村
+- 得到
+- 表
+- od
+- ert
+- ▁heute
+- けど
+- ল
+- ▁Com
+- ▁근데
+- ça
+- 解
+- ▁game
+- 接
+- ▁news
+- ▁peace
+- ▁weiter
+- ய
+- ▁pleasure
+- sti
+- ▁darüber
+- for
+- ▁situation
+- ▁learn
+- ▁ou
+- ▁ihrer
+- 局
+- 笑
+- ▁lie
+- ▁안
+- バ
+- 是什么
+- tie
+- ▁على
+- ▁silence
+- ▁grow
+- R
+- 是不是
+- '30'
+- ▁sister
+- tri
+- ▁sei
+- 安全
+- ther
+- த
+- ▁class
+- ید
+- 音
+- 派
+- 甚至
+- ▁zoo
+- ▁pan
+- ▁moved
+- ▁strength
+- ib
+- ili
+- ▁sta
+- ということで
+- ▁modern
+- ▁success
+- ante
+- side
+- 雪
+- ▁secret
+- log
+- ▁Va
+- ł
+- 君
+- 初
+- 哥
+- ▁dog
+- ▁follow
+- 指
+- ani
+- cher
+- се
+- 船
+- 切
+- 继续
+- ▁board
+- 男
+- ▁comme
+- ▁ru
+- ▁mag
+- 提供
+- ▁뭐
+- vel
+- 古
+- nes
+- U
+- ▁och
+- ▁davon
+- 다
+- 中的
+- 国家
+- 雨
+- 来说
+- ▁french
+- ッ
+- 早
+- ▁gegen
+- ро
+- mit
+- lichen
+- ▁middle
+- 两个
+- ▁project
+- ▁С
+- lang
+- ▁uit
+- ▁afraid
+- どう
+- ía
+- ▁eat
+- ▁expected
+- ▁dar
+- cal
+- への
+- ▁bear
+- む
+- 实际上
+- 管
+- غ
+- nce
+- 落
+- ▁Я
+- よく
+- ▁performance
+- ▁Arbeit
+- っ
+- 难
+- ца
+- ▁wouldn
+- ▁ara
+- tte
+- ▁army
+- こう
+- ▁entered
+- uri
+- 到了
+- mas
+- 字
+- ▁spring
+- ▁fit
+- ▁Ho
+- mor
+- ize
+- 如此
+- ier
+- ▁impact
+- ▁object
+- 七
+- 帮助
+- ▁national
+- can
+- ▁drive
+- rea
+- ▁cool
+- ▁private
+- 片
+- ”
+- ▁difficult
+- 在一起
+- 咱们
+- 土
+- 曲
+- ▁joy
+- ized
+- ப்
+- ү
+- ▁standing
+- 其实
+- ▁progress
+- ▁trouble
+- 低
+- ▁floor
+- tres
+- ▁pu
+- 選手
+- ▁только
+- 東京
+- ▁ship
+- ▁Nun
+- かな
+- 哈
+- ▁Su
+- ▁break
+- чи
+- 一直
+- 命
+- form
+- ▁mai
+- ول
+- ▁zusammen
+- ئ
+- ple
+- ▁av
+- ▁cho
+- エ
+- ズ
+- ▁provide
+- 救
+- pen
+- ▁She
+- ами
+- ▁happen
+- ving
+- 病
+- ▁din
+- 感じ
+- 作为
+- zeit
+- 活
+- になって
+- ▁nice
+- ウ
+- ▁ways
+- ▁american
+- ▁everybody
+- ▁special
+- ▁smile
+- же
+- ▁major
+- 率
+- ▁ju
+- sse
+- ▁chief
+- ▁hem
+- 理解
+- 一起
+- およそ
+- 处
+- ▁sont
+- ত
+- ▁distance
+- 罗
+- ▁И
+- 看看
+- чы
+- 电
+- ció
+- vin
+- ▁prince
+- ▁dies
+- ▁sign
+- لا
+- ▁fight
+- ▁greater
+- ▁That
+- ен
+- ▁mouth
+- 朋友
+- ▁Jahren
+- dig
+- ido
+- ▁kein
+- ▁consider
+- ▁demand
+- ව
+- 反
+- ▁sitting
+- ого
+- ▁written
+- ▁lang
+- ▁oku
+- ▁Wa
+- ní
+- cla
+- ▁remain
+- ▁couldn
+- 李
+- art
+- dd
+- ▁Eine
+- 은
+- 街
+- 场
+- ▁design
+- ▁spot
+- ▁потому
+- house
+- cken
+- 不知道
+- ▁vom
+- すると
+- ▁length
+- nis
+- win
+- ▁ба
+- 〉
+- ▁job
+- ▁пять
+- すごい
+- ship
+- 岁
+- hen
+- ▁heaven
+- де
+- ა
+- san
+- 必须
+- 河
+- ▁books
+- 条
+- ▁final
+- of
+- ▁grew
+- ▁base
+- 一次
+- ие
+- ▁walked
+- ▁isn
+- ▁increase
+- 官
+- ▁просто
+- 奥
+- ▁society
+- ▁amount
+- ▁On
+- 断
+- ero
+- cur
+- гу
+- ▁trees
+- ▁program
+- ▁これ
+- ې
+- ▁был
+- न
+- 有点
+- ى
+- ▁ம
+- ations
+- â
+- iza
+- ▁faith
+- vor
+- ▁heavy
+- ▁immediately
+- ▁battle
+- ▁president
+- ین
+- ▁financial
+- ▁What
+- ▁〈
+- のが
+- ▁dort
+- ▁четыре
+- ▁running
+- ▁begin
+- ▁рас
+- par
+- 間
+- サ
+- ▁personal
+- へ
+- ▁action
+- ха
+- wan
+- ▁cor
+- ▁тридцать
+- sie
+- ▁places
+- ▁development
+- ▁visit
+- ▁во
+- ▁train
+- ▁eu
+- ▁mary
+- ▁learned
+- ▁không
+- ▁write
+- ▁cre
+- ▁ohne
+- ですよね
+- ▁Gu
+- ст
+- 一个人
+- 东
+- 最近
+- 首
+- ется
+- ▁darauf
+- nas
+- ▁hot
+- さんが
+- ▁died
+- 地方
+- 支持
+- ую
+- ▁laid
+- ▁perfect
+- 她的
+- ces
+- ries
+- ▁queen
+- 工
+- 却
+- そして
+- ▁Ar
+- 决定
+- できる
+- ▁einmal
+- 서
+- 足
+- ▁command
+- ▁safe
+- ии
+- yu
+- される
+- ▁slowly
+- 连
+- dra
+- ә
+- ▁price
+- ▁Ti
+- ▁super
+- 玩
+- ı
+- ▁Mar
+- ий
+- ▁quick
+- ▁wood
+- ▁glaube
+- ▁сейчас
+- ▁add
+- 女性
+- ram
+- ndi
+- ▁estis
+- liche
+- ▁build
+- 只有
+- 实
+- っている
+- ▁sales
+- ▁nga
+- ▁Zu
+- ▁stopped
+- ą
+- 队
+- dro
+- ▁sobre
+- キ
+- 森
+- ▁嗯
+- ▁finally
+- 红
+- 思
+- nia
+- ▁various
+- ▁conversation
+- 差
+- 当然
+- ▁color
+- ▁changed
+- ▁age
+- 推
+- ө
+- цы
+- ▁married
+- 远
+- ▁west
+- ▁caught
+- mm
+- 酒
+- ▁gentleman
+- vid
+- 深
+- mus
+- ▁més
+- ▁cette
+- lik
+- 战
+- ▁hast
+- clock
+- uch
+- 所有的
+- 强
+- ве
+- ▁или
+- ▁film
+- ▁desire
+- shi
+- mel
+- ▁straight
+- ▁study
+- ins
+- メ
+- ▁pi
+- isi
+- ▁может
+- вы
+- ▁model
+- ▁warm
+- ▁ging
+- rie
+- sion
+- ▁которые
+- ▁tree
+- 当时
+- ▁ill
+- த்த
+- ▁cross
+- ▁hay
+- ▁ад
+- dem
+- ▁built
+- ▁Land
+- ▁science
+- ▁бол
+- ▁J
+- ▁bra
+- ட
+- ans
+- 亚
+- ところ
+- ▁danger
+- 商
+- 努力
+- ska
+- ▁kwa
+- 啦
+- 每
+- ▁talked
+- ▁remained
+- 部分
+- 老师
+- 士
+- 室
+- 变
+- sis
+- 番
+- ▁paar
+- schen
+- ▁giving
+- gang
+- 話
+- そうです
+- ▁gonna
+- ▁All
+- pp
+- ité
+- ▁quiet
+- 让我们
+- ▁rate
+- ного
+- 转
+- ▁york
+- ▁meiner
+- 热
+- ▁их
+- rus
+- チ
+- ▁الأ
+- ɣ
+- ▁tre
+- 我是
+- 事件
+- ▁wrote
+- ▁picture
+- ▁loved
+- ip
+- yan
+- аў
+- ▁Diese
+- ps
+- 整个
+- ▁Ur
+- ▁tr
+- ▁Ko
+- ▁snow
+- ▁gemacht
+- ▁waiting
+- ▁uncle
+- ث
+- tive
+- ▁семь
+- 肉
+- ▁那么
+- ▁fort
+- ▁yang
+- ▁race
+- ito
+- 失
+- ▁nichts
+- ▁For
+- ▁london
+- ▁кто
+- мо
+- ▁これは
+- ▁minute
+- ать
+- ino
+- ▁boys
+- 报
+- ski
+- бы
+- 千
+- ▁dinner
+- til
+- ▁gar
+- ▁march
+- ය
+- хо
+- 房
+- ▁fourth
+- rem
+- 边
+- ▁works
+- ▁sprechen
+- ▁broken
+- tus
+- 关
+- ▁tatsächlich
+- ち
+- ▁below
+- 有什么
+- ▁đ
+- ties
+- 感觉
+- ona
+- ▁piece
+- xi
+- ▁similar
+- より
+- ▁according
+- 保
+- ▁simply
+- 最后
+- ▁trust
+- 往
+- 达
+- なく
+- ம
+- ▁seinem
+- ḥ
+- ▁regard
+- ug
+- ▁hardly
+- 如果你
+- ▁zwischen
+- ▁direction
+- ▁seu
+- ▁drew
+- 世
+- lä
+- ▁mil
+- сан
+- ▁brain
+- ▁corner
+- 真正
+- cle
+- 种
+- 以上
+- 关系
+- ▁using
+- 这里
+- ▁summer
+- اس
+- ▁buy
+- ich
+- ▁language
+- ▁Ke
+- ▁impossible
+- ▁sus
+- 不同
+- چ
+- ше
+- eren
+- ▁Tu
+- ▁bon
+- 权
+- ▁hätte
+- ade
+- ▁Tag
+- ▁beauty
+- ▁lips
+- ue
+- 画
+- 第一
+- ▁evil
+- ▁Now
+- õ
+- 单
+- ▁france
+- ▁garden
+- ▁بود
+- ▁month
+- bil
+- ▁station
+- ec
+- さんの
+- ev
+- 进入
+- ▁lead
+- ré
+- int
+- 包
+- 県
+- sco
+- 기
+- ▁Ga
+- gehen
+- ▁denen
+- ▁presence
+- amente
+- ▁girls
+- ▁opinion
+- ▁touch
+- dia
+- そういう
+- ▁gran
+- 的问题
+- ▁fresh
+- ▁آن
+- 过去
+- ▁united
+- 系统
+- field
+- ▁Hu
+- gra
+- 야
+- rit
+- ▁mas
+- ▁political
+- ▁showed
+- とは
+- ят
+- fo
+- یم
+- ▁ма
+- ▁г
+- ser
+- ▁looks
+- ться
+- 眼
+- ず
+- ▁Frage
+- 以及
+- را
+- ▁vier
+- ▁나
+- ▁гэта
+- ▁placed
+- ney
+- パ
+- pu
+- ▁brown
+- 冷
+- 発
+- ▁сорок
+- ал
+- cra
+- 根
+- met
+- ▁Pe
+- ato
+- ▁我想
+- 星
+- ava
+- ción
+- ▁growing
+- ei
+- ions
+- 省
+- ▁risk
+- 了一个
+- ▁că
+- ▁В
+- ▁sick
+- ツ
+- ▁bank
+- ▁переулок
+- ▁current
+- ▁decided
+- ▁soft
+- ▁village
+- 板
+- ですが
+- 夫
+- 我就
+- 今年
+- ব
+- ▁filled
+- cent
+- 青
+- 好的
+- ▁hit
+- 実
+- ▁еще
+- ▁speaking
+- nh
+- ih
+- ют
+- ▁dazu
+- ice
+- گ
+- 其
+- 图
+- ral
+- น
+- V
+- ▁вас
+- ර
+- ▁hin
+- ▁Hi
+- ▁Ca
+- ▁led
+- 来了
+- kon
+- ▁ஆ
+- ou
+- ▁برای
+- 装
+- schaft
+- ▁wasn
+- ▁cent
+- ▁har
+- ▁muri
+- iti
+- эр
+- ▁weeks
+- 上的
+- 找到
+- ▁zich
+- ▁pero
+- 非
+- well
+- ▁inside
+- ial
+- like
+- ▁Sta
+- ▁Ihr
+- ▁writing
+- berg
+- ▁mentioned
+- 在那里
+- ▁expression
+- 出去
+- ▁trade
+- 脚
+- ▁born
+- fin
+- ▁pain
+- ▁á
+- ▁worked
+- ika
+- 風
+- ament
+- 比较
+- 交
+- ▁twelve
+- 这样的
+- ▁type
+- ▁cy
+- ده
+- ▁kan
+- ▁Jahre
+- ▁revenue
+- nte
+- ▁rain
+- ▁serve
+- ова
+- ute
+- ▁reach
+- よね
+- ▁H
+- oli
+- ▁meant
+- ▁würden
+- har
+- 妈妈
+- ▁direct
+- ▁இ
+- ң
+- やって
+- ▁plain
+- ▁för
+- ▁Ru
+- ▁pleased
+- ▁win
+- ▁winter
+- ▁influence
+- ▁parts
+- ▁doch
+- isch
+- kat
+- uk
+- ▁mij
+- ▁Lu
+- 車
+- 你可以
+- ▁naar
+- ▁worth
+- via
+- 这一
+- ▁community
+- ▁thoughts
+- 突然
+- 》
+- 包括
+- ▁kommen
+- hin
+- ▁しかし
+- ▁raised
+- ▁wide
+- ▁col
+- ▁große
+- ▁của
+- дзе
+- eb
+- 传
+- リー
+- ▁haven
+- ▁chair
+- ▁bri
+- ▁field
+- fall
+- 亲
+- ▁umu
+- 松
+- 里面
+- wood
+- 存在
+- 让你
+- 皮
+- 零
+- ▁boat
+- ▁forget
+- bro
+- ▁exclaimed
+- 代表
+- uka
+- ded
+- 听到
+- '11'
+- 血
+- ут
+- ▁carry
+- ▁chúng
+- mmer
+- mis
+- das
+- 华
+- 别人
+- ió
+- ▁Geschichte
+- ford
+- bb
+- ▁broad
+- ▁od
+- ▁paid
+- ▁likely
+- ▁condition
+- ▁ersten
+- ▁afternoon
+- 以前
+- ▁someone
+- ▁grave
+- ▁și
+- put
+- ای
+- ▁genau
+- 々
+- ▁dream
+- сы
+- ▁Fa
+- ых
+- න
+- ▁мо
+- ▁На
+- 说话
+- ком
+- ▁ourselves
+- ▁waar
+- ô
+- 草
+- ▁band
+- 我觉得
+- ▁content
+- ▁wenig
+- ora
+- 自然
+- 家庭
+- 方面
+- ে
+- 黑
+- ▁imp
+- ▁industry
+- ▁terrible
+- dos
+- 尼
+- ым
+- rig
+- ▁report
+- ▁somebody
+- 要求
+- ▁allowed
+- さんは
+- ▁path
+- rá
+- ▁pale
+- 那里
+- ▁appearance
+- ssen
+- ▁mark
+- ▁worden
+- umu
+- よう
+- 学生
+- ments
+- мен
+- 院
+- ▁க
+- أ
+- sur
+- 円
+- ▁journey
+- жа
+- ▁usually
+- ▁دو
+- 卖
+- ную
+- ves
+- ите
+- gli
+- 的方式
+- だと
+- 调
+- ▁listen
+- 警察
+- び
+- wen
+- ▁Frau
+- 兰
+- tik
+- グ
+- ī
+- 温
+- '50'
+- 線
+- んですよ
+- ▁vast
+- يا
+- ▁anar
+- ▁material
+- ە
+- ▁generally
+- ට
+- ▁fait
+- ▁enemy
+- пер
+- ▁area
+- ▁struck
+- dar
+- aient
+- 重要
+- tir
+- ▁rock
+- ような
+- 喝
+- 的に
+- ▁shot
+- ると
+- ▁finden
+- ▁sorry
+- ▁gra
+- ▁evidence
+- ▁آ
+- া
+- ний
+- 我认为
+- ход
+- ula
+- sam
+- 了解
+- ▁daran
+- wu
+- uma
+- ▁Gi
+- ▁Nach
+- いました
+- ков
+- ▁easily
+- 件
+- ▁individual
+- 节
+- ▁duty
+- 座
+- ▁tout
+- mie
+- 过来
+- ▁những
+- ▁unless
+- まだ
+- 它的
+- ু
+- ros
+- ▁bill
+- ▁fifteen
+- heid
+- mann
+- ▁Her
+- lt
+- 成功
+- iv
+- pri
+- اد
+- iya
+- それ
+- lia
+- ▁laughed
+- ▁шесть
+- ▁надо
+- ▁clean
+- ▁normal
+- sar
+- ▁ن
+- ▁tea
+- ala
+- ▁kar
+- ami
+- var
+- ながら
+- 恩
+- oc
+- ▁gesagt
+- llen
+- 是否
+- ▁meeting
+- 纳
+- ▁create
+- ▁ஒரு
+- ҡ
+- ▁Dann
+- ▁ré
+- ▁products
+- ▁Et
+- ▁вам
+- tis
+- ▁fun
+- ▁während
+- 了吗
+- ▁focus
+- ses
+- あの
+- ▁fond
+- ▁sí
+- んですけど
+- あと
+- ▁star
+- ▁Weise
+- 健康
+- ▁considered
+- َ
+- ▁não
+- 油
+- स
+- せ
+- んですね
+- ▁Weg
+- ▁broke
+- stra
+- ▁moving
+- ▁sah
+- set
+- ▁быть
+- 此
+- ▁leaving
+- ▁supposed
+- stan
+- ぶ
+- ▁million
+- ▁services
+- ひ
+- ▁charge
+- ▁ber
+- ▁drink
+- ▁test
+- ская
+- ▁enter
+- ▁gerade
+- 技术
+- ▁sudden
+- 式
+- wr
+- ги
+- ista
+- ▁wonderful
+- त
+- ▁season
+- ▁어
+- '",'
+- ▁difference
+- ▁jo
+- ▁আ
+- 未
+- اء
+- ▁knows
+- ▁geben
+- ▁rep
+- きました
+- ified
+- 为了
+- leg
+- iri
+- ▁mad
+- ▁positive
+- ▁grace
+- ▁пры
+- dri
+- ▁stone
+- なんか
+- ▁spent
+- 我在
+- ▁everyone
+- W
+- als
+- られる
+- ē
+- ▁bat
+- ▁Kinder
+- 修
+- 方法
+- э
+- ▁principal
+- 怎么样
+- ▁sagt
+- ▁iron
+- dor
+- pt
+- ▁том
+- ก
+- 坐
+- ▁Ve
+- アメリカ
+- ▁ball
+- ▁fünf
+- ▁Jo
+- ▁Fall
+- ped
+- ▁energy
+- ▁sixty
+- ▁honor
+- 雷
+- ius
+- لی
+- ▁min
+- رو
+- ジ
+- ▁ganze
+- ▁بر
+- というのは
+- 间
+- чу
+- ▁ப
+- ▁imagine
+- zar
+- ţi
+- ▁covered
+- ▁exist
+- 人的
+- ▁latter
+- ▁receive
+- ▁wollte
+- ▁cast
+- 任
+- ▁daar
+- 不同的
+- nor
+- ▁moral
+- vers
+- ▁global
+- ▁fo
+- ▁esta
+- pro
+- 強
+- eu
+- ように
+- たい
+- تر
+- ▁natürlich
+- ぐらい
+- bed
+- сти
+- uru
+- ▁allow
+- ே
+- ▁closed
+- ▁cases
+- 离
+- nos
+- ▁bound
+- ஸ்
+- but
+- 把它
+- ▁kun
+- ▁shut
+- 左
+- ▁dafür
+- ▁arrived
+- ▁huge
+- pf
+- ▁technology
+- ▁besides
+- ▁term
+- ா
+- ット
+- ことが
+- ph
+- 自由
+- 改变
+- ▁Kon
+- 服务
+- 二十
+- ニ
+- 许多
+- 注意
+- ろ
+- ay
+- ▁At
+- cin
+- ▁entirely
+- ▁tears
+- ですか
+- ojn
+- 教育
+- ▁particularly
+- ▁pod
+- ▁девять
+- 有人
+- ey
+- ▁hill
+- 直接
+- 试图
+- ▁ل
+- ▁großen
+- ão
+- ▁Ze
+- ▁而且
+- ella
+- ▁actual
+- looking
+- ata
+- bli
+- по
+- のです
+- all
+- ▁І
+- 方式
+- 状況
+- ants
+- ▁anti
+- pre
+- ▁Our
+- ▁turning
+- ació
+- しか
+- هم
+- 学习
+- ▁ко
+- 一天
+- 步
+- yn
+- ▁lassen
+- んですが
+- rt
+- yon
+- 准备
+- ビ
+- 维
+- 药
+- ▁cat
+- ward
+- ▁percent
+- ▁ahead
+- ▁notice
+- spe
+- 离开
+- 人类
+- ▁justice
+- ▁sollten
+- iga
+- ▁spend
+- pul
+- ▁significant
+- ▁fest
+- ▁ي
+- ▁needs
+- 收
+- hand
+- ай
+- ical
+- ▁sand
+- だから
+- ட்
+- лю
+- ▁sky
+- ▁killed
+- 语
+- ▁dos
+- ▁bak
+- nder
+- eva
+- 를
+- ▁Beispiel
+- rà
+- лу
+- 留
+- 考え
+- ▁holy
+- 宝
+- 百
+- ▁Dr
+- ▁Pi
+- ▁Go
+- ส
+- ▁europe
+- ▁rise
+- ▁animal
+- های
+- 完
+- ▁macht
+- ение
+- ▁jeder
+- ▁labor
+- ▁бо
+- ▁John
+- ▁deux
+- 参加
+- ▁island
+- ▁kids
+- ▁size
+- ▁occasion
+- cio
+- ▁beside
+- ぎ
+- iki
+- ▁park
+- ул
+- illa
+- ви
+- og
+- 中心
+- され
+- 福
+- board
+- 島
+- ▁jack
+- ▁complete
+- 提
+- ▁east
+- ▁religion
+- ▁note
+- ein
+- ▁usual
+- ture
+- ▁horses
+- ▁police
+- ▁youth
+- ▁unserer
+- vil
+- ▁uma
+- ▁wise
+- 短
+- ハ
+- ón
+- ola
+- 角
+- tru
+- ▁crowd
+- ▁george
+- ▁welche
+- ▁пред
+- ḍ
+- ▁gute
+- ▁silent
+- 吉
+- 兵
+- 跑
+- していた
+- 影响
+- ム
+- qu
+- ▁пятьдесят
+- 查
+- ▁opportunities
+- hr
+- ▁tend
+- lon
+- ▁peter
+- av
+- になった
+- ima
+- 师
+- ▁education
+- 見て
+- 好好
+- ▁К
+- ▁«
+- лен
+- bert
+- ți
+- ▁countries
+- ボ
+- ably
+- ▁sam
+- ability
+- ▁нам
+- 你会
+- கள்
+- ▁blind
+- ど
+- ▁trans
+- '40'
+- ▁восемь
+- ▁former
+- ▁مع
+- vy
+- nan
+- ▁ただ
+- ▁molt
+- ▁tut
+- ▁су
+- ▁“
+- 完成
+- ▁sicher
+- ▁prop
+- ▁noble
+- ▁ubu
+- ▁tax
+- ▁della
+- ▁meinen
+- lé
+- mun
+- 案
+- mba
+- 客
+- ▁ziemlich
+- เ
+- тор
+- ▁Hand
+- ▁warum
+- ▁lange
+- ман
+- ▁letters
+- ▁была
+- ▁reading
+- ▁Denn
+- ▁parents
+- 演
+- 配
+- ▁Car
+- rei
+- ▁attack
+- ▁чем
+- лся
+- teil
+- ▁surprise
+- 来自
+- にある
+- 破
+- ▁heraus
+- pon
+- 文化
+- 走了
+- ▁aunt
+- ▁needed
+- ▁ngo
+- ▁slow
+- low
+- 数据
+- ▁offer
+- といいます
+- 至
+- ▁pleasant
+- ▁list
+- ett
+- arbeit
+- 罪
+- ▁entire
+- ৰ
+- 无法
+- uta
+- ▁lines
+- sent
+- ▁Dies
+- 만
+- ▁merely
+- ▁cry
+- ▁está
+- ▁ac
+- ierte
+- iro
+- tha
+- mbo
+- ▁worse
+- ▁pra
+- 待
+- kla
+- ▁cha
+- 另一个
+- huh
+- tag
+- ▁unto
+- があります
+- ▁absolutely
+- ▁marriage
+- tim
+- 跳
+- ened
+- ▁п
+- sia
+- 算
+- ▁gesehen
+- ▁ни
+- ▁original
+- ▁этом
+- nou
+- ▁tall
+- ネ
+- ▁based
+- aka
+- ร
+- ov
+- 管理
+- 似乎
+- ▁ideas
+- ador
+- mut
+- bla
+- ▁этого
+- 我会
+- ые
+- نی
+- ▁هو
+- ▁store
+- 取り
+- 全部
+- 也不
+- ▁kandi
+- 获得
+- ▁somewhat
+- 대
+- ▁kleine
+- ▁watching
+- 我要
+- 经济
+- ▁forest
+- 原因
+- ▁weak
+- kommen
+- ats
+- 問題
+- ▁takes
+- шы
+- 总是
+- தி
+- سی
+- ▁steps
+- ▁nog
+- ▁всё
+- rat
+- 利用
+- ▁mà
+- 一定
+- 政治
+- ▁Or
+- ▁đó
+- ▁members
+- ▁silver
+- ▁discovered
+- 你在
+- ге
+- án
+- ▁ع
+- 割
+- า
+- 好了
+- ▁including
+- 科学
+- ский
+- tta
+- эл
+- ▁tan
+- ▁property
+- тер
+- ▁bow
+- け
+- ▁appear
+- 计划
+- gal
+- itat
+- 通常
+- ▁draw
+- 飞
+- 守
+- 掉
+- 谷
+- 未来
+- ▁serious
+- ▁bottom
+- ils
+- rn
+- night
+- ▁circumstances
+- ▁是的
+- vre
+- ▁Geld
+- اب
+- ▁escape
+- ▁chapter
+- ét
+- ▁movement
+- J
+- ▁proud
+- ell
+- “
+- ▁fortune
+- ▁customer
+- そ
+- nden
+- tung
+- ▁iz
+- лы
+- ▁breath
+- ů
+- ▁san
+- 满
+- ▁ph
+- ▁Jahr
+- werk
+- 发生
+- 好像
+- ▁Wo
+- ▁وال
+- ▁thick
+- ▁mountain
+- 党
+- tz
+- 可能会
+- ū
+- ▁changes
+- ения
+- ав
+- ▁sondern
+- 故事
+- бо
+- ი
+- ந்த
+- ケ
+- ▁prepared
+- ▁sad
+- ▁Stadt
+- ▁completely
+- 母
+- 感染
+- 迪
+- ▁fish
+- dis
+- ▁mort
+- ete
+- до
+- ▁points
+- 击
+- ▁besser
+- ▁gw
+- oni
+- rwa
+- ▁dels
+- oma
+- ев
+- ▁increased
+- ▁bij
+- ▁america
+- ▁Herr
+- ।
+- ▁instant
+- ica
+- ▁Moment
+- ▁Über
+- ▁где
+- rin
+- ▁erste
+- ▁named
+- ரி
+- 如
+- モ
+- ▁conditions
+- 停
+- ▁guard
+- 리
+- lic
+- ▁здесь
+- ▁Ya
+- bit
+- ис
+- ස
+- 共
+- tek
+- 夏
+- ep
+- ын
+- ُ
+- ▁mijn
+- これは
+- ظ
+- 几乎
+- ட்ட
+- 精
+- ▁effort
+- ▁judge
+- 仍然
+- ▁habit
+- ▁credit
+- sim
+- 牛
+- ▁jesus
+- 江
+- ▁тем
+- ▁afterwards
+- ม
+- ▁ok
+- lung
+- 信息
+- 追
+- ▁cal
+- най
+- ▁sono
+- 皆さん
+- ное
+- ▁gives
+- aga
+- 忙
+- '12'
+- ▁advantage
+- ně
+- rum
+- 谈论
+- itu
+- ray
+- 毛
+- 素
+- ret
+- ▁research
+- ▁meaning
+- ator
+- ▁zou
+- ці
+- lay
+- ック
+- ▁Grund
+- ▁glass
+- ▁nobody
+- ▁video
+- kle
+- ▁Ende
+- gy
+- 程
+- 感到
+- イン
+- 养
+- හ
+- ▁Ort
+- ▁cash
+- hir
+- ▁count
+- ▁inform
+- ▁また
+- ▁neue
+- encia
+- 歳
+- lü
+- ▁taste
+- ▁porque
+- ▁box
+- ▁fin
+- ற
+- ことを
+- ân
+- ▁watched
+- ▁college
+- ▁막
+- ▁York
+- ▁того
+- ▁storm
+- ▁mass
+- みんな
+- 约
+- 我们在
+- ▁weather
+- 拍
+- े
+- å
+- 排
+- ▁scene
+- ▁laugh
+- ▁dry
+- ▁total
+- 确实
+- ということ
+- zin
+- 对于
+- ▁beiden
+- ▁memory
+- ▁się
+- ▁Els
+- ▁madame
+- 其中
+- '15'
+- তা
+- mak
+- ▁events
+- 'off'
+- ▁greatest
+- тай
+- ▁aba
+- 界
+- ping
+- 型
+- press
+- ▁Sache
+- ▁speech
+- ▁pick
+- ados
+- ▁played
+- ▁companies
+- idad
+- ▁passage
+- 下来
+- こんな
+- ▁weg
+- ▁benefit
+- ▁alt
+- ▁War
+- 退
+- light
+- 视
+- ▁economic
+- ▁тут
+- ▁edge
+- ▁international
+- gue
+- net
+- 晚上
+- 照
+- 医生
+- ▁Bar
+- ева
+- ▁record
+- bol
+- ロシア
+- zel
+- 屋
+- ▁fallen
+- ▁tak
+- ▁animals
+- 超
+- ż
+- ほど
+- ▁تا
+- odd
+- ▁我是
+- ▁kill
+- 菜
+- ක
+- ille
+- ación
+- ს
+- 今日
+- ▁app
+- ▁tone
+- cos
+- ▁shore
+- ▁balance
+- abo
+- ▁إلى
+- ▁dia
+- ▁solid
+- ın
+- 香
+- 勝
+- 思い
+- wed
+- ো
+- 肯定
+- zy
+- ری
+- ぐ
+- ▁denken
+- ux
+- rang
+- kul
+- äh
+- ▁spread
+- kar
+- ノ
+- ily
+- table
+- ▁moon
+- 班
+- 娘
+- 端
+- ад
+- over
+- ua
+- эн
+- じゃない
+- 昨日
+- なら
+- wing
+- 服
+- die
+- ще
+- 因
+- ▁için
+- ▁sharp
+- tem
+- いく
+- ▁без
+- ▁jeden
+- sk
+- 苦
+- vad
+- eta
+- fre
+- 시
+- ▁comfort
+- ப
+- mb
+- ɛ
+- ран
+- 云
+- 回到
+- ▁understood
+- 機
+- ▁الت
+- ▁Haus
+- 称
+- 树
+- ガ
+- ▁ihrem
+- ▁های
+- ▁можно
+- ▁search
+- iko
+- тра
+- vé
+- 费
+- ▁bag
+- られた
+- ▁honour
+- 经常
+- ▁tom
+- ría
+- டி
+- 無
+- ▁casa
+- 洗
+- テ
+- ▁bekommen
+- ▁shape
+- 必要
+- rent
+- 望
+- ▁fla
+- 非常に
+- ▁cover
+- ▁даже
+- 井
+- 員
+- 换
+- ▁kommt
+- ved
+- ちゃん
+- 武
+- 是个
+- ▁exp
+- ▁interested
+- ▁prove
+- 나
+- qua
+- ▁ben
+- 全国
+- ▁believed
+- ▁fixed
+- म
+- ▁brave
+- law
+- ▁surface
+- рэ
+- ▁double
+- ▁reply
+- 場
+- प
+- dir
+- ني
+- ▁Daten
+- ▁dress
+- ▁Gefühl
+- от
+- pped
+- 的时间
+- ▁song
+- some
+- ▁sau
+- ▁stock
+- ▁If
+- मा
+- ▁nó
+- ara
+- ย
+- ▁laws
+- ▁square
+- ▁bru
+- ير
+- ここ
+- ▁gehört
+- ▁professor
+- ▁emp
+- ▁excited
+- ▁لل
+- ▁Unternehmen
+- stone
+- 玉
+- 市场
+- ▁finding
+- ▁sing
+- ith
+- ▁environment
+- gg
+- ▁schnell
+- enden
+- gera
+- 最终
+- 刘
+- ▁popular
+- ▁ont
+- ▁ancient
+- স
+- 联系
+- asi
+- 帮
+- ▁royal
+- 右
+- ▁pie
+- став
+- 身上
+- ▁leben
+- стра
+- ▁seventy
+- ▁parte
+- 个人
+- ▁throughout
+- ▁Fi
+- ▁いや
+- ▁ces
+- 大きな
+- ер
+- ▁smoke
+- 求
+- bri
+- 基
+- ນ
+- டு
+- ▁markets
+- ▁These
+- ため
+- ▁lake
+- '0'
+- ▁bal
+- ▁mere
+- 接受
+- ▁driven
+- 密
+- 登
+- 之间
+- ▁persons
+- ▁người
+- ▁explain
+- ▁seat
+- ▁putting
+- ▁af
+- 鱼
+- мы
+- imi
+- ார்
+- ▁pos
+- ▁telling
+- ▁час
+- ▁其实
+- ▁بال
+- 龙
+- ▁region
+- ▁center
+- ▁Ĝi
+- 系
+- ▁walls
+- ▁century
+- ▁mor
+- ▁mid
+- ว
+- ▁club
+- nel
+- 便
+- ▁pat
+- ▁range
+- ▁determined
+- ering
+- ▁Han
+- ▁prevent
+- ▁author
+- ப்ப
+- ▁sold
+- 呃
+- ▁எ
+- 音乐
+- ▁civil
+- ▁bas
+- com
+- aŭ
+- 黄
+- ▁accept
+- ▁stage
+- din
+- ▁narrow
+- ▁foreign
+- ob
+- ▁neck
+- 害
+- ▁رو
+- ▁issue
+- hor
+- 解决
+- 发展
+- tig
+- ▁production
+- ▁yellow
+- ▁soll
+- 证
+- ▁ladies
+- ▁hurt
+- ▁perfectly
+- ▁fill
+- und
+- 各
+- tant
+- 法律
+- 就会
+- jen
+- 杀
+- ол
+- 告
+- их
+- ▁german
+- ▁你知道
+- tura
+- 跟我
+- 付
+- ▁plant
+- 頭
+- ▁wel
+- ина
+- 想要
+- 大会
+- ِ
+- мі
+- вед
+- ▁approach
+- recht
+- gas
+- ▁access
+- ▁St
+- ▁familiar
+- ▁Ce
+- 去了
+- ▁proper
+- 常
+- ▁tri
+- との
+- ▁hotel
+- 伤
+- শ
+- ▁Dar
+- ▁learning
+- ▁Ju
+- 塔
+- ▁numbers
+- ▁fan
+- ▁bla
+- bin
+- ast
+- とも
+- rar
+- 有很多
+- ▁stick
+- ual
+- ▁hebben
+- gestellt
+- ▁因此
+- ী
+- pin
+- ත
+- ▁students
+- 対
+- lli
+- 站
+- りました
+- 投
+- ▁wished
+- ▁paris
+- ▁iki
+- ▁offered
+- ▁19
+- 关注
+- ▁cloud
+- тар
+- ▁grass
+- 城市
+- سا
+- rec
+- ур
+- war
+- ▁observed
+- ▁هم
+- ▁tam
+- ii
+- 之一
+- ▁favor
+- れば
+- ▁meinem
+- 乱
+- ته
+- ▁confidence
+- ▁agree
+- 父
+- 女人
+- cour
+- ?"
+- でしょう
+- 少し
+- ▁som
+- twa
+- osa
+- ea
+- ▁keinen
+- له
+- ▁degree
+- ▁который
+- ▁beneath
+- 秋
+- ▁était
+- فر
+- ▁mental
+- ▁Problem
+- work
+- 場所
+- tā
+- eux
+- biri
+- ▁marry
+- 微
+- ün
+- ged
+- ▁quality
+- ▁Seite
+- ła
+- 结果
+- лись
+- ▁firm
+- ▁baby
+- 奇
+- ları
+- rah
+- ▁clearly
+- ▁oil
+- 做的
+- ▁promise
+- ▁roman
+- ▁한
+- gin
+- lid
+- 伊
+- チーム
+- れる
+- '100'
+- ද
+- ▁farm
+- ▁человек
+- ▁finished
+- ًا
+- ▁darkness
+- ego
+- leri
+- lum
+- ▁clothes
+- ্
+- '60'
+- 死亡
+- 久
+- مان
+- цца
+- 保持
+- っと
+- 迷
+- 俺
+- ▁produce
+- ▁happiness
+- ▁Elle
+- ▁rule
+- ▁کار
+- 是我
+- みたいな
+- ▁playing
+- ▁gas
+- 列
+- ▁шестьдесят
+- 节目
+- たら
+- tet
+- uz
+- ane
+- ▁تو
+- وم
+- को
+- 家族
+- ▁golden
+- ▁あっ
+- 相信
+- ▁உ
+- ▁rein
+- 选
+- ▁scarcely
+- ▁seit
+- 你就
+- 几个
+- 友
+- 一般
+- ▁equal
+- ▁knowing
+- 回家
+- tern
+- ▁Ex
+- ச
+- 役
+- 回答
+- 果
+- ▁management
+- ▁lose
+- ール
+- ná
+- ▁brand
+- ▁sal
+- 啥
+- ▁könnten
+- ▁可是
+- зна
+- ▁opening
+- 試合
+- ター
+- 人生
+- morrow
+- ▁drawing
+- デ
+- ▁operating
+- ▁bạn
+- ▁profit
+- ▁Gra
+- ▁paul
+- 让他
+- rak
+- оч
+- glo
+- ette
+- ß
+- ▁loss
+- ▁stories
+- ▁wine
+- ▁sell
+- があった
+- 电话
+- hm
+- 脸
+- ▁attempt
+- ▁address
+- ▁bay
+- uku
+- 町
+- ▁leaves
+- ▁courage
+- spir
+- ▁media
+- ▁leading
+- ▁schwer
+- ▁ses
+- 的小
+- ▁efforts
+- ▁soldiers
+- ▁surely
+- ▁San
+- hel
+- lip
+- rü
+- iger
+- ▁Hier
+- ві
+- pot
+- 电影
+- ▁är
+- ▁passiert
+- ▁Але
+- än
+- ▁etwa
+- 他在
+- 春
+- ow
+- non
+- ▁produced
+- ▁flowers
+- ▁valley
+- ▁earlier
+- ▁Ik
+- cie
+- 人は
+- ▁holding
+- ▁eigenen
+- 理由
+- لي
+- ▁Mr
+- ▁sword
+- ▁supply
+- bot
+- ▁được
+- ▁weight
+- 副
+- ▁さあ
+- 주
+- 网
+- 视频
+- 妈
+- ி
+- ▁check
+- ▁walking
+- ▁flu
+- 連
+- 告诉我
+- ▁Europe
+- 枪
+- ▁additional
+- 这一点
+- ls
+- ▁pla
+- ிய
+- 助
+- pit
+- ▁fra
+- 因为我
+- ena
+- ification
+- eau
+- ako
+- ▁choice
+- による
+- 骨
+- бу
+- ▁vain
+- 自分の
+- ▁decision
+- cing
+- ▁如果你
+- 背
+- ▁Б
+- ▁gro
+- 라
+- ▁powerful
+- 弱
+- 地区
+- лан
+- 陈
+- きた
+- 拥有
+- 東
+- 这样做
+- ▁welcome
+- ▁recent
+- lam
+- 残
+- ▁happens
+- ▁structure
+- 在这个
+- fan
+- lit
+- وی
+- ▁formed
+- ▁han
+- ▁speed
+- ddy
+- zione
+- ▁meisten
+- ా
+- 是在
+- '14'
+- 狗
+- lí
+- 로
+- ▁gli
+- ▁seek
+- 一番
+- 组织
+- rio
+- ▁nation
+- tat
+- ▁houses
+- ▁travel
+- ▁uz
+- ▁тоже
+- ▁bird
+- ▁passion
+- ▁directly
+- 去年
+- ▁그냥
+- である
+- ious
+- lı
+- mul
+- вод
+- ▁basis
+- 明天
+- прав
+- ▁Can
+- ▁platform
+- ▁як
+- aire
+- どんな
+- 的这个
+- cker
+- The
+- ists
+- 治
+- gna
+- serv
+- ▁threw
+- ▁dropped
+- ▁divine
+- ▁deze
+- ▁그런
+- 这次
+- んですか
+- 读
+- ▁flat
+- ง
+- ▁Buch
+- 考
+- ир
+- uza
+- 考虑
+- dol
+- න්
+- ▁deliver
+- ак
+- vá
+- かった
+- 는데
+- ▁شد
+- スト
+- 」。
+- mat
+- fully
+- ugu
+- hör
+- ▁military
+- ▁letzten
+- 睡
+- ▁birds
+- ▁sake
+- ▁lying
+- ください
+- ▁Fe
+- жи
+- ▁required
+- ▁existence
+- ▁ничего
+- ▁addition
+- 横
+- 気持ち
+- ▁contract
+- 爸爸
+- ्
+- ள
+- バー
+- stu
+- из
+- bie
+- бе
+- ▁passing
+- 不了
+- try
+- 若
+- ▁ibi
+- ▁declared
+- セ
+- 唱
+- ▁Par
+- уч
+- ▁конечно
+- ▁эти
+- ▁anybody
+- 身体
+- ▁coast
+- aux
+- tum
+- きょう
+- си
+- ▁cri
+- কে
+- ▁내가
+- ныя
+- ▁available
+- rich
+- क
+- 建
+- '18'
+- ▁glance
+- ▁track
+- ▁unser
+- 尽
+- ▁page
+- بر
+- ること
+- bl
+- 良
+- chten
+- mic
+- ▁understanding
+- ▁orders
+- ▁practice
+- 源
+- су
+- 带着
+- ▁seva
+- ▁wichtig
+- vas
+- sin
+- 三个
+- bau
+- zwe
+- ole
+- kuru
+- 愿
+- 人家
+- stat
+- mond
+- こちら
+- she
+- ▁你们
+- ▁william
+- ▁caused
+- stein
+- ▁christian
+- ible
+- ient
+- ▁Mas
+- ▁catch
+- cas
+- ▁Wasser
+- ▁guy
+- ction
+- 時に
+- ▁uno
+- ▁vint
+- 脱
+- 轻
+- ▁eighty
+- ▁allen
+- 封
+- ▁şi
+- ▁host
+- ▁fly
+- ▁–
+- rate
+- rick
+- ▁Д
+- gil
+- uko
+- ▁bedeutet
+- ▁hung
+- ▁ears
+- ▁prison
+- ▁più
+- ▁ф
+- ▁двести
+- more
+- 例
+- ▁muy
+- 前に
+- ▁text
+- ▁centre
+- ▁presently
+- 熱
+- 情况
+- ▁wahrscheinlich
+- gle
+- ポイント
+- によって
+- ▁Augen
+- вал
+- genommen
+- 普
+- อ
+- chan
+- gre
+- 社区
+- ▁že
+- ▁này
+- 托
+- ▁curious
+- になります
+- ön
+- 해
+- ▁larger
+- jar
+- ▁этот
+- ▁pel
+- ін
+- ▁shows
+- ▁voi
+- 新的
+- つの
+- geben
+- 类
+- 夫人
+- 处理
+- ket
+- ▁instance
+- ▁colonel
+- ▁Okay
+- quer
+- ungs
+- ▁net
+- ▁indi
+- 比如
+- 年前
+- thi
+- 错
+- ▁ре
+- すごく
+- nne
+- 第二
+- ▁hath
+- 자
+- க்கு
+- 及
+- ▁pr
+- 这是一个
+- 首先
+- igkeit
+- ▁population
+- 면
+- ▁pentru
+- 了我
+- cen
+- ▁vision
+- 的事
+- ▁accident
+- じゃ
+- ▁matters
+- ▁elle
+- 解释
+- 返
+- ▁richtig
+- 也许
+- 群
+- व
+- 极
+- ▁spoken
+- ▁indian
+- ▁kwi
+- ▁coat
+- ▁hearing
+- っています
+- ▁gets
+- ざ
+- nic
+- 级
+- ▁shown
+- ▁هذا
+- ▁hospital
+- kes
+- での
+- би
+- ロー
+- ी
+- ▁officers
+- ▁port
+- ais
+- 一切
+- ग
+- ▁kleinen
+- ▁statements
+- 回去
+- ▁cro
+- 刚
+- ▁Bei
+- 데
+- ative
+- ▁gre
+- ним
+- '19'
+- 不好
+- 办
+- 乐
+- 沙
+- ▁shadow
+- ہ
+- ▁fool
+- 团
+- ▁今天
+- vert
+- ▁calm
+- 担心
+- bye
+- ▁motion
+- stru
+- プ
+- 現在
+- ▁ook
+- ▁faire
+- ▁aux
+- ▁thin
+- ▁basically
+- まして
+- ▁shook
+- 事故
+- 保护
+- গ
+- ▁henry
+- ▁member
+- lis
+- ▁thanks
+- ▁aware
+- ▁время
+- あります
+- されました
+- eri
+- ▁opposite
+- ო
+- ▁pocket
+- ▁task
+- ▁willing
+- ▁successful
+- فت
+- 秒
+- ▁bread
+- uw
+- চ
+- ▁ama
+- 变得
+- ▁tem
+- なので
+- ▁trong
+- ▁extra
+- ▁religious
+- ▁люди
+- ▁strategy
+- ▁Har
+- یه
+- ▁remains
+- ▁cousin
+- ус
+- ▁إ
+- 票
+- 挺
+- などの
+- 不用
+- ぞ
+- ▁fre
+- ですよ
+- ‘
+- 游戏
+- ▁beat
+- ese
+- dde
+- 而不是
+- 苏
+- ▁guys
+- ▁Aba
+- ▁central
+- 最高
+- それを
+- ches
+- jan
+- 容
+- ▁officer
+- pic
+- ▁wants
+- ched
+- ▁related
+- ▁bw
+- ふ
+- ▁noise
+- 杰
+- ▁possession
+- 開
+- lat
+- ▁pure
+- そんな
+- ▁Person
+- 造
+- ▁nti
+- 語
+- ▁otherwise
+- ▁gray
+- ▁carefully
+- ▁sogar
+- 産
+- вид
+- 一年
+- 项目
+- ண
+- ග
+- ▁thể
+- kom
+- ние
+- ด
+- 個
+- ▁daily
+- 衣
+- ▁comment
+- ▁我觉得
+- mek
+- ෙ
+- ors
+- 咱
+- ▁10
+- 穿
+- bat
+- ▁areas
+- mme
+- vol
+- ś
+- ▁pres
+- ▁sum
+- 防
+- cause
+- ですから
+- ற்ற
+- ▁stream
+- ▁今
+- ▁были
+- 手机
+- 段
+- igi
+- ▁ear
+- ▁scale
+- ▁activity
+- ▁drawn
+- uh
+- ▁smiled
+- ▁Oder
+- 出现
+- 控制
+- pat
+- ▁conduct
+- られ
+- istic
+- ▁ninety
+- ston
+- ▁surprised
+- ▁са
+- ▁cas
+- ▁engaged
+- pie
+- ▁你说
+- ▁Ent
+- nc
+- ▁fate
+- 動
+- 相当
+- ▁policy
+- 你想
+- ▁Form
+- ▁شما
+- あった
+- ரு
+- ▁aid
+- ▁bur
+- nar
+- ▁repeated
+- ▁jedoch
+- 休
+- ▁throw
+- 给他
+- 才能
+- ▁excellent
+- 結構
+- plo
+- ▁event
+- ▁Ben
+- ign
+- 我们可以
+- ▁семьдесят
+- دي
+- ▁больше
+- 渡
+- eh
+- ▁apart
+- 快乐
+- али
+- 改
+- ▁neu
+- ▁district
+- ij
+- sed
+- 男人
+- ▁gun
+- kte
+- lem
+- ▁versch
+- 应
+- ▁এ
+- يد
+- 《
+- ▁ay
+- мер
+- 様
+- ▁Ye
+- 层
+- ▁fue
+- dat
+- 饭
+- ismus
+- kä
+- ▁spite
+- ▁dire
+- ▁skin
+- ▁castle
+- sz
+- hl
+- ▁خ
+- ▁bil
+- ▁My
+- lig
+- ▁digital
+- 已
+- ▁dick
+- ும்
+- ▁monsieur
+- ▁species
+- ▁О
+- iku
+- ▁machine
+- 愛
+- ▁reform
+- ▁gentlemen
+- ▁fur
+- '16'
+- ▁regular
+- ши
+- far
+- ु
+- ▁mountains
+- ▁fat
+- ć
+- ас
+- ▁på
+- ▁fruit
+- ierung
+- ▁correct
+- 直到
+- 分享
+- ▁difficulty
+- ▁نه
+- こちらの
+- ি
+- ▁Spiel
+- ▁image
+- setzen
+- ▁review
+- ▁todo
+- ▁getan
+- 楼
+- об
+- heim
+- ım
+- ▁hoe
+- ï
+- ດ
+- вар
+- ▁advance
+- 放在
+- dic
+- 底
+- ▁reality
+- 根据
+- ▁portion
+- 強い
+- sor
+- ска
+- ▁ausge
+- ▁commercial
+- ▁created
+- ▁spa
+- 不想
+- tí
+- če
+- ▁还有
+- ela
+- ном
+- ▁temple
+- 同じ
+- ポ
+- ▁anyone
+- 姐
+- ▁보
+- 持
+- ▁keeping
+- udi
+- ▁message
+- 看着
+- ල
+- 数字
+- row
+- یت
+- ▁später
+- isa
+- 并不
+- 上帝
+- ▁crime
+- ▁forced
+- ▁noticed
+- ▁tired
+- ▁choose
+- ▁pieces
+- ▁đã
+- 想象
+- ソ
+- place
+- 活动
+- ▁creature
+- dom
+- ▁union
+- 茶
+- 父母
+- 更多的
+- 兄弟
+- legen
+- ▁wahr
+- '25'
+- 初めて
+- кой
+- fra
+- ques
+- anya
+- 我说
+- ник
+- 没事
+- ずっと
+- ▁proved
+- ▁plans
+- gh
+- ▁legal
+- 每个
+- ▁improve
+- لو
+- ▁pride
+- ▁bevor
+- 午後
+- 组
+- ▁erhalten
+- ▁знаю
+- ゴ
+- hy
+- ▁عن
+- ▁ту
+- ▁كان
+- 아
+- 大人
+- nem
+- ▁suffering
+- rad
+- 楽
+- ▁Well
+- ▁кон
+- ▁duke
+- ダ
+- ▁served
+- ▁sol
+- ša
+- пи
+- 我可以
+- ys
+- ạ
+- yle
+- 小姐
+- ▁dachte
+- fel
+- ▁obviously
+- 再次
+- ▁suit
+- ▁fully
+- ▁frank
+- 作品
+- ▁Kar
+- ピ
+- 性的
+- kim
+- 双
+- こういう
+- ▁bridge
+- pus
+- mont
+- ▁eh
+- 除
+- ▁res
+- ▁mention
+- ▁sounds
+- されている
+- ێ
+- 有些
+- 限
+- 情報
+- ▁لا
+- its
+- ▁blow
+- 精神
+- 做了
+- ▁été
+- ▁然而
+- ▁だから
+- nye
+- kwa
+- ▁peculiar
+- ore
+- ▁burst
+- ▁planet
+- ▁faint
+- ▁jemand
+- 样
+- сто
+- 伯
+- ▁nineteen
+- ▁tur
+- ▁asking
+- ▁pounds
+- ▁bringen
+- んですよね
+- ▁allem
+- 的工作
+- bona
+- ري
+- ▁starting
+- 明白
+- ヤ
+- ▁press
+- 款
+- ▁avait
+- ▁pray
+- 圣
+- 可能性
+- ▁gij
+- 紧
+- ▁height
+- 女孩
+- 告诉
+- nova
+- kir
+- eɣ
+- 梅
+- parti
+- ▁bereits
+- ▁att
+- ▁අ
+- wel
+- ▁david
+- ▁Thank
+- ▁measure
+- ▁date
+- ▁진짜
+- ▁nom
+- fru
+- 痛
+- stro
+- gt
+- 引
+- てる
+- hood
+- নি
+- dal
+- ▁forma
+- 我也
+- 余
+- ิ
+- ▁servant
+- 结
+- ங்க
+- fle
+- ▁stranger
+- ву
+- wana
+- '17'
+- ▁Frauen
+- ▁blo
+- ric
+- ▁Tom
+- auf
+- ней
+- ▁enjoy
+- 料
+- 書
+- 한
+- してる
+- ▁Unter
+- ▁moi
+- wie
+- nni
+- 他说
+- ▁Nu
+- ▁names
+- ult
+- ▁provided
+- ▁breast
+- vez
+- 政策
+- ▁palace
+- となる
+- 大约
+- ल
+- লে
+- ▁costs
+- ▁tap
+- тан
+- amu
+- 男性
+- 而且
+- ▁devil
+- ▁freedom
+- ▁charles
+- ھ
+- 存
+- ▁authority
+- bone
+- mble
+- ▁defend
+- ▁hoch
+- pens
+- ▁бар
+- ▁reasons
+- しい
+- 业
+- ▁Sam
+- ▁sechs
+- ません
+- ▁fancy
+- ▁fashion
+- 毒
+- ос
+- ▁intention
+- ▁alive
+- 倍
+- py
+- 静
+- 今回
+- ▁pair
+- 设计
+- ▁impression
+- 儿子
+- ör
+- ▁sex
+- 伦
+- ellen
+- ▁cyane
+- ▁desert
+- ▁gentle
+- 认识
+- ▁bell
+- ۆ
+- 잖아
+- ала
+- 多少
+- 压
+- 都有
+- ▁style
+- ṛ
+- ▁forms
+- ▁anxious
+- ▁teach
+- 哦
+- 词
+- ▁Pu
+- 更多
+- vent
+- 的生活
+- ▁genug
+- 怕
+- 很好
+- род
+- voll
+- ▁sup
+- ▁Menge
+- 假
+- 方向
+- mark
+- ▁circle
+- ▁weit
+- ▁тебя
+- ddi
+- cul
+- 自分
+- ▁twice
+- তে
+- 政
+- قا
+- ▁ordered
+- ▁gla
+- ▁mer
+- ▁honest
+- ▁сказал
+- のお
+- ▁Zi
+- ▁stra
+- ▁possibly
+- ▁angry
+- ▁س
+- ▁council
+- sol
+- loo
+- dam
+- ▁vida
+- 能力
+- gara
+- ši
+- ▁brief
+- ▁کرد
+- eln
+- 基本
+- ▁лет
+- ró
+- 打开
+- ▁20
+- 犯
+- nik
+- br
+- ▁leur
+- ▁gy
+- mine
+- ration
+- uli
+- ▁ocean
+- ▁origin
+- ▁Sant
+- ▁З
+- ▁geen
+- icht
+- 英国
+- ▁patient
+- ▁ordinary
+- ▁bent
+- ැ
+- ▁british
+- alt
+- ▁ای
+- ▁importance
+- ▁qua
+- ▁són
+- 喜
+- vie
+- ▁яго
+- 多くの
+- ▁legs
+- ство
+- ния
+- kins
+- ▁university
+- long
+- ▁вопрос
+- ▁eigentlich
+- ▁юм
+- ▁hören
+- 好吧
+- shed
+- lijk
+- rag
+- 姆
+- 令人
+- 인
+- 结婚
+- ▁rising
+- дер
+- leid
+- ▁Vater
+- ▁culture
+- ▁或者
+- ▁tongue
+- ▁murder
+- els
+- はい
+- '200'
+- 当中
+- ▁Kopf
+- arm
+- ▁somewhere
+- ▁volume
+- ▁heads
+- ega
+- ▁qual
+- ▁với
+- گر
+- nnen
+- 清楚
+- ▁safety
+- halt
+- ▁ganzen
+- কা
+- ▁zo
+- ades
+- 支
+- pend
+- 同时
+- 嗯
+- 스
+- ▁Mutter
+- 激
+- 選
+- تی
+- сть
+- ví
+- pet
+- ▁problems
+- ▁agreed
+- ▁physical
+- ▁investment
+- 内容
+- ▁truly
+- gus
+- wal
+- ▁row
+- わけ
+- ि
+- ▁security
+- iĝis
+- hab
+- ээ
+- ▁fe
+- ▁тебе
+- ology
+- ▁hate
+- ▁shoulder
+- ▁forgotten
+- ord
+- ▁breakfast
+- ▁rode
+- пла
+- ▁seventeen
+- ▁那你
+- ▁taught
+- ▁ему
+- 你是
+- ▁roof
+- vir
+- ▁aim
+- ▁cook
+- 迫
+- tä
+- ▁med
+- ▁mistake
+- ▁promised
+- ▁много
+- ▁dance
+- 运动
+- ▁сам
+- ▁Cha
+- されています
+- pun
+- ود
+- ▁China
+- ыш
+- ▁join
+- še
+- geb
+- 感谢
+- ▁versuchen
+- ▁waited
+- elijk
+- ▁exercise
+- ▁roll
+- ight
+- uda
+- ▁ability
+- ▁anne
+- rou
+- ▁altogether
+- ▁dare
+- izi
+- ▁remembered
+- plan
+- 息
+- нов
+- 氏
+- гүй
+- люб
+- 主要
+- ▁Jetzt
+- ▁kor
+- ▁би
+- ▁calling
+- fl
+- ▁làm
+- ▁Ch
+- lent
+- ▁fingers
+- ▁dangerous
+- சி
+- ▁weniger
+- ▁hart
+- 不可
+- ▁feelings
+- аль
+- ▁Tra
+- ▁kuri
+- ▁target
+- வா
+- ていた
+- ▁потом
+- old
+- 三十
+- ▁tent
+- ▁salt
+- part
+- 质
+- jn
+- けれども
+- ▁failed
+- 这不是
+- ▁glory
+- ule
+- 每天
+- از
+- ▁восемьдесят
+- 永远
+- ▁chose
+- uit
+- ▁そう
+- اق
+- ▁бу
+- 曾经
+- ▁streets
+- 汉
+- கி
+- ▁specific
+- nje
+- ▁wil
+- раз
+- 関係
+- any
+- を見
+- ▁вообще
+- tch
+- ▁terror
+- tausend
+- すること
+- 一位
+- 税
+- ▁bist
+- ға
+- 跟你
+- ▁empty
+- lief
+- 景
+- ▁helped
+- ▁method
+- ▁james
+- 刚刚
+- ▁vielen
+- zon
+- uni
+- bili
+- jā
+- stellen
+- ▁rot
+- ▁quietly
+- bon
+- ねえ
+- ▁famous
+- pping
+- パー
+- rim
+- 社
+- gor
+- ▁drop
+- 一条
+- ▁fil
+- ▁auto
+- 调查
+- urs
+- ▁express
+- zig
+- ▁grant
+- ziehen
+- ▁active
+- 都会
+- 消
+- ▁wear
+- ▁после
+- bal
+- 婚
+- 亮
+- ▁байна
+- iye
+- ▁пре
+- য়
+- ▁princess
+- ▁limit
+- 后来
+- 万円
+- ld
+- ▁ха
+- ło
+- ▁wij
+- 生命
+- keit
+- の中で
+- ائ
+- ▁ச
+- ▁bul
+- 探
+- ▁Für
+- ▁kingdom
+- cke
+- ▁series
+- ▁sufficient
+- 越来越
+- ▁busy
+- ▁grown
+- бор
+- '13'
+- 有一些
+- ▁Мы
+- ▁aussi
+- tika
+- 企业
+- ▁các
+- 行动
+- ▁issues
+- wert
+- bus
+- ▁rapid
+- зы
+- 告诉你
+- ூ
+- ▁Za
+- ▁شده
+- ▁такой
+- 赛
+- eli
+- もある
+- rz
+- ▁delight
+- ▁sang
+- なんです
+- ▁struggle
+- 堂
+- 在我
+- ▁online
+- ▁그거
+- tori
+- ▁explained
+- ño
+- 軍
+- про
+- ▁fault
+- aw
+- ▁deg
+- প
+- ім
+- rom
+- ▁ended
+- ▁guide
+- 目前
+- ート
+- ▁signal
+- ▁heat
+- či
+- ▁flow
+- 日本の
+- ▁そうですね
+- phy
+- nin
+- 念
+- 旧
+- tto
+- ▁aside
+- ▁gr
+- û
+- ium
+- ▁그래서
+- ▁liegt
+- 散
+- ▁És
+- ▁potential
+- ▁yesterday
+- ▁rights
+- ▁девяносто
+- ▁false
+- gia
+- 医
+- يل
+- nze
+- 違う
+- ▁pré
+- ▁sorrow
+- ▁highest
+- 医院
+- sp
+- されて
+- ▁hinter
+- ▁Wer
+- kā
+- ▁extremely
+- ▁Dia
+- ▁experiment
+- Laughter
+- иться
+- ▁judgment
+- 网络
+- ▁exact
+- ▁сегодня
+- اف
+- ▁fel
+- ▁hell
+- 你说
+- aya
+- 绝对
+- ▁dev
+- ▁verstehen
+- 駅
+- ▁listening
+- おいしい
+- 第一次
+- 的孩子
+- ▁strike
+- ▁connection
+- ▁sentence
+- ▁income
+- 赤
+- 居
+- ล
+- ▁European
+- ▁kuba
+- ▁pou
+- ▁greatly
+- ▁Па
+- 房子
+- ▁described
+- ского
+- したい
+- ກ
+- ▁klar
+- ▁recently
+- 萨
+- ▁priest
+- 旅行
+- ి
+- ▁margin
+- ое
+- ▁pressure
+- ▁touched
+- ▁trial
+- haus
+- ▁separate
+- ▁network
+- ▁refer
+- وي
+- était
+- tle
+- ▁fix
+- ▁previous
+- ▁sides
+- 요
+- 出了
+- 降
+- ▁companion
+- uf
+- uff
+- спе
+- 洛
+- dio
+- ▁때
+- ▁Fragen
+- 真是
+- ▁vers
+- ▁де
+- 那种
+- ▁older
+- ▁disease
+- ▁почему
+- 大的
+- ▁angel
+- lá
+- нем
+- ▁zehn
+- ▁beim
+- 進
+- ▁settled
+- হ
+- oso
+- ude
+- ▁gay
+- rung
+- ▁ride
+- 合作
+- ▁grande
+- ются
+- horn
+- cies
+- ▁becomes
+- ym
+- 剧
+- pla
+- 置
+- からの
+- দ
+- tom
+- 你要
+- ▁eleven
+- ▁When
+- bs
+- ▁людей
+- ▁meer
+- یا
+- ْ
+- ▁map
+- ▁gleich
+- ▁apparently
+- ▁naturally
+- ства
+- ҙ
+- 那是
+- gun
+- دی
+- 很难
+- 来的
+- ▁sage
+- ime
+- ▁onder
+- 英
+- ▁гор
+- ▁Prozent
+- 致
+- ming
+- ▁superior
+- aus
+- tă
+- ▁имени
+- sem
+- 游
+- ウクライナ
+- ▁Lebens
+- 即使
+- ▁hun
+- ▁intended
+- ▁philip
+- lio
+- ▁sixteen
+- сон
+- ▁involved
+- ыя
+- эд
+- ился
+- ▁liked
+- ▁capacity
+- 诺
+- まず
+- 轮
+- ▁vie
+- ▁Ci
+- iste
+- wand
+- ана
+- ▁interview
+- ▁troops
+- ▁Auto
+- eye
+- antes
+- лась
+- ▁même
+- ées
+- ▁belief
+- ້
+- 一家
+- gem
+- cara
+- າ
+- nal
+- ▁него
+- ▁title
+- nge
+- 我们将
+- 参与
+- 你能
+- 机会
+- 除了
+- 产
+- amos
+- ▁bald
+- ▁release
+- ▁corn
+- 谈
+- 粉
+- 创造
+- 親
+- 冲
+- でしょうか
+- ▁ice
+- ▁مت
+- ▁driving
+- ▁stars
+- щ
+- rry
+- ▁student
+- hundert
+- ▁highly
+- 展示
+- 止
+- ▁vote
+- ▁shop
+- ht
+- nehmen
+- ande
+- нд
+- ▁operations
+- eller
+- inde
+- ash
+- ек
+- lot
+- ruh
+- ա
+- ▁career
+- ct
+- ▁definitely
+- ▁chamber
+- ▁argument
+- ▁happening
+- tier
+- fr
+- phi
+- bes
+- 晚
+- гийн
+- ▁sans
+- 讨论
+- ono
+- ▁admit
+- 抓
+- ましょう
+- olo
+- zwa
+- 料理
+- ila
+- ▁ap
+- той
+- 収
+- ε
+- ▁avoid
+- burg
+- ▁thrown
+- vä
+- 実は
+- 校
+- ив
+- によりますと
+- ▁todos
+- ▁contra
+- 架
+- ▁aller
+- 整
+- やはり
+- ▁flo
+- 富
+- ▁suffer
+- ▁shoulders
+- 认
+- ▁minister
+- ▁Th
+- gro
+- ès
+- ▁match
+- ▁ந
+- tud
+- 滑
+- ▁bereit
+- spiel
+- tür
+- 俩
+- 期待
+- ▁concerned
+- 弄
+- ▁windows
+- 一定要
+- ийг
+- ▁harm
+- ▁cup
+- ori
+- hora
+- 末
+- বে
+- ज
+- ario
+- ▁thousands
+- ▁kennen
+- тур
+- লা
+- ▁pointed
+- ▁nay
+- ▁His
+- get
+- ▁ideal
+- 下去
+- 它是
+- ži
+- 箱
+- sing
+- rd
+- '80'
+- ▁fairly
+- ▁standard
+- ▁ее
+- 只能
+- ▁nose
+- 的故事
+- lus
+- kal
+- iamo
+- ▁spiritual
+- ▁해
+- ▁Pri
+- 田さん
+- dale
+- ▁carriage
+- яв
+- رس
+- ▁verwenden
+- ▁kinds
+- 要是
+- ▁Gott
+- 呼
+- tten
+- 块
+- ▁loud
+- ▁helfen
+- ▁joined
+- ▁себя
+- ▁bro
+- 吃饭
+- ▁kid
+- 索
+- 하는
+- 报告
+- vit
+- 可能是
+- ▁novel
+- tain
+- 县
+- 不管
+- ▁weitere
+- gam
+- ejo
+- anza
+- stri
+- ▁claim
+- reich
+- ▁excuse
+- த்
+- வு
+- 复
+- 叶
+- 就在
+- ▁fighting
+- ▁mari
+- пу
+- ▁ones
+- ▁temps
+- 猫
+- ▁affairs
+- gwa
+- ust
+- kana
+- ▁wealth
+- 症
+- 重新
+- ▁useful
+- dé
+- 百分之
+- ▁presented
+- ▁flight
+- 鲁
+- kam
+- 灯
+- ▁grey
+- cut
+- ▁пад
+- 记
+- 每个人都
+- cz
+- ▁papa
+- ique
+- ▁fri
+- rik
+- できない
+- ▁mix
+- lock
+- ▁loose
+- ность
+- fort
+- ▁Cor
+- بي
+- ▁niemand
+- ян
+- ▁cuando
+- ▁prayer
+- ▁faces
+- pad
+- bare
+- 歳の
+- ▁debt
+- ▁evidently
+- abi
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+- ▁möglich
+- まあ
+- ▁suggest
+- 付け
+- pha
+- ща
+- 动物
+- ▁woods
+- का
+- 你不
+- ▁Х
+- vien
+- ▁pull
+- ▁develop
+- 当你
+- ▁cruel
+- ▁そんな
+- ità
+- igung
+- 借
+- したら
+- ▁anders
+- ▁ف
+- بل
+- மா
+- 令
+- dus
+- 因此
+- 市の
+- になりました
+- ▁indem
+- ▁Regierung
+- ▁الح
+- ▁average
+- 时代
+- شر
+- ▁native
+- 速
+- ked
+- ▁ange
+- ▁خود
+- ▁X
+- ▁아니
+- ▁tin
+- ි
+- tü
+- 梦
+- hau
+- ила
+- тал
+- 愿意
+- ▁worst
+- por
+- வி
+- じゃないですか
+- 続いて
+- თ
+- bwa
+- wick
+- ssa
+- ▁lack
+- 表示
+- ▁considerable
+- ▁нужно
+- olu
+- hn
+- 志
+- ▁trail
+- ace
+- rup
+- ▁dogs
+- 勒
+- ath
+- ▁suggested
+- ▁torn
+- パン
+- ▁nos
+- ▁pal
+- 程度
+- 也有
+- ify
+- ▁bid
+- mwe
+- eix
+- 慢
+- ▁raz
+- ụ
+- 都在
+- tent
+- ▁пока
+- 父亲
+- 贵
+- haft
+- 条件
+- ск
+- र्
+- ▁Dan
+- 注目
+- ▁arbeiten
+- lichkeit
+- 是谁
+- bas
+- ▁gate
+- ▁மற்றும்
+- ▁male
+- uel
+- нь
+- ▁dabei
+- ▁sought
+- ▁raise
+- ters
+- elles
+- ▁какой
+- それで
+- ▁이제
+- ▁gain
+- 的想法
+- ▁bodies
+- book
+- ▁wohl
+- ▁それは
+- ゆ
+- hind
+- ыр
+- ▁constant
+- 明日
+- ▁heb
+- ▁好吧
+- 威
+- 心里
+- وت
+- ▁sacrifice
+- 然
+- ▁Männer
+- 普通
+- ▁labour
+- 产品
+- ете
+- ▁bekannt
+- 分析
+- 込み
+- 看起来
+- とき
+- 莫
+- ▁refused
+- 占
+- gé
+- 顿
+- comp
+- しない
+- ▁slightly
+- ▁хорошо
+- 道路
+- ▁fail
+- しっかり
+- ▁sheet
+- 招
+- ▁source
+- ▁Meinung
+- ▁dot
+- ▁Dank
+- 经
+- sign
+- 是你
+- ening
+- ум
+- ▁birth
+- ▁també
+- bury
+- コン
+- ▁hills
+- 顔
+- ui
+- ▁rapidly
+- ▁pur
+- ▁becoming
+- 我知道
+- 谢谢
+- 盘
+- ▁saved
+- kken
+- hang
+- ▁поэтому
+- ▁bringing
+- 谢谢你
+- ▁Nicht
+- mia
+- ▁tender
+- ▁showing
+- дел
+- える
+- 以来
+- pal
+- 也没有
+- ской
+- ▁computer
+- নে
+- ギ
+- ▁virtue
+- 曼
+- ▁nehmen
+- ره
+- 硬
+- ▁unknown
+- ▁tradition
+- fon
+- cr
+- ws
+- なる
+- ▁finger
+- ▁advanced
+- inte
+- мат
+- 句
+- сэн
+- ▁Film
+- 到底
+- avi
+- ▁papers
+- ▁governor
+- trag
+- ▁upper
+- 就可以
+- ▁じゃあ
+- cé
+- produ
+- кам
+- ▁Post
+- ▁audience
+- たち
+- anga
+- 切り
+- با
+- ▁extent
+- ▁peut
+- ▁pin
+- なぜ
+- halten
+- ▁solche
+- ▁gift
+- ▁э
+- endo
+- 側
+- ▁neuen
+- லை
+- ▁pity
+- ▁theory
+- sal
+- 提出
+- ▁Em
+- ▁¿
+- ▁alte
+- 場合
+- ▁division
+- 历史
+- ▁everywhere
+- iva
+- '70'
+- ▁Bir
+- 들
+- なって
+- pli
+- 北京
+- ization
+- 沉
+- ▁çok
+- ▁praise
+- ▁cities
+- term
+- yer
+- ▁Des
+- ▁Vo
+- ▁portfolio
+- ▁Tre
+- これから
+- று
+- 会社
+- 价
+- ▁первый
+- ▁dressed
+- ▁Auch
+- punkt
+- ел
+- 教授
+- ▁servants
+- 建立
+- ▁charm
+- ▁nächsten
+- ▁cell
+- ▁System
+- ベ
+- 靠
+- 育
+- зи
+- стр
+- cept
+- хи
+- ▁See
+- ▁talent
+- ▁Idee
+- wir
+- ▁où
+- 你有
+- right
+- ál
+- 魔
+- ▁кор
+- 射
+- ▁trip
+- ▁established
+- ül
+- ▁sua
+- ▁features
+- ▁الس
+- ▁distant
+- 至少
+- ▁rome
+- ▁Punkt
+- ▁kitchen
+- ced
+- ets
+- 都不
+- stellung
+- ▁wake
+- yl
+- 具
+- ецца
+- ▁advice
+- ешь
+- xa
+- 国内
+- なんて
+- ▁quarters
+- 坏
+- ibi
+- না
+- 折
+- 一百
+- ▁gewesen
+- ▁occurred
+- esa
+- эх
+- ▁Hause
+- kus
+- 結果
+- 刀
+- ér
+- ▁respond
+- 満
+- ▁reflect
+- phe
+- (
+- ır
+- ▁moments
+- ▁Park
+- isten
+- ワ
+- ේ
+- ▁yr
+- ▁beg
+- ▁аб
+- کن
+- ▁mode
+- ▁ذلك
+- jou
+- ▁forces
+- 家里
+- ised
+- ▁committed
+- ▁hy
+- ▁Г
+- 是如何
+- ña
+- zza
+- 判断
+- mber
+- その後
+- ▁announced
+- ▁Tri
+- ▁quatre
+- ▁bought
+- дар
+- уд
+- ности
+- 空间
+- 立ち
+- cro
+- 核
+- 怪
+- 刚才
+- ▁думаю
+- ständig
+- main
+- ▁robert
+- ▁discover
+- ара
+- ▁pace
+- ▁guidance
+- раб
+- ▁include
+- 業
+- ▁kiss
+- mbe
+- 藤
+- ▁pulled
+- ▁había
+- ▁بعد
+- cions
+- 言葉
+- ▁Gesicht
+- بار
+- nell
+- ▁darin
+- сі
+- 恶
+- lf
+- ▁teeth
+- ▁How
+- ▁пе
+- ej
+- を受け
+- 活動
+- ▁lamp
+- '24'
+- ▁smart
+- ▁leg
+- ீ
+- 礼
+- rac
+- ▁lovely
+- pose
+- きます
+- বা
+- த்து
+- 总统
+- 都没有
+- зе
+- ает
+- 所有这些
+- ▁card
+- ▁jane
+- 警
+- ▁facts
+- dur
+- ▁ankaŭ
+- ▁我就
+- mir
+- 身边
+- ▁sunday
+- 投票
+- ▁prefer
+- ră
+- 黒
+- ema
+- ▁detail
+- ▁shame
+- ▁oli
+- 電
+- 마
+- ▁fut
+- str
+- 主义
+- ож
+- ▁block
+- บ
+- ▁Cu
+- 世纪
+- 番組
+- 航
+- ▁هذه
+- ▁frei
+- ▁этой
+- ře
+- ▁closely
+- ▁intelligence
+- ▁erst
+- 卫
+- 意识到
+- ì
+- ▁hang
+- mma
+- ▁hearts
+- ▁leicht
+- ▁drove
+- app
+- nga
+- нос
+- 支援
+- ske
+- 厚
+- ▁bud
+- ▁அவர்
+- ▁One
+- 进来
+- ▁afford
+- back
+- ▁zal
+- 什么时候
+- ▁whispered
+- 現場
+- ▁medical
+- 旅
+- 真正的
+- pra
+- être
+- kor
+- ▁pop
+- 建议
+- tı
+- 増
+- ico
+- ▁Warum
+- ▁போ
+- ▁protection
+- ▁details
+- ▁Ol
+- ▁milk
+- 搞
+- 顶
+- eza
+- いろんな
+- ▁comfortable
+- ことは
+- ▁Ob
+- 论
+- ▁section
+- 属
+- oon
+- ▁department
+- ат
+- ▁다
+- 博
+- ▁gathered
+- 证明
+- ▁existing
+- ން
+- 게
+- von
+- 尾
+- izing
+- ▁affection
+- かなり
+- ech
+- ▁metal
+- ▁liebe
+- 蒙
+- sko
+- ここで
+- 他是
+- ür
+- pé
+- 势
+- ▁dozen
+- фи
+- 影
+- тро
+- 最好的
+- ▁secure
+- lab
+- ▁extraordinary
+- ▁anyway
+- ▁cosa
+- ▁listened
+- 货
+- sus
+- ▁brauchen
+- ▁slight
+- nta
+- ▁Namen
+- ますね
+- ▁conclusion
+- ▁female
+- нее
+- سم
+- 抗
+- пол
+- tos
+- ▁gene
+- 观
+- ▁laughing
+- ▁represent
+- ▁manage
+- 鬼
+- ▁syn
+- ▁solo
+- ▁crown
+- ▁handsome
+- ▁Pre
+- town
+- ▁Video
+- 领
+- ▁ở
+- ▁heeft
+- 我们有
+- 延
+- ▁bitter
+- ▁carrying
+- 一会儿
+- ▁daha
+- 我有
+- ▁„
+- 感情
+- ▁nurse
+- ు
+- 胡
+- ▁smooth
+- ▁iran
+- ▁él
+- 運
+- 経済
+- ▁indians
+- 是为了
+- sicht
+- 位置
+- eḍ
+- ▁donc
+- ▁больш
+- ▁Hal
+- მ
+- nek
+- 象
+- ▁treatment
+- 我已经
+- っていうのは
+- medi
+- ත්
+- ▁অ
+- டை
+- なんだ
+- ப்பு
+- ▁consideration
+- ▁த
+- 制作
+- тэй
+- tions
+- 不到
+- ▁appears
+- ong
+- дал
+- 的一些
+- fini
+- ▁Wissen
+- ▁当然
+- 今回の
+- ▁developed
+- 宫
+- 运
+- ▁careful
+- 新闻
+- ▁obliged
+- ế
+- ▁projects
+- もっと
+- ▁treat
+- kh
+- ŝ
+- ের
+- ▁discussion
+- ▁clar
+- ▁sự
+- べ
+- ▁về
+- zie
+- 思考
+- ▁reward
+- 环境
+- ヒ
+- ▁П
+- kö
+- ▁дело
+- 参
+- ボール
+- гі
+- 冬
+- ente
+- ▁resources
+- ▁வி
+- ▁второй
+- 日に
+- 네
+- ència
+- cks
+- şi
+- ා
+- ▁independent
+- ▁professional
+- さらに
+- ▁Is
+- 地说
+- 各种
+- blick
+- пе
+- ▁staff
+- ▁Raum
+- iyo
+- 铁
+- 是因为
+- zan
+- 十分
+- ▁role
+- 阳
+- 引き
+- ▁тогда
+- pl
+- ティ
+- ▁rough
+- 举
+- ່
+- 冰
+- ▁nta
+- ▁jest
+- ▁focused
+- ▁drug
+- ▁mighty
+- 記録
+- ▁ප
+- द
+- fli
+- aha
+- ctor
+- bor
+- ên
+- bank
+- 日の
+- az
+- ो
+- ▁Q
+- 了吧
+- ▁transport
+- hält
+- ▁lies
+- ▁wow
+- ▁schools
+- wit
+- ▁reg
+- ▁smaller
+- それは
+- 最大
+- ▁Woche
+- ▁кол
+- rap
+- 想到
+- ን
+- ▁systems
+- ▁mé
+- ball
+- あれ
+- なかなか
+- 幸福
+- cion
+- のに
+- ▁response
+- tá
+- ▁compared
+- ▁Mon
+- ▁imagination
+- ▁pare
+- 是吧
+- ค
+- رف
+- ▁200
+- nyi
+- 做出
+- рав
+- ▁louis
+- ppen
+- 曾
+- 会有
+- ▁realized
+- каз
+- ▁Wort
+- ▁Chris
+- ▁artist
+- ção
+- くる
+- ▁element
+- sho
+- ▁sieht
+- 归
+- 说了
+- gl
+- este
+- تا
+- ý
+- gate
+- einander
+- dí
+- ciones
+- 随着
+- ต
+- 欢迎
+- 年代
+- uga
+- 岛
+- 独
+- nom
+- nken
+- げ
+- 职
+- ▁kurz
+- ▁permit
+- 尽管
+- ▁organization
+- vier
+- ère
+- ▁extreme
+- 危险
+- ژ
+- fold
+- ▁training
+- 现
+- hat
+- 寻找
+- ▁spr
+- ings
+- ▁University
+- 行为
+- カー
+- ▁Dollar
+- ▁incident
+- mbi
+- gon
+- 塞
+- ▁David
+- ▁instrument
+- ▁hal
+- star
+- ▁questo
+- ▁rare
+- でしょ
+- 价值
+- ▁Schw
+- 嫌
+- ▁cop
+- ã
+- უ
+- ▁organis
+- 把我
+- ▁Neu
+- யா
+- られて
+- ▁cya
+- ität
+- ▁leader
+- 歩
+- 仕事
+- ▁smiling
+- ▁まずは
+- imo
+- ▁Kind
+- бер
+- ం
+- ▁beide
+- peri
+- 受到
+- 暗
+- ▁contrary
+- ▁saint
+- ▁deine
+- ▁treasure
+- llo
+- ▁hidden
+- ierten
+- iyor
+- ▁patients
+- 状態
+- ▁mess
+- ▁unbe
+- ▁skill
+- nza
+- ▁eso
+- ▁joe
+- ▁principle
+- 示
+- ▁realize
+- 领域
+- ▁EU
+- ▁relief
+- ▁ain
+- 的歌
+- يت
+- ▁Pen
+- ちゃんと
+- ▁arab
+- فا
+- مر
+- ▁Che
+- lim
+- ▁succeeded
+- pos
+- ▁fleet
+- wind
+- өр
+- 同样
+- ▁measures
+- 豆
+- 公共
+- ▁actions
+- ▁temper
+- жы
+- ▁不过
+- һ
+- ▁gan
+- ayo
+- ってる
+- ▁carrer
+- ▁liberty
+- রা
+- ▁ئا
+- ▁setting
+- оў
+- ट
+- ▁protect
+- 藏
+- 我是说
+- ▁colour
+- ▁более
+- 刺
+- ▁function
+- blo
+- ▁estate
+- ▁invest
+- sid
+- ▁families
+- ▁Sha
+- ыл
+- ун
+- ▁wore
+- nat
+- ▁steht
+- 同意
+- 劳
+- ppe
+- ▁falling
+- 必
+- 私は
+- 委
+- 简单
+- ▁suffered
+- に対して
+- тэ
+- টা
+- 随
+- ▁Sp
+- ▁frequently
+- hol
+- ▁somehow
+- 不可能
+- 十二
+- ▁partner
+- itude
+- ▁flesh
+- ▁powers
+- اع
+- 你看
+- ▁olarak
+- ▁ships
+- '500'
+- cus
+- 』
+- ▁yi
+- اه
+- ▁ج
+- 唯一
+- ист
+- ▁stellen
+- 有时
+- ▁campaign
+- uje
+- 『
+- ▁solution
+- aron
+- 上了
+- ubu
+- ery
+- shy
+- ydd
+- ▁nevertheless
+- 投资
+- ▁overall
+- 床
+- 対応
+- ▁affair
+- 我不
+- ▁awful
+- ▁Europa
+- ▁như
+- ▁stark
+- ▁begann
+- ▁Recht
+- ▁picked
+- ▁Ihrer
+- kazi
+- 基本上
+- 说的
+- bia
+- ▁accepted
+- ▁sp
+- ▁request
+- фе
+- ▁Freund
+- ency
+- 功
+- vari
+- shaka
+- ▁favour
+- ▁direkt
+- ▁euch
+- ක්
+- ▁plenty
+- ▁ĉi
+- 有了
+- unt
+- ём
+- 等等
+- ▁businesses
+- ▁seized
+- もあります
+- dien
+- ów
+- ▁grim
+- 特に
+- 不过
+- ▁glauben
+- hal
+- ▁kra
+- üh
+- ▁conference
+- ▁zeigen
+- ▁motor
+- ▁пера
+- 患者
+- юць
+- ▁peu
+- ▁thế
+- 以为
+- 薬
+- sey
+- 产生
+- ▁faithful
+- ▁satisfied
+- fic
+- spect
+- yor
+- lly
+- ▁dust
+- ▁dreadful
+- 即
+- ▁하
+- 私の
+- ▁うん
+- ▁jim
+- ▁pet
+- 套
+- 一直在
+- 에서
+- ▁funny
+- viv
+- 股
+- weg
+- cs
+- ▁expressed
+- ▁مو
+- 累
+- 上げ
+- mara
+- ▁foundation
+- аны
+- ont
+- ▁tip
+- gul
+- وس
+- 職
+- 把他
+- さんに
+- uv
+- にして
+- ▁Sal
+- ▁remarked
+- ここに
+- ▁appeal
+- pel
+- dul
+- ं
+- ▁meal
+- ▁Internet
+- ▁bob
+- ▁fields
+- 前の
+- 生物
+- ▁possess
+- ▁soldier
+- эт
+- つけ
+- ▁horror
+- ▁alla
+- лет
+- 変
+- んだよ
+- ▁kal
+- 你也
+- veni
+- 还没有
+- cri
+- 贝
+- می
+- 当我们
+- лар
+- ome
+- 洋
+- дан
+- ▁воз
+- ▁ක
+- gie
+- 吸
+- 预
+- ▁excitement
+- ▁거야
+- 是一种
+- ▁contact
+- ▁rules
+- 両
+- ▁mac
+- ▁negative
+- ▁Ist
+- 敢
+- ▁Гэта
+- ▁connected
+- ▁universal
+- ▁Gar
+- ▁irgendwie
+- pil
+- ▁majority
+- ▁destroy
+- ▁Los
+- 蒂
+- hod
+- 我去
+- prob
+- kol
+- がありました
+- ▁Den
+- ோ
+- ▁sé
+- ▁relationship
+- со
+- ▁bore
+- ▁lifted
+- 编
+- tory
+- ▁Körper
+- ▁fu
+- ▁whenever
+- かもしれない
+- ▁sprang
+- வே
+- と思う
+- 了一
+- cat
+- ▁我要
+- これを
+- top
+- 汽车
+- зі
+- 害怕
+- '90'
+- ▁iyi
+- ▁With
+- ▁şey
+- ▁qué
+- ▁emperor
+- ▁lock
+- oh
+- ▁Và
+- ▁flag
+- ▁Reihe
+- 结束
+- ▁tail
+- ▁pardon
+- ians
+- ред
+- ▁proof
+- ▁fal
+- ▁protest
+- ▁parties
+- 地域
+- 死了
+- rian
+- 你必须
+- яр
+- ▁rid
+- ▁amazing
+- kas
+- మ
+- nig
+- 袋
+- Z
+- ▁hello
+- ட்டு
+- ké
+- кова
+- ▁challenge
+- 但我
+- сад
+- ▁Pan
+- act
+- isto
+- 我没有
+- زی
+- ▁savage
+- tisch
+- ▁Angst
+- ▁spo
+- 丁
+- mise
+- 弹
+- 我都
+- 可是
+- ▁prior
+- だって
+- ▁West
+- ▁adam
+- ▁nest
+- 我还
+- ▁resist
+- ▁Antwort
+- ▁rev
+- ▁수
+- ▁mot
+- Y
+- ▁இந்த
+- ▁decide
+- ▁wondering
+- ▁phone
+- ▁所以我
+- 境
+- ▁crossed
+- down
+- 疑
+- ▁radio
+- 母亲
+- 印象
+- ▁Saint
+- ▁те
+- mobil
+- ▁wisdom
+- ▁để
+- 战争
+- nna
+- ▁anger
+- ップ
+- ▁flower
+- ▁Familie
+- kli
+- ▁zei
+- مل
+- ▁Не
+- rze
+- ▁screen
+- aniran
+- ington
+- ▁کو
+- ▁frame
+- 食べ
+- ktor
+- ange
+- kü
+- '""'
+- 露
+- ▁đi
+- ▁occupied
+- ả
+- ▁それでは
+- nit
+- 翻
+- ▁despair
+- ▁washington
+- five
+- 人に
+- 務
+- 和你
+- ▁kunnen
+- ров
+- ▁demanded
+- 里的
+- ose
+- 的名字
+- uti
+- 天气
+- col
+- シャ
+- ▁Liebe
+- бай
+- ▁dawn
+- 烟
+- colo
+- 做什么
+- ▁schi
+- 最初
+- ▁statement
+- دار
+- nam
+- نى
+- させて
+- 商品
+- 까
+- band
+- 杨
+- position
+- tage
+- 土地
+- ▁gerne
+- ▁ghost
+- 谢
+- ▁пол
+- ▁fundamental
+- ▁managed
+- 池
+- 治疗
+- duc
+- ▁Ihren
+- 人员
+- ▁enemies
+- eurs
+- ▁School
+- ▁kur
+- ▁rank
+- ель
+- nah
+- なんですが
+- fern
+- ▁yer
+- 衣服
+- 凯
+- ▁communication
+- ▁agreement
+- ▁marked
+- treten
+- ெ
+- tti
+- ▁Son
+- бра
+- 共同
+- 赶
+- みたい
+- ▁一方
+- ▁increasing
+- य
+- 니까
+- ▁Let
+- ▁removed
+- وب
+- 浮
+- 発表
+- 有没有
+- をして
+- ▁dying
+- ▁slave
+- 关键
+- ▁remarkable
+- 进去
+- ▁Krieg
+- 権
+- dzi
+- tó
+- iş
+- ▁Het
+- ▁теперь
+- ▁supper
+- ▁ari
+- ஜ
+- ▁cow
+- न्
+- 他们在
+- 年の
+- ▁improvement
+- ▁mistress
+- 计
+- 舞台
+- 团队
+- ු
+- 面前
+- ered
+- ▁equally
+- ▁суд
+- ▁jak
+- iem
+- ▁violence
+- بی
+- ▁strategic
+- ▁burning
+- öl
+- 没有人
+- 今の
+- くらい
+- ▁шу
+- liegen
+- ▁très
+- ▁schien
+- ▁However
+- ▁mü
+- などを
+- ▁poet
+- ▁الك
+- ▁bishop
+- ▁clo
+- ▁deck
+- お願いします
+- ▁baron
+- ▁Mor
+- stig
+- 有多
+- ▁farther
+- 皇
+- 课
+- 恋
+- кая
+- ень
+- ▁primer
+- 的声音
+- 人民
+- ▁말
+- ▁regarded
+- ▁Spe
+- 你好
+- 值
+- ▁groups
+- ▁asleep
+- 尤其是
+- ▁Э
+- 組
+- пом
+- ▁kom
+- 戴
+- ▁effective
+- ት
+- stin
+- sky
+- ▁mile
+- ▁verschiedene
+- ▁Alle
+- யில்
+- 机构
+- 如果我
+- ında
+- の方
+- 聞いて
+- ▁educa
+- jas
+- ▁code
+- ▁kas
+- ▁Cap
+- stellt
+- ▁Ste
+- chter
+- テレビ
+- ▁generation
+- 坐在
+- 秘密
+- од
+- もし
+- ▁wedi
+- роз
+- พ
+- vita
+- ▁sprach
+- ▁dengan
+- ▁cab
+- ▁describe
+- ▁route
+- ಿ
+- ▁weißt
+- ▁nahm
+- ед
+- いない
+- ▁좀
+- grad
+- ▁esto
+- 원
+- ▁calls
+- 务
+- ▁deeply
+- că
+- انی
+- ▁continues
+- னை
+- オリンピック
+- 了很多
+- ▁meat
+- お前
+- 的大
+- bul
+- thy
+- 先ほど
+- ngu
+- ▁gew
+- uba
+- ▁pack
+- сты
+- wyd
+- 丸
+- ▁arch
+- ▁播放
+- 怀疑
+- 了你
+- weise
+- ага
+- آ
+- ▁topic
+- oz
+- ган
+- ▁herum
+- 看见
+- 和我
+- ▁Amerika
+- 移
+- ▁behold
+- schau
+- 这个人
+- ▁inner
+- 营
+- ▁import
+- angle
+- rice
+- ▁capable
+- гла
+- 女儿
+- ▁nervous
+- ▁Kra
+- 介绍
+- ▁flying
+- 熊
+- ▁hacer
+- ▁chain
+- 唐
+- ▁minds
+- がない
+- whi
+- 妹
+- 混
+- tische
+- 姑娘
+- mah
+- ▁acht
+- ▁будзе
+- 地球
+- ▁أو
+- ▁innocent
+- gui
+- run
+- ▁Men
+- vě
+- ▁software
+- ffer
+- 背景
+- ific
+- らない
+- bet
+- களை
+- تى
+- 宁
+- ▁begun
+- uzi
+- ▁levels
+- vă
+- 年轻
+- ▁yield
+- lap
+- よりも
+- ▁Informationen
+- ▁بی
+- ▁spirits
+- ▁alarm
+- ▁проезд
+- ▁machte
+- ▁explanation
+- 对我
+- 不仅
+- ▁jam
+- zone
+- ▁younger
+- র্
+- 优
+- لت
+- гал
+- toj
+- ▁hide
+- ▁buried
+- マン
+- 攻撃
+- ▁worthy
+- 希
+- 吹
+- ▁Commission
+- ▁konnten
+- ा
+- ▁gods
+- tors
+- lama
+- ▁aspect
+- ▁eene
+- このあと
+- dum
+- 那样
+- ▁pred
+- ison
+- org
+- 并且
+- ▁rear
+- ரா
+- 具体
+- ▁wave
+- 加入
+- клад
+- ▁complex
+- 你现在
+- ▁Arm
+- を見て
+- loc
+- ▁southern
+- ▁mayor
+- ງ
+- gs
+- тель
+- ▁doors
+- лә
+- ▁citizens
+- 麻
+- 眼睛
+- ▁forgive
+- نت
+- ▁evident
+- 事儿
+- 人で
+- 帮我
+- வை
+- ▁commission
+- ▁site
+- 自身
+- ▁included
+- igt
+- 宮
+- 莉
+- 不太
+- 莱
+- ▁unique
+- setzt
+- ▁अ
+- 第三
+- ▁sons
+- 展
+- ▁dull
+- pass
+- ▁我说
+- ▁bottle
+- 容疑者
+- ▁dari
+- ▁الع
+- wydd
+- ▁verschiedenen
+- ▁bull
+- 一只
+- ▁stehen
+- 関
+- っていた
+- ▁کا
+- ▁чего
+- pers
+- ν
+- ступ
+- anda
+- 高い
+- তো
+- worth
+- 你还
+- ▁dim
+- ▁tower
+- ▁millions
+- ▁satisfaction
+- ▁effects
+- ▁rates
+- ▁gener
+- 表明
+- ▁虽然
+- 灵
+- 전
+- kuwa
+- 叫做
+- 下面
+- ▁cheer
+- من
+- کر
+- 领导
+- 变化
+- عد
+- 正常
+- 第一个
+- ▁clever
+- ▁treated
+- ▁divers
+- water
+- lös
+- ő
+- ▁rocks
+- 路上
+- ▁necessity
+- もちろん
+- 的一部分
+- licht
+- ingly
+- ۇ
+- 我们会
+- yard
+- 了他
+- аг
+- ▁ново
+- oka
+- ▁zero
+- ▁fund
+- ▁Natur
+- 接種
+- ridge
+- ▁pipe
+- 老板
+- قد
+- ▁figures
+- 丽
+- vant
+- 在哪里
+- maze
+- だけで
+- 替
+- lah
+- ▁edward
+- ▁shade
+- 人間
+- 乡
+- 在他
+- nim
+- ĉ
+- 备
+- それが
+- 新しい
+- ▁friendship
+- oi
+- ホ
+- 你这
+- nud
+- ▁liber
+- ▁sheep
+- ▁вер
+- ▁pictures
+- 义
+- ▁election
+- bung
+- gira
+- ▁Tur
+- эм
+- alo
+- ▁attitude
+- ▁них
+- jes
+- 橋
+- ▁conscious
+- že
+- ▁proportion
+- ▁ruin
+- cil
+- ▁себе
+- cyo
+- 其他人
+- ▁document
+- ▁western
+- ▁oo
+- ез
+- ▁economy
+- ▁twee
+- cient
+- ư
+- ▁palm
+- gua
+- elli
+- ств
+- と思って
+- raz
+- ▁execution
+- 視
+- 麦
+- ными
+- ▁entrance
+- 食物
+- кт
+- ▁manifest
+- ци
+- ▁perspective
+- ▁nations
+- ▁ху
+- lea
+- anti
+- ▁Bre
+- ▁plate
+- ▁desired
+- ço
+- ペ
+- ▁alten
+- ▁الب
+- ▁expense
+- 女子
+- そこ
+- vou
+- 目的
+- 坚持
+- ▁tiny
+- voir
+- ▁allein
+- ▁confi
+- шел
+- 导
+- ります
+- tica
+- ▁gradually
+- ▁Chi
+- cial
+- ▁pli
+- 壁
+- ▁mem
+- tab
+- 会议
+- wis
+- ış
+- 私が
+- ▁smith
+- 一人
+- ▁obtained
+- في
+- ▁relation
+- ▁grup
+- ▁gaze
+- رب
+- rous
+- していました
+- か月
+- 彩
+- rk
+- 许
+- hä
+- ▁tour
+- ▁giant
+- ▁perform
+- いや
+- ▁lad
+- ▁triumph
+- ▁finish
+- oku
+- ニュース
+- 艺术
+- ▁бер
+- 席
+- ▁всех
+- やすい
+- hung
+- ▁mí
+- jí
+- ▁sail
+- ▁require
+- ▁core
+- ▁einzige
+- 調査
+- рам
+- 马上
+- ▁сказать
+- 影響
+- ▁wounded
+- griff
+- 大哥
+- ▁Tür
+- 完了
+- ▁Ziel
+- 一场
+- 一旦
+- gent
+- ▁untuk
+- ▁criminal
+- nny
+- 企業
+- ▁revolution
+- 还要
+- ▁mystery
+- ▁mercy
+- aban
+- ▁constantly
+- cli
+- ▁teaching
+- taj
+- shobora
+- rod
+- cast
+- roll
+- ▁teacher
+- итель
+- ologie
+- bly
+- ▁update
+- 僕
+- ▁acquaintance
+- 休息
+- 今月
+- ▁article
+- 挑战
+- ▁эта
+- ▁bisschen
+- 某
+- ▁あの
+- fri
+- 照顾
+- 酸
+- ▁nói
+- وق
+- ▁sul
+- ▁visible
+- ▁instantly
+- gegangen
+- ▁dei
+- ▁それ
+- ▁значит
+- ▁versucht
+- 対策
+- ▁работа
+- өө
+- ▁через
+- ▁Ed
+- 测试
+- 护
+- year
+- というのが
+- ▁counsel
+- сла
+- ҫ
+- ですけど
+- 接下来
+- ▁你看
+- 難しい
+- ▁tres
+- 实现
+- ▁триста
+- last
+- 按
+- 觉
+- 宗教
+- ▁Plan
+- کی
+- 这么多
+- 确定
+- 悪
+- ▁letzte
+- ец
+- ▁violent
+- ▁knees
+- ▁knight
+- 在我们
+- 如果我们
+- isha
+- aria
+- ını
+- ▁пу
+- '21'
+- gru
+- ▁nel
+- ▁crack
+- ്
+- 因为他
+- ttle
+- ▁splendid
+- ▁richard
+- ▁Mont
+- ▁brilliant
+- ▁assured
+- script
+- 对你
+- rel
+- ▁mill
+- sca
+- imu
+- 減
+- ▁jede
+- ▁какие
+- 责任
+- apo
+- ▁être
+- ▁az
+- 补
+- 就是说
+- ▁earnings
+- 新たな
+- ▁fragte
+- ვ
+- ▁necessarily
+- tiv
+- ▁wit
+- ▁critical
+- ▁harry
+- 一张
+- gol
+- 赶紧
+- 潜
+- ではない
+- cou
+- 一緒に
+- ▁confident
+- ▁lag
+- 本来
+- ew
+- In
+- лася
+- 言って
+- 建筑
+- ާ
+- 上がって
+- ▁multi
+- ically
+- ▁turns
+- 惊
+- ию
+- ▁website
+- よかった
+- ▁worship
+- ▁unable
+- ▁throat
+- ӱ
+- ▁grief
+- lement
+- ここから
+- مي
+- ▁relations
+- iɣ
+- ▁كل
+- ière
+- 额
+- ▁published
+- 纸
+- dina
+- ▁vis
+- umi
+- ▁suspicion
+- 陆
+- test
+- ▁دارد
+- ўся
+- ▁sẽ
+- ▁Det
+- 也会
+- dad
+- eten
+- ▁petit
+- در
+- জ
+- 我们要
+- ▁darum
+- ▁maintain
+- ▁director
+- ▁الن
+- غا
+- 这个问题
+- 馬
+- 我很
+- aja
+- ▁Paul
+- 库
+- fy
+- ▁official
+- rí
+- ▁delivered
+- ▁cart
+- ▁goed
+- 上面
+- ▁schön
+- ▁prisoner
+- enga
+- 让他们
+- ▁universe
+- 挑
+- ▁rooms
+- оп
+- ▁bija
+- аць
+- 站在
+- pper
+- ایی
+- ▁оста
+- ▁regret
+- ▁differ
+- ▁나는
+- даг
+- ▁Col
+- んですけれども
+- ene
+- ந்து
+- 这儿
+- トップ
+- ▁mat
+- 言う
+- ▁puis
+- tail
+- だろう
+- ▁実は
+- 来到
+- 起こ
+- ▁tal
+- ▁objection
+- ▁Not
+- üm
+- ▁reden
+- 我自己
+- 经历
+- ▁midst
+- ▁admitted
+- ▁እ
+- 耳
+- ▁نمی
+- にかけて
+- ится
+- ▁threat
+- ás
+- ▁stronger
+- ingen
+- 考えて
+- ხ
+- كم
+- Т
+- ▁county
+- zem
+- ▁halten
+- 遇到
+- eff
+- 聊
+- ▁worry
+- 错误
+- fla
+- 观察
+- fte
+- ▁proposed
+- 免
+- ▁hence
+- ▁attend
+- لة
+- ume
+- ▁cada
+- ▁nearer
+- ▁Mädchen
+- 联
+- 大事
+- 甲
+- lassen
+- өн
+- ▁такое
+- ▁roz
+- の中
+- ▁guns
+- ик
+- ট
+- cade
+- 牌
+- 岡
+- ▁description
+- ▁Ка
+- ▁lots
+- ないと
+- ▁web
+- 男孩
+- ▁잘
+- 最大的
+- اند
+- 争
+- ▁adalah
+- рад
+- ▁concerning
+- ▁singing
+- ▁blame
+- lè
+- ▁affected
+- ▁folks
+- ▁verwendet
+- kira
+- ા
+- fas
+- ▁shock
+- ▁さらに
+- 容易
+- ick
+- 日本人
+- ously
+- 你自己
+- ▁possessed
+- mittel
+- ▁cabin
+- бі
+- ▁liberal
+- ▁我认为
+- ▁stones
+- 根本
+- 缺
+- ▁engine
+- graf
+- vat
+- ▁vice
+- ▁plants
+- 变成
+- dung
+- 录
+- lerin
+- ▁Bro
+- 唔
+- ▁mc
+- dü
+- ▁бул
+- ▁changing
+- ▁scientific
+- ท
+- ▁immense
+- ▁val
+- zug
+- ▁sport
+- ▁largest
+- über
+- lk
+- ▁magic
+- ▁гар
+- meter
+- 届
+- gger
+- ე
+- 笔
+- ▁私は
+- ▁absence
+- 京
+- ▁absolute
+- ▁дела
+- லி
+- cons
+- 媒体
+- ▁пи
+- 这件事
+- 原来
+- ▁wy
+- 丹
+- قل
+- aries
+- ▁Kan
+- ▁conflict
+- تم
+- 对吗
+- 府
+- ▁над
+- ischer
+- 園
+- ▁brothers
+- しく
+- oth
+- னி
+- ▁proceeded
+- ▁sem
+- nak
+- ▁intent
+- ▁کند
+- ▁disappeared
+- 只要
+- ово
+- bag
+- chte
+- ▁govern
+- ugh
+- きのう
+- 秀
+- yar
+- ▁mortal
+- 停止
+- ▁voices
+- 之间的
+- ▁nächste
+- ディ
+- ▁Au
+- ▁rue
+- گی
+- ප
+- zz
+- ▁bleiben
+- ▁لم
+- ▁favorite
+- pak
+- zähl
+- ▁hoped
+- ▁Nein
+- ▁mae
+- なり
+- 声音
+- ▁seated
+- дет
+- ▁bold
+- meye
+- ▁affect
+- ▁tempo
+- ે
+- ▁soil
+- 乔
+- 宇宙
+- ह
+- 镇
+- 骗
+- یک
+- ▁мар
+- 暴
+- aan
+- ▁waters
+- 中で
+- ั
+- ucht
+- мет
+- ▁awake
+- elo
+- ▁expectations
+- ▁sieben
+- знач
+- মা
+- としては
+- 撃
+- 工具
+- usa
+- 记录
+- 일
+- ▁arrival
+- atu
+- 在这
+- ▁Que
+- ▁hole
+- ffe
+- 港
+- ▁coffee
+- 選挙
+- gleich
+- ▁studies
+- 幅
+- ▁gente
+- 嘴
+- komp
+- ▁wusste
+- tric
+- ▁library
+- 增加
+- nzi
+- ▁delicate
+- ▁domestic
+- mina
+- ▁scheme
+- ▁перед
+- rö
+- bild
+- ▁profession
+- ▁experienced
+- ▁jedes
+- ▁interests
+- 后面
+- ▁Kom
+- 经过
+- ▁elizabeth
+- enda
+- プロ
+- ▁vessel
+- hold
+- ▁eating
+- ▁quando
+- czy
+- abil
+- merk
+- ▁remind
+- 盛
+- ▁channel
+- ▁Nacht
+- ▁না
+- 有个
+- 也没
+- 意思
+- ▁link
+- ▁تر
+- ▁numerous
+- zt
+- のある
+- 今後
+- bun
+- نگ
+- ▁recognized
+- ▁scheint
+- ▁الج
+- 这就是
+- рын
+- ▁custom
+- ▁mundo
+- ▁occur
+- nau
+- 的原因
+- 所以我
+- lect
+- сці
+- そうな
+- ▁vez
+- 行业
+- ▁bath
+- ▁Gehirn
+- ax
+- 由于
+- 描述
+- ▁pol
+- 陪
+- ▁silk
+- ort
+- ▁crew
+- ▁Wi
+- bt
+- ▁frightened
+- ▁всего
+- 现实
+- dies
+- 娜
+- ザ
+- ▁seines
+- たくさん
+- ▁えっ
+- ▁hor
+- 我不知道
+- ▁Bereich
+- ▁ceased
+- 湖
+- 爹
+- ▁israel
+- ▁wondered
+- ▁objects
+- 是我们
+- ▁prime
+- 丝
+- ▁apartment
+- ▁steel
+- ▁ещё
+- ただ
+- 我相信
+- ▁resolved
+- ▁Nor
+- ▁Bur
+- 固
+- Al
+- 洞
+- ▁更に
+- 中央
+- 舞
+- ▁china
+- ▁cert
+- ▁Mein
+- 确保
+- mini
+- 怀
+- ▁stepped
+- 依
+- ▁shelter
+- ▁fourteen
+- ching
+- ▁bou
+- ▁failure
+- ▁investments
+- амі
+- кон
+- ▁christmas
+- ▁signs
+- 看到了
+- ▁khi
+- ref
+- ছে
+- ▁groß
+- மை
+- ▁committee
+- autre
+- ▁anywhere
+- ▁organ
+- ▁게
+- ▁apply
+- ບ
+- いません
+- ▁chosen
+- ook
+- тә
+- ▁buck
+- んだけど
+- 음
+- ▁Wal
+- ular
+- erung
+- 優勝
+- ▁mysterious
+- ▁پر
+- 決め
+- ĝ
+- ▁friendly
+- 上がり
+- 打ち
+- rez
+- ▁Abend
+- ▁tous
+- xe
+- 万人
+- ▁Энэ
+- ▁кар
+- ぬ
+- سل
+- prav
+- scher
+- adi
+- ▁ён
+- lyn
+- スター
+- jun
+- ை
+- 一部
+- බ
+- thing
+- ▁principles
+- ▁너
+- eka
+- ອ
+- ▁voyage
+- ラン
+- nut
+- 最好
+- ▁rent
+- 方が
+- ▁Tod
+- ▁Staaten
+- ▁دە
+- 奖
+- ▁majesty
+- wij
+- 易
+- ▁tiene
+- ▁закон
+- 麻烦
+- ▁Rat
+- 过了
+- 達
+- ▁حال
+- 传统
+- ۈ
+- iyi
+- 週間
+- つく
+- radi
+- ▁Haupt
+- hil
+- ▁bet
+- 对不起
+- ▁prices
+- ▁manchmal
+- ▁manera
+- 习惯
+- ▁construction
+- gat
+- X
+- 的所有
+- ▁error
+- 抱
+- ▁està
+- なくて
+- ▁heißt
+- weit
+- 我现在
+- ▁mission
+- nica
+- àn
+- cor
+- zna
+- scha
+- ときに
+- hui
+- ago
+- ▁ee
+- ▁herr
+- 模式
+- 人気
+- 高兴
+- 准
+- ▁为什么
+- どこ
+- стан
+- 学院
+- 我看
+- 結
+- gne
+- fahren
+- ▁foolish
+- 枚
+- ə
+- ที่
+- ifi
+- já
+- homme
+- 检查
+- 银行
+- 允许
+- дзі
+- ▁pause
+- ▁rief
+- ො
+- 醒
+- uto
+- というか
+- টি
+- list
+- 午前
+- ▁problema
+- char
+- 勢
+- ▁Licht
+- nov
+- 乗
+- ▁Pla
+- ▁char
+- ▁internet
+- ▁coach
+- кан
+- 还没
+- enza
+- ▁gently
+- ▁beach
+- fried
+- 标准
+- ñ
+- ▁currently
+- 不行
+- ▁encore
+- 圈
+- ለ
+- iß
+- fect
+- 年轻人
+- ▁College
+- ọ
+- ▁discovery
+- ▁practical
+- ▁ĝi
+- 电视
+- poli
+- ▁oben
+- 命令
+- дзя
+- 艾
+- ▁stands
+- 虽然
+- ▁besteht
+- ▁Möglichkeit
+- ▁git
+- ▁paused
+- ▁sooner
+- ksi
+- ▁Mais
+- unk
+- ▁사
+- த்தில்
+- iden
+- cc
+- ▁这就是
+- زن
+- 名字
+- кра
+- ▁trois
+- 您的
+- door
+- 刻
+- ▁mille
+- ген
+- スタート
+- ▁cui
+- ▁Will
+- ▁sacred
+- やり
+- च
+- зу
+- system
+- ▁mud
+- inter
+- ▁одна
+- ▁quand
+- dienst
+- ▁hon
+- แ
+- 这位
+- ▁blessed
+- ▁pushed
+- ▁reco
+- 瓦
+- ▁ariko
+- 大多数
+- ▁наш
+- ▁Wahl
+- ▁reign
+- 장
+- air
+- kum
+- 来看
+- دة
+- ▁delay
+- стро
+- жу
+- ▁mont
+- ▁fought
+- seite
+- ог
+- ▁reported
+- chester
+- 他就
+- gged
+- ▁bare
+- ▁sigh
+- 薄
+- ▁thunder
+- شی
+- 年間
+- いつも
+- アン
+- ▁Yes
+- ▁Präsident
+- ▁northern
+- ▁Sir
+- ले
+- 当地
+- dik
+- sy
+- tia
+- liv
+- عمل
+- essa
+- 軽
+- 议
+- ▁modest
+- 蛋
+- ngi
+- gesetzt
+- oto
+- ▁empire
+- geführt
+- 人たち
+- nej
+- ▁Ihrem
+- ъ
+- ▁waste
+- ыг
+- 被害
+- ▁İ
+- bile
+- ▁delle
+- 弗
+- でき
+- 当我
+- ▁quan
+- ▁collection
+- ▁கா
+- ▁prima
+- 杯
+- ▁photograph
+- ▁veel
+- 压力
+- tif
+- 典
+- ▁wings
+- мә
+- ச்
+- rij
+- ▁dreams
+- 司
+- ▁dur
+- 予想
+- ▁gained
+- 怎么了
+- при
+- ▁내
+- ▁سر
+- ▁horrible
+- 人物
+- пар
+- 我能
+- ▁fled
+- 超过
+- 干什么
+- cí
+- rais
+- фа
+- 調
+- vision
+- ăm
+- ars
+- ▁easier
+- ▁могу
+- ▁Probleme
+- 있
+- 顺
+- facebook
+- ▁sofort
+- たま
+- not
+- zog
+- 戏
+- isme
+- ▁آنها
+- ే
+- 显示
+- ▁funktioniert
+- wali
+- なんと
+- 本身
+- 姿
+- 永
+- ▁stairs
+- ▁sale
+- ulo
+- ép
+- roj
+- ▁Port
+- ▁proceed
+- 甘
+- ▁copy
+- ▁burn
+- ▁bearing
+- 確認
+- ▁consequence
+- ▁completed
+- 不错
+- 在一个
+- ▁се
+- fred
+- 我喜欢
+- ▁employed
+- 瑞
+- ▁Oh
+- ▁banks
+- ห
+- 棒
+- ▁enormous
+- 烧
+- ▁sympathy
+- ▁Lage
+- vet
+- ▁Some
+- ▁concern
+- cré
+- ங்கள்
+- α
+- faced
+- richtet
+- ▁factors
+- اح
+- 哭
+- ▁nada
+- ▁Tor
+- ▁philosophy
+- ▁clouds
+- レー
+- ▁gesprochen
+- ▁tight
+- asta
+- born
+- 既
+- head
+- indi
+- ▁By
+- ▁instinct
+- ▁algo
+- 幕
+- ゲーム
+- 困难
+- rr
+- 我这
+- ҡа
+- ▁elements
+- の中に
+- lık
+- ▁congress
+- ▁你要
+- ▁album
+- ▁London
+- ▁relative
+- typ
+- cted
+- 专业
+- ▁connect
+- ▁restaurant
+- lier
+- ▁climate
+- ▁goal
+- ▁Gespräch
+- 则
+- ▁mood
+- ▁classes
+- ▁introduced
+- ▁gì
+- üt
+- ο
+- ▁schwierig
+- ▁anche
+- лег
+- ▁maid
+- 持续
+- ава
+- ▁König
+- 振
+- tique
+- dda
+- ▁odd
+- cord
+- tit
+- 试
+- dit
+- ▁segment
+- ņ
+- 転
+- ▁conscience
+- ▁retreat
+- ▁По
+- ▁jar
+- 難
+- ▁earn
+- ▁Por
+- sión
+- 我真的
+- 盗
+- それから
+- ▁التي
+- kro
+- عة
+- '!?'
+- ▁version
+- 施設
+- ▁Fin
+- 那就是
+- 徒
+- 对我来说
+- ▁cave
+- ▁medicine
+- ▁application
+- ции
+- 约翰
+- ▁Bild
+- ▁informed
+- zio
+- rant
+- ▁gör
+- 说你
+- ▁谢谢
+- 大量
+- ajn
+- 腿
+- جا
+- 傷
+- аж
+- ▁previously
+- pho
+- ▁immediate
+- 痛苦
+- 敬
+- 他们会
+- ▁witness
+- ▁interpret
+- 避難
+- gebracht
+- じゃあ
+- ▁benefits
+- ▁morgen
+- ▁reference
+- ▁feed
+- ▁chu
+- 终于
+- mmel
+- 年に
+- 하고
+- ▁එ
+- ▁anxiety
+- ▁severe
+- ▁via
+- اش
+- 移动
+- れて
+- пы
+- ẓ
+- ▁discuss
+- 到达
+- のように
+- ▁但是我
+- ▁returning
+- オー
+- нес
+- 捕
+- ▁recognize
+- ▁Vielleicht
+- ▁nord
+- 取材
+- 怎么办
+- 打电话
+- ▁falsch
+- ช
+- ▁Mark
+- gomba
+- ▁cheap
+- ишь
+- 突
+- ▁recon
+- 動き
+- 好吗
+- 炎
+- ▁kit
+- ▁Of
+- ▁ease
+- ▁yards
+- рь
+- ▁engagement
+- だと思います
+- oro
+- 大丈夫
+- ubi
+- ▁games
+- ▁Musik
+- となりました
+- 状
+- 季
+- ものを
+- ▁slide
+- aza
+- segu
+- führen
+- 注
+- さんと
+- 他会
+- 想法
+- ▁hurried
+- termin
+- تي
+- ▁mostly
+- sun
+- める
+- ▁wheel
+- kem
+- 反对
+- ▁intend
+- いって
+- 知识
+- ▁amongst
+- ún
+- ▁countenance
+- fite
+- ワクチン
+- 全然
+- ▁variety
+- ▁thomas
+- 形式
+- ▁بە
+- ▁intelligent
+- force
+- 值得
+- acht
+- jem
+- 用户
+- ▁الش
+- нага
+- quin
+- rri
+- hle
+- ▁rush
+- тов
+- 说什么
+- ▁అ
+- овать
+- ▁Sin
+- ▁کنم
+- anto
+- 吓
+- いっぱい
+- ▁partly
+- ▁hinaus
+- ▁guilty
+- isse
+- ▁fox
+- stead
+- 確
+- ▁rope
+- 出場
+- ▁вос
+- 面对
+- ▁assistance
+- ▁gesch
+- ▁Fo
+- いつ
+- கொண்ட
+- tech
+- 据
+- ▁Miss
+- ▁tools
+- lau
+- 逃
+- 大臣
+- тив
+- лов
+- ▁которая
+- ▁Ber
+- なんで
+- 显然
+- ▁solemn
+- 楽しみ
+- 辛
+- ▁geworden
+- ald
+- ▁eager
+- ▁counter
+- 我们现在
+- mana
+- ▁consciousness
+- iendo
+- ▁为了
+- iba
+- ▁너무
+- 胸
+- ▁També
+- ▁scho
+- ▁vent
+- xon
+- ▁candle
+- ▁Ш
+- ▁lion
+- ▁combat
+- 关心
+- ije
+- ▁located
+- ▁شود
+- staat
+- ▁resolution
+- 検査
+- ▁august
+- ▁disse
+- 交通
+- گو
+- ▁depend
+- ▁Von
+- かい
+- ▁Gen
+- 不安
+- はこの
+- ▁你是
+- chu
+- ▁Programm
+- aku
+- luc
+- ▁joke
+- مه
+- lands
+- эг
+- ▁adult
+- ▁Да
+- が出
+- 成员
+- 姐姐
+- 照片
+- ▁Ap
+- ▁consent
+- rer
+- ▁tief
+- ▁rub
+- 察
+- ып
+- 全球
+- 族
+- 落ち
+- ▁ließ
+- ▁High
+- feld
+- tul
+- cl
+- 事实
+- 하
+- 今日は
+- 响
+- rés
+- ▁Ger
+- рт
+- ও
+- ▁Herz
+- 你别
+- ▁سال
+- 的朋友
+- ▁neces
+- ▁pitch
+- gno
+- ▁tai
+- kie
+- ▁notion
+- ▁yu
+- eks
+- kora
+- ▁victory
+- hur
+- ▁Bas
+- agi
+- のでしょうか
+- 映画
+- 评论
+- کە
+- ▁Pol
+- どんどん
+- ▁enable
+- ▁marketing
+- ▁número
+- 摩
+- ▁reduce
+- ▁cela
+- වි
+- ్
+- ▁cru
+- byo
+- ril
+- ▁push
+- ▁ເ
+- yen
+- ▁bre
+- ▁flash
+- やる
+- ▁خو
+- すぐ
+- ▁improved
+- 持ち
+- まる
+- ät
+- had
+- ря
+- 糖
+- かもしれません
+- 负
+- دم
+- anyi
+- baza
+- ▁überhaupt
+- 鸡
+- れた
+- ▁rushed
+- 回来了
+- оль
+- ▁Una
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+- 사
+- ▁refuse
+- 语言
+- fangen
+- ▁Eu
+- 選手が
+- kka
+- 魚
+- ▁wider
+- 骑
+- 善
+- ▁eternal
+- قر
+- ▁trick
+- ▁así
+- ongo
+- ▁worn
+- ▁stores
+- рос
+- 制造
+- lied
+- rica
+- raf
+- 十年
+- omo
+- 、2
+- ▁smell
+- rav
+- ▁pensa
+- ▁continent
+- ▁stupid
+- となった
+- 接触
+- ▁werd
+- ▁Para
+- 闹
+- ▁stir
+- ▁score
+- 구
+- とても
+- 者の
+- ▁こちら
+- 属于
+- çi
+- том
+- 説明
+- 受け
+- ▁gest
+- サン
+- ului
+- ▁slip
+- 없
+- 増え
+- ▁apple
+- のかな
+- 偏
+- 承认
+- ▁surrounded
+- ▁Zukunft
+- ▁valuable
+- ▁führen
+- 答案
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+- teilen
+- ▁einigen
+- 孙
+- ▁rw
+- handel
+- ▁approached
+- cis
+- ▁exchange
+- 、1
+- ▁internal
+- ▁мал
+- まり
+- ラー
+- ▁gegenüber
+- 埃
+- lain
+- ▁absurd
+- 全体
+- ция
+- ▁phil
+- 上海
+- ▁associated
+- ▁điều
+- ▁cam
+- ▁William
+- ▁dont
+- ▁পা
+- шла
+- 技
+- ここまで
+- ▁abantu
+- kna
+- 章
+- hem
+- 例如
+- ▁Dass
+- ▁softly
+- qa
+- ▁Pra
+- 不再
+- 抽
+- ▁họ
+- sehen
+- ▁sunt
+- rada
+- を見せ
+- ▁நா
+- lou
+- ▁kindly
+- رة
+- уб
+- 巨大的
+- 作り
+- bling
+- ▁fet
+- ▁moreover
+- quel
+- ▁uniform
+- laufen
+- ▁stable
+- ▁байсан
+- ▁dal
+- ▁clients
+- wol
+- だよ
+- ismo
+- コロナ
+- 材料
+- ▁bitte
+- ▁genius
+- mā
+- があって
+- fat
+- ▁escaped
+- guard
+- 男子
+- 在你
+- ▁beginnen
+- ▁substance
+- ▁bush
+- 小心
+- 呆
+- ▁suggestion
+- دان
+- 跟他
+- ▁limited
+- ーン
+- ▁offen
+- ▁unserem
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+- cel
+- ▁يمكن
+- mate
+- 独立
+- giye
+- ▁mur
+- ▁robot
+- etti
+- esc
+- grund
+- ▁Bra
+- 负责
+- ▁situa
+- 开心
+- ▁Don
+- 羽
+- ▁dessen
+- ▁plane
+- ことで
+- gü
+- ▁куда
+- ▁closer
+- ▁precious
+- 거든
+- овская
+- است
+- ▁convinced
+- ▁bomb
+- tà
+- จ
+- 種
+- ▁früher
+- май
+- ał
+- ▁confess
+- そこに
+- ▁жа
+- 因为我们
+- ▁phải
+- вой
+- кс
+- ▁curiosity
+- نو
+- 図
+- 別
+- ▁десять
+- tting
+- йте
+- ▁kiel
+- ▁Meine
+- できた
+- ▁sap
+- 大きく
+- ▁addressed
+- 康
+- ota
+- சா
+- نه
+- kali
+- тым
+- ▁shouted
+- ▁этим
+- гә
+- ▁сер
+- gere
+- 総
+- ▁우리
+- ▁concept
+- here
+- のために
+- ▁unten
+- ▁National
+- sek
+- ▁crea
+- ▁hare
+- ▁button
+- なのか
+- উ
+- これまで
+- 関東
+- quet
+- 当他
+- 弟
+- ▁نو
+- ▁நி
+- 博士
+- stal
+- 佐
+- amo
+- ▁gal
+- mé
+- ár
+- zing
+- ▁zwanzig
+- ▁nachdem
+- بو
+- していく
+- ▁pressed
+- ▁neza
+- 有关
+- ▁dignity
+- ▁meanwhile
+- ▁Millionen
+- ▁causes
+- ▁poison
+- を取り
+- pir
+- ▁arrive
+- ▁mia
+- ▁flood
+- ory
+- ▁thu
+- 放弃
+- 早く
+- ckt
+- 的那个
+- anar
+- 虚
+- ▁lights
+- 雲
+- wart
+- ▁같애
+- مو
+- ▁doen
+- ▁phrase
+- そうですね
+- 的一
+- ▁cum
+- யை
+- Co
+- 込んで
+- ▁tiu
+- idos
+- سب
+- ▁今日は
+- ▁musste
+- 似
+- қ
+- ▁flew
+- ▁Mittel
+- 给我们
+- ライ
+- ▁bigger
+- prov
+- ▁Qui
+- ▁hanging
+- schrift
+- してください
+- ▁За
+- 犯罪
+- ▁purchase
+- ▁quarrel
+- üs
+- ▁seg
+- ণ
+- ▁federal
+- ී
+- 说我
+- ▁también
+- ▁yari
+- apa
+- nja
+- ▁succeed
+- ▁hielt
+- ▁recht
+- Ma
+- 正如
+- 焼き
+- ▁besten
+- fam
+- 論
+- おります
+- ุ
+- くて
+- tation
+- 住宅
+- وه
+- ▁mod
+- ▁coal
+- ▁italian
+- ほ
+- пан
+- tine
+- ▁però
+- ▁render
+- ▁South
+- 欠
+- 广告
+- реш
+- をする
+- кий
+- schuldig
+- ▁programs
+- рен
+- ぼ
+- hé
+- ▁cancer
+- ▁verk
+- ük
+- ▁miserable
+- श
+- azione
+- ▁bunch
+- ık
+- ▁fierce
+- fie
+- ▁appropriate
+- ▁healthy
+- ▁subjects
+- ▁kre
+- qué
+- ▁día
+- ▁Ama
+- ych
+- ▁fatal
+- 馆
+- kiri
+- ▁لە
+- 菲
+- 每一个
+- ились
+- форм
+- hoo
+- вор
+- vingt
+- ▁assume
+- 消息
+- ▁swift
+- ▁سو
+- elt
+- ▁competitive
+- ▁hasta
+- ▁aquí
+- 小时
+- 既然
+- させ
+- gene
+- stoff
+- ▁opera
+- ▁М
+- shing
+- lies
+- 連続
+- voy
+- ▁mamma
+- めて
+- unda
+- ▁hint
+- ▁offering
+- 吴
+- ▁luck
+- ▁Medi
+- iu
+- 賞
+- lte
+- 一份
+- 国际
+- ▁crisis
+- ▁creatures
+- raum
+- لك
+- ▁Roedd
+- lut
+- ▁tremendous
+- ▁Esta
+- swa
+- রে
+- ために
+- ρ
+- ▁micro
+- 发布
+- kov
+- ▁monde
+- ▁shoot
+- ▁rage
+- ín
+- ன்ற
+- ident
+- ▁reflection
+- ▁stern
+- 部屋
+- 太多
+- ▁forgot
+- 殺
+- ▁arthur
+- 広
+- ▁zwar
+- lina
+- を使って
+- ▁rob
+- эй
+- ▁destroyed
+- idi
+- ello
+- ▁activities
+- cole
+- ▁nein
+- ▁dell
+- pada
+- 여
+- ▁Ser
+- ▁tells
+- ▁painful
+- ones
+- 子供
+- ▁他说
+- ▁politics
+- 还在
+- 观众
+- ▁Seine
+- ines
+- 记住
+- ▁branches
+- 僕は
+- 更加
+- wala
+- ▁Nord
+- satz
+- ▁appointed
+- ▁że
+- きて
+- რა
+- uring
+- 決
+- äng
+- ▁sens
+- tischen
+- 表达
+- pia
+- ▁acquisition
+- ▁تم
+- 疾病
+- ▁directed
+- ▁bwa
+- 持って
+- 某种
+- ▁chap
+- یر
+- хан
+- ▁keen
+- はない
+- ▁lover
+- ▁nou
+- 有一
+- ▁Sicherheit
+- ▁hum
+- ▁observe
+- 岩
+- ▁accompanied
+- ▁laughter
+- ▁ஒ
+- jor
+- دە
+- ▁Hel
+- rā
+- ▁whilst
+- 時代
+- ▁waves
+- 意味
+- ▁survey
+- ид
+- 作業
+- 伝え
+- ▁Sprache
+- schrei
+- ▁distinct
+- 沈
+- един
+- يم
+- வர்
+- dzie
+- 赵
+- ▁spin
+- amba
+- 雅
+- ▁دست
+- 很多人
+- 北海道
+- ありがとうございました
+- schließen
+- ▁porta
+- லா
+- vic
+- ▁tren
+- hum
+- ▁Eltern
+- பெ
+- 澳大利亚
+- なんですね
+- ▁많이
+- ▁branch
+- 咋
+- 商业
+- ▁throne
+- ▁burden
+- ▁martin
+- ▁alors
+- fertig
+- ▁rela
+- 情绪
+- 抓住
+- 留下
+- 因为你
+- ▁totally
+- 并没有
+- ▁sector
+- ▁launch
+- ▁leaders
+- ▁shouldn
+- 众
+- ▁cái
+- 形成
+- 寺
+- ▁decline
+- ▁Thema
+- ▁fifth
+- 从未
+- ▁thoroughly
+- ▁Prozess
+- La
+- ệ
+- ▁Bla
+- 一块
+- box
+- will
+- なんですけど
+- 终
+- ▁sugar
+- ří
+- роб
+- ▁»
+- én
+- ▁votre
+- 撮影
+- ▁warning
+- ▁hundreds
+- ▁Это
+- ▁divided
+- からは
+- 重要的
+- 我希望
+- ▁string
+- 合い
+- دن
+- ▁fand
+- ▁Politik
+- dı
+- ▁stated
+- 充
+- 映像
+- 目标
+- سي
+- ▁household
+- ▁suis
+- ण
+- ▁hut
+- ι
+- 几天
+- ▁dollar
+- 鸟
+- 病院
+- 在这儿
+- ís
+- rid
+- riye
+- 我对
+- プレー
+- ▁punt
+- 戸
+- ▁City
+- ologi
+- صد
+- ▁tear
+- رد
+- emos
+- ணி
+- turn
+- ▁pea
+- 设
+- ье
+- 齐
+- からね
+- 表现
+- 早上
+- уул
+- 盖
+- ▁degrees
+- 四个
+- ▁hurry
+- 爆
+- ▁প
+- ▁plej
+- ▁forever
+- ė
+- пад
+- ders
+- ▁دی
+- ▁remote
+- ▁mount
+- ▁privilege
+- uj
+- bata
+- のこと
+- ぜひ
+- cket
+- ▁hari
+- ▁critic
+- ▁Fer
+- ▁hopes
+- மான
+- ▁applied
+- かけ
+- تون
+- ▁kwam
+- ▁cyangwa
+- ▁Macht
+- ▁lift
+- 流れ
+- gram
+- 平台
+- 写真
+- 拒绝
+- гор
+- ು
+- ▁Gemeinschaft
+- ников
+- ▁regarding
+- ▁reduced
+- 生产
+- 頂
+- ▁joseph
+- ▁Boden
+- ▁shining
+- pela
+- ວ
+- dacht
+- 植物
+- ▁heel
+- 학
+- これが
+- ったら
+- 是有
+- ▁Während
+- ▁weary
+- 大概
+- 坂
+- ▁völlig
+- ▁años
+- chy
+- ▁день
+- ▁wound
+- えて
+- ▁combination
+- oy
+- autres
+- инская
+- ван
+- asse
+- кол
+- ▁States
+- ▁эту
+- دو
+- ▁meu
+- ▁sollen
+- 出て
+- 人口
+- ል
+- 我们需要
+- ▁humble
+- به
+- ▁bang
+- most
+- 自我
+- inda
+- ▁rolled
+- 报道
+- 睡觉
+- 暖
+- ے
+- ▁crazy
+- ▁seriously
+- 较
+- 니
+- 我只是
+- 虫
+- ▁remaining
+- rra
+- ▁Ten
+- unter
+- pr
+- 入り
+- ngo
+- ▁punishment
+- ▁episode
+- ▁zeide
+- kop
+- ▁sank
+- いきます
+- ▁file
+- ▁Durch
+- ▁Selbst
+- ▁Projekt
+- ▁ring
+- ▁prisoners
+- ▁pes
+- ▁Fu
+- 絶
+- ால்
+- 言い
+- ▁народ
+- cca
+- ▁dad
+- ▁hero
+- ▁vos
+- ▁Mus
+- ▁helen
+- ▁생각
+- ▁Schritt
+- ていました
+- shire
+- adas
+- ▁pricing
+- ▁Entwicklung
+- 子ども
+- 的情况
+- dim
+- 码
+- gur
+- gging
+- vul
+- ▁pays
+- ліся
+- 夢
+- ▁holiday
+- hus
+- ▁remarks
+- ▁Rolle
+- ған
+- ほとんど
+- ිය
+- '23'
+- 专
+- ▁strongly
+- ▁solutions
+- tang
+- ▁اما
+- ake
+- tara
+- 比赛
+- lad
+- 律师
+- ▁uw
+- ап
+- 団
+- bur
+- ▁types
+- 欧
+- ▁assist
+- 海外
+- ▁california
+- ▁pig
+- ▁trap
+- ▁gall
+- ös
+- dica
+- gled
+- ▁architect
+- ▁Ph
+- ▁Blick
+- 自宅
+- ▁onze
+- ▁我也
+- ▁Geist
+- 把你
+- ▁wicked
+- ▁wolf
+- 結婚
+- 记得
+- ▁ああ
+- ▁breaking
+- şe
+- 尝试
+- ound
+- ▁views
+- 每个人
+- ское
+- তি
+- ▁pool
+- 過
+- ▁trat
+- ▁attended
+- عا
+- ▁profound
+- 大部分
+- 应该是
+- ▁humanity
+- în
+- mad
+- ここは
+- 調べ
+- ▁loving
+- 流行
+- ▁arose
+- 高校
+- 智
+- あっ
+- 不仅仅是
+- 国会
+- 最も
+- てきた
+- ▁sous
+- ▁Но
+- зд
+- ▁movie
+- camp
+- lieb
+- шу
+- ▁kindness
+- ς
+- ▁我知道
+- gut
+- ▁literature
+- 斯特
+- tam
+- ▁presentation
+- ことし
+- ▁designed
+- わけです
+- km
+- ▁expenses
+- oko
+- 我们都
+- ▁George
+- 優
+- 交流
+- 献
+- 失败
+- ひと
+- ▁vy
+- ▁border
+- ▁pilot
+- 集中
+- ▁tegen
+- ም
+- 基础
+- ▁inquired
+- つか
+- ▁assets
+- ▁ibyo
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+- ▁mismo
+- ▁zien
+- 登場
+- iḍ
+- ▁interrupted
+- ▁constitution
+- ▁nodded
+- archi
+- ключ
+- 还不
+- ▁هي
+- ▁alas
+- ▁merit
+- 비
+- ▁начал
+- ▁буду
+- ▁Nur
+- ▁thrust
+- luci
+- ▁الق
+- ▁adventure
+- ▁puede
+- ▁debate
+- пуст
+- ▁paying
+- 诗
+- mata
+- four
+- hari
+- mě
+- ▁enterprise
+- については
+- ▁Ei
+- fit
+- প্র
+- সা
+- ▁atmosphere
+- ண்ட
+- ▁இது
+- 煮
+- ▁cottage
+- アル
+- ▁ஏ
+- ▁cattle
+- ில்
+- arte
+- ▁pole
+- luk
+- ▁secretary
+- লি
+- ▁leven
+- 因为他们
+- stä
+- 困
+- ▁Aquest
+- 豪
+- 功能
+- ▁peer
+- вол
+- 两个人
+- ມ
+- ▁earl
+- 偷
+- ▁möchten
+- ▁kuko
+- ▁گ
+- ost
+- тен
+- ▁прав
+- ▁manager
+- ▁пыта
+- ▁Robert
+- த்தை
+- ject
+- iten
+- ғы
+- 本当
+- 异
+- ▁faster
+- ▁interior
+- füg
+- вя
+- ▁Wert
+- ▁cel
+- frage
+- مة
+- baka
+- 目の
+- ▁Energie
+- ▁leap
+- muntu
+- 的新
+- ▁margaret
+- ▁weakness
+- ▁steam
+- ▁souls
+- 정
+- ▁danke
+- ▁Ма
+- ▁eventually
+- ché
+- 我们就
+- 哥哥
+- ▁약간
+- 安排
+- lant
+- 扔
+- ▁guests
+- ско
+- ▁click
+- 頃
+- ▁Gruppe
+- organiz
+- ნ
+- 水平
+- lop
+- ▁tanto
+- vý
+- பி
+- ▁imagin
+- ▁Peter
+- ▁rosa
+- ▁Tat
+- ▁bai
+- 成了
+- uur
+- gio
+- ▁Ac
+- haw
+- が多い
+- esse
+- そうだ
+- ▁کنند
+- avoir
+- ▁seus
+- 国際
+- ា
+- نىڭ
+- 売
+- ▁spanish
+- 宽
+- 聪明
+- 反应
+- ▁widow
+- 不敢
+- ▁unit
+- アメリカの
+- ▁suc
+- 質
+- ▁unhappy
+- ▁flame
+- 对吧
+- ▁tide
+- ▁dijo
+- ậ
+- ▁shift
+- 帕
+- 韓国
+- azi
+- ▁déc
+- 经验
+- 达到
+- ▁notes
+- 랑
+- ▁fare
+- 怒
+- ▁catholic
+- 技術
+- 资源
+- ▁Nous
+- bila
+- ▁stared
+- ▁tied
+- quest
+- 各位
+- ▁thirteen
+- vano
+- 对方
+- ağı
+- ▁abge
+- ssel
+- 因为它
+- ▁veil
+- ▁chat
+- ▁또
+- establish
+- 以外
+- ި
+- raj
+- மாக
+- ▁auszu
+- გ
+- lance
+- ▁anzu
+- غير
+- kara
+- 答え
+- 晓
+- மே
+- ▁essential
+- 当時
+- 번
+- nyuma
+- ▁Stimme
+- lage
+- ै
+- ört
+- を持って
+- ▁anderes
+- 亿
+- ▁Sol
+- игр
+- 兴
+- リン
+- த்தி
+- wachsen
+- ってこと
+- ▁plu
+- ▁partners
+- ▁eggs
+- ▁largely
+- ▁hadn
+- ▁enjoyed
+- ው
+- aid
+- 상
+- ▁despite
+- ▁Os
+- 房间
+- ▁Union
+- кр
+- 五十
+- ▁distribution
+- ▁pues
+- 人々
+- 客户
+- ▁intellectual
+- ▁cred
+- ▁buildings
+- 国王
+- ▁Show
+- ios
+- ▁baş
+- ▁wholly
+- ▁parent
+- ▁neat
+- лын
+- ▁ehhe
+- ▁braucht
+- ▁stud
+- 入れ
+- ▁verloren
+- ▁guest
+- wyr
+- ちゃう
+- enti
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+- ران
+- ▁kr
+- аа
+- 简
+- شا
+- gegeben
+- ▁mate
+- 亲爱的
+- 增长
+- 意义
+- あり
+- ▁đang
+- ▁delighted
+- 婚姻
+- trau
+- ▁handle
+- ▁مح
+- ゲ
+- 伟大的
+- مت
+- ▁steady
+- 健
+- ▁Cal
+- ▁Bank
+- ances
+- ▁Tôi
+- ▁physician
+- his
+- pren
+- ambi
+- ڭ
+- laden
+- ▁stayed
+- などで
+- ▁patience
+- ▁utterly
+- ▁오
+- ▁Dieser
+- 牙
+- ▁primarily
+- ▁Grunde
+- ▁esper
+- كون
+- י
+- ony
+- ▁context
+- kaj
+- தா
+- ▁sud
+- là
+- Le
+- ▁іх
+- ▁Mer
+- ▁annual
+- сил
+- 中の
+- ▁fro
+- цу
+- вали
+- ▁contrast
+- ▁Technologie
+- 爷
+- ▁lernen
+- 泰
+- burn
+- 有一种
+- দি
+- 禁
+- An
+- 和他
+- uld
+- ▁будут
+- 幸
+- 史
+- coming
+- ▁entwickelt
+- ache
+- 呼吸
+- hara
+- ▁trump
+- ▁hey
+- post
+- 子さん
+- ▁beste
+- asa
+- ▁Zo
+- füll
+- 这么说
+- 挂
+- ▁arranged
+- 悲
+- 一つ
+- ics
+- лек
+- ▁coup
+- 办法
+- மி
+- があり
+- 一边
+- ▁pink
+- ▁расс
+- 保证
+- ▁follows
+- ▁scar
+- ировать
+- 御
+- ▁retail
+- 另一
+- ダー
+- эс
+- 文件
+- ▁symbol
+- جر
+- 爸
+- 现场
+- 透
+- diri
+- след
+- teur
+- σ
+- というふうに
+- 给了
+- ▁contained
+- 放送
+- ddle
+- ▁budget
+- ▁""
+- өл
+- ▁kamen
+- ▁Met
+- wag
+- ▁کل
+- ▁biggest
+- ▁gross
+- ▁feature
+- Л
+- fun
+- 圆
+- ▁distinguished
+- ▁studied
+- kunda
+- ▁dispose
+- 人を
+- ▁Hy
+- 得到了
+- ▁Af
+- Ar
+- fus
+- バス
+- rai
+- ▁obwohl
+- innen
+- ▁shoes
+- eme
+- そうなんです
+- او
+- kra
+- ▁sensation
+- mission
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+- raga
+- ▁hoffe
+- 心理
+- ▁ŝi
+- 했
+- ▁properly
+- ▁pala
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+- 大統領
+- ▁prophet
+- ▁parliament
+- нова
+- tera
+- fil
+- というところ
+- 就不
+- hou
+- ▁consum
+- ▁tomb
+- ު
+- uze
+- ていく
+- 羊
+- ▁lit
+- 适合
+- ▁Gesellschaft
+- としています
+- ▁созда
+- ▁desk
+- ▁হ
+- ▁አ
+- rek
+- ▁đến
+- 带来
+- твор
+- 题
+- год
+- ▁russian
+- に出
+- ▁Luft
+- '22'
+- dank
+- 再说
+- 斗
+- వ
+- ▁jedem
+- ▁பா
+- ▁knife
+- 反正
+- 開発
+- ▁crying
+- ▁moet
+- sser
+- ▁operation
+- ▁confusion
+- ರ
+- に入って
+- master
+- 缩
+- ▁этих
+- ▁continuing
+- ▁rival
+- ły
+- ▁articles
+- ▁кажется
+- ▁remark
+- ▁damage
+- ご覧
+- nto
+- ▁Google
+- ▁strip
+- 銀
+- お店
+- tse
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+- ▁devoted
+- 絵
+- ▁jump
+- 园
+- 女士
+- 世界上
+- ▁log
+- 丈夫
+- ▁traffic
+- ▁所以我们
+- ünde
+- ▁deu
+- ▁abandon
+- ▁tale
+- 审
+- şa
+- ▁bah
+- ▁тот
+- bble
+- 寄
+- ▁tis
+- ▁musical
+- 开发
+- ▁deposit
+- 阅读
+- two
+- のような
+- كن
+- 项
+- 决
+- ▁num
+- tiva
+- 中に
+- ▁چه
+- ıyor
+- ▁persönlich
+- kta
+- ён
+- ▁Mc
+- that
+- ander
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+- 東京都
+- ▁Ter
+- ▁hunting
+- ▁companions
+- ▁emotion
+- cious
+- 부
+- 升
+- さまざまな
+- 记者
+- 绝
+- ್
+- ▁ends
+- ▁الإ
+- 细
+- ias
+- thro
+- ▁bor
+- fei
+- ▁Inter
+- 的感觉
+- 任务
+- ▁surrounding
+- ▁хочу
+- ▁tar
+- ▁mixed
+- ▁Schüler
+- ▁স
+- さま
+- ▁spending
+- ▁Parlament
+- see
+- пор
+- 一段
+- 我和
+- guru
+- ▁beau
+- еш
+- аш
+- ▁என்று
+- zor
+- ▁sisters
+- вать
+- ის
+- bei
+- ites
+- ▁yours
+- лось
+- ▁друг
+- ▁Just
+- ▁expedition
+- ▁utter
+- ▁செ
+- ▁transform
+- ▁hollow
+- lev
+- ▁lonely
+- ▁sonra
+- క
+- ▁zijne
+- үү
+- ▁erkennen
+- ▁té
+- ▁هر
+- ▁komen
+- ktion
+- сло
+- 利益
+- 真的是
+- ड
+- tzen
+- ▁mare
+- late
+- ▁shake
+- ▁servi
+- ▁homes
+- しながら
+- भ
+- 法院
+- ▁multiple
+- ▁Fri
+- あまり
+- ▁exception
+- ▁reader
+- 潮
+- ▁discussed
+- 什么样的
+- ▁trop
+- ▁две
+- nä
+- irwa
+- ▁uko
+- 塁
+- hing
+- sack
+- いただきます
+- 肯
+- 盛り
+- ological
+- ▁Facebook
+- ▁poco
+- cles
+- schauen
+- 出し
+- تن
+- 她说
+- чит
+- ▁spi
+- zelf
+- 沢
+- ▁helping
+- খ
+- ▁Dat
+- 价格
+- 看到的
+- ▁деле
+- ▁kap
+- ▁accordingly
+- ӹ
+- ▁operate
+- ▁wer
+- 委员会
+- ▁meng
+- ▁literally
+- tari
+- 左右
+- лом
+- ▁bus
+- ▁permanent
+- 效
+- 四十
+- хий
+- pol
+- vous
+- rè
+- こうした
+- ▁arise
+- こっち
+- ▁lại
+- ্যা
+- teri
+- ▁driver
+- 十五
+- ▁American
+- соб
+- ù
+- ▁heap
+- ▁barn
+- nger
+- ▁Cas
+- すれば
+- ▁rifle
+- ▁Produkt
+- ▁After
+- ▁まず
+- 中间
+- ▁stem
+- ▁background
+- stehen
+- ▁Tage
+- bij
+- ▁schließlich
+- ▁কি
+- rte
+- кай
+- ▁writer
+- 額
+- ▁duties
+- ▁hungry
+- ▁wegen
+- ▁piano
+- ▁pretend
+- 運転
+- ▁sleeping
+- mpa
+- 大脑
+- iw
+- 部门
+- ▁doctrine
+- ▁define
+- dian
+- 美元
+- mmen
+- 提醒
+- ▁efficient
+- ▁続いては
+- ו
+- eo
+- ▁plötzlich
+- ສ
+- ▁несколько
+- きょうは
+- denken
+- дә
+- ▁President
+- ▁occasionally
+- 不喜欢
+- ▁James
+- ▁perd
+- ▁eigen
+- 今日の
+- ▁nombre
+- under
+- 津
+- ▁estaba
+- ▁бел
+- ▁rod
+- ▁mala
+- ▁sovereign
+- 今回は
+- anno
+- ▁понял
+- مە
+- ▁tiempo
+- 平均
+- ▁initiatives
+- ▁Tro
+- ▁тре
+- got
+- ▁industrial
+- uro
+- ▁Weil
+- ▁beast
+- ▁spell
+- ことに
+- ▁conviction
+- ▁sorts
+- ▁sistema
+- ▁visited
+- lines
+- ▁olma
+- enz
+- 寝
+- 伴
+- ▁《
+- 状态
+- ▁tele
+- ▁quit
+- ▁poder
+- registr
+- ▁你怎么
+- 将军
+- 战斗
+- leben
+- 途
+- ্য
+- 触
+- ▁absolut
+- 朱
+- bis
+- 懂
+- ▁cells
+- 里边
+- stadt
+- ▁kings
+- はず
+- ▁divi
+- ▁bless
+- ▁einge
+- 不需要
+- ်
+- ▁queer
+- bir
+- ▁свет
+- ич
+- alter
+- çe
+- 先月
+- ▁lawyer
+- ▁кур
+- now
+- еж
+- ▁formal
+- ▁город
+- ▁extended
+- ▁poverty
+- 許
+- буд
+- 湿
+- bringen
+- 贴
+- ▁từ
+- ▁verlassen
+- ▁Folge
+- ▁lest
+- 那天
+- ▁composed
+- ▁innovation
+- quen
+- ▁вз
+- ▁striking
+- 锁
+- ▁fellows
+- サー
+- ▁Team
+- ▁lieutenant
+- ряд
+- ▁loro
+- ▁gefunden
+- ▁Freunde
+- ▁Minuten
+- 地震
+- ▁これが
+- ▁generous
+- ▁толькі
+- rib
+- 経験
+- ヘ
+- hap
+- 家的
+- pid
+- iente
+- 丢
+- chel
+- mod
+- ▁sagten
+- enge
+- ▁exciting
+- 되
+- 月に
+- ▁spielen
+- ▁destruction
+- ▁すごい
+- 接近
+- ович
+- ▁stiff
+- constru
+- pur
+- 想想
+- ▁Wochen
+- 岸
+- tsi
+- ▁premier
+- 见过
+- ▁Non
+- ▁possibility
+- ▁responsible
+- ší
+- ▁expand
+- স্
+- ị
+- Be
+- ▁strain
+- ڵ
+- ▁интерес
+- ister
+- dau
+- ▁singular
+- ▁location
+- ▁prospect
+- 変化
+- ▁suo
+- ▁genannt
+- ▁stress
+- лд
+- ubwo
+- 検
+- るの
+- бли
+- жээ
+- ▁Sein
+- ▁cả
+- 相手
+- ▁вторая
+- ▁Morgen
+- comb
+- ▁troba
+- вин
+- igo
+- 那么多
+- ▁должны
+- いで
+- ās
+- ▁Dis
+- ▁那我
+- 昨天
+- geld
+- ▁acts
+- ▁Trump
+- ▁boats
+- ジャ
+- ▁eigene
+- 隔
+- 输
+- іць
+- лин
+- ▁Schule
+- ▁যা
+- пре
+- ▁association
+- ▁trembling
+- 猜
+- вая
+- ரை
+- loop
+- ▁recall
+- どうぞ
+- buch
+- '300'
+- ▁gibi
+- ▁stretched
+- ட்டி
+- ▁россии
+- ▁нэг
+- 5%
+- ▁performed
+- グループ
+- ıl
+- ▁proposal
+- ▁semi
+- 捜査
+- ▁sehe
+- 隆
+- ▁accustomed
+- したのは
+- ▁schlecht
+- 长大
+- ▁unusual
+- ress
+- 很快
+- ▁frequent
+- activ
+- gezogen
+- ▁germany
+- ▁ça
+- bound
+- ▁باش
+- ▁Fla
+- ifica
+- ▁anni
+- یو
+- ▁brings
+- ▁вельмі
+- лог
+- ▁individuals
+- maz
+- »
+- 걸
+- ▁Ordnung
+- лё
+- 大阪
+- ▁یا
+- ேன்
+- erte
+- 不断
+- தான்
+- ▁ale
+- кла
+- ▁glanced
+- jya
+- pis
+- 是不
+- aro
+- ▁Yo
+- 我们必须
+- sprech
+- چی
+- ▁nav
+- graph
+- ▁großer
+- uten
+- 温度
+- ▁Cre
+- ▁modo
+- ▁mewn
+- ▁wearing
+- figur
+- uye
+- ▁North
+- chung
+- ▁kay
+- ▁european
+- るのは
+- ▁billion
+- служ
+- 細
+- ▁hunt
+- ▁responsibility
+- ▁ও
+- ▁говорит
+- ▁grateful
+- 太阳
+- ▁fragen
+- 抢
+- ▁অঁ
+- ▁dice
+- ▁riding
+- ▁tard
+- dition
+- ファン
+- ▁있어
+- ▁sports
+- π
+- ▁india
+- ья
+- বি
+- cum
+- 妻
+- 无论
+- 意外
+- produkt
+- やっぱ
+- 而是
+- ありません
+- 症状
+- ▁Op
+- bio
+- ▁victim
+- ▁wanna
+- owa
+- ▁Ak
+- ▁самом
+- ▁Glück
+- rse
+- سه
+- 我们已经
+- rede
+- sept
+- 霍
+- ▁Gre
+- dwa
+- nant
+- 仲
+- rack
+- ▁thì
+- 我将
+- ▁butter
+- ▁hace
+- ▁Š
+- 特朗普
+- át
+- bera
+- ▁делать
+- 力量
+- ▁yok
+- ▁داد
+- het
+- gis
+- ▁Leuten
+- 다고
+- för
+- ▁agent
+- ป
+- 監督
+- تح
+- 誰
+- anc
+- ▁mehrere
+- 作为一个
+- ▁değil
+- 瓶
+- ▁지금
+- 下午
+- ▁katika
+- 同学
+- ▁سا
+- ▁invited
+- ▁spare
+- ▁represented
+- eld
+- 印
+- 撞
+- ▁swept
+- ▁wishes
+- 之外
+- などが
+- ▁maiden
+- ▁الف
+- ▁prize
+- ase
+- phone
+- 問
+- خر
+- ▁concluded
+- ▁repair
+- gos
+- ню
+- liz
+- ее
+- 有可能
+- ▁ак
+- ▁neben
+- 造成
+- ▁nennen
+- ▁Cy
+- ▁oak
+- овский
+- rif
+- lern
+- ezi
+- ▁Gemeinde
+- ▁guten
+- ▁hid
+- ▁poetry
+- ▁mano
+- ▁такая
+- ▁plot
+- ▁selling
+- ▁gekommen
+- ▁Maria
+- ▁Min
+- 导致
+- ີ
+- gelegt
+- 操
+- fod
+- 就算
+- لب
+- 家人
+- 墙
+- ところで
+- 脑
+- ▁Komm
+- 版
+- なの
+- ▁yap
+- ▁proposition
+- eze
+- ▁latin
+- 喂
+- gebaut
+- cinc
+- politik
+- ▁そこで
+- aquesta
+- ▁але
+- gala
+- ▁actor
+- ggi
+- тя
+- არ
+- unu
+- 荷
+- ĩ
+- ээр
+- ▁sil
+- ▁поселок
+- ▁Pat
+- 了一些
+- uren
+- found
+- 摇
+- ент
+- ▁signed
+- くなる
+- ▁display
+- 不一样
+- やつ
+- 内部
+- ▁tema
+- 演讲
+- ▁select
+- ▁ashamed
+- 決勝
+- 户
+- ▁elder
+- 寒
+- jin
+- 发生了
+- wyn
+- स्
+- 孩子们
+- ちゃ
+- ▁rude
+- ▁именно
+- әй
+- 生气
+- 販売
+- ▁yw
+- ▁movements
+- 好き
+- ▁feared
+- 实际
+- ▁dun
+- ▁creation
+- ▁pied
+- 腕
+- point
+- жил
+- யி
+- Bri
+- ▁cad
+- genda
+- ▁kissed
+- 标
+- ▁chest
+- ▁cheeks
+- 避
+- 冒
+- 宗
+- kwi
+- ▁Tal
+- ▁cycle
+- ▁geschrieben
+- られない
+- lian
+- 映
+- lebt
+- 帰
+- ▁adopt
+- たり
+- kola
+- ▁payment
+- ான்
+- ▁hol
+- త
+- ▁bible
+- ▁achieve
+- 意味着
+- hü
+- пят
+- ▁rail
+- おい
+- ▁действительно
+- ▁Platz
+- اط
+- 卵
+- ▁materials
+- رت
+- ッと
+- ware
+- sses
+- ▁disposition
+- ▁glücklich
+- tial
+- fund
+- ▁continu
+- stimmt
+- 話を
+- kur
+- ▁attached
+- ▁mild
+- ▁locked
+- 染
+- フェ
+- ▁recover
+- ▁Qu
+- 顾
+- 办公室
+- ▁sou
+- kata
+- hak
+- 立て
+- 桥
+- ள்
+- лт
+- バイ
+- グラ
+- ▁shout
+- 一切都
+- 忍
+- ▁comments
+- ▁United
+- аться
+- үүл
+- ▁transition
+- ▁manufacture
+- 所以你
+- aine
+- 習
+- Ge
+- accord
+- ▁przy
+- ▁leurs
+- ▁jsem
+- ▁verb
+- 也可以
+- 就要
+- 你去
+- ▁brush
+- lov
+- шта
+- ▁jobs
+- 份
+- ▁appreciate
+- ガー
+- 围
+- ▁maj
+- emo
+- ▁supported
+- ▁abroad
+- ▁unu
+- ▁thấy
+- ▁sc
+- ้า
+- ▁못
+- pes
+- ▁Mag
+- ின்
+- ▁salut
+- tatu
+- ▁juga
+- ්
+- ох
+- つまり
+- 几年
+- ▁நான்
+- ▁font
+- ▁Kirche
+- ▁knee
+- работ
+- ▁Zwei
+- ▁distress
+- ▁Bau
+- 猪
+- wur
+- hof
+- ▁blessing
+- ழ
+- mera
+- ▁heavily
+- ▁الص
+- cover
+- 逆
+- 磨
+- 려
+- 签
+- 強く
+- гра
+- 含
+- ▁behavior
+- وان
+- ▁pine
+- bewusst
+- ▁neun
+- ▁bones
+- ▁それが
+- ▁armed
+- ▁quant
+- нут
+- 同志
+- るか
+- ▁refuge
+- 说过
+- ирован
+- 播
+- 见到
+- いった
+- ▁Bill
+- ▁reserve
+- ▁farmer
+- roy
+- 没有什么
+- ▁intimate
+- شن
+- சு
+- ▁essentially
+- ▁aquest
+- ▁corre
+- ▁sala
+- ▁ändern
+- 郎
+- んじゃないか
+- ▁Tages
+- ▁ここで
+- ▁сама
+- 厂
+- ▁хар
+- 错了
+- ளி
+- ▁june
+- ▁competition
+- ▁fois
+- ilen
+- リア
+- 我不会
+- ▁ruth
+- ▁opposed
+- ▁penn
+- ▁هستند
+- 拖
+- ▁antwortete
+- ▁jeune
+- إ
+- ▁repeat
+- ▁greek
+- 就说
+- ș
+- '!」'
+- ▁teachers
+- ▁sy
+- あなた
+- 看来
+- isation
+- ▁rya
+- 一段时间
+- ▁belong
+- obu
+- ാ
+- ▁traditional
+- ▁painted
+- ことも
+- чь
+- ▁Mrs
+- сов
+- ▁settlement
+- いろいろ
+- ▁کنید
+- load
+- ▁massive
+- ▁zag
+- ▁alternative
+- dá
+- aren
+- メートル
+- ▁retired
+- ▁consult
+- ▁feels
+- njye
+- 训练
+- ▁charming
+- hop
+- ▁尽管
+- wl
+- ▁sole
+- gad
+- ▁karo
+- nez
+- tec
+- 我告诉你
+- 国の
+- jon
+- ▁bio
+- крас
+- tali
+- ▁việc
+- ▁reasonable
+- 制度
+- ▁kate
+- bw
+- юсь
+- نش
+- ▁schaffen
+- 搭
+- ▁slipped
+- 気温
+- tī
+- ゴール
+- ّ
+- ▁adopted
+- 更に
+- para
+- となっています
+- ind
+- ▁etc
+- んでしょうか
+- ▁scan
+- ▁ambition
+- iques
+- ▁basket
+- rau
+- ▁bark
+- 鞋
+- させる
+- dora
+- igu
+- éc
+- ían
+- ▁supreme
+- يف
+- krat
+- mund
+- 卷
+- avo
+- 暴力
+- ▁seldom
+- hack
+- urt
+- ▁approaching
+- 幼
+- др
+- ▁murmured
+- kni
+- ▁goodness
+- ▁Так
+- zeug
+- 合わせ
+- Po
+- 腰
+- ▁Val
+- ▁consistent
+- ▁rever
+- ▁gap
+- ▁gehe
+- ▁dame
+- witt
+- ▁handelt
+- ści
+- dreh
+- ▁pages
+- يو
+- 另外
+- ě
+- nach
+- ▁unfortunate
+- headed
+- 恨
+- path
+- ましたね
+- 主人
+- ▁совет
+- rī
+- ▁aren
+- ять
+- ▁depends
+- flo
+- miss
+- ▁таки
+- ▁zeigt
+- ▁terre
+- ▁commanded
+- ▁noi
+- bell
+- ▁Tatsache
+- cycl
+- رك
+- mani
+- ்
+- ▁muito
+- duk
+- lir
+- ▁tidak
+- ▁regiment
+- izo
+- ほか
+- ▁clerk
+- ▁vào
+- ▁года
+- 雄
+- ▁bullet
+- tree
+- ▁sempre
+- hlen
+- дэг
+- erin
+- rimo
+- 银
+- ▁teams
+- ▁foi
+- ums
+- ▁shortly
+- ▁cure
+- atge
+- 恢复
+- ▁Hol
+- づ
+- ▁Test
+- ▁drinking
+- ▁released
+- tics
+- ▁eastern
+- St
+- ▁zaman
+- 作用
+- ▁versus
+- ense
+- সে
+- aught
+- ▁жив
+- 라고
+- 押
+- ▁reflected
+- イギリス
+- ▁Hund
+- கை
+- وع
+- 交易
+- ▁blank
+- ▁Situation
+- ▁жизни
+- ▁minor
+- 大変
+- 으로
+- ▁obvious
+- 环
+- 攻
+- ▁useless
+- ▁blieb
+- のほう
+- ▁slaves
+- 材
+- ▁également
+- fal
+- сын
+- ▁tras
+- овой
+- ▁wherever
+- рез
+- ▁contest
+- care
+- ▁institution
+- дав
+- гло
+- 的那
+- ▁contribution
+- 这边
+- нне
+- ▁administration
+- 互联网
+- 了啊
+- ▁donde
+- она
+- ▁Demà
+- ление
+- ▁也许
+- ▁jour
+- ▁বা
+- ес
+- 打算
+- ▁быў
+- awa
+- mere
+- रा
+- 我不是
+- platz
+- 私も
+- pè
+- geven
+- ▁ensure
+- ▁importante
+- 彼
+- ▁داشت
+- 表情
+- тру
+- ところが
+- ▁vital
+- bab
+- 給
+- nio
+- ▁nutzen
+- ją
+- 泡
+- 不得不
+- rend
+- یان
+- 柱
+- ▁glorious
+- ensi
+- 这事
+- ாக
+- abu
+- 有一天
+- ▁feast
+- 동
+- ▁olan
+- 把这个
+- ▁inhabitants
+- キロ
+- ließ
+- 资金
+- 庄
+- ▁Tas
+- area
+- 気が
+- gues
+- يق
+- шин
+- 積
+- bug
+- 練習
+- ▁stroke
+- ▁Sy
+- ▁või
+- 狂
+- 迟
+- ▁Tot
+- arse
+- ▁plum
+- 不应该
+- ▁handed
+- ▁earnest
+- 勤
+- 对此
+- 我来
+- 敌人
+- 佳
+- ▁acquired
+- lak
+- жал
+- ▁fever
+- ▁impulse
+- 任何人
+- 南部
+- ▁dove
+- ▁ihres
+- 저
+- nega
+- 前面
+- готов
+- ▁newspaper
+- ックス
+- rait
+- мар
+- 碎
+- 老公
+- 所做的
+- ▁otro
+- ▁difficulties
+- 開催
+- ▁owner
+- 批
+- bü
+- aver
+- дать
+- ▁trace
+- ▁gleichen
+- தை
+- weisen
+- aɣ
+- ▁coin
+- شه
+- 드
+- 例えば
+- agon
+- ▁print
+- ▁treffen
+- اً
+- 乗り
+- 法国
+- ذا
+- ▁hombre
+- 隐
+- 不足
+- ▁Spa
+- kı
+- ▁departure
+- 的方法
+- 老婆
+- ▁хотел
+- ▁creating
+- nten
+- 台風
+- 人も
+- ▁وجود
+- ▁Sk
+- ates
+- ▁tough
+- ▁ўсё
+- ▁unlike
+- flu
+- ▁zweite
+- 冠
+- oba
+- 以上の
+- 一件
+- sell
+- ova
+- ▁accomplished
+- ально
+- てください
+- 峰
+- pati
+- 住在
+- ▁ignorant
+- 事实上
+- ▁deeper
+- ▁travers
+- ▁venture
+- 予定
+- らず
+- 病人
+- يك
+- হা
+- ▁Wirtschaft
+- 是由
+- ワン
+- cola
+- صل
+- 妻子
+- ▁我在
+- ▁reputation
+- ▁entfernt
+- trieb
+- ▁fiscal
+- ▁combined
+- การ
+- ▁plainly
+- ▁closing
+- ▁og
+- غر
+- ▁eher
+- 押し
+- ▁فقط
+- тры
+- 多的
+- 要去
+- イメージ
+- 欧洲
+- och
+- ṣ
+- ▁reckon
+- ▁granted
+- 際
+- ể
+- ▁italy
+- 熟
+- ļ
+- 淡
+- ▁whisper
+- лай
+- ▁painting
+- ▁poi
+- ▁smoking
+- ▁понимаю
+- ёр
+- krank
+- ▁insist
+- うまく
+- ▁fairy
+- 但他
+- アップ
+- мест
+- цен
+- quis
+- まさに
+- tör
+- ▁mono
+- 試
+- 続
+- ▁orange
+- キャ
+- 你不能
+- お金
+- ▁Sohn
+- гд
+- 哪儿
+- 网站
+- 你怎么
+- amento
+- icia
+- 深刻
+- тр
+- ▁mist
+- تو
+- ▁weder
+- 以降
+- 乳
+- ▁attractive
+- 漂亮
+- 广
+- ▁sue
+- ▁咱们
+- にとって
+- 追い
+- ден
+- ▁Cla
+- ▁такие
+- 始まり
+- ках
+- حق
+- ▁Ali
+- 内心
+- ▁jemals
+- ▁suspect
+- 次の
+- ▁wollten
+- ▁hunter
+- ▁slope
+- lee
+- agenda
+- ▁spre
+- ká
+- 供
+- ▁pond
+- 你了
+- になり
+- 还能
+- ▁risks
+- ものが
+- ▁Av
+- юцца
+- ▁Because
+- ▁мой
+- usi
+- 叔
+- ▁seeking
+- ▁momento
+- ▁margins
+- 近く
+- ▁General
+- ▁epi
+- hö
+- ▁compelled
+- так
+- тон
+- mä
+- ▁وأ
+- 艺
+- ▁pound
+- ์
+- ▁admiration
+- ▁songs
+- 行動
+- ▁dein
+- 館
+- ▁ښه
+- ▁abuse
+- ▁Stück
+- ▁pope
+- ois
+- ▁Umu
+- كي
+- ▁igihe
+- になっています
+- ấ
+- ッド
+- manda
+- hush
+- ▁interessant
+- ▁hörte
+- ▁bya
+- ▁jean
+- 的手
+- ▁infinite
+- ▁entonces
+- ают
+- 感兴趣
+- ける
+- 一体
+- foot
+- дол
+- ому
+- эв
+- chas
+- ând
+- rück
+- zia
+- ▁이런
+- 难道
+- ▁adapt
+- ▁Kor
+- 心配
+- ▁regions
+- ▁eagerly
+- ▁Mensch
+- 一项
+- sab
+- 殿
+- ▁Kol
+- ▁communities
+- ▁kaum
+- odor
+- わり
+- ▁Kla
+- 而言
+- ń
+- ▁yard
+- dine
+- ино
+- ▁lunch
+- 武器
+- ▁我现在
+- ▁forehead
+- ▁Bon
+- ▁fame
+- Ж
+- ▁那是
+- ▁withdraw
+- ▁mask
+- ▁تع
+- ▁dag
+- liness
+- 佩
+- 三年
+- ▁всем
+- ▁aho
+- レン
+- 哪里
+- полит
+- 냐
+- ▁idle
+- ▁dealing
+- bern
+- 的信息
+- ▁روی
+- ご紹介
+- ками
+- geze
+- eti
+- has
+- ▁Mary
+- ▁planning
+- сь
+- 每次
+- ュ
+- 控
+- 阶段
+- ▁Kunst
+- iff
+- لى
+- liga
+- ▁جو
+- ▁remove
+- 老人
+- ▁mister
+- 準備
+- ▁islands
+- ▁erreichen
+- 多い
+- ▁characters
+- ▁Bis
+- ▁вся
+- 一致
+- 祖
+- stieg
+- ▁mom
+- ▁само
+- geri
+- boy
+- ▁Rwanda
+- ▁estat
+- 印度
+- ▁client
+- wacht
+- 收入
+- ▁Mes
+- bles
+- jä
+- ▁acting
+- ▁infrastructure
+- ▁predict
+- ổ
+- щи
+- ロン
+- cido
+- grav
+- ▁мог
+- ▁burned
+- ▁gebracht
+- ▁shell
+- ▁australia
+- cult
+- 金融
+- ▁steep
+- ▁rang
+- 出生
+- 假设
+- ▁مر
+- ▁anirà
+- сен
+- ▁まあ
+- 这是我
+- ▁되게
+- voj
+- ——
+- ▁organic
+- छ
+- 的影响
+- ▁gather
+- 对话
+- 很重要
+- ât
+- ffen
+- dauer
+- ват
+- tons
+- 跟着
+- 并不是
+- 今晚
+- ▁Sinn
+- ▁nghĩ
+- ▁Bericht
+- ▁É
+- cono
+- lose
+- 辞
+- こちらです
+- ▁일
+- 爱情
+- だな
+- 说明
+- ях
+- ▁tag
+- ▁اون
+- ▁electric
+- 昔
+- ▁commander
+- 凡
+- கு
+- ▁chin
+- det
+- 続け
+- ▁fy
+- 比如说
+- ▁thrill
+- 一千
+- 证据
+- だったら
+- ▁яны
+- ▁causa
+- ▁insult
+- ▁fou
+- час
+- 实际上是
+- 莎
+- ▁Rück
+- ▁vessels
+- ▁junge
+- ▁eens
+- ▁inclined
+- ▁opposition
+- 計
+- ▁Forschung
+- spec
+- ▁unfortunately
+- ▁towns
+- ▁الر
+- 提到
+- ▁shirt
+- ▁сделать
+- 执行
+- 野菜
+- 快速
+- hole
+- ▁இரு
+- isiert
+- ▁ella
+- ▁Richtung
+- ▁wedding
+- ▁Viele
+- ▁leadership
+- 義
+- 你觉得
+- 销
+- 逃げ
+- ▁ali
+- ▁institutions
+- nā
+- aven
+- ▁billy
+- ▁schrecklich
+- licher
+- ▁icyo
+- ब
+- ▁descend
+- 答应
+- 判
+- eck
+- ▁gihe
+- 注意到
+- 情感
+- dha
+- '35'
+- ▁undertake
+- ▁extrem
+- 拼
+- ▁Unterschied
+- won
+- ▁equipment
+- لم
+- 多く
+- år
+- lässt
+- ▁philosopher
+- ▁uttered
+- 毕业
+- 主任
+- 星期
+- 施
+- ▁sed
+- маг
+- 再见
+- ▁Yu
+- 我跟
+- 长的
+- やった
+- いか
+- 发现了
+- くん
+- ▁shared
+- 租
+- ların
+- ▁doubtless
+- aires
+- كل
+- යි
+- 가지고
+- ソン
+- 的吗
+- ▁Einige
+- ▁vrouw
+- 不够
+- ▁intense
+- fol
+- ▁glow
+- ▁permitted
+- ▁pis
+- quelle
+- ёт
+- 成长
+- طور
+- lib
+- 跟我说
+- 最後の
+- 価格
+- 逼
+- ▁settle
+- 结构
+- stände
+- κ
+- fallen
+- ▁negro
+- ▁♫
+- chin
+- ▁лучше
+- য়ে
+- schein
+- нул
+- ▁While
+- ike
+- ▁tijd
+- ▁học
+- ्या
+- ▁emotional
+- 模
+- city
+- zed
+- ▁passat
+- ▁avons
+- 래
+- tah
+- ▁человека
+- 是一
+- говор
+- ҡы
+- ▁desde
+- ▁deny
+- ▁obligation
+- ভ
+- رق
+- 異
+- stad
+- ▁providing
+- 专家
+- ▁Ag
+- zak
+- ▁Sand
+- ▁lighted
+- ▁baba
+- hall
+- ografi
+- ▁nella
+- stab
+- ▁substantial
+- иль
+- கோ
+- ▁vague
+- 一方
+- ▁cease
+- ▁Erfahrung
+- 严重
+- λ
+- 的眼睛
+- ▁ありがとうございます
+- 去找
+- ▁historical
+- олог
+- 踏
+- ▁Name
+- 意识
+- 何で
+- 将来
+- 域
+- یس
+- どういう
+- したり
+- gura
+- ▁curve
+- ▁Aufgabe
+- ▁fence
+- ▁dancing
+- ▁flung
+- ▁investigation
+- folge
+- ▁evolution
+- ▁და
+- ▁Мо
+- گە
+- ▁conserva
+- нии
+- ▁load
+- ▁nhiều
+- ▁parole
+- 尊重
+- ',"'
+- psy
+- 効果
+- ▁egypt
+- geht
+- たちが
+- ulu
+- ús
+- 表演
+- 候
+- そういった
+- ▁speaker
+- ▁East
+- ▁utiliz
+- 记忆
+- ▁wooden
+- ▁beloved
+- 一度
+- ▁allows
+- ▁pursue
+- keeper
+- 環境
+- leh
+- 胜
+- ▁phase
+- 긴
+- 更好
+- 没想到
+- ím
+- tě
+- ▁lecture
+- ▁응
+- 订
+- 犬
+- ▁vorbei
+- ▁Mü
+- ▁lately
+- nosti
+- ▁bod
+- 一定会
+- programm
+- ▁arme
+- ▁irish
+- ữ
+- 恐惧
+- 생
+- idade
+- 按照
+- 佛
+- ▁Gesetz
+- ▁rabbit
+- 钟
+- ▁wilde
+- 国民
+- west
+- ה
+- 我々
+- 慢慢
+- 刑
+- 頼
+- tun
+- 搜索
+- note
+- ▁grandfather
+- larını
+- ▁decisions
+- ▁murmur
+- ▁terra
+- ▁할
+- ▁explore
+- ▁Ven
+- していて
+- கள
+- ▁TED
+- tivo
+- 文字
+- メンバー
+- bà
+- 驚
+- ▁staring
+- 電話
+- 满足
+- 支付
+- pac
+- ▁cliff
+- erson
+- のため
+- 穿过
+- 插
+- пал
+- ▁trunk
+- ▁league
+- ▁acest
+- ▁Tan
+- orient
+- cele
+- ▁desperate
+- ▁彼は
+- tical
+- ना
+- ▁expensive
+- ڕ
+- 适
+- ▁stuck
+- ▁Stunden
+- ▁dared
+- ▁Jahrhundert
+- gres
+- ஹ
+- ▁cheerful
+- かどうか
+- oor
+- 議員
+- 更新
+- 益
+- һы
+- ▁Rest
+- 分かって
+- 创
+- ▁après
+- ▁إن
+- иде
+- Q
+- ông
+- ▁obey
+- しても
+- iad
+- tje
+- nner
+- zimmer
+- ますよね
+- ▁sistem
+- ▁workers
+- ▁거기
+- 员工
+- ▁spain
+- ▁nad
+- ▁rend
+- ▁observation
+- ▁llama
+- ▁volumes
+- 地下
+- 北朝鮮
+- ந
+- gir
+- ▁declare
+- ▁upward
+- 極
+- طر
+- ▁deserve
+- ▁klein
+- ▁defence
+- ▁increases
+- ▁simp
+- ▁tramp
+- 台湾
+- 对他
+- bord
+- ▁camera
+- ancy
+- ▁jacob
+- ▁kwe
+- ată
+- 问题是
+- 実際に
+- ▁Sche
+- welt
+- ▁Arten
+- gla
+- ▁Bürger
+- የ
+- ộ
+- ▁Worte
+- ever
+- 梦想
+- bec
+- ▁begged
+- ▁wretched
+- ▁hơn
+- ▁хотя
+- 些
+- ስ
+- ант
+- ▁sending
+- 降り
+- ▁gilt
+- lando
+- że
+- ▁India
+- ▁questa
+- grün
+- 手を
+- qi
+- ▁Mel
+- ▁assure
+- 这意味着
+- あって
+- смотр
+- iber
+- ナー
+- шка
+- ця
+- ▁entering
+- ▁prze
+- tak
+- ▁hervor
+- ages
+- ▁offset
+- اج
+- ▁今日
+- ▁cũng
+- ▁consumer
+- zzi
+- 신
+- ▁adding
+- ▁virginia
+- ▁capture
+- ▁pistol
+- ▁curl
+- ▁accounts
+- 律
+- ▁Zahl
+- rut
+- なん
+- 紧张
+- ▁pursuit
+- ▁Michael
+- ası
+- ▁Far
+- ▁Berg
+- itza
+- stück
+- mira
+- ▁besonders
+- ▁enhance
+- fulness
+- dhi
+- யாக
+- ▁چې
+- ube
+- 肩
+- ▁jews
+- ▁أنه
+- ▁ils
+- ▁swear
+- ▁erreicht
+- ▁sentiment
+- ▁reaching
+- илась
+- かり
+- ▁cents
+- 중
+- 让她
+- ▁volta
+- ▁yell
+- 今天的
+- ские
+- ▁expansion
+- ▁notre
+- ▁torture
+- ▁purposes
+- 她们
+- ▁disgust
+- ެ
+- ▁aktiv
+- ▁highlight
+- yol
+- ้
+- ▁pit
+- 所以我们
+- 力を
+- ▁magazine
+- ▁America
+- rance
+- 它会
+- 崎
+- ▁saddle
+- ▁pont
+- 来た
+- blau
+- ક
+- tama
+- ვი
+- ▁Richtig
+- ▁depart
+- dag
+- ▁只要
+- ▁Trans
+- lagen
+- ▁кан
+- になっている
+- 思って
+- 不起
+- ▁indulge
+- polis
+- 鳥
+- ▁четырнадцать
+- ▁тэр
+- ভা
+- gabe
+- ▁langsam
+- በ
+- ▁должен
+- ▁leverage
+- ▁bench
+- 摆
+- ▁distinction
+- /
+- 大雨
+- 地上
+- ने
+- куп
+- ▁biết
+- действ
+- tore
+- east
+- ▁brook
+- brach
+- issa
+- 해서
+- ▁lesen
+- ▁Erde
+- ▁bari
+- ▁quelque
+- say
+- sinn
+- ▁vend
+- 階
+- rand
+- نە
+- ▁damals
+- рак
+- ▁recovered
+- 過去
+- の大
+- ▁verd
+- ники
+- 运行
+- ▁begins
+- дум
+- ▁mbere
+- 圧
+- ▁alice
+- 范
+- ▁cutting
+- ▁rescue
+- бар
+- 奶奶
+- ▁incredible
+- 理论
+- ▁grain
+- ▁habits
+- なし
+- ▁merry
+- ▁جا
+- ったり
+- уме
+- 现代
+- نس
+- ▁whereas
+- ▁cloth
+- ▁havas
+- ää
+- 时期
+- 启
+- ▁gesture
+- ▁belonged
+- ▁Ко
+- تە
+- ▁случае
+- jet
+- 拿着
+- ▁journal
+- ▁rien
+- 暮らし
+- 活躍
+- èrent
+- 得多
+- 合わせて
+- vuga
+- ▁lodge
+- 是他
+- ▁gingen
+- தே
+- ロシア軍
+- してきた
+- يس
+- ação
+- ата
+- 一方で
+- ▁tat
+- 観
+- 一名
+- ▁بد
+- ▁irre
+- ▁خیلی
+- 過ぎ
+- ▁sowohl
+- 歌曲
+- nett
+- ▁früh
+- ▁Та
+- ▁гэж
+- 宋
+- 荒
+- 掌
+- kad
+- ▁Reise
+- ▁dread
+- ▁fog
+- 没错
+- bled
+- うわ
+- 积极
+- ▁Verbindung
+- ▁sensible
+- ▁mounted
+- ▁interes
+- mur
+- 设备
+- 音楽
+- '2000'
+- ▁никто
+- rug
+- ಾ
+- We
+- 承
+- ▁shadows
+- ▁vin
+- nung
+- 被称为
+- ▁Weißt
+- ▁arrow
+- jal
+- زه
+- ▁misery
+- 狼
+- lets
+- 的机会
+- wig
+- glich
+- 遗
+- fehl
+- ▁komm
+- ▁なるほど
+- 信任
+- ▁brachte
+- iel
+- ▁editor
+- ▁Alter
+- 他对
+- ости
+- ▁gegeben
+- над
+- جي
+- نم
+- gard
+- lja
+- 傻
+- лав
+- ť
+- 、3
+- ▁padre
+- 厳しい
+- 我不能
+- んじゃない
+- ▁observ
+- उ
+- ▁prepare
+- 珠
+- tanga
+- でございます
+- ▁bowed
+- ▁Super
+- '27'
+- iers
+- ись
+- ▁Sen
+- 不如
+- ▁jumped
+- ▁Gal
+- ▁permet
+- 仕
+- 很少
+- ރ
+- 投げ
+- 了这个
+- ▁troubled
+- ▁sufficiently
+- 我不想
+- hun
+- ▁cars
+- rzy
+- ▁그래
+- 欢
+- gelijk
+- erlei
+- ▁whence
+- рук
+- și
+- ▁Martin
+- stelle
+- ▁Van
+- ▁باید
+- 塩
+- ▁schl
+- 采取
+- ング
+- 让人
+- 为什么要
+- ▁lamb
+- 진
+- ▁fired
+- ▁Wissenschaft
+- 沿
+- 多了
+- daki
+- れない
+- ▁señor
+- dura
+- zahl
+- ▁zeer
+- ▁external
+- ▁كانت
+- mour
+- 失去
+- ▁Vereinigte
+- ▁муж
+- ર
+- cchi
+- فعل
+- ▁schreiben
+- ▁Deshalb
+- ▁falls
+- ▁disc
+- ▁drum
+- ▁нибудь
+- ▁рэ
+- 革命
+- ▁faut
+- 離
+- みると
+- ▁dalam
+- ▁Eis
+- ▁helpless
+- bbi
+- 闻
+- ▁голос
+- 的脸
+- ▁schöne
+- 电脑
+- ▁scorn
+- 没什么
+- 正解
+- spann
+- ▁Danke
+- 飞机
+- ▁runs
+- ▁includes
+- assa
+- ▁gedacht
+- ▁fins
+- verse
+- aged
+- enzi
+- bé
+- mento
+- ▁Arbeits
+- fang
+- ▁straw
+- ▁Schul
+- 訪
+- ▁experiences
+- ▁beam
+- ▁rendered
+- データ
+- ▁lesson
+- おり
+- ▁verse
+- ▁mirror
+- زا
+- ▁cheek
+- カメラ
+- ▁Gegen
+- ял
+- 部队
+- 穿着
+- ▁leads
+- ત
+- ▁technical
+- ▁conceal
+- ▁zeal
+- ▁authorities
+- ▁losing
+- arri
+- 我们是
+- ▁wagon
+- ▁Bett
+- ▁reports
+- ▁weapons
+- spel
+- ▁kick
+- ▁그게
+- ▁assumed
+- ▁qualities
+- あげ
+- 魅力
+- ▁dwell
+- マイ
+- ▁peak
+- 見せ
+- ▁jug
+- ▁endlich
+- ▁tio
+- 我没
+- ▁stretch
+- À
+- falls
+- ្
+- пі
+- nih
+- ▁aufge
+- 公開
+- аз
+- lju
+- گاه
+- ▁eve
+- 谈谈
+- მა
+- ▁namens
+- ▁করে
+- ジェ
+- وو
+- aging
+- tered
+- вай
+- ▁zit
+- とこ
+- ස්
+- iah
+- ▁discipline
+- ര
+- 미
+- Re
+- bara
+- ▁exposed
+- شت
+- ▁infant
+- ▁Ä
+- ▁email
+- ▁owe
+- 期间
+- ▁icy
+- 一句话
+- 徐
+- ょ
+- ▁yara
+- ▁Ana
+- ▁carbon
+- 行了
+- eja
+- あす
+- ▁pile
+- ▁planned
+- kita
+- ▁railway
+- ▁знаете
+- 一句
+- 製
+- üb
+- 今夜
+- ▁ahora
+- ▁naj
+- ?」
+- dou
+- şte
+- 正是
+- ▁ultimately
+- 沖縄
+- bahn
+- vine
+- 减少
+- ▁پس
+- ▁Washington
+- ▁considering
+- ▁Era
+- ▁persona
+- nimmt
+- 有时候
+- ▁bara
+- 用的
+- ▁tomorrow
+- ifa
+- 他们是
+- 看了
+- を中心に
+- front
+- ayı
+- ▁primary
+- 的心
+- 过程
+- ▁spur
+- ▁distribu
+- 要么
+- уш
+- ▁nk
+- 戦争
+- fir
+- ▁измен
+- 案件
+- ▁neighborhood
+- 意識
+- ▁drunk
+- ▁よろしくお願いします
+- 黑人
+- ▁tran
+- ▁мир
+- ▁extend
+- ▁USA
+- 神经
+- 时候
+- ▁그렇게
+- که
+- ▁raw
+- nus
+- 学会
+- mpel
+- impo
+- 军队
+- ▁Gericht
+- arra
+- ▁winds
+- ▁jungen
+- ప
+- ▁Lassen
+- ▁деньги
+- ▁sabe
+- ▁mobile
+- пло
+- ති
+- ▁cub
+- illo
+- ecek
+- ▁こちらは
+- ▁Gri
+- ി
+- ▁rằng
+- ▁Mil
+- piece
+- ▁mucho
+- 处于
+- ▁pier
+- ▁aloud
+- ▁Gold
+- ద
+- ▁Hilfe
+- قي
+- ▁americans
+- geno
+- ▁gates
+- vio
+- 早期
+- ▁separated
+- ▁நீ
+- ન
+- 出す
+- 最新
+- ▁mam
+- illes
+- ▁ведь
+- ▁avant
+- ▁заб
+- уй
+- ▁এই
+- 更好的
+- key
+- ▁kannst
+- ▁quantity
+- ▁свое
+- ▁toujours
+- ▁ages
+- ു
+- ませんでした
+- ▁opinions
+- ▁images
+- Se
+- 窗
+- setzung
+- 开放
+- 就能
+- みて
+- tritt
+- 辺
+- range
+- 運動
+- ▁wander
+- вет
+- 費
+- ▁haste
+- 正式
+- ▁sexual
+- ṭ
+- ▁Gedanken
+- ▁reli
+- それに
+- ▁100
+- ▁weird
+- ದ
+- 细胞
+- ▁همه
+- ▁portrait
+- ▁chill
+- 生活中
+- чер
+- 纽约
+- estra
+- ▁sera
+- 全く
+- anz
+- ▁слова
+- ▁County
+- もん
+- ▁det
+- وج
+- ▁deed
+- ▁claims
+- ▁delightful
+- ▁vroeg
+- ▁яе
+- ข
+- 別の
+- ▁тех
+- 면은
+- ark
+- ▁scattered
+- ▁gazed
+- rado
+- гром
+- वा
+- Any
+- обра
+- 仙
+- ality
+- ▁missed
+- ▁prin
+- 宣布
+- ▁flatter
+- 都没
+- ▁essere
+- ▁personally
+- 子里
+- 後ろ
+- 离婚
+- ▁magnificent
+- ▁miracle
+- ▁rolling
+- ▁thread
+- 述
+- ▁fuel
+- ▁altar
+- ▁قبل
+- دى
+- お母さん
+- 行く
+- ▁daughters
+- өг
+- ▁crash
+- ະ
+- ू
+- ▁challenges
+- его
+- 哪个
+- 介
+- ▁rồi
+- tron
+- akt
+- ▁ص
+- undzwanzig
+- ▁gens
+- maid
+- できます
+- plaats
+- ▁bosom
+- ロシアの
+- ▁mm
+- ▁methods
+- staan
+- 兄
+- ection
+- 握
+- ▁그때
+- ான
+- كى
+- せる
+- بع
+- colored
+- ▁ĉe
+- нда
+- mbre
+- ▁robe
+- マスク
+- ▁Mars
+- ▁concert
+- mov
+- you
+- ▁midnight
+- ▁missing
+- 分钟
+- ▁drag
+- ার
+- ▁joint
+- nost
+- つき
+- ▁Ён
+- ▁于是
+- sini
+- ▁Jag
+- ương
+- 历
+- ables
+- ▁restrain
+- ▁africa
+- ▁horizon
+- 以至于
+- خت
+- ▁reduction
+- ▁совершенно
+- ▁insisted
+- 邀请
+- 欲
+- 坦
+- bru
+- ▁Anfang
+- ▁zeggen
+- 우
+- ▁Feuer
+- freund
+- bwira
+- 减
+- ண்
+- ▁Afrika
+- ▁außer
+- 这么做
+- 宅
+- hearted
+- legt
+- 長い
+- にお
+- omen
+- ▁trou
+- ▁Brief
+- ▁anna
+- ▁vest
+- ▁你就
+- ских
+- ▁bestimmte
+- ▁sia
+- 练
+- ▁raising
+- 竹
+- 规则
+- ▁ignorance
+- cep
+- dek
+- ▁گفت
+- ▁einzu
+- 敗
+- ▁führte
+- ▁хорош
+- حل
+- ▁이렇게
+- 成本
+- 撤
+- ▁consequences
+- ▁factor
+- 旁边
+- ▁patron
+- ▁involve
+- pas
+- 风险
+- erie
+- cap
+- 宿
+- 够
+- ▁Ф
+- 一生
+- られました
+- ▁sink
+- 建造
+- ▁valor
+- ▁против
+- ▁cock
+- ▁نیست
+- 年龄
+- ティー
+- 严
+- izza
+- ▁момент
+- mäßig
+- ▁这是一个
+- ▁province
+- ▁convert
+- tina
+- cup
+- tiques
+- ▁breed
+- 干吗
+- rb
+- gefallen
+- ▁paint
+- ▁foe
+- ulation
+- 沃
+- ▁kugira
+- igh
+- っていうこと
+- ▁அவ
+- plic
+- koze
+- 亡
+- ▁prote
+- pok
+- ▁primera
+- ▁Sun
+- ▁Volk
+- ▁entra
+- ▁другой
+- ▁aufzu
+- емся
+- ▁cards
+- のよ
+- куль
+- 改善
+- ▁perché
+- 执
+- ▁Dingen
+- ▁mainly
+- wohn
+- ドル
+- ▁cet
+- ヒット
+- ವ
+- ▁folk
+- onde
+- このように
+- 劲
+- 明らかに
+- ▁trên
+- থ
+- 旗
+- 봐
+- ަ
+- ▁மா
+- ▁significantly
+- ▁chase
+- ▁مثل
+- 振り
+- 当初
+- 儿童
+- ▁nam
+- 会議
+- 剑
+- ӓ
+- ▁Richard
+- 날
+- ▁absent
+- რ
+- луч
+- 载
+- ▁هناك
+- ▁appar
+- せて
+- ▁Band
+- ▁我不
+- 後に
+- oedd
+- De
+- ▁минут
+- 没有任何
+- 距離
+- 竞争
+- ▁Ö
+- ▁Fahr
+- gaben
+- ▁15
+- sert
+- 危
+- ▁кого
+- 本人
+- ά
+- ▁Air
+- ▁Kultur
+- yal
+- ▁lust
+- 漫
+- ▁developing
+- '26'
+- ▁boots
+- あとは
+- stream
+- ▁ບໍ່
+- 弾
+- dress
+- ▁quelques
+- ▁Bewegung
+- ▁messenger
+- жен
+- ▁roads
+- ų
+- ▁menschen
+- gender
+- deck
+- ▁molto
+- mart
+- 学家
+- ▁basic
+- 也不会
+- ▁nag
+- チャ
+- を受けて
+- ի
+- 有的
+- ▁firmly
+- 提高
+- ▁одно
+- 団体
+- ender
+- ▁Bezug
+- kura
+- wort
+- ▁inclu
+- لار
+- 作家
+- 話題
+- 住民
+- bura
+- çı
+- ▁crowded
+- diği
+- ▁outcome
+- 有效
+- volle
+- ▁May
+- َّ
+- ▁honey
+- 行って
+- чет
+- boot
+- 分かる
+- یی
+- 家伙
+- boat
+- wara
+- stimmen
+- 公平
+- 発見
+- ▁Вы
+- 的情况下
+- ర
+- ▁sana
+- ▁ram
+- ▁тысяча
+- ▁especial
+- ▁hastily
+- されます
+- ▁Vielen
+- 和其他
+- 或者是
+- ▁hai
+- ▁nearest
+- ▁multitude
+- kraft
+- үүд
+- minister
+- ivi
+- ▁rất
+- ▁plays
+- ▁clay
+- 销售
+- ▁porte
+- 周围
+- wechsel
+- ууд
+- ▁erzählen
+- ▁ernst
+- ▁оно
+- ▁系
+- 市民
+- ▁leaning
+- ▁overcome
+- 长期
+- melt
+- ▁Groß
+- үр
+- ▁innerhalb
+- ▁scrap
+- cita
+- ▁returns
+- ▁april
+- 奈
+- gehalten
+- 应用程序
+- dela
+- ▁kri
+- amp
+- 滚
+- ▁grasp
+- ▁cách
+- 软
+- ▁corps
+- elde
+- ▁lincoln
+- ▁lee
+- 全て
+- ▁continually
+- ▁Rus
+- ▁خوب
+- 一步
+- 人と
+- 一九
+- ▁đầu
+- ▁lokal
+- ▁молод
+- に対し
+- ege
+- ▁disorder
+- ▁implement
+- 吐
+- 默
+- nick
+- 足够
+- 者が
+- ▁twin
+- ▁между
+- 餐
+- 下的
+- タイ
+- iment
+- 自分が
+- ▁reveal
+- ờ
+- schlagen
+- uki
+- richten
+- тельно
+- 相关
+- ▁kadar
+- ▁cosas
+- ▁chinese
+- ▁Paris
+- ▁rebel
+- 挑戦
+- ▁এক
+- ▁humor
+- ▁improving
+- һа
+- いただいて
+- ваць
+- ▁fühlen
+- ▁cerca
+- āk
+- ▁ornament
+- ▁Wahrheit
+- 带到
+- 科技
+- 找到了
+- 真的很
+- euse
+- ▁purple
+- 聞き
+- 外面
+- ▁deshalb
+- ▁amid
+- ▁Mac
+- 疯狂
+- ления
+- 颜色
+- ▁clau
+- ுக்கு
+- ▁continua
+- 职业
+- ネット
+- 張
+- کرد
+- 浪
+- 的行为
+- kleid
+- ல்ல
+- ▁september
+- ▁Schiff
+- tava
+- 软件
+- 遠
+- 也不是
+- ▁Este
+- 思う
+- ▁confirm
+- tete
+- になると
+- 乌
+- istan
+- どれ
+- äre
+- 皇上
+- ▁oath
+- 愤怒
+- دل
+- 朗
+- ▁apparent
+- ồ
+- ▁khác
+- ▁shaking
+- ▁acquainted
+- ▁citizen
+- stav
+- ▁biz
+- 陸
+- 得很
+- 权力
+- ▁lieben
+- ▁fost
+- ▁badly
+- ▁King
+- ▁mankind
+- util
+- ▁هیچ
+- ҳ
+- tele
+- sucht
+- 戻
+- Da
+- ▁centuries
+- ようです
+- нар
+- ▁urged
+- чен
+- matic
+- ▁throwing
+- ▁outlook
+- ▁loves
+- ▁religi
+- を感じ
+- OK
+- gab
+- ▁Nähe
+- ▁issued
+- zioni
+- ▁đây
+- iriza
+- gesch
+- 裁
+- aud
+- ▁Universität
+- さっき
+- lika
+- ▁studio
+- рат
+- 聴
+- чин
+- 分からない
+- ▁зада
+- ▁afterward
+- lerini
+- ▁conta
+- ましたが
+- 逊
+- 트
+- ▁있는
+- ▁Tr
+- ▁illness
+- 見え
+- ▁alter
+- ▁sounded
+- posta
+- ▁Job
+- ▁significa
+- 下降
+- ▁третий
+- nyo
+- elen
+- ▁کنیم
+- avuga
+- ▁poem
+- ▁Sur
+- 变成了
+- ख
+- 再び
+- sili
+- 早就
+- نج
+- greg
+- 絶対
+- ▁First
+- trac
+- ▁photo
+- 当年
+- greifen
+- iĝas
+- ▁wären
+- ▁caso
+- ode
+- '45'
+- 能不能
+- Me
+- らしい
+- ▁enthusiasm
+- マー
+- ▁bene
+- 警戒
+- انه
+- ừ
+- krieg
+- ř
+- 爬
+- ▁شهر
+- ▁preserve
+- ▁Kur
+- 震
+- ▁spear
+- 浅
+- ▁bwo
+- ▁moeten
+- imba
+- ▁comparison
+- したこと
+- ▁câ
+- 食べて
+- 胖
+- 拡大
+- ▁право
+- ▁kw
+- ▁الخ
+- 蓝
+- 臣
+- 什
+- ▁recorded
+- ▁rational
+- ▁estimate
+- 你知道吗
+- ▁thinks
+- 見た
+- ▁arrest
+- ssy
+- ▁trend
+- 表面
+- eke
+- ▁durant
+- ▁contain
+- ▁Dy
+- ற்க
+- ைய
+- 一件事
+- 临
+- кә
+- sicher
+- 人都
+- ▁gathering
+- ▁transaction
+- coll
+- ▁explo
+- ▁maria
+- ovan
+- ▁当我
+- ног
+- 什么事
+- ला
+- 同事
+- ▁toch
+- 議
+- ▁vorher
+- 说是
+- ▁Wat
+- ▁genuine
+- リーグ
+- ckte
+- jahr
+- ▁madam
+- ध
+- مون
+- タイム
+- ▁sig
+- scribe
+- 购买
+- premi
+- Pa
+- ▁হ্যাঁ
+- ога
+- 识
+- ▁Fern
+- 日子
+- 斜
+- 분
+- ника
+- ▁hook
+- ania
+- ▁schwa
+- ▁Zeitpunkt
+- ▁год
+- ▁meadow
+- ▁charity
+- ▁نام
+- ▁column
+- ministr
+- ▁Kraft
+- ঁ
+- 的钱
+- ▁revealed
+- 宣
+- ▁fearful
+- ▁melancholy
+- ▁daher
+- 机器
+- ▁первая
+- まずは
+- vesti
+- 把自己
+- ▁ansehen
+- ▁lugar
+- ▁Geschäft
+- 交给
+- ▁leaf
+- зо
+- 腹
+- 虎
+- 'No'
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+- ▁compliment
+- 但这
+- ▁trot
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+- ▁Fuß
+- 報告
+- ொ
+- 巡
+- iš
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+- 等待
+- خط
+- ▁brow
+- ▁mock
+- овая
+- tico
+- 生き
+- ▁wet
+- 怎样
+- passen
+- lur
+- ▁Yuba
+- シー
+- 不在
+- embro
+- ধ
+- ▁zonder
+- ន
+- Ü
+- ▁craft
+- nji
+- 档
+- فی
+- য
+- ▁misfortune
+- kaba
+- вес
+- ക
+- flow
+- ▁Pero
+- さんです
+- アウト
+- чым
+- 过程中
+- ppel
+- これで
+- cce
+- вае
+- ▁alike
+- 阻止
+- しましょう
+- ▁blu
+- 嘉
+- ▁senior
+- ▁stellte
+- version
+- 药物
+- ▁然后我
+- 忘
+- вала
+- ▁gloom
+- 复杂
+- 的节目
+- ▁mejor
+- ča
+- wende
+- 演员
+- ▁borne
+- センター
+- ▁спа
+- ▁ebi
+- ▁requires
+- 杜
+- ▁پا
+- ▁fac
+- 医療
+- pher
+- 第二个
+- ▁switch
+- yne
+- ▁theatre
+- 作った
+- 信号
+- 体验
+- лам
+- 你需要
+- ▁sixth
+- ▁abandoned
+- ▁tipo
+- 類
+- ▁noted
+- 规
+- ▁initial
+- 上去
+- شار
+- ▁charged
+- 宇
+- 돼
+- ▁ciudad
+- 当たり
+- 为你
+- ▁cares
+- އ
+- 活着
+- ▁encourage
+- 門
+- tures
+- 간
+- ▁Би
+- ▁referred
+- erde
+- ▁leaned
+- дом
+- ▁Blut
+- ▁шо
+- 非常感谢
+- انت
+- 画面
+- ▁marks
+- ▁flock
+- ะ
+- 以下
+- ▁công
+- ▁saß
+- ▁originally
+- ▁sowie
+- kti
+- ▁encouraged
+- cm
+- psi
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+- ▁hunger
+- ता
+- 我今天
+- 忠
+- stel
+- уп
+- 自信
+- قى
+- ▁volen
+- acco
+- ▁acu
+- ▁яшчэ
+- ▁而是
+- lation
+- aks
+- nish
+- angan
+- 芸
+- гад
+- 할
+- ям
+- ี
+- ogni
+- ▁hoping
+- 約
+- 紙
+- 出版
+- さんも
+- ▁Hat
+- ▁likewise
+- 치
+- ▁contempt
+- ▁Seiten
+- ▁свою
+- 我我
+- ▁wanting
+- ไ
+- య
+- anze
+- こそ
+- ▁operational
+- вести
+- سر
+- ▁gaan
+- کس
+- maß
+- 民族
+- 仅仅
+- 阿姨
+- فل
+- ▁тысяч
+- rò
+- DNA
+- 工程
+- ▁stout
+- 毎日
+- ▁senses
+- 研
+- dil
+- ▁fuhr
+- aca
+- 担当
+- ▁говорить
+- ▁dispute
+- rgi
+- ▁landscape
+- tima
+- ▁الذي
+- сте
+- ▁brick
+- بول
+- jer
+- َا
+- ▁examples
+- 对我们
+- ▁Hin
+- ▁großartig
+- ▁gir
+- ▁engineering
+- ▁cau
+- ▁monk
+- mona
+- ځ
+- 上班
+- 作者
+- ▁má
+- ▁الط
+- зов
+- 伝
+- ▁chỉ
+- dern
+- ▁provisions
+- '28'
+- ales
+- ▁Bal
+- 创建
+- atory
+- ▁introduce
+- ▁назад
+- bart
+- 好きな
+- mother
+- üz
+- ▁engage
+- гон
+- лад
+- ▁champion
+- 子的
+- 食品
+- 見える
+- SNS
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+- 九州
+- ▁trained
+- ▁mensen
+- 可怕的
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+- 走吧
+- stern
+- view
+- 玛
+- 开了
+- 単
+- ▁finance
+- ▁Gebäude
+- führung
+- 是这样
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+- すでに
+- 的关系
+- ▁personas
+- ▁bride
+- ▁occasions
+- 自分で
+- ▁priests
+- と同じ
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+- imiento
+- сем
+- ▁То
+- 呗
+- iche
+- used
+- стаў
+- ▁chicken
+- ▁Straße
+- 好处
+- 是非常
+- hom
+- 公司的
+- away
+- ▁temperature
+- ▁sob
+- ▁darling
+- ▁knock
+- tick
+- 术
+- 甜
+- franc
+- ▁гол
+- ▁Quan
+- based
+- 不得
+- しっかりと
+- ▁artificial
+- sburg
+- メディア
+- 大きい
+- නි
+- 增
+- ▁आ
+- られている
+- өөр
+- tät
+- 很大
+- ▁todas
+- 工作的
+- aş
+- 听说
+- 所说的
+- vali
+- schied
+- ▁objective
+- سو
+- 区域
+- じゃなくて
+- avait
+- likuwa
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+- ▁laut
+- ではなく
+- ▁monta
+- ▁swell
+- 主席
+- ▁anderer
+- ▁ellos
+- 是吗
+- ▁determine
+- 的第一
+- ▁Familien
+- ▁threatened
+- のも
+- 灰
+- 我的意思是
+- ▁شو
+- ▁utmost
+- miz
+- ▁nacht
+- ▁arts
+- 束
+- ▁commit
+- ▁backward
+- ▁efficiency
+- ▁surrender
+- ebe
+- とともに
+- نده
+- 通知
+- ەر
+- 这些人
+- baha
+- ▁sonst
+- git
+- vale
+- ▁зам
+- iness
+- ▁Preis
+- お伝え
+- ▁imagined
+- 内で
+- ▁visual
+- 의
+- ▁Bedeutung
+- んだろう
+- まった
+- чат
+- wang
+- ▁probable
+- 他也
+- ▁conclude
+- ▁Ili
+- 財
+- 設
+- वि
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+- dium
+- eerd
+- ▁employment
+- 的女人
+- ຮ
+- 梁
+- 変わって
+- 民主
+- quent
+- 十六
+- uge
+- ▁retain
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+- ▁monarch
+- ▁receiving
+- かん
+- utse
+- ▁dreißig
+- ▁Anne
+- ▁employ
+- оз
+- ▁attempted
+- ▁july
+- ▁nào
+- tief
+- すぐに
+- ēja
+- ▁draußen
+- ▁könig
+- 我们也
+- رج
+- ▁detective
+- kup
+- ▁geç
+- そのまま
+- ぶり
+- வர
+- ▁childhood
+- кал
+- ▁ciutat
+- ении
+- ▁bloom
+- かも
+- ▁wilt
+- koj
+- ▁poured
+- んの
+- ▁hinzu
+- 節
+- ager
+- 附近
+- ▁femme
+- 才是
+- cco
+- ▁ramp
+- によると
+- buka
+- ▁уг
+- ▁eki
+- ▁monument
+- ▁contains
+- 公众
+- öz
+- ▁items
+- Mo
+- поль
+- ▁kleiner
+- 从来没有
+- が入って
+- ▁instruments
+- ▁thine
+- ▁essen
+- 侧
+- habit
+- 桃
+- をお
+- ▁suchen
+- 敏
+- ▁Region
+- shin
+- تل
+- ▁Tiu
+- 尚
+- 葉
+- ators
+- наход
+- డ
+- ▁могут
+- zik
+- ステ
+- 态
+- wagen
+- ▁recognition
+- mez
+- フィ
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+- ▁Jack
+- ija
+- ▁телефон
+- овка
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+- 跟你说
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+- зда
+- සි
+- тын
+- 好不好
+- ▁мало
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+- ▁பு
+- ▁hasn
+- スの
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+- 悪い
+- ▁relatively
+- ▁Mur
+- ▁Bru
+- ▁relationships
+- ▁rat
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+- ▁Doch
+- ▁noon
+- 同样的
+- ▁integr
+- 简单的
+- ▁seal
+- гляд
+- ▁Uhr
+- 我怎么
+- ▁dining
+- ▁gebe
+- culture
+- 師
+- 他不
+- ▁kot
+- ▁huis
+- 帮你
+- 大家都
+- ▁solve
+- fur
+- ▁poll
+- ▁jamais
+- meri
+- ▁cared
+- bonne
+- 思想
+- 数量
+- 一半
+- ▁furniture
+- ▁territory
+- ▁verbunden
+- gaan
+- Imana
+- herr
+- ▁estar
+- ▁revis
+- hale
+- ▁Kü
+- ▁lucky
+- 账
+- ▁pursued
+- の皆さん
+- っていうのが
+- ▁mondo
+- 瞬間
+- スポーツ
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+- ▁amor
+- alis
+- haber
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+- 巨大
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+- kee
+- وە
+- ▁groot
+- ▁प्र
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+- 新たに
+- 谱
+- ары
+- نفس
+- ▁ваш
+- ▁кры
+- maal
+- ▁mum
+- iter
+- ▁mig
+- bá
+- 盟
+- 向け
+- ▁betty
+- ▁никогда
+- ▁кү
+- гэ
+- どの
+- ブラ
+- 北部
+- йн
+- ▁peasant
+- gren
+- 很容易
+- 下さい
+- ไม่
+- ▁Ek
+- ▁Are
+- friend
+- ▁invisible
+- evi
+- ▁climb
+- lek
+- rika
+- tua
+- ▁terug
+- эк
+- ▁mijnheer
+- ▁decir
+- ▁Mill
+- とおり
+- ▁studi
+- ▁shield
+- 伸
+- assi
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+- 需
+- pira
+- 禁止
+- ▁autor
+- ▁contre
+- ▁entend
+- 给他们
+- ▁hugh
+- '!"'
+- ▁passa
+- ▁erwähnt
+- 公开
+- ▁zweiten
+- iau
+- ▁Ok
+- mitten
+- ৈ
+- 喊
+- dle
+- 来る
+- inu
+- ▁ئە
+- ▁freely
+- 汤
+- なんですよ
+- ▁nuestro
+- ▁couch
+- ▁mistaken
+- '400'
+- ▁expecting
+- 攻击
+- ▁landing
+- 你对
+- 的就是
+- ▁größte
+- ▁那就
+- 这对
+- 点击
+- ried
+- ▁bowl
+- 裏
+- და
+- kah
+- بط
+- face
+- lekt
+- ▁urbo
+- ▁solchen
+- 距离
+- вели
+- ▁drama
+- をした
+- chip
+- اص
+- mouth
+- ▁statt
+- antwort
+- kho
+- โ
+- 她是
+- ▁literary
+- ▁بیا
+- fassen
+- ▁michael
+- ▁nights
+- ▁jag
+- 都可以
+- ▁Geschichten
+- дын
+- 出来的
+- yê
+- 模型
+- ▁آیا
+- ▁пятнадцать
+- ▁belle
+- ピー
+- ▁Stra
+- бит
+- ▁merchant
+- ▁russia
+- 骂
+- ▁Amb
+- 今は
+- youtube
+- 수
+- ▁grade
+- ▁ɣer
+- ც
+- 撒
+- ▁pipeline
+- 聚
+- ▁Möglichkeiten
+- たのは
+- ▁julia
+- 容疑者は
+- 微信
+- ▁얘기
+- くなって
+- ▁umuntu
+- sprung
+- などに
+- খা
+- ▁vorstellen
+- ▁decent
+- 動か
+- もいい
+- 的力量
+- トン
+- ▁acted
+- ▁Personen
+- 恐
+- 大型
+- ▁reproach
+- エン
+- ▁nhà
+- ▁Heute
+- に対する
+- マーク
+- ▁Street
+- ▁toutes
+- ▁hinein
+- ▁двенадцать
+- даа
+- ▁ब
+- ▁Ah
+- ера
+- ▁kama
+- ▁hob
+- ▁abzu
+- 黑暗
+- ▁hated
+- ▁steal
+- ▁더
+- ▁yıl
+- ▁事实上
+- volu
+- ▁powder
+- Ch
+- klu
+- 针
+- ং
+- ▁Entscheidung
+- ▁mail
+- 话题
+- ▁otra
+- rir
+- ▁rhy
+- ▁сразу
+- 你可能
+- cija
+- там
+- dow
+- 摸
+- rite
+- ienne
+- 感染者
+- play
+- lick
+- ▁denk
+- ▁están
+- 若い
+- ▁verändert
+- チャンス
+- 红色
+- ▁bade
+- ây
+- mt
+- nych
+- 世代
+- ▁johnson
+- ▁fears
+- som
+- 什么呢
+- 去做
+- ▁prompt
+- 最終
+- ▁capabilities
+- ுவ
+- ▁Anti
+- ▁harder
+- 在那
+- ides
+- ▁examination
+- ▁invitation
+- ▁Erfolg
+- 小的
+- 定义
+- 测
+- ▁això
+- 住了
+- 赫
+- ▁авто
+- ▁apt
+- ▁Roman
+- wahr
+- hard
+- ▁irà
+- ▁toute
+- 会場
+- ▁spectacle
+- 個人
+- 德国
+- حد
+- 别的
+- けて
+- ▁näher
+- mpe
+- 用意
+- ▁کی
+- ▁bedroom
+- ▁puc
+- ▁nascut
+- 一日
+- ▁30
+- ▁meantime
+- ということは
+- ▁Charles
+- ▁shareholders
+- 明白了
+- 국
+- 故
+- ыз
+- තු
+- 十一
+- usta
+- ೆ
+- ▁parla
+- اخت
+- ▁inches
+- power
+- 的这种
+- ずに
+- berry
+- 品牌
+- 引起
+- ▁historia
+- ▁pret
+- ▁fantastic
+- 威胁
+- kru
+- 当他们
+- ؤ
+- esi
+- ▁occupation
+- rose
+- ር
+- ▁statue
+- 決定
+- ясн
+- ો
+- ▁Ton
+- ▁blij
+- uɣ
+- ogo
+- ▁favorable
+- 我从
+- ▁outline
+- atur
+- んじゃ
+- ▁বি
+- ▁conception
+- ▁comun
+- 大谷
+- меш
+- 었
+- ▁session
+- ▁fed
+- 今まで
+- ▁ridiculous
+- 妹妹
+- бр
+- ▁maig
+- フランス
+- ▁2017
+- ترین
+- ▁глаза
+- ▁её
+- ▁Computer
+- ▁vanished
+- kauf
+- ▁Unterstützung
+- ▁gukora
+- 做的事情
+- 业务
+- ▁schm
+- 러
+- ▁gotten
+- ▁pār
+- ▁rece
+- ▁zuerst
+- கா
+- 猎
+- ▁vergessen
+- ▁weet
+- ný
+- ▁Japan
+- ▁pointing
+- ▁estos
+- ▁unconscious
+- だけど
+- னா
+- ▁permission
+- هایی
+- вел
+- 一定是
+- 头发
+- ине
+- 悔
+- ▁Zum
+- ッシュ
+- 杀了
+- 様子
+- ▁blush
+- ▁sharply
+- 做得
+- ▁befindet
+- ▁cyf
+- ▁football
+- ▁alguna
+- пов
+- 泥
+- ghi
+- 十八
+- ▁treball
+- ▁inspired
+- ▁pattern
+- ▁crystal
+- 効
+- ▁cultural
+- ▁monster
+- ▁Bücher
+- ▁என்ன
+- сел
+- ▁pomp
+- ▁これで
+- 記
+- eyed
+- mati
+- トリ
+- boo
+- 庭
+- commerce
+- pru
+- ▁records
+- 取得
+- culo
+- ▁Mau
+- ようと
+- ▁Ban
+- 特殊
+- ฉัน
+- 采访
+- дра
+- ош
+- イベント
+- 发出
+- ▁prayers
+- ▁employees
+- klo
+- count
+- ▁panel
+- ▁crossing
+- 擦
+- 这是我们
+- ▁Frank
+- ▁kinda
+- ghan
+- reichen
+- ▁shone
+- có
+- ▁tutti
+- 真实
+- ▁avenue
+- ちょうど
+- kultur
+- 在我的
+- ▁iyo
+- ▁fetch
+- essen
+- ▁detect
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+- ▁село
+- ▁난
+- ▁reaction
+- 特定
+- 采
+- person
+- 惊讶
+- ▁strangers
+- ▁gemeinsam
+- тыр
+- ▁beard
+- ▁gleiche
+- ▁Africa
+- 周り
+- ▁сколько
+- ▁steadily
+- voca
+- 两年
+- zira
+- ▁startled
+- ▁muttered
+- 寄せ
+- 你应该
+- 策
+- ▁jud
+- 在他们
+- 闪
+- 개
+- ▁millor
+- ▁vein
+- に向けて
+- 香港
+- 播放
+- ▁accurate
+- 扬
+- 살
+- ▁Website
+- ফ
+- 覚
+- ▁whirl
+- よし
+- ope
+- ລ
+- 英雄
+- ▁我会
+- ▁gek
+- фі
+- 是怎么
+- ▁rap
+- ますか
+- ▁attacked
+- ▁endure
+- ▁tie
+- يع
+- rp
+- rash
+- ▁specifically
+- ▁collected
+- nahme
+- uous
+- න්න
+- dru
+- 稳定
+- oid
+- と思った
+- ▁каб
+- how
+- ▁以及
+- ▁anchor
+- 行き
+- ▁pia
+- ingham
+- ▁также
+- 礼物
+- ▁Typ
+- 理想
+- ▁lleva
+- ないように
+- 角色
+- uck
+- ી
+- ▁abstract
+- ▁Steve
+- Pro
+- ▁secured
+- ▁бес
+- mist
+- 生意
+- ▁consequently
+- ▁vriend
+- 的世界
+- ▁führt
+- ໄ
+- ▁railroad
+- ▁ticket
+- igis
+- ▁Ji
+- 道德
+- ▁disgrace
+- 소
+- ▁compare
+- 我必须
+- ▁wandering
+- ▁knocked
+- ▁tant
+- liter
+- ▁procure
+- ▁oedd
+- media
+- ▁offensichtlich
+- 都市
+- fed
+- ▁続いて
+- کار
+- ▁resistance
+- ▁ئى
+- ▁menos
+- ▁maken
+- 空气
+- 제
+- stock
+- 負け
+- ▁disappointed
+- fee
+- 選手の
+- ▁admire
+- ่า
+- ▁marie
+- 立即
+- 質問
+- ▁directions
+- eron
+- ▁Jean
+- ясь
+- يات
+- კ
+- 汁
+- 兴奋
+- ▁egg
+- كا
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+- ▁Polizei
+- ய்
+- ▁È
+- 吸引
+- iere
+- ຍ
+- ▁dip
+- ▁starts
+- ▁свой
+- cir
+- ক্ষ
+- 兴趣
+- ्य
+- fekt
+- ▁Marc
+- ▁coward
+- 匹
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+- ▁dalla
+- とした
+- ▁brutal
+- ▁traf
+- holen
+- 肥
+- gian
+- 鲜
+- 课程
+- 会儿
+- ▁resumed
+- ຕ
+- ▁территория
+- schul
+- ▁complet
+- ▁sighed
+- 限制
+- ▁elsewhere
+- ança
+- 质量
+- ノー
+- ▁Versuch
+- образ
+- kwe
+- ▁virus
+- 天気
+- 筋
+- 的书
+- ▁timing
+- 熟悉
+- 促
+- ▁дома
+- 对了
+- ▁Damit
+- 我给你
+- ▁Bel
+- ối
+- ▁Chance
+- জা
+- ルー
+- 快点
+- これも
+- anca
+- ▁frau
+- 周辺
+- ▁pierre
+- 干嘛
+- ▁فکر
+- 教堂
+- ▁確かに
+- 香り
+- 道理
+- agit
+- 有多少
+- 指示
+- ம்ப
+- ▁patch
+- スーパー
+- ▁tonight
+- ▁Markt
+- رم
+- ▁Regel
+- migrant
+- miş
+- sió
+- 巧
+- kha
+- Ha
+- mination
+- ▁astonishment
+- ▁asia
+- má
+- kota
+- дээ
+- ▁Fehler
+- ▁Dieses
+- ▁persone
+- cant
+- ▁aquel
+- 消防
+- ▁perfection
+- cció
+- rren
+- 门口
+- fest
+- ▁viz
+- 粗
+- ▁nonsense
+- schätz
+- ができる
+- 程序
+- ▁говорил
+- ▁Clo
+- siz
+- ▁identify
+- ós
+- ▁omu
+- 祝
+- 続き
+- ▁род
+- ock
+- 表現
+- ▁slightest
+- aĵo
+- 豊
+- ▁пожалуйста
+- ски
+- ▁Lehrer
+- ▁erfolgreich
+- moor
+- பு
+- ▁players
+- ▁vì
+- ▁descended
+- qual
+- xo
+- ▁hopeless
+- ▁conducted
+- 手术
+- ▁Ay
+- ▁12
+- っちゃう
+- 生活的
+- ▁dragged
+- ▁بعض
+- нет
+- ▁inte
+- мон
+- ▁teu
+- тө
+- ▁liquid
+- ▁hip
+- find
+- moni
+- ▁State
+- Е
+- ▁shooting
+- ▁legend
+- 埋
+- ▁revenues
+- ▁unglaublich
+- abel
+- слав
+- ▁”
+- 听起来
+- 所有人
+- ▁düşün
+- 了他的
+- 残り
+- fro
+- ▁bend
+- ▁wirst
+- ▁akk
+- ▁hitherto
+- 成長
+- こうやって
+- ▁dirty
+- metri
+- 分の
+- ▁perceive
+- ▁jur
+- vra
+- خا
+- ▁năm
+- 晴れ
+- 酷
+- ▁poste
+- лер
+- lais
+- ▁sav
+- 认真
+- 毕竟
+- with
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+- ▁scholar
+- 苹果
+- 改めて
+- 因素
+- டா
+- кы
+- ▁sembla
+- zustellen
+- ▁pupil
+- ifying
+- 缓
+- 手に
+- 名前
+- ▁совсем
+- 相談
+- ▁ал
+- ▁kendi
+- ▁promote
+- ▁стороны
+- 了我们
+- 发生的
+- прост
+- ▁mateix
+- ▁дума
+- ково
+- ▁Green
+- 自动
+- lima
+- もらう
+- ели
+- ▁departed
+- ▁belonging
+- ▁Star
+- وز
+- テーマ
+- யின்
+- ▁hesitated
+- ▁Om
+- 评
+- ▁Hoch
+- 正确
+- 協力
+- ikan
+- はどう
+- ▁lane
+- machen
+- reba
+- ▁ব
+- 逮捕
+- 在美国
+- ▁гру
+- osi
+- '1000'
+- 泉
+- ▁examined
+- 方は
+- ▁disco
+- ▁Ale
+- ▁terwijl
+- ▁dig
+- 避免
+- ▁leisure
+- worm
+- ▁depth
+- 卢
+- ▁worried
+- 做好
+- 出身
+- 生まれ
+- ▁قرار
+- 废
+- ám
+- zzo
+- culp
+- ▁Gebiet
+- ibya
+- ач
+- ▁scenes
+- 王子
+- gelegen
+- 基于
+- ▁partie
+- lhe
+- ▁belt
+- রি
+- änder
+- ▁compassion
+- ♪
+- 下了
+- ями
+- ▁devotion
+- ろう
+- 他の
+- ケット
+- ▁monitor
+- ▁häufig
+- 検討
+- tö
+- enta
+- ▁sido
+- 補
+- 聊天
+- 我一直
+- ▁гэтага
+- ▁называ
+- 蒸
+- ▁当你
+- shyi
+- houden
+- conc
+- ▁device
+- この日
+- 凉
+- ▁Del
+- mica
+- 剂
+- ドイツ
+- 是要
+- 隣
+- ▁narrative
+- ▁Medien
+- ▁francis
+- Р
+- ▁beaucoup
+- ▁whistle
+- 她在
+- ▁kumu
+- ▁droit
+- 我们正在
+- nahm
+- etta
+- ▁fille
+- Do
+- ▁owned
+- 通り
+- もらって
+- 写的
+- 疼
+- ל
+- ām
+- пра
+- них
+- gali
+- shya
+- ▁France
+- ▁cord
+- だし
+- rab
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+- ▁folgen
+- 遺
+- 位于
+- ▁sä
+- ▁Sport
+- ▁kui
+- 页
+- の方が
+- nata
+- ▁facing
+- ▁பொ
+- もない
+- He
+- ▁inventory
+- 实验
+- uter
+- raten
+- Sa
+- ▁drank
+- قی
+- 伺
+- ▁Barcelona
+- ▁keeps
+- ▁하고
+- ▁mult
+- gni
+- 금
+- ▁astonished
+- ▁readily
+- ▁unexpected
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+- どうして
+- ҙә
+- 跡
+- еб
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+- 面临
+- onia
+- ▁cinema
+- čas
+- 创业
+- ▁зап
+- rücken
+- ▁silly
+- ▁facility
+- ▁除了
+- 用于
+- ▁Monsieur
+- ばかり
+- ▁collect
+- ▁انجام
+- ▁gifts
+- ▁doctors
+- ▁emotions
+- ▁republican
+- ▁maintained
+- ▁snake
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+- 身份
+- ▁ней
+- ▁models
+- ▁gracious
+- ibwa
+- leigh
+- بت
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+- 警察は
+- stall
+- ▁percentage
+- خوا
+- ▁crop
+- ▁ainsi
+- 忘了
+- cide
+- ▁killing
+- ▁tool
+- 的好
+- teria
+- ▁mutual
+- 剩
+- ▁wreck
+- ▁chiefly
+- ▁values
+- ▁strengthen
+- ▁cómo
+- 같
+- 的能力
+- ▁wunderbar
+- steigen
+- ▁heir
+- ▁2018
+- кат
+- zī
+- ▁அது
+- ▁கூ
+- breite
+- ▁guter
+- ▁duck
+- 相对
+- 承诺
+- 叔叔
+- ▁bana
+- 使う
+- زار
+- 组成
+- lö
+- としても
+- kap
+- 操作
+- 物质
+- Ba
+- 入って
+- halte
+- ▁tau
+- ▁weapon
+- ▁байгаа
+- rok
+- ▁graduate
+- 心情
+- ブル
+- ▁höher
+- leu
+- するため
+- ▁vai
+- ころ
+- 五年
+- ▁landed
+- 闭
+- ▁след
+- ▁selected
+- 放心
+- 秘
+- 等着
+- nol
+- gling
+- ▁对于
+- ▁lend
+- ▁povas
+- 券
+- ▁sources
+- ▁Kampf
+- はある
+- ▁Gro
+- ▁jemanden
+- 要做
+- ▁peaceful
+- як
+- 规定
+- dolph
+- 追求
+- univers
+- 添加
+- ▁pump
+- バン
+- 特徴
+- 多年
+- easing
+- 枝
+- ▁Louis
+- ▁Rob
+- 改革
+- 一本
+- ▁climbed
+- ▁bueno
+- ▁analysis
+- важ
+- シン
+- ▁roger
+- sein
+- ▁riesige
+- organ
+- ▁plat
+- ▁seventh
+- imper
+- ика
+- ニー
+- ▁breeze
+- ▁slavery
+- モン
+- ▁Seg
+- cru
+- 这个时候
+- zte
+- nici
+- ▁grandmother
+- 这是一种
+- 最後に
+- 厉害
+- ề
+- ▁usted
+- ▁split
+- 的儿子
+- 速度
+- gers
+- ▁heavens
+- شان
+- ▁planta
+- 奇怪
+- ▁Sed
+- ▁yüz
+- дай
+- ▁saturday
+- うれしい
+- 奇怪的
+- 联邦
+- جو
+- ydi
+- ▁cyn
+- ▁зах
+- üste
+- agne
+- мол
+- ায়
+- ▁bwe
+- бира
+- toria
+- 尖
+- 好多
+- undu
+- ▁urban
+- гч
+- ыч
+- 穴
+- ▁hiding
+- ▁loan
+- ແ
+- ▁kant
+- ▁complain
+- ▁apa
+- ▁точно
+- 抜け
+- 建物
+- ▁Win
+- يب
+- 재
+- 政府は
+- ▁Professor
+- ▁naked
+- 期間
+- ▁essay
+- 翔
+- ▁propose
+- ▁circumstance
+- めた
+- 不见
+- tour
+- 使われ
+- гар
+- 我妈
+- ▁zuvor
+- 坚
+- mik
+- 财
+- ▁museum
+- ▁건
+- ▁engineer
+- 日から
+- uß
+- ▁solar
+- nın
+- ▁gesamte
+- ▁后来
+- となって
+- 写了
+- ▁agreeable
+- ▁canoe
+- ▁rug
+- ▁Hor
+- جان
+- 什么呀
+- ▁marble
+- meister
+- ەت
+- ▁dorothy
+- ▁confused
+- ноў
+- лат
+- ffi
+- née
+- 否则
+- Applause
+- ண்டு
+- ▁complicated
+- ▁guerra
+- キー
+- 你不是
+- ▁最后
+- class
+- ▁swiftly
+- lton
+- 滴
+- ▁vollständig
+- ▁будем
+- ▁gleam
+- ▁finest
+- ザー
+- ▁fiel
+- ▁gusa
+- ▁motive
+- 時の
+- ▁circ
+- timo
+- ▁こんにちは
+- rated
+- هی
+- ect
+- ▁moins
+- ▁rested
+- fau
+- dden
+- elijke
+- 鼓
+- ough
+- kub
+- ▁quart
+- ຫ
+- 菌
+- skri
+- まれ
+- ▁Juli
+- جه
+- ▁dorm
+- ▁selfish
+- 混乱
+- ▁佢
+- なんですか
+- ▁examine
+- ▁insurance
+- гай
+- 一开始
+- ▁disturb
+- cción
+- 政権
+- 伏
+- 征
+- ▁Foto
+- ▁stati
+- 讯
+- xed
+- ▁chemical
+- ром
+- ships
+- ligen
+- ▁halb
+- ▁introduction
+- ▁дальше
+- ▁dish
+- 联合
+- posa
+- 积
+- 基因
+- ይ
+- 商店
+- 粒
+- 不去
+- ▁Fran
+- 彼此
+- ▁nieder
+- ▁mache
+- 都要
+- три
+- 気になる
+- ▁основ
+- ▁achieved
+- ▁نظر
+- 込む
+- زم
+- 高的
+- Di
+- 使い
+- 某些
+- ▁Joe
+- пам
+- ▁cloak
+- 投入
+- ▁captured
+- 週
+- 就这样
+- сс
+- 所谓的
+- ▁arī
+- ▁spoil
+- nuncia
+- ▁ɣef
+- word
+- ▁materially
+- ▁thực
+- ▁öffentlichen
+- ▁яв
+- gum
+- ▁ئۇ
+- ▁quote
+- ▁indicated
+- ▁stor
+- tare
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+- ▁google
+- ▁unterstützen
+- 对自己
+- ▁wichtige
+- ▁recovery
+- mensch
+- ▁Ehe
+- ▁lessons
+- neɣ
+- 던
+- 怎么回事
+- octubre
+- 頑張って
+- أكثر
+- ▁testing
+- އި
+- ști
+- dır
+- ▁byose
+- ▁polite
+- рова
+- imana
+- ▁exhausted
+- ▁rivers
+- を行う
+- 帝国
+- oval
+- ▁例えば
+- ▁Rom
+- 这不
+- ▁Yn
+- ▁patent
+- 動画
+- рас
+- ▁mole
+- 不必
+- cura
+- ▁anh
+- ▁überall
+- 美国人
+- ▁disappointment
+- ▁soup
+- ktu
+- ▁transformation
+- ▁Verhalten
+- 後半
+- ▁байдаг
+- 合う
+- ▁Sil
+- ぜ
+- 扎
+- trud
+- ży
+- ▁shepherd
+- ▁возможно
+- レス
+- cier
+- ▁sunshine
+- ▁Telefon
+- 愉快
+- ▁havia
+- aşı
+- のだ
+- 进行了
+- 农
+- ▁continuous
+- ▁públic
+- ▁poz
+- ▁давай
+- 满意
+- 悲伤
+- lma
+- ▁curse
+- reb
+- 空気
+- قال
+- ▁player
+- 安定
+- maker
+- 的那样
+- ▁ntabwo
+- ▁способ
+- heben
+- ▁hecho
+- wide
+- Ho
+- then
+- ありました
+- ▁pendant
+- ▁咁
+- 但它
+- 能量
+- ▁Himmel
+- 十九
+- 有些人
+- 年的
+- ▁свои
+- ▁practically
+- ость
+- ▁deserted
+- 说他
+- ▁buying
+- ▁thorn
+- ▁holds
+- 信仰
+- lings
+- ▁glimpse
+- ▁CA
+- ▁Kal
+- slav
+- ҙа
+- 思います
+- 何も
+- ँ
+- regen
+- ッチ
+- druk
+- قص
+- ▁preferred
+- 续
+- بة
+- ▁мин
+- fälle
+- ▁travail
+- ▁他是
+- ழு
+- ▁matt
+- ít
+- eği
+- 勝ち
+- মি
+- ▁ваше
+- するのは
+- ෝ
+- 出した
+- ▁bite
+- 黄色
+- مال
+- ▁preparing
+- ▁há
+- ử
+- digit
+- ▁World
+- かせ
+- ieron
+- 答
+- 遇
+- カル
+- ファ
+- بن
+- 화
+- ▁priv
+- kaza
+- ▁giờ
+- ப்பட்ட
+- ▁bé
+- ▁چند
+- kut
+- ▁darf
+- ▁دوست
+- 具有
+- fes
+- еть
+- ▁Cette
+- konstru
+- ▁thứ
+- ▁jako
+- ছিল
+- organisation
+- ▁Essen
+- ▁surprising
+- 渴望
+- 资
+- 浦
+- isieren
+- ▁байв
+- ▁Ideen
+- స
+- ▁größer
+- besitz
+- bour
+- ән
+- werfen
+- ▁Ebene
+- dah
+- 美丽
+- 监
+- ドラマ
+- じゃん
+- ▁калі
+- 独自
+- 時期
+- ▁cose
+- ▁چیزی
+- ▁joining
+- ▁confession
+- 天天
+- ▁processes
+- 보
+- ▁比如说
+- 也就是
+- ーム
+- 是我的
+- ▁Stil
+- スピード
+- 少年
+- ▁fühlte
+- 予
+- vig
+- ики
+- ▁nap
+- ▁angels
+- ▁schedule
+- rán
+- ▁boston
+- ▁oude
+- まま
+- душ
+- 腾
+- ху
+- 咖啡
+- валі
+- ▁лес
+- 刷
+- ▁suspected
+- ▁inquiry
+- ▁amusement
+- ordnung
+- ▁Klasse
+- 食べる
+- 我记得
+- 连接
+- فق
+- ▁precisely
+- ープ
+- ▁왜
+- ▁Thomas
+- 場合は
+- schlag
+- னர்
+- ▁instructions
+- turi
+- ▁passieren
+- ▁medium
+- 姓
+- ▁укра
+- にする
+- wasser
+- ▁cigar
+- ▁pessoas
+- ▁thirst
+- ▁gö
+- がこの
+- ▁norman
+- geni
+- 拜
+- rina
+- اک
+- 出现了
+- 纯
+- ffin
+- 忘れ
+- 戦い
+- 效果
+- こういった
+- kõ
+- ▁مق
+- ▁روز
+- 妙
+- ▁jones
+- ▁During
+- 碰
+- 这条
+- っちゃ
+- рон
+- pack
+- gina
+- が起き
+- ▁sins
+- gelassen
+- prä
+- ▁Wand
+- istes
+- plat
+- ▁telephone
+- conf
+- 桌
+- ▁shine
+- 不知
+- جل
+- ▁Red
+- ▁pearl
+- ▁prec
+- 家の
+- ▁Süd
+- ▁într
+- 牧
+- agu
+- お話
+- ▁sym
+- 意見
+- case
+- ▁2016
+- ▁tenía
+- ▁Netz
+- lusion
+- ▁sola
+- なくなって
+- 克斯
+- 习
+- енно
+- ▁lucy
+- 日間
+- 단
+- መ
+- 肺
+- Sch
+- 文章
+- ▁festival
+- нае
+- дам
+- ppy
+- ▁glaubt
+- ило
+- 漏
+- सा
+- は何
+- rico
+- 吃了
+- ▁première
+- 移動
+- ▁River
+- 穷
+- ▁sensitive
+- 今週
+- ▁provides
+- нуть
+- 今度は
+- 获
+- ▁attract
+- ▁durante
+- 桑
+- ▁கு
+- を作って
+- volv
+- ື
+- ▁armen
+- 現
+- abwe
+- 세
+- ých
+- 隊
+- chet
+- ▁euro
+- kula
+- ▁hoofd
+- ▁visitor
+- ▁nooit
+- ятся
+- にした
+- ▁August
+- 每年
+- ▁هست
+- ▁colors
+- ▁buri
+- ▁definitiv
+- ▁Wu
+- かない
+- eno
+- গে
+- ▁لكن
+- egi
+- ▁своей
+- ▁Jordi
+- pfen
+- ▁solicit
+- ▁goods
+- ▁composition
+- gine
+- 第二天
+- までの
+- ▁intellect
+- ▁vow
+- 现在是
+- ▁frown
+- ▁furnished
+- ▁обе
+- য়া
+- 收到
+- ▁sicherlich
+- ▁setzte
+- ▁harsh
+- 専門家
+- ▁Тэр
+- 社長
+- 的一切
+- ハー
+- 迅速
+- hend
+- られています
+- 毁
+- ന
+- 順
+- ▁lunga
+- 最初の
+- anta
+- ▁expectation
+- ▁أي
+- ью
+- ▁дээр
+- ▁মা
+- udo
+- ▁jail
+- 计算
+- ▁나도
+- お父さん
+- fä
+- '*'
+- ▁Studenten
+- ▁Gut
+- ▁你可以
+- 合同
+- рус
+- zam
+- rul
+- 帅
+- ▁Why
+- ▁moderate
+- 块钱
+- හි
+- alia
+- ▁timid
+- ▁cigarette
+- 没关系
+- ologist
+- rufen
+- legi
+- دید
+- ▁முடி
+- 敷
+- 方も
+- 刺激
+- 误
+- ▁fortunate
+- ▁crow
+- kehr
+- ーション
+- ▁nut
+- 你认为
+- frei
+- ▁mình
+- ▁existed
+- 烈
+- lij
+- agost
+- ▁partnership
+- ู
+- ▁ده
+- ▁così
+- ▁hebt
+- fire
+- ▁país
+- druck
+- Ә
+- ▁demon
+- ▁improvements
+- 这样一个
+- ▁jealous
+- 化学
+- 連絡
+- 医学
+- huit
+- ▁transfer
+- گیر
+- ▁favourite
+- 强烈
+- お伝えします
+- 卫生
+- ▁jerusalem
+- ঘ
+- ارت
+- 化的
+- ▁parallel
+- ções
+- 概念
+- 我们知道
+- anu
+- したと
+- ▁我不知道
+- 候補
+- 在他的
+- iens
+- 적
+- مى
+- ▁Jud
+- に戻
+- ▁Gleich
+- ▁dynamic
+- ▁arrested
+- 母親
+- ▁establishment
+- 何を
+- ▁père
+- ▁Regi
+- لف
+- 科学家
+- ▁xwe
+- 갔
+- ▁Fan
+- ▁television
+- bach
+- zes
+- 数学
+- கே
+- ってくる
+- ớ
+- ▁граждан
+- 下一个
+- ▁стран
+- loj
+- ▁是啊
+- ▁masters
+- лез
+- ▁какая
+- オープン
+- ▁option
+- ັ
+- ▁identity
+- ▁submit
+- ▁jusqu
+- 抑え
+- gefühl
+- ▁medio
+- bira
+- 我应该
+- 勝利
+- ĝis
+- ▁handkerchief
+- 夫妻
+- 孩子的
+- ▁vue
+- ▁sober
+- 阵
+- ▁lightly
+- clos
+- ▁laten
+- chie
+- şti
+- 那个人
+- informa
+- rough
+- ▁links
+- ▁distinguish
+- tego
+- ▁civilization
+- ▁jaar
+- ▁drugs
+- ▁convenient
+- いると
+- ನ
+- ▁например
+- dock
+- ▁disturbed
+- würdig
+- ▁deutlich
+- hri
+- destin
+- 免费
+- zina
+- ▁revenge
+- zogen
+- чка
+- 瀬
+- ▁concealed
+- rwanda
+- ▁باز
+- fahr
+- ilo
+- 한테
+- 奶
+- いるんです
+- аба
+- 得点
+- institu
+- нан
+- ▁hedge
+- ▁Every
+- 違い
+- ▁Ly
+- ▁بس
+- свя
+- 你这个
+- ▁dressing
+- 猛
+- 俗
+- ச்சி
+- ▁Ang
+- 问我
+- ▁بزرگ
+- ▁chapel
+- ходит
+- ▁kiu
+- ений
+- 我跟你说
+- 堡
+- utter
+- 躲
+- ▁ໄປ
+- ▁fabric
+- ▁frankly
+- を出
+- 漁
+- 队长
+- ▁هل
+- 的歌曲
+- 쫌
+- baar
+- 还有一个
+- ▁boast
+- ▁Pf
+- ▁bunu
+- ▁Kas
+- сол
+- rock
+- 元気
+- お願い
+- ▁Santa
+- imento
+- 抜
+- ▁чувств
+- ණ
+- 書いて
+- ▁dwelling
+- 聞いた
+- ▁Laufe
+- brechen
+- stol
+- 也在
+- раж
+- 给她
+- ▁arrangement
+- ▁Са
+- zat
+- jung
+- '29'
+- minded
+- ▁vivid
+- ▁18
+- 走到
+- 去看
+- ▁weren
+- うち
+- ▁Mir
+- 背后
+- abilir
+- ホームラン
+- ures
+- 上がる
+- nischen
+- bridge
+- lara
+- hound
+- ▁technologies
+- ▁accompany
+- დ
+- tempe
+- ▁cream
+- ▁anticipated
+- même
+- лей
+- ▁Linie
+- が行われ
+- ▁Pres
+- 言った
+- past
+- ruf
+- ▁trait
+- uche
+- 모
+- ▁scotland
+- ▁struggling
+- ▁vậy
+- posit
+- 患
+- ミサイル
+- ▁preserved
+- ▁зачем
+- ▁defeat
+- avu
+- ▁aby
+- ▁pint
+- iji
+- leute
+- known
+- 済
+- サービス
+- ▁ёсць
+- 実際
+- 的国家
+- ▁são
+- ▁copper
+- 宾
+- ▁Partei
+- ziel
+- ▁illustration
+- ими
+- politi
+- ハン
+- ▁US
+- bry
+- ▁verge
+- öd
+- 法官
+- itud
+- 番の
+- 差不多
+- 很有
+- ▁jury
+- ▁Pas
+- 知道了
+- ▁объ
+- edifici
+- 雨の
+- 类似
+- ▁stamp
+- stimul
+- 我们应该
+- ▁monte
+- ީ
+- 称之为
+- 细节
+- ote
+- ▁номер
+- ライン
+- 日中
+- 赌
+- ctive
+- 页面
+- gion
+- ▁creative
+- 始
+- ▁Venim
+- ▁beings
+- schloss
+- ▁influ
+- 它在
+- ▁гэтым
+- 传播
+- 子どもたち
+- ▁Vous
+- mış
+- ▁sino
+- fach
+- ▁Stelle
+- 対象
+- 个月
+- big
+- ▁autumn
+- chron
+- ▁давайте
+- ▁eaten
+- ▁crept
+- цэ
+- 値
+- ▁defense
+- 激しい
+- ▁echo
+- nelle
+- ▁owing
+- ▁Black
+- ▁moest
+- ▁behalf
+- ▁происходит
+- impa
+- ▁minimum
+- niz
+- ▁investiga
+- 着急
+- ▁october
+- られます
+- ▁virgin
+- ▁lean
+- ළ
+- ▁ответ
+- えた
+- ▁звони
+- いけ
+- 享受
+- äch
+- 你们的
+- onge
+- ▁Hände
+- trat
+- ▁outward
+- 这里的
+- ▁notwithstanding
+- ▁volunteer
+- ▁Smith
+- чная
+- 去吧
+- 湾
+- 帯
+- きている
+- вез
+- 制限
+- maya
+- どうですか
+- ▁clothing
+- 性格
+- ▁Baby
+- bbing
+- වා
+- 花了
+- аю
+- ▁entwickeln
+- ▁approval
+- ▁irrit
+- 他没有
+- ▁Alles
+- ▁associate
+- ▁kenne
+- ▁bolt
+- 永远不会
+- lug
+- ▁perfekt
+- gué
+- nutz
+- 想知道
+- ▁bestimmten
+- ▁nail
+- ビー
+- ыми
+- ▁hatred
+- glia
+- 保存
+- ▁answers
+- ▁hefyd
+- 不懂
+- 催
+- aeth
+- ▁math
+- 的一种
+- ounce
+- eco
+- қа
+- ĝa
+- regel
+- うん
+- ▁betrachten
+- பா
+- nyama
+- 起き
+- 玛丽
+- ▁Fort
+- 願
+- ▁Chúng
+- wald
+- moto
+- ▁sustain
+- 명
+- ▁founded
+- ▁bringt
+- 工资
+- undi
+- Emp
+- ▁isso
+- ▁pris
+- と一緒に
+- eaux
+- ủ
+- aĵoj
+- ても
+- 海洋
+- ▁passes
+- otto
+- ▁Gesundheit
+- կ
+- 先週
+- unga
+- зал
+- ▁immortal
+- ▁covering
+- ▁wash
+- ích
+- bula
+- казать
+- wak
+- issen
+- 担
+- 泽
+- nico
+- ▁hyn
+- ▁react
+- ▁мор
+- mol
+- ▁definite
+- ▁decrease
+- ▁scarce
+- ▁beating
+- print
+- 強化
+- ▁Wind
+- egu
+- ▁справ
+- леп
+- gga
+- ▁nhưng
+- ▁poc
+- 是最
+- ங்கள
+- ▁compete
+- かかる
+- ▁territori
+- hana
+- 続く
+- ground
+- 経
+- 就是我
+- 買
+- وری
+- 邦
+- ミリ
+- ▁Gib
+- nă
+- ▁segui
+- 种族
+- gung
+- ▁formerly
+- ▁gün
+- ents
+- யும்
+- кие
+- ▁Kindern
+- ▁farewell
+- ▁cotton
+- пон
+- ▁mechanical
+- ▁enabled
+- थ
+- wesen
+- ▁prejudice
+- 과
+- ▁Organisation
+- کان
+- ▁beasts
+- 敌
+- ▁breathing
+- ▁đề
+- ▁dues
+- ▁grandes
+- ▁letting
+- wegen
+- ▁provision
+- ▁fishing
+- ▁ongoing
+- ŭ
+- 地位
+- ▁그러
+- 和平
+- ▁länger
+- ▁canada
+- 过的
+- ▁alma
+- ▁Pal
+- cular
+- வெ
+- ▁elegant
+- 司令
+- かね
+- ▁interessiert
+- ▁feu
+- ▁violet
+- маш
+- ▁Landes
+- ▁gazing
+- gestalt
+- 一个小
+- нат
+- klar
+- ló
+- ▁gallant
+- pä
+- ▁tutto
+- ▁În
+- kore
+- 正确的
+- lever
+- ▁restrict
+- ▁Lösung
+- ▁applications
+- ▁witch
+- ительно
+- yong
+- 会在
+- تج
+- 彼女
+- stärk
+- たちは
+- гре
+- ▁Spi
+- ▁tribes
+- η
+- six
+- sitz
+- komen
+- rê
+- ▁geboren
+- ▁niets
+- ▁prominent
+- けどね
+- 诉
+- 添
+- 发生了什么
+- tamente
+- ▁unver
+- 韩
+- ỗ
+- ▁viņa
+- heure
+- ያ
+- 兹
+- るんだ
+- 巨
+- дж
+- ඉ
+- ři
+- ▁upstairs
+- ▁entertain
+- цию
+- ▁indicate
+- kab
+- ▁rarely
+- 株
+- ▁imperial
+- pression
+- дем
+- 应用
+- 形で
+- crib
+- ▁replace
+- ▁beaten
+- 阴
+- ▁আছে
+- 平时
+- 立刻
+- 一群
+- ▁Außerdem
+- джа
+- ▁seltsam
+- ▁ته
+- 유
+- いわゆる
+- দা
+- ▁dimension
+- ▁Bob
+- 岁的
+- 思い出
+- open
+- 孤独
+- ▁disaster
+- களில்
+- baba
+- ▁touching
+- をかけ
+- ▁tribe
+- kati
+- 航空
+- ▁Nation
+- ▁maxim
+- 留在
+- 방
+- となり
+- 手が
+- ▁ähnlich
+- ēt
+- ▁strict
+- いきたい
+- により
+- гән
+- ▁wives
+- ▁политик
+- クラ
+- どちら
+- ▁Herausforderung
+- 这一切
+- ▁sailed
+- lash
+- ▁hanno
+- тка
+- 月の
+- ▁Vorstellung
+- sible
+- участ
+- 倾
+- namen
+- हरू
+- 込まれ
+- daw
+- 揚げ
+- っていく
+- nsen
+- ▁collar
+- ނ
+- hill
+- ▁petite
+- alität
+- ▁grab
+- ▁Kam
+- ▁egal
+- 绿
+- 事业
+- ores
+- ▁bewegen
+- besch
+- ▁Titel
+- 了她
+- きょうの
+- 这两个
+- 危険
+- labor
+- ▁funds
+- 十三
+- 갈
+- ▁jaw
+- ▁senator
+- ▁doorway
+- ationen
+- ્
+- зар
+- litz
+- dó
+- ▁определ
+- ▁あれ
+- ▁accent
+- yana
+- ▁wrath
+- Li
+- ▁moses
+- arna
+- ▁Hay
+- ええ
+- ▁interfere
+- ▁Text
+- ▁можна
+- 臭
+- なんですけれども
+- embra
+- zó
+- лич
+- 描
+- 发表
+- ぁ
+- itor
+- ▁cheese
+- ▁stato
+- 돈
+- ▁Mitarbeiter
+- ▁label
+- কার
+- ▁spun
+- ▁propos
+- ▁speaks
+- 在家
+- 赞
+- ór
+- ▁finds
+- 市で
+- 眠
+- ነ
+- flor
+- ニア
+- ▁мяне
+- ▁اگر
+- ▁operator
+- tention
+- ▁definition
+- 見事
+- вра
+- ▁büyük
+- tribut
+- порт
+- 非常非常
+- вяз
+- rov
+- ▁walter
+- oti
+- ▁marched
+- ▁restored
+- ells
+- ▁trug
+- 好啊
+- onda
+- ▁riches
+- ▁Hoffnung
+- 太太
+- ுடன்
+- lob
+- ▁belongs
+- ▁hans
+- 非常重要
+- ▁présent
+- 分子
+- 某个
+- 讨厌
+- ▁attracted
+- しまう
+- ▁encounter
+- nnes
+- zzle
+- レイ
+- ▁attain
+- ▁вами
+- ▁mutter
+- 开车
+- 바
+- ▁funeral
+- щу
+- اره
+- ▁shiver
+- 创新
+- ▁стал
+- treiben
+- 保险
+- ▁lightning
+- ▁complaint
+- ▁african
+- 重大
+- 你跟
+- ▁같은
+- چى
+- 对他们
+- ▁quel
+- ▁sama
+- ▁обо
+- 遊
+- aves
+- ▁manufacturing
+- 汗
+- 教会
+- 做一个
+- ▁wid
+- мент
+- ▁november
+- ▁месяц
+- ▁elbow
+- ▁tank
+- ほぼ
+- лем
+- ति
+- NHK
+- кин
+- ▁Bilder
+- ▁zeker
+- стве
+- 一个非常
+- ▁curt
+- ტ
+- ▁asset
+- 破坏
+- anye
+- ▁differences
+- ▁нашей
+- clin
+- ウクライナの
+- 行われた
+- ▁Shi
+- dors
+- ▁democratic
+- ▁Obwohl
+- صف
+- هر
+- ▁feeble
+- leş
+- 这种情况
+- ssin
+- を受けた
+- kai
+- ▁robust
+- ▁besondere
+- ▁elected
+- ▁Modell
+- ▁seconds
+- lauf
+- ダイ
+- ګ
+- セット
+- 時は
+- Applaus
+- 白人
+- ▁свобод
+- 病毒
+- 损
+- ජ
+- ▁Mat
+- ▁executive
+- ▁onto
+- lag
+- ▁важно
+- ან
+- ▁concerns
+- ء
+- ebbe
+- ▁Tru
+- ▁seed
+- 推荐
+- ▁Staat
+- 祭
+- ▁cleared
+- მო
+- eixen
+- 让它
+- သ
+- wiesen
+- Ro
+- train
+- ▁acknowledge
+- 取れ
+- 風が
+- 依然
+- ▁indifferent
+- 人数
+- ▁generate
+- ▁frost
+- itaj
+- ▁reminded
+- ▁romance
+- 隠
+- ▁ireland
+- ▁Ха
+- 最多
+- ▁layer
+- 柳
+- 就有
+- ▁caution
+- ▁bezeichnet
+- ▁denied
+- سە
+- ▁profile
+- 的位置
+- tato
+- ▁politische
+- ▁Dra
+- 高度
+- ちゃった
+- ▁Bay
+- 成立
+- ▁stephen
+- laş
+- 変わり
+- neb
+- ält
+- oza
+- coloured
+- рок
+- ▁independence
+- ▁traveller
+- 接着
+- utu
+- 许多人
+- ▁leather
+- 这项
+- 进步
+- ▁exists
+- kia
+- 进一步
+- ړ
+- ▁Char
+- ▁English
+- cional
+- maakt
+- ▁zá
+- ▁wereld
+- ▁дэ
+- 他们都
+- ▁Sicht
+- 出来了
+- وك
+- ▁christians
+- 種類
+- gone
+- ▁говорят
+- ▁danach
+- ▁positions
+- 问你
+- यो
+- ▁daylight
+- wall
+- えない
+- 不住
+- ▁echt
+- 脳
+- 搜
+- ▁consists
+- děl
+- 人在
+- ▁Adam
+- umva
+- ▁formula
+- anzi
+- ▁тысячи
+- ▁casual
+- ины
+- ▁eighth
+- plica
+- guer
+- 世界的
+- ▁استفاده
+- ▁German
+- ▁بیشتر
+- ▁Ŝi
+- ează
+- 反対
+- ▁determination
+- 时刻
+- iller
+- 히
+- 不愿意
+- ▁republic
+- respon
+- alen
+- liste
+- ▁outer
+- ▁echte
+- 对待
+- 私たち
+- ▁которых
+- って言って
+- 你先
+- ่
+- ▁aveva
+- nym
+- acions
+- ▁然后他
+- ▁democracy
+- 填
+- ▁гэты
+- ▁Bruder
+- ▁characteristic
+- ▁Kris
+- ▁neuro
+- politic
+- ▁visitors
+- ໂ
+- ▁gale
+- tır
+- ▁protestant
+- ▁befinden
+- ▁одиннадцать
+- ▁Schi
+- いまして
+- öhn
+- 연
+- そういうこと
+- arbeiten
+- лас
+- ▁porter
+- 做到
+- cun
+- нер
+- nette
+- atan
+- ▁zweitausend
+- ల
+- ట
+- öö
+- ▁relevant
+- 鼓励
+- 很棒
+- fford
+- ▁dios
+- ▁Tak
+- 个小时
+- 我发现
+- ▁Ul
+- rating
+- ▁creek
+- ▁aggressive
+- ▁трэба
+- それでも
+- 手里
+- へと
+- بح
+- нік
+- 一个问题
+- 公主
+- 见面
+- ▁saa
+- 始め
+- 崩
+- ivo
+- Mi
+- ▁extensive
+- ▁спасибо
+- ▁properties
+- ▁Asia
+- ▁Gran
+- 序
+- 애
+- posing
+- 发生的事情
+- 想起
+- 十七
+- ▁Anwendung
+- лө
+- ▁Member
+- トー
+- 孔
+- ▁наши
+- ▁span
+- ▁swung
+- ▁perception
+- हा
+- ▁ox
+- 劝
+- ▁laying
+- ▁carpet
+- wch
+- vē
+- はもう
+- 赚
+- ▁Demo
+- mala
+- daten
+- მე
+- quar
+- ▁تمام
+- anima
+- ▁radical
+- ▁власти
+- anı
+- ▁Ils
+- 証
+- '00'
+- hub
+- ало
+- の姿
+- るのが
+- раў
+- 読
+- '%'
+- ваў
+- 监狱
+- すいません
+- ▁execute
+- bred
+- ▁profess
+- ▁Fenster
+- 不上
+- exc
+- ▁torment
+- 互相
+- ▁unge
+- 着我
+- ரிய
+- の一
+- ▁sci
+- 設置
+- versa
+- ▁größten
+- vê
+- ▁graceful
+- popul
+- 夺
+- ಸ
+- 採
+- とにかく
+- name
+- ▁Partner
+- あるいは
+- ▁nuestra
+- ▁impressed
+- ức
+- 对不对
+- IT
+- 方便
+- pek
+- 一周
+- ਾ
+- ම්
+- ▁California
+- ▁assembly
+- ▁bran
+- жда
+- 可以在
+- ▁lei
+- 在她
+- 有趣的
+- ▁divide
+- 直播
+- nemen
+- の話
+- 求め
+- 彼の
+- ▁trends
+- ▁veröffentlicht
+- илось
+- 助け
+- க்கப்பட்ட
+- ▁دې
+- ▁rapport
+- ▁tones
+- nego
+- 怎么说
+- ົ
+- 也很
+- grat
+- träge
+- ▁großartige
+- ▁cape
+- ▁successfully
+- سى
+- علم
+- 撮
+- jah
+- 安妮
+- ▁whit
+- inin
+- ▁Funk
+- 他自己
+- ▁funding
+- jär
+- 女朋友
+- ホテル
+- ▁poly
+- ▁awkward
+- iyorum
+- wich
+- lish
+- ▁Jung
+- cata
+- جم
+- ▁Then
+- bby
+- embe
+- ▁Kein
+- 防止
+- 访问
+- ▁blanket
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+- ▁andrew
+- ▁grounds
+- ▁چا
+- ▁Israel
+- ▁pap
+- 出てくる
+- ▁convention
+- றி
+- えっ
+- ▁Wieder
+- 充满
+- ▁Australia
+- fik
+- аем
+- ▁schlechte
+- ▁Tier
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+- 屈
+- 肖
+- 課題
+- idas
+- ▁milli
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+- borough
+- 進め
+- ▁Zeug
+- عب
+- En
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+- 柔
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+- leb
+- 最低
+- 当て
+- ▁japan
+- 你把
+- ächtig
+- sort
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+- ▁profitable
+- 如果他们
+- ▁放一下
+- クロ
+- ände
+- 他们说
+- ▁Durant
+- 很高兴
+- pare
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+- schlossen
+- ▁ora
+- ▁அந்த
+- cà
+- 掉了
+- fè
+- ▁canal
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+- ▁bears
+- 出现在
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+- contra
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+- 間に
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+- ▁package
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+- ▁gelernt
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+- 大体
+- ▁anyhow
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+- stoß
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+- 奔
+- lp
+- män
+- кар
+- mesi
+- 的观点
+- gani
+- Mail
+- ▁mwa
+- サッカー
+- ▁alien
+- tiu
+- 是真的
+- 計画
+- りの
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+- иш
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+- ▁meta
+- CO
+- lec
+- ▁但是我们
+- argent
+- 炸
+- 我先
+- ▁studying
+- shaped
+- 的样子
+- ▁anymore
+- ▁attendant
+- 可怕
+- ▁erfahren
+- nade
+- ▁zona
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+- ▁containing
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+- ▁endeavour
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+- จะ
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+- ▁truck
+- 电子
+- ຊ
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+- ▁beheld
+- 焼
+- 这也是
+- energie
+- ▁acid
+- ▁communicate
+- ▁Хо
+- 估计
+- 怎么会
+- зем
+- මා
+- ▁Australien
+- fish
+- 嫁
+- igheid
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+- 区别
+- нев
+- liko
+- tale
+- ▁Camp
+- 文学
+- krit
+- hielt
+- ▁tube
+- ▁карт
+- igne
+- ді
+- 我再
+- 的身体
+- obo
+- 告诉我们
+- ▁dhateng
+- ▁invention
+- ări
+- ▁அவர
+- ▁Herrn
+- าย
+- ▁cunning
+- folk
+- をしている
+- ▁Fra
+- ▁ignor
+- ▁Denken
+- 付き
+- ▁слуша
+- 実験
+- patri
+- ▁Ganz
+- ▁kurze
+- ▁saving
+- 東北
+- 韦
+- るのか
+- ▁baz
+- ▁Freude
+- ▁километр
+- 責任
+- ▁restless
+- ском
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+- ▁whip
+- 收集
+- となります
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+- ▁folly
+- ▁wool
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+- મ
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+- 全員
+- стат
+- ▁regards
+- ▁Flu
+- El
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+- るような
+- tering
+- ству
+- ీ
+- cope
+- 每天都
+- harmoni
+- ▁کرده
+- ▁appointment
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+- 广泛
+- ehr
+- für
+- 向き
+- cell
+- юр
+- വ
+- 设置
+- 这只是
+- ▁erzählt
+- publik
+- ▁nämlich
+- 经理
+- ▁senate
+- ▁perpetual
+- 的研究
+- 昼
+- ற்று
+- ▁Grand
+- 和我们
+- ものです
+- まって
+- hra
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+- 可能性がある
+- ▁Cho
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+- ▁Cam
+- chair
+- ▁chart
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+- ▁injury
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+- ▁наверное
+- nisse
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+- ▁amendment
+- 応援
+- ▁barrier
+- ▁upset
+- 独特
+- 白色
+- ▁dedi
+- 無理
+- ▁extract
+- 時間が
+- 小说
+- ▁pains
+- 就是你
+- 観測
+- ▁所以说
+- 粘
+- 選手は
+- yin
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+- ▁lass
+- 湯
+- 指挥
+- ▁illumin
+- rush
+- 進んで
+- দের
+- élé
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+- fia
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+- 亏
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+- ご覧ください
+- gress
+- ▁romans
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+- トラ
+- 国家的
+- mato
+- hut
+- 支え
+- ▁tudi
+- ▁disappear
+- ына
+- ▁Gesundheits
+- ▁ነው
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+- cam
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+- 扣
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+- weis
+- ▁Hon
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+- 碗
+- ▁acquisitions
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+- 一面
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+- ibility
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+- ilor
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+- ӧ
+- 手段
+- бил
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+- phon
+- 途中
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+- ена
+- drückt
+- Н
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+- めちゃくちゃ
+- 自民党
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+- hnen
+- 也不能
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+- とっても
+- ټ
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+- ▁stake
+- kou
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+- schap
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+- zwi
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+- 如果他
+- 最重要的
+- γ
+- ますが
+- 軍事
+- ▁woe
+- ウェ
+- 息子
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+- 肯定是
+- ▁tay
+- ▁boss
+- граф
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+- lasse
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+- lini
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+- ウル
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+- ▁monkey
+- 私たちは
+- صور
+- ▁тому
+- સ
+- 真相
+- 这就
+- 削
+- ▁Sim
+- ▁squire
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+- 败
+- ۰
+- しかった
+- ちなみに
+- ستان
+- ▁trifle
+- dist
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+- empat
+- 敲
+- ▁Monat
+- 会说
+- стой
+- しようと
+- ▁langen
+- ப்பி
+- ▁Az
+- 焦
+- ますので
+- ছ
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+- 糟糕
+- erei
+- ▁subtle
+- 胎
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+- ▁яна
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+- ▁сообщ
+- 普遍
+- yim
+- pē
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+- 你没有
+- —
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+- ml
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+- ▁gardens
+- ▁ceremony
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+- ▁Ĉi
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+- کش
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+- ▁isabel
+- 向你
+- ె
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+- chang
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+- 首都
+- 地点
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+- вен
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+- ப்பா
+- ▁Dum
+- 的意思
+- ▁dragon
+- ▁silently
+- なければ
+- 荣
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+- emba
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+- GAAP
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+- რი
+- を使った
+- 배
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+- eight
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+- cada
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+- ▁residence
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+- ▁Hauptstadt
+- 京都
+- iling
+- 我把
+- ▁appetite
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+- ▁cardinal
+- häng
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+- reiz
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+- 来て
+- 慌
+- ғ
+- しょう
+- ▁bother
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+- ▁reverse
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+- ของ
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+- 不像
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+- ▁boil
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+- μ
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+- 一声
+- ār
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+- ầ
+- api
+- ố
+- ở
+- ▁Aufmerksamkeit
+- shore
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+- ▁центр
+- 議論
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+- ですし
+- 如今
+- ▁passions
+- ▁limbs
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+- verein
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+- lica
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+- 大事な
+- の問題
+- ▁zwölf
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+- ▁assault
+- 发生在
+- ▁vede
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+- ▁lloc
+- は今
+- tré
+- といった
+- court
+- ▁exquisite
+- wunde
+- ның
+- ▁Lee
+- ▁bundle
+- duct
+- 高速
+- ▁nas
+- mac
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+- 体育
+- チャー
+- さあ
+- ▁desires
+- ▁Geschäfts
+- gana
+- 辛苦
+- 略
+- 交谈
+- 的那种
+- bí
+- レベル
+- ▁brass
+- 说得
+- 무
+- dala
+- 歯
+- ▁shalt
+- kuba
+- スタ
+- ▁взгляд
+- 对象
+- ▁clinical
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+- 殖
+- industrie
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+- 的主要
+- ▁Sel
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+- enne
+- ▁ralph
+- ▁resting
+- ась
+- aran
+- body
+- ▁esteem
+- ▁hail
+- ඩ
+- ▁вместе
+- 仔细
+- ▁rip
+- ▁Aquesta
+- ▁Welche
+- Bo
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+- ▁multe
+- 锅
+- 否
+- ▁наша
+- ▁export
+- ▁vigorous
+- ▁Phil
+- мын
+- ▁scandal
+- 炮
+- カレー
+- ▁Gla
+- ▁بازی
+- تان
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+- ▁tranquil
+- ▁tobacco
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+- 집
+- ▁tire
+- ▁schwarze
+- リング
+- ▁embrace
+- ▁schu
+- ほら
+- ▁rural
+- ҙы
+- ▁skirt
+- ▁каждый
+- るという
+- ▁Bitte
+- 仍
+- ຄ
+- ▁wrap
+- vara
+- ەوە
+- 者は
+- воз
+- нал
+- ▁offers
+- έ
+- 恐怖
+- 安心
+- ▁geweest
+- ▁dumb
+- ▁municipal
+- фон
+- 上げて
+- tē
+- 小组
+- ▁Carl
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+- ▁Position
+- ▁después
+- 绑
+- '2019'
+- ▁scientists
+- ▁但是你
+- 경
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+- ▁excess
+- লো
+- erweise
+- pence
+- ▁prey
+- 把它们
+- قة
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+- ▁fünfzig
+- пле
+- 微笑
+- ▁cabinet
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+- muzi
+- 確保
+- ▁решил
+- ▁Büro
+- 的最
+- كر
+- ▁formation
+- ▁princes
+- ですけれども
+- still
+- әр
+- ▁баш
+- ▁entertainment
+- ິ
+- 奉
+- '800'
+- ▁собственно
+- 放下
+- ということを
+- bird
+- システム
+- ток
+- ▁Tochter
+- 被告
+- ▁uncertain
+- ▁нельзя
+- ▁شخص
+- ほん
+- arma
+- ▁insight
+- ▁ruler
+- ▁altijd
+- ▁habla
+- ären
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+- zunehmen
+- ▁passionate
+- ▁nunca
+- ▁typically
+- 童
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+- ▁supplies
+- räum
+- 温暖
+- anche
+- ▁lustig
+- stop
+- ▁ascend
+- かし
+- ▁Stunde
+- ▁начина
+- وار
+- றை
+- ර්
+- ▁marquis
+- ▁Quin
+- 伙伴
+- ▁إذا
+- ▁Pop
+- 惠
+- кую
+- のない
+- anne
+- していきます
+- 友達
+- ▁retire
+- 来源
+- almente
+- frau
+- ▁ўжо
+- mates
+- няя
+- нич
+- ▁seda
+- 消失
+- ısı
+- ▁wrapped
+- வில்
+- ▁política
+- 行われ
+- 見えて
+- ▁mau
+- lager
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+- 上来
+- lett
+- ލ
+- dang
+- ▁bez
+- енный
+- 哲学
+- ены
+- جز
+- ▁barrel
+- 相反
+- ▁neighbourhood
+- パス
+- zeichne
+- esten
+- メダル
+- ▁Kosten
+- ▁relativ
+- коп
+- ▁älter
+- 编辑
+- الي
+- gründe
+- голов
+- 体を
+- ஷ
+- 有机会
+- ▁nobles
+- 这件事情
+- が発生
+- ということなんです
+- 告诉他
+- 往往
+- ▁دارم
+- ũ
+- yah
+- 五个
+- న
+- ▁argue
+- ශ
+- ピン
+- imos
+- ▁trusted
+- ▁прямо
+- рог
+- ▁depuis
+- idades
+- Ч
+- ▁যে
+- ではありません
+- ていて
+- 億
+- ▁unpleasant
+- smith
+- わない
+- ▁latest
+- рг
+- んじゃないかな
+- nische
+- ▁pulling
+- を持つ
+- puesto
+- 乘
+- 酒店
+- ▁такого
+- ▁Gelegenheit
+- rats
+- ▁svoj
+- ▁viva
+- 这就是为什么
+- 杂
+- ▁مورد
+- পর
+- ▁pushing
+- ▁depths
+- しよう
+- ヨ
+- Ne
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+- ▁guards
+- 均
+- ▁hadden
+- ▁maison
+- ▁sketch
+- вых
+- 宝宝
+- 弁
+- ▁surgeon
+- ▁aŭ
+- ▁siempre
+- sea
+- 除非
+- ▁далее
+- quí
+- 感觉到
+- capacit
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+- ▁chant
+- umwa
+- bä
+- 森林
+- ▁foul
+- مار
+- 矿
+- 跨
+- ▁staying
+- 热情
+- мысл
+- ▁paw
+- ▁Straßen
+- ▁forecast
+- ▁собира
+- اری
+- 互
+- obli
+- енные
+- ப்பட
+- ▁fum
+- ▁cá
+- ▁distinctly
+- ада
+- wari
+- 飲
+- 一部分
+- 市内
+- 怎么能
+- gge
+- ▁عنوان
+- 出演
+- ▁sterben
+- ▁reproduc
+- ▁register
+- ▁Mitte
+- tiga
+- bron
+- ▁Software
+- ▁knights
+- たちの
+- ▁место
+- ▁menschliche
+- ising
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+- ▁mechanism
+- taka
+- ▁haut
+- 需求
+- ▁stopping
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+- ▁breathe
+- 濃
+- 不容易
+- stick
+- sagen
+- ▁guarantee
+- 这些东西
+- 詰め
+- 缝
+- ▁sabi
+- 所在
+- ランナー
+- ▁представи
+- ▁является
+- 特别是
+- شي
+- Đ
+- ▁després
+- 辺り
+- 也要
+- ▁образом
+- 听着
+- ▁earnestly
+- ▁Tim
+- воль
+- 就没有
+- ост
+- ▁oleh
+- 何が
+- ▁donne
+- فة
+- trägt
+- ▁brains
+- 幸运
+- ▁deadly
+- станов
+- valu
+- ürü
+- いえ
+- ▁skull
+- gebiet
+- Mar
+- 敵
+- 攻め
+- ▁spielt
+- source
+- baren
+- ▁Bevölkerung
+- 都能
+- ▁stirred
+- ▁commonly
+- ▁pang
+- ▁Before
+- 角度
+- ▁tener
+- ▁winning
+- ▁öffentliche
+- 截
+- نة
+- ▁parce
+- dler
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+- уль
+- ▁Größe
+- ▁nei
+- ▁conservative
+- ▁туда
+- というもの
+- 用の
+- ▁boxes
+- ▁reception
+- ▁Şi
+- ▁timber
+- ▁Druck
+- 発生
+- мир
+- 適
+- 弃
+- ▁Sud
+- 解決
+- க்கி
+- ▁baga
+- からも
+- وح
+- ▁comprend
+- 在你的
+- 工人
+- 划
+- ложен
+- 原则
+- 史上
+- klä
+- ե
+- markt
+- 荡
+- kari
+- walt
+- オン
+- ▁neues
+- ▁Ergebnisse
+- ▁courts
+- нг
+- 버
+- shot
+- ең
+- ▁blew
+- haltung
+- ▁Herzen
+- грамм
+- ▁participate
+- ▁competi
+- ちょ
+- 알
+- ▁gerek
+- ▁президент
+- ▁esti
+- ண்ண
+- 공
+- 교
+- ▁fatigue
+- ▁fatto
+- ▁aquella
+- altra
+- ▁blossom
+- 恵
+- 鮮
+- 瑟
+- ▁jewel
+- ▁Rock
+- 帝
+- ось
+- ▁remainder
+- ▁راه
+- 特別
+- ▁technique
+- ▁pillow
+- opa
+- 많
+- ▁category
+- ▁таким
+- ませ
+- ▁monday
+- それぞれ
+- pio
+- läuft
+- ▁zunächst
+- ▁Second
+- volg
+- motiv
+- ▁nerves
+- ▁programme
+- omu
+- tious
+- ▁Bell
+- ▁augen
+- дон
+- 一把
+- инский
+- ▁ஆஹ்
+- ▁omdat
+- ▁dutch
+- سته
+- ▁profitability
+- affe
+- ▁standards
+- 付近
+- 俄罗斯
+- kreis
+- ▁gelang
+- овых
+- кор
+- ▁procession
+- ơ
+- 将其
+- 他有
+- 爱的
+- 很有趣
+- ▁Omu
+- кія
+- ▁summit
+- ▁fünfzehn
+- چه
+- 生活在
+- コー
+- 情况下
+- ▁gesamten
+- 海岸
+- 非常好
+- 家长
+- écri
+- ▁murderer
+- ▁Он
+- ▁مست
+- ▁accelerate
+- itas
+- cía
+- ▁diejenigen
+- ▁contracts
+- ▁commun
+- 高中
+- hte
+- ▁delivering
+- たっぷり
+- kala
+- ▁popula
+- 公园
+- ▁domain
+- ▁journalist
+- ▁repar
+- crit
+- ための
+- ▁Auswirkungen
+- ▁былі
+- ▁Papier
+- 所以他们
+- lē
+- பார்
+- 外交
+- ▁Bundes
+- 我们家
+- そんなに
+- ▁stolen
+- 低い
+- 附
+- zaba
+- иа
+- ▁egyptian
+- ା
+- 달
+- 脏
+- ▁uwo
+- lă
+- 会不会
+- ▁Web
+- ▁olduğu
+- дор
+- regul
+- 時点で
+- 必ず
+- ță
+- wissenschaftlich
+- ▁scripture
+- mper
+- を示
+- ▁alterna
+- kke
+- حر
+- shuri
+- ▁format
+- amerikanische
+- ння
+- ▁serving
+- ▁doll
+- ▁resemble
+- ▁slain
+- 走进
+- 美丽的
+- 展開
+- ▁wilderness
+- ▁creo
+- kind
+- ▁Stein
+- ▁mussten
+- gramm
+- ▁абсолютно
+- ని
+- film
+- 不好意思
+- ▁sailor
+- 我看到
+- 笑声
+- 嘅
+- 火车
+- 便宜
+- gid
+- うまい
+- 也就是说
+- 中継
+- ▁cats
+- ango
+- wehr
+- чил
+- ▁flashed
+- 小孩
+- 으면
+- ▁occupy
+- 不对
+- ▁victims
+- rink
+- ▁shan
+- ▁dife
+- ▁должно
+- あんまり
+- чай
+- ▁وقتی
+- 谈话
+- 珍
+- 庁
+- コース
+- rechte
+- ▁компани
+- 后的
+- ▁fem
+- ▁utili
+- struktur
+- 付出
+- mount
+- ▁humour
+- ții
+- ▁respectable
+- を迎え
+- бри
+- ▁бог
+- myaka
+- mast
+- ▁accomplish
+- оро
+- 什么意思
+- ▁allowing
+- ▁streams
+- 扫
+- mming
+- ▁Pferd
+- ▁aya
+- ގެ
+- ▁Canada
+- chief
+- ▁erhielt
+- ▁scared
+- ▁burnt
+- ▁gloomy
+- ▁выбор
+- lha
+- ▁さて
+- нулся
+- ▁Link
+- ımı
+- ベル
+- ிருந்த
+- 仪
+- 睡眠
+- ▁God
+- ▁Vorteil
+- ▁Stellen
+- ístic
+- を作り
+- ▁Reich
+- 重点
+- 氷
+- つい
+- лийн
+- 早速
+- ▁số
+- ▁стол
+- ▁Haar
+- ゾ
+- 説
+- ▁equity
+- ▁grove
+- ▁زندگی
+- お客さん
+- 选举
+- 妮
+- फ
+- ывать
+- 開かれ
+- kamer
+- хай
+- ▁adjusted
+- парт
+- ▁insanlar
+- 垃圾
+- ան
+- だが
+- 您可以
+- mena
+- ▁suitable
+- ދ
+- fähig
+- bikorwa
+- 上升
+- ▁needle
+- ▁hawk
+- มี
+- ベスト
+- ▁prend
+- ▁clima
+- poko
+- 地元の
+- ▁jos
+- قابل
+- ▁prudent
+- 什么是
+- ▁agony
+- ▁inevitable
+- losen
+- ▁таких
+- ▁wages
+- ▁otros
+- date
+- 双方
+- 诊
+- ▁cluster
+- 桂
+- 杉
+- インド
+- ▁meaningful
+- iet
+- ▁fruits
+- 継
+- ▁status
+- 的男人
+- ▁trage
+- ▁polly
+- zehn
+- でしたが
+- ▁самое
+- amour
+- 和她
+- ufer
+- ▁transparent
+- rier
+- tero
+- スク
+- を入れ
+- ▁offence
+- ▁இருந்த
+- ライブ
+- ▁washed
+- ▁obscure
+- esso
+- 規模
+- ▁resident
+- ▁resolve
+- ▁betrachtet
+- ▁foarte
+- 幻
+- 正好
+- ▁damn
+- zugehen
+- 那儿
+- ஓ
+- წ
+- tian
+- 層
+- ▁assembled
+- 中国人
+- ▁chairs
+- 夸
+- ▁сур
+- 解除
+- ▁Dit
+- 終わり
+- To
+- ▁menyang
+- tanz
+- ▁officials
+- ▁libro
+- ▁Glauben
+- ibu
+- ▁prim
+- гы
+- ▁Most
+- 构建
+- ▁entweder
+- ▁bauen
+- 是关于
+- 動物
+- ▁Mund
+- ambu
+- かけて
+- rop
+- ▁generations
+- ▁Joan
+- ▁dash
+- ▁masses
+- ▁mögen
+- بات
+- oud
+- 込んだ
+- ▁brows
+- 演奏
+- 一気に
+- 防衛
+- cula
+- ▁Interesse
+- еры
+- 方針
+- 和他的
+- mog
+- ຈ
+- 輝
+- ▁disguise
+- ▁varia
+- ▁leaped
+- नि
+- ▁leer
+- шь
+- zieren
+- ļa
+- ına
+- rique
+- 彼女は
+- ளை
+- 雑
+- amendement
+- 这本书
+- ▁confined
+- 不久
+- 将在
+- ▁nephew
+- imit
+- бан
+- 缘
+- gado
+- 我们今天
+- ereza
+- 墓
+- ▁domina
+- 买了
+- 体制
+- 稳
+- صر
+- 一万
+- 競
+- 浴
+- 的结果
+- 時から
+- पा
+- ▁hopefully
+- ▁còn
+- 什么都
+- ▁poe
+- ▁fastened
+- ▁brands
+- ▁ấy
+- 烂
+- ▁florence
+- ▁legislation
+- gari
+- ▁высо
+- barra
+- ▁Plu
+- aciones
+- dna
+- 一首
+- 賀
+- ▁december
+- ▁valid
+- cine
+- ▁erinnert
+- pole
+- familie
+- 琳
+- strat
+- ▁humans
+- gā
+- ▁kinder
+- ▁scri
+- ampl
+- ▁blast
+- 琴
+- 曹
+- ▁dispar
+- 绿色
+- 挣
+- ▁продолжа
+- кру
+- ▁steamer
+- ▁arguments
+- ▁toda
+- を超える
+- ▁kullan
+- tli
+- ▁Zustand
+- ▁suspicious
+- 脉
+- ▁obstacle
+- 競技
+- 椅子
+- ▁Maß
+- ▁temporary
+- 的任何
+- ▁persuaded
+- 如果你想
+- 逐
+- ▁cambia
+- ウン
+- ό
+- ▁abbiamo
+- ▁vary
+- ▁понятно
+- ぐらいの
+- ものの
+- 辈
+- ▁commenced
+- ▁Schrei
+- ligt
+- 主题
+- 都知道
+- schule
+- ▁separa
+- web
+- ▁Kaj
+- 飞行
+- thon
+- ▁nnyo
+- ump
+- ▁Service
+- ▁irgendeine
+- eus
+- 件事
+- 这么大
+- 的老
+- ▁protected
+- ▁effectively
+- 分かります
+- dür
+- 主要是
+- кус
+- ▁enorme
+- ष
+- エネルギー
+- ▁sunset
+- нэ
+- wah
+- ▁призна
+- кино
+- ▁deinen
+- ▁costume
+- くない
+- کو
+- ▁homo
+- ▁کردن
+- tò
+- ума
+- もありました
+- いきましょう
+- ▁seats
+- ▁Dort
+- ▁mars
+- ▁supporting
+- ▁replaced
+- ▁hammer
+- ▁Two
+- ▁interven
+- sley
+- ▁машин
+- 把这
+- ▁genauso
+- مۇ
+- нская
+- ▁furnish
+- ▁units
+- 的爱
+- ▁strangely
+- 먹
+- steuer
+- 受伤
+- 関係者
+- 是这样的
+- 失去了
+- ▁اینجا
+- 分け
+- ທ
+- ▁globe
+- 市場
+- 播客
+- иг
+- 资本
+- ▁contents
+- hanga
+- zicht
+- ▁großes
+- 还会
+- ▁jove
+- кер
+- ▁saints
+- 丰富
+- ▁Vol
+- ▁fazer
+- ▁mou
+- دون
+- ▁Ntabwo
+- ▁Pass
+- ▁fires
+- ▁devices
+- ▁tienen
+- нымі
+- ▁mademoiselle
+- ガス
+- taba
+- ▁achter
+- 鬼子
+- 还真
+- 買い
+- ▁rays
+- ▁Cent
+- ▁buryo
+- ▁cual
+- ▁amigo
+- ▁murdered
+- んでいる
+- ▁vulgar
+- elte
+- ▁seas
+- ▁либо
+- пуска
+- ▁aceste
+- 申请
+- そば
+- ▁Brit
+- ▁bitten
+- 的历史
+- ▁Natürlich
+- ▁Texas
+- 炭
+- ▁Schwester
+- ▁блок
+- ▁neighbors
+- ாள்
+- 一時
+- ▁двух
+- rita
+- ▁effet
+- ▁часть
+- מ
+- ▁speziell
+- ▁групп
+- われ
+- 亲自
+- جی
+- ashobora
+- ▁armies
+- tuk
+- ▁succession
+- 上昇
+- ▁Minister
+- 一些人
+- ৃ
+- 究
+- ▁wing
+- いう
+- 的呀
+- anthrop
+- の一部
+- ▁fountain
+- 兼
+- ▁minut
+- りとか
+- 初の
+- 把他们
+- ว่า
+- ▁Design
+- ▁familia
+- 神秘
+- lui
+- ▁testimony
+- 教えて
+- ▁abruptly
+- ▁Une
+- ▁decidi
+- riet
+- 类型的
+- ▁meetings
+- 받
+- 월
+- рай
+- space
+- iadau
+- ▁pall
+- ▁antwoordde
+- 職員
+- مز
+- каў
+- think
+- ▁insbesondere
+- ▁solitary
+- ヨーロッパ
+- ▁responded
+- 中国的
+- ▁puzzled
+- 逆に
+- ▁Mul
+- 赢得
+- గ
+- сөн
+- 不是我
+- ▁sauce
+- ついて
+- ▁diferente
+- life
+- 民主党
+- ▁pronounced
+- までに
+- 公民
+- ▁hohe
+- ▁diverse
+- ▁namely
+- ▁weigh
+- pian
+- 这张
+- ▁thereby
+- வீ
+- ▁tragedy
+- が出て
+- ▁但是他
+- 谷歌
+- іцца
+- ök
+- nika
+- ▁chứ
+- 剪
+- ▁offices
+- 你们俩
+- ▁channels
+- zette
+- sozi
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+- 公園
+- gize
+- rata
+- 養
+- ▁scott
+- ▁grip
+- ▁longing
+- ▁texas
+- ▁diamond
+- 事業
+- 这场
+- ▁Kä
+- bou
+- 想过
+- ுள்ள
+- 一遍
+- 尝
+- schutz
+- ▁clara
+- ▁уу
+- вяр
+- 善良
+- ▁memories
+- üyor
+- ▁producing
+- ▁sadly
+- ▁wink
+- 生存
+- 现在的
+- 协议
+- ▁scent
+- ىنى
+- hine
+- ▁veces
+- ▁pří
+- ▁Ng
+- 购
+- ▁Werk
+- ▁cog
+- 行政
+- ワールドカップ
+- ▁Schmerz
+- ▁milit
+- liği
+- ▁neglect
+- pier
+- 黑色
+- ▁என்
+- ▁functions
+- ▁harvest
+- 시간
+- ▁floating
+- が今
+- ▁metro
+- lingen
+- яць
+- ▁partially
+- ▁indication
+- ▁Rei
+- スタッフ
+- ▁Sub
+- 予報
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+- gesehen
+- 的学生
+- ▁sergeant
+- mari
+- ады
+- Wa
+- 平等
+- 房间里
+- 않
+- ▁documents
+- ▁cavalry
+- ▁பல
+- fashioned
+- ▁sets
+- бой
+- ▁nehme
+- த்தின்
+- ▁پیش
+- क्
+- 广播
+- ае
+- 动作
+- ▁integration
+- 石头
+- ▁результат
+- ▁amas
+- ▁brig
+- দে
+- 饼
+- ▁fiction
+- 題
+- ▁кстати
+- ▁shu
+- ▁பிர
+- しまって
+- flex
+- eorang
+- made
+- рал
+- shop
+- 領
+- scheiden
+- ĉa
+- 旁
+- 舍
+- ї
+- 不是说
+- truc
+- schiff
+- ▁Scott
+- ія
+- ▁roses
+- ▁但是在
+- 胆
+- ▁Ра
+- داری
+- 我们想
+- Ab
+- ▁trabajo
+- に関する
+- ▁defect
+- ▁Gil
+- ერ
+- ▁wandered
+- 会談
+- ckle
+- 勝負
+- Au
+- meli
+- ▁bose
+- 飲み
+- šo
+- 植
+- ▁애들
+- 所说
+- vian
+- ▁لقد
+- 作って
+- ▁satisfy
+- ffle
+- ▁Arzt
+- ▁wann
+- ▁deeds
+- ▁myth
+- ▁Nummer
+- ▁criticism
+- ▁Kro
+- 故意
+- 单位
+- ▁맞아
+- ستا
+- ▁guardian
+- ▁indignation
+- Ver
+- ▁recollection
+- 会見
+- ▁whoever
+- break
+- 気温が
+- সি
+- ▁gleichzeitig
+- 陛下
+- ▁delicious
+- 开始了
+- 读者
+- beeld
+- 起了
+- ▁illegal
+- 地面
+- ▁yeni
+- ▁enjoyment
+- ▁outrage
+- 并在
+- ▁flee
+- 屏幕
+- больш
+- ▁interact
+- ▁momentum
+- ▁hano
+- ▁inspiration
+- liches
+- ලා
+- iser
+- ▁glasses
+- 加上
+- 暑
+- орон
+- 耐
+- ▁dramatic
+- ▁равно
+- hla
+- gó
+- 协
+- 障碍
+- 倉
+- ▁sunk
+- ▁шестнадцать
+- ▁tendency
+- ▁Island
+- するなど
+- 预测
+- よい
+- ▁unbedingt
+- ▁bleibt
+- ▁шмат
+- ▁disp
+- angira
+- arca
+- ▁crimes
+- onne
+- ▁loyal
+- ▁apostle
+- ▁betray
+- оны
+- ▁inward
+- ilia
+- 烦
+- 的日子
+- ▁사람
+- lg
+- 見る
+- rinner
+- 沉默
+- ▁seize
+- ஞ்ச
+- iler
+- ▁правильно
+- なあ
+- ▁بسیار
+- 媳妇
+- ▁говоря
+- 主意
+- 물
+- ▁ruhig
+- ▁Turn
+- 不出
+- сом
+- ▁Nel
+- 紫
+- unddreißig
+- を務め
+- unta
+- ▁Autor
+- ▁diana
+- ▁alexander
+- ▁telegraph
+- ▁ساخت
+- нен
+- ▁amen
+- 焦虑
+- ▁Artikel
+- аар
+- حة
+- ▁seves
+- ▁пост
+- һә
+- 天下
+- Un
+- 明显
+- ▁لدي
+- лаг
+- ▁Rand
+- ▁personality
+- hart
+- abandi
+- っていました
+- zek
+- ▁arrangements
+- بد
+- ▁sermon
+- が多く
+- 的照片
+- ▁ruined
+- ahu
+- なと
+- 智慧
+- ัง
+- ▁теле
+- tou
+- ▁geheel
+- ению
+- ▁своих
+- ▁virtues
+- ▁widely
+- ▁mingled
+- ▁Kir
+- 我们没有
+- ccio
+- ▁Club
+- 我们不
+- езд
+- してた
+- ▁rider
+- teilung
+- كو
+- 一片
+- ▁женщин
+- سان
+- 重复
+- 生日
+- ▁approved
+- 绕
+- 機能
+- ishi
+- ograph
+- 讲述
+- 危机
+- 広がって
+- 获得了
+- رە
+- 意见
+- 妇
+- ▁loaded
+- cation
+- いいですか
+- ▁fran
+- услов
+- ▁pastor
+- 统
+- 提供了
+- 再開
+- ▁puff
+- ն
+- 障害
+- ▁financi
+- 给大家
+- kund
+- ▁tune
+- arro
+- ▁없어
+- ▁levi
+- lade
+- 今も
+- ▁yea
+- ▁serpent
+- ▁endless
+- いいですね
+- いき
+- ▁Where
+- ▁Beste
+- riz
+- 离开了
+- 径
+- ränk
+- ному
+- 伸び
+- ▁Fähigkeit
+- ▁Schlaf
+- ▁duchess
+- 你和
+- ▁لأن
+- 就这么
+- ▁executed
+- ▁heels
+- ▁chicago
+- 성
+- ▁vader
+- zahlen
+- ▁periods
+- called
+- just
+- ▁тринадцать
+- さい
+- dron
+- 改变了
+- रि
+- ▁vera
+- ▁sharing
+- inta
+- ▁Feld
+- 負
+- ▁ihe
+- кров
+- dog
+- ▁чт
+- pool
+- ▁dreamed
+- ▁heilig
+- ▁slender
+- 在家里
+- ▁uncon
+- മ
+- ▁그리고
+- ▁Kandi
+- Union
+- 英里
+- ▁chances
+- ryo
+- 场景
+- 遥
+- 链接
+- 移民
+- 十四
+- ▁sac
+- fact
+- ▁selten
+- ▁Arab
+- 谈到
+- ▁feather
+- ▁inch
+- 尽可能
+- 都被
+- 日常
+- ▁attorney
+- human
+- ▁helpful
+- 劳动
+- 经营
+- ▁mă
+- ▁Cro
+- ▁languages
+- ▁Esperanto
+- лев
+- ▁sustainable
+- タン
+- 生産
+- 纵
+- うちの
+- ▁promotion
+- lateral
+- stamm
+- making
+- 華
+- ▁risen
+- geist
+- ▁vehicle
+- 大量的
+- сна
+- ▁careless
+- ваю
+- 村さん
+- 一刻
+- 育て
+- ▁Ча
+- 取り組み
+- ▁passengers
+- 雇
+- း
+- ▁chimney
+- ▁엄청
+- ▁abundant
+- ใน
+- 调整
+- 場で
+- moc
+- ▁sao
+- ▁Generation
+- 男女
+- ▁provin
+- ▁funktionieren
+- ▁limp
+- ▁calmly
+- gesellschaft
+- ▁Ram
+- ▁quello
+- ▁tv
+- ▁agents
+- 、4
+- あら
+- 和他们
+- ▁беларус
+- ▁Bezirk
+- どっち
+- ▁villages
+- ▁intervals
+- ▁cooking
+- ▁printed
+- ▁revelation
+- ▁Gene
+- 也就
+- ǧ
+- 今シーズン
+- 女王
+- ▁mwen
+- 我们对
+- стер
+- 上で
+- 链
+- ▁politique
+- ▁sunlight
+- ▁differently
+- god
+- 보고
+- 暑さ
+- 对她
+- ▁dost
+- 图像
+- ▁poden
+- igno
+- çu
+- ▁holes
+- かれ
+- 多久
+- ▁consumers
+- ▁sweat
+- bac
+- 喝酒
+- ▁کم
+- ▁movies
+- ▁одной
+- τ
+- テン
+- arian
+- ▁verdad
+- قول
+- 司机
+- 承担
+- ▁exceed
+- низ
+- 今日も
+- 措施
+- ▁clan
+- uso
+- ▁Last
+- 回应
+- ▁nowhere
+- ência
+- ▁islam
+- ɣa
+- gende
+- بان
+- 報
+- quir
+- ▁Lehr
+- Ц
+- ▁nerve
+- ▁january
+- 批判
+- 很大的
+- 男朋友
+- 通信
+- کت
+- 栏
+- いきました
+- ▁путин
+- 町の
+- 混合
+- ▁absorbed
+- wami
+- もともと
+- èn
+- 有趣
+- ▁hinunter
+- state
+- ▁fathers
+- ▁sweep
+- 情報を
+- ▁alcohol
+- bonye
+- ggy
+- රි
+- fitting
+- isierung
+- aq
+- ▁exposure
+- 紹介
+- iker
+- indu
+- ▁friday
+- цыя
+- brü
+- ▁faculty
+- ▁convict
+- ▁tad
+- 誰か
+- نظر
+- 新型コロナ
+- 업
+- ို
+- 遺体
+- ▁addressing
+- 什么的
+- たって
+- ▁veu
+- ▁presents
+- ▁depression
+- ハイ
+- ▁Suche
+- 财产
+- ▁Ту
+- 前半
+- ▁joan
+- を引き
+- стрел
+- 田中
+- ▁user
+- ▁constitu
+- ▁Publikum
+- ▁Tour
+- ەکە
+- 機関
+- ▁muốn
+- овал
+- ▁moonlight
+- ▁welke
+- まだまだ
+- ▁которой
+- ▁participa
+- 彻底
+- ▁discussions
+- ▁году
+- idée
+- 醉
+- 出して
+- ▁Nachrichten
+- fordert
+- ▁proces
+- 高さ
+- ▁Esto
+- 예
+- 切って
+- ▁Inhalt
+- ▁fortunately
+- ево
+- ndre
+- ▁قو
+- arı
+- 你都
+- فه
+- 披露
+- market
+- ▁сами
+- tano
+- 了一下
+- 平衡
+- '150'
+- gekommen
+- 多大
+- ተ
+- ▁lieber
+- zas
+- ▁demands
+- 多少钱
+- べき
+- 飛び
+- шә
+- ▁stare
+- 他们有
+- abantu
+- 하지
+- ▁hı
+- 魂
+- ▁supplied
+- 豚
+- шен
+- ▁preparation
+- bing
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+- 規
+- ▁nhất
+- selt
+- ▁გა
+- ▁gig
+- ▁можем
+- 銃
+- ضا
+- ▁raus
+- ▁Ergebnis
+- ▁forests
+- ▁erstellen
+- ▁Fle
+- ஸ
+- ▁behaviour
+- 年轻的
+- lder
+- энд
+- тел
+- யான
+- ▁Please
+- ▁travelling
+- 多么
+- volution
+- 더라고
+- ▁größere
+- を変え
+- ▁procedure
+- 遭
+- ▁Material
+- ▁campus
+- を行って
+- 級
+- руу
+- roch
+- ▁Mä
+- 現在の
+- 你已经
+- чын
+- ▁erwartet
+- ▁requirements
+- 就不会
+- 専門
+- médi
+- 转向
+- Lachen
+- 未来的
+- ▁commune
+- ▁Fluss
+- 孝
+- tega
+- ▁Zimmer
+- 我认为这
+- ▁Cra
+- レース
+- 状况
+- ▁جدا
+- 一大
+- 不是你
+- зор
+- ▁choses
+- ▁Tie
+- ▁representative
+- ▁pent
+- ▁knot
+- 有点像
+- െ
+- ▁slim
+- ▁Sho
+- っか
+- чных
+- 法案
+- ța
+- ▁barbari
+- ▁Sto
+- ▁promptly
+- камі
+- изм
+- oog
+- ▁verme
+- ▁adorn
+- やめ
+- ▁commence
+- ▁Hör
+- schritt
+- emt
+- ところに
+- ▁vz
+- ▁тип
+- 债
+- леч
+- がん
+- ▁добра
+- abend
+- excel
+- ▁aceast
+- のもの
+- 你真的
+- ▁invented
+- 矛盾
+- ▁neutral
+- と思うんですけど
+- erna
+- rf
+- zima
+- 规模
+- ワー
+- 喺
+- ▁currency
+- ▁eagle
+- lom
+- magn
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+- ▁pockets
+- 점
+- etto
+- 的目标
+- ▁হয়
+- ▁blade
+- ▁laŭ
+- евская
+- 重要な
+- dī
+- ▁кабинет
+- ▁zéro
+- ▁francs
+- 大多数人
+- вил
+- ▁identified
+- 你可
+- ▁logic
+- 你给我
+- تس
+- axe
+- ▁achievement
+- ▁contribute
+- ▁Ober
+- 反応
+- 宝贝
+- もあり
+- ▁itu
+- 基地
+- ▁admiral
+- nydd
+- shyira
+- 中学
+- drin
+- teen
+- こんなに
+- رات
+- 脸上
+- ში
+- ▁colleagues
+- 同情
+- ▁random
+- 覆
+- 挤
+- 声が
+- ▁bewegt
+- 结束了
+- 安静
+- 영
+- ▁weiteren
+- ▁okul
+- ▁начальник
+- erfolg
+- ▁grava
+- ▁pove
+- ▁Pod
+- 的数据
+- ▁typical
+- クリ
+- schaffen
+- 也好
+- ▁wrought
+- জন
+- kommt
+- だったり
+- dust
+- terra
+- 講
+- ▁bushes
+- ▁muß
+- ユ
+- ▁хэл
+- ▁waist
+- 我一
+- 跌
+- 工业
+- ▁dave
+- ئو
+- 回头
+- ▁Radio
+- '600'
+- ▁confirmed
+- ということが
+- 外国
+- 来讲
+- ža
+- chal
+- ▁mouse
+- 仲間
+- ▁alert
+- ላ
+- ▁Council
+- 肌
+- ▁beginnt
+- 铺
+- ▁Day
+- ▁hired
+- 全国の
+- だい
+- 伟
+- ук
+- ▁condemned
+- いよいよ
+- ▁stap
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+- ithi
+- ▁conform
+- performing
+- ▁incredibly
+- ▁cresc
+- ▁lingvo
+- 扭
+- 胃
+- ▁thumb
+- ▁honestly
+- isk
+- ffel
+- ▁specimen
+- glass
+- はこちら
+- ▁spark
+- munt
+- 西方
+- ▁grupo
+- 你不会
+- ▁söyle
+- histoire
+- વ
+- 随便
+- rama
+- ಕ
+- なきゃいけない
+- ▁circuit
+- の声
+- ▁Rue
+- 练习
+- ▁prayed
+- 更大的
+- 納
+- برا
+- ほかの
+- 회
+- ▁entdeckt
+- ▁команд
+- фор
+- 这也
+- ▁nasıl
+- nehmer
+- 相关的
+- 原谅
+- рыв
+- 送到
+- ▁deutsche
+- IS
+- ▁purse
+- 上有
+- ▁soziale
+- ▁rhe
+- 代码
+- ует
+- 輪
+- ▁Hallo
+- 比我
+- iest
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+- ▁tid
+- posició
+- ▁daring
+- してくれ
+- sprach
+- 東部
+- 毎
+- ▁agency
+- poj
+- 知って
+- gamba
+- ▁carr
+- 指导
+- ▁yeux
+- 飛
+- ▁followers
+- 贾
+- ▁obedience
+- ▁spät
+- ▁mischief
+- 報道
+- 川さん
+- action
+- 迎
+- ▁resort
+- ▁deals
+- ▁warned
+- 摔
+- 丑
+- ▁Einzel
+- லாம்
+- ほどの
+- ▁mob
+- ああ
+- もんね
+- jed
+- 하면
+- precia
+- 百万
+- ▁represents
+- российск
+- ▁entitled
+- ▁derived
+- ▁lace
+- ▁resource
+- ▁certainty
+- ▁gott
+- 又是
+- ▁gallop
+- ジュ
+- iwa
+- 電気
+- 这就是我
+- руч
+- ▁dolan
+- npr
+- Il
+- ▁coarse
+- ▁процесс
+- みます
+- ▁tenderness
+- ▁charges
+- 野さん
+- ▁emerge
+- umber
+- 喊道
+- 检
+- ▁satu
+- ▁sahen
+- є
+- ▁notwendig
+- தோ
+- ▁flames
+- 士兵
+- ▁Seele
+- خبر
+- ▁Nhưng
+- ▁från
+- ▁کښې
+- ▁아니야
+- ▁affectionate
+- ▁curtain
+- ▁detailed
+- やら
+- 我叫
+- 一系列
+- ▁пара
+- oce
+- ▁exempl
+- 范围
+- がいる
+- sions
+- ▁artists
+- ària
+- 凭
+- ▁divorce
+- ▁хоёр
+- ▁corridor
+- ▁lofty
+- 最高気温
+- roma
+- 陷入
+- 開け
+- ▁nivel
+- 集团
+- waka
+- ב
+- ▁Feind
+- 损失
+- ▁орган
+- mite
+- polo
+- 内の
+- ▁Zugang
+- 使って
+- ▁fein
+- ▁numéro
+- ▁allí
+- ற்ப
+- ▁pressing
+- afrika
+- ষ
+- dores
+- 可以看到
+- ▁roughly
+- ▁стара
+- 技能
+- ▁savings
+- ▁farmers
+- ▁theater
+- glück
+- magnet
+- ▁când
+- ▁barely
+- ▁briefly
+- вялі
+- போது
+- ôn
+- 児
+- احت
+- と呼ばれる
+- cloth
+- ▁aka
+- 和一个
+- ▁painter
+- ▁challenging
+- 初めての
+- ▁borrow
+- 完美
+- ▁pillar
+- ί
+- ரோ
+- ▁Frei
+- 军事
+- ▁advertising
+- ▁solitude
+- ことができる
+- やってる
+- 力が
+- ▁bargain
+- のではないか
+- 不少
+- ジャンプ
+- ბა
+- ビル
+- жо
+- ▁sees
+- يح
+- ▁normalerweise
+- bih
+- ▁před
+- ▁telefon
+- ▁injured
+- ▁marsh
+- uburyo
+- ▁правда
+- 就把
+- ▁газ
+- 瞧
+- ▁moves
+- के
+- ▁writes
+- ▁advocate
+- なんですよね
+- ▁sebagai
+- ▁wherefore
+- ▁hoop
+- maga
+- ▁sphere
+- 来月
+- guin
+- 咬
+- ▁underlying
+- ▁olduğunu
+- にいる
+- 主动
+- ▁plusieurs
+- みんなで
+- ً
+- nwa
+- 燃
+- lijke
+- 私人
+- ▁Studie
+- 例子
+- ূ
+- 牢
+- ▁happily
+- 따
+- ciò
+- ▁идет
+- ▁парти
+- を入れて
+- ▁ranks
+- īt
+- ctic
+- 就是这样
+- ▁blows
+- 有用
+- ▁colon
+- 吵
+- 找你
+- ▁pensar
+- кім
+- 彼は
+- 迹
+- ▁organized
+- 柄
+- дог
+- ▁countess
+- niu
+- liest
+- ▁construct
+- ో
+- ▁newly
+- ▁Kontrolle
+- ▁சிற
+- ▁nahe
+- ▁тү
+- стей
+- 指摘
+- ▁announcement
+- ▁tenant
+- ▁mexico
+- おはようございます
+- vuit
+- 突破
+- ▁видел
+- terie
+- ▁energ
+- houd
+- 봤
+- ▁harbor
+- տ
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+- avaient
+- aquest
+- ▁Dal
+- 优势
+- ▁deja
+- ember
+- 讨
+- 竟然
+- ▁slecht
+- initiative
+- ehrt
+- るんです
+- ativ
+- ▁Studien
+- letter
+- 观点
+- 到底是
+- ▁stond
+- ▁penetrate
+- ▁dividend
+- ▁investors
+- 警方
+- ▁иван
+- 面白い
+- っちゃった
+- ▁বল
+- ▁revolver
+- ateur
+- руш
+- misch
+- ▁vengeance
+- シュ
+- quatre
+- ▁wheat
+- shaw
+- ▁nawe
+- trop
+- cret
+- があると
+- 指出
+- рә
+- ▁simon
+- 创作
+- ▁thither
+- ивать
+- ▁beschäftigt
+- ▁brethren
+- igita
+- ▁Nie
+- 这个词
+- ▁Brown
+- قط
+- ribu
+- ▁bitterly
+- ▁sofa
+- ▁component
+- ▁ventured
+- これだけ
+- bora
+- 很久
+- 았
+- ▁Religion
+- 缺乏
+- ▁Pin
+- 咱俩
+- ▁panic
+- 很明显
+- zorg
+- 的父亲
+- ▁survive
+- 一辈子
+- ▁gravely
+- ▁Tagen
+- ▁counted
+- ▁positively
+- 召
+- дым
+- ූ
+- ▁dacă
+- 関連
+- ினார்
+- 沿着
+- ▁bisher
+- はありません
+- だろ
+- ▁elf
+- ް
+- 共产党
+- ▁clutch
+- anten
+- 咲
+- ▁casi
+- یل
+- ▁conversion
+- 知道的
+- ▁warten
+- ьте
+- ▁پای
+- feuer
+- 瓜
+- ▁بۆ
+- ▁tenir
+- ▁Ну
+- 不明白
+- ▁napoleon
+- の映像
+- 两种
+- 自从
+- 幸せ
+- hib
+- jwe
+- ▁podemos
+- ▁pirate
+- ▁bedeuten
+- лова
+- 我得
+- 切れ
+- orden
+- つ目
+- ▁weeping
+- をつけて
+- ছি
+- 衰
+- ▁интересно
+- ▁tradu
+- ▁сме
+- 互动
+- ស
+- 행
+- がい
+- ▁versuchte
+- 恒
+- 르
+- ▁Anzahl
+- 彼ら
+- ▁practices
+- ▁мед
+- menti
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+- ▁restore
+- ologia
+- ▁computers
+- ▁algun
+- 用来
+- 干净
+- school
+- аза
+- ▁vivo
+- ▁Grundlage
+- ukan
+- なくても
+- ▁estado
+- ▁attempts
+- ▁Ĉu
+- ▁sample
+- bund
+- ▁analog
+- ▁celle
+- ▁salva
+- ▁providence
+- すばらしい
+- ▁House
+- ▁trim
+- ▁erkannt
+- ▁rushing
+- 旋
+- ゼ
+- ▁bulk
+- はおよそ
+- 世界中
+- ▁findet
+- cultura
+- زو
+- ▁instances
+- зан
+- ගේ
+- であり
+- енных
+- ▁santa
+- ▁jewish
+- gele
+- ▁gusta
+- ▁condemn
+- '5000'
+- 司法
+- führer
+- ▁sais
+- үл
+- 東京オリンピック
+- に入り
+- 诚
+- ධ
+- ▁nouvelle
+- ▁invite
+- ovat
+- ▁treaty
+- ▁navy
+- urteil
+- minate
+- іх
+- 襲
+- ▁mexican
+- ggle
+- اي
+- ▁germans
+- ▁aqui
+- 在我们的
+- ▁canvas
+- ▁selection
+- ijo
+- 送り
+- ▁blaze
+- lığı
+- ▁gesund
+- gora
+- ▁آب
+- ▁Who
+- 知り
+- nız
+- ▁ôl
+- ▁suck
+- ▁displayed
+- えば
+- ▁undoubtedly
+- 的过程
+- طة
+- ▁Así
+- ▁warrant
+- ▁ursprünglich
+- ▁کردم
+- пен
+- 婴儿
+- 蛇
+- ▁هغه
+- ▁illustrate
+- 事実
+- ▁bureau
+- fata
+- なか
+- ▁Richter
+- mbwa
+- 费用
+- 那就
+- ▁Teile
+- ▁Daniel
+- ▁nên
+- ▁Ад
+- ▁deploy
+- ký
+- Č
+- 純
+- ▁entscheiden
+- ▁چیز
+- ▁Reaktion
+- ▁можете
+- währ
+- アイ
+- က
+- ▁acquire
+- лив
+- ▁caesar
+- 有事
+- ské
+- ▁doth
+- ▁tête
+- 蓝色
+- ▁conversations
+- ▁wept
+- 入れる
+- ▁politischen
+- ▁jerk
+- ▁elaborate
+- 然后你
+- ▁весь
+- ▁ladder
+- に行って
+- 分开
+- ▁advancing
+- macht
+- ▁které
+- 電力
+- Wi
+- дыр
+- ▁hamwe
+- とう
+- ▁lasted
+- タイミング
+- ▁afite
+- 报纸
+- 事態
+- ▁Ker
+- 漂
+- ▁employer
+- ▁discourse
+- ▁cannon
+- ▁Kommission
+- yat
+- shim
+- ▁которое
+- valo
+- даў
+- istoj
+- breng
+- ▁prevail
+- ニング
+- ▁julie
+- nine
+- ljen
+- вым
+- ▁pension
+- 縮
+- ▁сум
+- ▁kleines
+- ▁publish
+- lden
+- ▁alex
+- ▁niece
+- 閉
+- に来て
+- ▁simplicity
+- もらった
+- 吃的
+- ▁idol
+- ester
+- ਰ
+- はね
+- ээс
+- 针对
+- 阳光
+- たく
+- 書き
+- ▁brood
+- ୍
+- が続く
+- craft
+- 专门
+- 調整
+- んですけども
+- 批评
+- ▁dependent
+- 沟通
+- 全面
+- 两千
+- 掌握
+- ▁других
+- kere
+- 으니까
+- 基金
+- iska
+- ▁kang
+- empresa
+- ▁daniel
+- ▁четыреста
+- 着你
+- 반
+- utilitza
+- 会被
+- ▁riu
+- 主張
+- نية
+- philosoph
+- ▁munsi
+- ▁помог
+- طل
+- ▁measured
+- 稍微
+- ▁وقت
+- 第四
+- ▁flows
+- ▁hogy
+- 都很
+- ▁Act
+- までは
+- 朋友们
+- ▁trials
+- ▁schlug
+- 符合
+- 目標
+- ▁shower
+- シーン
+- チョ
+- нные
+- 分手
+- ▁kent
+- ެއް
+- ින්
+- ▁владимир
+- ▁Angelegenheit
+- ▁ascertain
+- ▁gaat
+- ▁الث
+- oja
+- ▁birthday
+- 続いては
+- ▁robin
+- 緊張
+- ▁Fol
+- ▁сделал
+- ▁amused
+- plex
+- 刑事
+- 我还是
+- 储
+- ▁Zahlen
+- やす
+- ▁frozen
+- ごろ
+- このまま
+- ▁sailors
+- ▁önce
+- lite
+- ▁eerste
+- 补充
+- 走向
+- ▁».
+- 現地
+- 愚蠢
+- Lo
+- gă
+- artagnan
+- ▁suite
+- іў
+- 有两个
+- 机器人
+- ▁relate
+- ▁fisher
+- 财富
+- ▁vanity
+- 尊
+- 凶
+- ɛa
+- 谋
+- umba
+- きています
+- ▁Wild
+- ▁magistrate
+- ▁مرد
+- Ich
+- مب
+- appel
+- 姿勢
+- 疲
+- પ
+- yez
+- 工程师
+- 乎
+- მი
+- ▁exemple
+- ▁Even
+- ▁ample
+- 混ぜ
+- ▁Poli
+- ▁Ari
+- 皇帝
+- ▁schr
+- ▁picking
+- 庆
+- ▁quella
+- يز
+- 保護
+- 結局
+- блі
+- ▁Cur
+- 窗户
+- ическая
+- মান
+- '31'
+- 你来
+- ▁семнадцать
+- ▁scout
+- ▁Lebensmittel
+- 收听
+- Qu
+- 上次
+- قوم
+- கால
+- جميع
+- wür
+- ▁Good
+- 建设
+- 世界の
+- δ
+- ▁raison
+- ▁Mike
+- нный
+- шо
+- idio
+- るため
+- ვა
+- fällt
+- ▁declined
+- geza
+- 如果您
+- 大声
+- すべて
+- いるのは
+- 赢
+- ▁obeyed
+- ▁depending
+- ▁colony
+- 側に
+- стью
+- ក
+- 워
+- osten
+- ▁flourish
+- 是如此
+- を奪
+- ▁كنت
+- ▁swing
+- 脾气
+- ▁garments
+- 前进
+- brid
+- ▁Și
+- ▁sarah
+- 鼻
+- ▁Ny
+- Ja
+- ▁swallow
+- ▁entreat
+- pran
+- ▁angesehen
+- 警告
+- ▁experiments
+- 坏了
+- 援
+- kunst
+- ▁correspond
+- ▁быстро
+- 首相
+- ziert
+- ですからね
+- ▁judges
+- ▁passe
+- ▁frontier
+- 玩儿
+- ▁байх
+- の影響
+- gericht
+- zeichen
+- bari
+- 两位
+- ▁colours
+- 唯一的
+- 実現
+- 鳴
+- 寻求
+- 評価
+- ▁impre
+- ▁aspects
+- ▁zeigte
+- ▁compte
+- ▁secrets
+- ması
+- 句话
+- ifik
+- ň
+- 策略
+- ▁Henry
+- 一口
+- ▁Бо
+- ប
+- ං
+- ▁chính
+- gier
+- বার
+- нию
+- ケース
+- ▁fanny
+- ▁curs
+- 烤
+- wedd
+- bian
+- ▁twist
+- ▁mère
+- ▁почти
+- сим
+- 日本は
+- ▁idiot
+- ▁hoog
+- лиз
+- ▁comprehend
+- ▁Wald
+- 可怜的
+- 引用
+- 统治
+- тик
+- ▁들어
+- ▁lawn
+- きっかけ
+- práv
+- らし
+- なと思って
+- ▁ceiling
+- ijwe
+- ▁그럼
+- ▁susan
+- 側の
+- のところ
+- ▁проект
+- だという
+- 汇
+- ▁Freiheit
+- 좋
+- ▁تت
+- ▁trembled
+- ▁Bil
+- 祈祷
+- 樹
+- estima
+- cze
+- ▁goat
+- ße
+- ことです
+- ▁sai
+- 俊
+- ▁сюда
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+- ▁install
+- 威尔
+- bett
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+- ▁luxury
+- 聖
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+- ▁Dun
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+- などと
+- dec
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+- ▁uncertainties
+- lok
+- っしゃ
+- ▁signor
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+- 딱
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+- ▁servir
+- 復
+- 仅
+- ብ
+- 委員会
+- agira
+- ாய்
+- 我们能
+- ▁ministers
+- を加え
+- 使用的
+- paga
+- ▁urge
+- ▁phenomena
+- 翼
+- ahan
+- 指導
+- bruch
+- 标志
+- abord
+- osos
+- ▁essence
+- ए
+- atori
+- ▁dried
+- fica
+- ▁ratio
+- いますが
+- ▁Lang
+- 半年
+- 裁判
+- ▁drops
+- ▁вось
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+- 芬
+- ▁Beginn
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+- ▁investing
+- ديد
+- әк
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+- үн
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+- ▁باشد
+- ▁wheels
+- 最後
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+- ива
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+- ugi
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+- ▁September
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+- と言って
+- takse
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+- šu
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+- eres
+- station
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+- ggs
+- ყ
+- ▁diminish
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+- ▁instruction
+- ルド
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+- 的部分
+- 増加
+- 相似
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+- 泣
+- 在这种情况下
+- 味道
+- bourg
+- を確認
+- 叫我
+- ▁steve
+- 师父
+- kumi
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+- 小子
+- ▁folded
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+- ест
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+- ▁transmit
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+- 確かに
+- спор
+- ▁finde
+- грам
+- 可以说
+- ▁highway
+- 非洲
+- ▁crush
+- ▁tett
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+- 月份
+- ▁gelesen
+- なくなった
+- ▁whale
+- fällig
+- ▁ripe
+- ▁wherein
+- 이랑
+- ダメ
+- 勉強
+- ▁nang
+- ▁Mitglied
+- ▁famille
+- 繰り返し
+- カード
+- 智能
+- dain
+- ехал
+- 罗马
+- 複
+- 訳
+- '99'
+- 实在
+- 楽しい
+- 이야
+- 発言
+- 命运
+- aktiv
+- ▁dense
+- ▁Up
+- ▁reside
+- 大家好
+- ▁dug
+- ▁genetic
+- 方の
+- の間
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+- ▁condu
+- 听听
+- ▁dropping
+- 進出
+- या
+- まい
+- тек
+- ▁lowest
+- ▁хүн
+- ▁Kann
+- ▁trova
+- 妇女
+- шир
+- ▁sandy
+- ▁Això
+- ▁которую
+- ▁gossip
+- ▁Ме
+- 随时
+- ▁adventures
+- ▁jsme
+- 对手
+- 特征
+- ミス
+- ▁repose
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+- ▁jackson
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+- ▁Armee
+- систем
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+- 大卫
+- မ
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+- сә
+- 到现在
+- 我们如何
+- ▁summon
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+- gesetz
+- クション
+- そこから
+- サイ
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+- 帮忙
+- ▁lantern
+- ▁Klein
+- 果たして
+- fusion
+- hild
+- wirtschaftliche
+- 去世
+- цо
+- present
+- ▁users
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+- сид
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+- ▁Schuld
+- 老爷
+- 出席
+- ▁license
+- 臨
+- ину
+- ▁neglected
+- ▁Stu
+- දි
+- ▁maka
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+- ▁harp
+- mill
+- 是从
+- скай
+- 彼得
+- 度过
+- schte
+- 爽
+- 漫画
+- 杀死
+- 保留
+- عرف
+- ▁emergency
+- 他已经
+- ▁plead
+- するという
+- ▁destiny
+- 解放
+- 午
+- まさか
+- そんなこと
+- 滞
+- pell
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+- ތ
+- ▁Chicago
+- ический
+- ▁crois
+- ▁plastic
+- linda
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+- ▁jolly
+- uke
+- 董事会
+- يى
+- ▁dedicated
+- ຂ
+- ▁проста
+- 体の
+- ფ
+- 交代
+- いただく
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+- 边缘
+- ▁ninth
+- ▁flushed
+- 他想
+- ▁impatient
+- hst
+- гын
+- ▁agua
+- ▁basin
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+- posto
+- 罚
+- ▁lease
+- ические
+- ▁idee
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+- リスト
+- ▁frog
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+- 声明
+- čč
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+- 消费
+- road
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+- ደ
+- א
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+- زان
+- ▁segments
+- lise
+- あなたの
+- 着他
+- 还得
+- 思った
+- 俺は
+- ▁incorpora
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+- 其中一个
+- ▁induced
+- ▁lively
+- кән
+- ▁recruit
+- ▁viola
+- 雨が
+- ▁động
+- ▁peep
+- fter
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+- 是很
+- を探
+- 让自己
+- ▁occasional
+- лах
+- ▁выход
+- kud
+- స్
+- ▁верх
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+- мах
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+- hafte
+- 男が
+- ▁sites
+- 事を
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+- ▁realm
+- 纹
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+- kosten
+- 成熟
+- 仗
+- ِي
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+- մ
+- ತ
+- 입
+- はまだ
+- ▁luego
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+- 三百
+- ▁Pot
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+- ▁deaf
+- 物の
+- 其实是
+- iam
+- ▁impacted
+- цый
+- 生徒
+- ▁thankful
+- melde
+- '36'
+- ▁situated
+- 食材
+- barkeit
+- рем
+- 给你们
+- 为此
+- 他妈
+- ču
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+- ▁confessed
+- альный
+- 墨
+- ▁친구
+- 固定
+- 主义者
+- ▁spy
+- 驾驶
+- ises
+- ▁exit
+- lep
+- しまった
+- ர்கள்
+- ▁discern
+- ▁preparations
+- 群体
+- ▁menschlichen
+- zieht
+- ▁institute
+- 其他的
+- ▁neighbor
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+- رک
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+- 呐
+- পা
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+- 誰も
+- jj
+- ▁terme
+- жив
+- 眉
+- ansah
+- 带来了
+- iumenge
+- handlung
+- 电台
+- 少ない
+- ▁Farbe
+- 浪费
+- ▁Jugend
+- ▁чуть
+- 行為
+- ард
+- neuf
+- ガン
+- lch
+- mö
+- ukuri
+- ▁gewinnen
+- 得了
+- remo
+- 弥
+- ▁tại
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+- 当局
+- link
+- 黄金
+- тели
+- 的家庭
+- ▁suburb
+- ▁captive
+- ▁summoned
+- 堪
+- 葬
+- 不说
+- ▁bump
+- ▁camel
+- まとめ
+- ▁checked
+- 一回
+- ത
+- イル
+- ▁ebenfalls
+- gust
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+- ▁exhibit
+- ög
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+- únic
+- seven
+- オミクロン株
+- 过去了
+- 石油
+- racy
+- 飯
+- ▁Wil
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+- ▁flies
+- беж
+- ỏ
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+- idir
+- ▁variation
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+- ▁Hälfte
+- ▁aujourd
+- woman
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+- 头上
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+- ▁turk
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+- 没人
+- حي
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+- ▁benutzt
+- ですかね
+- nier
+- Rh
+- 适应
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+- 前から
+- ▁pledge
+- が必要です
+- 冷静
+- ▁fist
+- 咯
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+- 懸念
+- ▁брат
+- 予選
+- cock
+- つけて
+- fond
+- ▁кому
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+- 为自己
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+- 强大
+- ела
+- さが
+- uzu
+- 拉斯
+- ▁banda
+- 遍
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+- ソース
+- hoff
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+- වෙ
+- 拔
+- ▁threshold
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+- kay
+- カン
+- lā
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+- ип
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+- 艺术家
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+- ▁readers
+- ▁Element
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+- ации
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+- ▁scream
+- twitter
+- 解説
+- xes
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+- 0,000
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+- bola
+- ӑ
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+- ということですね
+- ▁celui
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+- 他人
+- ▁Ini
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+- ▁pře
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+- からです
+- 知道你
+- ▁reeds
+- கின்றன
+- 陣
+- zugeben
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+- 総理
+- ▁plen
+- 商量
+- 杆
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+- ▁Ell
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+- ▁права
+- 学者
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+- િ
+- ▁Erinnerung
+- 他被
+- ▁арт
+- ẽ
+- ać
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+- luft
+- 置いて
+- ▁Akt
+- سف
+- 心脏
+- ▁babies
+- tekereza
+- 食事
+- කා
+- торы
+- блюд
+- のみ
+- 分から
+- رى
+- ▁drain
+- aidd
+- ▁Abantu
+- ģi
+- 行った
+- 詳しい
+- ▁opge
+- ▁deiner
+- メニュー
+- mula
+- 得意
+- ▁struggled
+- 窓
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+- ▁enfants
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+- 困惑
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+- க்கா
+- できて
+- kond
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+- ▁christianity
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+- ڪ
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+- lique
+- 强大的
+- っぱ
+- ▁gains
+- 那时候
+- 카
+- '&'
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+- TV
+- 日本代表
+- 他们没有
+- lice
+- ▁наших
+- ▁Beruf
+- 兆
+- лес
+- ▁shrink
+- ▁Albert
+- ayi
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+- uloj
+- 州的
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+- クト
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+- lista
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+- ▁jemandem
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+- ckey
+- суд
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+- どうも
+- جمع
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+- قدم
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+- るから
+- ▁sobald
+- ▁Kap
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+- いている
+- 险
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+- ▁victoria
+- rato
+- simil
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+- ▁categories
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+- antoj
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+- ▁лишь
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+- bring
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+- нні
+- ▁дня
+- ற்
+- 当時の
+- ▁renewed
+- corn
+- ▁goals
+- 小学
+- 两天
+- ▁Gesetze
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+- どうやって
+- ▁livre
+- 案子
+- landa
+- провод
+- ម
+- 別に
+- ▁красно
+- 七十
+- ごはん
+- атор
+- ▁yonder
+- 听众
+- acre
+- ▁uncomfortable
+- sätze
+- ▁programa
+- bê
+- ▁observations
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+- кли
+- 大幅
+- 他把
+- ▁beef
+- ঠ
+- ▁dialogue
+- ▁marvel
+- 请你
+- ▁awakened
+- ▁Iran
+- automat
+- ▁чалавек
+- வில்லை
+- 实施
+- ▁contributed
+- ▁richtige
+- 它们的
+- みました
+- ▁сказала
+- ▁кем
+- ▁escrit
+- ▁hierher
+- 他现在
+- ▁indifference
+- யே
+- 亡くなった
+- 方面的
+- 数は
+- 川の
+- ಮ
+- ▁Objekt
+- シーズン
+- ▁teatre
+- 制定
+- 钢
+- ▁Ressourcen
+- 箭
+- ▁augment
+- 担任
+- 丰
+- ▁escort
+- iris
+- 月から
+- ница
+- ▁wenige
+- пут
+- ız
+- ▁footsteps
+- ▁Umwelt
+- شى
+- ▁mixture
+- 呼びかけ
+- 南方
+- ▁erg
+- 岸田総理
+- ି
+- cem
+- ▁tests
+- неш
+- mura
+- gré
+- ▁менее
+- há
+- ▁pepper
+- ங்கி
+- 代の
+- ▁разговор
+- yamba
+- 等你
+- ▁indignant
+- 했어
+- ▁энэ
+- ▁contented
+- ▁speci
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+- ție
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+- ුව
+- ▁alliance
+- 上学
+- おっ
+- 前線
+- చ
+- ▁schönen
+- 分かった
+- ация
+- 緑
+- 勇敢
+- ▁thoughtful
+- жан
+- ▁trouve
+- сно
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+- ର
+- ▁february
+- 一辆
+- 举行
+- ères
+- ▁minha
+- toxic
+- 牵
+- 释放
+- ▁gallery
+- кажу
+- ▁bestand
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+- ▁Charakter
+- ▁Sinne
+- sache
+- ▁Gast
+- 在这些
+- ▁sper
+- ヌ
+- ái
+- ▁Wood
+- 六十
+- ▁simul
+- しかない
+- 仮
+- 医師
+- ▁Florida
+- ▁distributed
+- ▁flour
+- oxid
+- ன்ன
+- の部分
+- 出る
+- ▁saat
+- ▁giá
+- ▁reporter
+- 就没
+- ▁amy
+- ▁profund
+- ▁International
+- ▁framework
+- ვე
+- zir
+- ложил
+- 加速
+- しまいました
+- ▁Ты
+- lion
+- 伪
+- 杀人
+- 准确
+- 値上げ
+- ▁Organ
+- することで
+- ▁funciona
+- нак
+- どうしても
+- 夕方
+- ▁вс
+- 为我们
+- ▁gezeigt
+- ponent
+- ワクチン接種
+- ▁velvet
+- ▁attraction
+- と思いますね
+- 巻
+- 購入
+- ▁Spaß
+- ▁här
+- ▁Baum
+- 必要な
+- ▁instruct
+- ▁lordship
+- 知らない
+- ▁arbeitet
+- 稍
+- 怖い
+- ▁Ehre
+- 실
+- 覆盖
+- ▁ладно
+- ▁absorb
+- ▁tread
+- 踏み
+- cate
+- 誘
+- 空港
+- ▁patriot
+- িয়া
+- trust
+- ▁Beth
+- ▁transactions
+- ▁tạo
+- firma
+- ▁Gö
+- 实践
+- teg
+- とり
+- ▁claro
+- もので
+- かというと
+- 打了
+- 我需要
+- ▁admired
+- ▁bronze
+- duction
+- Bu
+- ▁одного
+- yla
+- Ş
+- 的女儿
+- جن
+- ▁стала
+- ▁conceived
+- цей
+- ▁akan
+- biologi
+- ▁palabra
+- ▁stolz
+- デン
+- wärts
+- ▁jsou
+- ▁twilight
+- ▁Brazil
+- ▁verbracht
+- reka
+- ▁понимаете
+- つける
+- ppi
+- ▁calcul
+- general
+- ▁igual
+- ▁wealthy
+- usia
+- оо
+- ▁harmony
+- ▁Code
+- 協
+- ▁принят
+- 强调
+- пала
+- ▁symptoms
+- ▁trading
+- ▁chef
+- ▁answering
+- 降低
+- 著
+- ▁expedi
+- 这个地方
+- ▁swimming
+- ▁froh
+- ▁roused
+- 車が
+- フォ
+- ▁chatter
+- brun
+- Univers
+- ▁earliest
+- hita
+- ▁max
+- ▁awoke
+- 農
+- ladi
+- ▁scor
+- 垂
+- 教练
+- vollen
+- ▁уверен
+- 儿啊
+- pfel
+- Ko
+- ▁bate
+- 慎
+- 夫婦
+- ▁tunnel
+- ▁visto
+- あん
+- ▁uncertainty
+- 有限
+- ▁conquer
+- ▁decades
+- ▁Lü
+- ▁Mach
+- ▁Sehen
+- чыць
+- 差し
+- ▁warriors
+- なさい
+- 四年
+- под
+- illon
+- 狠
+- 感激
+- vik
+- ▁decade
+- ▁lime
+- 的确
+- ▁Elizabeth
+- 선
+- ボー
+- ▁plunged
+- 实验室
+- ▁Nachricht
+- ühr
+- 泊
+- ▁Viņa
+- ▁managing
+- ▁seul
+- heden
+- ▁Höhe
+- ▁robber
+- ▁testament
+- bereich
+- medic
+- ipun
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+- 外国人
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+- そのあと
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+- ▁pav
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+- pois
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+- ความ
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+- руб
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+- strahl
+- るために
+- ▁edition
+- ▁possibilities
+- 社交
+- ▁attribute
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+- ▁бизнес
+- 开玩笑
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+- ▁позвол
+- 贷款
+- 为他
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+- 比例
+- princip
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+- 爆炸
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+- êr
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+- enfant
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+- undvierzig
+- 尘
+- ▁whither
+- 不怕
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+- ▁그러면
+- ▁diversos
+- ▁medal
+- ifies
+- стары
+- valent
+- ▁ungefähr
+- 쪽
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+- 高齢者
+- ▁তিনি
+- ড়
+- 記念
+- larda
+- 态度
+- ▁leisten
+- ▁связан
+- ▁assez
+- ▁stove
+- 白い
+- ებ
+- 痴
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+- 創
+- ▁align
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+- ildi
+- ▁Karriere
+- ▁laat
+- richtung
+- 真っ
+- adores
+- 疫情
+- といえば
+- 맞
+- ▁proceedings
+- 不一定
+- ▁throng
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+- 美国的
+- ▁genom
+- 企
+- 泪
+- ▁greeks
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+- escola
+- どおり
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+- ట్
+- дад
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+- 年前に
+- three
+- ҵ
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+- 一眼
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+- デザイン
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+- 悩
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+- 一座
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+- 你再
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+- 작
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+- でしたね
+- ▁platforms
+- ▁clair
+- るんですね
+- ▁personne
+- 现在我们
+- において
+- ▁madness
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+- tius
+- енная
+- вит
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+- zep
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+- 国外
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+- gression
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+- ▁karşı
+- 做的是
+- ▁beobachtet
+- õi
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+- үүн
+- ▁anim
+- 徹底
+- キン
+- 下げ
+- ▁şu
+- 失敗
+- ▁Hab
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+- 怎么做
+- 地域の
+- ▁cough
+- ▁многие
+- 膜
+- دام
+- ▁calculation
+- ▁студ
+- 农民
+- に向かって
+- ▁machinery
+- fühl
+- ▁jonge
+- 这就是我们
+- 活用
+- 长时间
+- Ө
+- モデル
+- ▁mandat
+- ▁neighbours
+- 狙
+- వా
+- рот
+- ▁clue
+- ▁mentre
+- алга
+- شون
+- 这样的人
+- ▁fanden
+- ▁weekend
+- ▁heroic
+- ▁Kamera
+- ▁recommended
+- ▁Komp
+- ▁geschlossen
+- ▁Hintergrund
+- ▁normally
+- ▁viu
+- 感染拡大
+- ▁كۆ
+- eqq
+- 代理
+- ▁زمان
+- بدأ
+- alde
+- ▁түүний
+- ▁elector
+- 看一下
+- ▁thor
+- لند
+- ▁побед
+- うえで
+- 竜
+- ▁проблемы
+- ▁стране
+- aggi
+- چە
+- ▁selle
+- ストレート
+- Ү
+- 燃料
+- ▁constructed
+- ▁henri
+- stricken
+- ▁прошло
+- ▁misma
+- шке
+- рош
+- èl
+- ▁ehemalige
+- 亿美元
+- orna
+- тая
+- న్న
+- ということになります
+- 青年
+- gezeichnet
+- ረ
+- Ingenieur
+- ショット
+- ▁tricks
+- 我可
+- ßen
+- ▁glowing
+- 노
+- ▁hymn
+- ▁olive
+- ▁towers
+- 莲
+- sponsor
+- 自体
+- энне
+- фо
+- ▁feeding
+- ▁nueva
+- 下一
+- ▁کردند
+- makers
+- biro
+- 合法
+- 在线
+- andra
+- ▁spraw
+- 再来
+- პ
+- 培养
+- 乔治
+- いただきました
+- ▁знает
+- ▁mason
+- gence
+- umbu
+- どころ
+- ▁داشته
+- போ
+- ▁downstairs
+- ▁بم
+- 出た
+- 是一个非常
+- ▁judged
+- ល
+- muka
+- ▁ashore
+- ▁Ausbildung
+- opera
+- hour
+- بوو
+- 祸
+- ▁collapse
+- 的角色
+- 2%
+- 挨
+- ŵ
+- 脂
+- vallen
+- 急に
+- ▁campo
+- fili
+- ბ
+- тары
+- mien
+- ศ
+- ▁intercourse
+- ผ
+- ▁Rahmen
+- пита
+- ▁gast
+- 好奇
+- 轨
+- 乐队
+- ование
+- 没有办法
+- 脑子
+- ▁traveling
+- ural
+- 笑话
+- ▁polític
+- どこに
+- 这些是
+- 면서
+- ▁Wy
+- ически
+- 话说
+- jó
+- 過ごし
+- ической
+- 鈴木
+- に入れ
+- jährige
+- kurs
+- ▁formidable
+- ▁pinch
+- ▁assigned
+- ▁Können
+- ▁verdienen
+- уют
+- werte
+- ▁fluid
+- ▁پێ
+- брос
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+- чих
+- ▁memiliki
+- バランス
+- ▁kell
+- ▁Anem
+- ▁richtigen
+- сси
+- ▁amazed
+- برد
+- بال
+- ▁Quant
+- ▁могли
+- вест
+- ▁supplement
+- ▁Werkzeug
+- 暴露
+- unch
+- ▁terrace
+- voor
+- 戏剧
+- 大好き
+- ète
+- 姜
+- ▁어떻게
+- ▁Figur
+- raba
+- ▁sina
+- 最佳
+- 廷
+- 八年
+- ▁Rücken
+- 大夫
+- lustra
+- ▁flush
+- ▁difícil
+- ▁rejoined
+- ▁Oni
+- رز
+- ▁reinforce
+- 女的
+- ▁patterns
+- ありますね
+- avais
+- ▁ceux
+- çar
+- 膨
+- ▁triste
+- 場面
+- ちゃって
+- луу
+- шиг
+- கூட
+- 成分
+- ▁senza
+- ▁опас
+- ▁negoci
+- flamm
+- wirtschaft
+- もそう
+- 五百
+- 标签
+- ▁Auge
+- woord
+- を守る
+- 坑
+- アジア
+- ▁것도
+- ▁vaccin
+- 隐藏
+- ▁côté
+- теля
+- 复杂的
+- bö
+- ▁shells
+- 크
+- 履
+- それだけ
+- prise
+- control
+- zwei
+- ▁parlament
+- Italia
+- 邓
+- ▁alto
+- ▁chuck
+- していない
+- ならない
+- ▁yaşa
+- ให้
+- альна
+- шёл
+- ▁Obama
+- ▁Road
+- ▁exclamation
+- ▁tới
+- شكل
+- 给予
+- 有利
+- 現実
+- 跟我们
+- 世界各地
+- astro
+- ▁More
+- ▁Vergleich
+- だということです
+- 晃
+- 爆发
+- 父親
+- elimina
+- ▁completion
+- ▁kostenlos
+- ▁wussten
+- 年以上
+- 这么多年
+- たかった
+- ▁acute
+- таў
+- ▁conquered
+- ▁benshi
+- ▁Sau
+- ▁torch
+- ▁мире
+- ▁maintaining
+- ▁spider
+- child
+- ▁baker
+- ▁тийм
+- 鍋
+- ▁dasselbe
+- ▁Best
+- ▁offend
+- ▁язык
+- ▁insects
+- 踢
+- ਸ
+- ئی
+- لَ
+- ▁agitation
+- Alb
+- ▁Bord
+- ▁göre
+- ▁Quer
+- ▁attach
+- ▁sinking
+- 这可能
+- 呼び
+- 朕
+- 彭
+- ▁reluctant
+- ியா
+- zukommen
+- ике
+- ▁좋아
+- 国防
+- 掘
+- ▁Museum
+- ▁saba
+- ▁Zug
+- ▁mußte
+- лена
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+- ▁độ
+- 老鼠
+- ▁öffnen
+- ▁vorne
+- ▁batter
+- できません
+- Vi
+- 资格
+- ▁hazard
+- gata
+- ▁nancy
+- ▁гос
+- ▁economi
+- 太平洋
+- んじゃないですか
+- ▁выгляд
+- ▁الأمر
+- ▁marvellous
+- 西班牙
+- ходить
+- ▁Party
+- ▁stack
+- دىن
+- 但我们
+- ▁muchas
+- ▁ҡу
+- čí
+- ▁halbe
+- 葡萄
+- ▁Pil
+- ▁dwelt
+- ▁havis
+- ▁adoption
+- ▁différent
+- 各种各样的
+- 당
+- 会社の
+- gebrochen
+- ség
+- 的消息
+- 勢い
+- ின
+- ▁participation
+- ▁fühlt
+- 灵魂
+- 叹
+- дея
+- 指控
+- ▁möglichen
+- schlaf
+- icio
+- lili
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+- 各地で
+- ▁logical
+- 目が
+- ▁котором
+- ▁competitors
+- 待ち
+- 配合
+- ▁لأ
+- ▁adjustment
+- 足球
+- ▁muti
+- ▁germ
+- ▁이거
+- の情報
+- labora
+- っち
+- 古代
+- пат
+- ▁cai
+- 自杀
+- 打击
+- ▁charlie
+- лага
+- くれた
+- ▁север
+- kenntnis
+- 可爱
+- 公里
+- 看起来像
+- ▁virtual
+- ▁guitar
+- streit
+- gawa
+- ▁Such
+- 1%
+- ▁weiße
+- ▁ivan
+- ▁خلال
+- ▁Wohl
+- ▁leise
+- ansi
+- 逻辑
+- 하게
+- ▁oxford
+- ште
+- ▁revel
+- ▁sahip
+- bintu
+- ▁tien
+- ▁trauma
+- ▁trước
+- ▁honourable
+- ▁probability
+- Est
+- ▁Mak
+- gereza
+- 矢
+- ▁lớn
+- 回復
+- ▁brisk
+- rende
+- プレ
+- emis
+- 我开始
+- ▁potent
+- 客气
+- 几次
+- 还记得
+- това
+- ▁mevrouw
+- ▁eighteenth
+- ывает
+- ▁fing
+- wohl
+- こちらは
+- 是对的
+- iện
+- jana
+- 得更
+- 连续
+- ▁süre
+- ائل
+- தாக
+- 在里面
+- 走り
+- ▁ardent
+- 的事儿
+- buk
+- ▁chuyện
+- wuchs
+- زى
+- ▁allgemeine
+- 肠
+- 吻
+- 抱歉
+- 得太
+- ゼロ
+- 这个东西
+- ▁March
+- 听说过
+- ▁زمین
+- ▁fragment
+- ▁بودند
+- 舒服
+- ジャー
+- öpfe
+- ▁dieselbe
+- 要請
+- ▁reasoning
+- modell
+- لات
+- xxam
+- 斯坦
+- 的天气
+- ▁خل
+- ▁cùng
+- introdu
+- 有名
+- Й
+- 稀
+- meni
+- ▁Proto
+- 这是你
+- vocation
+- 大丈夫です
+- ▁плане
+- なもの
+- ▁Erfahrungen
+- しましたが
+- 賃
+- ▁welcher
+- ▁riep
+- ▁legisla
+- けた
+- ▁мной
+- hong
+- ▁você
+- ▁baseball
+- ▁slap
+- objet
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+- ▁شيء
+- ಯ
+- ijas
+- vēl
+- ĝo
+- mada
+- ▁mystic
+- EC
+- 課
+- ▁experts
+- 杂志
+- 昭和
+- 因为这
+- ▁yose
+- ▁preference
+- ▁Flug
+- 簡単
+- ▁impatience
+- 쓰
+- プレゼント
+- หน
+- ▁ولی
+- ▁slay
+- ▁så
+- 今後の
+- ▁числе
+- ▁ຢູ່
+- ▁хотите
+- ▁никаких
+- ▁நட
+- lette
+- mong
+- していると
+- ▁več
+- ▁dismissed
+- ▁Wissenschaftler
+- ▁liquor
+- ▁pursuing
+- を目指す
+- glaub
+- бро
+- ▁buff
+- 下班
+- ▁ilk
+- ▁Untersuchung
+- ▁Tradition
+- ▁linked
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+- linie
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+- ▁French
+- センチ
+- நேர
+- ário
+- ▁insect
+- aigua
+- qq
+- アフリカ
+- ރު
+- キング
+- の一つ
+- ▁converted
+- ▁vault
+- wain
+- schel
+- samkeit
+- ỉ
+- ▁personnes
+- ▁staircase
+- 咨询
+- ▁slumber
+- ▁Со
+- corr
+- schicht
+- ▁clasped
+- sigur
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+- 姉
+- ▁hẽe
+- ▁pueblo
+- ▁Cat
+- 任何事情
+- ▁جهان
+- 去哪儿
+- нных
+- marin
+- kaya
+- ▁Todes
+- ләр
+- ▁Gan
+- ੇ
+- ▁routine
+- 竞选
+- 如果是
+- 生病
+- ▁punished
+- ▁libre
+- قات
+- ▁bamb
+- ▁demonstration
+- ▁retained
+- ▁nhìn
+- ▁엄마
+- ▁Worten
+- kapa
+- ල්
+- ▁siege
+- ▁üç
+- を伝え
+- 女生
+- ▁schützen
+- ▁família
+- 严格
+- ▁singer
+- 青春
+- ▁Besitz
+- ▁poems
+- しております
+- 考试
+- わら
+- 女の子
+- バル
+- ▁Merc
+- ▁scope
+- なきゃ
+- 不是一个
+- ▁loyalty
+- 躺
+- 研究所
+- ▁juffrouw
+- 英尺
+- ▁verkauft
+- груз
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+- 贝尔
+- ▁أع
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+- 读书
+- 现在已经
+- 问道
+- 很长
+- щих
+- esca
+- ckel
+- ▁thanked
+- ▁Produktion
+- ▁Milliarden
+- 子供たち
+- ▁bodily
+- gada
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+- گل
+- 显
+- ▁Both
+- ▁carrier
+- fér
+- aime
+- 的许多
+- arrêt
+- profit
+- ▁breathless
+- いたら
+- 妖
+- が一番
+- ▁verbessern
+- 瘦
+- ▁mall
+- ないので
+- ▁traces
+- ▁timp
+- 后悔
+- téri
+- 向前
+- یز
+- 範囲
+- ▁dealt
+- 乖
+- ▁desirable
+- 去看看
+- 考える
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+- лик
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+- ıldı
+- клон
+- 即使是
+- ▁Home
+- ngingo
+- 際に
+- ▁abode
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+- ▁всю
+- ▁près
+- 興味
+- 街道
+- wè
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+- 芽
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+- 解决方案
+- ▁이러
+- 届け
+- ▁collective
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+- kina
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+- tata
+- 든
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+- Tech
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+- 最后一个
+- ▁Dorf
+- ▁Deutschland
+- ちゃんの
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+- ▁streng
+- வும்
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+- ▁Kontakt
+- ▁molly
+- 牧师
+- sprache
+- ▁Haushalt
+- 昌
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+- ▁Ländern
+- 考えた
+- 一起去
+- ህ
+- ▁terrified
+- ▁learnt
+- ▁witnessed
+- ▁trov
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+- ▁Beziehungen
+- 把我们
+- زل
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+- 起来了
+- ▁franchise
+- ▁abundance
+- ▁atlantic
+- ▁airport
+- كس
+- せない
+- kong
+- ▁conclu
+- 的态度
+- 的音乐
+- ▁Sind
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+- ▁nữa
+- たんですけど
+- 回报
+- ுடைய
+- ▁domini
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+- ▁encara
+- ▁entgegen
+- ţă
+- виз
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+- ▁Verwaltung
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+- 癌症
+- yandi
+- ▁seulement
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+- esprit
+- yaga
+- 想办法
+- ▁Francisco
+- の予想
+- ▁Wein
+- 晶
+- ït
+- تنا
+- ▁serie
+- ▁characteristics
+- ▁mesmo
+- ▁Schulter
+- 阔
+- ▁کے
+- laki
+- nood
+- 的状态
+- sett
+- フト
+- ▁Virginia
+- メーカー
+- ▁acum
+- ▁Vila
+- muş
+- кана
+- カラ
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+- ▁داده
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+- 任何东西
+- ▁части
+- ები
+- යේ
+- へん
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+- коў
+- lux
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+- iek
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+- itari
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+- 识别
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+- ▁என
+- 換
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+- ▁trotz
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+- 对对对
+- க்கிற
+- 跑了
+- 予約
+- 颗
+- ▁lawyers
+- ▁своим
+- ▁Nya
+- 嫂子
+- ▁mining
+- ▁submitted
+- ▁кил
+- ▁guided
+- 女性の
+- 안
+- 迁
+- ทํา
+- ▁bắt
+- ওয়া
+- 温泉
+- नी
+- ▁bike
+- ▁tossed
+- ஸ்ட
+- ▁Brand
+- ▁ثم
+- ▁Ти
+- 纠
+- ▁சரி
+- 었어
+- ▁emerged
+- ▁versuche
+- これまでの
+- 包含
+- ▁offended
+- ▁già
+- ▁passer
+- 您说
+- 锦
+- klin
+- ▁rechten
+- 地球上
+- тара
+- ▁machten
+- 下次
+- ▁privat
+- 疾
+- ను
+- ▁slice
+- தற்கு
+- ▁destination
+- てしまった
+- дали
+- 你可能会
+- ▁comprehensive
+- ワイ
+- 数が
+- τα
+- amiento
+- рать
+- ▁Theorie
+- らせ
+- Music
+- ▁columns
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+- 내
+- 一根
+- ▁Jun
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+- ▁prudence
+- ▁spielte
+- жим
+- kiwa
+- කි
+- ▁relax
+- ifiziert
+- ▁Slide
+- ▁errand
+- ▁drawer
+- 年生
+- 落とし
+- てない
+- ▁reserved
+- ▁мира
+- 惹
+- 鶏
+- ▁suffice
+- ▁premium
+- ▁handful
+- été
+- ▁олон
+- であれば
+- party
+- ▁истории
+- 看待
+- ▁работы
+- ▁اینکه
+- ▁borders
+- 最大の
+- енным
+- 終了
+- čno
+- ▁winding
+- 加拿大
+- あんな
+- ▁Johnson
+- ってください
+- beera
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+- 方案
+- ویل
+- ▁georgia
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+- ▁있고
+- ▁amidst
+- 扯
+- เขา
+- ▁emerging
+- ▁Roger
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+- ستی
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+- ▁drap
+- ▁spit
+- hund
+- мак
+- 议员
+- 际
+- zusetzen
+- ピッチャー
+- 意大利
+- விட
+- رض
+- ▁rép
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+- ▁Long
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+- ▁слож
+- 扮演
+- нк
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+- 超越
+- 引き続き
+- JR
+- 大手
+- fowl
+- pata
+- ▁goddess
+- 妃
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+- ディー
+- рис
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+- ラスト
+- ním
+- rän
+- gah
+- 多个
+- 教え
+- 佐藤
+- ▁boldly
+- 悩み
+- ▁которого
+- 自転車
+- ちゃんが
+- 核心
+- vacu
+- ▁resent
+- ▁último
+- 的大脑
+- 发言
+- cule
+- ▁wählen
+- ―
+- 辱
+- 강
+- ruka
+- 傾向
+- еду
+- ▁reicht
+- ▁répondit
+- дин
+- 绳
+- 雕
+- 你知道我
+- 工作人员
+- ▁boiling
+- ▁моск
+- 顺利
+- ▁polic
+- terior
+- ▁sect
+- 荣誉
+- хэн
+- わせ
+- irdi
+- itatea
+- ▁draft
+- 的衣服
+- تور
+- 3%
+- 堵
+- யோ
+- ාව
+- να
+- raub
+- 悟
+- 侯
+- ▁entschieden
+- ▁Zeichen
+- 感謝
+- ▁Planeten
+- ▁fueron
+- 就知道
+- ▁loi
+- iteit
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+- ▁otras
+- ▁Öl
+- ▁neighbour
+- ▁standpoint
+- сар
+- 完全に
+- 不舒服
+- VTR
+- ▁spaniards
+- ▁Sozial
+- ▁dirt
+- ▁hwn
+- 颜
+- 晨
+- abanya
+- рий
+- ▁chiar
+- 斤
+- ▁getötet
+- ▁unnecessary
+- bizi
+- brew
+- ▁پار
+- Plattform
+- ▁gewonnen
+- 規制
+- 发送
+- を使う
+- ▁priority
+- ▁Geheimnis
+- ▁suspended
+- 同時に
+- geteilt
+- ▁willingly
+- ▁seien
+- ▁Unser
+- 不动
+- ▁Fund
+- AS
+- ▁nama
+- ކަ
+- 感动
+- ţii
+- american
+- 你们都
+- ▁justify
+- ▁steward
+- ▁Kim
+- 舅
+- мур
+- 袭击
+- اشت
+- ▁реально
+- 正直
+- 没问题
+- līdz
+- 最後は
+- 阶
+- 남
+- ▁bản
+- ▁이게
+- ▁இரண்டு
+- 歴
+- ▁нэр
+- ▁Wol
+- ▁clung
+- 大使
+- ▁Flo
+- ▁Industrie
+- udge
+- ▁massa
+- ▁此外
+- ▁clergy
+- のもと
+- 免疫
+- ▁работает
+- 相撲
+- ▁থাক
+- ▁candidate
+- かなと思います
+- 跟大家
+- ordre
+- hala
+- ▁canadian
+- 小朋友
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+- PR
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+- hurst
+- ▁strait
+- 弄清楚
+- ▁класс
+- 它可以
+- ありますか
+- 拐
+- 체
+- バイデン大統領
+- 揺れ
+- gibt
+- reise
+- いただいた
+- ▁dump
+- ▁meek
+- ▁hayi
+- proof
+- fassung
+- ▁Stock
+- 坡
+- 煤
+- 遗憾
+- ▁Experiment
+- トマト
+- 协会
+- ▁contribu
+- 东北
+- ▁aynı
+- sız
+- ▁geschaffen
+- ▁secondary
+- ▁pitt
+- auto
+- гуля
+- mies
+- lohn
+- 離れた
+- сот
+- 所有人都
+- スペイン
+- 維持
+- ▁Tema
+- ▁আমি
+- 日まで
+- 銀行
+- 整理
+- 原子
+- 殿下
+- вернул
+- ▁goede
+- ▁relates
+- 近くの
+- ▁клуб
+- ▁douglas
+- 仪式
+- ▁paz
+- ▁implica
+- কি
+- korera
+- 试试
+- ▁translated
+- ▁тое
+- 单独
+- ியது
+- ▁algorithm
+- 몇
+- ▁случай
+- ▁anterior
+- 俳優
+- ▁comic
+- ▁candidates
+- もうちょっと
+- 一緒
+- ość
+- ▁nước
+- ▁thay
+- ▁policeman
+- 足を
+- 測
+- ▁combine
+- ார்கள்
+- ▁Pour
+- ▁gaining
+- 更容易
+- ▁bloß
+- ▁Punkte
+- જ
+- ▁chocolate
+- ည
+- 労働
+- それぞれの
+- ▁robbed
+- ▁fourteenth
+- 专辑
+- 物価
+- 北方
+- یده
+- ▁commanding
+- বু
+- ▁joyous
+- ▁drehte
+- ▁товарищ
+- ▁eingesetzt
+- ▁sullen
+- 五千
+- 姑
+- ▁девушк
+- 我以前
+- ▁elderly
+- を取って
+- けさ
+- 日目
+- 织
+- ▁elektr
+- ▁hearty
+- ▁trigger
+- トル
+- ▁waarom
+- ▁zač
+- หล
+- ▁schaute
+- ▁starve
+- erade
+- ▁yapma
+- ▁precise
+- ▁kinderen
+- ▁حول
+- rolle
+- ▁productive
+- мова
+- ▁bằng
+- 酬
+- ▁შე
+- ulin
+- ▁regulations
+- 直後
+- ▁насколько
+- 祝福
+- 结论
+- ▁Kanada
+- ▁philosophical
+- ▁slash
+- dynami
+- 他是一个
+- くなります
+- ▁shelf
+- '95'
+- ますけど
+- ▁deprived
+- ▁stray
+- 出门
+- 小屋
+- そうそう
+- вался
+- ▁mujer
+- ու
+- 泄
+- ▁моей
+- 窃
+- ▁überrascht
+- ▁diğer
+- ▁حتى
+- ▁loin
+- 雨雲が
+- ▁importantly
+- ▁fiery
+- ▁contradict
+- ▁Яна
+- ▁balloon
+- ając
+- ▁einfacher
+- ▁bestowed
+- 島の
+- город
+- ▁moyen
+- 浪漫
+- ▁தலை
+- ▁fortress
+- ▁lebih
+- schild
+- nimi
+- coli
+- というような
+- ▁sprak
+- ▁fright
+- ▁benutzen
+- 吞
+- ▁Schwierigkeiten
+- ▁estão
+- ▁vide
+- ▁frederick
+- 延長
+- 的城市
+- ināt
+- 貌
+- ▁Hey
+- 雰囲気
+- ▁aggr
+- ▁wiederholt
+- າຍ
+- ▁musket
+- ▁swan
+- TO
+- ▁Gang
+- cala
+- ێک
+- look
+- ▁الْ
+- 社員
+- ▁wales
+- ▁problème
+- こちらも
+- zusehen
+- ▁پو
+- sichtig
+- ▁electronic
+- гээр
+- 把你的
+- ▁server
+- ▁exertion
+- 屏
+- ല
+- ▁ejemplo
+- ▁invariably
+- fugi
+- 外部
+- gruppe
+- mord
+- 為
+- ந்தி
+- ▁señora
+- двига
+- ▁exert
+- 諸
+- ▁كبير
+- ▁feedback
+- ▁Terra
+- 大切な
+- ющий
+- そこまで
+- чар
+- ▁Var
+- 食べた
+- ▁glancing
+- 薪
+- 还有什么
+- ▁compact
+- 审查
+- 原さん
+- ▁хоть
+- ▁أحد
+- mén
+- 絶対に
+- ▁merge
+- ってきて
+- ▁swallowed
+- ▁Wachstum
+- ▁Technik
+- مدينة
+- ▁fossil
+- ▁organiza
+- 把这些
+- 引っ
+- を見つけ
+- 阁
+- てほしい
+- нө
+- ▁impart
+- いますね
+- ▁numero
+- あふれ
+- 立場
+- kutu
+- ▁correspondence
+- 增加了
+- ▁ragged
+- А
+- ▁hindi
+- ▁dyn
+- ▁successive
+- がこちら
+- ▁verdammt
+- 严肃
+- ▁barren
+- ▁prairie
+- లు
+- ▁Ora
+- ▁понять
+- ▁vamos
+- ▁conventional
+- に参加
+- ▁vegetables
+- ▁стали
+- ▁تغییر
+- 希腊
+- ▁kelkaj
+- 算了
+- '48'
+- ацыі
+- ▁kitty
+- 踊
+- ▁Wahrscheinlich
+- meza
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+- ▁cheerfully
+- zego
+- ▁storage
+- تك
+- ▁bezahlt
+- muz
+- 五月
+- jol
+- дей
+- oğu
+- icamente
+- ▁hinauf
+- син
+- ▁prova
+- ▁letzter
+- ▁temperament
+- ▁rencontre
+- ▁influenced
+- ラーメン
+- ▁некалькі
+- ▁parlour
+- ▁Mariya
+- fläche
+- ▁Meister
+- 提出了
+- を求め
+- 有意思
+- няў
+- ▁polar
+- ▁Verwendung
+- ▁стен
+- ەم
+- 的作品
+- ▁verstand
+- 所以如果你
+- ▁mieux
+- ▁요즘
+- ▁diversity
+- бак
+- 恐れ
+- 案例
+- 洲
+- ▁tamam
+- ▁salary
+- ▁возможность
+- 它将
+- kret
+- 氧
+- 龍
+- ▁industries
+- chá
+- ▁Vier
+- ▁swinging
+- ▁bacon
+- пись
+- ▁edith
+- 诶
+- ັດ
+- ▁знаком
+- фу
+- ою
+- ừng
+- ▁impress
+- 立って
+- ões
+- хад
+- 锐
+- ▁resemblance
+- 罢
+- 拯救
+- 我也是
+- ▁tightly
+- けがを
+- лева
+- 货币
+- ▁beautifully
+- యి
+- ▁Histori
+- ▁Harry
+- 訴え
+- 集団
+- даль
+- 短信
+- 可以通过
+- лаш
+- 苗
+- ▁summary
+- 프
+- ▁первых
+- 企画
+- oxy
+- っつ
+- ▁celebra
+- 准备好了
+- ▁обрат
+- 大胆
+- tuig
+- θ
+- ▁mabel
+- бла
+- метр
+- 50%
+- ▁fugitive
+- ▁babe
+- だよね
+- 名单
+- ▁haunted
+- 做到了
+- lerde
+- white
+- ▁technische
+- 意味で
+- 脂肪
+- ▁dissolve
+- zze
+- ▁일단
+- ▁loath
+- ▁intensity
+- 提案
+- 提交
+- ▁steckt
+- 耶稣
+- ▁orbit
+- ▁русски
+- ▁fiercely
+- 年後
+- 只是一个
+- Ex
+- ▁sakj
+- 查看
+- ▁آر
+- ▁Ost
+- ▁stump
+- ▁enthalten
+- ▁bitterness
+- 会合
+- 他又
+- ▁hesitate
+- 神奈川県
+- 键
+- كار
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+- ▁درباره
+- жиг
+- ▁cabeza
+- lada
+- ▁bidding
+- тыш
+- ▁cautiously
+- 主播
+- Gelächter
+- ▁erlaubt
+- ▁reconciliation
+- 崩溃
+- وارد
+- pion
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+- 協議
+- ▁gemeinsame
+- 키
+- ▁دارید
+- ▁Qual
+- 東日本
+- 三千
+- ▁میں
+- ▁venus
+- ▁undertaking
+- 所以我认为
+- 克拉
+- ▁câu
+- われる
+- гэр
+- aurait
+- ▁никак
+- かわい
+- 别说
+- hilfe
+- ▁writ
+- beug
+- ▁sombre
+- ptic
+- 如果有
+- NG
+- limi
+- 的事实
+- ▁cảm
+- rott
+- ▁район
+- ▁desolate
+- ▁picturesque
+- বো
+- 晕
+- ذهب
+- ることができる
+- ▁weinig
+- ▁siya
+- ▁stain
+- 惑
+- ▁pourquoi
+- ▁строительный
+- ▁doğru
+- ▁Institution
+- 送给
+- ▁Indien
+- 干啥
+- design
+- ゆっくり
+- 近い
+- ▁Sicher
+- ▁раньше
+- 용
+- ▁effektiv
+- NATO
+- ▁incentive
+- ▁assisted
+- 什么东西
+- ▁stirring
+- ▁corresponding
+- हि
+- 复制
+- 窝
+- ▁Füße
+- ▁tất
+- ències
+- ▁Ati
+- 钥匙
+- ▁mysteries
+- ▁bietet
+- ▁gegenseitig
+- ▁எழு
+- ▁tej
+- 庆祝
+- เล
+- ▁நகர
+- るもの
+- மீ
+- نز
+- sinde
+- サル
+- になってる
+- 上空
+- 言われた
+- ▁Julia
+- ۋا
+- ▁grundlegende
+- ▁comte
+- pida
+- ▁speedily
+- 我们希望
+- respect
+- 普通の
+- ▁thoughtfully
+- ▁Är
+- 宜
+- sorge
+- ▁betrifft
+- ▁dụng
+- ար
+- 你如何
+- 地址
+- ▁يكون
+- лета
+- 都说
+- 赏
+- 強調
+- ▁Sab
+- лос
+- tijd
+- ▁bezahlen
+- ▁Serie
+- イタリア
+- ▁flexible
+- ▁isolated
+- ▁angrily
+- ゲスト
+- 被认为是
+- 人工
+- tauchen
+- ▁точки
+- 張り
+- ▁Right
+- borg
+- ligi
+- about
+- 匠
+- さんから
+- ▁passive
+- ▁delayed
+- かす
+- ダン
+- জে
+- 想着
+- ▁vacant
+- 宴
+- ▁congregation
+- 分かり
+- خور
+- ▁juice
+- ستخدم
+- るべき
+- 子どもの
+- ▁poble
+- 三振
+- けれど
+- 緩和
+- ▁pigeon
+- сны
+- ▁большое
+- ▁Westen
+- әһе
+- сни
+- ▁ensuite
+- ▁agencies
+- ▁remorse
+- ▁смотрите
+- الات
+- 工事
+- 터
+- ▁отвеча
+- igkeiten
+- ▁Universitat
+- bwiye
+- という感じ
+- 통
+- ▁identific
+- 男性は
+- 冻
+- ▁quá
+- 日曜日
+- ▁সেই
+- ミン
+- ído
+- ▁parcel
+- ▁êtes
+- ▁ringing
+- 癌
+- ▁baixa
+- ▁благо
+- するか
+- 组合
+- ▁predomin
+- reisen
+- hnung
+- ▁கரு
+- 贪
+- ▁Häuser
+- ▁nowadays
+- ▁Victoria
+- ثلاث
+- imecres
+- ▁ошиб
+- 保健
+- ▁goose
+- ▁nieuwe
+- ▁consented
+- 匪
+- ▁geloof
+- ▁Würde
+- ▁pup
+- 集まって
+- ▁parade
+- これらの
+- 周围的
+- 美好的
+- bildung
+- ▁plank
+- ▁blink
+- ▁behalten
+- ▁сказали
+- ▁каких
+- ▁දෙ
+- 佑
+- 呂
+- ▁bestimmt
+- ▁بده
+- ţa
+- 但她
+- 寿
+- ▁ўз
+- iera
+- ▁shrewd
+- ▁рядом
+- меж
+- 毎年
+- ▁persistent
+- ▁addict
+- ▁mogelijk
+- ▁Christian
+- ▁disconnect
+- ▁dadurch
+- gisha
+- ▁politicians
+- ▁audio
+- 让人们
+- ▁Harvard
+- ▁Niemand
+- fí
+- ড়া
+- ▁digest
+- 同一个
+- 契約
+- ▁corona
+- kku
+- 広い
+- 以色列
+- ▁Kreuz
+- 陪伴
+- ゃ
+- ▁Gü
+- ▁uitge
+- ▁seasonal
+- ▁sanction
+- Th
+- යා
+- vär
+- 幻想
+- ▁Kate
+- 麦克
+- 榜
+- ▁exchanged
+- ▁verfolgt
+- メジャー
+- ▁jardin
+- ▁Kritik
+- ▁Islam
+- 心态
+- 侵
+- ▁console
+- ▁الان
+- ▁fulfil
+- 多数
+- fiel
+- ▁voraus
+- conscious
+- sohn
+- 부터
+- 時半
+- ▁canon
+- ▁zahlreiche
+- 盒子
+- মু
+- länge
+- pila
+- ▁линия
+- 偶然
+- ▁whatsoever
+- 转变
+- ▁eleventh
+- 暗示
+- 心理学
+- ▁albert
+- 表现出
+- 蜜
+- ▁pennsylvania
+- 새
+- らん
+- たくない
+- tev
+- плы
+- 扑
+- ▁taun
+- 手中
+- ▁onward
+- імі
+- 空間
+- ▁thời
+- ▁евро
+- ▁tuesday
+- ▁scotch
+- ▁смысле
+- しているということです
+- ▁redeem
+- čni
+- 評
+- лык
+- ▁Divendres
+- ประ
+- ▁tested
+- ▁passar
+- 偶尔
+- ▁Group
+- ▁знаем
+- 二十四
+- 想定
+- ▁Satz
+- 淘
+- 摧毁
+- ▁dapat
+- ▁helmet
+- ▁historic
+- 特定的
+- ▁youthful
+- ▁relieve
+- ▁chris
+- ▁argued
+- 十几
+- ▁eerst
+- ▁документ
+- ▁poate
+- мор
+- 退休
+- 你有没有
+- 帰り
+- ▁මෙ
+- হু
+- 鎖
+- 長く
+- ▁deliberate
+- казаць
+- 違
+- ▁shed
+- ographie
+- 舒
+- ▁jaun
+- ▁wanneer
+- ▁ببین
+- 燃烧
+- taking
+- ▁Schau
+- 发现自己
+- 出てきた
+- comm
+- وجه
+- ▁contro
+- ▁angenehm
+- চ্ছ
+- ▁awaiting
+- عَ
+- ▁watu
+- ▁overwhelming
+- 宠
+- ▁peuvent
+- ▁erlebt
+- が好き
+- 仕事を
+- 体系
+- vidi
+- ▁relating
+- ▁sweetness
+- ▁rhai
+- ▁круг
+- カリ
+- ▁هستم
+- ▁disput
+- 発達
+- schnitt
+- ▁Bü
+- ▁recognise
+- ▁tracta
+- 只是为了
+- 通過
+- ではなくて
+- 始まって
+- ▁discharge
+- 不出来
+- ▁inventor
+- ▁bog
+- ▁français
+- 贤
+- 锻炼
+- びっくり
+- аваны
+- 公正
+- ゅ
+- ▁страна
+- 安装
+- ▁kwenye
+- ніка
+- urage
+- 在网上
+- ▁drown
+- 年ぶりの
+- terri
+- zellen
+- ת
+- セル
+- いかがですか
+- 授業
+- ▁morgan
+- ▁miller
+- ほうが
+- ▁neuer
+- 论文
+- ▁wunder
+- ▁Fach
+- ▁skies
+- 标题
+- ▁Sitz
+- zional
+- خواهی
+- 見てみましょう
+- ▁transformed
+- 没办法
+- ▁melted
+- ▁encontrar
+- ▁genre
+- 擅长
+- ▁Middle
+- 说实话
+- ática
+- ności
+- グル
+- ▁cultivated
+- ▁pioneer
+- 基準
+- 生态
+- ▁இல்ல
+- ▁neden
+- スープ
+- ▁Questo
+- ▁pry
+- 食感
+- ▁Wall
+- ▁regulation
+- 锋
+- 我认为这是
+- 近くに
+- gemeen
+- ▁hospitality
+- ▁erected
+- ▁Have
+- ▁interaction
+- greif
+- ಂ
+- ▁tissue
+- ▁таго
+- ▁элек
+- 不要再
+- ▁Brun
+- ▁gill
+- っちゃって
+- what
+- 変え
+- ▁adapted
+- 铃
+- マンション
+- جار
+- ▁frighten
+- ▁Gw
+- ▁Top
+- ▁juist
+- ▁Jeff
+- ▁wrth
+- ▁frisch
+- 好听
+- ▁penalty
+- ▁څه
+- ▁marion
+- ▁Front
+- ▁Arch
+- ▁dicho
+- ော
+- ▁optimistic
+- ▁Zusammenhang
+- әл
+- 铁路
+- ▁accounting
+- ▁vocal
+- оби
+- ▁representing
+- ▁cancel
+- ĕ
+- 转移
+- రి
+- 七年
+- ▁kubera
+- 饮食
+- ▁unfold
+- 无聊
+- ▁Stephen
+- ▁bestow
+- ▁регион
+- ▁terrific
+- 监管
+- 場所に
+- وک
+- ▁matin
+- ▁conveyed
+- ▁склада
+- ペース
+- ▁adhere
+- ついに
+- And
+- ▁underground
+- tuvi
+- ▁apenas
+- ▁allowance
+- ▁противо
+- Rires
+- ▁нашего
+- ▁نیاز
+- ▁begon
+- ▁harold
+- ことから
+- 定期
+- 返し
+- ベース
+- ▁кост
+- 测量
+- ▁deceive
+- ダウン
+- 部署
+- 应对
+- 找个
+- 命名
+- ▁bucket
+- ▁traitor
+- ඇ
+- ʻ
+- ވަ
+- gá
+- ▁அமை
+- ▁Zweifel
+- овым
+- 殺人
+- 落下
+- ▁Tell
+- ▁стало
+- 跪
+- ▁Lieblings
+- ▁earthquake
+- రు
+- рик
+- 処
+- ▁pavement
+- 我真的很
+- 離れ
+- ▁mora
+- 開いて
+- 十个
+- ww
+- indre
+- eerde
+- 次に
+- 否定
+- ▁violin
+- ▁numa
+- ドリ
+- 加州
+- ましたけど
+- ▁моему
+- vēr
+- ▁enfin
+- いける
+- ▁plunder
+- grado
+- ジャン
+- ▁podía
+- били
+- ▁mourn
+- ▁circula
+- ▁screamed
+- ▁eternity
+- ▁privi
+- ほう
+- ▁되는
+- 正面
+- ▁üle
+- ombra
+- ▁வரு
+- pane
+- åg
+- ▁entlang
+- 团长
+- 驻
+- マル
+- 残る
+- ných
+- 思いを
+- ▁Jackson
+- ҭ
+- も多い
+- ▁එක
+- を行い
+- achtig
+- 的证据
+- ▁geschehen
+- 慮
+- ▁gentil
+- ▁найти
+- わし
+- 分裂
+- impi
+- 悦
+- ▁định
+- 弁護士
+- 陽性
+- zubringen
+- 早点
+- ▁autonom
+- 手術
+- 種目
+- 我说的
+- ޮ
+- 审判
+- 年纪
+- ▁其实我
+- ▁gris
+- ▁показал
+- ▁tourist
+- быт
+- 机场
+- ▁interrupt
+- 守り
+- 筋肉
+- ▁manière
+- ユニ
+- 作戦
+- ▁proced
+- 響
+- ▁morality
+- ▁Wunder
+- igeze
+- ▁awfully
+- ▁gesti
+- ▁Allerdings
+- ▁학교
+- ▁dwarf
+- 上市
+- ▁futbol
+- кен
+- ▁construir
+- 疯
+- 大雪
+- ▁physics
+- ▁beteiligt
+- オーストラリア
+- ▁hearth
+- эння
+- ▁cavern
+- бат
+- евский
+- 路线
+- іі
+- ▁Donc
+- ና
+- ▁appreciation
+- ▁trousers
+- 基础设施
+- ▁gewissen
+- 城镇
+- 你看到
+- ▁организаци
+- 悬
+- 抹
+- ▁hinzufügen
+- ▁brillant
+- ▁Donald
+- ご飯
+- ▁dominant
+- рах
+- 伙计
+- meɣ
+- ▁benim
+- 鉴
+- выше
+- 郁
+- 捷
+- クリスマス
+- ▁ມັນ
+- ▁اسم
+- その中で
+- 看不见
+- ывают
+- らせて
+- 饮
+- йын
+- かぶ
+- 掌声
+- ори
+- ▁puzzle
+- ្រ
+- 队伍
+- ▁carved
+- ది
+- ▁admission
+- слуш
+- 鼻子
+- ouvri
+- pta
+- ▁yani
+- ▁extending
+- ப்படுகிறது
+- தில்
+- 官方
+- шчы
+- ▁пойм
+- 皆さんに
+- रे
+- ▁minim
+- ▁forgetting
+- rseits
+- طي
+- 日々
+- unterschiedliche
+- teko
+- bewegung
+- ▁coffin
+- ▁Information
+- 挥
+- いきたいと思います
+- ▁arribar
+- kubernetes
+- ▁nachzudenken
+- '55'
+- 狙い
+- '3000'
+- ▁ultra
+- лым
+- 業者
+- ▁تحت
+- ▁menace
+- ▁Instrument
+- いかない
+- ▁Boy
+- گا
+- ▁Gehen
+- ەیە
+- ▁capability
+- 양
+- dział
+- වේ
+- 经典
+- の仕事
+- abri
+- 都不知道
+- 的家人
+- луг
+- 游泳
+- ▁owl
+- 仍然是
+- ▁attachment
+- 今から
+- ▁Apo
+- ▁наступ
+- Oh
+- 所以当
+- ▁тихо
+- ▁casting
+- 然后在
+- 常に
+- ▁laatste
+- weza
+- тир
+- マス
+- ▁bộ
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+- ▁عام
+- ▁скажем
+- ▁меньше
+- 溶
+- ▁делает
+- ▁Hinsicht
+- ▁ambient
+- ▁бя
+- 一号
+- illion
+- ▁Zweiten
+- ajja
+- ▁подобн
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+- 縁
+- ごめん
+- ▁trở
+- ▁crude
+- すみません
+- fanya
+- 灾难
+- นี้
+- ▁dritten
+- ▁transferred
+- ▁freight
+- ▁chemin
+- ▁milieu
+- 他认为
+- を発表
+- ▁cruise
+- ▁deepest
+- нап
+- леш
+- ▁Ecke
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+- 要不要
+- öß
+- ▁Minute
+- ▁процент
+- 广州
+- ▁sublime
+- ▁получается
+- ▁Brad
+- 然后是
+- ▁allgemein
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+- ▁болно
+- ▁Eigenschaft
+- 比較
+- ▁grandi
+- drängt
+- ۋ
+- ▁அதிக
+- ▁Cri
+- 場所で
+- سك
+- ▁çek
+- щий
+- 纽
+- 冯
+- ▁живот
+- ▁مې
+- 便利
+- krimin
+- 当然是
+- ▁kicked
+- MS
+- 友好
+- ▁Krankenhaus
+- ▁Trotz
+- じゃないか
+- 成果
+- 剩下的
+- ▁Vision
+- ▁verdade
+- ▁pê
+- ▁Түүний
+- ▁پیدا
+- '='
+- ▁пусть
+- ▁Privat
+- マネ
+- ▁Quar
+- 物体
+- ▁ນີ້
+- ませんか
+- 单词
+- 触れ
+- 山上
+- ママ
+- koloni
+- bogen
+- ▁đổi
+- ▁Tanzania
+- 在我看来
+- 太陽
+- нец
+- 生长
+- ▁Dev
+- ▁scrutin
+- نَ
+- ▁девятьсот
+- llah
+- の裏
+- trum
+- に加え
+- ▁fulfill
+- ▁regardless
+- ▁surpass
+- ▁வந்த
+- ▁seixanta
+- ▁inflation
+- ▁augenblick
+- орд
+- ▁infinitely
+- ▁ufite
+- 时光
+- ▁Lauf
+- 精力
+- ēr
+- が確認され
+- மெ
+- හා
+- ▁பேச
+- schmerz
+- unterricht
+- reɣ
+- 创造了
+- ▁veure
+- ▁sechzig
+- steller
+- ▁retour
+- ▁untersucht
+- に入った
+- 方がいい
+- ▁constitutional
+- 号码
+- ▁educational
+- มัน
+- ▁Those
+- ▁Quando
+- ▁herbert
+- 以便
+- ▁années
+- ▁acres
+- 帆
+- ▁maître
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+- 罗斯
+- ▁syria
+- office
+- ▁nebo
+- ▁контрол
+- ▁everyday
+- ▁ähnliche
+- ▁diferents
+- නවා
+- 密码
+- ▁Blue
+- ▁broadcast
+- žu
+- дают
+- أن
+- pik
+- 月亮
+- stimmung
+- ▁Krä
+- ▁satellite
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+- ▁tailor
+- くれる
+- 腐
+- ሽ
+- の写真
+- ▁كه
+- 変更
+- ▁negroes
+- ▁همین
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+- strom
+- مەن
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+- 妨
+- 一夜
+- 働いて
+- ▁lachte
+- ▁persisted
+- perto
+- larga
+- படி
+- ▁Wirkung
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+- geladen
+- овые
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+- ▁bemerkte
+- ▁beliebt
+- ▁гаражно
+- 싸
+- ないといけない
+- ▁provoca
+- ▁Ausdruck
+- 两者
+- ▁canvi
+- ▁complexion
+- гран
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+- DA
+- ӡ
+- häuser
+- とかも
+- 留下来
+- ▁چون
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+- 姿を
+- 死去
+- ▁bewildered
+- ▁refrain
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+- ▁dije
+- ▁praying
+- ▁fuera
+- 代价
+- ▁Selle
+- হে
+- ▁hyd
+- 全身
+- يين
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+- 年齢
+- ▁tension
+- Prince
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+- шет
+- 谦
+- ẩ
+- ucu
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+- ছা
+- ▁Ky
+- 知道他
+- 蔡
+- 拦
+- instagram
+- ovitch
+- ▁orphan
+- телей
+- ▁hablar
+- ▁subsist
+- ▁grape
+- アナ
+- давать
+- фан
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+- を使い
+- 汽
+- 另一种
+- нікі
+- 排除
+- ▁scientist
+- ভি
+- лыг
+- ▁tribute
+- таш
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+- 低気圧
+- ▁ohio
+- ▁universit
+- 简直
+- zaj
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+- țe
+- abaye
+- ▁nke
+- 週末
+- 仏
+- 主持人
+- ▁먹고
+- ▁recherch
+- ▁wohin
+- ▁pulse
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+- ▁impatiently
+- ▁invalid
+- ىدى
+- を巡って
+- 富有
+- ▁identical
+- 霸
+- 刊
+- responsabil
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+- ▁futuro
+- ▁داریم
+- ителей
+- ▁Zeitung
+- سون
+- 置き
+- ▁pete
+- 基督教
+- щения
+- 時代の
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+- 安全保障
+- ▁descri
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+- ▁regelmäßig
+- ▁verfolgen
+- 脅
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+- ▁Einrichtung
+- itarian
+- 大规模
+- 是个好
+- âr
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+- ▁herausfinden
+- ▁privacy
+- ▁важны
+- ▁Out
+- mitglied
+- ▁dedicat
+- 廃
+- ▁signature
+- ▁பகுதி
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+- ▁quiero
+- この時間
+- abba
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+- ாங்க
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+- 趁
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+- dokument
+- ▁quiere
+- ってしまう
+- 雨が降り
+- ▁hause
+- ▁discretion
+- 的风险
+- ほんとに
+- ▁Universum
+- ▁Foo
+- kê
+- ▁Pala
+- ▁exclusive
+- крат
+- ▁assumption
+- ▁sev
+- を持ち
+- ▁exhibited
+- ▁такую
+- ▁hỏi
+- ▁produziert
+- ▁jersey
+- ▁Schönheit
+- 额外的
+- ▁Vorstand
+- ▁sotto
+- ▁eagerness
+- funa
+- ▁unseen
+- ▁jumping
+- ▁legitimate
+- ▁physically
+- ▁hafi
+- ▁poetic
+- iyordu
+- 唱的歌
+- 崇
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+- ▁humility
+- 启动
+- ▁accessible
+- ▁hoarse
+- ▁nơi
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+- үй
+- حب
+- cari
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+- ▁Для
+- singa
+- 犹豫
+- ▁Bedingungen
+- ▁englische
+- ▁sneer
+- を挙げ
+- ▁sentimental
+- 运营
+- ▁jaroj
+- 覧
+- 我一直在
+- 潘
+- ▁ingredient
+- 真ん中
+- ▁Alexander
+- ▁brigade
+- ▁oxygen
+- 相同
+- 过度
+- 校园
+- 何だ
+- ключа
+- شە
+- تُ
+- ▁Rang
+- 远离
+- 相次いで
+- ▁byinshi
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+- umugore
+- ▁dictate
+- ▁nhau
+- ția
+- 船长
+- ▁năng
+- 侠
+- 誤
+- となっている
+- 但是如果你
+- ▁hyper
+- 该怎么办
+- 大学生
+- ればいい
+- 这很
+- тоў
+- ság
+- ▁fifteenth
+- ▁đâu
+- 길
+- ▁известно
+- ▁glacier
+- einig
+- сво
+- 那一刻
+- に乗って
+- ẻ
+- 罐
+- ▁arriving
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+- ▁закры
+- べる
+- 革
+- ▁retirement
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+- ှ
+- ▁annoyed
+- ▁üblich
+- yerek
+- говарива
+- kampf
+- දා
+- 提议
+- ▁Western
+- なぁ
+- ▁kubona
+- ▁کجا
+- gezi
+- 疏
+- 进展
+- ▁города
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+- ▁kiuj
+- 年ぶりに
+- ▁appearing
+- NA
+- ▁elfu
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+- 涛
+- ▁bernard
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+- бед
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+- 美しい
+- schneiden
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+- ▁Maßnahmen
+- ▁superstition
+- speicher
+- ▁embark
+- 観客
+- koko
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+- お話を
+- ▁joyful
+- হি
+- ▁incessant
+- ▁courtesy
+- ҳа
+- ▁fain
+- imbi
+- '['
+- 为他们
+- ▁determina
+- 本部
+- 民間
+- もらいたい
+- 裸
+- أت
+- 做了什么
+- лыш
+- kiye
+- bleib
+- ▁şeyler
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+- ▁Germany
+- 我很高兴
+- ありますよね
+- 呵
+- ▁вокруг
+- 专注于
+- ▁sequence
+- 予算
+- ▁indirect
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+- lässig
+- 合适
+- ຽ
+- ჩ
+- You
+- 所属
+- ▁tries
+- ▁speeches
+- カット
+- ▁trivial
+- ▁posterior
+- 見通し
+- ▁rhan
+- оруж
+- 去过
+- ҩ
+- ▁monstrous
+- 遊び
+- ▁hàng
+- ▁союз
+- 都不会
+- 見られます
+- ▁ernsthaft
+- ▁Tatsächlich
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+- しん
+- beho
+- horse
+- ▁breach
+- ファイ
+- ▁když
+- ▁muscle
+- ▁comparable
+- ▁grasped
+- 誕生
+- ▁Nta
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+- ▁Они
+- 有几个
+- 碑
+- Ю
+- ▁Firefox
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+- 控え
+- ່າ
+- 布莱
+- gruppen
+- 澤
+- ▁круп
+- 働く
+- نق
+- 볼
+- ▁yake
+- ▁tiek
+- 冠军
+- পে
+- baho
+- ▁Streit
+- 晋
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+- ▁направлен
+- 维尔
+- fär
+- presa
+- についても
+- ▁tõ
+- ▁administrative
+- ▁communicated
+- ubah
+- реп
+- க்கிறது
+- 老大
+- 選ばれ
+- ▁благодар
+- ▁desperately
+- corre
+- šķ
+- ▁sweeping
+- 怎么可能
+- ggie
+- ▁recur
+- ▁உட
+- ▁floated
+- 推出
+- прэ
+- 在上面
+- 織
+- ◆
+- ▁twelfth
+- 腺
+- 女孩子
+- ▁counting
+- ▁hurrying
+- 如果没有
+- ▁Abschluss
+- ▁stealing
+- ▁Deck
+- 稲
+- ▁Bevor
+- कि
+- みてください
+- ▁کمی
+- ▁Verkehr
+- ▁Martí
+- ▁assess
+- 逮捕された
+- ▁حرف
+- gekomen
+- 天才
+- 类似的
+- ▁Online
+- ď
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+- 订阅
+- 渋谷
+- ▁už
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+- 支配
+- чку
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+- шат
+- 対策を
+- かれて
+- 缠
+- ወ
+- ▁вполне
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+- ▁trường
+- grond
+- ▁echoed
+- ▁phát
+- だいぶ
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+- 指数
+- 祖父
+- wahl
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+- righteous
+- рап
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+- 的痛苦
+- 六年
+- 全都
+- ▁clasp
+- 浏览
+- топ
+- ▁horizontal
+- 坚定
+- ▁wengi
+- ▁bored
+- 局长
+- ▁travaille
+- த்திற்கு
+- fluss
+- 岗
+- 現状
+- ▁但是当
+- ▁Operation
+- فور
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+- 你到底
+- ▁convenience
+- 抑郁
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+- ▁образова
+- 公寓
+- लि
+- ▁mesure
+- ització
+- パラ
+- ▁mourning
+- ▁dart
+- ▁Grün
+- ސ
+- ਿ
+- ▁wednesday
+- 外出
+- ▁hasty
+- 完整的
+- ▁compre
+- வரை
+- 议会
+- ఏ
+- 會
+- ▁farklı
+- kracht
+- 你可以在
+- カップ
+- 无数
+- ▁Gute
+- гийг
+- ▁llarg
+- ▁elevated
+- ▁elsie
+- ▁Kommunikation
+- あたり
+- impine
+- 特殊的
+- ழி
+- тере
+- ▁そのため
+- нием
+- ▁armor
+- ▁Weiter
+- 在中国
+- ško
+- É
+- යට
+- ▁thyself
+- 沸
+- ▁achtzehn
+- ▁extinct
+- 太多了
+- ▁dresses
+- ▁Prime
+- ▁carrera
+- ுகிறது
+- 不认识
+- ▁expenditure
+- ▁innumerable
+- 気象庁
+- 公安
+- grown
+- 不开
+- ▁infantry
+- ▁abide
+- に合わせて
+- '39'
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+- ▁schuld
+- スキー
+- ▁jog
+- وضع
+- ▁européen
+- ▁seria
+- 合格
+- いら
+- ာ
+- 伐
+- ▁உம்ம்
+- రా
+- 无关
+- ▁diffus
+- দিন
+- 的语言
+- ▁Sarah
+- 书中
+- storm
+- 受害者
+- 仕組み
+- ▁banquet
+- 早餐
+- кос
+- 亿元
+- 10%
+- 供給
+- アイデア
+- технолог
+- ▁diwujudke
+- ่ง
+- ▁energetic
+- ▁bunny
+- 从来不
+- burgh
+- ிருக்கிற
+- ▁glittering
+- 嘘
+- 8%
+- 留言
+- 二十五
+- CD
+- さえ
+- ▁geändert
+- ▁увидел
+- ▁roedd
+- 你这么
+- BA
+- ▁spray
+- ことができます
+- 有能力
+- alina
+- ▁disposal
+- ▁système
+- ▁dreary
+- ▁quarante
+- ▁بأن
+- ▁vient
+- 第三个
+- ▁vacation
+- ▁Ohne
+- 币
+- 倡
+- ▁unnatural
+- 결
+- 少なくとも
+- ▁Another
+- みんなが
+- ▁fuss
+- ▁خواهد
+- ▁patro
+- ▁personage
+- ▁жыв
+- ▁ddim
+- альным
+- ▁scheinen
+- ▁probabil
+- ▁Bewusstsein
+- ▁Militär
+- 裙
+- ▁কথা
+- ▁Sagte
+- ▁whe
+- pola
+- ▁missus
+- 我觉得我
+- ▁quaint
+- viel
+- ▁telescope
+- 涵
+- ▁тупик
+- 在某种程度上
+- 言うと
+- 传递
+- 的灵魂
+- दा
+- ▁töten
+- 事务
+- ▁demonstrated
+- 教训
+- maro
+- ▁figura
+- しろ
+- 痕
+- ▁предлага
+- ▁metrics
+- ண்டி
+- ▁Iyo
+- を前に
+- 4%
+- ▁marian
+- hagen
+- ▁ради
+- цеп
+- ▁слушай
+- ▁демократ
+- ▁ситуация
+- ▁Drogen
+- ▁dose
+- افت
+- ▁اول
+- ▁suppress
+- bril
+- ▁beschlossen
+- 涂
+- 謎
+- ಪ
+- badde
+- ▁있는데
+- ▁drunken
+- ▁seule
+- ▁afge
+- 囲
+- 爸妈
+- 棚
+- 峡
+- ▁bacteria
+- ▁erscheinen
+- が続き
+- ț
+- قار
+- хоз
+- 紧急
+- 好朋友
+- рада
+- ▁examining
+- 同性恋
+- 韩国
+- 很高
+- ▁спец
+- ヘル
+- ере
+- 一歩
+- batu
+- ▁automobile
+- 執
+- を含む
+- ▁gerecht
+- ▁welcomed
+- ▁agricultural
+- っていうのを
+- ▁tudo
+- 相信我
+- 喜欢你
+- ▁woorden
+- 彻
+- তু
+- ارات
+- 这都是
+- 模糊
+- 逆転
+- ダブル
+- ▁мае
+- ショー
+- ▁dirigi
+- 检测
+- 哪怕
+- 我父亲
+- ▁eric
+- із
+- ▁parc
+- dodd
+- 饰
+- រ
+- ▁December
+- 겠지
+- ▁yali
+- ▁gordon
+- ▁Stamm
+- बा
+- ▁ikintu
+- مین
+- ச்சு
+- 美好
+- ▁interrog
+- Aplausos
+- EM
+- 警報
+- ▁Treffen
+- ▁Tau
+- ▁rive
+- を食べ
+- ▁sử
+- ▁останови
+- ▁oliver
+- onian
+- ▁Ez
+- stuk
+- 逛
+- ▁внутри
+- ▁decisive
+- ▁unglücklich
+- の様子
+- 広がる
+- 这个项目
+- ▁Klima
+- ▁primero
+- jang
+- ▁Betrieb
+- ▁sealed
+- ▁insane
+- ۔
+- ▁gewoon
+- 为您
+- ▁tenderly
+- ▁самый
+- ▁oppose
+- ▁ответил
+- eremoni
+- саб
+- ▁correctly
+- FIL
+- าน
+- ▁어디
+- ▁realidad
+- behörde
+- gerufen
+- уха
+- 房子里
+- ▁sujet
+- ▁classroom
+- ▁hübsch
+- ▁sekolah
+- ▁گروه
+- ▁Gli
+- 足以
+- ▁Carolina
+- yorum
+- ▁epidemi
+- áz
+- liwa
+- ボン
+- ▁feminine
+- ▁które
+- ▁primo
+- ▁verdi
+- кот
+- ▁graham
+- mele
+- 你们两个
+- िक
+- 感受到
+- 无人
+- 될
+- ▁knelt
+- ▁tìm
+- ▁hebrew
+- ▁Meter
+- กล
+- ▁clothed
+- 使える
+- 陽
+- amazi
+- Kanal
+- ▁бага
+- 心灵
+- 逸
+- ▁klingt
+- ▁видео
+- лоб
+- 撤退
+- 景色
+- nect
+- ▁vorsichtig
+- ಗ
+- ▁bachelor
+- ▁хочешь
+- ▁شرکت
+- 見ていきましょう
+- もあって
+- 6%
+- ▁Mah
+- ▁Су
+- லோ
+- வேண்டும்
+- 浓
+- ▁carpenter
+- ▁shrub
+- ▁nourish
+- 始める
+- ようになって
+- しまして
+- ▁awareness
+- 闯
+- ▁невозможно
+- ▁나오
+- boro
+- ▁strife
+- ▁dismiss
+- 综合
+- িয়ে
+- witz
+- حدث
+- pür
+- 图片
+- 놀
+- 鍵
+- ▁subdued
+- spoonful
+- ▁Finally
+- 看不到
+- 运动员
+- کات
+- ▁rector
+- 的角度
+- ▁faculties
+- 麺
+- 円安
+- 緊急事態宣言
+- ▁molecule
+- ▁plough
+- ▁вчера
+- ▁tiến
+- Pierre
+- مند
+- 市では
+- ▁Ми
+- bika
+- ▁obstinate
+- ▁saviour
+- 庞
+- pflicht
+- 大海
+- ▁habitat
+- 这是我的
+- urira
+- 識
+- ですとか
+- hani
+- ▁candid
+- 屁
+- ▁ممکن
+- ▁এবং
+- ▁ລະ
+- tsinda
+- شته
+- 农业
+- 不同意
+- ▁Februar
+- లో
+- ிலிருந்து
+- 太子
+- kleri
+- ▁яму
+- ▁draught
+- 前提
+- ▁envia
+- ▁também
+- ▁crust
+- ▁Калі
+- ▁Verfahren
+- ▁manifestation
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+- ▁честно
+- 做饭
+- 今天晚上
+- ▁видите
+- kò
+- ▁marshal
+- ▁Bäume
+- ▁astonishing
+- ▁conjecture
+- ▁embarrassment
+- 畅
+- 眼泪
+- koli
+- альные
+- ▁concentrated
+- 岩石
+- ười
+- ▁amiable
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+- ধ্য
+- 自行车
+- ▁societies
+- 舰
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+- ▁realise
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+- mico
+- に移
+- 就行了
+- 合适的
+- ▁setanta
+- fenster
+- ▁dismal
+- ▁Wunsch
+- ▁wolves
+- lil
+- 建設
+- ▁rwose
+- annu
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+- イチ
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+- 電車
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+- Episode
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+- ību
+- 並んで
+- 三点
+- cò
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+- знача
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+- この辺り
+- альных
+- いくと
+- ▁Album
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+- 层面
+- хим
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+- 谋杀
+- ▁resentment
+- ▁shark
+- 总结
+- 進化
+- 运作
+- ▁assertion
+- ູ
+- ▁Praxis
+- にあります
+- ▁confidential
+- uğu
+- wego
+- ▁Ро
+- ▁embraced
+- nutri
+- 与其
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+- ▁camino
+- يط
+- 视觉
+- achtet
+- 良い
+- ▁скорее
+- 珀
+- っていうふうに
+- 继承
+- 你刚才
+- ▁صورت
+- ▁países
+- имся
+- 是一样的
+- тэн
+- ▁гэтай
+- ▁находится
+- ▁voilà
+- ▁startling
+- 我想要
+- 维持
+- ▁tray
+- රු
+- ▁повтор
+- 的主题
+- contin
+- 的人来说
+- ▁demanda
+- 旧統一教会
+- ▁inspection
+- 先ほどの
+- ▁Senat
+- ▁breadth
+- sehn
+- ального
+- 明明
+- ▁respira
+- جنوب
+- 落在
+- 耻
+- 試験
+- ▁explaining
+- 确
+- ▁perdu
+- ▁gorgeous
+- 陌生人
+- ▁Absicht
+- ولد
+- 遇见
+- шан
+- 高級
+- ▁parlor
+- хлоп
+- ษ
+- ▁Dijous
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+- 改造
+- ▁miriam
+- ▁Té
+- ▁shipping
+- そこは
+- äck
+- ステージ
+- 十分钟
+- 太郎
+- ▁pasture
+- ワード
+- ▁nicely
+- 増えて
+- ▁beobachten
+- 网上
+- ▁fröhlich
+- ▁އަ
+- தொடர்
+- モー
+- льны
+- ▁стоял
+- дзень
+- ▁газет
+- 名为
+- を認め
+- 但这是
+- 决心
+- origine
+- 느
+- Gelach
+- foje
+- ▁charter
+- 这些问题
+- ▁vinden
+- ▁Nieder
+- yumba
+- பர
+- が出た
+- ▁everlasting
+- ▁trouver
+- zí
+- 成年人
+- ▁keer
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+- kaka
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+- овали
+- AD
+- 接到
+- କ
+- ĥ
+- 疫苗
+- 这一切都
+- 理性
+- ▁polished
+- ▁flavor
+- смотрел
+- 産業
+- 你爸
+- ▁பய
+- 逗
+- ▁Unión
+- ▁rumor
+- そうですか
+- 我就不
+- ▁attributed
+- ツー
+- kları
+- ▁passenger
+- ▁bliss
+- 喽
+- ሁ
+- ▁verheiratet
+- ▁mindestens
+- ▁پنج
+- ▁highness
+- verfahren
+- تَ
+- 利润
+- ▁funkci
+- ▁readiness
+- ▁escala
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+- ▁система
+- ராக
+- 抛
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+- arbre
+- 在此
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+- ▁واحد
+- ▁gosh
+- 拨
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+- ṛṛ
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+- 俺が
+- ▁거의
+- 小麦
+- ▁whereby
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+- mizi
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+- 名古屋
+- ภ
+- ấu
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+- ▁clergyman
+- 劣
+- équipe
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+- ший
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+- 遮
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+- パパ
+- 回到了
+- ▁await
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+- 构成
+- 滅
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+- 白天
+- ▁gerald
+- 旋转
+- 我想说
+- ▁surf
+- 酱
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+- 尸
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+- 激励
+- щик
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+- ▁món
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+- ▁upside
+- ่ว
+- umuryango
+- 収穫
+- 欺负
+- ▁engaging
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+- ▁chop
+- үнд
+- 的需求
+- 很简单
+- loch
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+- 龄
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+- For
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+- einheit
+- ちゃんは
+- 我们开始
+- ▁Option
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+- んだね
+- SA
+- ई
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+- ▁tyrant
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+- ▁animated
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+- 獲得
+- ▁кап
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+- ауы
+- 見えない
+- ▁그치
+- カフェ
+- が流れ
+- 亲密
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+- 等一下
+- 就好了
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+- 弦
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+- wszy
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+- 忌
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+- empi
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+- 一分钟
+- ▁kadın
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+- 我妈妈
+- živ
+- 袭
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+- 凌
+- ▁squirrel
+- ▁denomina
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+- цэн
+- 他们已经
+- ▁Theater
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+- 央
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+- hize
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+- ▁unworthy
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+- Instagram
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+- 接受了
+- ▁связи
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+- 你有什么
+- யூ
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+- نے
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+- ▁nhw
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+- ▁waiter
+- 容器
+- серьезн
+- 前後
+- schijn
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+- environ
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+- оф
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+- 文本
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+- 並み
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+- ਤ
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+- clav
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+- 然后他们
+- движ
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+- 这样的话
+- 考え方
+- 舒适
+- あるんですけど
+- லில்
+- 意味着什么
+- locat
+- ▁boiled
+- 不明
+- ▁fashionable
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+- sexual
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+- 谨慎
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+- 水果
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+- 一堆
+- netz
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+- mayı
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+- estructura
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+- 还想
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+- 져
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+- 娘娘
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+- 顶部
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+- 的时候呢
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+- 我们发现
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+- 意义上
+- 僕ら
+- 各个
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+- පා
+- ▁khu
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+- 六月
+- kker
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+- 只要你
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+- 耐心
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+- 的房间
+- に来た
+- ▁nursing
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+- 笼
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+- Domin
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+- ▁stepping
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+- と思うんですが
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+- 寒さ
+- ▁какую
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+- meid
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+- ić
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+- ▁historically
+- ▁punika
+- 画像
+- ▁weighed
+- 本地
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+- デー
+- あなたが
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+- 今大会
+- '`'
+- ▁archbishop
+- ▁иногда
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+- числ
+- 奇妙
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+- に関しては
+- ▁preaching
+- işle
+- ▁psychological
+- ▁survivor
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+- 这个想法
+- 脖子
+- 观看
+- ▁Verstand
+- ▁кож
+- ▁нужны
+- ▁diff
+- integra
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+- 我希望你
+- 循环
+- ▁Simon
+- 中止
+- ▁crucial
+- ▁праца
+- ▁thông
+- дук
+- ▁ownership
+- ந்தார்
+- ▁Paar
+- ▁carta
+- 大変な
+- ▁hayat
+- ់
+- ▁ситуации
+- 欺骗
+- ▁regime
+- まるで
+- مُ
+- وص
+- 一个女人
+- 資金
+- ം
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+- ▁Geburt
+- 衝突
+- ▁Play
+- ▁Ärzte
+- ▁snatched
+- خنده
+- īgi
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+- تحدث
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+- kî
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+- 认识到
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+- 扩
+- 男生
+- 仓
+- ▁Laura
+- ▁magnitude
+- ацыя
+- torial
+- ▁tercer
+- 岸田
+- テスト
+- بى
+- état
+- ▁máy
+- schläge
+- 激烈
+- ▁같은데
+- ▁kep
+- 这就像
+- পি
+- φ
+- 肩膀
+- ▁psychology
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+- ▁wiped
+- を求める
+- ظهر
+- ▁streak
+- ▁Aktion
+- لې
+- ▁vấn
+- ▁owed
+- ▁assessment
+- ▁solomon
+- 魔法
+- ▁digging
+- ▁Kleidung
+- ▁волн
+- піса
+- 직
+- ښ
+- 叠
+- 垫
+- ▁provoke
+- ▁disturbance
+- 范围内
+- 教学
+- ▁труб
+- ▁Deutsch
+- ێت
+- ▁করা
+- ▁Englisch
+- fluent
+- ▁какого
+- 陪你
+- sulta
+- ▁applause
+- ▁тэгээд
+- ას
+- ▁mmhm
+- ▁rascal
+- тәр
+- ▁honesty
+- ▁Phase
+- Risos
+- ▁постоянно
+- 申請
+- ັກ
+- ▁hängt
+- ▁affecting
+- ▁Veränderung
+- ▁опыт
+- હ
+- ▁اليوم
+- ▁друж
+- 碰到
+- ▁Bühne
+- ▁порядк
+- ҭа
+- 新聞
+- ▁Stimm
+- ▁Süden
+- مام
+- 演説
+- 世界上最
+- leitung
+- 遣
+- 北日本
+- 감
+- 碳
+- ▁collecting
+- っぽ
+- ができて
+- やりたい
+- ▁vardı
+- ▁reap
+- 背中
+- ▁Farb
+- 焼け
+- ないよ
+- 摄
+- 하다
+- 외
+- ▁appealed
+- ッキー
+- ▁prick
+- 上手
+- お届け
+- kono
+- ▁мэдэ
+- 顾问
+- 很清楚
+- 斑
+- ▁интернет
+- 刃
+- һын
+- ▁نور
+- awal
+- ▁flashing
+- ▁Since
+- 大师
+- ▁macro
+- เก
+- ▁unconsciously
+- ▁passé
+- ▁dreaded
+- ძ
+- ▁cependant
+- 초
+- ▁Там
+- ▁бело
+- ▁personnel
+- 犯行
+- ▁priorities
+- шев
+- ▁grote
+- neuron
+- ▁treating
+- âme
+- ▁Bach
+- 有许多
+- සු
+- ▁Pic
+- ▁Ereignisse
+- 感染対策
+- ▁discoveries
+- spoiled
+- lith
+- 喝了
+- ታ
+- ▁Geschlecht
+- ▁excursion
+- 保険
+- ▁fonction
+- 場に
+- maktadır
+- ▁judicial
+- 安倍元総理
+- ▁Sobald
+- lardan
+- кажет
+- ディング
+- ▁harmless
+- 这么一个
+- ▁ministry
+- ▁ຫັ້ນ
+- ▁cotxe
+- 嘉宾
+- を取る
+- ىسى
+- lomb
+- という話
+- ▁siaj
+- ambul
+- ▁referring
+- 小小的
+- ▁utterance
+- 角落
+- ▁kehrte
+- パリ
+- 捐
+- ▁provincial
+- 想出
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+- ▁Kong
+- ıp
+- 激情
+- 周期
+- ▁Ruf
+- 坐下
+- ▁ewig
+- ▁African
+- ▁çünkü
+- ▁academy
+- ▁Maybe
+- 无限
+- čči
+- ▁connu
+- 这孩子
+- ▁banker
+- 村上
+- ▁authentic
+- 寂
+- 郷
+- ▁kết
+- ヴィ
+- ▁luncheon
+- ▁알아
+- 冷凍
+- ▁resume
+- андр
+- ▁refined
+- clus
+- 这样的一个
+- ாளர்
+- 鲍
+- ▁nobility
+- ▁Ул
+- ໍາ
+- を重ね
+- ▁嗯嗯
+- 談
+- ▁awaited
+- فَ
+- ▁Aussage
+- ღ
+- ▁vermute
+- 涌
+- హ
+- 月曜日
+- ▁tốt
+- esque
+- ▁جدید
+- 選んだ
+- ▁Wann
+- 身后
+- アイス
+- カ月
+- ▁beschreiben
+- 诱
+- ▁chwarae
+- 附近的
+- inclou
+- 什么样
+- 請
+- කු
+- ल्
+- ▁initially
+- 期节目
+- ▁roaring
+- ▁Ар
+- ▁другим
+- ▁oven
+- 必须要
+- 20%
+- ▁saloon
+- ▁врач
+- せん
+- ▁lewis
+- ▁vicious
+- ▁Zentrum
+- ▁brazil
+- 的权利
+- 美味
+- 不许
+- ▁பயன்படுத்த
+- ▁вульф
+- ▁видимо
+- 先発
+- きれいに
+- ▁ужас
+- ▁obtaining
+- ▁Aufnahme
+- ▁podría
+- ▁sadness
+- 拿出来
+- 必要がある
+- лыҡ
+- 作业
+- 采用
+- ▁vya
+- ▁feat
+- ▁Although
+- なければならない
+- 大規模な
+- 推进
+- 我只想
+- ▁durum
+- 还有其他
+- どうする
+- œuvre
+- ▁eloquence
+- ▁бөгөөд
+- 栋
+- 鏡
+- būt
+- günstig
+- 开会
+- ▁Grab
+- ▁அவர்கள்
+- ロシアが
+- voja
+- ▁felicit
+- ▁attendance
+- 否认
+- ぇ
+- ლა
+- わずか
+- ▁hunted
+- ようやく
+- 爵士
+- ހަ
+- 然而
+- ▁mournful
+- 長期
+- wamu
+- 期限
+- 询问
+- ▁установ
+- ▁rainbow
+- 騒
+- ▁diligent
+- ▁vergangen
+- ▁asylum
+- ▁yanjye
+- koht
+- ▁comfortably
+- gerät
+- 凶手
+- ▁armour
+- pari
+- ▁dignified
+- ▁cuerpo
+- ▁Prinzip
+- associa
+- つま
+- ▁excite
+- ▁matches
+- ▁brauche
+- ▁كې
+- ddling
+- ▁baggage
+- ▁outstanding
+- 秒钟
+- ▁vierzehn
+- 克服
+- ▁blazing
+- பின்
+- 下がって
+- ▁руку
+- jící
+- わたし
+- න්නේ
+- ▁consegui
+- 他们正在
+- ▁qualified
+- ▁tradi
+- 统一
+- 人が死亡
+- buah
+- ▁защит
+- ▁donkey
+- 新幹線
+- 欲しい
+- クル
+- ifuza
+- miento
+- ▁lumber
+- jye
+- ▁meteor
+- ▁gradual
+- களும்
+- ▁crowned
+- quisition
+- ショート
+- 糊
+- ही
+- チャレンジ
+- னும்
+- ▁एक
+- ▁sydney
+- ▁Taylor
+- 克莱
+- 拿起
+- リーダー
+- 兔子
+- 其他地方
+- ▁Graf
+- ▁ужо
+- ël
+- өнгө
+- ▁fünfundzwanzig
+- ▁estimated
+- 一趟
+- ▁quoted
+- த்தா
+- spruch
+- 激しく
+- ▁Был
+- čil
+- トラブル
+- пустил
+- ▁seventeenth
+- ▁thirteenth
+- ▁compelling
+- ▁seemingly
+- ▁نگاه
+- 更好地
+- ▁bunk
+- のまま
+- nev
+- 借り
+- ▁кре
+- krist
+- 七月
+- ▁Atom
+- spezifisch
+- 一直是
+- Orient
+- йтесь
+- ▁meilleur
+- '120'
+- ▁habt
+- ▁liking
+- ▁legislature
+- どのような
+- 一块儿
+- いくつか
+- ▁romp
+- ▁trench
+- ▁сильн
+- 凑
+- 鸣
+- 登録
+- ▁frail
+- 获取
+- ▁Main
+- いること
+- 広く
+- プリン
+- 親子
+- 段階
+- 豆腐
+- 很长时间
+- ▁Castell
+- 真理
+- ▁треб
+- ▁endeavoured
+- Institut
+- 萌
+- Infrastruktur
+- 堀
+- 诉讼
+- ▁прекрасно
+- ってほしい
+- キャン
+- 徐々に
+- ▁يجب
+- ▁vall
+- ზ
+- eisen
+- ▁Gewicht
+- ▁العمل
+- ▁contemporary
+- 戦略
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+- కి
+- 予定です
+- っけ
+- ▁bello
+- ▁socialist
+- ということなんですね
+- ▁cement
+- ▁그걸
+- ▁shifting
+- ▁Sup
+- ▁дене
+- ▁governed
+- schäm
+- ▁jurisdiction
+- ▁giorno
+- ▁triple
+- funk
+- ▁sneak
+- 院长
+- 食料
+- чныя
+- vira
+- ▁spoon
+- сця
+- ▁silenci
+- 出售
+- 几十年
+- geordnet
+- ▁Philosophie
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+- リズム
+- ▁scheen
+- ▁пятый
+- ▁tidings
+- ▁podem
+- 北陸
+- 大姐
+- ▁hệ
+- ▁Lind
+- accus
+- иров
+- 另
+- 付いて
+- ▁vegades
+- ▁কৰি
+- ▁inherent
+- räumen
+- ▁Vorschlag
+- ▁Dabei
+- ッジ
+- ▁Interessen
+- 得很好
+- неп
+- ▁keel
+- 一阵
+- ▁treasury
+- ▁sculpture
+- ▁vật
+- ▁Beach
+- できなかった
+- ちょっと待って
+- 巨人
+- ▁Verein
+- 乗せ
+- ▁Ple
+- 冲动
+- 休み
+- syll
+- ත්ත
+- ോ
+- ▁غیر
+- ロック
+- чки
+- 回答说
+- うまみ
+- ලි
+- thorpe
+- 其中一些
+- をつか
+- ▁outdoor
+- に向けた
+- ▁amuse
+- К
+- ▁bantu
+- uwen
+- 辅
+- lulu
+- নী
+- ativa
+- 網
+- 詹姆斯
+- 懂得
+- 太空
+- 四处
+- いずれも
+- ▁Vertrag
+- ▁verbinden
+- 脊
+- ఎ
+- ▁обычно
+- ▁dispatch
+- 切った
+- と指摘
+- ▁komplett
+- 火事
+- ▁migration
+- kintu
+- 働
+- 札幌
+- ▁касается
+- 告诉他们
+- ▁ingenious
+- だとか
+- عادة
+- 金钱
+- ضع
+- 般的
+- ▁fling
+- 正如你
+- ▁durchführen
+- コーヒー
+- スケート
+- 的意思是
+- 很多事情
+- ▁crisp
+- ▁grunt
+- ▁kutoka
+- ES
+- 爵
+- ▁dự
+- ▁catastrophe
+- ▁tưởng
+- ▁disregard
+- ▁gezegd
+- ▁Kredit
+- 強さ
+- ▁بودم
+- OS
+- beruf
+- ▁handling
+- ▁pesca
+- 根本不
+- ▁Champ
+- ▁девят
+- 晒
+- ▁evaluate
+- ▁Dewi
+- ▁ответствен
+- щего
+- 认知
+- ovna
+- ▁paragraph
+- 決めて
+- ▁roared
+- ▁Quindi
+- 毫不
+- 름
+- ▁Sitzung
+- கிறது
+- ▁willow
+- 欢迎来到
+- 船上
+- 维护
+- ▁tanta
+- ▁Europäischen
+- ▁Molt
+- শ্
+- tiene
+- 種類の
+- を超えて
+- ▁terminal
+- ▁consulted
+- ▁portal
+- алды
+- ▁المع
+- assassinat
+- 証明
+- 标记
+- يار
+- 一起工作
+- ▁ئەو
+- ▁Pflanzen
+- ▁embarrassed
+- kawa
+- ▁serene
+- ▁streaming
+- ▁jason
+- ▁stride
+- ▁лиш
+- 多次
+- භ
+- ▁almighty
+- ▁hurricane
+- ▁Science
+- ▁سره
+- fashe
+- quadra
+- 常常
+- 国民の
+- ▁dạ
+- põ
+- ▁discount
+- を出す
+- 到目前为止
+- fähigkeit
+- 谈判
+- スパイ
+- ▁patiently
+- ▁lautet
+- ▁болох
+- 惊人的
+- fleur
+- ▁prosperous
+- 委屈
+- ică
+- ▁Brian
+- 两百
+- ▁ভাল
+- ▁brandy
+- 实在是
+- àng
+- ▁produit
+- палі
+- 客厅
+- を示す
+- ▁gezwungen
+- исто
+- stairs
+- ▁lapse
+- 镜头
+- 般
+- ▁Està
+- operative
+- ▁Veranstaltung
+- 二人
+- දී
+- ▁moll
+- ▁courteous
+- رَ
+- ▁Oberst
+- 谁呀
+- 車両
+- анг
+- ▁série
+- ▁behandeln
+- பட்ட
+- とかそういう
+- лок
+- ▁کړ
+- ▁Austr
+- 看起来很
+- 酒吧
+- ▁বলে
+- пок
+- インターネット
+- 天皇
+- ▁estaban
+- зур
+- 极端
+- ▁distrust
+- بری
+- 去哪里
+- ▁сельсовет
+- ▁نحن
+- ▁erhöht
+- ▁mặt
+- 軒
+- ▁Abendessen
+- ▁accusation
+- 领导人
+- льная
+- ▁görün
+- 年生の
+- 伊藤
+- 支払
+- 肌肉
+- ▁zitten
+- したうえで
+- chimp
+- 恋爱
+- ▁ег
+- ▁зелен
+- 动机
+- ▁Konflikt
+- 군
+- ▁sabbath
+- '".""'
+- ▁kapit
+- セント
+- ▁polish
+- ▁simultaneously
+- की
+- ▁kugeza
+- 相当于
+- ▁годы
+- علوم
+- итесь
+- ▁expressing
+- іла
+- 美女
+- umugabo
+- قطع
+- ▁schickte
+- ▁реша
+- ▁Fälle
+- ெய்
+- migr
+- 己
+- ální
+- ▁людям
+- ▁некоторые
+- バーディー
+- fungu
+- ▁executing
+- بحث
+- schieben
+- 引发
+- 质疑
+- ▁Gründen
+- grenzen
+- ▁persecution
+- ▁Zitat
+- 土砂災害
+- ▁besorgt
+- ▁unfair
+- 到这儿
+- 인데
+- عات
+- ▁ребята
+- ouvre
+- 第二次
+- рыс
+- ших
+- ▁тус
+- しまい
+- 思われ
+- 鹅
+- ▁Katze
+- ▁strengthened
+- kamu
+- ▁Royal
+- ombro
+- ▁folgt
+- ▁destaca
+- ▁همان
+- خورد
+- 倦
+- スポット
+- rechnung
+- ▁inheritance
+- ىدۇ
+- ▁பிற
+- ▁Kevin
+- ▁behaved
+- 数百
+- 明显的
+- roving
+- ▁наконец
+- 稚
+- 구나
+- 犹太人
+- 确认
+- ▁தீ
+- لِ
+- 二十年
+- 为何
+- ходил
+- жат
+- märk
+- ұ
+- ▁الوقت
+- ▁depois
+- ▁цар
+- ▁despised
+- 商人
+- ొ
+- ▁besuchen
+- 詐欺
+- スタイル
+- ▁überzeugt
+- ここの
+- wili
+- ▁Life
+- 特别好
+- ▁ziet
+- ラム
+- を続ける
+- rinda
+- 호
+- kanya
+- ▁chairman
+- 木さん
+- トイレ
+- крыва
+- ▁stooped
+- ▁Schatten
+- ▁kuwi
+- 一本书
+- ebene
+- вец
+- ▁занят
+- ▁garment
+- ▁Danach
+- ▁ăn
+- ▁Как
+- が続いています
+- 遂
+- क्ष
+- ▁acceptance
+- ▁körperlich
+- 義務
+- 该怎么
+- ائي
+- ▁krista
+- ителя
+- ымі
+- ▁Neuro
+- ▁semble
+- を進めて
+- 見られる
+- 遭受
+- ▁Einheit
+- ▁ditch
+- ▁Sydney
+- ▁üzerinde
+- 葛
+- ▁российской
+- ▁шестьсот
+- ▁advertisement
+- ▁lucruri
+- 伯特
+- 小林
+- ▁bamu
+- հ
+- ▁Pakistan
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+- わからない
+- ▁historische
+- kiem
+- ▁nghe
+- かつ
+- 约会
+- もらえる
+- tsiooni
+- ▁preached
+- 울
+- 胳膊
+- ▁exaggerate
+- ▁subscription
+- ▁sworn
+- ▁evolved
+- 认可
+- verhalten
+- ▁hạ
+- нде
+- ▁путина
+- ▁accumulate
+- いくら
+- ▁Dž
+- 先頭
+- ▁berlin
+- 僅か
+- ▁Knie
+- 数据库
+- ▁жест
+- ▁زیادی
+- ▁envoy
+- ▁самого
+- väg
+- 录音
+- جعل
+- タイトル
+- ▁protecting
+- brian
+- 緩
+- 勘
+- もしかしたら
+- ▁shrugged
+- willig
+- ▁иә
+- නය
+- 三个月
+- 時代に
+- 音频
+- 来看看
+- මි
+- ▁vicinity
+- ▁choosing
+- ▁வழி
+- 奇迹
+- ▁Schreiben
+- лээ
+- croft
+- ▁kontrol
+- бот
+- ▁izmanto
+- ▁좋은
+- пуск
+- parent
+- ▁container
+- 疯了
+- 尼斯
+- 確認された
+- ▁thicket
+- ▁верн
+- ▁dankbar
+- 并将
+- 大陆
+- ▁comercial
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+- jev
+- 对面
+- ▁tiefer
+- ▁cama
+- 不能再
+- だと思う
+- 則
+- 鸭
+- ▁soutenir
+- 幺
+- ▁Schlacht
+- ▁chaos
+- ▁meisje
+- を行った
+- ▁conjunt
+- ▁Ansatz
+- 皇后
+- 环境中
+- 님
+- 世紀
+- も含めて
+- '98'
+- ობ
+- त्य
+- ▁derecho
+- ▁davis
+- ▁Шу
+- お酒
+- ▁verhindern
+- ▁komunumo
+- ▁mochte
+- こだわり
+- 设施
+- 天使
+- ▁schade
+- позиц
+- 9%
+- пай
+- ▁richmond
+- ▁посмотрел
+- eusement
+- meester
+- ỹ
+- োৱা
+- 久しぶり
+- 候选人
+- ங்களில்
+- ন্দ
+- ▁Zusammenarbeit
+- 少しずつ
+- ▁Tausende
+- 东方
+- いただき
+- aquestes
+- ▁gefällt
+- гада
+- 薇
+- ▁mathematics
+- ▁처음
+- lago
+- amara
+- zentrum
+- ▁forgiveness
+- ▁Each
+- ▁அதன்
+- ▁Deci
+- 他にも
+- ▁mirth
+- 建立了
+- Ɣ
+- сор
+- ▁Wörter
+- Bahn
+- ▁bajo
+- ▁carne
+- 可怜
+- thought
+- größe
+- ガラス
+- ▁screaming
+- ▁ändert
+- 培
+- 给了我
+- અ
+- ▁thôi
+- ▁indicator
+- ▁alfred
+- ▁کنی
+- ▁gezien
+- جلس
+- ▁disciplined
+- ▁housekeeper
+- 体操
+- 还挺
+- ▁submarine
+- いきなり
+- كرة
+- ▁erneut
+- ▁Medizin
+- 资助
+- vå
+- ▁lucru
+- ▁разные
+- ▁oars
+- ливо
+- 可惜
+- ▁Oxford
+- Kampagne
+- ▁generosity
+- ▁무슨
+- ญ
+- 縦
+- 偽
+- ível
+- パワー
+- ▁moviment
+- 尽量
+- ▁universitat
+- ME
+- ễ
+- ▁нравится
+- ▁понимаешь
+- ▁böyle
+- 学术
+- こんにちは
+- ▁kvar
+- 摊
+- 棵
+- ▁hereafter
+- Franc
+- ▁prussia
+- ஷ்
+- 天哪
+- ▁Hügel
+- 最新の
+- ▁Korea
+- ▁мөн
+- ▁হয়ে
+- ▁kaiser
+- ático
+- ▁mooi
+- 晩
+- ึ
+- ▁cambio
+- holm
+- ственно
+- ▁implied
+- ▁év
+- 我个人
+- ▁jenny
+- 就是因为
+- Ĉ
+- を通じて
+- ▁değiş
+- ▁borrowed
+- 是否有
+- ▁Tout
+- ▁festgestellt
+- きょうも
+- ▁تک
+- 短暂
+- ▁franklin
+- ▁piled
+- 还是要
+- 自主
+- یەک
+- ▁contempor
+- 狩
+- 불
+- نظام
+- 亲戚
+- 活跃
+- 真心
+- 会让你
+- ▁کشور
+- ování
+- ▁stesso
+- бег
+- 临时
+- 八点
+- いろいろな
+- घ
+- 螺
+- க்கை
+- ▁consultation
+- ▁Wut
+- ▁Personal
+- ▁quedar
+- ▁こうした中
+- ▁Ку
+- ▁prolonged
+- ▁folgenden
+- ▁temporal
+- ▁bleef
+- ăng
+- バター
+- ▁Saya
+- ▁detected
+- ▁Про
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+- ▁besiege
+- 有趣的是
+- 集合
+- generation
+- ▁publisher
+- жым
+- ಡ
+- 穆斯林
+- ▁declaring
+- ビジネス
+- ▁elkaar
+- ▁visibility
+- 争い
+- ▁scary
+- 慢点
+- ▁leiten
+- って言った
+- 我也不知道
+- ড়ি
+- ▁westward
+- ▁repress
+- ▁fehlt
+- ृ
+- ▁installed
+- ▁сожалению
+- ▁언니
+- 雇佣
+- ▁repos
+- ▁парк
+- ▁accuse
+- سپ
+- みたいな感じ
+- 飛行
+- 阿里
+- ▁demonstra
+- ▁ridicule
+- ▁மிகவும்
+- 脑袋
+- ▁Company
+- চে
+- ▁Senator
+- AT
+- ▁veranda
+- 征服
+- 布里
+- يَ
+- 丈
+- ▁சேர்
+- 崇拜
+- ivamente
+- ▁Water
+- ▁glimmer
+- していること
+- II
+- 衛
+- 喜剧
+- 手紙
+- ▁집에
+- ējā
+- ▁Block
+- ▁väl
+- undneunzig
+- 詞
+- ▁слов
+- ▁Kalifornien
+- ει
+- haza
+- 趣
+- ▁Basis
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+- قۇ
+- 动议
+- 是多么
+- やろう
+- ▁neighboring
+- ▁Hast
+- алда
+- вание
+- どこまで
+- ▁lavora
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+- ▁кеше
+- ▁Perspektive
+- ▁cualquier
+- ▁chemistry
+- ліз
+- ▁inherited
+- もち
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+- ώ
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+- 須
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+- avons
+- mbri
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+- ▁europäischen
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+- ▁rocket
+- druž
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+- 任何一个
+- ぱ
+- вших
+- ▁wizard
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+- 谁知道
+- ▁batteries
+- historie
+- ▁laquelle
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+- тыя
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+- ▁vermeiden
+- ▁striving
+- ▁aufmerksam
+- 上课
+- 機械
+- ▁empower
+- 決して
+- 开门
+- ▁Gerechtigkeit
+- ▁힘들
+- 療養
+- ▁Three
+- existent
+- abhängig
+- ▁чём
+- ▁peggy
+- пресс
+- ވެ
+- ▁systematic
+- ▁než
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+- せっかく
+- されてる
+- 赞助
+- 预防
+- ▁två
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+- ▁существует
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+- soever
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+- わけですよね
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+- ēl
+- دۇ
+- sparen
+- 歌词
+- ▁Schließlich
+- 芒
+- 分かんない
+- ப்படுத்த
+- ▁Republic
+- 公表
+- ীয়
+- 几分钟
+- 晚餐
+- 桌子上
+- schlüssel
+- скія
+- ▁Цяпер
+- ▁музыка
+- ▁Russland
+- ▁найд
+- ▁merciful
+- 迹象
+- ▁printing
+- ▁growled
+- ▁речь
+- 気分
+- 可能性があります
+- ▁plunge
+- ▁பெண்
+- ▁melhor
+- ání
+- ▁Fä
+- ▁transit
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+- ándose
+- artisan
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+- кле
+- What
+- 一个巨大的
+- ▁Dreh
+- raad
+- 给出
+- ▁perished
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+- rides
+- studie
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+- ▁lingered
+- 完整
+- ▁scroll
+- 底部
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+- provi
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+- 福利
+- 钢琴
+- صنع
+- ▁подход
+- だったので
+- 遗产
+- ▁infirm
+- පු
+- するのか
+- ▁Geschwindigkeit
+- ▁contemplation
+- ▁loneliness
+- 芯
+- 토
+- 据说
+- acca
+- நா
+- ▁koran
+- ▁schimb
+- ңә
+- ▁Тут
+- ▁plug
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+- decorat
+- preservation
+- ސް
+- ▁трудно
+- を巡る
+- ائية
+- 黒い
+- ▁Mario
+- ▁handled
+- ▁biological
+- 场所
+- ▁تأ
+- 哲
+- ▁brightness
+- យ
+- ▁October
+- ▁adjoining
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+- 虹
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+- 问一下
+- ▁ethics
+- 微妙
+- 総合
+- reißen
+- ▁показыва
+- ▁sparkle
+- ▁faci
+- ▁Brasil
+- marsch
+- ▁entrepreneur
+- ucci
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+- плеч
+- 목
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+- 评价
+- ▁sniff
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+- こんな感じ
+- 村庄
+- губ
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+- ▁друга
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+- legation
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+- ▁intimacy
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+- ложить
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+- 致力于
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+- 整備
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+- われわれ
+- 小型
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+- ▁Manche
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+- ▁Bush
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+- 走路
+- を紹介
+- ▁promet
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+- ホームページ
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+- யு
+- を起こし
+- larına
+- prinz
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+- 奋
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+- 你会发现
+- 福島
+- 墨西哥
+- ▁latitude
+- ▁እን
+- 我就想
+- ▁horseback
+- ரெ
+- рская
+- っていきます
+- 哟
+- ▁psychische
+- ▁dainty
+- ▁inquiring
+- ▁başladı
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+- 하니까
+- 好听的
+- ▁chiama
+- ▁knocking
+- ▁carelessly
+- 达成
+- ▁понят
+- ▁precept
+- േ
+- 债务
+- ▁schlimmer
+- 最重要的是
+- 姥
+- 枯
+- 見た目
+- 转身
+- ħ
+- 破壊
+- ▁Wichtig
+- 典型的
+- ▁lawful
+- ▁caravan
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+- ▁самым
+- rühm
+- 凍
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+- 新規感染者
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+- ▁forsake
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+- 男の子
+- ▁Muslim
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+- моло
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+- 秩序
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+- берег
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+- ▁поднял
+- ▁athlete
+- 慢慢地
+- pharma
+- ▁bobby
+- entreprise
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+- ▁könne
+- ▁realizing
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+- ▁metaphor
+- ▁Investor
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+- ▁nadie
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+- คร
+- 即便
+- 一応
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+- ▁interface
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+- ància
+- 把它放在
+- アーティスト
+- ▁wußte
+- spitze
+- 很喜欢
+- って思って
+- 艘
+- კა
+- を訴え
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+- ▁shattered
+- garuka
+- 回复
+- saison
+- 友人
+- biza
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+- ছেন
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+- ▁Dach
+- 半島
+- ▁removing
+- 是什么样子
+- 有人说
+- ビア
+- 会話
+- 学位
+- ▁racing
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+- angwa
+- 价值观
+- czą
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+- ウム
+- باب
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+- ホント
+- ▁cynnwys
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+- 共和国
+- 亚洲
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+- ビール
+- zieh
+- 業界
+- dringen
+- niedrig
+- と見られる
+- ▁qualche
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+- Ž
+- ▁зүйл
+- ▁measurement
+- фарм
+- เร
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+- öffentlich
+- 暑い
+- ▁roland
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+- 皆さんも
+- ▁我想听
+- న్
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+- 幻灯片
+- ▁massacre
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+- 同伴
+- 使命
+- ▁consisting
+- бло
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+- プログラム
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+- 真诚
+- ▁rotten
+- ▁convertir
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+- 服用
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+- тычна
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+- ▁около
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+- bido
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+- 린
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+- ટ
+- ▁supplier
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+- ▁нашем
+- 深く
+- わかった
+- Direct
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+- 拝
+- әү
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+- ▁avea
+- ▁quoth
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+- simila
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+- gaciro
+- 通道
+- ▁jasper
+- 된
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+- ▁چهار
+- ıdır
+- ▁пасля
+- сцю
+- ▁falsehood
+- 好消息
+- ▁Golf
+- 斯顿
+- ▁boundary
+- 恰
+- ৌ
+- β
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+- 銭
+- uɣal
+- ▁حو
+- ▁stripped
+- ałem
+- சூ
+- ▁Kommentare
+- ▁countless
+- გი
+- 下がり
+- għ
+- ▁있다
+- 祈
+- ▁obedient
+- ▁precedent
+- ▁dialect
+- ště
+- を目指して
+- ▁charley
+- веж
+- に警戒
+- どうなって
+- 玄
+- 얘
+- ગ
+- ▁Innovation
+- ▁venerable
+- ▁Schaden
+- గా
+- ▁deployment
+- ▁discharged
+- ▁bribe
+- ▁choked
+- เด
+- ницы
+- ▁Бер
+- ▁shareholder
+- ▁irresistible
+- 색
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+- 猜测
+- håll
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+- ▁начала
+- jú
+- حاول
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+- ▁loaf
+- 후
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+- 治愈
+- 细菌
+- escence
+- ▁Diana
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+- ▁ermöglichen
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+- मि
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+- ▁встрет
+- ▁coneix
+- リップ
+- europäische
+- träger
+- 日期
+- ▁splendour
+- 準決勝
+- ▁Kauf
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+- 伊朗
+- ▁Verfassung
+- ▁racial
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+- يست
+- اقتصاد
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+- ▁überprüfen
+- 挣扎
+- ▁вижу
+- 聊聊
+- ▁greet
+- 躁
+- ק
+- 创伤
+- ▁ведаю
+- 旅程
+- ▁llegar
+- етесь
+- ▁mbili
+- 寒い
+- ▁calor
+- ▁conoce
+- ▁worte
+- undsiebzig
+- ▁stumbled
+- 剣
+- ▁займа
+- 楼梯
+- 市长
+- 低下
+- ▁вспомни
+- ▁holmes
+- 未知
+- ことになります
+- ډ
+- 辨
+- ▁contemptuous
+- '......'
+- ▁darted
+- zustand
+- ▁грани
+- ビデオ
+- ▁soothe
+- 짜
+- 创始人
+- ▁imprisonment
+- ▁intensely
+- 在乎
+- leɣ
+- traction
+- ificació
+- fellow
+- ంది
+- foli
+- 対決
+- بِ
+- 长官
+- 머
+- ▁Ankaŭ
+- 纯粹
+- ▁unmittelbar
+- ▁Ursache
+- овское
+- ▁granite
+- ▁avem
+- 一生中
+- گەن
+- анс
+- ▁epic
+- ▁virtually
+- ▁tylko
+- を防ぐ
+- ▁podia
+- ▁snatch
+- 替代
+- 費用
+- 购物
+- 組み合わせ
+- 長崎
+- ▁لذا
+- 더
+- ▁واقعا
+- ▁maior
+- ▁ieder
+- をはじめ
+- 点钟
+- ელ
+- ▁Kontext
+- ▁Verbesserung
+- サポート
+- geleitet
+- ތަ
+- ▁wickedness
+- ▁kugirango
+- 装饰
+- ▁azul
+- コロナ禍
+- 集体
+- ▁Null
+- Europe
+- 幹部
+- ▁Umfrage
+- 澄
+- স্থা
+- ▁cafe
+- 展开
+- пак
+- ▁приходит
+- 携
+- 教えてくれ
+- 晚安
+- 夫妇
+- εί
+- 如果不是
+- 谈过
+- ▁controversy
+- ▁nyingi
+- ▁lần
+- まとめて
+- につながる
+- ようになりました
+- ▁beeinflusst
+- ▁Italien
+- ▁classical
+- スリー
+- bilidad
+- нув
+- ピーク
+- ▁erleben
+- と述べ
+- ▁humid
+- 海军
+- brennen
+- ▁henceforth
+- ▁گرفته
+- 栄養
+- йшоў
+- ▁famine
+- 之所以
+- ▁improvis
+- жә
+- ▁المست
+- ▁burial
+- ів
+- ешься
+- 冷たい
+- 实话
+- ▁Fou
+- ▁przez
+- ▁Mathematik
+- ▁furnace
+- ▁ອື
+- 舞蹈
+- ▁Abteilung
+- ḥem
+- ▁Fair
+- ▁avut
+- ▁dringend
+- ▁Lincoln
+- ▁вариант
+- ▁bemerkenswert
+- 困扰
+- ంద
+- ▁fertile
+- 另一边
+- ▁sangat
+- 基金会
+- 注文
+- між
+- ▁Sagen
+- 告诉她
+- ಹ
+- ▁instinctively
+- อย่าง
+- 恳求
+- 製造
+- ▁gratify
+- ぼく
+- ▁grit
+- ▁Anderson
+- ▁turtle
+- ▁unusually
+- 赢了
+- 会导致
+- ▁Karl
+- ▁Wetter
+- gültig
+- ▁römische
+- 摄影
+- 吃完
+- ▁declara
+- '250'
+- 团结
+- 每当
+- 知ってる
+- 酵
+- ▁Kapital
+- 职业生涯
+- 重症化
+- вернуть
+- ambaye
+- 洪水
+- observa
+- ွ
+- スペシャル
+- ▁equation
+- 恭喜
+- ▁инде
+- 宪法
+- ▁northwest
+- ▁Müll
+- ▁oyster
+- ▁devons
+- 几年前
+- ந்தது
+- ▁Verteidigung
+- ミー
+- ▁Details
+- ▁gewann
+- 蛋糕
+- ▁Kleid
+- つながって
+- ▁combina
+- 被迫
+- ▁geldi
+- ▁confronted
+- 僵
+- 季节
+- ▁그건
+- ▁soothing
+- ത്ത
+- ▁хэрэг
+- 牛肉
+- ▁papel
+- ▁Meeres
+- ▁Fox
+- ▁Darüber
+- 偏见
+- メール
+- お茶
+- 卡尔
+- MA
+- Tool
+- 扮
+- ▁crise
+- ▁efficiencies
+- ▁participants
+- ▁refusal
+- ▁알바
+- ņēm
+- ▁여기
+- BM
+- école
+- ▁upgrade
+- ▁superb
+- ते
+- 言わ
+- ▁черт
+- ▁господин
+- ▁fireplace
+- ▁Campus
+- ▁Hollywood
+- ▁experiencing
+- 震度
+- ▁никого
+- ▁системы
+- 可靠
+- klima
+- 帽
+- 誕生日
+- ▁видим
+- ブルー
+- 惯
+- ▁biology
+- ▁annoyance
+- गा
+- 回去吧
+- に入れて
+- vogel
+- ▁современн
+- ▁Wolf
+- சோ
+- 失踪
+- ▁spill
+- 埃尔
+- 这让我
+- 大众
+- チュ
+- ▁ignored
+- 变得更加
+- ▁beforehand
+- ై
+- ▁anticipation
+- ▁imprisoned
+- 伴侣
+- トランプ
+- ▁ilgili
+- ▁பண்ண
+- ▁maggior
+- ▁hydro
+- ▁unexpectedly
+- ▁opportun
+- ▁jî
+- 肢
+- ባ
+- 孫
+- ▁entscheidend
+- ▁விளையாட
+- ▁salud
+- 英語
+- ▁смысл
+- কো
+- ▁fui
+- ▁pike
+- こんなこと
+- 分野
+- 艳
+- ը
+- ▁staggered
+- ▁League
+- னால்
+- 不幸的是
+- Datei
+- mdash
+- ▁cedar
+- 部隊
+- おうち
+- ▁biraz
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+- 因为这是
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+- 市長
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+- 很酷
+- 对我说
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+- ▁Entdeckung
+- ▁proclamation
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+- 我不得不
+- ಗಳ
+- 很开心
+- eater
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+- ▁fiddle
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+- 很奇怪
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+- 机关
+- 愁
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+- ▁potenc
+- 勃
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+- ▁transparency
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+- 守備
+- 作为一名
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+- ▁Walter
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+- 関心
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+- 鶴
+- CEO
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+- solució
+- ▁southwest
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+- வன்
+- ▁mosquito
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+- avion
+- ব্
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+- 垣
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+- おそれがあります
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+- ▁dispense
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+- ီ
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+- drž
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+- ▁самые
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+- 感染状況
+- ước
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+- lumina
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+- ▁косм
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+- klapp
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+- ▁Fond
+- ▁bizim
+- ▁portuguese
+- ▁rubbish
+- 복
+- oubli
+- 干吗呀
+- ▁хот
+- 运气
+- тыўна
+- ▁இப்ப
+- ▁kanggo
+- ெட்
+- 寡
+- ▁sanctuary
+- 써
+- よろしい
+- 邸
+- ▁tradicional
+- ▁bandage
+- ▁ukrain
+- 渡辺
+- ミニ
+- というわけで
+- ▁mängi
+- ちゃいます
+- ▁рух
+- abash
+- ▁Gedächtnis
+- ▁pièce
+- 医療機関
+- ▁immune
+- 火災
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+- genossen
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+- ţ
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+- 現代
+- podobn
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+- quote
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+- tumye
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+- 剥夺
+- ▁ensued
+- 这是一件
+- schwimm
+- 是不可能的
+- 辞め
+- ▁pueda
+- església
+- süchtig
+- ▁birçok
+- 出租车
+- 귀
+- 麗
+- 적으로
+- 注射
+- schränkung
+- ▁snare
+- ▁skate
+- ▁retard
+- 一生懸命
+- ▁ängstlich
+- ▁vegada
+- ডা
+- あるんですか
+- 起訴
+- räte
+- 徽
+- ▁Posteriorment
+- ▁nyinshi
+- ▁trabalho
+- 코
+- ▁చె
+- ▁Больш
+- ロンドン
+- ▁verwandelt
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+- 設計
+- zugreifen
+- ▁பழ
+- 语音
+- ▁naples
+- 世の中
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+- ఫ
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+- 世帯
+- ▁scottish
+- 見直し
+- ركز
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+- ▁Stich
+- ▁refinement
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+- 老虎
+- бега
+- ▁temporarily
+- コンピュータ
+- 腸
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+- アート
+- '1.5'
+- 維
+- ▁straightforward
+- ঐ
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+- 繁殖
+- ধি
+- beamte
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+- 積極的に
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+- ▁законопроект
+- liśmy
+- ▁cantonada
+- ▁مرکز
+- 杭州
+- ▁verurteilt
+- 噴火
+- ▁адказ
+- ▁Nacional
+- expliqu
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+- people
+- ıyordu
+- 눈
+- 설
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+- ▁لطفاً
+- ▁decât
+- ▁일본
+- ▁prodigious
+- ▁Jacob
+- 散歩
+- 传奇
+- ▁klassische
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+- 釣り
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+- 风暴
+- ▁Opera
+- ▁институт
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+- ▁clinton
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+- ハンド
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+- ▁площад
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+- geheimnis
+- 披
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+- ▁sorgfältig
+- 현
+- แต่
+- ▁discreet
+- chirurg
+- といわれる
+- ുന്ന
+- ▁возраст
+- ▁birkaç
+- schirm
+- 环节
+- ▁intact
+- ▁Então
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+- 亨
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+- 階段
+- 教导
+- auftrag
+- kümme
+- 所需的
+- ▁Jimmy
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+- ▁aquellos
+- እ
+- ▁susceptible
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+- ▁건데
+- ordination
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+- 判明
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+- 木曜日
+- PCR
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+- 可能性もある
+- ிங்
+- هدف
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+- ▁boulder
+- 逃避
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+- ▁Chief
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+- າກ
+- 항
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+- ▁implicit
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+- ▁humming
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+- ▁membership
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+- ▁دغه
+- 贫穷
+- ▁করতে
+- 多长时间
+- 赞美
+- ▁boost
+- باد
+- ▁успел
+- 没见过
+- 変わらない
+- 肾
+- ▁industrious
+- ▁конфликт
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+- atangiye
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+- 貫
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+- 投降
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+- ▁Усё
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+- ಾಗ
+- ▁мужик
+- ლე
+- լ
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+- 教師
+- ランチ
+- ▁тэг
+- сурс
+- 早已
+- ▁Bridge
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+- ▁되지
+- ▁triangle
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+- 玛丽拉
+- изова
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+- 異常
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+- ▁elevator
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+- 观念
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+- 哑
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+- ことはない
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+- ッカー
+- 怜
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+- τι
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+- förmig
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+- espai
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+- お疲れさま
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+- ङ
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+- ▁konkur
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+- 小さく
+- んねん
+- 的一件事是
+- ▁lehnte
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+- ύ
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+- 緊
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+- Б
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+- 锤
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+- $
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+- 灣
+- 甩
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+- 浆
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+- 蛙
+- 磊
+- ସ
+- 點
+- 汰
+- 棟
+- 陕
+- 憎
+- 绣
+- 歪
+- 頻
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+- 岂
+- О
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+- 胞
+- ష
+- 표
+- 噴
+- 堕
+- 圏
+- 咳
+- 淑
+- 庸
+- 疚
+- 嘎
+- ג
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+- 譲
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+- 绵
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+- 碍
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+- ű
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+- 詳
+- 圭
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+- 枉
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+- 鋭
+- 鲸
+- 栖
+- 姫
+- 嫩
+- 큰
+- 渊
+- 掠
+- ಳ
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+- 衔
+- 憋
+- 癖
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+- 확
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+- 厨
+- ડ
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+- 颖
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+- °
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+- Î
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+- 纷
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+- 渗
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+- 賄
+- 陀
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+- –
+- ჰ
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+- ೊ
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+- 徹
+- 鍛
+- ሱ
+- 儒
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+- 耸
+- ೂ
+- 淳
+- 翌
+- 餌
+- 庇
+- 捞
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+- 膏
+- ۵
+- 럽
+- 隅
+- 啪
+- 辐
+- 熙
+- ඔ
+- 紛
+- 捏
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+- 손
+- 駒
+- 愚
+- 椎
+- 朽
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+- 帘
+- 嘟
+- 颊
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+- 葵
+- 慕
+- 泛
+- 窒
+- ધ
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+- 昧
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+- 屿
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+- 팔
+- ယ
+- 粥
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+- 餅
+- 討
+- 廣
+- 붙
+- 씨
+- 犹
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+- 萩
+- 歹
+- 咽
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+- В
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+- П
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+- 囚
+- 鲨
+- 凳
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+- ۀ
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+- 옷
+- 쳐
+- 참
+- 痒
+- 혼
+- 眨
+- 揉
+- Ғ
+- 테
+- ծ
+- 咒
+- 绒
+- 厘
+- 변
+- 십
+- 厢
+- 琐
+- ਼
+- ẳ
+- 缴
+- 驼
+- ዲ
+- 嵐
+- 礁
+- 粹
+- 독
+- 俳
+- 妞
+- 쉬
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+- 岐
+- 闇
+- 肮
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+- 債
+- 盯
+- 銅
+- 卦
+- 莹
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+- ቅ
+- 樱
+- 笠
+- 薯
+- 醇
+- 栓
+- ़
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+- ካ
+- 刹
+- 奨
+- 迅
+- њ
+- җ
+- 릴
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+- 谭
+- 俯
+- 帳
+- 帐
+- 瑶
+- 떡
+- ោ
+- 溝
+- ೀ
+- 谍
+- 습
+- 噩
+- ҷ
+- ੁ
+- 淀
+- 勺
+- 簿
+- 曝
+- ւ
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+- 棺
+- 斩
+- ።
+- 貝
+- 鼎
+- Ё
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+- Ŝ
+- 별
+- ိ
+- 殻
+- 舗
+- 婿
+- 韵
+- ੈ
+- 충
+- 酔
+- 繋
+- 剛
+- 愤
+- 韧
+- 웠
+- ಣ
+- ฐ
+- 哺
+- 哼
+- ඟ
+- 嗡
+- 緒
+- 姬
+- 慶
+- 匈
+- 懐
+- ഷ
+- 挠
+- 氓
+- இ
+- 桩
+- ۍ
+- ៅ
+- 纲
+- 妾
+- 軌
+- 渴
+- 聘
+- ם
+- 衍
+- 랐
+- ŷ
+- 奄
+- 酋
+- ူ
+- ହ
+- 睹
+- 拌
+- ຶ
+- 绪
+- 瘫
+- 濱
+- 隧
+- 瞄
+- ൂ
+- 耿
+- ൻ
+- 吱
+- 喇
+- ഗ
+- շ
+- 嘻
+- ಷ
+- ಚ
+- 霧
+- 넣
+- 怠
+- 杖
+- 錦
+- 屠
+- 빼
+- 鞠
+- 眺
+- 藻
+- 栅
+- 矛
+- 冊
+- ထ
+- 聆
+- ശ
+- 怡
+- 宵
+- 악
+- Ы
+- 嚷
+- 패
+- ァ
+- 勋
+- 언
+- 慨
+- 赦
+- 萍
+- ռ
+- 막
+- አ
+- ζ
+- 豁
+- 휴
+- 肚
+- 捣
+- ٿ
+- 셔
+- 昨
+- 皓
+- 喃
+- എ
+- 喉
+- 貿
+- 趴
+- 迭
+- 謀
+- 孩
+- ቤ
+- 薦
+- 粛
+- 哒
+- ീ
+- 趾
+- 酿
+- 섯
+- 痘
+- 茜
+- 蚀
+- 菇
+- எ
+- 躬
+- 誌
+- 佬
+- ¿
+- 俵
+- ċ
+- 꺼
+- 渐
+- 卒
+- 鸽
+- 發
+- ళ
+- ហ
+- 亩
+- 唇
+- 壇
+- ಶ
+- 蝇
+- ચ
+- 蜀
+- 隻
+- 俱
+- 泌
+- 剿
+- 磕
+- ቸ
+- அ
+- 衬
+- 처
+- 垮
+- 琉
+- 墅
+- 쯤
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+- 険
+- 扁
+- 橱
+- 창
+- 咁
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+- 특
+- 沒
+- 倫
+- 噜
+- 钦
+- 傍
+- ӷ
+- 嗓
+- 枫
+- 답
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+- 茸
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+- 惰
+- 림
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+- 榄
+- 氢
+- 喧
+- 览
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+- 羅
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+- 拱
+- 轿
+- Ú
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+- 倩
+- ਅ
+- 剔
+- 距
+- 禀
+- 揍
+- ޒ
+- 嵌
+- 瞬
+- 憲
+- 蹭
+- 凹
+- 판
+- ィ
+- ९
+- 凸
+- 橘
+- 苔
+- 蕉
+- 浇
+- ৯
+- 獄
+- 穂
+- „
+- 贸
+- ʼ
+- 읽
+- 聪
+- 료
+- 옆
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+- 妝
+- 鸮
+- 떤
+- 圳
+- 捲
+- 陝
+- 獺
+- 媾
+- 魍
+- 鼩
+- 鴈
+- ゝ
+- 狲
+- 釐
+- 铄
+- 沮
+- 蘼
+- 邕
+- 钎
+- 靥
+- 鞣
+- ♥
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+- 稹
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+- 郅
+- 阆
+- 颱
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+- ־
+- 儋
+- 厝
+- 肅
+- 誊
+- 騷
+- 숟
+- "\x92"
+- "\x97"
+- Ճ
+- ՞
+- ኜ
+- ዢ
+- ዦ
+- Ố
+- 嘍
+- 揩
+- 曆
+- 栢
+- 潋
+- 箬
+- 糍
+- 遞
+- 髡
+- 鰐
+- 鲡
+- 鲢
+- 齬
+- 걀
+- 꺄
+- 꿉
+- 뗄
+- 빕
+- 얽
+- 읏
+- 잰
+- 쟀
+- 컥
+- 킷
+- 햐
+- 흩
+- ඣ
+- ፑ
+- ៣
+- 寳
+- 淖
+- 灑
+- 錨
+- 駁
+- 랠
+- 썽
+- 웰
+- 젬
+- 탬
+- 툼
+- 핍
+- 檳
+- 籃
+- 僑
+- 橼
+- 脹
+- 銛
+- 钜
+- 盱
+- 籲
+- 陲
+- 颔
+- 勖
+- 蓖
+- 郢
+- 檯
+- 粂
+- 獏
+- 燿
+- 祕
+- 鯊
+- 枡
+- 惕
+- 兌
+- 钼
+- 鞆
+- ●
+- 綦
+- 蕗
+- 埜
+- 焘
+- 劭
+- 愎
+- 橈
+- 鎚
+- 锷
+- 鞏
+- 齁
+- 龇
+- ဍ
+- ♂
+- 侪
+- 窠
+- 肪
+- 蜇
+- 逹
+- 邛
+- 卟
+- 撓
+- 燐
+- 纖
+- 郓
+- 闱
+- 餞
+- 鹓
+- 俬
+- 呔
+- 澧
+- 燴
+- 犍
+- 羧
+- 葶
+- 谲
+- ¬
+- Ć
+- ː
+- ̓
+- ڱ
+- ۚ
+- ඪ
+- ፁ
+- ᱪ
+- Ẩ
+- 呎
+- 哣
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+- 搣
+- 淒
+- 苕
+- 萋
+- 襪
+- 訇
+- 諄
+- 謢
+- 邳
+- 鉈
+- 鴉
+- 鸻
+- 갭
+- 곶
+- 넹
+- 뗐
+- 룽
+- 맷
+- 샾
+- 쐈
+- 쨍
+- 챠
+- 컹
+- 튠
+- 푠
+- ˮ
+- Ί
+- Ҿ
+- ޛ
+- ዟ
+- 悗
+- 耒
+- 躾
+- 鏟
+- 閂
+- 곁
+- 맏
+- 뮌
+- 찻
+- 睪
+- 礦
+- 筺
+- 艱
+- 賬
+- 镕
+- 蕕
+- 炝
+- 聾
+- 逡
+- ฌ
+- 瀋
+- 詭
+- 鲣
+- 侉
+- 埙
+- 慟
+- 蜉
+- 钡
+- 輯
+- 諧
+- 吳
+- 鐙
+- 陛
+- 撹
+- 苧
+- 劔
+- 濛
+- 齧
+- Ժ
+- 剝
+- 吩
+- 徕
+- 镓
+- ൌ
+- 佻
+- 嚅
+- 岘
+- 窨
+- 跬
+- 銜
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+- 魉
+- 鰓
+- 㩒
+- 嬗
+- 旎
+- 旖
+- 氫
+- 洄
+- 牺
+- 篙
+- 舂
+- 闌
+- 飄
+- Ծ
+- ḿ
+- 仞
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+- 噓
+- 囹
+- 圄
+- 岿
+- 恁
+- 揦
+- 殚
+- 沆
+- 簕
+- 莠
+- 莼
+- 萊
+- 鰆
+- ɔ
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+- 砣
+- 辇
+- 궐
+- 뽐
+- "\x84"
+- "\x9A"
+- §
+- Ť
+- Ձ
+- ۓ
+- ጢ
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+- Ổ
+- Ờ
+- 僆
+- 棂
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+- 瓏
+- 瘢
+- 聳
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+- 衿
+- 遒
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+- 镬
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+- 꺠
+- 끙
+- 늪
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+- 딥
+- 딧
+- 랖
+- 룻
+- 릅
+- 봔
+- 봬
+- 뼛
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+- 쉼
+- 싣
+- 쎘
+- 얏
+- 윌
+- 쥴
+- 쯧
+- 챈
+- 챌
+- 촥
+- 톰
+- 핬
+- 圪
+- 屜
+- 捽
+- 珮
+- 碛
+- 臟
+- 螫
+- 輋
+- 醪
+- 骠
+- 똠
+- 맸
+- 샬
+- 沔
+- 禊
+- 襦
+- 趄
+- 邇
+- 剜
+- 筍
+- 緞
+- 虧
+- 趔
+- 铩
+- 埒
+- 憚
+- 扦
+- 罟
+- 囮
+- 賈
+- 匁
+- 吲
+- 哚
+- 雎
+- 鄒
+- 飫
+- 縊
+- 讼
+- 濯
+- 竊
+- 铕
+- 蓣
+- ๅ
+- 潑
+- 瀉
+- Ґ
+- ಊ
+- 屓
+- 碣
+- 粳
+- 苈
+- 蕤
+- 誨
+- 跖
+- 騾
+- 魑
+- ⸺
+- 倅
+- 幇
+- 廂
+- 柺
+- 楡
+- 瀨
+- 狆
+- 籏
+- 籬
+- 跣
+- 锶
+- 镆
+- 韌
+- ΐ
+- 佚
+- 汜
+- 牍
+- 牴
+- 癱
+- 蟬
+- 顏
+- ϋ
+- ዉ
+- ▪
+- 佥
+- 刿
+- 噙
+- 孭
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+- 猷
+- 瓤
+- 疊
+- 癡
+- 矍
+- 硯
+- 稃
+- 讫
+- 贔
+- 꾀
+- 떵
+- 맬
+- 빳
+- 챕
+- "\x91"
+- "\x96"
+- ƴ
+- ؔ
+- ઔ
+- ෲ
+- ኣ
+- ១
+- 啞
+- 夘
+- 嵋
+- 徬
+- 慾
+- 斂
+- 渌
+- 滌
+- 漯
+- 燶
+- 砵
+- 稟
+- 笤
+- 蓆
+- 螞
+- 覓
+- 諺
+- 诳
+- 谡
+- 踎
+- 躉
+- 鈔
+- 铋
+- 陞
+- 顼
+- 麪
+- 갬
+- 궜
+- 궤
+- 꿇
+- 덱
+- 떳
+- 띨
+- 룐
+- 몹
+- 삑
+- 슁
+- 쏭
+- 앰
+- 욤
+- 웩
+- 잦
+- 죙
+- 챘
+- 첵
+- 촘
+- 쿤
+- 킴
+- 텁
+- 퓸
+- 훼
+- ઍ
+- 冧
+- 勐
+- 擸
+- 晉
+- 汨
+- 燊
+- 瘡
+- 癇
+- 眀
+- 鎹
+- 铷
+- 鯰
+- 鱒
+- 딤
+- 앓
+- 얍
+- 윷
+- 쟈
+- 팎
+- 囗
+- 諌
+- 鋤
+- 壆
+- 嶄
+- 碕
+- 綵
+- 闢
+- 鳐
+- 秣
+- 簗
+- 蕪
+- 氹
+- 兪
+- 恹
+- 鈕
+- 钇
+- 蝣
+- 軀
+- 畲
+- 埕
+- 潅
+- 瞼
+- 褄
+- 詈
+- 邏
+- 颶
+- 莟
+- 艙
+- 碩
+- 筅
+- 钐
+- 寤
+- 徭
+- 銑
+- 朊
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+- 峅
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+- 脩
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+- 鱸
+- 鸩
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+- 壢
+- 泫
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+- 耄
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+- 垭
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+- 捯
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+- 攋
+- 梼
+- 璟
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+- 紥
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+- 铒
+- 鬢
+- 魃
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+- 隸
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+- 鲛
+- 鹱
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+- 犧
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+- 鏝
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+- 鹘
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+- 咥
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+- 嵊
+- 椴
+- 濞
+- 緘
+- 藺
+- 蝮
+- 醴
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+- 鹗
+- 贲
+- ഃ
+- ဠ
+- 偈
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+- 嗳
+- 姶
+- 嫻
+- 孥
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+- 彧
+- 徂
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+- 皲
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+- ṃ
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+- 쉑
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+- "\x8A"
+- "\x9D"
+- Ğ
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+- ฯ
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+- ኼ
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+- 곪
+- 굼
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+- 덨
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+- 롹
+- 묽
+- 볐
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+- 뽂
+- 샛
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+- 숑
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+- 앳
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+- 웁
+- 윰
+- 쟨
+- 젼
+- 짹
+- 쫘
+- 쭌
+- 챗
+- 펩
+- 푯
+- 핌
+- 𢱕
+- 櫚
+- 煅
+- 甌
+- 莢
+- 驕
+- 髌
+- 랏
+- 쏜
+- 옐
+- 핼
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+- 翹
+- 蔀
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+- 慷
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+- 鲔
+- 끅
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+- ២
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+- 唻
+- 抾
+- 柞
+- 澱
+- 錙
+- 늄
+- 뒨
+- 믈
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+- ¶
+- ʾ
+- ̊
+- ଈ
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+- ஶ
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+- ጳ
+- ጼ
+- ጾ
+- ጿ
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+- 舁
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+- 铟
+- 颏
+- 黌
+- 栂
+- 瘻
+- 瞋
+- 窣
+- 窸
+- 絋
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+- 銕
+- √
+- 蚺
+- 蹕
+- 窶
+- 牻
+- 擯
+- 愼
+- 榾
+- 癩
+- 筜
+- 筼
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+- 蟄
+- 鍔
+- 頽
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+- 骈
+- 鸊
+- 倬
+- 唛
+- 嗶
+- 嘰
+- 嚐
+- 媪
+- 徛
+- 憺
+- 揠
+- 甦
+- 翃
+- 肭
+- 芗
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+- "\x81"
+- "\x8D"
+- "\x8E"
+- "\x98"
+- ¦
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+- 뛌
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+- 맀
+- 먀
+- 먙
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+- 묀
+- 뭍
+- 뭥
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+- 뭰
+- 믁
+- 믐
+- 밈
+- 밎
+- 뱁
+- 뱡
+- 벋
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+- 봥
+- 뵤
+- 붜
+- 뷘
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+- 쁩
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+- 솨
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+- 쉭
+- 쌘
+- 쎼
+- 쏸
+- 쐐
+- 쐴
+- 쑬
+- 쒯
+- 씃
+- 앎
+- 앏
+- 앝
+- 얉
+- 얋
+- 얐
+- 얬
+- 옙
+- 옜
+- 왝
+- 왯
+- 윅
+- 읒
+- 잿
+- 쟝
+- 젭
+- 젱
+- 졀
+- 좃
+- 좇
+- 좐
+- 죈
+- 죵
+- 줜
+- 줴
+- 쥔
+- 쥘
+- 짢
+- 쩟
+- ํ
+- ଋ
+- 닯
+- 뜀
+- 룟
+- 뭡
+- 쌂
+- Ѓ
+- ઁ
+- ః
+- 摅
+- 欉
+- 洶
+- 牘
+- 篪
+- 繃
+- 艻
+- 跩
+- 鷯
+- 뫼
+- 왐
+- ఙ
+- 仂
+- 俍
+- 埖
+- 堊
+- 尅
+- 悾
+- 掅
+- 摎
+- 柢
+- 樨
+- 橂
+- 歁
+- 殕
+- 狯
+- 珜
+- 珰
+- 瑆
+- 畭
+- 穰
+- 箨
+- 缳
+- 罌
+- 耨
+- 臌
+- 苁
+- 萄
+- 蠓
+- 蠖
+- 蠶
+- 褸
+- 襠
+- 觴
+- 謖
+- 酺
+- 铊
+- 駟
+- 鰍
+- 黾
+- ♡
+- 僢
+- 剉
+- 噘
+- 圞
+- 圹
+- 岨
+- 弉
+- 昺
+- 橢
+- 濶
+- 焮
+- 禩
+- 秡
+- 秫
+- 笊
+- 茌
+- 蕖
+- 藹
+- 螅
+- 袆
+- 郏
+- 鉋
+- 銹
+- 铌
+- 駸
+- 墻
+- 悋
+- 暱
+- 櫨
+- 浬
+- 筬
+- 糺
+- 紑
+- 誂
+- 賎
+- 跹
+- 蹁
+- 蹠
+- 鰤
+- ằ
+- 蒻
+- 鞨
+- ắ
+- 笵
+- <sos>
+- <eos>
+- <sop>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+use_preprocessor: true
+token_type: bpe
+bpemodel: data/token_list/bpe_unigram50000/bpe.model
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+frontend: default
+frontend_conf:
+    n_fft: 512
+    win_length: 400
+    hop_length: 160
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 1
+normalize: global_mvn
+normalize_conf:
+    stats_file: exp/s2t_stats_raw_bpe50000/train/feats_stats.npz
+model: espnet
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1
+    length_normalized_loss: false
+    sym_na: <na>
+preencoder: null
+preencoder_conf: {}
+encoder: e_branchformer
+encoder_conf:
+    output_size: 384
+    attention_heads: 6
+    attention_layer_type: selfattn
+    pos_enc_layer_type: abs_pos
+    rel_pos_type: latest
+    cgmlp_linear_units: 1536
+    cgmlp_conv_kernel: 31
+    use_linear_after_conv: false
+    gate_activation: identity
+    num_blocks: 6
+    dropout_rate: 0.05
+    positional_dropout_rate: 0.05
+    attention_dropout_rate: 0.05
+    input_layer: conv2d
+    layer_drop_rate: 0.0
+    linear_units: 1536
+    positionwise_layer_type: linear
+    use_ffn: true
+    macaron_ffn: true
+    merge_conv_kernel: 31
+postencoder: null
+postencoder_conf: {}
+decoder: transformer
+decoder_conf:
+    attention_heads: 6
+    linear_units: 1536
+    num_blocks: 6
+    dropout_rate: 0.05
+    positional_dropout_rate: 0.05
+    self_attention_dropout_rate: 0.05
+    src_attention_dropout_rate: 0.05
+preprocessor: s2t
+preprocessor_conf:
+    text_prev_name: text_prev
+    text_ctc_name: text_ctc
+    fs: 16000
+    na_symbol: <na>
+    speech_length: 30
+    speech_resolution: 0.02
+    speech_init_silence: 30
+    text_prev_apply_prob: 0.5
+    time_apply_prob: 0.5
+    notime_symbol: <notimestamps>
+    first_time_symbol: <0.00>
+    last_time_symbol: <30.00>
+required:
+- output_dir
+- token_list
+version: '202308'
+distributed: true
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/images/acc.png b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/images/acc.png
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diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.1.log b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.1.log
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+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.1.log
@@ -0,0 +1,5256 @@
+# Running on gpuc02.delta.ncsa.illinois.edu
+# Started at Sun Jan 14 12:48:05 CST 2024
+# SLURMD_NODENAME=gpuc02
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=128
+# SLURM_CPUS_PER_TASK=128
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GPUS_ON_NODE=8
+# SLURM_GTIDS=0
+# SLURM_JOBID=2858112
+# SLURM_JOB_ACCOUNT=bbjs-delta-gpu
+# SLURM_JOB_CPUS_PER_NODE='128(x2)'
+# SLURM_JOB_END_TIME=1705430862
+# SLURM_JOB_GID=202
+# SLURM_JOB_GPUS=0,1,2,3,4,5,6,7
+# SLURM_JOB_ID=2858112
+# SLURM_JOB_NAME=exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
+# SLURM_JOB_NODELIST='gpuc[02,04]'
+# SLURM_JOB_NUM_NODES=2
+# SLURM_JOB_PARTITION=gpuA100x8
+# SLURM_JOB_QOS=bbjs-delta-gpu
+# SLURM_JOB_START_TIME=1705258062
+# SLURM_JOB_UID=68077
+# SLURM_JOB_USER=peng6
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_NODE=2000000
+# SLURM_NNODES=2
+# SLURM_NODEID=0
+# SLURM_NODELIST='gpuc[02,04]'
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1
+# SLURM_SUBMIT_HOST=dt-login01.delta.ncsa.illinois.edu
+# SLURM_TASKS_PER_NODE='1(x2)'
+# SLURM_TASK_PID=1518488
+# SLURM_TOPOLOGY_ADDR=ss00.ss05.gpuc02
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9984:109
+# srun --export=ALL python3 -m espnet2.bin.s2t_train --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_bea4c054-f27d-4c8f-a870-da9e26899632 
+/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/spe/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_bea4c054-f27d-4c8f-a870-da9e26899632
+ech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_bea4c054-f27d-4c8f-a870-da9e26899632
+[gpuc02:0/16] 2024-01-14 12:51:26,855 (distributed_c10d:319) INFO: Added key: store_based_barrier_key:1 to store for rank: 0
+[gpuc02:0/16] 2024-01-14 12:51:27,207 (distributed_c10d:353) INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
+[gpuc02:0/16] 2024-01-14 12:51:27,285 (s2t:464) INFO: Vocabulary size: 50002
+[gpuc02:0/16] 2024-01-14 12:51:35,860 (abs_task:1231) INFO: pytorch.version=1.13.1, cuda.available=True, cudnn.version=8500, cudnn.benchmark=False, cudnn.deterministic=True
+[gpuc02:0/16] 2024-01-14 12:51:35,865 (abs_task:1232) INFO: Model structure:
+ESPnetS2TModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=512, win_length=400, hop_length=160, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=512, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=1, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=1, axis=time)
+  )
+  (normalize): GlobalMVN(stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz, norm_means=True, norm_vars=True)
+  (encoder): EBranchformerEncoder(
+    (embed): Conv2dSubsampling(
+      (conv): Sequential(
+        (0): Conv2d(1, 384, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=7296, out_features=384, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.05, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (1): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (2): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (3): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (4): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (5): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+  )
+  (decoder): TransformerDecoder(
+    (embed): Sequential(
+      (0): Embedding(50002, 384)
+      (1): PositionalEncoding(
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+    (output_layer): Linear(in_features=384, out_features=50002, bias=True)
+    (decoders): MultiSequential(
+      (0): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (1): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (2): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (3): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (4): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (5): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=384, out_features=50002, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetS2TModel
+    Total Number of model parameters: 101.18 M
+    Number of trainable parameters: 101.18 M (100.0%)
+    Size: 404.73 MB
+    Type: torch.float32
+[gpuc02:0/16] 2024-01-14 12:51:35,865 (abs_task:1235) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.9, 0.98]
+    capturable: False
+    eps: 1e-06
+    foreach: None
+    initial_lr: 0.001
+    lr: 3.3333333333333334e-09
+    maximize: False
+    weight_decay: 0.0
+)
+[gpuc02:0/16] 2024-01-14 12:51:35,865 (abs_task:1236) INFO: Scheduler: PiecewiseLinearWarmupLR(warmup_steps_list=[0, 30000, 60000], warmup_lr_list=[0.0, 0.0001, 0.001])
+[gpuc02:0/16] 2024-01-14 12:51:35,882 (abs_task:1245) INFO: Saving the configuration in exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
+[gpuc02:0/16] 2024-01-14 12:51:42,175 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 12:51:43,341 (abs_task:1616) INFO: [valid] dataset:
+ESPnetDataset(
+  speech: {"path": "dump/raw/dev_v3/wav.scp", "type": "kaldi_ark"}
+  text_prev: {"path": "dump/raw/dev_v3/text.prev", "type": "text"}
+  text_ctc: {"path": "dump/raw/dev_v3/text.ctc", "type": "text"}
+  text: {"path": "dump/raw/dev_v3/text", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f28b1d1f9a0>)
+[gpuc02:0/16] 2024-01-14 12:51:43,341 (abs_task:1617) INFO: [valid] Batch sampler: UnsortedBatchSampler(N-batch=4671, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/valid/speech_shape, 
+[gpuc02:0/16] 2024-01-14 12:51:43,342 (abs_task:1618) INFO: [valid] mini-batch sizes summary: N-batch=4671, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 12:51:48,755 (trainer:159) INFO: The training was resumed using exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/checkpoint.pth
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+[gpuc02:0/16] 2024-01-14 12:51:56,353 (trainer:284) INFO: 19/45epoch started
+[gpuc02:0/16] 2024-01-14 12:51:56,402 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 12:52:15,250 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 12:52:19,180 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f27df514df0>)
+[gpuc02:0/16] 2024-01-14 12:52:19,180 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 12:52:19,184 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
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+gpuc02:1518665:1518753 [4] NCCL INFO Channel 00/0 : 4[88000] -> 5[8b000] via P2P/IPC/read
+gpuc02:1518665:1518753 [4] NCCL INFO Channel 01/0 : 4[88000] -> 5[8b000] via P2P/IPC/read
+gpuc02:1518665:1518753 [4] NCCL INFO Connected all rings
+gpuc02:1518665:1518753 [4] NCCL INFO Channel 00/0 : 4[88000] -> 3[4c000] via P2P/IPC/read
+gpuc02:1518665:1518753 [4] NCCL INFO Channel 01/0 : 4[88000] -> 3[4c000] via P2P/IPC/read
+gpuc02:1518665:1518753 [4] NCCL INFO Connected all trees
+gpuc02:1518665:1518753 [4] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+gpuc02:1518665:1518753 [4] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
+gpuc02:1518665:1518753 [4] NCCL INFO comm 0x576209f0 rank 4 nranks 16 cudaDev 4 busId 88000 - Init COMPLETE
+[gpuc02:0/16] 2024-01-14 12:56:25,244 (distributed:1027) INFO: Reducer buckets have been rebuilt in this iteration.
+[gpuc02:0/16] 2024-01-14 12:57:09,047 (trainer:737) INFO: 19epoch:train:1-100batch: iter_time=2.540, forward_time=0.188, loss_ctc=42.663, loss_att=46.521, acc=0.744, loss=45.363, backward_time=0.111, grad_norm=35.667, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=4.714e-04, train_time=3.125
+[gpuc02:0/16] 2024-01-14 12:57:55,392 (trainer:737) INFO: 19epoch:train:101-200batch: iter_time=1.431e-04, forward_time=0.106, loss_ctc=63.155, loss_att=72.110, acc=0.706, loss=69.424, backward_time=0.100, grad_norm=50.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.713e-04, train_time=0.464
+[gpuc02:0/16] 2024-01-14 12:58:37,550 (trainer:737) INFO: 19epoch:train:201-300batch: iter_time=1.556e-04, forward_time=0.104, loss_ctc=49.167, loss_att=55.610, acc=0.721, loss=53.677, backward_time=0.098, grad_norm=37.109, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.712e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 12:59:19,587 (trainer:737) INFO: 19epoch:train:301-400batch: iter_time=1.282e-04, forward_time=0.103, loss_ctc=43.666, loss_att=44.551, acc=0.741, loss=44.286, backward_time=0.097, grad_norm=33.828, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.711e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 13:00:01,600 (trainer:737) INFO: 19epoch:train:401-500batch: iter_time=1.148e-04, forward_time=0.103, loss_ctc=55.486, loss_att=63.983, acc=0.693, loss=61.434, backward_time=0.097, grad_norm=44.118, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.710e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 13:00:43,642 (trainer:737) INFO: 19epoch:train:501-600batch: iter_time=1.228e-04, forward_time=0.103, loss_ctc=55.845, loss_att=54.945, acc=0.715, loss=55.215, backward_time=0.098, grad_norm=45.648, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.709e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 13:01:25,621 (trainer:737) INFO: 19epoch:train:601-700batch: iter_time=1.207e-04, forward_time=0.104, loss_ctc=47.211, loss_att=52.040, acc=0.734, loss=50.591, backward_time=0.097, grad_norm=35.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.709e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 13:02:07,590 (trainer:737) INFO: 19epoch:train:701-800batch: iter_time=1.075e-04, forward_time=0.103, loss_ctc=42.165, loss_att=44.128, acc=0.742, loss=43.539, backward_time=0.096, grad_norm=33.529, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.708e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 13:02:49,652 (trainer:737) INFO: 19epoch:train:801-900batch: iter_time=1.247e-04, forward_time=0.103, loss_ctc=61.894, loss_att=62.045, acc=0.701, loss=62.000, backward_time=0.096, grad_norm=60.288, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.707e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 13:03:31,593 (trainer:737) INFO: 19epoch:train:901-1000batch: iter_time=1.113e-04, forward_time=0.103, loss_ctc=49.577, loss_att=53.009, acc=0.740, loss=51.980, backward_time=0.097, grad_norm=38.686, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.706e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 13:04:13,882 (trainer:737) INFO: 19epoch:train:1001-1100batch: iter_time=1.199e-04, forward_time=0.108, loss_ctc=40.038, loss_att=47.764, acc=0.726, loss=45.446, backward_time=0.096, grad_norm=32.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.705e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 13:04:55,726 (trainer:737) INFO: 19epoch:train:1101-1200batch: iter_time=1.231e-04, forward_time=0.104, loss_ctc=41.041, loss_att=47.028, acc=0.728, loss=45.232, backward_time=0.096, grad_norm=35.630, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.040, optim0_lr0=4.704e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-14 13:05:31,740 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 13:05:50,707 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 13:05:54,212 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d219e40>)
+[gpuc02:0/16] 2024-01-14 13:05:54,212 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 13:05:54,215 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 13:09:59,338 (trainer:737) INFO: 19epoch:train:1201-1300batch: iter_time=2.544, forward_time=0.105, loss_ctc=51.048, loss_att=52.927, acc=0.727, loss=52.363, backward_time=0.097, grad_norm=42.484, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.703e-04, train_time=3.036
+[gpuc02:0/16] 2024-01-14 13:10:42,092 (trainer:737) INFO: 19epoch:train:1301-1400batch: iter_time=1.227e-04, forward_time=0.105, loss_ctc=57.835, loss_att=61.129, acc=0.731, loss=60.141, backward_time=0.098, grad_norm=42.086, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.703e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 13:11:24,913 (trainer:737) INFO: 19epoch:train:1401-1500batch: iter_time=1.211e-04, forward_time=0.104, loss_ctc=43.884, loss_att=58.745, acc=0.723, loss=54.287, backward_time=0.097, grad_norm=36.121, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.702e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 13:12:10,124 (trainer:737) INFO: 19epoch:train:1501-1600batch: iter_time=1.287e-04, forward_time=0.105, loss_ctc=49.194, loss_att=47.852, acc=0.732, loss=48.255, backward_time=0.098, grad_norm=35.624, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.701e-04, train_time=0.452
+[gpuc02:0/16] 2024-01-14 13:12:52,977 (trainer:737) INFO: 19epoch:train:1601-1700batch: iter_time=1.303e-04, forward_time=0.104, loss_ctc=47.879, loss_att=58.553, acc=0.710, loss=55.351, backward_time=0.098, grad_norm=37.948, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.700e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 13:12:56,321 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 13:13:35,414 (trainer:737) INFO: 19epoch:train:1701-1800batch: iter_time=1.511e-04, forward_time=0.103, loss_ctc=54.818, loss_att=51.836, acc=0.713, loss=52.731, backward_time=0.097, grad_norm=45.928, clip=100.000, loss_scale=2.224e+34, optim_step_time=0.041, optim0_lr0=4.699e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 13:14:18,085 (trainer:737) INFO: 19epoch:train:1801-1900batch: iter_time=1.388e-04, forward_time=0.104, loss_ctc=47.663, loss_att=54.000, acc=0.738, loss=52.099, backward_time=0.098, grad_norm=36.821, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.698e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:15:00,560 (trainer:737) INFO: 19epoch:train:1901-2000batch: iter_time=1.377e-04, forward_time=0.104, loss_ctc=42.627, loss_att=44.326, acc=0.734, loss=43.816, backward_time=0.097, grad_norm=34.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.697e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:15:43,102 (trainer:737) INFO: 19epoch:train:2001-2100batch: iter_time=1.406e-04, forward_time=0.104, loss_ctc=50.445, loss_att=55.701, acc=0.716, loss=54.124, backward_time=0.098, grad_norm=40.253, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.696e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:16:25,769 (trainer:737) INFO: 19epoch:train:2101-2200batch: iter_time=1.310e-04, forward_time=0.104, loss_ctc=58.434, loss_att=63.006, acc=0.722, loss=61.634, backward_time=0.098, grad_norm=53.947, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.696e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:17:08,212 (trainer:737) INFO: 19epoch:train:2201-2300batch: iter_time=1.396e-04, forward_time=0.103, loss_ctc=39.658, loss_att=48.255, acc=0.746, loss=45.676, backward_time=0.098, grad_norm=31.304, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.695e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 13:17:50,624 (trainer:737) INFO: 19epoch:train:2301-2400batch: iter_time=1.783e-04, forward_time=0.106, loss_ctc=41.157, loss_att=47.997, acc=0.728, loss=45.945, backward_time=0.099, grad_norm=33.142, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.694e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 13:18:33,214 (trainer:737) INFO: 19epoch:train:2401-2500batch: iter_time=1.963e-04, forward_time=0.106, loss_ctc=48.677, loss_att=49.408, acc=0.723, loss=49.189, backward_time=0.101, grad_norm=45.504, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=4.693e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:18:38,612 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 13:18:58,396 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 13:19:02,324 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f27c473fe80>)
+[gpuc02:0/16] 2024-01-14 13:19:02,325 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 13:19:02,328 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 13:23:24,229 (trainer:737) INFO: 19epoch:train:2501-2600batch: iter_time=2.474, forward_time=0.105, loss_ctc=41.908, loss_att=47.479, acc=0.734, loss=45.808, backward_time=0.098, grad_norm=35.494, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.692e-04, train_time=2.910
+[gpuc02:0/16] 2024-01-14 13:24:07,120 (trainer:737) INFO: 19epoch:train:2601-2700batch: iter_time=1.293e-04, forward_time=0.105, loss_ctc=58.531, loss_att=70.685, acc=0.705, loss=67.039, backward_time=0.099, grad_norm=46.637, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.691e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 13:24:49,744 (trainer:737) INFO: 19epoch:train:2701-2800batch: iter_time=1.494e-04, forward_time=0.104, loss_ctc=47.840, loss_att=56.663, acc=0.711, loss=54.016, backward_time=0.098, grad_norm=36.114, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.690e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:25:32,297 (trainer:737) INFO: 19epoch:train:2801-2900batch: iter_time=1.282e-04, forward_time=0.103, loss_ctc=42.704, loss_att=44.597, acc=0.729, loss=44.029, backward_time=0.097, grad_norm=34.257, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.690e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:26:14,991 (trainer:737) INFO: 19epoch:train:2901-3000batch: iter_time=1.379e-04, forward_time=0.103, loss_ctc=53.496, loss_att=64.211, acc=0.690, loss=60.996, backward_time=0.097, grad_norm=44.224, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.689e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 13:26:57,825 (trainer:737) INFO: 19epoch:train:3001-3100batch: iter_time=1.352e-04, forward_time=0.103, loss_ctc=50.011, loss_att=51.923, acc=0.715, loss=51.350, backward_time=0.097, grad_norm=42.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.688e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 13:27:43,638 (trainer:737) INFO: 19epoch:train:3101-3200batch: iter_time=1.581e-04, forward_time=0.104, loss_ctc=46.205, loss_att=48.044, acc=0.736, loss=47.492, backward_time=0.097, grad_norm=34.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.687e-04, train_time=0.458
+[gpuc02:0/16] 2024-01-14 13:28:26,320 (trainer:737) INFO: 19epoch:train:3201-3300batch: iter_time=1.342e-04, forward_time=0.103, loss_ctc=41.562, loss_att=43.690, acc=0.735, loss=43.052, backward_time=0.096, grad_norm=32.945, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.686e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 13:29:08,808 (trainer:737) INFO: 19epoch:train:3301-3400batch: iter_time=1.288e-04, forward_time=0.103, loss_ctc=61.053, loss_att=63.257, acc=0.702, loss=62.596, backward_time=0.097, grad_norm=56.972, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.685e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:29:51,366 (trainer:737) INFO: 19epoch:train:3401-3500batch: iter_time=1.231e-04, forward_time=0.102, loss_ctc=48.640, loss_att=51.611, acc=0.732, loss=50.719, backward_time=0.097, grad_norm=37.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.684e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:30:33,763 (trainer:737) INFO: 19epoch:train:3501-3600batch: iter_time=1.322e-04, forward_time=0.102, loss_ctc=38.911, loss_att=47.007, acc=0.725, loss=44.578, backward_time=0.096, grad_norm=31.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.684e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 13:31:16,131 (trainer:737) INFO: 19epoch:train:3601-3700batch: iter_time=1.277e-04, forward_time=0.102, loss_ctc=39.958, loss_att=47.036, acc=0.724, loss=44.913, backward_time=0.096, grad_norm=36.396, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.683e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 13:31:43,353 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 13:32:02,339 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 13:32:05,895 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d29fcd0>)
+[gpuc02:0/16] 2024-01-14 13:32:05,895 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 13:32:05,898 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 13:36:09,083 (trainer:737) INFO: 19epoch:train:3701-3800batch: iter_time=2.436, forward_time=0.104, loss_ctc=50.612, loss_att=52.656, acc=0.722, loss=52.042, backward_time=0.097, grad_norm=43.374, clip=100.000, loss_scale=3.988e+34, optim_step_time=0.040, optim0_lr0=4.682e-04, train_time=2.929
+[gpuc02:0/16] 2024-01-14 13:36:51,929 (trainer:737) INFO: 19epoch:train:3801-3900batch: iter_time=1.050e-04, forward_time=0.103, loss_ctc=55.505, loss_att=62.286, acc=0.714, loss=60.252, backward_time=0.098, grad_norm=45.054, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.681e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 13:37:34,513 (trainer:737) INFO: 19epoch:train:3901-4000batch: iter_time=1.194e-04, forward_time=0.104, loss_ctc=42.949, loss_att=57.708, acc=0.720, loss=53.280, backward_time=0.096, grad_norm=36.014, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.680e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:38:17,157 (trainer:737) INFO: 19epoch:train:4001-4100batch: iter_time=1.226e-04, forward_time=0.102, loss_ctc=48.435, loss_att=47.795, acc=0.722, loss=47.987, backward_time=0.097, grad_norm=36.054, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.679e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:38:54,637 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 13:38:59,747 (trainer:737) INFO: 19epoch:train:4101-4200batch: iter_time=1.216e-04, forward_time=0.103, loss_ctc=47.400, loss_att=58.468, acc=0.697, loss=55.148, backward_time=0.096, grad_norm=39.589, clip=100.000, loss_scale=3.902e+34, optim_step_time=0.041, optim0_lr0=4.678e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:39:42,316 (trainer:737) INFO: 19epoch:train:4201-4300batch: iter_time=1.166e-04, forward_time=0.103, loss_ctc=50.627, loss_att=49.551, acc=0.722, loss=49.874, backward_time=0.096, grad_norm=42.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.678e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:40:24,859 (trainer:737) INFO: 19epoch:train:4301-4400batch: iter_time=1.205e-04, forward_time=0.104, loss_ctc=47.178, loss_att=51.352, acc=0.733, loss=50.100, backward_time=0.096, grad_norm=34.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.677e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:41:07,360 (trainer:737) INFO: 19epoch:train:4401-4500batch: iter_time=1.299e-04, forward_time=0.103, loss_ctc=42.152, loss_att=42.397, acc=0.736, loss=42.323, backward_time=0.096, grad_norm=33.057, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.676e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:41:49,921 (trainer:737) INFO: 19epoch:train:4501-4600batch: iter_time=1.302e-04, forward_time=0.104, loss_ctc=49.152, loss_att=54.157, acc=0.710, loss=52.656, backward_time=0.096, grad_norm=39.625, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.675e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:42:32,595 (trainer:737) INFO: 19epoch:train:4601-4700batch: iter_time=1.257e-04, forward_time=0.104, loss_ctc=57.344, loss_att=61.913, acc=0.713, loss=60.542, backward_time=0.097, grad_norm=55.116, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.674e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 13:43:15,121 (trainer:737) INFO: 19epoch:train:4701-4800batch: iter_time=1.317e-04, forward_time=0.103, loss_ctc=39.093, loss_att=48.043, acc=0.743, loss=45.358, backward_time=0.096, grad_norm=30.834, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.673e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:43:59,555 (trainer:737) INFO: 19epoch:train:4801-4900batch: iter_time=1.128e-04, forward_time=0.103, loss_ctc=40.197, loss_att=47.020, acc=0.724, loss=44.973, backward_time=0.096, grad_norm=32.751, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.673e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-14 13:44:43,186 (trainer:737) INFO: 19epoch:train:4901-5000batch: iter_time=1.050e-04, forward_time=0.103, loss_ctc=47.594, loss_att=49.483, acc=0.714, loss=48.916, backward_time=0.096, grad_norm=43.663, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.672e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-14 13:44:47,613 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 13:45:06,889 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 13:45:10,608 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f27dd11bf70>)
+[gpuc02:0/16] 2024-01-14 13:45:10,608 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 13:45:10,611 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 13:50:16,722 (trainer:737) INFO: 19epoch:train:5001-5100batch: iter_time=2.877, forward_time=0.124, loss_ctc=41.358, loss_att=43.539, acc=0.746, loss=42.885, backward_time=0.097, grad_norm=34.580, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.671e-04, train_time=3.335
+[gpuc02:0/16] 2024-01-14 13:50:59,612 (trainer:737) INFO: 19epoch:train:5101-5200batch: iter_time=1.020e-04, forward_time=0.103, loss_ctc=57.842, loss_att=69.754, acc=0.707, loss=66.180, backward_time=0.097, grad_norm=44.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.670e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 13:51:42,216 (trainer:737) INFO: 19epoch:train:5201-5300batch: iter_time=1.030e-04, forward_time=0.102, loss_ctc=47.669, loss_att=53.821, acc=0.719, loss=51.975, backward_time=0.096, grad_norm=34.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.669e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 13:52:24,762 (trainer:737) INFO: 19epoch:train:5301-5400batch: iter_time=1.068e-04, forward_time=0.102, loss_ctc=42.417, loss_att=42.701, acc=0.734, loss=42.616, backward_time=0.096, grad_norm=33.108, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.668e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:53:07,329 (trainer:737) INFO: 19epoch:train:5401-5500batch: iter_time=1.224e-04, forward_time=0.102, loss_ctc=52.274, loss_att=61.355, acc=0.698, loss=58.631, backward_time=0.096, grad_norm=41.376, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.667e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:53:49,821 (trainer:737) INFO: 19epoch:train:5501-5600batch: iter_time=1.190e-04, forward_time=0.102, loss_ctc=47.425, loss_att=50.837, acc=0.718, loss=49.813, backward_time=0.096, grad_norm=43.223, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.667e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:54:32,758 (trainer:737) INFO: 19epoch:train:5601-5700batch: iter_time=1.044e-04, forward_time=0.106, loss_ctc=46.100, loss_att=47.576, acc=0.737, loss=47.133, backward_time=0.096, grad_norm=36.459, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.666e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 13:55:15,277 (trainer:737) INFO: 19epoch:train:5701-5800batch: iter_time=1.166e-04, forward_time=0.102, loss_ctc=41.236, loss_att=43.896, acc=0.735, loss=43.098, backward_time=0.096, grad_norm=32.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.665e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:55:57,735 (trainer:737) INFO: 19epoch:train:5801-5900batch: iter_time=1.225e-04, forward_time=0.102, loss_ctc=57.970, loss_att=59.001, acc=0.705, loss=58.692, backward_time=0.096, grad_norm=57.517, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.664e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 13:56:40,219 (trainer:737) INFO: 19epoch:train:5901-6000batch: iter_time=1.214e-04, forward_time=0.103, loss_ctc=47.511, loss_att=50.962, acc=0.734, loss=49.926, backward_time=0.096, grad_norm=38.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.663e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 13:57:22,388 (trainer:737) INFO: 19epoch:train:6001-6100batch: iter_time=1.175e-04, forward_time=0.102, loss_ctc=38.587, loss_att=47.208, acc=0.728, loss=44.621, backward_time=0.095, grad_norm=30.709, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.662e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 13:58:04,676 (trainer:737) INFO: 19epoch:train:6101-6200batch: iter_time=1.234e-04, forward_time=0.101, loss_ctc=39.059, loss_att=47.076, acc=0.724, loss=44.671, backward_time=0.096, grad_norm=33.527, clip=100.000, loss_scale=2.326e+34, optim_step_time=0.040, optim0_lr0=4.661e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 13:58:30,661 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 13:58:49,687 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 13:58:53,238 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d7b930ca0>)
+[gpuc02:0/16] 2024-01-14 13:58:53,239 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 13:58:53,242 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 14:02:57,180 (trainer:737) INFO: 19epoch:train:6201-6300batch: iter_time=2.414, forward_time=0.104, loss_ctc=49.137, loss_att=52.243, acc=0.724, loss=51.311, backward_time=0.097, grad_norm=40.277, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.661e-04, train_time=2.925
+[gpuc02:0/16] 2024-01-14 14:03:40,149 (trainer:737) INFO: 19epoch:train:6301-6400batch: iter_time=1.033e-04, forward_time=0.104, loss_ctc=54.930, loss_att=61.563, acc=0.716, loss=59.573, backward_time=0.097, grad_norm=42.582, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.660e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 14:04:22,964 (trainer:737) INFO: 19epoch:train:6401-6500batch: iter_time=9.844e-05, forward_time=0.102, loss_ctc=42.963, loss_att=58.352, acc=0.720, loss=53.735, backward_time=0.096, grad_norm=33.058, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.659e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 14:05:05,646 (trainer:737) INFO: 19epoch:train:6501-6600batch: iter_time=1.042e-04, forward_time=0.103, loss_ctc=48.447, loss_att=47.672, acc=0.723, loss=47.905, backward_time=0.096, grad_norm=35.560, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.658e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 14:05:48,268 (trainer:737) INFO: 19epoch:train:6601-6700batch: iter_time=1.017e-04, forward_time=0.103, loss_ctc=46.845, loss_att=58.460, acc=0.699, loss=54.976, backward_time=0.097, grad_norm=37.241, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.657e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:06:30,794 (trainer:737) INFO: 19epoch:train:6701-6800batch: iter_time=9.743e-05, forward_time=0.102, loss_ctc=49.215, loss_att=48.723, acc=0.724, loss=48.871, backward_time=0.096, grad_norm=43.324, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.656e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:07:13,409 (trainer:737) INFO: 19epoch:train:6801-6900batch: iter_time=1.003e-04, forward_time=0.103, loss_ctc=46.979, loss_att=50.660, acc=0.734, loss=49.556, backward_time=0.097, grad_norm=36.122, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.656e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:07:56,012 (trainer:737) INFO: 19epoch:train:6901-7000batch: iter_time=1.037e-04, forward_time=0.103, loss_ctc=42.267, loss_att=42.433, acc=0.736, loss=42.383, backward_time=0.096, grad_norm=32.782, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.655e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:08:38,543 (trainer:737) INFO: 19epoch:train:7001-7100batch: iter_time=1.077e-04, forward_time=0.103, loss_ctc=49.150, loss_att=53.362, acc=0.714, loss=52.098, backward_time=0.097, grad_norm=40.380, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.654e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:09:21,142 (trainer:737) INFO: 19epoch:train:7101-7200batch: iter_time=1.029e-04, forward_time=0.102, loss_ctc=55.345, loss_att=58.905, acc=0.716, loss=57.837, backward_time=0.097, grad_norm=52.859, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.653e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:10:03,636 (trainer:737) INFO: 19epoch:train:7201-7300batch: iter_time=1.110e-04, forward_time=0.103, loss_ctc=38.943, loss_att=47.366, acc=0.745, loss=44.839, backward_time=0.096, grad_norm=30.614, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.652e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:10:45,968 (trainer:737) INFO: 19epoch:train:7301-7400batch: iter_time=1.230e-04, forward_time=0.102, loss_ctc=40.220, loss_att=47.511, acc=0.723, loss=45.323, backward_time=0.096, grad_norm=33.086, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.651e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 14:11:28,378 (trainer:737) INFO: 19epoch:train:7401-7500batch: iter_time=1.029e-04, forward_time=0.102, loss_ctc=47.081, loss_att=48.754, acc=0.716, loss=48.252, backward_time=0.096, grad_norm=42.512, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.651e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 14:11:33,074 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 14:11:53,362 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 14:11:57,405 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f259cd54250>)
+[gpuc02:0/16] 2024-01-14 14:11:57,405 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 14:11:57,408 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 14:16:29,089 (trainer:737) INFO: 19epoch:train:7501-7600batch: iter_time=2.402, forward_time=0.107, loss_ctc=41.181, loss_att=45.997, acc=0.748, loss=44.552, backward_time=0.097, grad_norm=35.401, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.650e-04, train_time=3.007
+[gpuc02:0/16] 2024-01-14 14:17:11,918 (trainer:737) INFO: 19epoch:train:7601-7700batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=56.284, loss_att=69.957, acc=0.717, loss=65.855, backward_time=0.098, grad_norm=43.999, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.649e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 14:17:55,196 (trainer:737) INFO: 19epoch:train:7701-7800batch: iter_time=1.049e-04, forward_time=0.104, loss_ctc=47.428, loss_att=56.009, acc=0.725, loss=53.435, backward_time=0.097, grad_norm=35.791, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.648e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 14:18:38,200 (trainer:737) INFO: 19epoch:train:7801-7900batch: iter_time=1.242e-04, forward_time=0.104, loss_ctc=42.403, loss_att=44.641, acc=0.743, loss=43.969, backward_time=0.098, grad_norm=34.682, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.647e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 14:18:53,215 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 14:19:22,261 (trainer:737) INFO: 19epoch:train:7901-8000batch: iter_time=1.223e-04, forward_time=0.104, loss_ctc=51.904, loss_att=62.354, acc=0.701, loss=59.219, backward_time=0.098, grad_norm=44.067, clip=100.000, loss_scale=2.748e+34, optim_step_time=0.040, optim0_lr0=4.646e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-14 14:20:05,783 (trainer:737) INFO: 19epoch:train:8001-8100batch: iter_time=1.094e-04, forward_time=0.105, loss_ctc=46.950, loss_att=53.250, acc=0.718, loss=51.360, backward_time=0.097, grad_norm=42.018, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.646e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-14 14:20:49,003 (trainer:737) INFO: 19epoch:train:8101-8200batch: iter_time=1.050e-04, forward_time=0.105, loss_ctc=45.764, loss_att=51.991, acc=0.736, loss=50.123, backward_time=0.098, grad_norm=35.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.645e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 14:21:32,076 (trainer:737) INFO: 19epoch:train:8201-8300batch: iter_time=1.167e-04, forward_time=0.104, loss_ctc=40.794, loss_att=43.519, acc=0.745, loss=42.702, backward_time=0.097, grad_norm=33.197, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.644e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 14:22:14,975 (trainer:737) INFO: 19epoch:train:8301-8400batch: iter_time=1.172e-04, forward_time=0.104, loss_ctc=57.356, loss_att=60.654, acc=0.706, loss=59.665, backward_time=0.097, grad_norm=54.394, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.643e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 14:22:57,767 (trainer:737) INFO: 19epoch:train:8401-8500batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=47.469, loss_att=52.945, acc=0.741, loss=51.303, backward_time=0.098, grad_norm=37.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.642e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 14:23:40,296 (trainer:737) INFO: 19epoch:train:8501-8600batch: iter_time=1.084e-04, forward_time=0.103, loss_ctc=37.808, loss_att=47.544, acc=0.732, loss=44.623, backward_time=0.097, grad_norm=30.785, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.641e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:24:22,755 (trainer:737) INFO: 19epoch:train:8601-8700batch: iter_time=1.042e-04, forward_time=0.103, loss_ctc=39.102, loss_att=46.730, acc=0.733, loss=44.441, backward_time=0.097, grad_norm=33.210, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.641e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 14:24:47,590 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 14:25:07,152 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 14:25:10,813 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f259cd0f460>)
+[gpuc02:0/16] 2024-01-14 14:25:10,814 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 14:25:10,817 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 14:29:15,086 (trainer:737) INFO: 19epoch:train:8701-8800batch: iter_time=2.416, forward_time=0.105, loss_ctc=47.996, loss_att=51.205, acc=0.736, loss=50.242, backward_time=0.097, grad_norm=40.701, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.640e-04, train_time=2.923
+[gpuc02:0/16] 2024-01-14 14:29:57,806 (trainer:737) INFO: 19epoch:train:8801-8900batch: iter_time=9.169e-05, forward_time=0.104, loss_ctc=54.465, loss_att=62.906, acc=0.717, loss=60.374, backward_time=0.097, grad_norm=43.488, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.639e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 14:30:40,687 (trainer:737) INFO: 19epoch:train:8901-9000batch: iter_time=9.259e-05, forward_time=0.106, loss_ctc=42.341, loss_att=58.135, acc=0.722, loss=53.397, backward_time=0.096, grad_norm=34.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.638e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 14:31:23,216 (trainer:737) INFO: 19epoch:train:9001-9100batch: iter_time=9.689e-05, forward_time=0.103, loss_ctc=48.236, loss_att=48.702, acc=0.723, loss=48.562, backward_time=0.096, grad_norm=36.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.637e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:32:05,721 (trainer:737) INFO: 19epoch:train:9101-9200batch: iter_time=1.037e-04, forward_time=0.103, loss_ctc=46.577, loss_att=58.284, acc=0.700, loss=54.772, backward_time=0.097, grad_norm=39.683, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.636e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:32:48,116 (trainer:737) INFO: 19epoch:train:9201-9300batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=48.462, loss_att=49.262, acc=0.722, loss=49.022, backward_time=0.096, grad_norm=44.789, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.636e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 14:33:30,670 (trainer:737) INFO: 19epoch:train:9301-9400batch: iter_time=1.085e-04, forward_time=0.103, loss_ctc=46.843, loss_att=51.044, acc=0.734, loss=49.784, backward_time=0.097, grad_norm=36.791, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.635e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:34:13,072 (trainer:737) INFO: 19epoch:train:9401-9500batch: iter_time=1.230e-04, forward_time=0.104, loss_ctc=42.411, loss_att=42.988, acc=0.735, loss=42.815, backward_time=0.097, grad_norm=34.245, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.634e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 14:34:55,547 (trainer:737) INFO: 19epoch:train:9501-9600batch: iter_time=1.132e-04, forward_time=0.103, loss_ctc=47.941, loss_att=53.363, acc=0.713, loss=51.737, backward_time=0.097, grad_norm=40.179, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.633e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:35:38,157 (trainer:737) INFO: 19epoch:train:9601-9700batch: iter_time=1.142e-04, forward_time=0.103, loss_ctc=55.384, loss_att=60.517, acc=0.715, loss=58.977, backward_time=0.098, grad_norm=53.142, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.632e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:36:20,706 (trainer:737) INFO: 19epoch:train:9701-9800batch: iter_time=1.256e-04, forward_time=0.103, loss_ctc=38.742, loss_att=47.156, acc=0.746, loss=44.632, backward_time=0.097, grad_norm=30.508, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.631e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:37:03,294 (trainer:737) INFO: 19epoch:train:9801-9900batch: iter_time=1.213e-04, forward_time=0.103, loss_ctc=40.072, loss_att=47.406, acc=0.725, loss=45.205, backward_time=0.097, grad_norm=32.574, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.631e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:37:45,896 (trainer:737) INFO: 19epoch:train:9901-10000batch: iter_time=1.079e-04, forward_time=0.102, loss_ctc=46.031, loss_att=49.303, acc=0.712, loss=48.321, backward_time=0.096, grad_norm=41.338, clip=100.000, loss_scale=3.468e+34, optim_step_time=0.040, optim0_lr0=4.630e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:37:50,769 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 14:38:09,911 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 14:38:13,576 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da5bb74f0>)
+[gpuc02:0/16] 2024-01-14 14:38:13,576 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 14:38:13,580 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 14:42:37,578 (trainer:737) INFO: 19epoch:train:10001-10100batch: iter_time=2.423, forward_time=0.103, loss_ctc=41.580, loss_att=45.307, acc=0.751, loss=44.189, backward_time=0.097, grad_norm=33.964, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.629e-04, train_time=2.917
+[gpuc02:0/16] 2024-01-14 14:43:08,804 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 14:43:20,379 (trainer:737) INFO: 19epoch:train:10101-10200batch: iter_time=1.205e-04, forward_time=0.104, loss_ctc=55.889, loss_att=68.733, acc=0.717, loss=64.880, backward_time=0.098, grad_norm=42.778, clip=100.000, loss_scale=3.587e+34, optim_step_time=0.041, optim0_lr0=4.628e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 14:44:02,956 (trainer:737) INFO: 19epoch:train:10201-10300batch: iter_time=1.289e-04, forward_time=0.104, loss_ctc=46.669, loss_att=54.094, acc=0.729, loss=51.866, backward_time=0.097, grad_norm=33.211, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.627e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:44:45,589 (trainer:737) INFO: 19epoch:train:10301-10400batch: iter_time=1.270e-04, forward_time=0.103, loss_ctc=41.831, loss_att=43.400, acc=0.747, loss=42.929, backward_time=0.097, grad_norm=33.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.626e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:45:28,171 (trainer:737) INFO: 19epoch:train:10401-10500batch: iter_time=1.248e-04, forward_time=0.104, loss_ctc=51.289, loss_att=61.892, acc=0.703, loss=58.711, backward_time=0.097, grad_norm=42.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.626e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:46:10,762 (trainer:737) INFO: 19epoch:train:10501-10600batch: iter_time=1.247e-04, forward_time=0.102, loss_ctc=45.677, loss_att=52.393, acc=0.720, loss=50.378, backward_time=0.097, grad_norm=42.470, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.625e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:46:53,405 (trainer:737) INFO: 19epoch:train:10601-10700batch: iter_time=1.100e-04, forward_time=0.103, loss_ctc=45.479, loss_att=50.915, acc=0.740, loss=49.284, backward_time=0.097, grad_norm=34.968, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.624e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:47:35,975 (trainer:737) INFO: 19epoch:train:10701-10800batch: iter_time=1.160e-04, forward_time=0.102, loss_ctc=40.648, loss_att=43.363, acc=0.747, loss=42.549, backward_time=0.097, grad_norm=32.657, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.623e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 14:48:18,563 (trainer:737) INFO: 19epoch:train:10801-10900batch: iter_time=1.178e-04, forward_time=0.103, loss_ctc=58.422, loss_att=62.784, acc=0.708, loss=61.475, backward_time=0.097, grad_norm=55.453, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.622e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 14:49:01,237 (trainer:737) INFO: 19epoch:train:10901-11000batch: iter_time=1.224e-04, forward_time=0.103, loss_ctc=47.044, loss_att=51.601, acc=0.745, loss=50.234, backward_time=0.097, grad_norm=37.800, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.622e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 14:49:43,599 (trainer:737) INFO: 19epoch:train:11001-11100batch: iter_time=1.279e-04, forward_time=0.103, loss_ctc=37.898, loss_att=46.939, acc=0.733, loss=44.227, backward_time=0.096, grad_norm=31.029, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.621e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 14:50:26,361 (trainer:737) INFO: 19epoch:train:11101-11200batch: iter_time=1.255e-04, forward_time=0.104, loss_ctc=38.928, loss_att=46.285, acc=0.732, loss=44.078, backward_time=0.096, grad_norm=34.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.620e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 14:50:52,153 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 14:51:11,145 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 14:51:14,684 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e88fe20>)
+[gpuc02:0/16] 2024-01-14 14:51:14,684 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 14:51:14,687 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 14:55:21,978 (trainer:737) INFO: 19epoch:train:11201-11300batch: iter_time=2.432, forward_time=0.103, loss_ctc=48.118, loss_att=52.068, acc=0.732, loss=50.883, backward_time=0.097, grad_norm=37.665, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.619e-04, train_time=2.956
+[gpuc02:0/16] 2024-01-14 14:56:04,940 (trainer:737) INFO: 19epoch:train:11301-11400batch: iter_time=1.091e-04, forward_time=0.104, loss_ctc=54.842, loss_att=59.681, acc=0.734, loss=58.229, backward_time=0.097, grad_norm=42.309, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.618e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 14:56:48,787 (trainer:737) INFO: 19epoch:train:11401-11500batch: iter_time=1.131e-04, forward_time=0.104, loss_ctc=42.119, loss_att=57.416, acc=0.728, loss=52.827, backward_time=0.097, grad_norm=33.645, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.617e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-14 14:57:32,126 (trainer:737) INFO: 19epoch:train:11501-11600batch: iter_time=1.149e-04, forward_time=0.104, loss_ctc=47.789, loss_att=48.473, acc=0.734, loss=48.268, backward_time=0.097, grad_norm=35.620, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.617e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 14:58:15,274 (trainer:737) INFO: 19epoch:train:11601-11700batch: iter_time=1.111e-04, forward_time=0.102, loss_ctc=46.219, loss_att=57.392, acc=0.714, loss=54.040, backward_time=0.097, grad_norm=38.906, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.616e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 14:58:58,078 (trainer:737) INFO: 19epoch:train:11701-11800batch: iter_time=1.222e-04, forward_time=0.102, loss_ctc=47.711, loss_att=50.132, acc=0.718, loss=49.405, backward_time=0.096, grad_norm=43.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.615e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 14:59:40,770 (trainer:737) INFO: 19epoch:train:11801-11900batch: iter_time=1.125e-04, forward_time=0.103, loss_ctc=46.910, loss_att=53.599, acc=0.742, loss=51.592, backward_time=0.097, grad_norm=37.093, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.614e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 15:00:23,654 (trainer:737) INFO: 19epoch:train:11901-12000batch: iter_time=1.128e-04, forward_time=0.102, loss_ctc=41.976, loss_att=44.814, acc=0.736, loss=43.962, backward_time=0.097, grad_norm=32.957, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.613e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 15:01:06,244 (trainer:737) INFO: 19epoch:train:12001-12100batch: iter_time=1.053e-04, forward_time=0.103, loss_ctc=48.240, loss_att=54.375, acc=0.719, loss=52.534, backward_time=0.097, grad_norm=38.524, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.612e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 15:01:48,926 (trainer:737) INFO: 19epoch:train:12101-12200batch: iter_time=1.140e-04, forward_time=0.103, loss_ctc=55.969, loss_att=59.975, acc=0.725, loss=58.773, backward_time=0.097, grad_norm=55.995, clip=100.000, loss_scale=2.638e+34, optim_step_time=0.041, optim0_lr0=4.612e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 15:02:31,471 (trainer:737) INFO: 19epoch:train:12201-12300batch: iter_time=1.190e-04, forward_time=0.103, loss_ctc=38.328, loss_att=47.982, acc=0.748, loss=45.086, backward_time=0.096, grad_norm=30.759, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.611e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:03:13,876 (trainer:737) INFO: 19epoch:train:12301-12400batch: iter_time=1.192e-04, forward_time=0.102, loss_ctc=39.812, loss_att=47.058, acc=0.733, loss=44.885, backward_time=0.096, grad_norm=31.507, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.610e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 15:03:56,376 (trainer:737) INFO: 19epoch:train:12401-12500batch: iter_time=1.084e-04, forward_time=0.104, loss_ctc=46.217, loss_att=48.141, acc=0.727, loss=47.564, backward_time=0.096, grad_norm=39.221, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.609e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:04:01,663 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 15:04:20,824 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 15:04:24,431 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6c92be50>)
+[gpuc02:0/16] 2024-01-14 15:04:24,431 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 15:04:24,434 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 15:08:48,570 (trainer:737) INFO: 19epoch:train:12501-12600batch: iter_time=2.439, forward_time=0.107, loss_ctc=40.801, loss_att=43.186, acc=0.752, loss=42.471, backward_time=0.098, grad_norm=33.189, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.608e-04, train_time=2.922
+[gpuc02:0/16] 2024-01-14 15:09:31,303 (trainer:737) INFO: 19epoch:train:12601-12700batch: iter_time=1.134e-04, forward_time=0.104, loss_ctc=55.849, loss_att=67.721, acc=0.719, loss=64.160, backward_time=0.098, grad_norm=43.226, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.608e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 15:10:13,815 (trainer:737) INFO: 19epoch:train:12701-12800batch: iter_time=1.151e-04, forward_time=0.104, loss_ctc=47.106, loss_att=54.459, acc=0.728, loss=52.253, backward_time=0.097, grad_norm=35.378, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.607e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:10:56,356 (trainer:737) INFO: 19epoch:train:12801-12900batch: iter_time=1.209e-04, forward_time=0.104, loss_ctc=42.323, loss_att=43.292, acc=0.746, loss=43.001, backward_time=0.097, grad_norm=34.108, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.606e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:11:38,864 (trainer:737) INFO: 19epoch:train:12901-13000batch: iter_time=1.165e-04, forward_time=0.103, loss_ctc=51.406, loss_att=61.488, acc=0.704, loss=58.463, backward_time=0.097, grad_norm=42.056, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.605e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:12:21,324 (trainer:737) INFO: 19epoch:train:13001-13100batch: iter_time=1.217e-04, forward_time=0.104, loss_ctc=45.790, loss_att=51.414, acc=0.722, loss=49.727, backward_time=0.097, grad_norm=42.942, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.604e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 15:13:03,796 (trainer:737) INFO: 19epoch:train:13101-13200batch: iter_time=1.142e-04, forward_time=0.102, loss_ctc=45.315, loss_att=51.240, acc=0.739, loss=49.463, backward_time=0.097, grad_norm=35.736, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.604e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:13:46,186 (trainer:737) INFO: 19epoch:train:13201-13300batch: iter_time=1.151e-04, forward_time=0.102, loss_ctc=40.697, loss_att=42.920, acc=0.747, loss=42.253, backward_time=0.096, grad_norm=32.798, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.603e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 15:14:28,754 (trainer:737) INFO: 19epoch:train:13301-13400batch: iter_time=9.684e-05, forward_time=0.103, loss_ctc=57.020, loss_att=59.758, acc=0.709, loss=58.937, backward_time=0.097, grad_norm=54.371, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.602e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:15:11,317 (trainer:737) INFO: 19epoch:train:13401-13500batch: iter_time=1.031e-04, forward_time=0.103, loss_ctc=47.073, loss_att=51.355, acc=0.747, loss=50.070, backward_time=0.097, grad_norm=36.824, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.601e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:15:53,854 (trainer:737) INFO: 19epoch:train:13501-13600batch: iter_time=1.033e-04, forward_time=0.102, loss_ctc=37.822, loss_att=46.481, acc=0.736, loss=43.883, backward_time=0.096, grad_norm=31.909, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.600e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:16:36,388 (trainer:737) INFO: 19epoch:train:13601-13700batch: iter_time=1.101e-04, forward_time=0.103, loss_ctc=38.811, loss_att=46.054, acc=0.737, loss=43.881, backward_time=0.096, grad_norm=34.423, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.599e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:17:02,754 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 15:17:22,146 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 15:17:25,845 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f252fd06ad0>)
+[gpuc02:0/16] 2024-01-14 15:17:25,846 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 15:17:25,849 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 15:21:34,920 (trainer:737) INFO: 19epoch:train:13701-13800batch: iter_time=2.428, forward_time=0.106, loss_ctc=47.122, loss_att=50.727, acc=0.738, loss=49.645, backward_time=0.098, grad_norm=40.090, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.599e-04, train_time=2.985
+[gpuc02:0/16] 2024-01-14 15:22:17,867 (trainer:737) INFO: 19epoch:train:13801-13900batch: iter_time=1.112e-04, forward_time=0.104, loss_ctc=53.714, loss_att=64.211, acc=0.715, loss=61.062, backward_time=0.098, grad_norm=40.907, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.598e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 15:23:00,543 (trainer:737) INFO: 19epoch:train:13901-14000batch: iter_time=1.111e-04, forward_time=0.104, loss_ctc=42.180, loss_att=57.986, acc=0.723, loss=53.244, backward_time=0.098, grad_norm=35.349, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.597e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 15:23:43,138 (trainer:737) INFO: 19epoch:train:14001-14100batch: iter_time=1.170e-04, forward_time=0.103, loss_ctc=47.749, loss_att=48.698, acc=0.723, loss=48.413, backward_time=0.097, grad_norm=35.546, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.596e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 15:24:25,699 (trainer:737) INFO: 19epoch:train:14101-14200batch: iter_time=1.209e-04, forward_time=0.103, loss_ctc=46.349, loss_att=58.526, acc=0.702, loss=54.873, backward_time=0.098, grad_norm=38.433, clip=100.000, loss_scale=5.275e+34, optim_step_time=0.041, optim0_lr0=4.595e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:25:08,179 (trainer:737) INFO: 19epoch:train:14201-14300batch: iter_time=1.084e-04, forward_time=0.102, loss_ctc=47.783, loss_att=49.342, acc=0.723, loss=48.874, backward_time=0.097, grad_norm=41.968, clip=100.000, loss_scale=8.308e+34, optim_step_time=0.041, optim0_lr0=4.595e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:25:35,804 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 15:25:50,760 (trainer:737) INFO: 19epoch:train:14301-14400batch: iter_time=1.195e-04, forward_time=0.104, loss_ctc=46.467, loss_att=50.985, acc=0.735, loss=49.629, backward_time=0.098, grad_norm=36.035, clip=100.000, loss_scale=6.839e+34, optim_step_time=0.041, optim0_lr0=4.594e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 15:26:33,186 (trainer:737) INFO: 19epoch:train:14401-14500batch: iter_time=1.225e-04, forward_time=0.103, loss_ctc=41.365, loss_att=42.462, acc=0.738, loss=42.133, backward_time=0.097, grad_norm=33.891, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.593e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 15:27:15,735 (trainer:737) INFO: 19epoch:train:14501-14600batch: iter_time=1.097e-04, forward_time=0.104, loss_ctc=48.637, loss_att=54.385, acc=0.714, loss=52.661, backward_time=0.097, grad_norm=42.909, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.592e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:28:01,726 (trainer:737) INFO: 19epoch:train:14601-14700batch: iter_time=1.069e-04, forward_time=0.123, loss_ctc=56.205, loss_att=61.793, acc=0.716, loss=60.116, backward_time=0.111, grad_norm=55.604, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.591e-04, train_time=0.460
+[gpuc02:0/16] 2024-01-14 15:28:44,216 (trainer:737) INFO: 19epoch:train:14701-14800batch: iter_time=1.163e-04, forward_time=0.103, loss_ctc=38.268, loss_att=47.570, acc=0.746, loss=44.780, backward_time=0.097, grad_norm=30.303, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.591e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 15:29:29,749 (trainer:737) INFO: 19epoch:train:14801-14900batch: iter_time=1.237e-04, forward_time=0.103, loss_ctc=39.830, loss_att=48.154, acc=0.726, loss=45.657, backward_time=0.097, grad_norm=33.435, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.590e-04, train_time=0.455
+[gpuc02:0/16] 2024-01-14 15:30:13,115 (trainer:737) INFO: 19epoch:train:14901-15000batch: iter_time=1.084e-04, forward_time=0.108, loss_ctc=46.440, loss_att=49.507, acc=0.713, loss=48.587, backward_time=0.097, grad_norm=42.501, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.589e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 15:52:42,926 (trainer:343) INFO: 19epoch results: [train] iter_time=0.199, forward_time=0.104, loss_ctc=47.019, loss_att=52.524, acc=0.725, loss=50.873, backward_time=0.097, grad_norm=38.835, clip=100.000, loss_scale=2.937e+34, optim_step_time=0.041, optim0_lr0=4.651e-04, train_time=0.633, time=2 hours, 38 minutes and 39.4 seconds, total_count=285000, gpu_max_cached_mem_GB=24.980, [valid] loss_ctc=56.202, cer_ctc=0.287, loss_att=56.914, acc=0.577, cer=0.344, wer=0.999, loss=56.700, time=22 minutes and 6.9 seconds, total_count=88749, gpu_max_cached_mem_GB=24.980
+[gpuc02:0/16] 2024-01-14 15:52:52,030 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-14 15:52:52,098 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/14epoch.pth
+[gpuc02:0/16] 2024-01-14 15:52:52,098 (trainer:272) INFO: 20/45epoch started. Estimated time to finish: 3 days, 6 hours and 24 minutes
+[gpuc02:0/16] 2024-01-14 15:52:52,109 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 15:53:10,882 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 15:53:14,336 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96e83f10>)
+[gpuc02:0/16] 2024-01-14 15:53:14,336 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 15:53:14,340 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 15:57:49,836 (trainer:737) INFO: 20epoch:train:1-100batch: iter_time=2.388, forward_time=0.131, loss_ctc=39.873, loss_att=48.220, acc=0.716, loss=45.716, backward_time=0.104, grad_norm=35.381, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.588e-04, train_time=2.977
+[gpuc02:0/16] 2024-01-14 15:58:32,029 (trainer:737) INFO: 20epoch:train:101-200batch: iter_time=1.207e-04, forward_time=0.105, loss_ctc=40.991, loss_att=46.428, acc=0.732, loss=44.797, backward_time=0.099, grad_norm=35.191, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.587e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 15:59:16,408 (trainer:737) INFO: 20epoch:train:201-300batch: iter_time=1.247e-04, forward_time=0.116, loss_ctc=47.437, loss_att=50.480, acc=0.732, loss=49.567, backward_time=0.101, grad_norm=38.525, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.587e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-14 15:59:59,164 (trainer:737) INFO: 20epoch:train:301-400batch: iter_time=1.398e-04, forward_time=0.106, loss_ctc=55.261, loss_att=56.613, acc=0.724, loss=56.208, backward_time=0.099, grad_norm=44.773, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.586e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:00:16,118 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 16:00:41,784 (trainer:737) INFO: 20epoch:train:401-500batch: iter_time=1.233e-04, forward_time=0.106, loss_ctc=48.823, loss_att=54.133, acc=0.741, loss=52.540, backward_time=0.099, grad_norm=38.472, clip=100.000, loss_scale=2.895e+34, optim_step_time=0.042, optim0_lr0=4.585e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 16:01:33,774 (trainer:737) INFO: 20epoch:train:501-600batch: iter_time=1.263e-04, forward_time=0.104, loss_ctc=46.233, loss_att=48.064, acc=0.723, loss=47.515, backward_time=0.099, grad_norm=38.927, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.584e-04, train_time=0.520
+[gpuc02:0/16] 2024-01-14 16:02:17,000 (trainer:737) INFO: 20epoch:train:601-700batch: iter_time=1.194e-04, forward_time=0.106, loss_ctc=56.825, loss_att=63.194, acc=0.706, loss=61.283, backward_time=0.099, grad_norm=43.276, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.583e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 16:02:59,704 (trainer:737) INFO: 20epoch:train:701-800batch: iter_time=1.198e-04, forward_time=0.105, loss_ctc=46.640, loss_att=64.783, acc=0.699, loss=59.340, backward_time=0.099, grad_norm=41.863, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.583e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:03:42,591 (trainer:737) INFO: 20epoch:train:801-900batch: iter_time=1.304e-04, forward_time=0.106, loss_ctc=51.718, loss_att=64.628, acc=0.704, loss=60.755, backward_time=0.099, grad_norm=41.795, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.582e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 16:04:25,420 (trainer:737) INFO: 20epoch:train:901-1000batch: iter_time=1.195e-04, forward_time=0.108, loss_ctc=63.962, loss_att=66.841, acc=0.693, loss=65.978, backward_time=0.099, grad_norm=46.831, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.581e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 16:05:07,994 (trainer:737) INFO: 20epoch:train:1001-1100batch: iter_time=1.061e-04, forward_time=0.105, loss_ctc=48.338, loss_att=49.428, acc=0.722, loss=49.101, backward_time=0.099, grad_norm=37.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.580e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 16:05:50,693 (trainer:737) INFO: 20epoch:train:1101-1200batch: iter_time=1.117e-04, forward_time=0.105, loss_ctc=50.914, loss_att=57.291, acc=0.700, loss=55.378, backward_time=0.098, grad_norm=43.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.579e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:06:28,298 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 16:06:47,271 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 16:06:50,857 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96ee0be0>)
+[gpuc02:0/16] 2024-01-14 16:06:50,857 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 16:06:50,861 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 16:11:45,578 (trainer:737) INFO: 20epoch:train:1201-1300batch: iter_time=3.108, forward_time=0.109, loss_ctc=43.781, loss_att=47.820, acc=0.744, loss=46.608, backward_time=0.104, grad_norm=32.910, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.579e-04, train_time=3.549
+[gpuc02:0/16] 2024-01-14 16:12:28,272 (trainer:737) INFO: 20epoch:train:1301-1400batch: iter_time=1.510e-04, forward_time=0.105, loss_ctc=41.787, loss_att=53.855, acc=0.733, loss=50.234, backward_time=0.098, grad_norm=35.997, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.578e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:13:10,805 (trainer:737) INFO: 20epoch:train:1401-1500batch: iter_time=1.347e-04, forward_time=0.105, loss_ctc=43.100, loss_att=44.030, acc=0.752, loss=43.751, backward_time=0.098, grad_norm=35.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.577e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 16:13:53,795 (trainer:737) INFO: 20epoch:train:1501-1600batch: iter_time=1.239e-04, forward_time=0.107, loss_ctc=45.154, loss_att=56.252, acc=0.721, loss=52.923, backward_time=0.098, grad_norm=37.392, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.576e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 16:14:37,095 (trainer:737) INFO: 20epoch:train:1601-1700batch: iter_time=1.210e-04, forward_time=0.106, loss_ctc=51.945, loss_att=58.242, acc=0.741, loss=56.353, backward_time=0.098, grad_norm=41.956, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.575e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 16:15:19,536 (trainer:737) INFO: 20epoch:train:1701-1800batch: iter_time=1.255e-04, forward_time=0.105, loss_ctc=48.991, loss_att=49.709, acc=0.741, loss=49.494, backward_time=0.098, grad_norm=42.281, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.575e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:16:02,270 (trainer:737) INFO: 20epoch:train:1801-1900batch: iter_time=1.191e-04, forward_time=0.105, loss_ctc=46.707, loss_att=54.979, acc=0.734, loss=52.498, backward_time=0.098, grad_norm=35.643, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.574e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:16:44,962 (trainer:737) INFO: 20epoch:train:1901-2000batch: iter_time=1.318e-04, forward_time=0.105, loss_ctc=54.001, loss_att=65.211, acc=0.704, loss=61.848, backward_time=0.098, grad_norm=45.982, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.573e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:17:27,508 (trainer:737) INFO: 20epoch:train:2001-2100batch: iter_time=1.171e-04, forward_time=0.106, loss_ctc=46.673, loss_att=66.667, acc=0.712, loss=60.669, backward_time=0.098, grad_norm=39.009, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.572e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 16:18:10,797 (trainer:737) INFO: 20epoch:train:2101-2200batch: iter_time=1.221e-04, forward_time=0.105, loss_ctc=47.835, loss_att=59.421, acc=0.725, loss=55.945, backward_time=0.099, grad_norm=35.342, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.571e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 16:18:53,831 (trainer:737) INFO: 20epoch:train:2201-2300batch: iter_time=1.265e-04, forward_time=0.105, loss_ctc=61.622, loss_att=63.402, acc=0.716, loss=62.868, backward_time=0.098, grad_norm=44.284, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.571e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 16:19:36,715 (trainer:737) INFO: 20epoch:train:2301-2400batch: iter_time=1.435e-04, forward_time=0.104, loss_ctc=47.731, loss_att=52.890, acc=0.720, loss=51.342, backward_time=0.097, grad_norm=35.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.570e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 16:20:19,483 (trainer:737) INFO: 20epoch:train:2401-2500batch: iter_time=1.141e-04, forward_time=0.105, loss_ctc=53.322, loss_att=63.376, acc=0.715, loss=60.359, backward_time=0.098, grad_norm=44.275, clip=100.000, loss_scale=3.323e+34, optim_step_time=0.041, optim0_lr0=4.569e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:20:32,221 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 16:20:51,200 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 16:20:55,132 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d7aa678b0>)
+[gpuc02:0/16] 2024-01-14 16:20:55,132 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 16:20:55,135 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 16:25:32,843 (trainer:737) INFO: 20epoch:train:2501-2600batch: iter_time=2.667, forward_time=0.105, loss_ctc=38.582, loss_att=47.989, acc=0.735, loss=45.167, backward_time=0.097, grad_norm=32.255, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.568e-04, train_time=3.133
+[gpuc02:0/16] 2024-01-14 16:26:15,233 (trainer:737) INFO: 20epoch:train:2601-2700batch: iter_time=1.474e-04, forward_time=0.104, loss_ctc=39.602, loss_att=45.943, acc=0.739, loss=44.041, backward_time=0.097, grad_norm=34.699, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.567e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:26:57,930 (trainer:737) INFO: 20epoch:train:2701-2800batch: iter_time=1.500e-04, forward_time=0.105, loss_ctc=44.336, loss_att=48.416, acc=0.747, loss=47.192, backward_time=0.099, grad_norm=35.819, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.567e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:27:40,642 (trainer:737) INFO: 20epoch:train:2801-2900batch: iter_time=1.287e-04, forward_time=0.106, loss_ctc=51.906, loss_att=55.456, acc=0.737, loss=54.391, backward_time=0.099, grad_norm=42.782, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.566e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:28:23,317 (trainer:737) INFO: 20epoch:train:2901-3000batch: iter_time=1.334e-04, forward_time=0.104, loss_ctc=47.322, loss_att=55.035, acc=0.745, loss=52.721, backward_time=0.098, grad_norm=39.353, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.565e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:29:05,862 (trainer:737) INFO: 20epoch:train:3001-3100batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=44.943, loss_att=47.728, acc=0.736, loss=46.892, backward_time=0.098, grad_norm=36.244, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.564e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 16:29:48,707 (trainer:737) INFO: 20epoch:train:3101-3200batch: iter_time=1.416e-04, forward_time=0.105, loss_ctc=53.551, loss_att=62.608, acc=0.717, loss=59.891, backward_time=0.098, grad_norm=40.296, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.563e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 16:30:31,700 (trainer:737) INFO: 20epoch:train:3201-3300batch: iter_time=1.391e-04, forward_time=0.105, loss_ctc=46.360, loss_att=63.855, acc=0.715, loss=58.607, backward_time=0.098, grad_norm=41.946, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.563e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 16:31:14,842 (trainer:737) INFO: 20epoch:train:3301-3400batch: iter_time=1.342e-04, forward_time=0.106, loss_ctc=49.553, loss_att=63.517, acc=0.723, loss=59.328, backward_time=0.099, grad_norm=38.008, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.562e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 16:31:57,598 (trainer:737) INFO: 20epoch:train:3401-3500batch: iter_time=1.340e-04, forward_time=0.105, loss_ctc=61.846, loss_att=68.604, acc=0.699, loss=66.576, backward_time=0.099, grad_norm=43.708, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.561e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:32:40,512 (trainer:737) INFO: 20epoch:train:3501-3600batch: iter_time=1.545e-04, forward_time=0.106, loss_ctc=46.736, loss_att=47.772, acc=0.744, loss=47.461, backward_time=0.098, grad_norm=36.810, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.560e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 16:33:22,930 (trainer:737) INFO: 20epoch:train:3601-3700batch: iter_time=1.409e-04, forward_time=0.105, loss_ctc=49.411, loss_att=57.167, acc=0.705, loss=54.840, backward_time=0.097, grad_norm=40.966, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.559e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:33:33,193 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 16:33:49,148 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 16:34:08,148 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 16:34:11,854 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da5aaf610>)
+[gpuc02:0/16] 2024-01-14 16:34:11,854 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 16:34:11,858 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 16:39:08,857 (trainer:737) INFO: 20epoch:train:3701-3800batch: iter_time=3.000, forward_time=0.119, loss_ctc=42.767, loss_att=51.425, acc=0.737, loss=48.828, backward_time=0.099, grad_norm=32.883, clip=100.000, loss_scale=2.559e+34, optim_step_time=0.042, optim0_lr0=4.559e-04, train_time=3.459
+[gpuc02:0/16] 2024-01-14 16:39:51,579 (trainer:737) INFO: 20epoch:train:3801-3900batch: iter_time=1.295e-04, forward_time=0.108, loss_ctc=41.485, loss_att=51.075, acc=0.740, loss=48.198, backward_time=0.097, grad_norm=34.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.558e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:40:34,292 (trainer:737) INFO: 20epoch:train:3901-4000batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=42.547, loss_att=43.230, acc=0.755, loss=43.025, backward_time=0.097, grad_norm=35.884, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.557e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:41:16,744 (trainer:737) INFO: 20epoch:train:4001-4100batch: iter_time=1.466e-04, forward_time=0.105, loss_ctc=44.144, loss_att=53.992, acc=0.726, loss=51.038, backward_time=0.097, grad_norm=36.617, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.556e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:41:59,398 (trainer:737) INFO: 20epoch:train:4101-4200batch: iter_time=1.416e-04, forward_time=0.104, loss_ctc=50.847, loss_att=57.356, acc=0.745, loss=55.404, backward_time=0.098, grad_norm=40.372, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.556e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 16:42:41,805 (trainer:737) INFO: 20epoch:train:4201-4300batch: iter_time=1.535e-04, forward_time=0.104, loss_ctc=48.099, loss_att=49.311, acc=0.742, loss=48.948, backward_time=0.097, grad_norm=38.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.555e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:43:24,408 (trainer:737) INFO: 20epoch:train:4301-4400batch: iter_time=1.414e-04, forward_time=0.105, loss_ctc=46.160, loss_att=53.878, acc=0.736, loss=51.563, backward_time=0.098, grad_norm=36.348, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.554e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 16:44:06,871 (trainer:737) INFO: 20epoch:train:4401-4500batch: iter_time=1.680e-04, forward_time=0.104, loss_ctc=51.959, loss_att=63.898, acc=0.708, loss=60.316, backward_time=0.097, grad_norm=44.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.553e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:44:49,511 (trainer:737) INFO: 20epoch:train:4501-4600batch: iter_time=1.682e-04, forward_time=0.105, loss_ctc=45.713, loss_att=65.765, acc=0.715, loss=59.750, backward_time=0.098, grad_norm=38.261, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.552e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 16:45:32,210 (trainer:737) INFO: 20epoch:train:4601-4700batch: iter_time=1.558e-04, forward_time=0.106, loss_ctc=47.125, loss_att=57.952, acc=0.729, loss=54.704, backward_time=0.098, grad_norm=34.594, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.552e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 16:46:14,781 (trainer:737) INFO: 20epoch:train:4701-4800batch: iter_time=1.450e-04, forward_time=0.105, loss_ctc=60.932, loss_att=63.242, acc=0.717, loss=62.549, backward_time=0.098, grad_norm=44.829, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.551e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 16:46:57,158 (trainer:737) INFO: 20epoch:train:4801-4900batch: iter_time=1.419e-04, forward_time=0.105, loss_ctc=46.867, loss_att=52.953, acc=0.720, loss=51.127, backward_time=0.097, grad_norm=36.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.550e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:47:40,038 (trainer:737) INFO: 20epoch:train:4901-5000batch: iter_time=1.379e-04, forward_time=0.105, loss_ctc=53.143, loss_att=62.594, acc=0.718, loss=59.758, backward_time=0.098, grad_norm=42.080, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.549e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 16:47:46,148 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 16:48:05,766 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 16:48:09,522 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f252d465540>)
+[gpuc02:0/16] 2024-01-14 16:48:09,522 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 16:48:09,526 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 16:53:02,659 (trainer:737) INFO: 20epoch:train:5001-5100batch: iter_time=2.599, forward_time=0.168, loss_ctc=38.346, loss_att=50.238, acc=0.715, loss=46.670, backward_time=0.115, grad_norm=33.953, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.548e-04, train_time=3.226
+[gpuc02:0/16] 2024-01-14 16:53:45,017 (trainer:737) INFO: 20epoch:train:5101-5200batch: iter_time=1.591e-04, forward_time=0.105, loss_ctc=39.205, loss_att=46.255, acc=0.735, loss=44.140, backward_time=0.096, grad_norm=33.788, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.548e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 16:54:28,100 (trainer:737) INFO: 20epoch:train:5201-5300batch: iter_time=1.461e-04, forward_time=0.107, loss_ctc=44.154, loss_att=49.583, acc=0.737, loss=47.955, backward_time=0.097, grad_norm=37.734, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.547e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 16:55:10,586 (trainer:737) INFO: 20epoch:train:5301-5400batch: iter_time=1.431e-04, forward_time=0.106, loss_ctc=50.693, loss_att=55.725, acc=0.724, loss=54.215, backward_time=0.097, grad_norm=41.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.546e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 16:55:53,019 (trainer:737) INFO: 20epoch:train:5401-5500batch: iter_time=1.441e-04, forward_time=0.105, loss_ctc=46.656, loss_att=52.961, acc=0.744, loss=51.069, backward_time=0.097, grad_norm=36.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.545e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:56:35,353 (trainer:737) INFO: 20epoch:train:5501-5600batch: iter_time=1.241e-04, forward_time=0.105, loss_ctc=43.744, loss_att=47.619, acc=0.729, loss=46.457, backward_time=0.096, grad_norm=36.387, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.545e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 16:57:18,320 (trainer:737) INFO: 20epoch:train:5601-5700batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=53.139, loss_att=62.557, acc=0.709, loss=59.732, backward_time=0.097, grad_norm=41.489, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.544e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 16:58:00,728 (trainer:737) INFO: 20epoch:train:5701-5800batch: iter_time=1.270e-04, forward_time=0.105, loss_ctc=45.317, loss_att=64.517, acc=0.702, loss=58.757, backward_time=0.097, grad_norm=41.514, clip=100.000, loss_scale=3.655e+34, optim_step_time=0.041, optim0_lr0=4.543e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 16:58:43,565 (trainer:737) INFO: 20epoch:train:5801-5900batch: iter_time=1.250e-04, forward_time=0.108, loss_ctc=49.515, loss_att=63.923, acc=0.708, loss=59.601, backward_time=0.097, grad_norm=37.995, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.542e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 16:59:26,104 (trainer:737) INFO: 20epoch:train:5901-6000batch: iter_time=1.102e-04, forward_time=0.106, loss_ctc=61.186, loss_att=66.254, acc=0.693, loss=64.734, backward_time=0.097, grad_norm=42.963, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.541e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:00:08,517 (trainer:737) INFO: 20epoch:train:6001-6100batch: iter_time=1.192e-04, forward_time=0.104, loss_ctc=46.237, loss_att=47.535, acc=0.732, loss=47.145, backward_time=0.097, grad_norm=36.370, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.541e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:00:50,910 (trainer:737) INFO: 20epoch:train:6101-6200batch: iter_time=1.190e-04, forward_time=0.106, loss_ctc=48.402, loss_att=54.293, acc=0.710, loss=52.526, backward_time=0.096, grad_norm=39.601, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.540e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:01:18,251 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 17:01:37,353 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 17:01:40,931 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d44ac6ec0>)
+[gpuc02:0/16] 2024-01-14 17:01:40,931 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 17:01:40,934 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 17:05:48,857 (trainer:737) INFO: 20epoch:train:6201-6300batch: iter_time=2.442, forward_time=0.104, loss_ctc=42.519, loss_att=47.650, acc=0.736, loss=46.111, backward_time=0.097, grad_norm=32.311, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.539e-04, train_time=2.979
+[gpuc02:0/16] 2024-01-14 17:06:31,261 (trainer:737) INFO: 20epoch:train:6301-6400batch: iter_time=1.258e-04, forward_time=0.105, loss_ctc=40.763, loss_att=49.003, acc=0.730, loss=46.531, backward_time=0.097, grad_norm=36.436, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.538e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:07:14,025 (trainer:737) INFO: 20epoch:train:6401-6500batch: iter_time=1.298e-04, forward_time=0.106, loss_ctc=41.712, loss_att=41.342, acc=0.758, loss=41.453, backward_time=0.097, grad_norm=32.981, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.538e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:07:56,809 (trainer:737) INFO: 20epoch:train:6501-6600batch: iter_time=1.333e-04, forward_time=0.105, loss_ctc=43.734, loss_att=54.580, acc=0.715, loss=51.326, backward_time=0.097, grad_norm=37.687, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.537e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:08:39,963 (trainer:737) INFO: 20epoch:train:6601-6700batch: iter_time=1.342e-04, forward_time=0.106, loss_ctc=49.846, loss_att=56.018, acc=0.736, loss=54.167, backward_time=0.097, grad_norm=40.071, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.536e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 17:09:22,783 (trainer:737) INFO: 20epoch:train:6701-6800batch: iter_time=1.275e-04, forward_time=0.106, loss_ctc=47.463, loss_att=46.922, acc=0.742, loss=47.085, backward_time=0.097, grad_norm=38.838, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.535e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:10:05,993 (trainer:737) INFO: 20epoch:train:6801-6900batch: iter_time=1.156e-04, forward_time=0.107, loss_ctc=45.577, loss_att=54.153, acc=0.724, loss=51.580, backward_time=0.098, grad_norm=37.316, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.534e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 17:10:49,090 (trainer:737) INFO: 20epoch:train:6901-7000batch: iter_time=1.142e-04, forward_time=0.106, loss_ctc=50.998, loss_att=63.636, acc=0.698, loss=59.845, backward_time=0.097, grad_norm=43.828, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.534e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 17:11:31,947 (trainer:737) INFO: 20epoch:train:7001-7100batch: iter_time=1.369e-04, forward_time=0.106, loss_ctc=44.950, loss_att=64.385, acc=0.700, loss=58.555, backward_time=0.097, grad_norm=38.396, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.533e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:12:14,886 (trainer:737) INFO: 20epoch:train:7101-7200batch: iter_time=1.111e-04, forward_time=0.105, loss_ctc=46.700, loss_att=54.714, acc=0.726, loss=52.309, backward_time=0.098, grad_norm=35.028, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.532e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 17:12:40,032 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 17:12:57,468 (trainer:737) INFO: 20epoch:train:7201-7300batch: iter_time=1.091e-04, forward_time=0.105, loss_ctc=59.343, loss_att=63.090, acc=0.706, loss=61.966, backward_time=0.097, grad_norm=43.979, clip=100.000, loss_scale=3.294e+34, optim_step_time=0.042, optim0_lr0=4.531e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:13:39,932 (trainer:737) INFO: 20epoch:train:7301-7400batch: iter_time=1.123e-04, forward_time=0.106, loss_ctc=46.866, loss_att=51.382, acc=0.726, loss=50.027, backward_time=0.097, grad_norm=36.297, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.531e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:14:22,626 (trainer:737) INFO: 20epoch:train:7401-7500batch: iter_time=1.140e-04, forward_time=0.106, loss_ctc=53.188, loss_att=57.860, acc=0.721, loss=56.459, backward_time=0.098, grad_norm=42.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.530e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:14:28,424 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 17:14:47,419 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 17:14:50,979 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d9638b4f0>)
+[gpuc02:0/16] 2024-01-14 17:14:50,979 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 17:14:50,982 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 17:19:17,712 (trainer:737) INFO: 20epoch:train:7501-7600batch: iter_time=2.427, forward_time=0.104, loss_ctc=37.966, loss_att=46.400, acc=0.724, loss=43.870, backward_time=0.097, grad_norm=32.334, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.529e-04, train_time=2.951
+[gpuc02:0/16] 2024-01-14 17:20:00,021 (trainer:737) INFO: 20epoch:train:7601-7700batch: iter_time=1.118e-04, forward_time=0.104, loss_ctc=38.783, loss_att=43.953, acc=0.741, loss=42.402, backward_time=0.097, grad_norm=33.607, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.528e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 17:20:42,511 (trainer:737) INFO: 20epoch:train:7701-7800batch: iter_time=1.044e-04, forward_time=0.105, loss_ctc=43.830, loss_att=48.268, acc=0.739, loss=46.937, backward_time=0.097, grad_norm=36.352, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.527e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:21:24,984 (trainer:737) INFO: 20epoch:train:7801-7900batch: iter_time=1.060e-04, forward_time=0.105, loss_ctc=50.465, loss_att=54.217, acc=0.728, loss=53.091, backward_time=0.097, grad_norm=41.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.527e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:22:07,820 (trainer:737) INFO: 20epoch:train:7901-8000batch: iter_time=1.041e-04, forward_time=0.106, loss_ctc=46.492, loss_att=52.238, acc=0.747, loss=50.514, backward_time=0.097, grad_norm=37.043, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.526e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:22:50,280 (trainer:737) INFO: 20epoch:train:8001-8100batch: iter_time=1.063e-04, forward_time=0.105, loss_ctc=43.944, loss_att=46.505, acc=0.732, loss=45.737, backward_time=0.097, grad_norm=35.281, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.525e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:23:33,111 (trainer:737) INFO: 20epoch:train:8101-8200batch: iter_time=9.876e-05, forward_time=0.105, loss_ctc=52.534, loss_att=61.369, acc=0.714, loss=58.719, backward_time=0.097, grad_norm=41.245, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.524e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:24:15,622 (trainer:737) INFO: 20epoch:train:8201-8300batch: iter_time=1.120e-04, forward_time=0.105, loss_ctc=44.665, loss_att=62.730, acc=0.705, loss=57.311, backward_time=0.097, grad_norm=38.938, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.524e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:24:58,522 (trainer:737) INFO: 20epoch:train:8301-8400batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=48.537, loss_att=62.036, acc=0.711, loss=57.987, backward_time=0.098, grad_norm=36.341, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.523e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 17:25:41,360 (trainer:737) INFO: 20epoch:train:8401-8500batch: iter_time=1.156e-04, forward_time=0.105, loss_ctc=60.966, loss_att=64.997, acc=0.701, loss=63.788, backward_time=0.098, grad_norm=45.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.522e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:26:24,155 (trainer:737) INFO: 20epoch:train:8501-8600batch: iter_time=1.264e-04, forward_time=0.105, loss_ctc=46.217, loss_att=48.204, acc=0.730, loss=47.608, backward_time=0.097, grad_norm=35.233, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.521e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:27:06,793 (trainer:737) INFO: 20epoch:train:8601-8700batch: iter_time=1.172e-04, forward_time=0.104, loss_ctc=46.872, loss_att=55.173, acc=0.706, loss=52.683, backward_time=0.097, grad_norm=39.997, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.520e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:27:34,132 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 17:27:53,785 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 17:27:57,497 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d81aee620>)
+[gpuc02:0/16] 2024-01-14 17:27:57,497 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 17:27:57,500 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 17:32:04,990 (trainer:737) INFO: 20epoch:train:8701-8800batch: iter_time=2.434, forward_time=0.105, loss_ctc=42.517, loss_att=47.764, acc=0.746, loss=46.190, backward_time=0.097, grad_norm=32.468, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.520e-04, train_time=2.982
+[gpuc02:0/16] 2024-01-14 17:32:47,484 (trainer:737) INFO: 20epoch:train:8801-8900batch: iter_time=1.337e-04, forward_time=0.107, loss_ctc=40.329, loss_att=53.258, acc=0.735, loss=49.379, backward_time=0.097, grad_norm=36.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.519e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:33:29,924 (trainer:737) INFO: 20epoch:train:8901-9000batch: iter_time=1.407e-04, forward_time=0.105, loss_ctc=41.716, loss_att=43.227, acc=0.755, loss=42.774, backward_time=0.096, grad_norm=33.847, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.518e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:34:12,557 (trainer:737) INFO: 20epoch:train:9001-9100batch: iter_time=1.424e-04, forward_time=0.106, loss_ctc=43.796, loss_att=55.662, acc=0.721, loss=52.102, backward_time=0.097, grad_norm=36.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.517e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:34:55,189 (trainer:737) INFO: 20epoch:train:9101-9200batch: iter_time=1.633e-04, forward_time=0.106, loss_ctc=50.002, loss_att=57.588, acc=0.745, loss=55.312, backward_time=0.097, grad_norm=40.012, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.517e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:35:37,648 (trainer:737) INFO: 20epoch:train:9201-9300batch: iter_time=1.535e-04, forward_time=0.105, loss_ctc=47.196, loss_att=49.091, acc=0.746, loss=48.522, backward_time=0.096, grad_norm=37.477, clip=100.000, loss_scale=2.928e+34, optim_step_time=0.041, optim0_lr0=4.516e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:36:20,377 (trainer:737) INFO: 20epoch:train:9301-9400batch: iter_time=1.428e-04, forward_time=0.107, loss_ctc=44.952, loss_att=53.881, acc=0.738, loss=51.203, backward_time=0.097, grad_norm=36.369, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.515e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:37:02,959 (trainer:737) INFO: 20epoch:train:9401-9500batch: iter_time=1.638e-04, forward_time=0.106, loss_ctc=50.546, loss_att=63.922, acc=0.708, loss=59.909, backward_time=0.097, grad_norm=42.749, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.514e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:37:45,913 (trainer:737) INFO: 20epoch:train:9501-9600batch: iter_time=1.322e-04, forward_time=0.109, loss_ctc=44.996, loss_att=65.775, acc=0.717, loss=59.541, backward_time=0.098, grad_norm=38.954, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.514e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 17:38:28,628 (trainer:737) INFO: 20epoch:train:9601-9700batch: iter_time=1.334e-04, forward_time=0.106, loss_ctc=46.866, loss_att=58.950, acc=0.730, loss=55.325, backward_time=0.098, grad_norm=35.147, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.513e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:39:11,201 (trainer:737) INFO: 20epoch:train:9701-9800batch: iter_time=1.464e-04, forward_time=0.106, loss_ctc=59.554, loss_att=62.587, acc=0.721, loss=61.677, backward_time=0.097, grad_norm=44.024, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.512e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:39:53,635 (trainer:737) INFO: 20epoch:train:9801-9900batch: iter_time=1.311e-04, forward_time=0.105, loss_ctc=46.503, loss_att=53.375, acc=0.721, loss=51.313, backward_time=0.096, grad_norm=37.823, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.511e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:40:36,506 (trainer:737) INFO: 20epoch:train:9901-10000batch: iter_time=1.293e-04, forward_time=0.106, loss_ctc=52.373, loss_att=62.163, acc=0.717, loss=59.226, backward_time=0.097, grad_norm=41.978, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.511e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 17:40:41,919 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 17:41:00,811 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 17:41:04,275 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1daf72ebc0>)
+[gpuc02:0/16] 2024-01-14 17:41:04,275 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 17:41:04,278 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 17:45:34,745 (trainer:737) INFO: 20epoch:train:10001-10100batch: iter_time=2.425, forward_time=0.104, loss_ctc=38.114, loss_att=48.730, acc=0.720, loss=45.545, backward_time=0.096, grad_norm=32.790, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.510e-04, train_time=2.982
+[gpuc02:0/16] 2024-01-14 17:46:17,239 (trainer:737) INFO: 20epoch:train:10101-10200batch: iter_time=1.263e-04, forward_time=0.103, loss_ctc=38.694, loss_att=45.004, acc=0.740, loss=43.111, backward_time=0.096, grad_norm=33.552, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.509e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:46:59,989 (trainer:737) INFO: 20epoch:train:10201-10300batch: iter_time=1.305e-04, forward_time=0.105, loss_ctc=43.266, loss_att=48.780, acc=0.739, loss=47.126, backward_time=0.097, grad_norm=35.521, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.508e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:47:42,729 (trainer:737) INFO: 20epoch:train:10301-10400batch: iter_time=1.292e-04, forward_time=0.104, loss_ctc=49.987, loss_att=54.077, acc=0.729, loss=52.850, backward_time=0.096, grad_norm=42.753, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.507e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 17:48:13,802 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 17:48:25,728 (trainer:737) INFO: 20epoch:train:10401-10500batch: iter_time=1.412e-04, forward_time=0.105, loss_ctc=46.173, loss_att=52.096, acc=0.747, loss=50.319, backward_time=0.097, grad_norm=42.567, clip=100.000, loss_scale=3.566e+34, optim_step_time=0.041, optim0_lr0=4.507e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 17:49:08,094 (trainer:737) INFO: 20epoch:train:10501-10600batch: iter_time=1.383e-04, forward_time=0.105, loss_ctc=43.592, loss_att=46.522, acc=0.732, loss=45.643, backward_time=0.096, grad_norm=34.789, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.506e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 17:49:50,553 (trainer:737) INFO: 20epoch:train:10601-10700batch: iter_time=1.436e-04, forward_time=0.105, loss_ctc=52.433, loss_att=61.961, acc=0.713, loss=59.103, backward_time=0.097, grad_norm=41.744, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.505e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:50:33,018 (trainer:737) INFO: 20epoch:train:10701-10800batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=44.816, loss_att=63.174, acc=0.706, loss=57.667, backward_time=0.097, grad_norm=40.205, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.504e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:51:16,329 (trainer:737) INFO: 20epoch:train:10801-10900batch: iter_time=1.471e-04, forward_time=0.106, loss_ctc=48.983, loss_att=62.485, acc=0.712, loss=58.435, backward_time=0.098, grad_norm=39.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.504e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 17:51:58,880 (trainer:737) INFO: 20epoch:train:10901-11000batch: iter_time=1.357e-04, forward_time=0.105, loss_ctc=61.128, loss_att=65.252, acc=0.698, loss=64.015, backward_time=0.097, grad_norm=43.663, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.503e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 17:52:41,312 (trainer:737) INFO: 20epoch:train:11001-11100batch: iter_time=1.377e-04, forward_time=0.106, loss_ctc=45.904, loss_att=48.401, acc=0.729, loss=47.652, backward_time=0.097, grad_norm=36.549, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.502e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 17:53:23,658 (trainer:737) INFO: 20epoch:train:11101-11200batch: iter_time=1.316e-04, forward_time=0.105, loss_ctc=46.666, loss_att=55.296, acc=0.708, loss=52.707, backward_time=0.096, grad_norm=38.273, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.501e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 17:53:50,323 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 17:54:09,331 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 17:54:12,873 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d492cb6d0>)
+[gpuc02:0/16] 2024-01-14 17:54:12,873 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 17:54:12,877 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 17:58:24,639 (trainer:737) INFO: 20epoch:train:11201-11300batch: iter_time=2.405, forward_time=0.105, loss_ctc=42.067, loss_att=47.250, acc=0.739, loss=45.695, backward_time=0.097, grad_norm=33.548, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.501e-04, train_time=3.010
+[gpuc02:0/16] 2024-01-14 17:59:07,274 (trainer:737) INFO: 20epoch:train:11301-11400batch: iter_time=1.228e-04, forward_time=0.104, loss_ctc=40.749, loss_att=48.762, acc=0.732, loss=46.358, backward_time=0.097, grad_norm=36.661, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.500e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 17:59:49,568 (trainer:737) INFO: 20epoch:train:11401-11500batch: iter_time=1.069e-04, forward_time=0.105, loss_ctc=41.381, loss_att=41.102, acc=0.758, loss=41.185, backward_time=0.097, grad_norm=33.744, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.499e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 18:00:32,313 (trainer:737) INFO: 20epoch:train:11501-11600batch: iter_time=1.061e-04, forward_time=0.105, loss_ctc=43.471, loss_att=53.701, acc=0.717, loss=50.632, backward_time=0.096, grad_norm=37.635, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.498e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 18:01:15,079 (trainer:737) INFO: 20epoch:train:11601-11700batch: iter_time=1.158e-04, forward_time=0.105, loss_ctc=49.598, loss_att=55.899, acc=0.737, loss=54.009, backward_time=0.097, grad_norm=41.094, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.498e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 18:01:57,328 (trainer:737) INFO: 20epoch:train:11701-11800batch: iter_time=1.228e-04, forward_time=0.103, loss_ctc=47.151, loss_att=46.651, acc=0.744, loss=46.801, backward_time=0.096, grad_norm=37.119, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.497e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 18:02:39,760 (trainer:737) INFO: 20epoch:train:11801-11900batch: iter_time=1.110e-04, forward_time=0.104, loss_ctc=45.004, loss_att=53.490, acc=0.728, loss=50.945, backward_time=0.097, grad_norm=36.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.496e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 18:03:22,387 (trainer:737) INFO: 20epoch:train:11901-12000batch: iter_time=1.189e-04, forward_time=0.104, loss_ctc=50.628, loss_att=64.174, acc=0.700, loss=60.110, backward_time=0.096, grad_norm=44.036, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.495e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 18:04:05,039 (trainer:737) INFO: 20epoch:train:12001-12100batch: iter_time=1.293e-04, forward_time=0.105, loss_ctc=44.412, loss_att=64.034, acc=0.701, loss=58.147, backward_time=0.097, grad_norm=39.817, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.495e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 18:04:47,493 (trainer:737) INFO: 20epoch:train:12101-12200batch: iter_time=1.265e-04, forward_time=0.105, loss_ctc=46.545, loss_att=54.815, acc=0.728, loss=52.334, backward_time=0.097, grad_norm=36.366, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.494e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 18:05:30,740 (trainer:737) INFO: 20epoch:train:12201-12300batch: iter_time=1.093e-04, forward_time=0.104, loss_ctc=58.990, loss_att=62.443, acc=0.707, loss=61.407, backward_time=0.097, grad_norm=45.347, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.493e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 18:06:13,564 (trainer:737) INFO: 20epoch:train:12301-12400batch: iter_time=1.086e-04, forward_time=0.104, loss_ctc=46.481, loss_att=50.534, acc=0.728, loss=49.318, backward_time=0.096, grad_norm=36.213, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.492e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 18:06:56,695 (trainer:737) INFO: 20epoch:train:12401-12500batch: iter_time=1.099e-04, forward_time=0.104, loss_ctc=51.139, loss_att=56.789, acc=0.727, loss=55.094, backward_time=0.097, grad_norm=39.716, clip=100.000, loss_scale=2.658e+34, optim_step_time=0.041, optim0_lr0=4.492e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 18:07:01,253 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 18:07:20,259 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 18:07:23,909 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1db01925c0>)
+[gpuc02:0/16] 2024-01-14 18:07:23,909 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 18:07:23,912 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 18:11:50,801 (trainer:737) INFO: 20epoch:train:12501-12600batch: iter_time=2.409, forward_time=0.104, loss_ctc=37.813, loss_att=49.545, acc=0.734, loss=46.025, backward_time=0.097, grad_norm=32.410, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.491e-04, train_time=2.941
+[gpuc02:0/16] 2024-01-14 18:12:33,106 (trainer:737) INFO: 20epoch:train:12601-12700batch: iter_time=1.132e-04, forward_time=0.104, loss_ctc=38.428, loss_att=46.364, acc=0.741, loss=43.983, backward_time=0.097, grad_norm=32.979, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.490e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 18:12:33,916 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 18:13:15,793 (trainer:737) INFO: 20epoch:train:12701-12800batch: iter_time=1.167e-04, forward_time=0.106, loss_ctc=43.259, loss_att=48.719, acc=0.749, loss=47.081, backward_time=0.098, grad_norm=37.392, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.041, optim0_lr0=4.489e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 18:13:58,384 (trainer:737) INFO: 20epoch:train:12801-12900batch: iter_time=1.259e-04, forward_time=0.106, loss_ctc=50.385, loss_att=54.877, acc=0.741, loss=53.529, backward_time=0.097, grad_norm=41.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.489e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 18:14:40,953 (trainer:737) INFO: 20epoch:train:12901-13000batch: iter_time=1.193e-04, forward_time=0.106, loss_ctc=45.571, loss_att=54.164, acc=0.750, loss=51.586, backward_time=0.097, grad_norm=35.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.488e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:15:23,371 (trainer:737) INFO: 20epoch:train:13001-13100batch: iter_time=1.243e-04, forward_time=0.106, loss_ctc=43.008, loss_att=47.312, acc=0.738, loss=46.021, backward_time=0.097, grad_norm=34.338, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.487e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 18:16:06,299 (trainer:737) INFO: 20epoch:train:13101-13200batch: iter_time=1.207e-04, forward_time=0.106, loss_ctc=51.490, loss_att=61.343, acc=0.723, loss=58.387, backward_time=0.097, grad_norm=39.351, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.486e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 18:16:48,819 (trainer:737) INFO: 20epoch:train:13201-13300batch: iter_time=1.372e-04, forward_time=0.106, loss_ctc=44.023, loss_att=63.552, acc=0.720, loss=57.693, backward_time=0.097, grad_norm=39.632, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.486e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:17:31,555 (trainer:737) INFO: 20epoch:train:13301-13400batch: iter_time=1.443e-04, forward_time=0.107, loss_ctc=48.399, loss_att=64.054, acc=0.722, loss=59.358, backward_time=0.098, grad_norm=37.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.485e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 18:18:14,164 (trainer:737) INFO: 20epoch:train:13401-13500batch: iter_time=1.359e-04, forward_time=0.106, loss_ctc=59.182, loss_att=68.431, acc=0.702, loss=65.656, backward_time=0.098, grad_norm=42.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.484e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 18:18:56,673 (trainer:737) INFO: 20epoch:train:13501-13600batch: iter_time=1.341e-04, forward_time=0.106, loss_ctc=45.776, loss_att=47.939, acc=0.743, loss=47.290, backward_time=0.097, grad_norm=36.895, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.483e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:19:40,779 (trainer:737) INFO: 20epoch:train:13601-13700batch: iter_time=1.258e-04, forward_time=0.105, loss_ctc=46.741, loss_att=55.217, acc=0.712, loss=52.674, backward_time=0.096, grad_norm=38.579, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.482e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-14 18:20:08,184 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 18:20:27,431 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 18:20:31,431 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d578bf1c0>)
+[gpuc02:0/16] 2024-01-14 18:20:31,431 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 18:20:31,434 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 18:25:30,281 (trainer:737) INFO: 20epoch:train:13701-13800batch: iter_time=2.466, forward_time=0.105, loss_ctc=41.681, loss_att=51.304, acc=0.739, loss=48.417, backward_time=0.098, grad_norm=33.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.482e-04, train_time=3.495
+[gpuc02:0/16] 2024-01-14 18:26:12,941 (trainer:737) INFO: 20epoch:train:13801-13900batch: iter_time=9.169e-05, forward_time=0.105, loss_ctc=40.628, loss_att=50.486, acc=0.743, loss=47.528, backward_time=0.098, grad_norm=34.527, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.481e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 18:26:55,931 (trainer:737) INFO: 20epoch:train:13901-14000batch: iter_time=1.133e-04, forward_time=0.105, loss_ctc=41.419, loss_att=42.474, acc=0.758, loss=42.157, backward_time=0.097, grad_norm=34.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.480e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 18:28:01,515 (trainer:737) INFO: 20epoch:train:14001-14100batch: iter_time=1.173e-04, forward_time=0.106, loss_ctc=42.780, loss_att=53.910, acc=0.724, loss=50.571, backward_time=0.097, grad_norm=35.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.479e-04, train_time=0.656
+[gpuc02:0/16] 2024-01-14 18:28:43,992 (trainer:737) INFO: 20epoch:train:14101-14200batch: iter_time=1.217e-04, forward_time=0.106, loss_ctc=49.577, loss_att=56.518, acc=0.748, loss=54.435, backward_time=0.097, grad_norm=43.148, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.479e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:29:26,869 (trainer:737) INFO: 20epoch:train:14201-14300batch: iter_time=1.206e-04, forward_time=0.105, loss_ctc=46.861, loss_att=48.761, acc=0.746, loss=48.191, backward_time=0.097, grad_norm=38.497, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.478e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 18:30:09,957 (trainer:737) INFO: 20epoch:train:14301-14400batch: iter_time=1.034e-04, forward_time=0.106, loss_ctc=44.730, loss_att=53.739, acc=0.739, loss=51.036, backward_time=0.098, grad_norm=35.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.477e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 18:30:52,453 (trainer:737) INFO: 20epoch:train:14401-14500batch: iter_time=1.202e-04, forward_time=0.105, loss_ctc=50.135, loss_att=62.742, acc=0.710, loss=58.960, backward_time=0.098, grad_norm=42.120, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.477e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:31:35,455 (trainer:737) INFO: 20epoch:train:14501-14600batch: iter_time=1.259e-04, forward_time=0.106, loss_ctc=44.951, loss_att=65.250, acc=0.718, loss=59.160, backward_time=0.098, grad_norm=38.036, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.476e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 18:32:18,210 (trainer:737) INFO: 20epoch:train:14601-14700batch: iter_time=1.234e-04, forward_time=0.105, loss_ctc=46.317, loss_att=57.873, acc=0.734, loss=54.406, backward_time=0.098, grad_norm=34.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.475e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 18:33:01,009 (trainer:737) INFO: 20epoch:train:14701-14800batch: iter_time=1.060e-04, forward_time=0.106, loss_ctc=58.240, loss_att=62.376, acc=0.721, loss=61.135, backward_time=0.098, grad_norm=43.426, clip=100.000, loss_scale=4.112e+34, optim_step_time=0.042, optim0_lr0=4.474e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 18:33:43,510 (trainer:737) INFO: 20epoch:train:14801-14900batch: iter_time=1.102e-04, forward_time=0.105, loss_ctc=45.734, loss_att=52.122, acc=0.723, loss=50.205, backward_time=0.097, grad_norm=36.511, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.474e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 18:34:26,879 (trainer:737) INFO: 20epoch:train:14901-15000batch: iter_time=1.127e-04, forward_time=0.105, loss_ctc=50.757, loss_att=61.946, acc=0.717, loss=58.590, backward_time=0.098, grad_norm=41.786, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.473e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 18:54:52,157 (trainer:343) INFO: 20epoch results: [train] iter_time=0.205, forward_time=0.106, loss_ctc=47.358, loss_att=55.124, acc=0.726, loss=52.795, backward_time=0.098, grad_norm=38.352, clip=100.000, loss_scale=2.769e+34, optim_step_time=0.042, optim0_lr0=4.530e-04, train_time=0.646, time=2 hours, 41 minutes and 45.41 seconds, total_count=300000, gpu_max_cached_mem_GB=26.227, [valid] loss_ctc=57.394, cer_ctc=0.284, loss_att=55.944, acc=0.579, cer=0.389, wer=0.998, loss=56.379, time=20 minutes and 14.46 seconds, total_count=93420, gpu_max_cached_mem_GB=26.227
+[gpuc02:0/16] 2024-01-14 18:54:57,278 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-14 18:54:57,332 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/15epoch.pth
+[gpuc02:0/16] 2024-01-14 18:54:57,333 (trainer:272) INFO: 21/45epoch started. Estimated time to finish: 3 days, 3 hours and 37 minutes
+[gpuc02:0/16] 2024-01-14 18:54:57,343 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 18:55:16,180 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 18:55:19,883 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96eede70>)
+[gpuc02:0/16] 2024-01-14 18:55:19,883 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 18:55:19,886 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 18:59:43,965 (trainer:737) INFO: 21epoch:train:1-100batch: iter_time=2.296, forward_time=0.111, loss_ctc=47.508, loss_att=51.453, acc=0.713, loss=50.269, backward_time=0.099, grad_norm=40.127, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.045, optim0_lr0=4.472e-04, train_time=2.866
+[gpuc02:0/16] 2024-01-14 19:00:30,783 (trainer:737) INFO: 21epoch:train:101-200batch: iter_time=9.681e-05, forward_time=0.124, loss_ctc=48.500, loss_att=56.311, acc=0.716, loss=53.967, backward_time=0.103, grad_norm=41.453, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.044, optim0_lr0=4.471e-04, train_time=0.468
+[gpuc02:0/16] 2024-01-14 19:01:17,392 (trainer:737) INFO: 21epoch:train:201-300batch: iter_time=1.014e-04, forward_time=0.105, loss_ctc=44.675, loss_att=51.271, acc=0.720, loss=49.292, backward_time=0.098, grad_norm=35.168, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.471e-04, train_time=0.466
+[gpuc02:0/16] 2024-01-14 19:02:00,337 (trainer:737) INFO: 21epoch:train:301-400batch: iter_time=1.009e-04, forward_time=0.106, loss_ctc=46.899, loss_att=53.766, acc=0.745, loss=51.706, backward_time=0.098, grad_norm=38.600, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.470e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:02:45,395 (trainer:737) INFO: 21epoch:train:401-500batch: iter_time=1.050e-04, forward_time=0.111, loss_ctc=35.049, loss_att=39.087, acc=0.756, loss=37.876, backward_time=0.111, grad_norm=30.777, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.044, optim0_lr0=4.469e-04, train_time=0.450
+[gpuc02:0/16] 2024-01-14 19:03:27,384 (trainer:737) INFO: 21epoch:train:501-600batch: iter_time=1.084e-04, forward_time=0.103, loss_ctc=39.365, loss_att=44.240, acc=0.736, loss=42.777, backward_time=0.097, grad_norm=35.166, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.468e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 19:04:10,357 (trainer:737) INFO: 21epoch:train:601-700batch: iter_time=1.082e-04, forward_time=0.104, loss_ctc=49.150, loss_att=53.615, acc=0.736, loss=52.276, backward_time=0.099, grad_norm=46.319, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.468e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 19:04:52,342 (trainer:737) INFO: 21epoch:train:701-800batch: iter_time=1.141e-04, forward_time=0.104, loss_ctc=47.761, loss_att=48.262, acc=0.724, loss=48.111, backward_time=0.097, grad_norm=40.031, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.467e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 19:05:42,040 (trainer:737) INFO: 21epoch:train:801-900batch: iter_time=1.065e-04, forward_time=0.103, loss_ctc=52.454, loss_att=59.542, acc=0.681, loss=57.415, backward_time=0.098, grad_norm=42.706, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.466e-04, train_time=0.497
+[gpuc02:0/16] 2024-01-14 19:06:25,947 (trainer:737) INFO: 21epoch:train:901-1000batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=45.267, loss_att=60.224, acc=0.723, loss=55.737, backward_time=0.098, grad_norm=36.568, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.465e-04, train_time=0.439
+[gpuc02:0/16] 2024-01-14 19:07:08,010 (trainer:737) INFO: 21epoch:train:1001-1100batch: iter_time=1.194e-04, forward_time=0.104, loss_ctc=46.974, loss_att=62.980, acc=0.709, loss=58.178, backward_time=0.098, grad_norm=38.728, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.465e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 19:07:50,338 (trainer:737) INFO: 21epoch:train:1101-1200batch: iter_time=1.189e-04, forward_time=0.104, loss_ctc=45.596, loss_att=55.235, acc=0.741, loss=52.343, backward_time=0.098, grad_norm=38.174, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.464e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:08:25,051 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 19:08:44,478 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 19:08:47,980 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6d0850c0>)
+[gpuc02:0/16] 2024-01-14 19:08:47,980 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 19:08:47,983 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 19:13:59,179 (trainer:737) INFO: 21epoch:train:1201-1300batch: iter_time=3.214, forward_time=0.120, loss_ctc=46.007, loss_att=50.094, acc=0.701, loss=48.868, backward_time=0.099, grad_norm=41.265, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.463e-04, train_time=3.688
+[gpuc02:0/16] 2024-01-14 19:14:41,542 (trainer:737) INFO: 21epoch:train:1301-1400batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=46.293, loss_att=46.597, acc=0.761, loss=46.506, backward_time=0.097, grad_norm=40.196, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.462e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:14:49,176 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 19:15:23,912 (trainer:737) INFO: 21epoch:train:1401-1500batch: iter_time=1.210e-04, forward_time=0.105, loss_ctc=43.820, loss_att=53.143, acc=0.723, loss=50.346, backward_time=0.098, grad_norm=36.510, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.042, optim0_lr0=4.462e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:16:06,363 (trainer:737) INFO: 21epoch:train:1501-1600batch: iter_time=1.267e-04, forward_time=0.105, loss_ctc=46.051, loss_att=55.161, acc=0.732, loss=52.428, backward_time=0.099, grad_norm=36.580, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.461e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:16:49,309 (trainer:737) INFO: 21epoch:train:1601-1700batch: iter_time=1.338e-04, forward_time=0.105, loss_ctc=41.400, loss_att=48.074, acc=0.761, loss=46.072, backward_time=0.099, grad_norm=34.871, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.460e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:17:31,473 (trainer:737) INFO: 21epoch:train:1701-1800batch: iter_time=1.290e-04, forward_time=0.104, loss_ctc=37.933, loss_att=39.638, acc=0.759, loss=39.127, backward_time=0.098, grad_norm=33.827, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.459e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 19:18:13,801 (trainer:737) INFO: 21epoch:train:1801-1900batch: iter_time=1.190e-04, forward_time=0.105, loss_ctc=42.911, loss_att=54.800, acc=0.735, loss=51.234, backward_time=0.098, grad_norm=39.717, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.459e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:18:56,222 (trainer:737) INFO: 21epoch:train:1901-2000batch: iter_time=1.190e-04, forward_time=0.105, loss_ctc=47.826, loss_att=50.394, acc=0.741, loss=49.623, backward_time=0.098, grad_norm=38.346, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.458e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:19:38,259 (trainer:737) INFO: 21epoch:train:2001-2100batch: iter_time=1.023e-04, forward_time=0.104, loss_ctc=47.806, loss_att=47.781, acc=0.725, loss=47.789, backward_time=0.097, grad_norm=38.669, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.457e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 19:20:20,498 (trainer:737) INFO: 21epoch:train:2101-2200batch: iter_time=1.293e-04, forward_time=0.104, loss_ctc=44.848, loss_att=57.222, acc=0.721, loss=53.510, backward_time=0.097, grad_norm=33.227, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.456e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 19:21:03,221 (trainer:737) INFO: 21epoch:train:2201-2300batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=50.002, loss_att=68.421, acc=0.723, loss=62.895, backward_time=0.098, grad_norm=40.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.456e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 19:21:46,105 (trainer:737) INFO: 21epoch:train:2301-2400batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=46.002, loss_att=56.407, acc=0.735, loss=53.285, backward_time=0.098, grad_norm=36.351, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.455e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:22:28,187 (trainer:737) INFO: 21epoch:train:2401-2500batch: iter_time=1.181e-04, forward_time=0.103, loss_ctc=40.021, loss_att=47.853, acc=0.732, loss=45.503, backward_time=0.097, grad_norm=35.462, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.454e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 19:22:33,941 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 19:22:53,924 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 19:22:57,661 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d9601b6d0>)
+[gpuc02:0/16] 2024-01-14 19:22:57,661 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 19:22:57,664 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 19:30:14,604 (trainer:737) INFO: 21epoch:train:2501-2600batch: iter_time=2.390, forward_time=0.141, loss_ctc=45.949, loss_att=47.315, acc=0.737, loss=46.905, backward_time=0.102, grad_norm=37.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.454e-04, train_time=4.664
+[gpuc02:0/16] 2024-01-14 19:30:57,092 (trainer:737) INFO: 21epoch:train:2601-2700batch: iter_time=1.204e-04, forward_time=0.104, loss_ctc=47.722, loss_att=53.296, acc=0.741, loss=51.624, backward_time=0.098, grad_norm=40.649, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.453e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 19:31:40,059 (trainer:737) INFO: 21epoch:train:2701-2800batch: iter_time=1.179e-04, forward_time=0.105, loss_ctc=42.980, loss_att=49.241, acc=0.735, loss=47.363, backward_time=0.098, grad_norm=33.338, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.452e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:32:23,104 (trainer:737) INFO: 21epoch:train:2801-2900batch: iter_time=9.530e-05, forward_time=0.108, loss_ctc=45.920, loss_att=53.899, acc=0.757, loss=51.505, backward_time=0.098, grad_norm=36.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.451e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 19:33:05,366 (trainer:737) INFO: 21epoch:train:2901-3000batch: iter_time=1.270e-04, forward_time=0.104, loss_ctc=34.442, loss_att=39.148, acc=0.764, loss=37.736, backward_time=0.097, grad_norm=29.725, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.451e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 19:33:47,634 (trainer:737) INFO: 21epoch:train:3001-3100batch: iter_time=1.318e-04, forward_time=0.104, loss_ctc=38.217, loss_att=44.173, acc=0.747, loss=42.386, backward_time=0.098, grad_norm=34.833, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.450e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 19:34:30,469 (trainer:737) INFO: 21epoch:train:3101-3200batch: iter_time=1.208e-04, forward_time=0.105, loss_ctc=46.540, loss_att=52.012, acc=0.750, loss=50.371, backward_time=0.098, grad_norm=39.558, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.449e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 19:35:13,214 (trainer:737) INFO: 21epoch:train:3201-3300batch: iter_time=1.277e-04, forward_time=0.104, loss_ctc=46.201, loss_att=47.882, acc=0.731, loss=47.378, backward_time=0.097, grad_norm=37.120, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.448e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 19:35:55,613 (trainer:737) INFO: 21epoch:train:3301-3400batch: iter_time=1.248e-04, forward_time=0.105, loss_ctc=50.764, loss_att=56.608, acc=0.705, loss=54.854, backward_time=0.098, grad_norm=39.554, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.448e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:36:38,194 (trainer:737) INFO: 21epoch:train:3401-3500batch: iter_time=1.225e-04, forward_time=0.105, loss_ctc=44.122, loss_att=60.260, acc=0.735, loss=55.418, backward_time=0.099, grad_norm=36.359, clip=100.000, loss_scale=3.780e+34, optim_step_time=0.042, optim0_lr0=4.447e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 19:37:20,546 (trainer:737) INFO: 21epoch:train:3501-3600batch: iter_time=1.214e-04, forward_time=0.104, loss_ctc=46.061, loss_att=63.169, acc=0.718, loss=58.037, backward_time=0.098, grad_norm=36.030, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.446e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:38:02,977 (trainer:737) INFO: 21epoch:train:3601-3700batch: iter_time=1.189e-04, forward_time=0.104, loss_ctc=44.779, loss_att=55.413, acc=0.747, loss=52.223, backward_time=0.098, grad_norm=35.653, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.445e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:38:30,026 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 19:38:50,183 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 19:38:53,957 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f247a4bbaf0>)
+[gpuc02:0/16] 2024-01-14 19:38:53,957 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 19:38:53,960 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 19:43:56,614 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 19:43:58,748 (trainer:737) INFO: 21epoch:train:3701-3800batch: iter_time=2.778, forward_time=0.108, loss_ctc=44.914, loss_att=49.044, acc=0.713, loss=47.805, backward_time=0.099, grad_norm=38.769, clip=100.000, loss_scale=4.049e+34, optim_step_time=0.041, optim0_lr0=4.445e-04, train_time=3.557
+[gpuc02:0/16] 2024-01-14 19:44:41,464 (trainer:737) INFO: 21epoch:train:3801-3900batch: iter_time=1.056e-04, forward_time=0.104, loss_ctc=45.524, loss_att=48.284, acc=0.741, loss=47.456, backward_time=0.097, grad_norm=40.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.444e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 19:45:24,352 (trainer:737) INFO: 21epoch:train:3901-4000batch: iter_time=1.068e-04, forward_time=0.104, loss_ctc=43.011, loss_att=53.943, acc=0.712, loss=50.663, backward_time=0.097, grad_norm=36.870, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.443e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:46:07,187 (trainer:737) INFO: 21epoch:train:4001-4100batch: iter_time=1.008e-04, forward_time=0.104, loss_ctc=45.647, loss_att=54.542, acc=0.722, loss=51.873, backward_time=0.098, grad_norm=36.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.443e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 19:46:49,685 (trainer:737) INFO: 21epoch:train:4101-4200batch: iter_time=1.014e-04, forward_time=0.104, loss_ctc=40.344, loss_att=47.816, acc=0.752, loss=45.575, backward_time=0.098, grad_norm=33.891, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.442e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 19:47:31,965 (trainer:737) INFO: 21epoch:train:4201-4300batch: iter_time=1.106e-04, forward_time=0.103, loss_ctc=36.834, loss_att=38.506, acc=0.760, loss=38.005, backward_time=0.097, grad_norm=32.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.441e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:48:14,333 (trainer:737) INFO: 21epoch:train:4301-4400batch: iter_time=9.781e-05, forward_time=0.103, loss_ctc=41.472, loss_att=54.328, acc=0.727, loss=50.471, backward_time=0.097, grad_norm=40.247, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.440e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 19:48:57,356 (trainer:737) INFO: 21epoch:train:4401-4500batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=46.895, loss_att=50.725, acc=0.733, loss=49.576, backward_time=0.097, grad_norm=37.936, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.440e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 19:49:39,761 (trainer:737) INFO: 21epoch:train:4501-4600batch: iter_time=9.819e-05, forward_time=0.103, loss_ctc=46.427, loss_att=46.509, acc=0.723, loss=46.484, backward_time=0.097, grad_norm=39.319, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.439e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:50:22,267 (trainer:737) INFO: 21epoch:train:4601-4700batch: iter_time=9.791e-05, forward_time=0.103, loss_ctc=44.659, loss_att=59.725, acc=0.698, loss=55.205, backward_time=0.098, grad_norm=35.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.438e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 19:51:05,171 (trainer:737) INFO: 21epoch:train:4701-4800batch: iter_time=1.009e-04, forward_time=0.108, loss_ctc=49.166, loss_att=68.964, acc=0.718, loss=63.025, backward_time=0.099, grad_norm=40.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.437e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 19:51:47,897 (trainer:737) INFO: 21epoch:train:4801-4900batch: iter_time=9.533e-05, forward_time=0.104, loss_ctc=45.679, loss_att=54.289, acc=0.730, loss=51.706, backward_time=0.098, grad_norm=36.800, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.437e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 19:52:30,659 (trainer:737) INFO: 21epoch:train:4901-5000batch: iter_time=9.571e-05, forward_time=0.103, loss_ctc=39.718, loss_att=49.110, acc=0.719, loss=46.293, backward_time=0.097, grad_norm=35.959, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.436e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 19:52:36,576 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 19:52:56,357 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 19:53:00,031 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1df7877190>)
+[gpuc02:0/16] 2024-01-14 19:53:00,031 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 19:53:00,034 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 19:57:36,638 (trainer:737) INFO: 21epoch:train:5001-5100batch: iter_time=2.358, forward_time=0.127, loss_ctc=45.562, loss_att=47.942, acc=0.722, loss=47.228, backward_time=0.103, grad_norm=37.504, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.435e-04, train_time=3.060
+[gpuc02:0/16] 2024-01-14 19:58:19,043 (trainer:737) INFO: 21epoch:train:5101-5200batch: iter_time=1.076e-04, forward_time=0.104, loss_ctc=47.742, loss_att=54.347, acc=0.721, loss=52.365, backward_time=0.097, grad_norm=42.173, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.435e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:59:01,434 (trainer:737) INFO: 21epoch:train:5201-5300batch: iter_time=1.109e-04, forward_time=0.104, loss_ctc=43.233, loss_att=49.502, acc=0.728, loss=47.621, backward_time=0.097, grad_norm=36.225, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.434e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 19:59:43,946 (trainer:737) INFO: 21epoch:train:5301-5400batch: iter_time=1.172e-04, forward_time=0.105, loss_ctc=46.278, loss_att=52.434, acc=0.751, loss=50.587, backward_time=0.098, grad_norm=40.762, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.433e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 20:00:26,382 (trainer:737) INFO: 21epoch:train:5401-5500batch: iter_time=1.272e-04, forward_time=0.103, loss_ctc=34.555, loss_att=38.120, acc=0.760, loss=37.051, backward_time=0.096, grad_norm=29.884, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.432e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:01:09,376 (trainer:737) INFO: 21epoch:train:5501-5600batch: iter_time=1.110e-04, forward_time=0.103, loss_ctc=38.145, loss_att=43.825, acc=0.741, loss=42.121, backward_time=0.096, grad_norm=35.208, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.432e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 20:01:51,829 (trainer:737) INFO: 21epoch:train:5601-5700batch: iter_time=1.059e-04, forward_time=0.105, loss_ctc=46.641, loss_att=52.254, acc=0.742, loss=50.570, backward_time=0.097, grad_norm=41.732, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.431e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:02:34,255 (trainer:737) INFO: 21epoch:train:5701-5800batch: iter_time=1.091e-04, forward_time=0.103, loss_ctc=45.412, loss_att=47.364, acc=0.727, loss=46.779, backward_time=0.096, grad_norm=36.092, clip=100.000, loss_scale=2.181e+34, optim_step_time=0.041, optim0_lr0=4.430e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:03:17,049 (trainer:737) INFO: 21epoch:train:5801-5900batch: iter_time=1.055e-04, forward_time=0.103, loss_ctc=49.822, loss_att=57.972, acc=0.688, loss=55.527, backward_time=0.097, grad_norm=40.685, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.429e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 20:03:59,553 (trainer:737) INFO: 21epoch:train:5901-6000batch: iter_time=1.000e-04, forward_time=0.104, loss_ctc=43.690, loss_att=59.144, acc=0.730, loss=54.507, backward_time=0.098, grad_norm=35.068, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.429e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 20:04:41,964 (trainer:737) INFO: 21epoch:train:6001-6100batch: iter_time=1.123e-04, forward_time=0.104, loss_ctc=45.973, loss_att=62.087, acc=0.713, loss=57.253, backward_time=0.097, grad_norm=37.930, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.428e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:05:24,333 (trainer:737) INFO: 21epoch:train:6101-6200batch: iter_time=1.014e-04, forward_time=0.104, loss_ctc=43.978, loss_att=54.557, acc=0.744, loss=51.383, backward_time=0.097, grad_norm=34.375, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.427e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:05:49,966 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 20:06:09,962 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 20:06:13,650 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1de2c5f160>)
+[gpuc02:0/16] 2024-01-14 20:06:13,651 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 20:06:13,654 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 20:10:27,209 (trainer:737) INFO: 21epoch:train:6201-6300batch: iter_time=2.304, forward_time=0.103, loss_ctc=45.123, loss_att=49.072, acc=0.701, loss=47.887, backward_time=0.096, grad_norm=41.481, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.427e-04, train_time=3.029
+[gpuc02:0/16] 2024-01-14 20:10:30,569 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 20:11:09,615 (trainer:737) INFO: 21epoch:train:6301-6400batch: iter_time=9.633e-05, forward_time=0.104, loss_ctc=44.613, loss_att=45.618, acc=0.746, loss=45.317, backward_time=0.097, grad_norm=36.484, clip=100.000, loss_scale=2.224e+34, optim_step_time=0.042, optim0_lr0=4.426e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:11:52,276 (trainer:737) INFO: 21epoch:train:6401-6500batch: iter_time=1.066e-04, forward_time=0.104, loss_ctc=43.149, loss_att=53.037, acc=0.713, loss=50.071, backward_time=0.097, grad_norm=36.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.425e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:12:35,022 (trainer:737) INFO: 21epoch:train:6501-6600batch: iter_time=1.057e-04, forward_time=0.104, loss_ctc=45.894, loss_att=54.036, acc=0.724, loss=51.594, backward_time=0.098, grad_norm=38.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.424e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 20:13:17,622 (trainer:737) INFO: 21epoch:train:6601-6700batch: iter_time=1.193e-04, forward_time=0.104, loss_ctc=40.372, loss_att=47.201, acc=0.755, loss=45.152, backward_time=0.097, grad_norm=33.386, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.424e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:13:59,870 (trainer:737) INFO: 21epoch:train:6701-6800batch: iter_time=1.337e-04, forward_time=0.103, loss_ctc=37.090, loss_att=37.845, acc=0.761, loss=37.619, backward_time=0.096, grad_norm=32.240, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.423e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 20:14:42,558 (trainer:737) INFO: 21epoch:train:6801-6900batch: iter_time=1.205e-04, forward_time=0.104, loss_ctc=41.458, loss_att=53.921, acc=0.729, loss=50.182, backward_time=0.097, grad_norm=40.661, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.422e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 20:15:25,232 (trainer:737) INFO: 21epoch:train:6901-7000batch: iter_time=1.215e-04, forward_time=0.104, loss_ctc=46.793, loss_att=49.541, acc=0.734, loss=48.716, backward_time=0.097, grad_norm=39.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.421e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 20:16:07,438 (trainer:737) INFO: 21epoch:train:7001-7100batch: iter_time=1.231e-04, forward_time=0.103, loss_ctc=46.301, loss_att=45.844, acc=0.725, loss=45.981, backward_time=0.096, grad_norm=39.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.421e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 20:16:49,720 (trainer:737) INFO: 21epoch:train:7101-7200batch: iter_time=1.316e-04, forward_time=0.104, loss_ctc=44.166, loss_att=58.450, acc=0.700, loss=54.165, backward_time=0.097, grad_norm=33.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.420e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:17:32,306 (trainer:737) INFO: 21epoch:train:7201-7300batch: iter_time=1.036e-04, forward_time=0.105, loss_ctc=48.189, loss_att=67.501, acc=0.721, loss=61.708, backward_time=0.098, grad_norm=38.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.419e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:18:15,060 (trainer:737) INFO: 21epoch:train:7301-7400batch: iter_time=1.165e-04, forward_time=0.105, loss_ctc=45.151, loss_att=53.644, acc=0.733, loss=51.096, backward_time=0.098, grad_norm=35.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.419e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 20:18:58,039 (trainer:737) INFO: 21epoch:train:7401-7500batch: iter_time=1.027e-04, forward_time=0.104, loss_ctc=39.587, loss_att=49.197, acc=0.719, loss=46.314, backward_time=0.097, grad_norm=34.872, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.418e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 20:19:00,329 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 20:19:20,476 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 20:19:24,145 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1de020fc70>)
+[gpuc02:0/16] 2024-01-14 20:19:24,145 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 20:19:24,148 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 20:23:58,975 (trainer:737) INFO: 21epoch:train:7501-7600batch: iter_time=2.307, forward_time=0.105, loss_ctc=45.444, loss_att=49.756, acc=0.731, loss=48.462, backward_time=0.097, grad_norm=38.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.417e-04, train_time=3.009
+[gpuc02:0/16] 2024-01-14 20:24:41,248 (trainer:737) INFO: 21epoch:train:7601-7700batch: iter_time=1.047e-04, forward_time=0.105, loss_ctc=45.700, loss_att=54.048, acc=0.740, loss=51.544, backward_time=0.097, grad_norm=41.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.416e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:25:23,689 (trainer:737) INFO: 21epoch:train:7701-7800batch: iter_time=1.219e-04, forward_time=0.106, loss_ctc=42.674, loss_att=50.527, acc=0.733, loss=48.171, backward_time=0.097, grad_norm=33.474, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.416e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:26:07,099 (trainer:737) INFO: 21epoch:train:7801-7900batch: iter_time=1.170e-04, forward_time=0.106, loss_ctc=45.346, loss_att=54.366, acc=0.755, loss=51.660, backward_time=0.098, grad_norm=35.804, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.415e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 20:26:49,406 (trainer:737) INFO: 21epoch:train:7901-8000batch: iter_time=1.291e-04, forward_time=0.104, loss_ctc=34.172, loss_att=39.253, acc=0.765, loss=37.729, backward_time=0.096, grad_norm=30.082, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.414e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:27:31,787 (trainer:737) INFO: 21epoch:train:8001-8100batch: iter_time=1.226e-04, forward_time=0.104, loss_ctc=37.898, loss_att=44.566, acc=0.747, loss=42.566, backward_time=0.096, grad_norm=34.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.414e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:28:15,373 (trainer:737) INFO: 21epoch:train:8101-8200batch: iter_time=1.165e-04, forward_time=0.108, loss_ctc=45.627, loss_att=52.029, acc=0.752, loss=50.109, backward_time=0.098, grad_norm=41.013, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.413e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-14 20:28:57,659 (trainer:737) INFO: 21epoch:train:8201-8300batch: iter_time=1.285e-04, forward_time=0.104, loss_ctc=44.931, loss_att=46.840, acc=0.736, loss=46.267, backward_time=0.096, grad_norm=36.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.412e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:29:15,315 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 20:29:40,272 (trainer:737) INFO: 21epoch:train:8301-8400batch: iter_time=1.154e-04, forward_time=0.107, loss_ctc=49.215, loss_att=56.775, acc=0.702, loss=54.507, backward_time=0.097, grad_norm=37.817, clip=100.000, loss_scale=2.748e+34, optim_step_time=0.042, optim0_lr0=4.411e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:30:22,867 (trainer:737) INFO: 21epoch:train:8401-8500batch: iter_time=1.086e-04, forward_time=0.105, loss_ctc=43.218, loss_att=60.064, acc=0.738, loss=55.010, backward_time=0.098, grad_norm=35.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.411e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:31:05,358 (trainer:737) INFO: 21epoch:train:8501-8600batch: iter_time=1.116e-04, forward_time=0.105, loss_ctc=45.206, loss_att=63.096, acc=0.720, loss=57.729, backward_time=0.097, grad_norm=36.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.410e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 20:31:47,850 (trainer:737) INFO: 21epoch:train:8601-8700batch: iter_time=1.147e-04, forward_time=0.105, loss_ctc=43.674, loss_att=53.815, acc=0.752, loss=50.773, backward_time=0.098, grad_norm=33.972, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.409e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 20:32:11,404 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 20:32:31,446 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 20:32:35,159 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1de3aebe50>)
+[gpuc02:0/16] 2024-01-14 20:32:35,159 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 20:32:35,162 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 20:36:41,665 (trainer:737) INFO: 21epoch:train:8701-8800batch: iter_time=2.298, forward_time=0.104, loss_ctc=44.603, loss_att=49.499, acc=0.709, loss=48.030, backward_time=0.096, grad_norm=40.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.409e-04, train_time=2.938
+[gpuc02:0/16] 2024-01-14 20:37:24,183 (trainer:737) INFO: 21epoch:train:8801-8900batch: iter_time=1.357e-04, forward_time=0.104, loss_ctc=44.487, loss_att=46.811, acc=0.746, loss=46.113, backward_time=0.097, grad_norm=40.634, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.408e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 20:38:06,773 (trainer:737) INFO: 21epoch:train:8901-9000batch: iter_time=1.273e-04, forward_time=0.104, loss_ctc=43.030, loss_att=53.311, acc=0.715, loss=50.227, backward_time=0.097, grad_norm=37.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.407e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:38:49,557 (trainer:737) INFO: 21epoch:train:9001-9100batch: iter_time=1.353e-04, forward_time=0.104, loss_ctc=45.548, loss_att=54.159, acc=0.724, loss=51.576, backward_time=0.098, grad_norm=33.962, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.406e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 20:39:31,858 (trainer:737) INFO: 21epoch:train:9101-9200batch: iter_time=1.469e-04, forward_time=0.104, loss_ctc=40.413, loss_att=46.754, acc=0.756, loss=44.851, backward_time=0.098, grad_norm=34.381, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.406e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:40:14,057 (trainer:737) INFO: 21epoch:train:9201-9300batch: iter_time=1.442e-04, forward_time=0.103, loss_ctc=36.431, loss_att=37.502, acc=0.763, loss=37.181, backward_time=0.097, grad_norm=30.957, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.405e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 20:40:56,333 (trainer:737) INFO: 21epoch:train:9301-9400batch: iter_time=1.403e-04, forward_time=0.103, loss_ctc=41.225, loss_att=54.373, acc=0.726, loss=50.429, backward_time=0.097, grad_norm=39.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.404e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:41:38,731 (trainer:737) INFO: 21epoch:train:9401-9500batch: iter_time=1.255e-04, forward_time=0.104, loss_ctc=46.531, loss_att=49.893, acc=0.735, loss=48.884, backward_time=0.098, grad_norm=37.709, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.404e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:42:21,162 (trainer:737) INFO: 21epoch:train:9501-9600batch: iter_time=1.564e-04, forward_time=0.103, loss_ctc=46.450, loss_att=46.591, acc=0.723, loss=46.548, backward_time=0.097, grad_norm=39.238, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.403e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:43:04,064 (trainer:737) INFO: 21epoch:train:9601-9700batch: iter_time=1.254e-04, forward_time=0.103, loss_ctc=44.034, loss_att=58.625, acc=0.702, loss=54.247, backward_time=0.097, grad_norm=35.503, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.402e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 20:43:46,656 (trainer:737) INFO: 21epoch:train:9701-9800batch: iter_time=1.333e-04, forward_time=0.105, loss_ctc=48.243, loss_att=67.276, acc=0.724, loss=61.566, backward_time=0.099, grad_norm=38.819, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.401e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:44:29,098 (trainer:737) INFO: 21epoch:train:9801-9900batch: iter_time=1.245e-04, forward_time=0.104, loss_ctc=44.818, loss_att=54.189, acc=0.730, loss=51.378, backward_time=0.098, grad_norm=35.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.401e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 20:45:11,960 (trainer:737) INFO: 21epoch:train:9901-10000batch: iter_time=1.116e-04, forward_time=0.103, loss_ctc=38.830, loss_att=49.113, acc=0.720, loss=46.028, backward_time=0.097, grad_norm=34.628, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.400e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 20:45:14,828 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 20:45:34,625 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 20:45:38,336 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1de022d240>)
+[gpuc02:0/16] 2024-01-14 20:45:38,336 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 20:45:38,340 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 20:50:08,693 (trainer:737) INFO: 21epoch:train:10001-10100batch: iter_time=2.382, forward_time=0.104, loss_ctc=44.806, loss_att=49.091, acc=0.734, loss=47.806, backward_time=0.097, grad_norm=38.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.399e-04, train_time=2.967
+[gpuc02:0/16] 2024-01-14 20:50:51,551 (trainer:737) INFO: 21epoch:train:10101-10200batch: iter_time=9.418e-05, forward_time=0.105, loss_ctc=46.567, loss_att=54.378, acc=0.739, loss=52.035, backward_time=0.097, grad_norm=39.890, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.399e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 20:51:34,282 (trainer:737) INFO: 21epoch:train:10201-10300batch: iter_time=9.278e-05, forward_time=0.105, loss_ctc=42.085, loss_att=48.973, acc=0.737, loss=46.907, backward_time=0.097, grad_norm=33.305, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.398e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 20:52:16,891 (trainer:737) INFO: 21epoch:train:10301-10400batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=45.182, loss_att=54.625, acc=0.755, loss=51.792, backward_time=0.098, grad_norm=37.511, clip=100.000, loss_scale=3.302e+34, optim_step_time=0.042, optim0_lr0=4.397e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:52:59,085 (trainer:737) INFO: 21epoch:train:10401-10500batch: iter_time=1.191e-04, forward_time=0.104, loss_ctc=33.540, loss_att=38.844, acc=0.768, loss=37.253, backward_time=0.097, grad_norm=28.857, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.397e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 20:53:41,396 (trainer:737) INFO: 21epoch:train:10501-10600batch: iter_time=1.049e-04, forward_time=0.104, loss_ctc=37.480, loss_att=44.403, acc=0.748, loss=42.326, backward_time=0.097, grad_norm=34.476, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.396e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:54:24,185 (trainer:737) INFO: 21epoch:train:10601-10700batch: iter_time=1.022e-04, forward_time=0.105, loss_ctc=45.265, loss_att=52.056, acc=0.751, loss=50.019, backward_time=0.098, grad_norm=41.305, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.395e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 20:55:06,476 (trainer:737) INFO: 21epoch:train:10701-10800batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=44.873, loss_att=47.072, acc=0.736, loss=46.412, backward_time=0.097, grad_norm=37.532, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.394e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 20:55:49,110 (trainer:737) INFO: 21epoch:train:10801-10900batch: iter_time=1.037e-04, forward_time=0.104, loss_ctc=49.439, loss_att=56.881, acc=0.703, loss=54.649, backward_time=0.098, grad_norm=40.407, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.394e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 20:56:32,216 (trainer:737) INFO: 21epoch:train:10901-11000batch: iter_time=1.084e-04, forward_time=0.104, loss_ctc=42.692, loss_att=59.161, acc=0.738, loss=54.220, backward_time=0.098, grad_norm=33.634, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.393e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 20:57:15,038 (trainer:737) INFO: 21epoch:train:11001-11100batch: iter_time=1.024e-04, forward_time=0.104, loss_ctc=45.129, loss_att=62.992, acc=0.721, loss=57.633, backward_time=0.097, grad_norm=36.498, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.392e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 20:57:58,028 (trainer:737) INFO: 21epoch:train:11101-11200batch: iter_time=1.072e-04, forward_time=0.105, loss_ctc=43.699, loss_att=54.226, acc=0.751, loss=51.068, backward_time=0.098, grad_norm=34.605, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.392e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 20:58:22,097 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 20:58:41,749 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 20:58:45,391 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2508f665c0>)
+[gpuc02:0/16] 2024-01-14 20:58:45,391 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 20:58:45,408 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 21:02:53,684 (trainer:737) INFO: 21epoch:train:11201-11300batch: iter_time=2.335, forward_time=0.103, loss_ctc=44.179, loss_att=48.124, acc=0.718, loss=46.941, backward_time=0.097, grad_norm=39.321, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.391e-04, train_time=2.956
+[gpuc02:0/16] 2024-01-14 21:03:36,158 (trainer:737) INFO: 21epoch:train:11301-11400batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=43.872, loss_att=44.359, acc=0.766, loss=44.213, backward_time=0.098, grad_norm=36.759, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.390e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 21:04:18,621 (trainer:737) INFO: 21epoch:train:11401-11500batch: iter_time=1.286e-04, forward_time=0.104, loss_ctc=42.700, loss_att=52.089, acc=0.727, loss=49.272, backward_time=0.098, grad_norm=36.724, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.389e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:04:42,400 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 21:05:01,177 (trainer:737) INFO: 21epoch:train:11501-11600batch: iter_time=1.237e-04, forward_time=0.106, loss_ctc=45.316, loss_att=54.335, acc=0.736, loss=51.629, backward_time=0.098, grad_norm=36.645, clip=100.000, loss_scale=3.231e+34, optim_step_time=0.041, optim0_lr0=4.389e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 21:05:44,036 (trainer:737) INFO: 21epoch:train:11601-11700batch: iter_time=1.175e-04, forward_time=0.106, loss_ctc=39.967, loss_att=47.391, acc=0.766, loss=45.164, backward_time=0.097, grad_norm=33.277, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.388e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 21:06:26,399 (trainer:737) INFO: 21epoch:train:11701-11800batch: iter_time=1.078e-04, forward_time=0.105, loss_ctc=36.434, loss_att=38.529, acc=0.765, loss=37.901, backward_time=0.097, grad_norm=32.825, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.387e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 21:07:08,828 (trainer:737) INFO: 21epoch:train:11801-11900batch: iter_time=1.025e-04, forward_time=0.106, loss_ctc=40.908, loss_att=53.975, acc=0.737, loss=50.055, backward_time=0.098, grad_norm=37.843, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.387e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:07:51,405 (trainer:737) INFO: 21epoch:train:11901-12000batch: iter_time=1.089e-04, forward_time=0.106, loss_ctc=46.226, loss_att=49.354, acc=0.744, loss=48.415, backward_time=0.098, grad_norm=36.970, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.386e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 21:08:33,708 (trainer:737) INFO: 21epoch:train:12001-12100batch: iter_time=1.065e-04, forward_time=0.106, loss_ctc=45.775, loss_att=46.223, acc=0.728, loss=46.089, backward_time=0.097, grad_norm=37.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.385e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 21:09:16,166 (trainer:737) INFO: 21epoch:train:12101-12200batch: iter_time=1.046e-04, forward_time=0.106, loss_ctc=43.654, loss_att=56.931, acc=0.723, loss=52.948, backward_time=0.098, grad_norm=33.455, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.385e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:09:58,888 (trainer:737) INFO: 21epoch:train:12201-12300batch: iter_time=9.967e-05, forward_time=0.107, loss_ctc=47.823, loss_att=67.773, acc=0.727, loss=61.788, backward_time=0.098, grad_norm=37.530, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.384e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 21:10:41,622 (trainer:737) INFO: 21epoch:train:12301-12400batch: iter_time=1.049e-04, forward_time=0.106, loss_ctc=44.780, loss_att=55.640, acc=0.740, loss=52.382, backward_time=0.097, grad_norm=34.773, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.383e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 21:11:26,059 (trainer:737) INFO: 21epoch:train:12401-12500batch: iter_time=9.424e-05, forward_time=0.113, loss_ctc=39.063, loss_att=47.698, acc=0.736, loss=45.108, backward_time=0.107, grad_norm=34.722, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.382e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-14 21:11:28,498 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 21:11:48,861 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 21:11:52,553 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e14caedd0>)
+[gpuc02:0/16] 2024-01-14 21:11:52,553 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 21:11:52,557 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 21:16:20,123 (trainer:737) INFO: 21epoch:train:12501-12600batch: iter_time=2.463, forward_time=0.106, loss_ctc=45.037, loss_att=50.079, acc=0.720, loss=48.567, backward_time=0.097, grad_norm=38.499, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.382e-04, train_time=2.940
+[gpuc02:0/16] 2024-01-14 21:17:02,273 (trainer:737) INFO: 21epoch:train:12601-12700batch: iter_time=1.764e-04, forward_time=0.104, loss_ctc=45.381, loss_att=55.151, acc=0.719, loss=52.220, backward_time=0.098, grad_norm=40.482, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.381e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 21:17:44,588 (trainer:737) INFO: 21epoch:train:12701-12800batch: iter_time=1.885e-04, forward_time=0.106, loss_ctc=41.895, loss_att=49.549, acc=0.729, loss=47.253, backward_time=0.097, grad_norm=34.197, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.380e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 21:18:27,009 (trainer:737) INFO: 21epoch:train:12801-12900batch: iter_time=1.821e-04, forward_time=0.106, loss_ctc=45.498, loss_att=52.590, acc=0.749, loss=50.463, backward_time=0.098, grad_norm=38.595, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.380e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:19:09,096 (trainer:737) INFO: 21epoch:train:12901-13000batch: iter_time=1.879e-04, forward_time=0.104, loss_ctc=34.123, loss_att=38.803, acc=0.759, loss=37.399, backward_time=0.097, grad_norm=31.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.379e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 21:19:51,299 (trainer:737) INFO: 21epoch:train:13001-13100batch: iter_time=1.681e-04, forward_time=0.105, loss_ctc=37.582, loss_att=43.603, acc=0.740, loss=41.797, backward_time=0.097, grad_norm=34.183, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.378e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 21:20:33,730 (trainer:737) INFO: 21epoch:train:13101-13200batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=45.425, loss_att=52.205, acc=0.743, loss=50.171, backward_time=0.098, grad_norm=42.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.378e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:21:15,898 (trainer:737) INFO: 21epoch:train:13201-13300batch: iter_time=1.601e-04, forward_time=0.104, loss_ctc=45.065, loss_att=47.620, acc=0.729, loss=46.854, backward_time=0.096, grad_norm=36.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.377e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 21:21:58,763 (trainer:737) INFO: 21epoch:train:13301-13400batch: iter_time=1.461e-04, forward_time=0.105, loss_ctc=49.183, loss_att=57.920, acc=0.687, loss=55.299, backward_time=0.096, grad_norm=39.660, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.376e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 21:22:41,226 (trainer:737) INFO: 21epoch:train:13401-13500batch: iter_time=1.419e-04, forward_time=0.106, loss_ctc=43.130, loss_att=59.979, acc=0.726, loss=54.924, backward_time=0.097, grad_norm=38.944, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.375e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:23:23,889 (trainer:737) INFO: 21epoch:train:13501-13600batch: iter_time=1.490e-04, forward_time=0.105, loss_ctc=45.016, loss_att=61.607, acc=0.716, loss=56.630, backward_time=0.097, grad_norm=36.921, clip=100.000, loss_scale=2.991e+34, optim_step_time=0.041, optim0_lr0=4.375e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 21:24:06,922 (trainer:737) INFO: 21epoch:train:13601-13700batch: iter_time=1.430e-04, forward_time=0.106, loss_ctc=43.660, loss_att=55.133, acc=0.746, loss=51.691, backward_time=0.097, grad_norm=35.274, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.374e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 21:24:32,341 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 21:24:51,867 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 21:24:55,461 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dda053d00>)
+[gpuc02:0/16] 2024-01-14 21:24:55,461 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 21:24:55,464 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 21:29:01,762 (trainer:737) INFO: 21epoch:train:13701-13800batch: iter_time=2.496, forward_time=0.105, loss_ctc=43.725, loss_att=49.169, acc=0.706, loss=47.536, backward_time=0.097, grad_norm=39.316, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.373e-04, train_time=2.948
+[gpuc02:0/16] 2024-01-14 21:29:44,553 (trainer:737) INFO: 21epoch:train:13801-13900batch: iter_time=1.288e-04, forward_time=0.106, loss_ctc=43.971, loss_att=46.554, acc=0.761, loss=45.779, backward_time=0.097, grad_norm=37.706, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.373e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 21:30:26,936 (trainer:737) INFO: 21epoch:train:13901-14000batch: iter_time=1.156e-04, forward_time=0.105, loss_ctc=42.642, loss_att=52.112, acc=0.728, loss=49.271, backward_time=0.097, grad_norm=36.204, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.372e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:31:04,304 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 21:31:09,435 (trainer:737) INFO: 21epoch:train:14001-14100batch: iter_time=1.114e-04, forward_time=0.105, loss_ctc=44.995, loss_att=55.127, acc=0.732, loss=52.087, backward_time=0.098, grad_norm=36.948, clip=100.000, loss_scale=3.902e+34, optim_step_time=0.041, optim0_lr0=4.371e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 21:31:52,230 (trainer:737) INFO: 21epoch:train:14101-14200batch: iter_time=1.231e-04, forward_time=0.106, loss_ctc=39.860, loss_att=47.200, acc=0.767, loss=44.998, backward_time=0.097, grad_norm=33.248, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.371e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 21:32:34,826 (trainer:737) INFO: 21epoch:train:14201-14300batch: iter_time=1.210e-04, forward_time=0.109, loss_ctc=36.291, loss_att=38.477, acc=0.764, loss=37.821, backward_time=0.096, grad_norm=32.042, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.370e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 21:33:06,635 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 21:33:17,289 (trainer:737) INFO: 21epoch:train:14301-14400batch: iter_time=1.207e-04, forward_time=0.106, loss_ctc=41.115, loss_att=54.384, acc=0.738, loss=50.403, backward_time=0.097, grad_norm=38.167, clip=100.000, loss_scale=1.815e+34, optim_step_time=0.042, optim0_lr0=4.369e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 21:33:59,802 (trainer:737) INFO: 21epoch:train:14401-14500batch: iter_time=1.171e-04, forward_time=0.106, loss_ctc=46.695, loss_att=49.553, acc=0.748, loss=48.695, backward_time=0.097, grad_norm=37.922, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.368e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 21:34:42,085 (trainer:737) INFO: 21epoch:train:14501-14600batch: iter_time=1.187e-04, forward_time=0.105, loss_ctc=45.526, loss_att=47.143, acc=0.726, loss=46.658, backward_time=0.096, grad_norm=38.371, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.368e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 21:35:24,440 (trainer:737) INFO: 21epoch:train:14601-14700batch: iter_time=1.218e-04, forward_time=0.106, loss_ctc=43.419, loss_att=57.312, acc=0.722, loss=53.144, backward_time=0.096, grad_norm=33.471, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.367e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 21:36:07,384 (trainer:737) INFO: 21epoch:train:14701-14800batch: iter_time=1.187e-04, forward_time=0.106, loss_ctc=48.062, loss_att=67.737, acc=0.727, loss=61.834, backward_time=0.098, grad_norm=39.527, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.366e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 21:36:49,866 (trainer:737) INFO: 21epoch:train:14801-14900batch: iter_time=1.120e-04, forward_time=0.106, loss_ctc=44.743, loss_att=55.330, acc=0.742, loss=52.154, backward_time=0.097, grad_norm=35.958, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.366e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 21:37:35,811 (trainer:737) INFO: 21epoch:train:14901-15000batch: iter_time=1.055e-04, forward_time=0.108, loss_ctc=38.401, loss_att=47.211, acc=0.735, loss=44.568, backward_time=0.096, grad_norm=33.944, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.044, optim0_lr0=4.365e-04, train_time=0.459
+[gpuc02:0/16] 2024-01-14 21:57:46,643 (trainer:343) INFO: 21epoch results: [train] iter_time=0.198, forward_time=0.105, loss_ctc=43.884, loss_att=51.761, acc=0.733, loss=49.398, backward_time=0.098, grad_norm=36.936, clip=100.000, loss_scale=2.599e+34, optim_step_time=0.042, optim0_lr0=4.418e-04, train_time=0.650, time=2 hours, 42 minutes and 48.05 seconds, total_count=315000, gpu_max_cached_mem_GB=26.227, [valid] loss_ctc=56.804, cer_ctc=0.289, loss_att=55.203, acc=0.583, cer=0.378, wer=0.998, loss=55.684, time=20 minutes and 0.95 seconds, total_count=98091, gpu_max_cached_mem_GB=26.227
+[gpuc02:0/16] 2024-01-14 21:57:51,962 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-14 21:57:51,968 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/13epoch.pth, exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/16epoch.pth
+[gpuc02:0/16] 2024-01-14 21:57:51,968 (trainer:272) INFO: 22/45epoch started. Estimated time to finish: 3 days, 47 minutes and 24.92 seconds
+[gpuc02:0/16] 2024-01-14 21:57:51,978 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 21:58:10,800 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 21:58:14,220 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d790adbd0>)
+[gpuc02:0/16] 2024-01-14 21:58:14,220 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 21:58:14,223 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 22:02:42,138 (trainer:737) INFO: 22epoch:train:1-100batch: iter_time=2.393, forward_time=0.140, loss_ctc=52.153, loss_att=65.808, acc=0.703, loss=61.712, backward_time=0.103, grad_norm=47.682, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.043, optim0_lr0=4.364e-04, train_time=2.901
+[gpuc02:0/16] 2024-01-14 22:03:25,139 (trainer:737) INFO: 22epoch:train:101-200batch: iter_time=1.150e-04, forward_time=0.104, loss_ctc=48.274, loss_att=57.985, acc=0.695, loss=55.072, backward_time=0.098, grad_norm=43.541, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.364e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 22:04:08,198 (trainer:737) INFO: 22epoch:train:201-300batch: iter_time=1.227e-04, forward_time=0.109, loss_ctc=50.384, loss_att=48.896, acc=0.749, loss=49.342, backward_time=0.099, grad_norm=35.565, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.363e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 22:04:51,556 (trainer:737) INFO: 22epoch:train:301-400batch: iter_time=1.155e-04, forward_time=0.105, loss_ctc=48.825, loss_att=59.699, acc=0.713, loss=56.437, backward_time=0.098, grad_norm=43.142, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.362e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 22:05:35,798 (trainer:737) INFO: 22epoch:train:401-500batch: iter_time=1.232e-04, forward_time=0.108, loss_ctc=54.289, loss_att=55.185, acc=0.739, loss=54.916, backward_time=0.099, grad_norm=45.202, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.044, optim0_lr0=4.362e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-14 22:06:20,247 (trainer:737) INFO: 22epoch:train:501-600batch: iter_time=1.434e-04, forward_time=0.106, loss_ctc=48.852, loss_att=55.367, acc=0.718, loss=53.412, backward_time=0.099, grad_norm=39.740, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.361e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-14 22:07:04,481 (trainer:737) INFO: 22epoch:train:601-700batch: iter_time=1.353e-04, forward_time=0.108, loss_ctc=52.557, loss_att=49.530, acc=0.726, loss=50.438, backward_time=0.102, grad_norm=48.036, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.044, optim0_lr0=4.360e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-14 22:07:49,222 (trainer:737) INFO: 22epoch:train:701-800batch: iter_time=1.289e-04, forward_time=0.113, loss_ctc=52.957, loss_att=54.421, acc=0.735, loss=53.981, backward_time=0.107, grad_norm=43.630, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.043, optim0_lr0=4.359e-04, train_time=0.447
+[gpuc02:0/16] 2024-01-14 22:08:31,972 (trainer:737) INFO: 22epoch:train:801-900batch: iter_time=1.142e-04, forward_time=0.109, loss_ctc=48.694, loss_att=61.360, acc=0.717, loss=57.560, backward_time=0.098, grad_norm=43.338, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.359e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 22:09:17,937 (trainer:737) INFO: 22epoch:train:901-1000batch: iter_time=1.110e-04, forward_time=0.113, loss_ctc=50.807, loss_att=56.110, acc=0.705, loss=54.519, backward_time=0.104, grad_norm=41.252, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.043, optim0_lr0=4.358e-04, train_time=0.459
+[gpuc02:0/16] 2024-01-14 22:10:01,550 (trainer:737) INFO: 22epoch:train:1001-1100batch: iter_time=1.244e-04, forward_time=0.110, loss_ctc=56.162, loss_att=63.356, acc=0.718, loss=61.197, backward_time=0.099, grad_norm=45.677, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.357e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-14 22:10:46,836 (trainer:737) INFO: 22epoch:train:1101-1200batch: iter_time=1.232e-04, forward_time=0.112, loss_ctc=48.634, loss_att=55.461, acc=0.719, loss=53.413, backward_time=0.101, grad_norm=39.507, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.046, optim0_lr0=4.357e-04, train_time=0.453
+[gpuc02:0/16] 2024-01-14 22:11:37,847 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 22:11:56,806 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 22:12:00,301 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ddbc13280>)
+[gpuc02:0/16] 2024-01-14 22:12:00,301 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 22:12:00,304 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 22:19:12,690 (trainer:737) INFO: 22epoch:train:1201-1300batch: iter_time=4.577, forward_time=0.130, loss_ctc=48.065, loss_att=63.562, acc=0.708, loss=58.913, backward_time=0.106, grad_norm=42.759, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.044, optim0_lr0=4.356e-04, train_time=5.058
+[gpuc02:0/16] 2024-01-14 22:19:54,963 (trainer:737) INFO: 22epoch:train:1301-1400batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=51.531, loss_att=65.572, acc=0.676, loss=61.360, backward_time=0.097, grad_norm=44.164, clip=100.000, loss_scale=1.298e+34, optim_step_time=0.042, optim0_lr0=4.355e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 22:20:36,946 (trainer:737) INFO: 22epoch:train:1401-1500batch: iter_time=1.133e-04, forward_time=0.103, loss_ctc=39.376, loss_att=40.218, acc=0.739, loss=39.965, backward_time=0.096, grad_norm=32.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.355e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 22:21:19,436 (trainer:737) INFO: 22epoch:train:1501-1600batch: iter_time=1.184e-04, forward_time=0.105, loss_ctc=50.628, loss_att=56.615, acc=0.734, loss=54.819, backward_time=0.098, grad_norm=39.337, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.354e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:22:01,656 (trainer:737) INFO: 22epoch:train:1601-1700batch: iter_time=1.111e-04, forward_time=0.105, loss_ctc=52.385, loss_att=58.608, acc=0.716, loss=56.741, backward_time=0.097, grad_norm=39.768, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.353e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 22:22:44,172 (trainer:737) INFO: 22epoch:train:1701-1800batch: iter_time=1.261e-04, forward_time=0.105, loss_ctc=49.527, loss_att=51.383, acc=0.732, loss=50.826, backward_time=0.098, grad_norm=42.421, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.353e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:23:27,052 (trainer:737) INFO: 22epoch:train:1801-1900batch: iter_time=1.175e-04, forward_time=0.104, loss_ctc=44.723, loss_att=49.513, acc=0.713, loss=48.076, backward_time=0.097, grad_norm=40.096, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.352e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 22:24:09,253 (trainer:737) INFO: 22epoch:train:1901-2000batch: iter_time=1.203e-04, forward_time=0.104, loss_ctc=52.933, loss_att=48.434, acc=0.716, loss=49.784, backward_time=0.096, grad_norm=49.067, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.351e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 22:24:52,225 (trainer:737) INFO: 22epoch:train:2001-2100batch: iter_time=1.174e-04, forward_time=0.105, loss_ctc=57.050, loss_att=66.434, acc=0.714, loss=63.619, backward_time=0.098, grad_norm=47.261, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.351e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 22:25:34,885 (trainer:737) INFO: 22epoch:train:2101-2200batch: iter_time=1.446e-04, forward_time=0.104, loss_ctc=46.653, loss_att=59.763, acc=0.709, loss=55.830, backward_time=0.097, grad_norm=37.611, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.350e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 22:26:16,963 (trainer:737) INFO: 22epoch:train:2201-2300batch: iter_time=1.293e-04, forward_time=0.103, loss_ctc=45.673, loss_att=52.022, acc=0.713, loss=50.117, backward_time=0.096, grad_norm=39.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.349e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 22:26:59,177 (trainer:737) INFO: 22epoch:train:2301-2400batch: iter_time=1.257e-04, forward_time=0.105, loss_ctc=53.349, loss_att=61.749, acc=0.717, loss=59.229, backward_time=0.098, grad_norm=46.840, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.348e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 22:27:42,769 (trainer:737) INFO: 22epoch:train:2401-2500batch: iter_time=1.321e-04, forward_time=0.105, loss_ctc=45.658, loss_att=49.444, acc=0.714, loss=48.308, backward_time=0.097, grad_norm=39.696, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.348e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-14 22:27:47,029 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 22:28:06,958 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 22:28:10,909 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d97d07b80>)
+[gpuc02:0/16] 2024-01-14 22:28:10,910 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 22:28:10,913 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 22:33:20,333 (trainer:737) INFO: 22epoch:train:2501-2600batch: iter_time=2.591, forward_time=0.106, loss_ctc=50.708, loss_att=66.806, acc=0.702, loss=61.977, backward_time=0.098, grad_norm=41.631, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.347e-04, train_time=3.375
+[gpuc02:0/16] 2024-01-14 22:34:02,497 (trainer:737) INFO: 22epoch:train:2601-2700batch: iter_time=1.534e-04, forward_time=0.104, loss_ctc=45.698, loss_att=57.675, acc=0.703, loss=54.082, backward_time=0.097, grad_norm=40.739, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.346e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 22:34:45,679 (trainer:737) INFO: 22epoch:train:2701-2800batch: iter_time=1.387e-04, forward_time=0.106, loss_ctc=48.800, loss_att=48.235, acc=0.754, loss=48.405, backward_time=0.097, grad_norm=35.933, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.346e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 22:35:27,993 (trainer:737) INFO: 22epoch:train:2801-2900batch: iter_time=1.543e-04, forward_time=0.106, loss_ctc=47.366, loss_att=59.469, acc=0.717, loss=55.838, backward_time=0.097, grad_norm=40.687, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.345e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 22:36:10,398 (trainer:737) INFO: 22epoch:train:2901-3000batch: iter_time=1.555e-04, forward_time=0.105, loss_ctc=52.351, loss_att=55.629, acc=0.738, loss=54.646, backward_time=0.098, grad_norm=43.177, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.344e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:36:52,974 (trainer:737) INFO: 22epoch:train:3001-3100batch: iter_time=1.814e-04, forward_time=0.105, loss_ctc=46.558, loss_att=55.057, acc=0.719, loss=52.507, backward_time=0.097, grad_norm=38.555, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.344e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 22:37:35,476 (trainer:737) INFO: 22epoch:train:3101-3200batch: iter_time=1.611e-04, forward_time=0.104, loss_ctc=50.202, loss_att=48.925, acc=0.727, loss=49.308, backward_time=0.097, grad_norm=48.896, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.343e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:38:18,033 (trainer:737) INFO: 22epoch:train:3201-3300batch: iter_time=1.593e-04, forward_time=0.105, loss_ctc=51.334, loss_att=54.055, acc=0.737, loss=53.238, backward_time=0.097, grad_norm=40.660, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.342e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:39:00,920 (trainer:737) INFO: 22epoch:train:3301-3400batch: iter_time=1.592e-04, forward_time=0.106, loss_ctc=46.468, loss_att=60.345, acc=0.722, loss=56.182, backward_time=0.098, grad_norm=39.693, clip=100.000, loss_scale=2.596e+34, optim_step_time=0.042, optim0_lr0=4.342e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 22:39:43,429 (trainer:737) INFO: 22epoch:train:3401-3500batch: iter_time=1.463e-04, forward_time=0.105, loss_ctc=48.356, loss_att=55.133, acc=0.710, loss=53.099, backward_time=0.098, grad_norm=37.897, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.341e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:40:26,056 (trainer:737) INFO: 22epoch:train:3501-3600batch: iter_time=1.595e-04, forward_time=0.106, loss_ctc=54.848, loss_att=62.659, acc=0.721, loss=60.315, backward_time=0.098, grad_norm=43.624, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.340e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 22:40:28,983 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 22:41:08,492 (trainer:737) INFO: 22epoch:train:3601-3700batch: iter_time=1.489e-04, forward_time=0.106, loss_ctc=46.403, loss_att=53.402, acc=0.727, loss=51.303, backward_time=0.098, grad_norm=40.637, clip=100.000, loss_scale=2.203e+34, optim_step_time=0.042, optim0_lr0=4.340e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:41:35,281 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 22:41:54,534 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 22:41:58,206 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d97d17fa0>)
+[gpuc02:0/16] 2024-01-14 22:41:58,206 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 22:41:58,210 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 22:46:17,986 (trainer:737) INFO: 22epoch:train:3701-3800batch: iter_time=2.580, forward_time=0.114, loss_ctc=46.769, loss_att=61.307, acc=0.715, loss=56.945, backward_time=0.098, grad_norm=38.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.339e-04, train_time=3.095
+[gpuc02:0/16] 2024-01-14 22:47:00,433 (trainer:737) INFO: 22epoch:train:3801-3900batch: iter_time=1.472e-04, forward_time=0.105, loss_ctc=50.892, loss_att=64.322, acc=0.681, loss=60.293, backward_time=0.097, grad_norm=42.241, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.338e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:47:42,630 (trainer:737) INFO: 22epoch:train:3901-4000batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=39.098, loss_att=40.113, acc=0.740, loss=39.808, backward_time=0.097, grad_norm=32.321, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.338e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 22:48:25,063 (trainer:737) INFO: 22epoch:train:4001-4100batch: iter_time=1.624e-04, forward_time=0.104, loss_ctc=49.922, loss_att=55.549, acc=0.737, loss=53.861, backward_time=0.098, grad_norm=38.541, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.337e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:49:08,030 (trainer:737) INFO: 22epoch:train:4101-4200batch: iter_time=1.353e-04, forward_time=0.105, loss_ctc=51.993, loss_att=58.233, acc=0.720, loss=56.361, backward_time=0.097, grad_norm=39.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.336e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 22:49:50,467 (trainer:737) INFO: 22epoch:train:4201-4300batch: iter_time=1.525e-04, forward_time=0.104, loss_ctc=48.153, loss_att=50.753, acc=0.735, loss=49.973, backward_time=0.097, grad_norm=41.191, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.336e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:50:32,934 (trainer:737) INFO: 22epoch:train:4301-4400batch: iter_time=1.433e-04, forward_time=0.104, loss_ctc=43.455, loss_att=48.538, acc=0.716, loss=47.013, backward_time=0.097, grad_norm=37.454, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.335e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:51:15,660 (trainer:737) INFO: 22epoch:train:4401-4500batch: iter_time=1.623e-04, forward_time=0.103, loss_ctc=51.496, loss_att=48.181, acc=0.718, loss=49.176, backward_time=0.096, grad_norm=48.420, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.334e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 22:51:58,055 (trainer:737) INFO: 22epoch:train:4501-4600batch: iter_time=1.689e-04, forward_time=0.104, loss_ctc=55.380, loss_att=64.732, acc=0.718, loss=61.926, backward_time=0.097, grad_norm=47.159, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.333e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 22:52:40,607 (trainer:737) INFO: 22epoch:train:4601-4700batch: iter_time=1.456e-04, forward_time=0.104, loss_ctc=45.426, loss_att=59.283, acc=0.713, loss=55.126, backward_time=0.097, grad_norm=37.810, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.333e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:53:22,757 (trainer:737) INFO: 22epoch:train:4701-4800batch: iter_time=1.533e-04, forward_time=0.104, loss_ctc=44.753, loss_att=52.444, acc=0.714, loss=50.136, backward_time=0.096, grad_norm=37.514, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.332e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 22:54:05,344 (trainer:737) INFO: 22epoch:train:4801-4900batch: iter_time=1.441e-04, forward_time=0.104, loss_ctc=52.403, loss_att=60.335, acc=0.718, loss=57.955, backward_time=0.097, grad_norm=46.040, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.331e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 22:54:47,858 (trainer:737) INFO: 22epoch:train:4901-5000batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=44.839, loss_att=49.023, acc=0.717, loss=47.768, backward_time=0.097, grad_norm=38.781, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.331e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 22:54:53,364 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 22:55:13,254 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 22:55:16,881 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dd0d77f70>)
+[gpuc02:0/16] 2024-01-14 22:55:16,882 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 22:55:16,885 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 23:00:07,524 (trainer:737) INFO: 22epoch:train:5001-5100batch: iter_time=2.620, forward_time=0.114, loss_ctc=50.142, loss_att=64.878, acc=0.703, loss=60.457, backward_time=0.099, grad_norm=41.690, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.330e-04, train_time=3.196
+[gpuc02:0/16] 2024-01-14 23:00:49,638 (trainer:737) INFO: 22epoch:train:5101-5200batch: iter_time=1.885e-04, forward_time=0.104, loss_ctc=44.698, loss_att=54.909, acc=0.699, loss=51.846, backward_time=0.096, grad_norm=37.596, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.329e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 23:01:32,140 (trainer:737) INFO: 22epoch:train:5201-5300batch: iter_time=1.617e-04, forward_time=0.105, loss_ctc=48.561, loss_att=48.513, acc=0.744, loss=48.527, backward_time=0.097, grad_norm=36.601, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.329e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:02:14,739 (trainer:737) INFO: 22epoch:train:5301-5400batch: iter_time=1.499e-04, forward_time=0.105, loss_ctc=46.464, loss_att=57.506, acc=0.718, loss=54.193, backward_time=0.097, grad_norm=40.491, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.328e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 23:02:57,223 (trainer:737) INFO: 22epoch:train:5401-5500batch: iter_time=1.626e-04, forward_time=0.105, loss_ctc=51.489, loss_att=52.446, acc=0.736, loss=52.159, backward_time=0.098, grad_norm=41.790, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.327e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:03:39,566 (trainer:737) INFO: 22epoch:train:5501-5600batch: iter_time=1.636e-04, forward_time=0.105, loss_ctc=45.873, loss_att=54.832, acc=0.705, loss=52.144, backward_time=0.097, grad_norm=39.267, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.327e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:04:21,742 (trainer:737) INFO: 22epoch:train:5601-5700batch: iter_time=1.782e-04, forward_time=0.104, loss_ctc=49.433, loss_att=46.491, acc=0.725, loss=47.374, backward_time=0.097, grad_norm=44.283, clip=100.000, loss_scale=4.008e+34, optim_step_time=0.041, optim0_lr0=4.326e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:05:04,056 (trainer:737) INFO: 22epoch:train:5701-5800batch: iter_time=1.970e-04, forward_time=0.104, loss_ctc=50.660, loss_att=53.004, acc=0.734, loss=52.301, backward_time=0.097, grad_norm=41.625, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.325e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:05:47,043 (trainer:737) INFO: 22epoch:train:5801-5900batch: iter_time=1.842e-04, forward_time=0.108, loss_ctc=45.414, loss_att=60.188, acc=0.716, loss=55.756, backward_time=0.097, grad_norm=40.378, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.325e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 23:06:29,520 (trainer:737) INFO: 22epoch:train:5901-6000batch: iter_time=1.795e-04, forward_time=0.104, loss_ctc=47.860, loss_att=53.741, acc=0.708, loss=51.977, backward_time=0.097, grad_norm=39.311, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.324e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:07:11,946 (trainer:737) INFO: 22epoch:train:6001-6100batch: iter_time=1.762e-04, forward_time=0.105, loss_ctc=53.223, loss_att=61.800, acc=0.719, loss=59.227, backward_time=0.098, grad_norm=42.738, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.323e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:07:54,817 (trainer:737) INFO: 22epoch:train:6101-6200batch: iter_time=1.745e-04, forward_time=0.104, loss_ctc=45.503, loss_att=53.347, acc=0.719, loss=50.994, backward_time=0.097, grad_norm=38.526, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.323e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 23:08:22,201 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 23:08:42,014 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 23:08:45,701 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dc1e13b80>)
+[gpuc02:0/16] 2024-01-14 23:08:45,701 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 23:08:45,704 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 23:13:00,695 (trainer:737) INFO: 22epoch:train:6201-6300batch: iter_time=2.628, forward_time=0.107, loss_ctc=46.532, loss_att=60.428, acc=0.705, loss=56.259, backward_time=0.098, grad_norm=40.096, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.322e-04, train_time=3.059
+[gpuc02:0/16] 2024-01-14 23:13:43,163 (trainer:737) INFO: 22epoch:train:6301-6400batch: iter_time=2.017e-04, forward_time=0.105, loss_ctc=50.187, loss_att=62.919, acc=0.684, loss=59.100, backward_time=0.097, grad_norm=41.367, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.321e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:14:25,320 (trainer:737) INFO: 22epoch:train:6401-6500batch: iter_time=1.632e-04, forward_time=0.104, loss_ctc=38.753, loss_att=39.211, acc=0.742, loss=39.074, backward_time=0.097, grad_norm=32.476, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.321e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 23:15:08,071 (trainer:737) INFO: 22epoch:train:6501-6600batch: iter_time=1.530e-04, forward_time=0.105, loss_ctc=49.714, loss_att=55.020, acc=0.738, loss=53.428, backward_time=0.098, grad_norm=39.240, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.320e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 23:15:50,559 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 23:15:50,567 (trainer:737) INFO: 22epoch:train:6601-6700batch: iter_time=1.578e-04, forward_time=0.106, loss_ctc=51.139, loss_att=56.898, acc=0.724, loss=55.170, backward_time=0.097, grad_norm=36.781, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.319e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:16:33,168 (trainer:737) INFO: 22epoch:train:6701-6800batch: iter_time=1.400e-04, forward_time=0.105, loss_ctc=47.588, loss_att=49.812, acc=0.736, loss=49.145, backward_time=0.097, grad_norm=40.084, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.319e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 23:17:15,383 (trainer:737) INFO: 22epoch:train:6801-6900batch: iter_time=1.449e-04, forward_time=0.104, loss_ctc=43.458, loss_att=48.897, acc=0.715, loss=47.265, backward_time=0.096, grad_norm=39.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.318e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:17:57,543 (trainer:737) INFO: 22epoch:train:6901-7000batch: iter_time=1.508e-04, forward_time=0.104, loss_ctc=50.891, loss_att=46.970, acc=0.723, loss=48.146, backward_time=0.096, grad_norm=47.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.317e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 23:18:06,422 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 23:18:40,000 (trainer:737) INFO: 22epoch:train:7001-7100batch: iter_time=1.398e-04, forward_time=0.104, loss_ctc=55.088, loss_att=64.916, acc=0.715, loss=61.967, backward_time=0.097, grad_norm=47.533, clip=100.000, loss_scale=1.248e+34, optim_step_time=0.041, optim0_lr0=4.317e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:19:22,549 (trainer:737) INFO: 22epoch:train:7101-7200batch: iter_time=1.365e-04, forward_time=0.103, loss_ctc=45.184, loss_att=59.276, acc=0.712, loss=55.048, backward_time=0.097, grad_norm=37.955, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.316e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:20:04,694 (trainer:737) INFO: 22epoch:train:7201-7300batch: iter_time=1.264e-04, forward_time=0.102, loss_ctc=43.923, loss_att=52.859, acc=0.711, loss=50.178, backward_time=0.096, grad_norm=38.361, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.315e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 23:20:47,028 (trainer:737) INFO: 22epoch:train:7301-7400batch: iter_time=1.303e-04, forward_time=0.104, loss_ctc=51.313, loss_att=60.168, acc=0.721, loss=57.511, backward_time=0.097, grad_norm=41.855, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.315e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:21:29,203 (trainer:737) INFO: 22epoch:train:7401-7500batch: iter_time=1.162e-04, forward_time=0.104, loss_ctc=44.312, loss_att=48.525, acc=0.720, loss=47.261, backward_time=0.096, grad_norm=37.178, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.314e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:21:34,646 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 23:21:54,740 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 23:21:58,415 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2439d9e950>)
+[gpuc02:0/16] 2024-01-14 23:21:58,415 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 23:21:58,419 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 23:26:37,449 (trainer:737) INFO: 22epoch:train:7501-7600batch: iter_time=2.631, forward_time=0.123, loss_ctc=49.576, loss_att=64.429, acc=0.704, loss=59.973, backward_time=0.103, grad_norm=42.244, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.313e-04, train_time=3.082
+[gpuc02:0/16] 2024-01-14 23:27:19,861 (trainer:737) INFO: 22epoch:train:7601-7700batch: iter_time=1.346e-04, forward_time=0.104, loss_ctc=44.377, loss_att=53.477, acc=0.704, loss=50.747, backward_time=0.097, grad_norm=35.336, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.313e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:28:02,299 (trainer:737) INFO: 22epoch:train:7701-7800batch: iter_time=1.335e-04, forward_time=0.105, loss_ctc=48.650, loss_att=48.393, acc=0.744, loss=48.470, backward_time=0.098, grad_norm=34.385, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.312e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:28:44,950 (trainer:737) INFO: 22epoch:train:7801-7900batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=45.958, loss_att=56.419, acc=0.721, loss=53.280, backward_time=0.098, grad_norm=38.899, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.311e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 23:29:27,699 (trainer:737) INFO: 22epoch:train:7901-8000batch: iter_time=1.391e-04, forward_time=0.106, loss_ctc=51.678, loss_att=52.013, acc=0.736, loss=51.913, backward_time=0.098, grad_norm=43.249, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.311e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 23:30:09,948 (trainer:737) INFO: 22epoch:train:8001-8100batch: iter_time=1.320e-04, forward_time=0.105, loss_ctc=44.971, loss_att=53.132, acc=0.711, loss=50.683, backward_time=0.097, grad_norm=39.392, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.310e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:30:53,046 (trainer:737) INFO: 22epoch:train:8101-8200batch: iter_time=1.390e-04, forward_time=0.104, loss_ctc=49.222, loss_att=46.472, acc=0.724, loss=47.297, backward_time=0.097, grad_norm=46.250, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.309e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 23:31:35,469 (trainer:737) INFO: 22epoch:train:8201-8300batch: iter_time=1.682e-04, forward_time=0.106, loss_ctc=50.389, loss_att=52.960, acc=0.734, loss=52.188, backward_time=0.097, grad_norm=40.802, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.309e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:32:17,966 (trainer:737) INFO: 22epoch:train:8301-8400batch: iter_time=1.703e-04, forward_time=0.106, loss_ctc=44.977, loss_att=58.905, acc=0.722, loss=54.726, backward_time=0.097, grad_norm=40.153, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.308e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:33:00,553 (trainer:737) INFO: 22epoch:train:8401-8500batch: iter_time=1.568e-04, forward_time=0.106, loss_ctc=46.770, loss_att=52.433, acc=0.714, loss=50.734, backward_time=0.096, grad_norm=37.165, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.307e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 23:33:43,038 (trainer:737) INFO: 22epoch:train:8501-8600batch: iter_time=1.524e-04, forward_time=0.107, loss_ctc=52.871, loss_att=61.294, acc=0.721, loss=58.767, backward_time=0.097, grad_norm=45.225, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.307e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:34:25,573 (trainer:737) INFO: 22epoch:train:8601-8700batch: iter_time=1.579e-04, forward_time=0.106, loss_ctc=44.758, loss_att=52.882, acc=0.720, loss=50.444, backward_time=0.097, grad_norm=38.180, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.306e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:34:51,069 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 23:35:09,927 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 23:35:13,452 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dc0507430>)
+[gpuc02:0/16] 2024-01-14 23:35:13,452 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 23:35:13,455 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 23:39:27,206 (trainer:737) INFO: 22epoch:train:8701-8800batch: iter_time=2.554, forward_time=0.105, loss_ctc=46.236, loss_att=61.982, acc=0.710, loss=57.258, backward_time=0.098, grad_norm=39.870, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.305e-04, train_time=3.016
+[gpuc02:0/16] 2024-01-14 23:40:09,541 (trainer:737) INFO: 22epoch:train:8801-8900batch: iter_time=1.326e-04, forward_time=0.104, loss_ctc=49.620, loss_att=66.657, acc=0.684, loss=61.546, backward_time=0.098, grad_norm=42.314, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=4.305e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:40:51,682 (trainer:737) INFO: 22epoch:train:8901-9000batch: iter_time=1.261e-04, forward_time=0.103, loss_ctc=38.426, loss_att=40.199, acc=0.751, loss=39.667, backward_time=0.097, grad_norm=32.274, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=4.304e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 23:41:34,718 (trainer:737) INFO: 22epoch:train:9001-9100batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=49.311, loss_att=56.094, acc=0.743, loss=54.059, backward_time=0.099, grad_norm=36.756, clip=100.000, loss_scale=1.859e+34, optim_step_time=0.042, optim0_lr0=4.303e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 23:42:17,006 (trainer:737) INFO: 22epoch:train:9101-9200batch: iter_time=1.286e-04, forward_time=0.104, loss_ctc=51.119, loss_att=58.480, acc=0.726, loss=56.272, backward_time=0.097, grad_norm=39.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.303e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:42:59,277 (trainer:737) INFO: 22epoch:train:9201-9300batch: iter_time=1.446e-04, forward_time=0.104, loss_ctc=47.330, loss_att=53.034, acc=0.745, loss=51.323, backward_time=0.098, grad_norm=38.169, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.302e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:43:41,630 (trainer:737) INFO: 22epoch:train:9301-9400batch: iter_time=1.425e-04, forward_time=0.104, loss_ctc=42.750, loss_att=51.118, acc=0.718, loss=48.608, backward_time=0.097, grad_norm=37.573, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.301e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:44:24,045 (trainer:737) INFO: 22epoch:train:9401-9500batch: iter_time=1.296e-04, forward_time=0.103, loss_ctc=51.416, loss_att=47.629, acc=0.732, loss=48.765, backward_time=0.097, grad_norm=47.660, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.301e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:45:06,740 (trainer:737) INFO: 22epoch:train:9501-9600batch: iter_time=1.312e-04, forward_time=0.108, loss_ctc=53.817, loss_att=65.602, acc=0.727, loss=62.066, backward_time=0.098, grad_norm=43.260, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.300e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 23:45:49,303 (trainer:737) INFO: 22epoch:train:9601-9700batch: iter_time=1.300e-04, forward_time=0.104, loss_ctc=44.274, loss_att=58.605, acc=0.720, loss=54.306, backward_time=0.097, grad_norm=36.910, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.299e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:46:31,497 (trainer:737) INFO: 22epoch:train:9701-9800batch: iter_time=1.401e-04, forward_time=0.104, loss_ctc=44.021, loss_att=53.418, acc=0.715, loss=50.599, backward_time=0.097, grad_norm=39.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.299e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:47:13,850 (trainer:737) INFO: 22epoch:train:9801-9900batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=51.363, loss_att=61.109, acc=0.726, loss=58.185, backward_time=0.098, grad_norm=43.574, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.298e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:47:56,327 (trainer:737) INFO: 22epoch:train:9901-10000batch: iter_time=1.343e-04, forward_time=0.104, loss_ctc=43.877, loss_att=47.617, acc=0.737, loss=46.495, backward_time=0.097, grad_norm=37.119, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.297e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:48:00,719 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 23:48:20,144 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 23:48:23,856 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f243706f610>)
+[gpuc02:0/16] 2024-01-14 23:48:23,856 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 23:48:23,860 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 23:53:06,183 (trainer:737) INFO: 22epoch:train:10001-10100batch: iter_time=2.663, forward_time=0.113, loss_ctc=49.079, loss_att=63.801, acc=0.712, loss=59.384, backward_time=0.098, grad_norm=41.332, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.297e-04, train_time=3.098
+[gpuc02:0/16] 2024-01-14 23:53:48,497 (trainer:737) INFO: 22epoch:train:10101-10200batch: iter_time=1.055e-04, forward_time=0.103, loss_ctc=43.935, loss_att=56.728, acc=0.705, loss=52.890, backward_time=0.097, grad_norm=37.306, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.296e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:54:30,905 (trainer:737) INFO: 22epoch:train:10201-10300batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=47.639, loss_att=47.669, acc=0.755, loss=47.660, backward_time=0.098, grad_norm=34.602, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.295e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:55:13,145 (trainer:737) INFO: 22epoch:train:10301-10400batch: iter_time=1.177e-04, forward_time=0.103, loss_ctc=45.661, loss_att=57.797, acc=0.722, loss=54.156, backward_time=0.097, grad_norm=38.068, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.295e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 23:55:55,554 (trainer:737) INFO: 22epoch:train:10401-10500batch: iter_time=1.167e-04, forward_time=0.104, loss_ctc=51.281, loss_att=54.086, acc=0.744, loss=53.245, backward_time=0.098, grad_norm=42.470, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=4.294e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 23:56:37,874 (trainer:737) INFO: 22epoch:train:10501-10600batch: iter_time=1.383e-04, forward_time=0.103, loss_ctc=44.816, loss_att=52.919, acc=0.725, loss=50.488, backward_time=0.097, grad_norm=37.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.293e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:57:20,369 (trainer:737) INFO: 22epoch:train:10601-10700batch: iter_time=1.338e-04, forward_time=0.103, loss_ctc=49.222, loss_att=47.922, acc=0.732, loss=48.312, backward_time=0.097, grad_norm=47.856, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.293e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:58:02,683 (trainer:737) INFO: 22epoch:train:10701-10800batch: iter_time=1.518e-04, forward_time=0.104, loss_ctc=50.874, loss_att=53.466, acc=0.742, loss=52.688, backward_time=0.097, grad_norm=41.335, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.292e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 23:58:45,166 (trainer:737) INFO: 22epoch:train:10801-10900batch: iter_time=1.493e-04, forward_time=0.105, loss_ctc=44.952, loss_att=59.976, acc=0.727, loss=55.469, backward_time=0.097, grad_norm=39.143, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.291e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 23:59:27,479 (trainer:737) INFO: 22epoch:train:10901-11000batch: iter_time=1.517e-04, forward_time=0.105, loss_ctc=46.717, loss_att=54.266, acc=0.714, loss=52.001, backward_time=0.096, grad_norm=38.295, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.291e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:00:10,064 (trainer:737) INFO: 22epoch:train:11001-11100batch: iter_time=1.598e-04, forward_time=0.106, loss_ctc=52.519, loss_att=61.599, acc=0.723, loss=58.875, backward_time=0.097, grad_norm=42.848, clip=100.000, loss_scale=3.718e+34, optim_step_time=0.041, optim0_lr0=4.290e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 00:00:52,661 (trainer:737) INFO: 22epoch:train:11101-11200batch: iter_time=1.529e-04, forward_time=0.105, loss_ctc=44.585, loss_att=53.372, acc=0.727, loss=50.736, backward_time=0.097, grad_norm=37.786, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.289e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 00:01:20,742 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 00:01:40,019 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 00:01:43,550 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dd476a8c0>)
+[gpuc02:0/16] 2024-01-15 00:01:43,550 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 00:01:43,554 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 00:06:04,834 (trainer:737) INFO: 22epoch:train:11201-11300batch: iter_time=2.518, forward_time=0.117, loss_ctc=45.474, loss_att=61.713, acc=0.715, loss=56.842, backward_time=0.097, grad_norm=37.914, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.289e-04, train_time=3.122
+[gpuc02:0/16] 2024-01-15 00:06:47,091 (trainer:737) INFO: 22epoch:train:11301-11400batch: iter_time=1.403e-04, forward_time=0.105, loss_ctc=49.746, loss_att=64.990, acc=0.682, loss=60.417, backward_time=0.098, grad_norm=44.016, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.288e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 00:07:29,765 (trainer:737) INFO: 22epoch:train:11401-11500batch: iter_time=1.468e-04, forward_time=0.104, loss_ctc=38.437, loss_att=40.323, acc=0.740, loss=39.758, backward_time=0.097, grad_norm=32.346, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.287e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:08:12,265 (trainer:737) INFO: 22epoch:train:11501-11600batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=49.144, loss_att=55.517, acc=0.739, loss=53.605, backward_time=0.098, grad_norm=39.836, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.287e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:08:54,976 (trainer:737) INFO: 22epoch:train:11601-11700batch: iter_time=1.274e-04, forward_time=0.106, loss_ctc=50.950, loss_att=57.209, acc=0.724, loss=55.331, backward_time=0.098, grad_norm=38.552, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.286e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:09:37,710 (trainer:737) INFO: 22epoch:train:11701-11800batch: iter_time=1.335e-04, forward_time=0.105, loss_ctc=47.791, loss_att=50.995, acc=0.735, loss=50.034, backward_time=0.098, grad_norm=40.455, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.285e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:10:19,963 (trainer:737) INFO: 22epoch:train:11801-11900batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=43.218, loss_att=48.978, acc=0.716, loss=47.250, backward_time=0.097, grad_norm=38.223, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.285e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 00:11:02,135 (trainer:737) INFO: 22epoch:train:11901-12000batch: iter_time=1.304e-04, forward_time=0.104, loss_ctc=50.198, loss_att=47.096, acc=0.725, loss=48.027, backward_time=0.097, grad_norm=46.884, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.284e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 00:11:44,620 (trainer:737) INFO: 22epoch:train:12001-12100batch: iter_time=1.330e-04, forward_time=0.105, loss_ctc=53.845, loss_att=65.110, acc=0.716, loss=61.730, backward_time=0.098, grad_norm=47.300, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.283e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:12:27,304 (trainer:737) INFO: 22epoch:train:12101-12200batch: iter_time=1.316e-04, forward_time=0.105, loss_ctc=43.998, loss_att=59.188, acc=0.712, loss=54.631, backward_time=0.098, grad_norm=37.855, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.283e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:13:09,565 (trainer:737) INFO: 22epoch:train:12201-12300batch: iter_time=1.335e-04, forward_time=0.105, loss_ctc=43.343, loss_att=52.806, acc=0.712, loss=49.967, backward_time=0.097, grad_norm=36.513, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.282e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 00:13:52,373 (trainer:737) INFO: 22epoch:train:12301-12400batch: iter_time=1.527e-04, forward_time=0.105, loss_ctc=50.808, loss_att=60.714, acc=0.722, loss=57.742, backward_time=0.097, grad_norm=43.604, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.282e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 00:14:34,714 (trainer:737) INFO: 22epoch:train:12401-12500batch: iter_time=1.227e-04, forward_time=0.104, loss_ctc=43.562, loss_att=48.448, acc=0.719, loss=46.982, backward_time=0.097, grad_norm=36.842, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.281e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:14:41,233 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 00:15:00,686 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 00:15:04,225 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e44a680>)
+[gpuc02:0/16] 2024-01-15 00:15:04,225 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 00:15:04,228 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 00:19:40,082 (trainer:737) INFO: 22epoch:train:12501-12600batch: iter_time=2.520, forward_time=0.107, loss_ctc=48.638, loss_att=65.655, acc=0.711, loss=60.550, backward_time=0.098, grad_norm=39.563, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.280e-04, train_time=3.053
+[gpuc02:0/16] 2024-01-15 00:20:22,377 (trainer:737) INFO: 22epoch:train:12601-12700batch: iter_time=1.525e-04, forward_time=0.104, loss_ctc=43.858, loss_att=57.524, acc=0.705, loss=53.424, backward_time=0.097, grad_norm=37.615, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.280e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:21:04,916 (trainer:737) INFO: 22epoch:train:12701-12800batch: iter_time=1.642e-04, forward_time=0.105, loss_ctc=47.415, loss_att=47.453, acc=0.755, loss=47.442, backward_time=0.098, grad_norm=36.597, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.279e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:21:47,306 (trainer:737) INFO: 22epoch:train:12801-12900batch: iter_time=1.435e-04, forward_time=0.105, loss_ctc=45.285, loss_att=57.966, acc=0.722, loss=54.162, backward_time=0.098, grad_norm=37.735, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.278e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 00:22:30,089 (trainer:737) INFO: 22epoch:train:12901-13000batch: iter_time=1.471e-04, forward_time=0.106, loss_ctc=51.163, loss_att=54.696, acc=0.743, loss=53.636, backward_time=0.099, grad_norm=41.265, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.278e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 00:22:57,135 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 00:23:12,792 (trainer:737) INFO: 22epoch:train:13001-13100batch: iter_time=1.711e-04, forward_time=0.105, loss_ctc=45.221, loss_att=53.799, acc=0.726, loss=51.226, backward_time=0.098, grad_norm=38.651, clip=100.000, loss_scale=5.874e+34, optim_step_time=0.042, optim0_lr0=4.277e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:23:55,121 (trainer:737) INFO: 22epoch:train:13101-13200batch: iter_time=1.548e-04, forward_time=0.105, loss_ctc=48.663, loss_att=48.137, acc=0.733, loss=48.295, backward_time=0.098, grad_norm=46.354, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.276e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:23:58,474 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 00:24:37,763 (trainer:737) INFO: 22epoch:train:13201-13300batch: iter_time=1.490e-04, forward_time=0.106, loss_ctc=50.021, loss_att=52.594, acc=0.744, loss=51.822, backward_time=0.098, grad_norm=38.337, clip=100.000, loss_scale=2.224e+34, optim_step_time=0.042, optim0_lr0=4.276e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 00:25:20,189 (trainer:737) INFO: 22epoch:train:13301-13400batch: iter_time=1.508e-04, forward_time=0.107, loss_ctc=44.891, loss_att=59.608, acc=0.726, loss=55.193, backward_time=0.099, grad_norm=39.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.275e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 00:26:02,485 (trainer:737) INFO: 22epoch:train:13401-13500batch: iter_time=1.580e-04, forward_time=0.105, loss_ctc=46.345, loss_att=53.898, acc=0.716, loss=51.632, backward_time=0.097, grad_norm=38.238, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.274e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:26:45,250 (trainer:737) INFO: 22epoch:train:13501-13600batch: iter_time=1.475e-04, forward_time=0.106, loss_ctc=51.991, loss_att=62.128, acc=0.724, loss=59.087, backward_time=0.098, grad_norm=41.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.274e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:27:51,410 (trainer:737) INFO: 22epoch:train:13601-13700batch: iter_time=1.505e-04, forward_time=0.105, loss_ctc=44.229, loss_att=53.217, acc=0.728, loss=50.521, backward_time=0.097, grad_norm=38.060, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.273e-04, train_time=0.661
+[gpuc02:0/16] 2024-01-15 00:28:17,009 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 00:28:36,873 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 00:28:40,885 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dbc9bdbd0>)
+[gpuc02:0/16] 2024-01-15 00:28:40,885 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 00:28:40,889 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 00:32:57,067 (trainer:737) INFO: 22epoch:train:13701-13800batch: iter_time=2.571, forward_time=0.106, loss_ctc=45.839, loss_att=59.321, acc=0.724, loss=55.277, backward_time=0.098, grad_norm=38.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.272e-04, train_time=3.056
+[gpuc02:0/16] 2024-01-15 00:33:39,694 (trainer:737) INFO: 22epoch:train:13801-13900batch: iter_time=1.630e-04, forward_time=0.105, loss_ctc=49.265, loss_att=63.374, acc=0.691, loss=59.141, backward_time=0.098, grad_norm=43.497, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.272e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 00:34:21,978 (trainer:737) INFO: 22epoch:train:13901-14000batch: iter_time=1.411e-04, forward_time=0.104, loss_ctc=37.905, loss_att=38.978, acc=0.752, loss=38.656, backward_time=0.096, grad_norm=32.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.271e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:35:04,512 (trainer:737) INFO: 22epoch:train:14001-14100batch: iter_time=1.424e-04, forward_time=0.106, loss_ctc=48.847, loss_att=55.036, acc=0.745, loss=53.180, backward_time=0.098, grad_norm=38.444, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.270e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:35:47,108 (trainer:737) INFO: 22epoch:train:14101-14200batch: iter_time=1.438e-04, forward_time=0.106, loss_ctc=50.922, loss_att=57.064, acc=0.729, loss=55.221, backward_time=0.099, grad_norm=38.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.270e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 00:36:29,784 (trainer:737) INFO: 22epoch:train:14201-14300batch: iter_time=1.995e-04, forward_time=0.107, loss_ctc=47.224, loss_att=51.526, acc=0.748, loss=50.236, backward_time=0.099, grad_norm=40.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.269e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 00:37:12,177 (trainer:737) INFO: 22epoch:train:14301-14400batch: iter_time=1.767e-04, forward_time=0.106, loss_ctc=42.336, loss_att=50.743, acc=0.720, loss=48.221, backward_time=0.098, grad_norm=38.889, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.269e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 00:37:54,463 (trainer:737) INFO: 22epoch:train:14401-14500batch: iter_time=1.568e-04, forward_time=0.105, loss_ctc=49.665, loss_att=45.903, acc=0.736, loss=47.032, backward_time=0.097, grad_norm=45.464, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.268e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:38:37,405 (trainer:737) INFO: 22epoch:train:14501-14600batch: iter_time=1.590e-04, forward_time=0.106, loss_ctc=53.456, loss_att=64.720, acc=0.729, loss=61.341, backward_time=0.099, grad_norm=44.639, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.267e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 00:39:19,912 (trainer:737) INFO: 22epoch:train:14601-14700batch: iter_time=1.597e-04, forward_time=0.106, loss_ctc=43.889, loss_att=58.427, acc=0.722, loss=54.065, backward_time=0.098, grad_norm=37.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.267e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:40:02,217 (trainer:737) INFO: 22epoch:train:14701-14800batch: iter_time=1.722e-04, forward_time=0.106, loss_ctc=43.170, loss_att=52.727, acc=0.716, loss=49.860, backward_time=0.098, grad_norm=37.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.266e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 00:40:44,774 (trainer:737) INFO: 22epoch:train:14801-14900batch: iter_time=1.685e-04, forward_time=0.107, loss_ctc=50.241, loss_att=60.638, acc=0.727, loss=57.519, backward_time=0.098, grad_norm=42.930, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.265e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 00:41:27,823 (trainer:737) INFO: 22epoch:train:14901-15000batch: iter_time=1.405e-04, forward_time=0.106, loss_ctc=43.821, loss_att=47.257, acc=0.738, loss=46.227, backward_time=0.097, grad_norm=36.232, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.265e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 01:01:42,888 (trainer:343) INFO: 22epoch results: [train] iter_time=0.219, forward_time=0.106, loss_ctc=48.034, loss_att=55.251, acc=0.722, loss=53.086, backward_time=0.098, grad_norm=40.411, clip=100.000, loss_scale=2.340e+34, optim_step_time=0.042, optim0_lr0=4.314e-04, train_time=0.654, time=2 hours, 43 minutes and 45.65 seconds, total_count=330000, gpu_max_cached_mem_GB=26.227, [valid] loss_ctc=52.806, cer_ctc=0.278, loss_att=52.770, acc=0.586, cer=0.368, wer=0.999, loss=52.781, time=20 minutes and 5.05 seconds, total_count=102762, gpu_max_cached_mem_GB=26.227
+[gpuc02:0/16] 2024-01-15 01:01:47,956 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-15 01:01:48,016 (trainer:272) INFO: 23/45epoch started. Estimated time to finish: 2 days, 21 hours and 56 minutes
+[gpuc02:0/16] 2024-01-15 01:01:48,028 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 01:02:07,218 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 01:02:10,807 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f259cd0f3d0>)
+[gpuc02:0/16] 2024-01-15 01:02:10,807 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 01:02:10,810 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 01:06:44,940 (trainer:737) INFO: 23epoch:train:1-100batch: iter_time=2.489, forward_time=0.118, loss_ctc=45.475, loss_att=41.432, acc=0.752, loss=42.645, backward_time=0.099, grad_norm=41.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.264e-04, train_time=2.969
+[gpuc02:0/16] 2024-01-15 01:07:27,123 (trainer:737) INFO: 23epoch:train:101-200batch: iter_time=1.334e-04, forward_time=0.105, loss_ctc=42.797, loss_att=54.636, acc=0.717, loss=51.084, backward_time=0.098, grad_norm=39.912, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.263e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 01:08:10,815 (trainer:737) INFO: 23epoch:train:201-300batch: iter_time=1.253e-04, forward_time=0.105, loss_ctc=53.222, loss_att=49.677, acc=0.742, loss=50.740, backward_time=0.098, grad_norm=49.239, clip=100.000, loss_scale=3.988e+34, optim_step_time=0.042, optim0_lr0=4.263e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-15 01:08:53,881 (trainer:737) INFO: 23epoch:train:301-400batch: iter_time=1.237e-04, forward_time=0.104, loss_ctc=41.481, loss_att=54.702, acc=0.708, loss=50.736, backward_time=0.097, grad_norm=38.723, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.262e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 01:09:37,197 (trainer:737) INFO: 23epoch:train:401-500batch: iter_time=1.337e-04, forward_time=0.106, loss_ctc=59.068, loss_att=66.544, acc=0.707, loss=64.301, backward_time=0.098, grad_norm=51.679, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.261e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 01:10:19,881 (trainer:737) INFO: 23epoch:train:501-600batch: iter_time=1.369e-04, forward_time=0.105, loss_ctc=49.372, loss_att=51.359, acc=0.746, loss=50.763, backward_time=0.098, grad_norm=40.689, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.261e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 01:11:03,975 (trainer:737) INFO: 23epoch:train:601-700batch: iter_time=1.311e-04, forward_time=0.106, loss_ctc=48.616, loss_att=58.653, acc=0.717, loss=55.642, backward_time=0.098, grad_norm=40.542, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.260e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-15 01:11:16,995 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 01:11:48,220 (trainer:737) INFO: 23epoch:train:701-800batch: iter_time=1.364e-04, forward_time=0.118, loss_ctc=47.517, loss_att=45.624, acc=0.744, loss=46.192, backward_time=0.101, grad_norm=41.449, clip=100.000, loss_scale=2.706e+34, optim_step_time=0.043, optim0_lr0=4.259e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-15 01:12:31,891 (trainer:737) INFO: 23epoch:train:801-900batch: iter_time=1.311e-04, forward_time=0.109, loss_ctc=53.062, loss_att=50.818, acc=0.732, loss=51.491, backward_time=0.101, grad_norm=47.941, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.259e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-15 01:13:14,005 (trainer:737) INFO: 23epoch:train:901-1000batch: iter_time=1.298e-04, forward_time=0.105, loss_ctc=47.322, loss_att=58.803, acc=0.735, loss=55.359, backward_time=0.098, grad_norm=40.735, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.258e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 01:13:58,312 (trainer:737) INFO: 23epoch:train:1001-1100batch: iter_time=1.442e-04, forward_time=0.108, loss_ctc=44.903, loss_att=51.700, acc=0.720, loss=49.661, backward_time=0.099, grad_norm=39.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.258e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-15 01:14:45,689 (trainer:737) INFO: 23epoch:train:1101-1200batch: iter_time=1.280e-04, forward_time=0.111, loss_ctc=58.894, loss_att=73.533, acc=0.676, loss=69.141, backward_time=0.099, grad_norm=56.578, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.257e-04, train_time=0.474
+[gpuc02:0/16] 2024-01-15 01:15:34,525 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 01:15:53,269 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 01:15:56,856 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f242590b220>)
+[gpuc02:0/16] 2024-01-15 01:15:56,856 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 01:15:56,859 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 01:22:16,316 (trainer:737) INFO: 23epoch:train:1201-1300batch: iter_time=3.882, forward_time=0.135, loss_ctc=42.031, loss_att=39.265, acc=0.747, loss=40.095, backward_time=0.101, grad_norm=38.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.256e-04, train_time=4.506
+[gpuc02:0/16] 2024-01-15 01:22:58,490 (trainer:737) INFO: 23epoch:train:1301-1400batch: iter_time=1.448e-04, forward_time=0.104, loss_ctc=42.922, loss_att=47.155, acc=0.729, loss=45.885, backward_time=0.097, grad_norm=40.156, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.256e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 01:23:41,404 (trainer:737) INFO: 23epoch:train:1401-1500batch: iter_time=1.583e-04, forward_time=0.104, loss_ctc=50.651, loss_att=58.469, acc=0.732, loss=56.124, backward_time=0.098, grad_norm=46.095, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.255e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 01:24:23,592 (trainer:737) INFO: 23epoch:train:1501-1600batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=41.905, loss_att=49.876, acc=0.733, loss=47.484, backward_time=0.097, grad_norm=39.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.254e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 01:25:05,752 (trainer:737) INFO: 23epoch:train:1601-1700batch: iter_time=1.561e-04, forward_time=0.104, loss_ctc=44.480, loss_att=57.080, acc=0.702, loss=53.300, backward_time=0.097, grad_norm=39.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.254e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 01:25:48,682 (trainer:737) INFO: 23epoch:train:1701-1800batch: iter_time=1.598e-04, forward_time=0.106, loss_ctc=56.013, loss_att=59.637, acc=0.735, loss=58.550, backward_time=0.098, grad_norm=46.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.253e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 01:26:31,521 (trainer:737) INFO: 23epoch:train:1801-1900batch: iter_time=1.684e-04, forward_time=0.105, loss_ctc=45.857, loss_att=51.930, acc=0.738, loss=50.108, backward_time=0.099, grad_norm=36.485, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.252e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 01:27:14,055 (trainer:737) INFO: 23epoch:train:1901-2000batch: iter_time=1.887e-04, forward_time=0.104, loss_ctc=48.991, loss_att=51.747, acc=0.731, loss=50.920, backward_time=0.100, grad_norm=42.659, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.252e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 01:27:56,360 (trainer:737) INFO: 23epoch:train:2001-2100batch: iter_time=1.608e-04, forward_time=0.105, loss_ctc=51.686, loss_att=47.980, acc=0.747, loss=49.092, backward_time=0.098, grad_norm=42.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.251e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:28:39,153 (trainer:737) INFO: 23epoch:train:2101-2200batch: iter_time=1.710e-04, forward_time=0.105, loss_ctc=50.437, loss_att=60.158, acc=0.722, loss=57.242, backward_time=0.098, grad_norm=41.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.251e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 01:29:21,672 (trainer:737) INFO: 23epoch:train:2201-2300batch: iter_time=1.661e-04, forward_time=0.104, loss_ctc=41.709, loss_att=49.477, acc=0.742, loss=47.146, backward_time=0.097, grad_norm=34.361, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.250e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 01:30:04,134 (trainer:737) INFO: 23epoch:train:2301-2400batch: iter_time=1.680e-04, forward_time=0.105, loss_ctc=56.909, loss_att=63.741, acc=0.692, loss=61.691, backward_time=0.098, grad_norm=50.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.249e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 01:30:46,448 (trainer:737) INFO: 23epoch:train:2401-2500batch: iter_time=1.397e-04, forward_time=0.104, loss_ctc=42.334, loss_att=55.745, acc=0.709, loss=51.722, backward_time=0.097, grad_norm=44.046, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.249e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:30:51,807 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 01:31:11,207 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 01:31:14,845 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d370dc0>)
+[gpuc02:0/16] 2024-01-15 01:31:14,845 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 01:31:14,848 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 01:35:48,797 (trainer:737) INFO: 23epoch:train:2501-2600batch: iter_time=2.599, forward_time=0.105, loss_ctc=43.710, loss_att=44.464, acc=0.737, loss=44.237, backward_time=0.096, grad_norm=40.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.248e-04, train_time=3.023
+[gpuc02:0/16] 2024-01-15 01:36:31,079 (trainer:737) INFO: 23epoch:train:2601-2700batch: iter_time=1.358e-04, forward_time=0.103, loss_ctc=41.208, loss_att=53.509, acc=0.713, loss=49.819, backward_time=0.096, grad_norm=36.651, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.247e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:37:13,737 (trainer:737) INFO: 23epoch:train:2701-2800batch: iter_time=1.402e-04, forward_time=0.104, loss_ctc=48.081, loss_att=51.718, acc=0.729, loss=50.627, backward_time=0.097, grad_norm=47.151, clip=100.000, loss_scale=3.510e+34, optim_step_time=0.041, optim0_lr0=4.247e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 01:37:55,965 (trainer:737) INFO: 23epoch:train:2801-2900batch: iter_time=1.427e-04, forward_time=0.103, loss_ctc=40.192, loss_att=53.745, acc=0.712, loss=49.679, backward_time=0.096, grad_norm=38.367, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.246e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 01:38:38,918 (trainer:737) INFO: 23epoch:train:2901-3000batch: iter_time=1.403e-04, forward_time=0.107, loss_ctc=56.577, loss_att=64.085, acc=0.703, loss=61.832, backward_time=0.097, grad_norm=51.854, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.245e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 01:39:21,509 (trainer:737) INFO: 23epoch:train:3001-3100batch: iter_time=1.129e-04, forward_time=0.106, loss_ctc=47.919, loss_att=51.985, acc=0.733, loss=50.765, backward_time=0.097, grad_norm=40.000, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.245e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 01:39:45,984 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 01:40:03,802 (trainer:737) INFO: 23epoch:train:3101-3200batch: iter_time=1.343e-04, forward_time=0.104, loss_ctc=47.483, loss_att=56.119, acc=0.715, loss=53.528, backward_time=0.097, grad_norm=39.505, clip=100.000, loss_scale=3.273e+34, optim_step_time=0.041, optim0_lr0=4.244e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:40:46,088 (trainer:737) INFO: 23epoch:train:3201-3300batch: iter_time=1.366e-04, forward_time=0.106, loss_ctc=45.674, loss_att=44.353, acc=0.747, loss=44.749, backward_time=0.097, grad_norm=40.376, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.243e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:41:29,396 (trainer:737) INFO: 23epoch:train:3301-3400batch: iter_time=1.351e-04, forward_time=0.105, loss_ctc=51.371, loss_att=48.184, acc=0.740, loss=49.140, backward_time=0.097, grad_norm=44.809, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.243e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 01:42:12,037 (trainer:737) INFO: 23epoch:train:3401-3500batch: iter_time=1.320e-04, forward_time=0.106, loss_ctc=46.661, loss_att=58.794, acc=0.731, loss=55.154, backward_time=0.097, grad_norm=39.968, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.242e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 01:42:54,393 (trainer:737) INFO: 23epoch:train:3501-3600batch: iter_time=1.335e-04, forward_time=0.105, loss_ctc=44.040, loss_att=51.668, acc=0.718, loss=49.380, backward_time=0.097, grad_norm=40.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.242e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:43:36,935 (trainer:737) INFO: 23epoch:train:3601-3700batch: iter_time=1.339e-04, forward_time=0.107, loss_ctc=57.279, loss_att=70.571, acc=0.677, loss=66.584, backward_time=0.098, grad_norm=53.245, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.241e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 01:44:03,689 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 01:44:23,124 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 01:44:26,827 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1db83a7d90>)
+[gpuc02:0/16] 2024-01-15 01:44:26,827 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 01:44:26,830 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 01:48:54,323 (trainer:737) INFO: 23epoch:train:3701-3800batch: iter_time=2.583, forward_time=0.105, loss_ctc=40.497, loss_att=39.778, acc=0.746, loss=39.994, backward_time=0.096, grad_norm=37.413, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.240e-04, train_time=3.174
+[gpuc02:0/16] 2024-01-15 01:49:36,564 (trainer:737) INFO: 23epoch:train:3801-3900batch: iter_time=1.385e-04, forward_time=0.105, loss_ctc=42.749, loss_att=48.476, acc=0.728, loss=46.758, backward_time=0.096, grad_norm=39.763, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.240e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 01:50:18,887 (trainer:737) INFO: 23epoch:train:3901-4000batch: iter_time=1.346e-04, forward_time=0.105, loss_ctc=49.377, loss_att=58.497, acc=0.732, loss=55.761, backward_time=0.097, grad_norm=43.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.239e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:51:01,171 (trainer:737) INFO: 23epoch:train:4001-4100batch: iter_time=1.350e-04, forward_time=0.105, loss_ctc=41.918, loss_att=50.330, acc=0.735, loss=47.806, backward_time=0.097, grad_norm=38.824, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.238e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:51:43,603 (trainer:737) INFO: 23epoch:train:4101-4200batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=43.652, loss_att=57.067, acc=0.706, loss=53.043, backward_time=0.097, grad_norm=39.283, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.238e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 01:52:26,349 (trainer:737) INFO: 23epoch:train:4201-4300batch: iter_time=1.448e-04, forward_time=0.106, loss_ctc=56.030, loss_att=61.008, acc=0.736, loss=59.514, backward_time=0.098, grad_norm=46.605, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.237e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 01:53:08,640 (trainer:737) INFO: 23epoch:train:4301-4400batch: iter_time=1.415e-04, forward_time=0.104, loss_ctc=44.782, loss_att=51.574, acc=0.741, loss=49.537, backward_time=0.097, grad_norm=36.153, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.236e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:53:51,429 (trainer:737) INFO: 23epoch:train:4401-4500batch: iter_time=1.490e-04, forward_time=0.105, loss_ctc=48.213, loss_att=51.705, acc=0.733, loss=50.657, backward_time=0.097, grad_norm=41.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.236e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 01:54:33,764 (trainer:737) INFO: 23epoch:train:4501-4600batch: iter_time=1.484e-04, forward_time=0.105, loss_ctc=50.837, loss_att=47.213, acc=0.749, loss=48.300, backward_time=0.097, grad_norm=44.866, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.235e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 01:55:16,487 (trainer:737) INFO: 23epoch:train:4601-4700batch: iter_time=1.340e-04, forward_time=0.105, loss_ctc=49.419, loss_att=59.624, acc=0.723, loss=56.563, backward_time=0.098, grad_norm=40.674, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.235e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 01:55:59,052 (trainer:737) INFO: 23epoch:train:4701-4800batch: iter_time=1.340e-04, forward_time=0.104, loss_ctc=41.541, loss_att=49.503, acc=0.741, loss=47.114, backward_time=0.097, grad_norm=34.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.234e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 01:56:41,494 (trainer:737) INFO: 23epoch:train:4801-4900batch: iter_time=1.509e-04, forward_time=0.105, loss_ctc=55.771, loss_att=63.242, acc=0.693, loss=61.000, backward_time=0.097, grad_norm=51.520, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.233e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 01:57:24,065 (trainer:737) INFO: 23epoch:train:4901-5000batch: iter_time=1.346e-04, forward_time=0.107, loss_ctc=41.618, loss_att=55.947, acc=0.709, loss=51.648, backward_time=0.097, grad_norm=41.708, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.233e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 01:57:31,692 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 01:57:50,601 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 01:57:54,191 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e093ff5b0>)
+[gpuc02:0/16] 2024-01-15 01:57:54,191 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 01:57:54,194 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 02:03:11,998 (trainer:737) INFO: 23epoch:train:5001-5100batch: iter_time=3.036, forward_time=0.114, loss_ctc=43.490, loss_att=40.986, acc=0.755, loss=41.737, backward_time=0.098, grad_norm=39.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.232e-04, train_time=3.479
+[gpuc02:0/16] 2024-01-15 02:03:54,486 (trainer:737) INFO: 23epoch:train:5101-5200batch: iter_time=1.619e-04, forward_time=0.106, loss_ctc=40.900, loss_att=54.219, acc=0.721, loss=50.223, backward_time=0.096, grad_norm=37.240, clip=100.000, loss_scale=2.949e+34, optim_step_time=0.041, optim0_lr0=4.231e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:04:36,961 (trainer:737) INFO: 23epoch:train:5201-5300batch: iter_time=1.400e-04, forward_time=0.106, loss_ctc=47.743, loss_att=49.758, acc=0.747, loss=49.154, backward_time=0.097, grad_norm=43.870, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.231e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:04:47,121 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 02:05:19,320 (trainer:737) INFO: 23epoch:train:5301-5400batch: iter_time=1.645e-04, forward_time=0.106, loss_ctc=39.605, loss_att=52.503, acc=0.718, loss=48.634, backward_time=0.096, grad_norm=35.432, clip=100.000, loss_scale=2.559e+34, optim_step_time=0.041, optim0_lr0=4.230e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:06:01,966 (trainer:737) INFO: 23epoch:train:5401-5500batch: iter_time=1.603e-04, forward_time=0.107, loss_ctc=54.952, loss_att=65.739, acc=0.712, loss=62.503, backward_time=0.097, grad_norm=52.086, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.230e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:06:44,810 (trainer:737) INFO: 23epoch:train:5501-5600batch: iter_time=1.477e-04, forward_time=0.107, loss_ctc=47.834, loss_att=49.798, acc=0.755, loss=49.209, backward_time=0.098, grad_norm=37.383, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.229e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 02:07:27,290 (trainer:737) INFO: 23epoch:train:5601-5700batch: iter_time=1.510e-04, forward_time=0.106, loss_ctc=47.205, loss_att=57.436, acc=0.721, loss=54.367, backward_time=0.097, grad_norm=37.964, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.228e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:08:09,545 (trainer:737) INFO: 23epoch:train:5701-5800batch: iter_time=1.412e-04, forward_time=0.106, loss_ctc=44.765, loss_att=43.768, acc=0.752, loss=44.067, backward_time=0.097, grad_norm=39.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.228e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 02:08:52,217 (trainer:737) INFO: 23epoch:train:5801-5900batch: iter_time=1.434e-04, forward_time=0.107, loss_ctc=50.601, loss_att=49.976, acc=0.738, loss=50.163, backward_time=0.097, grad_norm=43.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.227e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:09:34,672 (trainer:737) INFO: 23epoch:train:5901-6000batch: iter_time=1.405e-04, forward_time=0.107, loss_ctc=45.986, loss_att=58.237, acc=0.737, loss=54.562, backward_time=0.097, grad_norm=37.143, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.226e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:10:17,237 (trainer:737) INFO: 23epoch:train:6001-6100batch: iter_time=1.551e-04, forward_time=0.106, loss_ctc=43.879, loss_att=51.290, acc=0.723, loss=49.066, backward_time=0.096, grad_norm=39.334, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.226e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:10:59,898 (trainer:737) INFO: 23epoch:train:6101-6200batch: iter_time=1.496e-04, forward_time=0.107, loss_ctc=55.652, loss_att=71.615, acc=0.681, loss=66.826, backward_time=0.098, grad_norm=50.194, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.225e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:11:30,760 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 02:11:49,974 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 02:11:53,547 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1df2cd7370>)
+[gpuc02:0/16] 2024-01-15 02:11:53,548 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 02:11:53,551 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 02:16:19,396 (trainer:737) INFO: 23epoch:train:6201-6300batch: iter_time=2.582, forward_time=0.105, loss_ctc=40.386, loss_att=39.800, acc=0.748, loss=39.976, backward_time=0.096, grad_norm=38.362, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.225e-04, train_time=3.195
+[gpuc02:0/16] 2024-01-15 02:17:01,681 (trainer:737) INFO: 23epoch:train:6301-6400batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=41.596, loss_att=49.775, acc=0.711, loss=47.321, backward_time=0.096, grad_norm=40.495, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.224e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:17:44,106 (trainer:737) INFO: 23epoch:train:6401-6500batch: iter_time=1.559e-04, forward_time=0.107, loss_ctc=48.519, loss_att=57.490, acc=0.729, loss=54.799, backward_time=0.097, grad_norm=44.652, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.223e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:18:26,584 (trainer:737) INFO: 23epoch:train:6501-6600batch: iter_time=1.753e-04, forward_time=0.106, loss_ctc=40.909, loss_att=51.118, acc=0.724, loss=48.055, backward_time=0.097, grad_norm=38.468, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.223e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:19:09,351 (trainer:737) INFO: 23epoch:train:6601-6700batch: iter_time=1.873e-04, forward_time=0.106, loss_ctc=43.016, loss_att=54.291, acc=0.708, loss=50.909, backward_time=0.097, grad_norm=39.307, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.222e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:19:51,884 (trainer:737) INFO: 23epoch:train:6701-6800batch: iter_time=1.760e-04, forward_time=0.107, loss_ctc=54.271, loss_att=62.180, acc=0.720, loss=59.807, backward_time=0.098, grad_norm=45.870, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.221e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:20:34,371 (trainer:737) INFO: 23epoch:train:6801-6900batch: iter_time=1.781e-04, forward_time=0.106, loss_ctc=45.091, loss_att=50.702, acc=0.732, loss=49.019, backward_time=0.097, grad_norm=34.585, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.221e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:21:17,268 (trainer:737) INFO: 23epoch:train:6901-7000batch: iter_time=1.608e-04, forward_time=0.106, loss_ctc=47.623, loss_att=51.655, acc=0.728, loss=50.446, backward_time=0.097, grad_norm=42.752, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.220e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 02:21:59,710 (trainer:737) INFO: 23epoch:train:7001-7100batch: iter_time=1.722e-04, forward_time=0.107, loss_ctc=49.417, loss_att=46.279, acc=0.753, loss=47.220, backward_time=0.098, grad_norm=42.990, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.220e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:22:42,220 (trainer:737) INFO: 23epoch:train:7101-7200batch: iter_time=1.778e-04, forward_time=0.106, loss_ctc=49.052, loss_att=57.579, acc=0.718, loss=55.021, backward_time=0.097, grad_norm=42.047, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.219e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:23:24,605 (trainer:737) INFO: 23epoch:train:7201-7300batch: iter_time=1.630e-04, forward_time=0.107, loss_ctc=40.979, loss_att=49.386, acc=0.740, loss=46.864, backward_time=0.097, grad_norm=35.999, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.218e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:24:07,048 (trainer:737) INFO: 23epoch:train:7301-7400batch: iter_time=1.752e-04, forward_time=0.106, loss_ctc=55.054, loss_att=62.372, acc=0.694, loss=60.177, backward_time=0.097, grad_norm=50.180, clip=100.000, loss_scale=3.655e+34, optim_step_time=0.042, optim0_lr0=4.218e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:24:49,626 (trainer:737) INFO: 23epoch:train:7401-7500batch: iter_time=1.469e-04, forward_time=0.106, loss_ctc=41.266, loss_att=54.809, acc=0.702, loss=50.746, backward_time=0.097, grad_norm=41.300, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.217e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:24:54,706 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 02:25:14,752 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 02:25:18,567 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f247cef8760>)
+[gpuc02:0/16] 2024-01-15 02:25:18,567 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 02:25:18,570 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 02:30:03,020 (trainer:737) INFO: 23epoch:train:7501-7600batch: iter_time=2.706, forward_time=0.106, loss_ctc=42.483, loss_att=41.036, acc=0.744, loss=41.470, backward_time=0.097, grad_norm=38.549, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.216e-04, train_time=3.134
+[gpuc02:0/16] 2024-01-15 02:30:21,592 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 02:30:45,441 (trainer:737) INFO: 23epoch:train:7601-7700batch: iter_time=1.955e-04, forward_time=0.105, loss_ctc=40.890, loss_att=52.657, acc=0.716, loss=49.127, backward_time=0.098, grad_norm=34.568, clip=100.000, loss_scale=2.979e+34, optim_step_time=0.042, optim0_lr0=4.216e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:31:27,810 (trainer:737) INFO: 23epoch:train:7701-7800batch: iter_time=1.846e-04, forward_time=0.105, loss_ctc=48.408, loss_att=53.423, acc=0.732, loss=51.919, backward_time=0.097, grad_norm=46.155, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.215e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:32:10,678 (trainer:737) INFO: 23epoch:train:7801-7900batch: iter_time=1.784e-04, forward_time=0.105, loss_ctc=39.318, loss_att=51.513, acc=0.721, loss=47.854, backward_time=0.097, grad_norm=37.187, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.215e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 02:32:53,084 (trainer:737) INFO: 23epoch:train:7901-8000batch: iter_time=1.790e-04, forward_time=0.105, loss_ctc=53.865, loss_att=63.589, acc=0.707, loss=60.671, backward_time=0.097, grad_norm=49.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.214e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:33:35,578 (trainer:737) INFO: 23epoch:train:8001-8100batch: iter_time=1.799e-04, forward_time=0.106, loss_ctc=47.374, loss_att=51.048, acc=0.737, loss=49.946, backward_time=0.097, grad_norm=37.224, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.213e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:34:18,029 (trainer:737) INFO: 23epoch:train:8101-8200batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=46.332, loss_att=55.842, acc=0.716, loss=52.989, backward_time=0.097, grad_norm=37.118, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.213e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:35:00,878 (trainer:737) INFO: 23epoch:train:8201-8300batch: iter_time=1.789e-04, forward_time=0.104, loss_ctc=45.182, loss_att=43.829, acc=0.751, loss=44.235, backward_time=0.097, grad_norm=39.376, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.212e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 02:35:43,578 (trainer:737) INFO: 23epoch:train:8301-8400batch: iter_time=1.291e-04, forward_time=0.106, loss_ctc=49.841, loss_att=47.200, acc=0.742, loss=47.992, backward_time=0.097, grad_norm=44.268, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.211e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:36:26,396 (trainer:737) INFO: 23epoch:train:8401-8500batch: iter_time=1.669e-04, forward_time=0.105, loss_ctc=45.870, loss_att=57.980, acc=0.733, loss=54.347, backward_time=0.098, grad_norm=40.814, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.211e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 02:37:08,991 (trainer:737) INFO: 23epoch:train:8501-8600batch: iter_time=1.480e-04, forward_time=0.107, loss_ctc=43.501, loss_att=51.457, acc=0.721, loss=49.070, backward_time=0.097, grad_norm=38.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.210e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:37:51,890 (trainer:737) INFO: 23epoch:train:8601-8700batch: iter_time=1.503e-04, forward_time=0.105, loss_ctc=55.344, loss_att=69.269, acc=0.679, loss=65.092, backward_time=0.098, grad_norm=51.302, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.210e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 02:38:20,401 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 02:38:39,983 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 02:38:43,557 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1df3803700>)
+[gpuc02:0/16] 2024-01-15 02:38:43,557 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 02:38:43,560 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 02:43:02,214 (trainer:737) INFO: 23epoch:train:8701-8800batch: iter_time=2.678, forward_time=0.105, loss_ctc=39.944, loss_att=40.138, acc=0.747, loss=40.080, backward_time=0.097, grad_norm=37.590, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.209e-04, train_time=3.103
+[gpuc02:0/16] 2024-01-15 02:43:44,455 (trainer:737) INFO: 23epoch:train:8801-8900batch: iter_time=1.514e-04, forward_time=0.105, loss_ctc=41.582, loss_att=48.114, acc=0.731, loss=46.154, backward_time=0.097, grad_norm=40.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.208e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 02:44:27,163 (trainer:737) INFO: 23epoch:train:8901-9000batch: iter_time=1.575e-04, forward_time=0.106, loss_ctc=48.699, loss_att=59.192, acc=0.732, loss=56.044, backward_time=0.098, grad_norm=45.481, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.208e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:45:09,517 (trainer:737) INFO: 23epoch:train:9001-9100batch: iter_time=1.684e-04, forward_time=0.105, loss_ctc=40.633, loss_att=49.664, acc=0.738, loss=46.955, backward_time=0.097, grad_norm=37.827, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.207e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:45:51,764 (trainer:737) INFO: 23epoch:train:9101-9200batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=42.605, loss_att=57.218, acc=0.705, loss=52.834, backward_time=0.097, grad_norm=38.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.206e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 02:46:34,338 (trainer:737) INFO: 23epoch:train:9201-9300batch: iter_time=1.474e-04, forward_time=0.105, loss_ctc=54.429, loss_att=59.637, acc=0.740, loss=58.074, backward_time=0.097, grad_norm=48.934, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.206e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:47:16,756 (trainer:737) INFO: 23epoch:train:9301-9400batch: iter_time=1.662e-04, forward_time=0.104, loss_ctc=44.539, loss_att=51.777, acc=0.742, loss=49.606, backward_time=0.097, grad_norm=37.927, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.205e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 02:47:59,467 (trainer:737) INFO: 23epoch:train:9401-9500batch: iter_time=1.624e-04, forward_time=0.104, loss_ctc=47.554, loss_att=51.118, acc=0.735, loss=50.049, backward_time=0.097, grad_norm=38.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.205e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:48:42,182 (trainer:737) INFO: 23epoch:train:9501-9600batch: iter_time=1.582e-04, forward_time=0.104, loss_ctc=49.174, loss_att=46.863, acc=0.750, loss=47.556, backward_time=0.097, grad_norm=42.688, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.204e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:49:24,709 (trainer:737) INFO: 23epoch:train:9601-9700batch: iter_time=1.346e-04, forward_time=0.105, loss_ctc=48.374, loss_att=60.066, acc=0.723, loss=56.558, backward_time=0.097, grad_norm=40.604, clip=100.000, loss_scale=3.240e+34, optim_step_time=0.041, optim0_lr0=4.203e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 02:50:07,429 (trainer:737) INFO: 23epoch:train:9701-9800batch: iter_time=1.439e-04, forward_time=0.104, loss_ctc=40.714, loss_att=49.029, acc=0.745, loss=46.534, backward_time=0.097, grad_norm=33.255, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.203e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 02:50:50,072 (trainer:737) INFO: 23epoch:train:9801-9900batch: iter_time=1.650e-04, forward_time=0.105, loss_ctc=54.488, loss_att=63.417, acc=0.693, loss=60.739, backward_time=0.097, grad_norm=49.417, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.202e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:51:10,469 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 02:51:32,646 (trainer:737) INFO: 23epoch:train:9901-10000batch: iter_time=1.370e-04, forward_time=0.105, loss_ctc=40.872, loss_att=56.134, acc=0.708, loss=51.555, backward_time=0.097, grad_norm=41.597, clip=100.000, loss_scale=3.063e+34, optim_step_time=0.041, optim0_lr0=4.201e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:51:39,215 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 02:51:58,444 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 02:52:02,039 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f243cf9da80>)
+[gpuc02:0/16] 2024-01-15 02:52:02,039 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 02:52:02,042 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 02:56:54,406 (trainer:737) INFO: 23epoch:train:10001-10100batch: iter_time=2.616, forward_time=0.126, loss_ctc=42.954, loss_att=42.627, acc=0.742, loss=42.725, backward_time=0.099, grad_norm=39.525, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.201e-04, train_time=3.217
+[gpuc02:0/16] 2024-01-15 02:57:36,748 (trainer:737) INFO: 23epoch:train:10101-10200batch: iter_time=1.734e-04, forward_time=0.104, loss_ctc=40.896, loss_att=53.294, acc=0.714, loss=49.575, backward_time=0.097, grad_norm=36.417, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.200e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:58:19,399 (trainer:737) INFO: 23epoch:train:10201-10300batch: iter_time=1.849e-04, forward_time=0.105, loss_ctc=46.724, loss_att=51.172, acc=0.734, loss=49.838, backward_time=0.098, grad_norm=48.176, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.200e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 02:59:01,731 (trainer:737) INFO: 23epoch:train:10301-10400batch: iter_time=1.856e-04, forward_time=0.104, loss_ctc=38.955, loss_att=53.320, acc=0.714, loss=49.010, backward_time=0.098, grad_norm=41.581, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.199e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 02:59:44,731 (trainer:737) INFO: 23epoch:train:10401-10500batch: iter_time=1.974e-04, forward_time=0.105, loss_ctc=54.100, loss_att=64.137, acc=0.706, loss=61.126, backward_time=0.098, grad_norm=52.307, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.198e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 03:00:27,139 (trainer:737) INFO: 23epoch:train:10501-10600batch: iter_time=1.686e-04, forward_time=0.105, loss_ctc=47.435, loss_att=51.264, acc=0.735, loss=50.115, backward_time=0.098, grad_norm=38.260, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.198e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:01:09,603 (trainer:737) INFO: 23epoch:train:10601-10700batch: iter_time=1.968e-04, forward_time=0.105, loss_ctc=46.857, loss_att=55.704, acc=0.719, loss=53.050, backward_time=0.098, grad_norm=39.749, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.197e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:01:51,980 (trainer:737) INFO: 23epoch:train:10701-10800batch: iter_time=1.893e-04, forward_time=0.105, loss_ctc=44.139, loss_att=43.611, acc=0.751, loss=43.769, backward_time=0.097, grad_norm=38.257, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.197e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:02:34,504 (trainer:737) INFO: 23epoch:train:10801-10900batch: iter_time=2.200e-04, forward_time=0.105, loss_ctc=49.997, loss_att=47.292, acc=0.744, loss=48.103, backward_time=0.098, grad_norm=45.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.196e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 03:03:17,150 (trainer:737) INFO: 23epoch:train:10901-11000batch: iter_time=2.393e-04, forward_time=0.106, loss_ctc=45.465, loss_att=58.272, acc=0.734, loss=54.430, backward_time=0.100, grad_norm=38.468, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.195e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 03:03:59,953 (trainer:737) INFO: 23epoch:train:11001-11100batch: iter_time=2.300e-04, forward_time=0.105, loss_ctc=43.488, loss_att=51.078, acc=0.721, loss=48.801, backward_time=0.099, grad_norm=40.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.195e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 03:04:42,928 (trainer:737) INFO: 23epoch:train:11101-11200batch: iter_time=2.108e-04, forward_time=0.107, loss_ctc=55.560, loss_att=69.143, acc=0.682, loss=65.068, backward_time=0.099, grad_norm=51.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.194e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 03:05:09,486 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 03:05:28,964 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 03:05:32,655 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da11d1420>)
+[gpuc02:0/16] 2024-01-15 03:05:32,655 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 03:05:32,659 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 03:10:07,206 (trainer:737) INFO: 23epoch:train:11201-11300batch: iter_time=2.816, forward_time=0.106, loss_ctc=39.495, loss_att=38.690, acc=0.748, loss=38.932, backward_time=0.096, grad_norm=36.698, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.193e-04, train_time=3.243
+[gpuc02:0/16] 2024-01-15 03:10:49,428 (trainer:737) INFO: 23epoch:train:11301-11400batch: iter_time=2.234e-04, forward_time=0.104, loss_ctc=41.149, loss_att=46.106, acc=0.719, loss=44.619, backward_time=0.096, grad_norm=40.166, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.193e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 03:11:31,867 (trainer:737) INFO: 23epoch:train:11401-11500batch: iter_time=2.014e-04, forward_time=0.105, loss_ctc=47.534, loss_att=55.161, acc=0.732, loss=52.873, backward_time=0.097, grad_norm=43.438, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.192e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:12:14,183 (trainer:737) INFO: 23epoch:train:11501-11600batch: iter_time=2.079e-04, forward_time=0.105, loss_ctc=40.640, loss_att=49.463, acc=0.730, loss=46.816, backward_time=0.097, grad_norm=36.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.192e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:12:56,486 (trainer:737) INFO: 23epoch:train:11601-11700batch: iter_time=2.004e-04, forward_time=0.104, loss_ctc=42.282, loss_att=54.239, acc=0.704, loss=50.652, backward_time=0.097, grad_norm=40.439, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.191e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:13:38,914 (trainer:737) INFO: 23epoch:train:11701-11800batch: iter_time=1.820e-04, forward_time=0.105, loss_ctc=54.065, loss_att=59.910, acc=0.725, loss=58.157, backward_time=0.098, grad_norm=49.122, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.190e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:14:21,669 (trainer:737) INFO: 23epoch:train:11801-11900batch: iter_time=1.833e-04, forward_time=0.105, loss_ctc=44.431, loss_att=49.588, acc=0.736, loss=48.041, backward_time=0.098, grad_norm=35.281, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.190e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 03:14:50,216 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 03:15:04,544 (trainer:737) INFO: 23epoch:train:11901-12000batch: iter_time=1.855e-04, forward_time=0.104, loss_ctc=47.839, loss_att=50.996, acc=0.732, loss=50.049, backward_time=0.097, grad_norm=41.132, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.041, optim0_lr0=4.189e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 03:15:46,890 (trainer:737) INFO: 23epoch:train:12001-12100batch: iter_time=1.730e-04, forward_time=0.105, loss_ctc=49.090, loss_att=46.862, acc=0.754, loss=47.530, backward_time=0.098, grad_norm=43.363, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.189e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:16:29,963 (trainer:737) INFO: 23epoch:train:12101-12200batch: iter_time=1.782e-04, forward_time=0.105, loss_ctc=48.944, loss_att=57.692, acc=0.719, loss=55.068, backward_time=0.098, grad_norm=43.117, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.188e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 03:17:12,471 (trainer:737) INFO: 23epoch:train:12201-12300batch: iter_time=1.954e-04, forward_time=0.104, loss_ctc=40.586, loss_att=49.018, acc=0.742, loss=46.489, backward_time=0.097, grad_norm=36.018, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.187e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 03:17:54,899 (trainer:737) INFO: 23epoch:train:12301-12400batch: iter_time=1.831e-04, forward_time=0.105, loss_ctc=54.455, loss_att=61.741, acc=0.695, loss=59.555, backward_time=0.098, grad_norm=49.211, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.187e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:18:37,408 (trainer:737) INFO: 23epoch:train:12401-12500batch: iter_time=1.698e-04, forward_time=0.104, loss_ctc=40.608, loss_att=54.212, acc=0.704, loss=50.131, backward_time=0.097, grad_norm=40.745, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.186e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 03:18:44,500 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 03:19:03,433 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 03:19:07,244 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da0853d60>)
+[gpuc02:0/16] 2024-01-15 03:19:07,244 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 03:19:07,247 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 03:23:58,632 (trainer:737) INFO: 23epoch:train:12501-12600batch: iter_time=2.512, forward_time=0.103, loss_ctc=42.317, loss_att=42.157, acc=0.756, loss=42.205, backward_time=0.098, grad_norm=38.236, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.186e-04, train_time=3.212
+[gpuc02:0/16] 2024-01-15 03:24:40,997 (trainer:737) INFO: 23epoch:train:12601-12700batch: iter_time=1.338e-04, forward_time=0.104, loss_ctc=40.506, loss_att=56.188, acc=0.719, loss=51.484, backward_time=0.097, grad_norm=35.587, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.185e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:25:23,407 (trainer:737) INFO: 23epoch:train:12701-12800batch: iter_time=1.374e-04, forward_time=0.104, loss_ctc=47.372, loss_att=50.708, acc=0.746, loss=49.707, backward_time=0.098, grad_norm=44.237, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.184e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:26:05,699 (trainer:737) INFO: 23epoch:train:12801-12900batch: iter_time=1.575e-04, forward_time=0.104, loss_ctc=39.015, loss_att=52.740, acc=0.718, loss=48.622, backward_time=0.097, grad_norm=36.562, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.184e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:26:48,179 (trainer:737) INFO: 23epoch:train:12901-13000batch: iter_time=1.497e-04, forward_time=0.104, loss_ctc=53.116, loss_att=65.364, acc=0.716, loss=61.689, backward_time=0.097, grad_norm=48.545, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.183e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 03:27:30,621 (trainer:737) INFO: 23epoch:train:13001-13100batch: iter_time=1.379e-04, forward_time=0.104, loss_ctc=47.465, loss_att=50.976, acc=0.753, loss=49.923, backward_time=0.098, grad_norm=39.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.182e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:28:13,062 (trainer:737) INFO: 23epoch:train:13101-13200batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=46.890, loss_att=58.058, acc=0.724, loss=54.708, backward_time=0.098, grad_norm=39.385, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.182e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:28:55,319 (trainer:737) INFO: 23epoch:train:13201-13300batch: iter_time=1.571e-04, forward_time=0.103, loss_ctc=44.552, loss_att=43.862, acc=0.752, loss=44.069, backward_time=0.097, grad_norm=38.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.181e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 03:29:38,066 (trainer:737) INFO: 23epoch:train:13301-13400batch: iter_time=1.209e-04, forward_time=0.104, loss_ctc=49.094, loss_att=50.334, acc=0.739, loss=49.962, backward_time=0.098, grad_norm=43.035, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.181e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 03:30:22,125 (trainer:737) INFO: 23epoch:train:13401-13500batch: iter_time=1.361e-04, forward_time=0.104, loss_ctc=45.318, loss_att=58.022, acc=0.740, loss=54.211, backward_time=0.098, grad_norm=36.783, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.180e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-15 03:31:04,796 (trainer:737) INFO: 23epoch:train:13501-13600batch: iter_time=1.484e-04, forward_time=0.104, loss_ctc=42.936, loss_att=51.196, acc=0.724, loss=48.718, backward_time=0.097, grad_norm=37.899, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.179e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 03:31:47,826 (trainer:737) INFO: 23epoch:train:13601-13700batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=55.005, loss_att=71.580, acc=0.682, loss=66.608, backward_time=0.098, grad_norm=51.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.179e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 03:32:14,352 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 03:32:33,928 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 03:32:37,508 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dc0263700>)
+[gpuc02:0/16] 2024-01-15 03:32:37,508 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 03:32:37,511 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 03:36:50,751 (trainer:737) INFO: 23epoch:train:13701-13800batch: iter_time=2.481, forward_time=0.102, loss_ctc=39.244, loss_att=38.806, acc=0.750, loss=38.937, backward_time=0.096, grad_norm=37.206, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.178e-04, train_time=3.029
+[gpuc02:0/16] 2024-01-15 03:37:32,784 (trainer:737) INFO: 23epoch:train:13801-13900batch: iter_time=1.008e-04, forward_time=0.103, loss_ctc=40.822, loss_att=48.283, acc=0.715, loss=46.045, backward_time=0.096, grad_norm=39.839, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.178e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 03:38:15,403 (trainer:737) INFO: 23epoch:train:13901-14000batch: iter_time=1.347e-04, forward_time=0.104, loss_ctc=48.074, loss_att=56.612, acc=0.732, loss=54.050, backward_time=0.097, grad_norm=45.827, clip=100.000, loss_scale=2.783e+34, optim_step_time=0.041, optim0_lr0=4.177e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 03:38:57,559 (trainer:737) INFO: 23epoch:train:14001-14100batch: iter_time=1.385e-04, forward_time=0.104, loss_ctc=40.599, loss_att=50.145, acc=0.729, loss=47.281, backward_time=0.097, grad_norm=37.337, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.176e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 03:39:39,810 (trainer:737) INFO: 23epoch:train:14101-14200batch: iter_time=1.551e-04, forward_time=0.104, loss_ctc=42.208, loss_att=53.854, acc=0.709, loss=50.360, backward_time=0.097, grad_norm=37.826, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.176e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 03:39:46,209 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 03:40:22,315 (trainer:737) INFO: 23epoch:train:14201-14300batch: iter_time=1.595e-04, forward_time=0.106, loss_ctc=52.777, loss_att=59.938, acc=0.723, loss=57.790, backward_time=0.098, grad_norm=48.515, clip=100.000, loss_scale=2.371e+34, optim_step_time=0.041, optim0_lr0=4.175e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 03:41:05,100 (trainer:737) INFO: 23epoch:train:14301-14400batch: iter_time=1.645e-04, forward_time=0.109, loss_ctc=44.173, loss_att=49.828, acc=0.736, loss=48.132, backward_time=0.097, grad_norm=35.570, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.175e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 03:41:47,810 (trainer:737) INFO: 23epoch:train:14401-14500batch: iter_time=1.642e-04, forward_time=0.105, loss_ctc=47.789, loss_att=51.174, acc=0.731, loss=50.158, backward_time=0.097, grad_norm=41.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.174e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 03:42:30,238 (trainer:737) INFO: 23epoch:train:14501-14600batch: iter_time=1.844e-04, forward_time=0.105, loss_ctc=48.781, loss_att=46.390, acc=0.754, loss=47.108, backward_time=0.097, grad_norm=42.453, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.173e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:43:12,701 (trainer:737) INFO: 23epoch:train:14601-14700batch: iter_time=1.622e-04, forward_time=0.106, loss_ctc=48.688, loss_att=57.310, acc=0.719, loss=54.724, backward_time=0.098, grad_norm=40.921, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.173e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:43:54,999 (trainer:737) INFO: 23epoch:train:14701-14800batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=40.291, loss_att=48.587, acc=0.742, loss=46.098, backward_time=0.097, grad_norm=37.152, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.172e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 03:44:37,378 (trainer:737) INFO: 23epoch:train:14801-14900batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=54.207, loss_att=61.719, acc=0.694, loss=59.466, backward_time=0.097, grad_norm=50.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.172e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 03:45:19,957 (trainer:737) INFO: 23epoch:train:14901-15000batch: iter_time=1.174e-04, forward_time=0.104, loss_ctc=40.576, loss_att=54.458, acc=0.702, loss=50.294, backward_time=0.097, grad_norm=42.035, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.171e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 04:05:28,059 (trainer:343) INFO: 23epoch results: [train] iter_time=0.220, forward_time=0.106, loss_ctc=46.570, loss_att=53.332, acc=0.727, loss=51.303, backward_time=0.097, grad_norm=41.669, clip=100.000, loss_scale=2.354e+34, optim_step_time=0.041, optim0_lr0=4.217e-04, train_time=0.654, time=2 hours, 43 minutes and 41.2 seconds, total_count=345000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=55.549, cer_ctc=0.276, loss_att=56.743, acc=0.588, cer=0.336, wer=0.998, loss=56.385, time=19 minutes and 58.63 seconds, total_count=107433, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 04:05:33,381 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-15 04:05:33,466 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/17epoch.pth, exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/18epoch.pth
+[gpuc02:0/16] 2024-01-15 04:05:33,467 (trainer:272) INFO: 24/45epoch started. Estimated time to finish: 2 days, 18 hours and 59 minutes
+[gpuc02:0/16] 2024-01-15 04:05:33,477 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 04:05:52,572 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 04:05:56,014 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d5d6b8040>)
+[gpuc02:0/16] 2024-01-15 04:05:56,014 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 04:05:56,017 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 04:10:22,807 (trainer:737) INFO: 24epoch:train:1-100batch: iter_time=2.350, forward_time=0.104, loss_ctc=46.687, loss_att=54.969, acc=0.721, loss=52.485, backward_time=0.098, grad_norm=41.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.170e-04, train_time=2.893
+[gpuc02:0/16] 2024-01-15 04:11:04,794 (trainer:737) INFO: 24epoch:train:101-200batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=44.012, loss_att=45.740, acc=0.740, loss=45.221, backward_time=0.098, grad_norm=39.403, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.170e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 04:11:47,121 (trainer:737) INFO: 24epoch:train:201-300batch: iter_time=1.274e-04, forward_time=0.104, loss_ctc=47.360, loss_att=56.225, acc=0.717, loss=53.566, backward_time=0.098, grad_norm=43.775, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.169e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 04:12:30,421 (trainer:737) INFO: 24epoch:train:301-400batch: iter_time=1.336e-04, forward_time=0.103, loss_ctc=42.486, loss_att=51.276, acc=0.710, loss=48.639, backward_time=0.098, grad_norm=39.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.169e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 04:13:14,272 (trainer:737) INFO: 24epoch:train:401-500batch: iter_time=1.373e-04, forward_time=0.108, loss_ctc=46.461, loss_att=60.993, acc=0.716, loss=56.633, backward_time=0.099, grad_norm=41.749, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.168e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 04:13:56,402 (trainer:737) INFO: 24epoch:train:501-600batch: iter_time=1.395e-04, forward_time=0.104, loss_ctc=42.900, loss_att=48.582, acc=0.721, loss=46.877, backward_time=0.098, grad_norm=41.125, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.167e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 04:14:38,617 (trainer:737) INFO: 24epoch:train:601-700batch: iter_time=1.158e-04, forward_time=0.107, loss_ctc=42.091, loss_att=54.228, acc=0.714, loss=50.587, backward_time=0.098, grad_norm=38.868, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.167e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 04:15:21,100 (trainer:737) INFO: 24epoch:train:701-800batch: iter_time=1.416e-04, forward_time=0.105, loss_ctc=45.202, loss_att=51.215, acc=0.720, loss=49.411, backward_time=0.098, grad_norm=40.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.166e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 04:16:03,068 (trainer:737) INFO: 24epoch:train:801-900batch: iter_time=1.508e-04, forward_time=0.104, loss_ctc=49.433, loss_att=61.306, acc=0.691, loss=57.744, backward_time=0.097, grad_norm=44.888, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.165e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 04:16:45,348 (trainer:737) INFO: 24epoch:train:901-1000batch: iter_time=1.332e-04, forward_time=0.104, loss_ctc=54.687, loss_att=55.744, acc=0.718, loss=55.427, backward_time=0.098, grad_norm=59.529, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.165e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 04:17:27,390 (trainer:737) INFO: 24epoch:train:1001-1100batch: iter_time=1.461e-04, forward_time=0.104, loss_ctc=45.828, loss_att=56.433, acc=0.729, loss=53.251, backward_time=0.099, grad_norm=38.722, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.164e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 04:18:09,797 (trainer:737) INFO: 24epoch:train:1101-1200batch: iter_time=1.534e-04, forward_time=0.105, loss_ctc=47.891, loss_att=43.604, acc=0.754, loss=44.890, backward_time=0.099, grad_norm=42.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.164e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:18:38,780 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 04:18:57,435 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 04:19:00,859 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e88d630>)
+[gpuc02:0/16] 2024-01-15 04:19:00,859 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 04:19:00,862 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 04:23:56,188 (trainer:737) INFO: 24epoch:train:1201-1300batch: iter_time=2.697, forward_time=0.135, loss_ctc=54.309, loss_att=69.704, acc=0.691, loss=65.085, backward_time=0.106, grad_norm=45.325, clip=100.000, loss_scale=3.842e+34, optim_step_time=0.043, optim0_lr0=4.163e-04, train_time=3.464
+[gpuc02:0/16] 2024-01-15 04:24:21,316 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 04:24:38,354 (trainer:737) INFO: 24epoch:train:1301-1400batch: iter_time=1.198e-04, forward_time=0.102, loss_ctc=43.957, loss_att=46.005, acc=0.739, loss=45.391, backward_time=0.097, grad_norm=39.795, clip=100.000, loss_scale=3.315e+34, optim_step_time=0.041, optim0_lr0=4.162e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 04:25:21,075 (trainer:737) INFO: 24epoch:train:1401-1500batch: iter_time=1.211e-04, forward_time=0.104, loss_ctc=46.452, loss_att=62.345, acc=0.722, loss=57.577, backward_time=0.098, grad_norm=42.413, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.162e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 04:26:03,141 (trainer:737) INFO: 24epoch:train:1501-1600batch: iter_time=1.230e-04, forward_time=0.103, loss_ctc=41.864, loss_att=46.818, acc=0.737, loss=45.332, backward_time=0.097, grad_norm=38.432, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.161e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 04:26:45,523 (trainer:737) INFO: 24epoch:train:1601-1700batch: iter_time=1.170e-04, forward_time=0.103, loss_ctc=44.987, loss_att=59.749, acc=0.708, loss=55.321, backward_time=0.098, grad_norm=38.021, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.161e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:27:27,724 (trainer:737) INFO: 24epoch:train:1701-1800batch: iter_time=1.249e-04, forward_time=0.103, loss_ctc=42.041, loss_att=54.008, acc=0.744, loss=50.418, backward_time=0.097, grad_norm=39.272, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.160e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 04:28:10,254 (trainer:737) INFO: 24epoch:train:1801-1900batch: iter_time=1.357e-04, forward_time=0.103, loss_ctc=40.090, loss_att=40.338, acc=0.741, loss=40.264, backward_time=0.097, grad_norm=37.960, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.159e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 04:28:52,712 (trainer:737) INFO: 24epoch:train:1901-2000batch: iter_time=1.468e-04, forward_time=0.104, loss_ctc=46.132, loss_att=66.539, acc=0.697, loss=60.417, backward_time=0.099, grad_norm=41.223, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.159e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:29:34,837 (trainer:737) INFO: 24epoch:train:2001-2100batch: iter_time=1.463e-04, forward_time=0.104, loss_ctc=43.132, loss_att=53.897, acc=0.720, loss=50.668, backward_time=0.098, grad_norm=40.719, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.158e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 04:30:17,617 (trainer:737) INFO: 24epoch:train:2101-2200batch: iter_time=1.421e-04, forward_time=0.109, loss_ctc=48.145, loss_att=57.778, acc=0.717, loss=54.888, backward_time=0.098, grad_norm=41.324, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.158e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:31:00,038 (trainer:737) INFO: 24epoch:train:2201-2300batch: iter_time=1.425e-04, forward_time=0.106, loss_ctc=54.954, loss_att=62.028, acc=0.742, loss=59.906, backward_time=0.098, grad_norm=52.062, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.157e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:31:42,889 (trainer:737) INFO: 24epoch:train:2301-2400batch: iter_time=1.350e-04, forward_time=0.110, loss_ctc=47.433, loss_att=52.614, acc=0.753, loss=51.059, backward_time=0.098, grad_norm=38.659, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.156e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:32:26,306 (trainer:737) INFO: 24epoch:train:2401-2500batch: iter_time=1.179e-04, forward_time=0.104, loss_ctc=50.326, loss_att=58.803, acc=0.699, loss=56.259, backward_time=0.098, grad_norm=44.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.156e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 04:32:33,770 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 04:32:53,877 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 04:32:57,596 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2439babc70>)
+[gpuc02:0/16] 2024-01-15 04:32:57,596 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 04:32:57,600 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 04:38:21,781 (trainer:737) INFO: 24epoch:train:2501-2600batch: iter_time=2.669, forward_time=0.118, loss_ctc=44.334, loss_att=54.401, acc=0.721, loss=51.381, backward_time=0.100, grad_norm=39.757, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.155e-04, train_time=3.555
+[gpuc02:0/16] 2024-01-15 04:39:04,223 (trainer:737) INFO: 24epoch:train:2601-2700batch: iter_time=1.082e-04, forward_time=0.103, loss_ctc=42.909, loss_att=45.663, acc=0.743, loss=44.837, backward_time=0.097, grad_norm=38.484, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.155e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:39:46,660 (trainer:737) INFO: 24epoch:train:2701-2800batch: iter_time=1.141e-04, forward_time=0.104, loss_ctc=46.376, loss_att=58.015, acc=0.715, loss=54.523, backward_time=0.098, grad_norm=41.454, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.154e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:40:28,942 (trainer:737) INFO: 24epoch:train:2801-2900batch: iter_time=1.144e-04, forward_time=0.103, loss_ctc=41.870, loss_att=51.045, acc=0.712, loss=48.293, backward_time=0.097, grad_norm=39.181, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.154e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 04:41:11,772 (trainer:737) INFO: 24epoch:train:2901-3000batch: iter_time=1.211e-04, forward_time=0.104, loss_ctc=44.690, loss_att=59.803, acc=0.720, loss=55.269, backward_time=0.098, grad_norm=38.534, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.153e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:41:54,625 (trainer:737) INFO: 24epoch:train:3001-3100batch: iter_time=1.208e-04, forward_time=0.104, loss_ctc=41.696, loss_att=47.702, acc=0.727, loss=45.900, backward_time=0.097, grad_norm=39.643, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.152e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:42:37,155 (trainer:737) INFO: 24epoch:train:3101-3200batch: iter_time=1.148e-04, forward_time=0.104, loss_ctc=41.661, loss_att=54.418, acc=0.713, loss=50.591, backward_time=0.097, grad_norm=39.821, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.152e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 04:43:19,647 (trainer:737) INFO: 24epoch:train:3201-3300batch: iter_time=1.177e-04, forward_time=0.104, loss_ctc=43.921, loss_att=50.438, acc=0.725, loss=48.483, backward_time=0.097, grad_norm=38.826, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.151e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 04:44:01,897 (trainer:737) INFO: 24epoch:train:3301-3400batch: iter_time=1.162e-04, forward_time=0.104, loss_ctc=47.789, loss_att=61.324, acc=0.693, loss=57.263, backward_time=0.097, grad_norm=43.125, clip=100.000, loss_scale=2.908e+34, optim_step_time=0.042, optim0_lr0=4.151e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 04:44:44,416 (trainer:737) INFO: 24epoch:train:3401-3500batch: iter_time=1.274e-04, forward_time=0.105, loss_ctc=52.232, loss_att=55.739, acc=0.721, loss=54.687, backward_time=0.097, grad_norm=52.265, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.150e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 04:45:27,222 (trainer:737) INFO: 24epoch:train:3501-3600batch: iter_time=1.114e-04, forward_time=0.105, loss_ctc=44.564, loss_att=55.809, acc=0.732, loss=52.436, backward_time=0.098, grad_norm=39.843, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.149e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:46:09,469 (trainer:737) INFO: 24epoch:train:3601-3700batch: iter_time=1.155e-04, forward_time=0.104, loss_ctc=46.900, loss_att=44.336, acc=0.751, loss=45.105, backward_time=0.097, grad_norm=42.301, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.149e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 04:46:33,047 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 04:46:52,803 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 04:46:56,557 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2485bcac20>)
+[gpuc02:0/16] 2024-01-15 04:46:56,558 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 04:46:56,561 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 04:52:01,705 (trainer:737) INFO: 24epoch:train:3701-3800batch: iter_time=2.384, forward_time=0.110, loss_ctc=53.144, loss_att=68.796, acc=0.693, loss=64.100, backward_time=0.099, grad_norm=46.230, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.148e-04, train_time=3.522
+[gpuc02:0/16] 2024-01-15 04:52:44,556 (trainer:737) INFO: 24epoch:train:3801-3900batch: iter_time=9.480e-05, forward_time=0.103, loss_ctc=42.590, loss_att=45.208, acc=0.742, loss=44.422, backward_time=0.097, grad_norm=39.196, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.148e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 04:53:26,997 (trainer:737) INFO: 24epoch:train:3901-4000batch: iter_time=9.848e-05, forward_time=0.104, loss_ctc=46.056, loss_att=61.030, acc=0.724, loss=56.538, backward_time=0.098, grad_norm=42.651, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.147e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:54:09,734 (trainer:737) INFO: 24epoch:train:4001-4100batch: iter_time=1.033e-04, forward_time=0.104, loss_ctc=40.911, loss_att=45.934, acc=0.740, loss=44.427, backward_time=0.097, grad_norm=37.640, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.146e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 04:54:52,716 (trainer:737) INFO: 24epoch:train:4101-4200batch: iter_time=9.996e-05, forward_time=0.104, loss_ctc=44.689, loss_att=58.589, acc=0.712, loss=54.419, backward_time=0.097, grad_norm=38.717, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.146e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 04:55:35,013 (trainer:737) INFO: 24epoch:train:4201-4300batch: iter_time=9.773e-05, forward_time=0.104, loss_ctc=41.418, loss_att=53.675, acc=0.746, loss=49.998, backward_time=0.097, grad_norm=40.428, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.145e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 04:56:17,174 (trainer:737) INFO: 24epoch:train:4301-4400batch: iter_time=1.128e-04, forward_time=0.103, loss_ctc=39.869, loss_att=40.457, acc=0.741, loss=40.280, backward_time=0.096, grad_norm=36.627, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.145e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 04:56:59,589 (trainer:737) INFO: 24epoch:train:4401-4500batch: iter_time=1.341e-04, forward_time=0.104, loss_ctc=45.274, loss_att=65.940, acc=0.698, loss=59.740, backward_time=0.098, grad_norm=41.271, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.144e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 04:57:41,837 (trainer:737) INFO: 24epoch:train:4501-4600batch: iter_time=1.397e-04, forward_time=0.103, loss_ctc=42.285, loss_att=53.611, acc=0.722, loss=50.213, backward_time=0.097, grad_norm=41.010, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.143e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 04:58:24,156 (trainer:737) INFO: 24epoch:train:4601-4700batch: iter_time=1.355e-04, forward_time=0.104, loss_ctc=47.805, loss_att=57.733, acc=0.717, loss=54.754, backward_time=0.098, grad_norm=41.340, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.143e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 04:59:07,386 (trainer:737) INFO: 24epoch:train:4701-4800batch: iter_time=1.591e-04, forward_time=0.104, loss_ctc=52.835, loss_att=56.876, acc=0.744, loss=55.663, backward_time=0.098, grad_norm=53.230, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.142e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 04:59:50,148 (trainer:737) INFO: 24epoch:train:4801-4900batch: iter_time=1.292e-04, forward_time=0.104, loss_ctc=46.541, loss_att=52.448, acc=0.754, loss=50.676, backward_time=0.098, grad_norm=40.312, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.142e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 05:00:32,485 (trainer:737) INFO: 24epoch:train:4901-5000batch: iter_time=1.272e-04, forward_time=0.104, loss_ctc=49.900, loss_att=58.208, acc=0.701, loss=55.716, backward_time=0.098, grad_norm=43.658, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.141e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:00:38,387 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 05:00:58,212 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 05:01:01,858 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1df2ae2740>)
+[gpuc02:0/16] 2024-01-15 05:01:01,858 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 05:01:01,872 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 05:07:31,936 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 05:07:31,943 (trainer:737) INFO: 24epoch:train:5001-5100batch: iter_time=3.387, forward_time=0.105, loss_ctc=43.650, loss_att=51.303, acc=0.741, loss=49.007, backward_time=0.097, grad_norm=40.166, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.140e-04, train_time=4.194
+[gpuc02:0/16] 2024-01-15 05:08:14,117 (trainer:737) INFO: 24epoch:train:5101-5200batch: iter_time=1.013e-04, forward_time=0.103, loss_ctc=42.483, loss_att=46.066, acc=0.740, loss=44.991, backward_time=0.097, grad_norm=37.122, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.140e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 05:08:56,584 (trainer:737) INFO: 24epoch:train:5201-5300batch: iter_time=1.068e-04, forward_time=0.103, loss_ctc=45.549, loss_att=58.605, acc=0.726, loss=54.688, backward_time=0.097, grad_norm=41.073, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.139e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:09:38,906 (trainer:737) INFO: 24epoch:train:5301-5400batch: iter_time=1.153e-04, forward_time=0.104, loss_ctc=41.094, loss_att=51.668, acc=0.723, loss=48.496, backward_time=0.098, grad_norm=37.850, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.139e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:10:21,237 (trainer:737) INFO: 24epoch:train:5401-5500batch: iter_time=1.227e-04, forward_time=0.104, loss_ctc=43.904, loss_att=58.035, acc=0.733, loss=53.796, backward_time=0.098, grad_norm=38.298, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.138e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:11:03,655 (trainer:737) INFO: 24epoch:train:5501-5600batch: iter_time=1.154e-04, forward_time=0.103, loss_ctc=41.502, loss_att=47.545, acc=0.736, loss=45.732, backward_time=0.097, grad_norm=40.938, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.137e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:11:46,069 (trainer:737) INFO: 24epoch:train:5601-5700batch: iter_time=1.125e-04, forward_time=0.103, loss_ctc=41.031, loss_att=54.614, acc=0.723, loss=50.539, backward_time=0.097, grad_norm=39.585, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.137e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:12:28,671 (trainer:737) INFO: 24epoch:train:5701-5800batch: iter_time=1.004e-04, forward_time=0.106, loss_ctc=43.768, loss_att=53.605, acc=0.722, loss=50.654, backward_time=0.097, grad_norm=39.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.136e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:13:11,733 (trainer:737) INFO: 24epoch:train:5801-5900batch: iter_time=1.070e-04, forward_time=0.104, loss_ctc=47.205, loss_att=62.496, acc=0.702, loss=57.909, backward_time=0.097, grad_norm=44.140, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.136e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 05:13:54,538 (trainer:737) INFO: 24epoch:train:5901-6000batch: iter_time=1.052e-04, forward_time=0.103, loss_ctc=52.889, loss_att=56.644, acc=0.731, loss=55.518, backward_time=0.097, grad_norm=53.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.135e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 05:14:36,916 (trainer:737) INFO: 24epoch:train:6001-6100batch: iter_time=1.105e-04, forward_time=0.104, loss_ctc=44.624, loss_att=56.615, acc=0.739, loss=53.017, backward_time=0.098, grad_norm=38.908, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.135e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:15:19,141 (trainer:737) INFO: 24epoch:train:6101-6200batch: iter_time=1.155e-04, forward_time=0.103, loss_ctc=45.815, loss_att=44.312, acc=0.758, loss=44.763, backward_time=0.097, grad_norm=41.292, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.134e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 05:15:45,426 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 05:16:05,057 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 05:16:08,753 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e84fad390>)
+[gpuc02:0/16] 2024-01-15 05:16:08,753 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 05:16:08,756 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 05:20:40,814 (trainer:737) INFO: 24epoch:train:6201-6300batch: iter_time=2.402, forward_time=0.104, loss_ctc=52.426, loss_att=66.205, acc=0.705, loss=62.072, backward_time=0.098, grad_norm=42.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.133e-04, train_time=3.217
+[gpuc02:0/16] 2024-01-15 05:21:23,396 (trainer:737) INFO: 24epoch:train:6301-6400batch: iter_time=1.054e-04, forward_time=0.104, loss_ctc=42.227, loss_att=43.908, acc=0.742, loss=43.404, backward_time=0.097, grad_norm=38.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.133e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:22:06,217 (trainer:737) INFO: 24epoch:train:6401-6500batch: iter_time=1.085e-04, forward_time=0.104, loss_ctc=45.718, loss_att=59.232, acc=0.728, loss=55.178, backward_time=0.098, grad_norm=43.349, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.132e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 05:22:48,624 (trainer:737) INFO: 24epoch:train:6501-6600batch: iter_time=1.081e-04, forward_time=0.105, loss_ctc=41.185, loss_att=45.810, acc=0.738, loss=44.422, backward_time=0.097, grad_norm=36.975, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.132e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:23:32,013 (trainer:737) INFO: 24epoch:train:6601-6700batch: iter_time=1.199e-04, forward_time=0.105, loss_ctc=43.930, loss_att=58.770, acc=0.713, loss=54.318, backward_time=0.098, grad_norm=36.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.131e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 05:24:14,416 (trainer:737) INFO: 24epoch:train:6701-6800batch: iter_time=9.807e-05, forward_time=0.104, loss_ctc=41.078, loss_att=53.456, acc=0.746, loss=49.742, backward_time=0.097, grad_norm=42.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.130e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:24:56,565 (trainer:737) INFO: 24epoch:train:6801-6900batch: iter_time=9.772e-05, forward_time=0.102, loss_ctc=39.638, loss_att=39.990, acc=0.744, loss=39.885, backward_time=0.096, grad_norm=36.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.130e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 05:25:39,137 (trainer:737) INFO: 24epoch:train:6901-7000batch: iter_time=9.951e-05, forward_time=0.103, loss_ctc=45.724, loss_att=66.710, acc=0.696, loss=60.414, backward_time=0.097, grad_norm=40.424, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.129e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:26:21,442 (trainer:737) INFO: 24epoch:train:7001-7100batch: iter_time=1.080e-04, forward_time=0.105, loss_ctc=42.050, loss_att=53.128, acc=0.720, loss=49.805, backward_time=0.097, grad_norm=40.444, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.129e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:27:03,859 (trainer:737) INFO: 24epoch:train:7101-7200batch: iter_time=1.152e-04, forward_time=0.106, loss_ctc=47.342, loss_att=56.269, acc=0.723, loss=53.591, backward_time=0.098, grad_norm=41.651, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.128e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:27:46,411 (trainer:737) INFO: 24epoch:train:7201-7300batch: iter_time=1.102e-04, forward_time=0.106, loss_ctc=52.648, loss_att=57.890, acc=0.744, loss=56.317, backward_time=0.098, grad_norm=51.438, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.127e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 05:28:28,980 (trainer:737) INFO: 24epoch:train:7301-7400batch: iter_time=9.899e-05, forward_time=0.105, loss_ctc=45.780, loss_att=51.520, acc=0.759, loss=49.798, backward_time=0.098, grad_norm=39.857, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.127e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 05:29:11,357 (trainer:737) INFO: 24epoch:train:7401-7500batch: iter_time=9.602e-05, forward_time=0.104, loss_ctc=49.317, loss_att=57.850, acc=0.704, loss=55.290, backward_time=0.097, grad_norm=44.219, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.126e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:29:14,387 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 05:29:34,155 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 05:29:37,785 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e97a53130>)
+[gpuc02:0/16] 2024-01-15 05:29:37,786 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 05:29:37,789 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 05:34:29,014 (trainer:737) INFO: 24epoch:train:7501-7600batch: iter_time=2.424, forward_time=0.105, loss_ctc=43.581, loss_att=51.787, acc=0.740, loss=49.325, backward_time=0.098, grad_norm=39.716, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.126e-04, train_time=3.176
+[gpuc02:0/16] 2024-01-15 05:35:11,401 (trainer:737) INFO: 24epoch:train:7601-7700batch: iter_time=1.534e-04, forward_time=0.104, loss_ctc=42.109, loss_att=46.050, acc=0.741, loss=44.868, backward_time=0.098, grad_norm=38.141, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.125e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:35:54,176 (trainer:737) INFO: 24epoch:train:7701-7800batch: iter_time=1.401e-04, forward_time=0.105, loss_ctc=46.424, loss_att=57.394, acc=0.729, loss=54.103, backward_time=0.099, grad_norm=40.345, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.125e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 05:36:36,817 (trainer:737) INFO: 24epoch:train:7801-7900batch: iter_time=1.431e-04, forward_time=0.105, loss_ctc=41.338, loss_att=50.502, acc=0.727, loss=47.753, backward_time=0.098, grad_norm=37.791, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.124e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:37:19,353 (trainer:737) INFO: 24epoch:train:7901-8000batch: iter_time=1.413e-04, forward_time=0.106, loss_ctc=43.723, loss_att=58.047, acc=0.735, loss=53.750, backward_time=0.098, grad_norm=38.587, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.123e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 05:38:01,961 (trainer:737) INFO: 24epoch:train:8001-8100batch: iter_time=1.451e-04, forward_time=0.106, loss_ctc=41.216, loss_att=46.821, acc=0.741, loss=45.140, backward_time=0.097, grad_norm=38.886, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.123e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:38:46,491 (trainer:737) INFO: 24epoch:train:8101-8200batch: iter_time=1.429e-04, forward_time=0.120, loss_ctc=40.956, loss_att=53.358, acc=0.727, loss=49.637, backward_time=0.101, grad_norm=38.114, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.044, optim0_lr0=4.122e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-15 05:39:29,480 (trainer:737) INFO: 24epoch:train:8201-8300batch: iter_time=1.499e-04, forward_time=0.105, loss_ctc=43.386, loss_att=53.051, acc=0.726, loss=50.151, backward_time=0.098, grad_norm=38.966, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.122e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 05:40:12,641 (trainer:737) INFO: 24epoch:train:8301-8400batch: iter_time=1.483e-04, forward_time=0.106, loss_ctc=47.164, loss_att=62.543, acc=0.704, loss=57.929, backward_time=0.098, grad_norm=43.686, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.121e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 05:40:55,193 (trainer:737) INFO: 24epoch:train:8401-8500batch: iter_time=1.465e-04, forward_time=0.105, loss_ctc=51.904, loss_att=55.813, acc=0.734, loss=54.640, backward_time=0.098, grad_norm=52.577, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.120e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 05:41:37,793 (trainer:737) INFO: 24epoch:train:8501-8600batch: iter_time=1.325e-04, forward_time=0.106, loss_ctc=44.145, loss_att=57.045, acc=0.739, loss=53.175, backward_time=0.098, grad_norm=40.226, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.120e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:41:56,923 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 05:42:20,184 (trainer:737) INFO: 24epoch:train:8601-8700batch: iter_time=1.398e-04, forward_time=0.105, loss_ctc=45.699, loss_att=44.217, acc=0.760, loss=44.662, backward_time=0.097, grad_norm=41.561, clip=100.000, loss_scale=3.000e+34, optim_step_time=0.042, optim0_lr0=4.119e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:42:48,449 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 05:43:07,533 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 05:43:11,058 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8393a9e0>)
+[gpuc02:0/16] 2024-01-15 05:43:11,058 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 05:43:11,061 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 05:47:52,942 (trainer:737) INFO: 24epoch:train:8701-8800batch: iter_time=2.837, forward_time=0.105, loss_ctc=52.388, loss_att=68.981, acc=0.692, loss=64.003, backward_time=0.098, grad_norm=45.059, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.119e-04, train_time=3.327
+[gpuc02:0/16] 2024-01-15 05:48:35,782 (trainer:737) INFO: 24epoch:train:8801-8900batch: iter_time=1.183e-04, forward_time=0.103, loss_ctc=41.791, loss_att=45.706, acc=0.736, loss=44.532, backward_time=0.097, grad_norm=40.935, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.118e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 05:49:18,097 (trainer:737) INFO: 24epoch:train:8901-9000batch: iter_time=1.266e-04, forward_time=0.104, loss_ctc=45.270, loss_att=58.798, acc=0.724, loss=54.740, backward_time=0.098, grad_norm=41.277, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.118e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:50:00,729 (trainer:737) INFO: 24epoch:train:9001-9100batch: iter_time=1.247e-04, forward_time=0.102, loss_ctc=40.433, loss_att=47.067, acc=0.724, loss=45.077, backward_time=0.097, grad_norm=38.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.117e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 05:50:43,005 (trainer:737) INFO: 24epoch:train:9101-9200batch: iter_time=1.216e-04, forward_time=0.103, loss_ctc=44.387, loss_att=59.034, acc=0.704, loss=54.640, backward_time=0.097, grad_norm=38.513, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.116e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 05:51:25,263 (trainer:737) INFO: 24epoch:train:9201-9300batch: iter_time=1.221e-04, forward_time=0.103, loss_ctc=40.561, loss_att=53.568, acc=0.737, loss=49.666, backward_time=0.098, grad_norm=39.896, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.116e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 05:52:07,389 (trainer:737) INFO: 24epoch:train:9301-9400batch: iter_time=1.256e-04, forward_time=0.102, loss_ctc=39.414, loss_att=40.690, acc=0.734, loss=40.308, backward_time=0.096, grad_norm=35.671, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.115e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 05:52:49,605 (trainer:737) INFO: 24epoch:train:9401-9500batch: iter_time=1.157e-04, forward_time=0.103, loss_ctc=45.024, loss_att=63.869, acc=0.699, loss=58.216, backward_time=0.097, grad_norm=42.702, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.115e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 05:53:32,018 (trainer:737) INFO: 24epoch:train:9501-9600batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=41.447, loss_att=53.342, acc=0.713, loss=49.773, backward_time=0.097, grad_norm=41.139, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.114e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 05:54:14,580 (trainer:737) INFO: 24epoch:train:9601-9700batch: iter_time=1.193e-04, forward_time=0.103, loss_ctc=47.371, loss_att=54.518, acc=0.708, loss=52.374, backward_time=0.097, grad_norm=42.014, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.113e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 05:54:57,283 (trainer:737) INFO: 24epoch:train:9701-9800batch: iter_time=1.170e-04, forward_time=0.103, loss_ctc=51.949, loss_att=56.712, acc=0.739, loss=55.283, backward_time=0.097, grad_norm=51.032, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.113e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 05:55:39,969 (trainer:737) INFO: 24epoch:train:9801-9900batch: iter_time=1.307e-04, forward_time=0.103, loss_ctc=45.650, loss_att=50.931, acc=0.752, loss=49.347, backward_time=0.097, grad_norm=41.582, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.112e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 05:56:22,232 (trainer:737) INFO: 24epoch:train:9901-10000batch: iter_time=1.372e-04, forward_time=0.103, loss_ctc=49.265, loss_att=58.293, acc=0.696, loss=55.584, backward_time=0.097, grad_norm=44.066, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.112e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 05:56:24,833 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 05:56:45,046 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 05:56:48,710 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e99cbeec0>)
+[gpuc02:0/16] 2024-01-15 05:56:48,711 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 05:56:48,714 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 06:01:45,262 (trainer:737) INFO: 24epoch:train:10001-10100batch: iter_time=2.406, forward_time=0.118, loss_ctc=43.407, loss_att=52.219, acc=0.728, loss=49.575, backward_time=0.099, grad_norm=39.750, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.111e-04, train_time=3.230
+[gpuc02:0/16] 2024-01-15 06:02:27,397 (trainer:737) INFO: 24epoch:train:10101-10200batch: iter_time=1.046e-04, forward_time=0.103, loss_ctc=42.182, loss_att=44.727, acc=0.745, loss=43.963, backward_time=0.097, grad_norm=36.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.111e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:03:09,648 (trainer:737) INFO: 24epoch:train:10201-10300batch: iter_time=1.120e-04, forward_time=0.104, loss_ctc=45.663, loss_att=56.765, acc=0.719, loss=53.435, backward_time=0.097, grad_norm=39.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.110e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:03:51,726 (trainer:737) INFO: 24epoch:train:10301-10400batch: iter_time=1.312e-04, forward_time=0.102, loss_ctc=40.727, loss_att=49.997, acc=0.717, loss=47.216, backward_time=0.097, grad_norm=37.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.109e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:04:33,981 (trainer:737) INFO: 24epoch:train:10401-10500batch: iter_time=1.202e-04, forward_time=0.103, loss_ctc=43.351, loss_att=58.965, acc=0.724, loss=54.281, backward_time=0.098, grad_norm=39.240, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.109e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:05:16,098 (trainer:737) INFO: 24epoch:train:10501-10600batch: iter_time=1.157e-04, forward_time=0.102, loss_ctc=41.195, loss_att=46.976, acc=0.730, loss=45.242, backward_time=0.097, grad_norm=40.347, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.108e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:05:58,197 (trainer:737) INFO: 24epoch:train:10601-10700batch: iter_time=1.145e-04, forward_time=0.103, loss_ctc=40.506, loss_att=53.525, acc=0.715, loss=49.619, backward_time=0.097, grad_norm=38.286, clip=100.000, loss_scale=3.219e+34, optim_step_time=0.041, optim0_lr0=4.108e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:06:40,577 (trainer:737) INFO: 24epoch:train:10701-10800batch: iter_time=1.087e-04, forward_time=0.102, loss_ctc=43.396, loss_att=50.137, acc=0.727, loss=48.115, backward_time=0.097, grad_norm=37.766, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.107e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:07:22,711 (trainer:737) INFO: 24epoch:train:10801-10900batch: iter_time=1.159e-04, forward_time=0.103, loss_ctc=47.008, loss_att=60.425, acc=0.695, loss=56.400, backward_time=0.097, grad_norm=43.244, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.107e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:08:06,020 (trainer:737) INFO: 24epoch:train:10901-11000batch: iter_time=1.330e-04, forward_time=0.103, loss_ctc=51.515, loss_att=53.795, acc=0.724, loss=53.111, backward_time=0.097, grad_norm=53.176, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.106e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 06:08:48,836 (trainer:737) INFO: 24epoch:train:11001-11100batch: iter_time=1.313e-04, forward_time=0.104, loss_ctc=43.654, loss_att=55.394, acc=0.734, loss=51.872, backward_time=0.098, grad_norm=37.669, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.105e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 06:09:30,967 (trainer:737) INFO: 24epoch:train:11101-11200batch: iter_time=1.245e-04, forward_time=0.103, loss_ctc=45.620, loss_att=43.964, acc=0.755, loss=44.461, backward_time=0.097, grad_norm=41.803, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.105e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:09:55,205 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 06:10:15,348 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 06:10:19,044 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e86d92fb0>)
+[gpuc02:0/16] 2024-01-15 06:10:19,044 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 06:10:19,048 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 06:14:56,313 (trainer:737) INFO: 24epoch:train:11201-11300batch: iter_time=2.427, forward_time=0.104, loss_ctc=52.471, loss_att=67.602, acc=0.693, loss=63.063, backward_time=0.098, grad_norm=44.813, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.104e-04, train_time=3.253
+[gpuc02:0/16] 2024-01-15 06:15:38,797 (trainer:737) INFO: 24epoch:train:11301-11400batch: iter_time=1.233e-04, forward_time=0.103, loss_ctc=41.860, loss_att=44.289, acc=0.740, loss=43.560, backward_time=0.097, grad_norm=38.748, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.104e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 06:16:20,995 (trainer:737) INFO: 24epoch:train:11401-11500batch: iter_time=1.339e-04, forward_time=0.104, loss_ctc=45.550, loss_att=55.575, acc=0.733, loss=52.567, backward_time=0.098, grad_norm=40.806, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.103e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:16:46,534 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 06:17:02,925 (trainer:737) INFO: 24epoch:train:11501-11600batch: iter_time=1.211e-04, forward_time=0.104, loss_ctc=41.249, loss_att=44.998, acc=0.728, loss=43.873, backward_time=0.097, grad_norm=37.856, clip=100.000, loss_scale=3.336e+34, optim_step_time=0.042, optim0_lr0=4.103e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 06:17:45,006 (trainer:737) INFO: 24epoch:train:11601-11700batch: iter_time=1.298e-04, forward_time=0.104, loss_ctc=43.828, loss_att=57.113, acc=0.708, loss=53.127, backward_time=0.098, grad_norm=39.592, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.102e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:18:27,141 (trainer:737) INFO: 24epoch:train:11701-11800batch: iter_time=1.400e-04, forward_time=0.104, loss_ctc=40.181, loss_att=52.972, acc=0.737, loss=49.134, backward_time=0.098, grad_norm=38.234, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.101e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:19:09,454 (trainer:737) INFO: 24epoch:train:11801-11900batch: iter_time=1.605e-04, forward_time=0.103, loss_ctc=39.013, loss_att=40.060, acc=0.736, loss=39.746, backward_time=0.097, grad_norm=35.745, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.101e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:19:52,077 (trainer:737) INFO: 24epoch:train:11901-12000batch: iter_time=1.403e-04, forward_time=0.104, loss_ctc=44.889, loss_att=62.654, acc=0.703, loss=57.324, backward_time=0.098, grad_norm=40.534, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.100e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 06:20:34,199 (trainer:737) INFO: 24epoch:train:12001-12100batch: iter_time=1.577e-04, forward_time=0.103, loss_ctc=41.762, loss_att=53.733, acc=0.712, loss=50.141, backward_time=0.096, grad_norm=40.202, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.100e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:21:16,743 (trainer:737) INFO: 24epoch:train:12101-12200batch: iter_time=1.473e-04, forward_time=0.104, loss_ctc=46.997, loss_att=52.264, acc=0.714, loss=50.684, backward_time=0.096, grad_norm=40.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.099e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 06:21:59,167 (trainer:737) INFO: 24epoch:train:12201-12300batch: iter_time=1.395e-04, forward_time=0.104, loss_ctc=52.394, loss_att=56.549, acc=0.740, loss=55.302, backward_time=0.097, grad_norm=51.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.099e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:22:41,520 (trainer:737) INFO: 24epoch:train:12301-12400batch: iter_time=1.215e-04, forward_time=0.104, loss_ctc=45.487, loss_att=50.292, acc=0.754, loss=48.851, backward_time=0.097, grad_norm=41.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.098e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:23:23,767 (trainer:737) INFO: 24epoch:train:12401-12500batch: iter_time=1.321e-04, forward_time=0.103, loss_ctc=49.038, loss_att=56.656, acc=0.704, loss=54.371, backward_time=0.097, grad_norm=43.844, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.097e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:23:30,589 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 06:23:49,762 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 06:23:53,272 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e46573f70>)
+[gpuc02:0/16] 2024-01-15 06:23:53,272 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 06:23:53,275 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 06:28:45,405 (trainer:737) INFO: 24epoch:train:12501-12600batch: iter_time=2.453, forward_time=0.104, loss_ctc=42.975, loss_att=51.739, acc=0.729, loss=49.109, backward_time=0.098, grad_norm=38.909, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.097e-04, train_time=3.216
+[gpuc02:0/16] 2024-01-15 06:29:27,623 (trainer:737) INFO: 24epoch:train:12601-12700batch: iter_time=8.815e-05, forward_time=0.103, loss_ctc=42.300, loss_att=44.830, acc=0.747, loss=44.071, backward_time=0.097, grad_norm=38.874, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.096e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:30:10,074 (trainer:737) INFO: 24epoch:train:12701-12800batch: iter_time=1.007e-04, forward_time=0.103, loss_ctc=46.083, loss_att=55.405, acc=0.723, loss=52.608, backward_time=0.097, grad_norm=41.493, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.096e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:30:52,202 (trainer:737) INFO: 24epoch:train:12801-12900batch: iter_time=1.079e-04, forward_time=0.102, loss_ctc=40.831, loss_att=49.289, acc=0.718, loss=46.752, backward_time=0.097, grad_norm=37.890, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.095e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:31:34,513 (trainer:737) INFO: 24epoch:train:12901-13000batch: iter_time=1.134e-04, forward_time=0.103, loss_ctc=43.256, loss_att=59.225, acc=0.723, loss=54.435, backward_time=0.097, grad_norm=38.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.094e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:32:16,902 (trainer:737) INFO: 24epoch:train:13001-13100batch: iter_time=1.158e-04, forward_time=0.103, loss_ctc=40.685, loss_att=47.411, acc=0.728, loss=45.393, backward_time=0.097, grad_norm=39.740, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.094e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:32:59,044 (trainer:737) INFO: 24epoch:train:13101-13200batch: iter_time=1.172e-04, forward_time=0.103, loss_ctc=40.509, loss_att=53.441, acc=0.719, loss=49.561, backward_time=0.097, grad_norm=38.810, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.093e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:33:41,372 (trainer:737) INFO: 24epoch:train:13201-13300batch: iter_time=1.122e-04, forward_time=0.103, loss_ctc=43.328, loss_att=50.163, acc=0.728, loss=48.112, backward_time=0.098, grad_norm=38.002, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.093e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:34:23,724 (trainer:737) INFO: 24epoch:train:13301-13400batch: iter_time=1.090e-04, forward_time=0.103, loss_ctc=46.338, loss_att=59.851, acc=0.697, loss=55.797, backward_time=0.098, grad_norm=42.650, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.092e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:35:06,081 (trainer:737) INFO: 24epoch:train:13401-13500batch: iter_time=1.018e-04, forward_time=0.104, loss_ctc=51.755, loss_att=53.356, acc=0.726, loss=52.876, backward_time=0.098, grad_norm=49.303, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.092e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:35:48,913 (trainer:737) INFO: 24epoch:train:13501-13600batch: iter_time=1.049e-04, forward_time=0.105, loss_ctc=43.545, loss_att=55.005, acc=0.736, loss=51.567, backward_time=0.098, grad_norm=38.037, clip=100.000, loss_scale=2.887e+34, optim_step_time=0.042, optim0_lr0=4.091e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 06:36:31,918 (trainer:737) INFO: 24epoch:train:13601-13700batch: iter_time=9.965e-05, forward_time=0.103, loss_ctc=45.259, loss_att=42.712, acc=0.762, loss=43.476, backward_time=0.098, grad_norm=41.715, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.090e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 06:36:55,941 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 06:37:16,122 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 06:37:20,264 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e6530bf40>)
+[gpuc02:0/16] 2024-01-15 06:37:20,264 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 06:37:20,268 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 06:41:44,937 (trainer:737) INFO: 24epoch:train:13701-13800batch: iter_time=2.436, forward_time=0.108, loss_ctc=51.813, loss_att=67.046, acc=0.702, loss=62.476, backward_time=0.098, grad_norm=43.877, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.090e-04, train_time=3.130
+[gpuc02:0/16] 2024-01-15 06:42:27,046 (trainer:737) INFO: 24epoch:train:13801-13900batch: iter_time=1.114e-04, forward_time=0.103, loss_ctc=41.635, loss_att=45.565, acc=0.742, loss=44.386, backward_time=0.097, grad_norm=38.372, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.089e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:43:09,328 (trainer:737) INFO: 24epoch:train:13901-14000batch: iter_time=1.111e-04, forward_time=0.104, loss_ctc=44.888, loss_att=61.625, acc=0.725, loss=56.604, backward_time=0.098, grad_norm=53.718, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.089e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:43:51,408 (trainer:737) INFO: 24epoch:train:14001-14100batch: iter_time=1.151e-04, forward_time=0.103, loss_ctc=40.708, loss_att=46.391, acc=0.738, loss=44.686, backward_time=0.097, grad_norm=37.980, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.088e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:44:33,667 (trainer:737) INFO: 24epoch:train:14101-14200batch: iter_time=1.170e-04, forward_time=0.103, loss_ctc=43.757, loss_att=58.495, acc=0.715, loss=54.074, backward_time=0.097, grad_norm=40.229, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.088e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:45:16,049 (trainer:737) INFO: 24epoch:train:14201-14300batch: iter_time=1.056e-04, forward_time=0.103, loss_ctc=40.278, loss_att=54.000, acc=0.745, loss=49.884, backward_time=0.098, grad_norm=40.377, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.087e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:45:58,198 (trainer:737) INFO: 24epoch:train:14301-14400batch: iter_time=1.099e-04, forward_time=0.103, loss_ctc=38.812, loss_att=39.769, acc=0.746, loss=39.482, backward_time=0.097, grad_norm=34.890, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.087e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 06:46:40,563 (trainer:737) INFO: 24epoch:train:14401-14500batch: iter_time=1.146e-04, forward_time=0.103, loss_ctc=44.464, loss_att=66.929, acc=0.696, loss=60.190, backward_time=0.098, grad_norm=41.507, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.086e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:47:22,755 (trainer:737) INFO: 24epoch:train:14501-14600batch: iter_time=1.018e-04, forward_time=0.103, loss_ctc=41.142, loss_att=53.268, acc=0.722, loss=49.630, backward_time=0.097, grad_norm=39.013, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.085e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 06:48:05,038 (trainer:737) INFO: 24epoch:train:14601-14700batch: iter_time=1.089e-04, forward_time=0.103, loss_ctc=46.861, loss_att=56.775, acc=0.723, loss=53.801, backward_time=0.097, grad_norm=41.528, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=4.085e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 06:48:47,487 (trainer:737) INFO: 24epoch:train:14701-14800batch: iter_time=1.210e-04, forward_time=0.104, loss_ctc=51.472, loss_att=57.114, acc=0.745, loss=55.421, backward_time=0.098, grad_norm=48.625, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.084e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:49:29,955 (trainer:737) INFO: 24epoch:train:14801-14900batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=45.170, loss_att=51.993, acc=0.758, loss=49.946, backward_time=0.098, grad_norm=40.948, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.084e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 06:50:12,289 (trainer:737) INFO: 24epoch:train:14901-15000batch: iter_time=1.147e-04, forward_time=0.103, loss_ctc=48.693, loss_att=57.732, acc=0.705, loss=55.021, backward_time=0.097, grad_norm=43.049, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.083e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 07:10:10,475 (trainer:343) INFO: 24epoch results: [train] iter_time=0.206, forward_time=0.104, loss_ctc=44.999, loss_att=53.929, acc=0.726, loss=51.250, backward_time=0.098, grad_norm=41.291, clip=100.000, loss_scale=2.878e+34, optim_step_time=0.042, optim0_lr0=4.126e-04, train_time=0.658, time=2 hours, 44 minutes and 48.41 seconds, total_count=360000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=54.737, cer_ctc=0.278, loss_att=53.331, acc=0.588, cer=0.376, wer=0.998, loss=53.753, time=19 minutes and 48.42 seconds, total_count=112104, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 07:10:15,563 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-15 07:10:15,582 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/19epoch.pth
+[gpuc02:0/16] 2024-01-15 07:10:15,582 (trainer:272) INFO: 25/45epoch started. Estimated time to finish: 2 days, 16 hours and 4 minutes
+[gpuc02:0/16] 2024-01-15 07:10:15,591 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 07:10:35,555 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 07:10:39,388 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f259cd0f3d0>)
+[gpuc02:0/16] 2024-01-15 07:10:39,388 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 07:10:39,391 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 07:15:25,701 (trainer:737) INFO: 25epoch:train:1-100batch: iter_time=2.516, forward_time=0.105, loss_ctc=42.483, loss_att=54.754, acc=0.718, loss=51.073, backward_time=0.099, grad_norm=41.933, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.083e-04, train_time=3.101
+[gpuc02:0/16] 2024-01-15 07:16:07,912 (trainer:737) INFO: 25epoch:train:101-200batch: iter_time=1.184e-04, forward_time=0.104, loss_ctc=44.371, loss_att=40.287, acc=0.757, loss=41.512, backward_time=0.098, grad_norm=37.793, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.082e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:16:50,209 (trainer:737) INFO: 25epoch:train:201-300batch: iter_time=1.186e-04, forward_time=0.104, loss_ctc=42.838, loss_att=47.413, acc=0.749, loss=46.041, backward_time=0.098, grad_norm=37.868, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.081e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 07:17:33,189 (trainer:737) INFO: 25epoch:train:301-400batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=58.307, loss_att=63.173, acc=0.708, loss=61.713, backward_time=0.099, grad_norm=46.127, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.081e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 07:18:16,062 (trainer:737) INFO: 25epoch:train:401-500batch: iter_time=1.259e-04, forward_time=0.104, loss_ctc=45.923, loss_att=43.515, acc=0.731, loss=44.237, backward_time=0.098, grad_norm=40.101, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.080e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 07:18:58,350 (trainer:737) INFO: 25epoch:train:501-600batch: iter_time=1.199e-04, forward_time=0.104, loss_ctc=38.310, loss_att=45.274, acc=0.734, loss=43.185, backward_time=0.098, grad_norm=37.431, clip=100.000, loss_scale=5.774e+34, optim_step_time=0.041, optim0_lr0=4.080e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 07:19:41,225 (trainer:737) INFO: 25epoch:train:601-700batch: iter_time=1.368e-04, forward_time=0.105, loss_ctc=48.543, loss_att=55.971, acc=0.718, loss=53.742, backward_time=0.100, grad_norm=40.195, clip=100.000, loss_scale=8.308e+34, optim_step_time=0.042, optim0_lr0=4.079e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 07:19:54,405 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 07:20:23,823 (trainer:737) INFO: 25epoch:train:701-800batch: iter_time=1.289e-04, forward_time=0.107, loss_ctc=53.183, loss_att=59.188, acc=0.719, loss=57.387, backward_time=0.098, grad_norm=46.104, clip=100.000, loss_scale=5.413e+34, optim_step_time=0.043, optim0_lr0=4.079e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 07:21:06,619 (trainer:737) INFO: 25epoch:train:801-900batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=52.509, loss_att=62.590, acc=0.722, loss=59.566, backward_time=0.098, grad_norm=50.883, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.078e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 07:21:51,451 (trainer:737) INFO: 25epoch:train:901-1000batch: iter_time=1.334e-04, forward_time=0.104, loss_ctc=47.945, loss_att=50.584, acc=0.733, loss=49.792, backward_time=0.098, grad_norm=45.345, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.077e-04, train_time=0.448
+[gpuc02:0/16] 2024-01-15 07:22:30,516 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 07:22:33,947 (trainer:737) INFO: 25epoch:train:1001-1100batch: iter_time=1.345e-04, forward_time=0.106, loss_ctc=49.045, loss_att=51.190, acc=0.734, loss=50.547, backward_time=0.098, grad_norm=45.314, clip=100.000, loss_scale=3.986e+34, optim_step_time=0.042, optim0_lr0=4.077e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 07:23:20,226 (trainer:737) INFO: 25epoch:train:1101-1200batch: iter_time=1.209e-04, forward_time=0.126, loss_ctc=51.172, loss_att=62.224, acc=0.727, loss=58.909, backward_time=0.103, grad_norm=43.525, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.076e-04, train_time=0.463
+[gpuc02:0/16] 2024-01-15 07:23:57,884 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 07:24:18,120 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 07:24:21,965 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f252df92170>)
+[gpuc02:0/16] 2024-01-15 07:24:21,966 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 07:24:21,969 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 07:30:26,364 (trainer:737) INFO: 25epoch:train:1201-1300batch: iter_time=3.254, forward_time=0.125, loss_ctc=43.036, loss_att=58.292, acc=0.710, loss=53.715, backward_time=0.100, grad_norm=40.793, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=4.076e-04, train_time=4.261
+[gpuc02:0/16] 2024-01-15 07:31:08,575 (trainer:737) INFO: 25epoch:train:1301-1400batch: iter_time=1.422e-04, forward_time=0.104, loss_ctc=40.784, loss_att=40.391, acc=0.758, loss=40.509, backward_time=0.097, grad_norm=36.978, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.075e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:31:50,848 (trainer:737) INFO: 25epoch:train:1401-1500batch: iter_time=1.248e-04, forward_time=0.105, loss_ctc=44.291, loss_att=49.077, acc=0.744, loss=47.641, backward_time=0.098, grad_norm=40.495, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.075e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 07:32:33,074 (trainer:737) INFO: 25epoch:train:1501-1600batch: iter_time=1.364e-04, forward_time=0.105, loss_ctc=47.715, loss_att=48.623, acc=0.743, loss=48.350, backward_time=0.098, grad_norm=41.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.074e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:33:15,803 (trainer:737) INFO: 25epoch:train:1601-1700batch: iter_time=1.419e-04, forward_time=0.104, loss_ctc=46.171, loss_att=52.194, acc=0.722, loss=50.387, backward_time=0.099, grad_norm=39.182, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.074e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 07:33:58,363 (trainer:737) INFO: 25epoch:train:1701-1800batch: iter_time=1.437e-04, forward_time=0.104, loss_ctc=47.273, loss_att=51.892, acc=0.723, loss=50.506, backward_time=0.097, grad_norm=40.401, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.073e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 07:34:41,197 (trainer:737) INFO: 25epoch:train:1801-1900batch: iter_time=1.298e-04, forward_time=0.105, loss_ctc=47.368, loss_att=50.876, acc=0.727, loss=49.823, backward_time=0.098, grad_norm=39.717, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.072e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 07:35:24,195 (trainer:737) INFO: 25epoch:train:1901-2000batch: iter_time=1.407e-04, forward_time=0.107, loss_ctc=44.159, loss_att=55.977, acc=0.723, loss=52.431, backward_time=0.099, grad_norm=41.185, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.072e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 07:36:06,338 (trainer:737) INFO: 25epoch:train:2001-2100batch: iter_time=1.349e-04, forward_time=0.105, loss_ctc=55.210, loss_att=57.278, acc=0.725, loss=56.657, backward_time=0.098, grad_norm=51.238, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.071e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 07:36:49,435 (trainer:737) INFO: 25epoch:train:2101-2200batch: iter_time=1.446e-04, forward_time=0.110, loss_ctc=42.421, loss_att=55.781, acc=0.730, loss=51.773, backward_time=0.100, grad_norm=39.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.071e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 07:37:32,054 (trainer:737) INFO: 25epoch:train:2201-2300batch: iter_time=1.189e-04, forward_time=0.104, loss_ctc=50.501, loss_att=49.832, acc=0.730, loss=50.033, backward_time=0.097, grad_norm=48.546, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.070e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 07:38:14,793 (trainer:737) INFO: 25epoch:train:2301-2400batch: iter_time=1.150e-04, forward_time=0.105, loss_ctc=52.127, loss_att=60.879, acc=0.739, loss=58.253, backward_time=0.098, grad_norm=39.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.070e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 07:38:56,894 (trainer:737) INFO: 25epoch:train:2401-2500batch: iter_time=1.103e-04, forward_time=0.103, loss_ctc=44.182, loss_att=54.129, acc=0.728, loss=51.145, backward_time=0.097, grad_norm=38.738, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.069e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 07:39:06,910 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 07:39:26,362 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 07:39:29,991 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f243883b310>)
+[gpuc02:0/16] 2024-01-15 07:39:29,992 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 07:39:29,995 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 07:44:54,652 (trainer:737) INFO: 25epoch:train:2501-2600batch: iter_time=3.052, forward_time=0.104, loss_ctc=41.329, loss_att=54.453, acc=0.720, loss=50.516, backward_time=0.096, grad_norm=42.209, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.068e-04, train_time=3.577
+[gpuc02:0/16] 2024-01-15 07:45:40,348 (trainer:737) INFO: 25epoch:train:2601-2700batch: iter_time=1.441e-04, forward_time=0.121, loss_ctc=43.015, loss_att=39.690, acc=0.758, loss=40.688, backward_time=0.104, grad_norm=37.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.068e-04, train_time=0.457
+[gpuc02:0/16] 2024-01-15 07:46:22,591 (trainer:737) INFO: 25epoch:train:2701-2800batch: iter_time=1.709e-04, forward_time=0.105, loss_ctc=41.396, loss_att=47.028, acc=0.748, loss=45.338, backward_time=0.097, grad_norm=37.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.067e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:47:04,960 (trainer:737) INFO: 25epoch:train:2801-2900batch: iter_time=1.596e-04, forward_time=0.104, loss_ctc=54.274, loss_att=62.684, acc=0.711, loss=60.161, backward_time=0.097, grad_norm=48.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.067e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 07:47:47,197 (trainer:737) INFO: 25epoch:train:2901-3000batch: iter_time=1.575e-04, forward_time=0.104, loss_ctc=42.833, loss_att=42.033, acc=0.735, loss=42.273, backward_time=0.097, grad_norm=38.807, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.066e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:48:29,372 (trainer:737) INFO: 25epoch:train:3001-3100batch: iter_time=1.791e-04, forward_time=0.104, loss_ctc=38.078, loss_att=45.675, acc=0.733, loss=43.396, backward_time=0.097, grad_norm=37.484, clip=100.000, loss_scale=2.243e+34, optim_step_time=0.041, optim0_lr0=4.066e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:49:12,077 (trainer:737) INFO: 25epoch:train:3101-3200batch: iter_time=1.590e-04, forward_time=0.108, loss_ctc=46.530, loss_att=55.309, acc=0.720, loss=52.676, backward_time=0.098, grad_norm=42.962, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.065e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 07:49:54,762 (trainer:737) INFO: 25epoch:train:3201-3300batch: iter_time=1.661e-04, forward_time=0.105, loss_ctc=50.463, loss_att=58.247, acc=0.725, loss=55.912, backward_time=0.098, grad_norm=44.955, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.065e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 07:50:37,449 (trainer:737) INFO: 25epoch:train:3301-3400batch: iter_time=1.443e-04, forward_time=0.104, loss_ctc=50.316, loss_att=62.778, acc=0.722, loss=59.040, backward_time=0.097, grad_norm=50.284, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.064e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 07:51:20,021 (trainer:737) INFO: 25epoch:train:3401-3500batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=47.079, loss_att=49.959, acc=0.736, loss=49.095, backward_time=0.097, grad_norm=44.768, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.063e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 07:52:02,577 (trainer:737) INFO: 25epoch:train:3501-3600batch: iter_time=1.344e-04, forward_time=0.104, loss_ctc=47.602, loss_att=49.628, acc=0.741, loss=49.020, backward_time=0.097, grad_norm=43.234, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.063e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 07:52:44,969 (trainer:737) INFO: 25epoch:train:3601-3700batch: iter_time=1.617e-04, forward_time=0.104, loss_ctc=49.634, loss_att=61.200, acc=0.731, loss=57.730, backward_time=0.097, grad_norm=39.447, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.062e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 07:53:03,521 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 07:53:14,150 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 07:53:33,006 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 07:53:36,589 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f248c23c220>)
+[gpuc02:0/16] 2024-01-15 07:53:36,589 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 07:53:36,592 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 07:58:08,560 (trainer:737) INFO: 25epoch:train:3701-3800batch: iter_time=2.743, forward_time=0.105, loss_ctc=42.123, loss_att=57.621, acc=0.714, loss=52.972, backward_time=0.097, grad_norm=42.527, clip=100.000, loss_scale=2.979e+34, optim_step_time=0.041, optim0_lr0=4.062e-04, train_time=3.236
+[gpuc02:0/16] 2024-01-15 07:58:50,796 (trainer:737) INFO: 25epoch:train:3801-3900batch: iter_time=1.291e-04, forward_time=0.105, loss_ctc=40.033, loss_att=40.229, acc=0.757, loss=40.170, backward_time=0.097, grad_norm=35.752, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.061e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 07:59:33,827 (trainer:737) INFO: 25epoch:train:3901-4000batch: iter_time=1.093e-04, forward_time=0.105, loss_ctc=43.179, loss_att=48.745, acc=0.746, loss=47.075, backward_time=0.097, grad_norm=41.282, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.061e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 08:00:16,246 (trainer:737) INFO: 25epoch:train:4001-4100batch: iter_time=1.245e-04, forward_time=0.105, loss_ctc=46.100, loss_att=48.650, acc=0.743, loss=47.885, backward_time=0.097, grad_norm=42.263, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.060e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:00:59,154 (trainer:737) INFO: 25epoch:train:4101-4200batch: iter_time=1.257e-04, forward_time=0.105, loss_ctc=44.634, loss_att=51.350, acc=0.727, loss=49.335, backward_time=0.098, grad_norm=38.236, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.059e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 08:01:44,298 (trainer:737) INFO: 25epoch:train:4201-4300batch: iter_time=1.478e-04, forward_time=0.105, loss_ctc=46.090, loss_att=51.482, acc=0.726, loss=49.865, backward_time=0.097, grad_norm=42.385, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.059e-04, train_time=0.451
+[gpuc02:0/16] 2024-01-15 08:02:28,563 (trainer:737) INFO: 25epoch:train:4301-4400batch: iter_time=1.424e-04, forward_time=0.112, loss_ctc=46.203, loss_att=50.222, acc=0.729, loss=49.016, backward_time=0.103, grad_norm=39.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.058e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-15 08:03:11,015 (trainer:737) INFO: 25epoch:train:4401-4500batch: iter_time=1.623e-04, forward_time=0.107, loss_ctc=43.550, loss_att=55.427, acc=0.724, loss=51.864, backward_time=0.097, grad_norm=40.031, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.058e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:03:53,404 (trainer:737) INFO: 25epoch:train:4501-4600batch: iter_time=1.664e-04, forward_time=0.106, loss_ctc=54.723, loss_att=58.499, acc=0.726, loss=57.366, backward_time=0.098, grad_norm=49.551, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.057e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:04:35,836 (trainer:737) INFO: 25epoch:train:4601-4700batch: iter_time=1.479e-04, forward_time=0.105, loss_ctc=41.480, loss_att=55.181, acc=0.732, loss=51.071, backward_time=0.097, grad_norm=40.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.057e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:05:18,522 (trainer:737) INFO: 25epoch:train:4701-4800batch: iter_time=3.025e-04, forward_time=0.107, loss_ctc=49.780, loss_att=49.507, acc=0.732, loss=49.589, backward_time=0.097, grad_norm=45.083, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.056e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:06:01,133 (trainer:737) INFO: 25epoch:train:4801-4900batch: iter_time=1.380e-04, forward_time=0.106, loss_ctc=51.230, loss_att=59.959, acc=0.741, loss=57.340, backward_time=0.097, grad_norm=41.484, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.056e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 08:06:43,632 (trainer:737) INFO: 25epoch:train:4901-5000batch: iter_time=1.350e-04, forward_time=0.104, loss_ctc=44.049, loss_att=53.815, acc=0.733, loss=50.885, backward_time=0.097, grad_norm=39.507, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.055e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:06:58,040 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 08:07:17,514 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 08:07:21,559 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ea0a87c70>)
+[gpuc02:0/16] 2024-01-15 08:07:21,559 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 08:07:21,563 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 08:12:51,295 (trainer:737) INFO: 25epoch:train:5001-5100batch: iter_time=3.156, forward_time=0.110, loss_ctc=40.378, loss_att=57.133, acc=0.713, loss=52.106, backward_time=0.098, grad_norm=41.444, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.054e-04, train_time=3.676
+[gpuc02:0/16] 2024-01-15 08:13:33,470 (trainer:737) INFO: 25epoch:train:5101-5200batch: iter_time=1.546e-04, forward_time=0.104, loss_ctc=42.132, loss_att=41.287, acc=0.749, loss=41.541, backward_time=0.097, grad_norm=37.855, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.054e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:14:15,715 (trainer:737) INFO: 25epoch:train:5201-5300batch: iter_time=1.705e-04, forward_time=0.104, loss_ctc=41.235, loss_att=45.701, acc=0.749, loss=44.361, backward_time=0.097, grad_norm=37.226, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.053e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:14:58,010 (trainer:737) INFO: 25epoch:train:5301-5400batch: iter_time=1.509e-04, forward_time=0.105, loss_ctc=53.323, loss_att=59.800, acc=0.708, loss=57.857, backward_time=0.098, grad_norm=47.411, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.053e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:15:40,374 (trainer:737) INFO: 25epoch:train:5401-5500batch: iter_time=1.398e-04, forward_time=0.103, loss_ctc=42.170, loss_att=43.582, acc=0.722, loss=43.159, backward_time=0.096, grad_norm=39.441, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.052e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:16:22,501 (trainer:737) INFO: 25epoch:train:5501-5600batch: iter_time=1.405e-04, forward_time=0.104, loss_ctc=37.583, loss_att=46.738, acc=0.723, loss=43.992, backward_time=0.095, grad_norm=37.259, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.052e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 08:17:04,643 (trainer:737) INFO: 25epoch:train:5601-5700batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=45.678, loss_att=55.907, acc=0.715, loss=52.838, backward_time=0.097, grad_norm=40.059, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.051e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 08:17:47,219 (trainer:737) INFO: 25epoch:train:5701-5800batch: iter_time=1.298e-04, forward_time=0.106, loss_ctc=50.257, loss_att=57.861, acc=0.718, loss=55.580, backward_time=0.097, grad_norm=43.731, clip=100.000, loss_scale=3.240e+34, optim_step_time=0.041, optim0_lr0=4.051e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 08:18:29,616 (trainer:737) INFO: 25epoch:train:5801-5900batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=50.851, loss_att=61.780, acc=0.709, loss=58.501, backward_time=0.096, grad_norm=49.351, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.050e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:19:12,182 (trainer:737) INFO: 25epoch:train:5901-6000batch: iter_time=1.367e-04, forward_time=0.104, loss_ctc=46.456, loss_att=50.493, acc=0.725, loss=49.282, backward_time=0.096, grad_norm=45.647, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.049e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:19:55,418 (trainer:737) INFO: 25epoch:train:6001-6100batch: iter_time=1.280e-04, forward_time=0.104, loss_ctc=46.844, loss_att=49.906, acc=0.730, loss=48.987, backward_time=0.097, grad_norm=43.172, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.049e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 08:20:37,893 (trainer:737) INFO: 25epoch:train:6101-6200batch: iter_time=1.427e-04, forward_time=0.105, loss_ctc=49.745, loss_att=61.573, acc=0.725, loss=58.025, backward_time=0.096, grad_norm=40.286, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.048e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:21:06,754 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 08:21:26,510 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 08:21:30,127 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8da7b670>)
+[gpuc02:0/16] 2024-01-15 08:21:30,128 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 08:21:30,131 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 08:25:51,073 (trainer:737) INFO: 25epoch:train:6201-6300batch: iter_time=2.678, forward_time=0.104, loss_ctc=41.277, loss_att=58.072, acc=0.708, loss=53.034, backward_time=0.097, grad_norm=40.390, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.048e-04, train_time=3.132
+[gpuc02:0/16] 2024-01-15 08:26:05,221 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 08:26:33,791 (trainer:737) INFO: 25epoch:train:6301-6400batch: iter_time=1.414e-04, forward_time=0.108, loss_ctc=39.904, loss_att=40.474, acc=0.753, loss=40.303, backward_time=0.097, grad_norm=36.129, clip=100.000, loss_scale=2.748e+34, optim_step_time=0.042, optim0_lr0=4.047e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:27:16,140 (trainer:737) INFO: 25epoch:train:6401-6500batch: iter_time=1.651e-04, forward_time=0.105, loss_ctc=43.176, loss_att=46.969, acc=0.742, loss=45.831, backward_time=0.097, grad_norm=39.512, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.047e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:27:58,521 (trainer:737) INFO: 25epoch:train:6501-6600batch: iter_time=1.966e-04, forward_time=0.105, loss_ctc=45.052, loss_att=49.219, acc=0.736, loss=47.969, backward_time=0.097, grad_norm=42.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.046e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:28:40,749 (trainer:737) INFO: 25epoch:train:6601-6700batch: iter_time=1.946e-04, forward_time=0.106, loss_ctc=44.306, loss_att=47.291, acc=0.724, loss=46.396, backward_time=0.097, grad_norm=38.116, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.046e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:29:22,920 (trainer:737) INFO: 25epoch:train:6701-6800batch: iter_time=1.911e-04, forward_time=0.104, loss_ctc=45.060, loss_att=52.006, acc=0.713, loss=49.922, backward_time=0.097, grad_norm=40.799, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.045e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 08:30:05,253 (trainer:737) INFO: 25epoch:train:6801-6900batch: iter_time=2.076e-04, forward_time=0.106, loss_ctc=45.931, loss_att=49.354, acc=0.734, loss=48.327, backward_time=0.098, grad_norm=39.139, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.045e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:30:47,729 (trainer:737) INFO: 25epoch:train:6901-7000batch: iter_time=2.295e-04, forward_time=0.106, loss_ctc=43.602, loss_att=55.481, acc=0.714, loss=51.917, backward_time=0.099, grad_norm=40.373, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.044e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:31:29,997 (trainer:737) INFO: 25epoch:train:7001-7100batch: iter_time=1.646e-04, forward_time=0.105, loss_ctc=53.840, loss_att=56.522, acc=0.717, loss=55.718, backward_time=0.097, grad_norm=50.243, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.043e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:32:12,249 (trainer:737) INFO: 25epoch:train:7101-7200batch: iter_time=1.539e-04, forward_time=0.105, loss_ctc=41.481, loss_att=53.827, acc=0.719, loss=50.123, backward_time=0.096, grad_norm=39.262, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.043e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:32:54,437 (trainer:737) INFO: 25epoch:train:7201-7300batch: iter_time=1.613e-04, forward_time=0.105, loss_ctc=49.040, loss_att=49.257, acc=0.723, loss=49.191, backward_time=0.096, grad_norm=45.957, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.042e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:33:36,845 (trainer:737) INFO: 25epoch:train:7301-7400batch: iter_time=1.653e-04, forward_time=0.106, loss_ctc=50.643, loss_att=59.166, acc=0.736, loss=56.609, backward_time=0.098, grad_norm=40.457, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.042e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:34:19,525 (trainer:737) INFO: 25epoch:train:7401-7500batch: iter_time=1.647e-04, forward_time=0.107, loss_ctc=43.369, loss_att=53.554, acc=0.726, loss=50.498, backward_time=0.099, grad_norm=39.893, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.041e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:34:26,138 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 08:34:45,297 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 08:34:48,978 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ea0a3a890>)
+[gpuc02:0/16] 2024-01-15 08:34:48,978 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 08:34:48,982 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 08:39:37,620 (trainer:737) INFO: 25epoch:train:7501-7600batch: iter_time=2.703, forward_time=0.105, loss_ctc=40.270, loss_att=55.458, acc=0.721, loss=50.902, backward_time=0.097, grad_norm=38.393, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.041e-04, train_time=3.181
+[gpuc02:0/16] 2024-01-15 08:40:19,893 (trainer:737) INFO: 25epoch:train:7601-7700batch: iter_time=1.567e-04, forward_time=0.105, loss_ctc=42.034, loss_att=39.909, acc=0.762, loss=40.547, backward_time=0.097, grad_norm=39.258, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.040e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:41:02,214 (trainer:737) INFO: 25epoch:train:7701-7800batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=40.796, loss_att=46.875, acc=0.754, loss=45.051, backward_time=0.097, grad_norm=37.202, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.040e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:41:44,699 (trainer:737) INFO: 25epoch:train:7801-7900batch: iter_time=1.577e-04, forward_time=0.106, loss_ctc=52.164, loss_att=63.071, acc=0.710, loss=59.799, backward_time=0.098, grad_norm=46.061, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.039e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:42:26,947 (trainer:737) INFO: 25epoch:train:7901-8000batch: iter_time=1.495e-04, forward_time=0.105, loss_ctc=42.117, loss_att=43.077, acc=0.735, loss=42.789, backward_time=0.097, grad_norm=38.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.038e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 08:43:09,104 (trainer:737) INFO: 25epoch:train:8001-8100batch: iter_time=1.478e-04, forward_time=0.103, loss_ctc=37.489, loss_att=45.409, acc=0.736, loss=43.033, backward_time=0.096, grad_norm=36.036, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.038e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 08:43:51,839 (trainer:737) INFO: 25epoch:train:8101-8200batch: iter_time=1.525e-04, forward_time=0.106, loss_ctc=45.182, loss_att=55.112, acc=0.723, loss=52.133, backward_time=0.097, grad_norm=38.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.037e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:44:34,599 (trainer:737) INFO: 25epoch:train:8201-8300batch: iter_time=1.397e-04, forward_time=0.105, loss_ctc=49.484, loss_att=57.840, acc=0.726, loss=55.333, backward_time=0.098, grad_norm=42.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.037e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:45:17,362 (trainer:737) INFO: 25epoch:train:8301-8400batch: iter_time=1.462e-04, forward_time=0.105, loss_ctc=49.901, loss_att=63.550, acc=0.725, loss=59.455, backward_time=0.097, grad_norm=49.264, clip=100.000, loss_scale=3.468e+34, optim_step_time=0.042, optim0_lr0=4.036e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:45:44,299 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 08:45:59,964 (trainer:737) INFO: 25epoch:train:8401-8500batch: iter_time=1.529e-04, forward_time=0.105, loss_ctc=46.111, loss_att=49.601, acc=0.739, loss=48.554, backward_time=0.097, grad_norm=43.971, clip=100.000, loss_scale=3.378e+34, optim_step_time=0.042, optim0_lr0=4.036e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 08:46:42,389 (trainer:737) INFO: 25epoch:train:8501-8600batch: iter_time=1.647e-04, forward_time=0.106, loss_ctc=46.283, loss_att=49.521, acc=0.741, loss=48.550, backward_time=0.097, grad_norm=41.539, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.035e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:47:25,230 (trainer:737) INFO: 25epoch:train:8601-8700batch: iter_time=1.499e-04, forward_time=0.109, loss_ctc=49.212, loss_att=61.576, acc=0.733, loss=57.867, backward_time=0.098, grad_norm=39.448, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.035e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 08:47:53,268 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 08:48:12,607 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 08:48:16,205 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ea12d78b0>)
+[gpuc02:0/16] 2024-01-15 08:48:16,205 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 08:48:16,208 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 08:52:44,472 (trainer:737) INFO: 25epoch:train:8701-8800batch: iter_time=2.745, forward_time=0.105, loss_ctc=41.373, loss_att=58.984, acc=0.707, loss=53.701, backward_time=0.097, grad_norm=41.836, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.034e-04, train_time=3.192
+[gpuc02:0/16] 2024-01-15 08:53:26,968 (trainer:737) INFO: 25epoch:train:8801-8900batch: iter_time=1.577e-04, forward_time=0.104, loss_ctc=39.477, loss_att=40.821, acc=0.755, loss=40.418, backward_time=0.097, grad_norm=36.563, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.034e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:54:09,292 (trainer:737) INFO: 25epoch:train:8901-9000batch: iter_time=1.601e-04, forward_time=0.106, loss_ctc=43.130, loss_att=47.456, acc=0.743, loss=46.158, backward_time=0.097, grad_norm=39.201, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.033e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 08:54:51,832 (trainer:737) INFO: 25epoch:train:9001-9100batch: iter_time=1.668e-04, forward_time=0.104, loss_ctc=44.912, loss_att=49.066, acc=0.737, loss=47.820, backward_time=0.097, grad_norm=40.629, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.032e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:55:34,338 (trainer:737) INFO: 25epoch:train:9101-9200batch: iter_time=1.735e-04, forward_time=0.104, loss_ctc=43.855, loss_att=48.701, acc=0.722, loss=47.248, backward_time=0.096, grad_norm=39.035, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.032e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 08:56:17,185 (trainer:737) INFO: 25epoch:train:9201-9300batch: iter_time=1.625e-04, forward_time=0.104, loss_ctc=44.487, loss_att=51.652, acc=0.715, loss=49.503, backward_time=0.096, grad_norm=40.622, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.031e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 08:56:59,838 (trainer:737) INFO: 25epoch:train:9301-9400batch: iter_time=1.602e-04, forward_time=0.106, loss_ctc=45.933, loss_att=50.026, acc=0.731, loss=48.798, backward_time=0.096, grad_norm=40.819, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.031e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 08:57:43,295 (trainer:737) INFO: 25epoch:train:9401-9500batch: iter_time=1.469e-04, forward_time=0.104, loss_ctc=43.339, loss_att=56.658, acc=0.711, loss=52.662, backward_time=0.096, grad_norm=39.631, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.030e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 08:58:26,011 (trainer:737) INFO: 25epoch:train:9501-9600batch: iter_time=1.594e-04, forward_time=0.104, loss_ctc=55.720, loss_att=57.598, acc=0.715, loss=57.035, backward_time=0.097, grad_norm=52.220, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.030e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 08:59:08,401 (trainer:737) INFO: 25epoch:train:9601-9700batch: iter_time=1.516e-04, forward_time=0.106, loss_ctc=41.250, loss_att=54.138, acc=0.720, loss=50.272, backward_time=0.097, grad_norm=37.709, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.029e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 08:59:50,709 (trainer:737) INFO: 25epoch:train:9701-9800batch: iter_time=1.644e-04, forward_time=0.105, loss_ctc=49.089, loss_att=49.127, acc=0.725, loss=49.115, backward_time=0.096, grad_norm=46.587, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.029e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:00:33,192 (trainer:737) INFO: 25epoch:train:9801-9900batch: iter_time=1.778e-04, forward_time=0.106, loss_ctc=50.537, loss_att=59.686, acc=0.735, loss=56.941, backward_time=0.097, grad_norm=41.950, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.028e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 09:01:15,535 (trainer:737) INFO: 25epoch:train:9901-10000batch: iter_time=1.409e-04, forward_time=0.105, loss_ctc=43.146, loss_att=54.006, acc=0.727, loss=50.748, backward_time=0.096, grad_norm=39.185, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.028e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:01:23,486 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 09:01:42,933 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 09:01:46,565 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e6d236c50>)
+[gpuc02:0/16] 2024-01-15 09:01:46,565 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 09:01:46,569 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 09:06:42,501 (trainer:737) INFO: 25epoch:train:10001-10100batch: iter_time=2.824, forward_time=0.108, loss_ctc=39.694, loss_att=53.887, acc=0.720, loss=49.630, backward_time=0.097, grad_norm=40.586, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.027e-04, train_time=3.269
+[gpuc02:0/16] 2024-01-15 09:07:24,532 (trainer:737) INFO: 25epoch:train:10101-10200batch: iter_time=1.637e-04, forward_time=0.105, loss_ctc=41.818, loss_att=39.705, acc=0.750, loss=40.339, backward_time=0.097, grad_norm=37.794, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.026e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 09:08:06,738 (trainer:737) INFO: 25epoch:train:10201-10300batch: iter_time=1.519e-04, forward_time=0.105, loss_ctc=40.364, loss_att=44.441, acc=0.752, loss=43.218, backward_time=0.097, grad_norm=38.551, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.026e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:08:48,947 (trainer:737) INFO: 25epoch:train:10301-10400batch: iter_time=1.576e-04, forward_time=0.105, loss_ctc=52.748, loss_att=59.836, acc=0.708, loss=57.710, backward_time=0.097, grad_norm=49.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.025e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:09:32,500 (trainer:737) INFO: 25epoch:train:10401-10500batch: iter_time=4.356e-04, forward_time=0.112, loss_ctc=41.192, loss_att=42.148, acc=0.726, loss=41.861, backward_time=0.098, grad_norm=41.612, clip=100.000, loss_scale=2.845e+34, optim_step_time=0.041, optim0_lr0=4.025e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-15 09:10:14,493 (trainer:737) INFO: 25epoch:train:10501-10600batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=37.523, loss_att=45.600, acc=0.729, loss=43.177, backward_time=0.096, grad_norm=37.968, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.024e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 09:10:56,853 (trainer:737) INFO: 25epoch:train:10601-10700batch: iter_time=1.313e-04, forward_time=0.106, loss_ctc=45.176, loss_att=54.014, acc=0.721, loss=51.363, backward_time=0.097, grad_norm=42.760, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.024e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:11:39,679 (trainer:737) INFO: 25epoch:train:10701-10800batch: iter_time=1.325e-04, forward_time=0.106, loss_ctc=49.862, loss_att=57.346, acc=0.722, loss=55.101, backward_time=0.097, grad_norm=43.754, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.023e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 09:12:22,078 (trainer:737) INFO: 25epoch:train:10801-10900batch: iter_time=1.361e-04, forward_time=0.106, loss_ctc=49.343, loss_att=61.151, acc=0.712, loss=57.609, backward_time=0.096, grad_norm=48.440, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.023e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:13:04,348 (trainer:737) INFO: 25epoch:train:10901-11000batch: iter_time=1.399e-04, forward_time=0.105, loss_ctc=45.568, loss_att=49.109, acc=0.730, loss=48.047, backward_time=0.096, grad_norm=43.114, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.022e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:13:46,958 (trainer:737) INFO: 25epoch:train:11001-11100batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=46.495, loss_att=49.897, acc=0.729, loss=48.877, backward_time=0.097, grad_norm=40.990, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.022e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 09:14:29,718 (trainer:737) INFO: 25epoch:train:11101-11200batch: iter_time=1.400e-04, forward_time=0.106, loss_ctc=48.734, loss_att=60.820, acc=0.728, loss=57.194, backward_time=0.097, grad_norm=39.568, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.021e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 09:14:55,838 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 09:15:15,257 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 09:15:18,887 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e9fed2a10>)
+[gpuc02:0/16] 2024-01-15 09:15:18,887 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 09:15:18,891 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 09:19:52,254 (trainer:737) INFO: 25epoch:train:11201-11300batch: iter_time=2.725, forward_time=0.106, loss_ctc=40.848, loss_att=59.061, acc=0.710, loss=53.597, backward_time=0.098, grad_norm=39.844, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.020e-04, train_time=3.225
+[gpuc02:0/16] 2024-01-15 09:20:34,331 (trainer:737) INFO: 25epoch:train:11301-11400batch: iter_time=1.195e-04, forward_time=0.105, loss_ctc=39.442, loss_att=40.952, acc=0.759, loss=40.499, backward_time=0.097, grad_norm=36.383, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.020e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 09:21:16,608 (trainer:737) INFO: 25epoch:train:11401-11500batch: iter_time=1.352e-04, forward_time=0.106, loss_ctc=42.455, loss_att=50.761, acc=0.744, loss=48.269, backward_time=0.098, grad_norm=39.009, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.019e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:21:59,016 (trainer:737) INFO: 25epoch:train:11501-11600batch: iter_time=1.308e-04, forward_time=0.105, loss_ctc=44.179, loss_att=49.079, acc=0.744, loss=47.609, backward_time=0.098, grad_norm=39.314, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.019e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:22:41,383 (trainer:737) INFO: 25epoch:train:11601-11700batch: iter_time=1.414e-04, forward_time=0.105, loss_ctc=43.638, loss_att=51.970, acc=0.727, loss=49.470, backward_time=0.097, grad_norm=37.977, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.018e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:23:23,431 (trainer:737) INFO: 25epoch:train:11701-11800batch: iter_time=1.368e-04, forward_time=0.104, loss_ctc=44.268, loss_att=50.723, acc=0.730, loss=48.787, backward_time=0.097, grad_norm=41.016, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.018e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 09:24:05,832 (trainer:737) INFO: 25epoch:train:11801-11900batch: iter_time=1.245e-04, forward_time=0.105, loss_ctc=45.637, loss_att=50.386, acc=0.729, loss=48.961, backward_time=0.097, grad_norm=40.109, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.017e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:24:48,459 (trainer:737) INFO: 25epoch:train:11901-12000batch: iter_time=1.319e-04, forward_time=0.104, loss_ctc=42.606, loss_att=56.030, acc=0.724, loss=52.003, backward_time=0.098, grad_norm=39.393, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.017e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 09:25:30,837 (trainer:737) INFO: 25epoch:train:12001-12100batch: iter_time=1.311e-04, forward_time=0.104, loss_ctc=53.491, loss_att=57.170, acc=0.726, loss=56.067, backward_time=0.098, grad_norm=50.674, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.016e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:26:13,201 (trainer:737) INFO: 25epoch:train:12101-12200batch: iter_time=1.309e-04, forward_time=0.105, loss_ctc=41.104, loss_att=55.904, acc=0.732, loss=51.464, backward_time=0.097, grad_norm=38.212, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.016e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:26:24,990 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 09:26:55,438 (trainer:737) INFO: 25epoch:train:12201-12300batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=49.062, loss_att=49.575, acc=0.734, loss=49.421, backward_time=0.097, grad_norm=44.864, clip=100.000, loss_scale=2.643e+34, optim_step_time=0.041, optim0_lr0=4.015e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:27:38,026 (trainer:737) INFO: 25epoch:train:12301-12400batch: iter_time=1.397e-04, forward_time=0.106, loss_ctc=50.339, loss_att=60.256, acc=0.742, loss=57.281, backward_time=0.098, grad_norm=40.297, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.015e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 09:28:20,354 (trainer:737) INFO: 25epoch:train:12401-12500batch: iter_time=1.303e-04, forward_time=0.106, loss_ctc=42.851, loss_att=53.455, acc=0.734, loss=50.274, backward_time=0.097, grad_norm=38.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.014e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:28:27,453 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 09:28:47,027 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 09:28:51,002 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e5ef59090>)
+[gpuc02:0/16] 2024-01-15 09:28:51,002 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 09:28:51,005 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 09:33:38,223 (trainer:737) INFO: 25epoch:train:12501-12600batch: iter_time=2.659, forward_time=0.104, loss_ctc=39.710, loss_att=55.630, acc=0.716, loss=50.854, backward_time=0.097, grad_norm=39.842, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.013e-04, train_time=3.178
+[gpuc02:0/16] 2024-01-15 09:34:20,268 (trainer:737) INFO: 25epoch:train:12601-12700batch: iter_time=1.176e-04, forward_time=0.103, loss_ctc=41.892, loss_att=40.386, acc=0.750, loss=40.838, backward_time=0.096, grad_norm=37.213, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.013e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 09:35:02,449 (trainer:737) INFO: 25epoch:train:12701-12800batch: iter_time=1.294e-04, forward_time=0.104, loss_ctc=40.113, loss_att=44.597, acc=0.752, loss=43.252, backward_time=0.097, grad_norm=35.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.012e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:35:45,063 (trainer:737) INFO: 25epoch:train:12801-12900batch: iter_time=1.217e-04, forward_time=0.104, loss_ctc=52.117, loss_att=58.974, acc=0.709, loss=56.917, backward_time=0.097, grad_norm=46.314, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.012e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 09:36:27,365 (trainer:737) INFO: 25epoch:train:12901-13000batch: iter_time=1.279e-04, forward_time=0.103, loss_ctc=40.895, loss_att=42.514, acc=0.726, loss=42.029, backward_time=0.097, grad_norm=37.293, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.011e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:37:10,328 (trainer:737) INFO: 25epoch:train:13001-13100batch: iter_time=1.287e-04, forward_time=0.104, loss_ctc=37.584, loss_att=45.774, acc=0.727, loss=43.317, backward_time=0.097, grad_norm=38.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.011e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 09:37:53,236 (trainer:737) INFO: 25epoch:train:13101-13200batch: iter_time=1.393e-04, forward_time=0.104, loss_ctc=44.728, loss_att=54.363, acc=0.720, loss=51.472, backward_time=0.097, grad_norm=40.204, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.010e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 09:38:35,832 (trainer:737) INFO: 25epoch:train:13201-13300batch: iter_time=1.348e-04, forward_time=0.105, loss_ctc=48.964, loss_att=56.748, acc=0.722, loss=54.412, backward_time=0.097, grad_norm=43.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.010e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 09:39:18,188 (trainer:737) INFO: 25epoch:train:13301-13400batch: iter_time=1.427e-04, forward_time=0.104, loss_ctc=50.262, loss_att=61.448, acc=0.712, loss=58.092, backward_time=0.097, grad_norm=50.601, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.009e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:40:00,425 (trainer:737) INFO: 25epoch:train:13401-13500batch: iter_time=1.466e-04, forward_time=0.105, loss_ctc=45.469, loss_att=49.553, acc=0.728, loss=48.328, backward_time=0.097, grad_norm=41.450, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.009e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:40:42,662 (trainer:737) INFO: 25epoch:train:13501-13600batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=45.860, loss_att=49.237, acc=0.733, loss=48.224, backward_time=0.096, grad_norm=42.728, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.008e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:41:25,073 (trainer:737) INFO: 25epoch:train:13601-13700batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=48.794, loss_att=60.970, acc=0.729, loss=57.317, backward_time=0.096, grad_norm=39.885, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.008e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:41:52,334 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 09:42:12,429 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 09:42:16,160 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e598877f0>)
+[gpuc02:0/16] 2024-01-15 09:42:16,161 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 09:42:16,164 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 09:46:48,686 (trainer:737) INFO: 25epoch:train:13701-13800batch: iter_time=2.781, forward_time=0.106, loss_ctc=40.324, loss_att=56.890, acc=0.711, loss=51.920, backward_time=0.097, grad_norm=38.967, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.007e-04, train_time=3.236
+[gpuc02:0/16] 2024-01-15 09:47:30,928 (trainer:737) INFO: 25epoch:train:13801-13900batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=39.051, loss_att=39.964, acc=0.757, loss=39.690, backward_time=0.097, grad_norm=35.304, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.006e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:48:13,192 (trainer:737) INFO: 25epoch:train:13901-14000batch: iter_time=1.645e-04, forward_time=0.104, loss_ctc=42.581, loss_att=46.424, acc=0.744, loss=45.271, backward_time=0.097, grad_norm=37.262, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.006e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:48:55,518 (trainer:737) INFO: 25epoch:train:14001-14100batch: iter_time=1.566e-04, forward_time=0.105, loss_ctc=44.584, loss_att=49.189, acc=0.738, loss=47.808, backward_time=0.097, grad_norm=39.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.005e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:49:37,746 (trainer:737) INFO: 25epoch:train:14101-14200batch: iter_time=1.497e-04, forward_time=0.104, loss_ctc=43.790, loss_att=46.402, acc=0.727, loss=45.618, backward_time=0.097, grad_norm=37.676, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.005e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:50:19,965 (trainer:737) INFO: 25epoch:train:14201-14300batch: iter_time=1.748e-04, forward_time=0.104, loss_ctc=44.330, loss_att=51.560, acc=0.716, loss=49.391, backward_time=0.097, grad_norm=41.582, clip=100.000, loss_scale=3.572e+34, optim_step_time=0.042, optim0_lr0=4.004e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:51:02,183 (trainer:737) INFO: 25epoch:train:14301-14400batch: iter_time=1.367e-04, forward_time=0.105, loss_ctc=45.688, loss_att=49.121, acc=0.735, loss=48.091, backward_time=0.097, grad_norm=40.254, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.004e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:51:44,899 (trainer:737) INFO: 25epoch:train:14401-14500batch: iter_time=1.444e-04, forward_time=0.109, loss_ctc=42.843, loss_att=55.813, acc=0.716, loss=51.922, backward_time=0.097, grad_norm=41.441, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.003e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 09:52:27,219 (trainer:737) INFO: 25epoch:train:14501-14600batch: iter_time=1.298e-04, forward_time=0.105, loss_ctc=54.174, loss_att=56.753, acc=0.720, loss=55.979, backward_time=0.098, grad_norm=53.075, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.003e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 09:53:09,683 (trainer:737) INFO: 25epoch:train:14601-14700batch: iter_time=1.505e-04, forward_time=0.105, loss_ctc=40.758, loss_att=53.934, acc=0.720, loss=49.981, backward_time=0.098, grad_norm=38.457, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.002e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 09:53:51,954 (trainer:737) INFO: 25epoch:train:14701-14800batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=48.250, loss_att=48.486, acc=0.726, loss=48.415, backward_time=0.098, grad_norm=46.128, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.002e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 09:54:34,766 (trainer:737) INFO: 25epoch:train:14801-14900batch: iter_time=1.453e-04, forward_time=0.106, loss_ctc=50.313, loss_att=59.105, acc=0.737, loss=56.467, backward_time=0.098, grad_norm=40.981, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.001e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 09:55:17,370 (trainer:737) INFO: 25epoch:train:14901-15000batch: iter_time=1.255e-04, forward_time=0.106, loss_ctc=42.738, loss_att=53.427, acc=0.728, loss=50.220, backward_time=0.098, grad_norm=40.369, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.001e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 10:15:29,310 (trainer:343) INFO: 25epoch results: [train] iter_time=0.226, forward_time=0.106, loss_ctc=45.470, loss_att=52.221, acc=0.729, loss=50.196, backward_time=0.097, grad_norm=41.415, clip=100.000, loss_scale=2.815e+34, optim_step_time=0.041, optim0_lr0=4.041e-04, train_time=0.660, time=2 hours, 45 minutes and 11.97 seconds, total_count=375000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=52.681, cer_ctc=0.265, loss_att=52.625, acc=0.596, cer=0.350, wer=0.998, loss=52.642, time=20 minutes and 1.43 seconds, total_count=116775, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 10:15:34,455 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-15 10:15:34,477 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/20epoch.pth
+[gpuc02:0/16] 2024-01-15 10:15:34,477 (trainer:272) INFO: 26/45epoch started. Estimated time to finish: 2 days, 13 hours and 7 minutes
+[gpuc02:0/16] 2024-01-15 10:15:34,486 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 10:15:54,593 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 10:15:58,219 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f259cd0f8e0>)
+[gpuc02:0/16] 2024-01-15 10:15:58,219 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 10:15:58,222 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 10:20:49,214 (trainer:737) INFO: 26epoch:train:1-100batch: iter_time=2.472, forward_time=0.152, loss_ctc=39.854, loss_att=42.975, acc=0.737, loss=42.038, backward_time=0.107, grad_norm=37.847, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.045, optim0_lr0=4.000e-04, train_time=3.147
+[gpuc02:0/16] 2024-01-15 10:21:31,897 (trainer:737) INFO: 26epoch:train:101-200batch: iter_time=1.486e-04, forward_time=0.104, loss_ctc=45.292, loss_att=48.228, acc=0.711, loss=47.347, backward_time=0.097, grad_norm=42.478, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.000e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 10:22:14,614 (trainer:737) INFO: 26epoch:train:201-300batch: iter_time=1.506e-04, forward_time=0.103, loss_ctc=42.294, loss_att=44.300, acc=0.736, loss=43.698, backward_time=0.097, grad_norm=41.124, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.999e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 10:22:56,916 (trainer:737) INFO: 26epoch:train:301-400batch: iter_time=1.653e-04, forward_time=0.104, loss_ctc=45.745, loss_att=60.978, acc=0.719, loss=56.408, backward_time=0.098, grad_norm=44.264, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.998e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 10:23:41,659 (trainer:737) INFO: 26epoch:train:401-500batch: iter_time=1.608e-04, forward_time=0.103, loss_ctc=45.172, loss_att=47.089, acc=0.734, loss=46.514, backward_time=0.098, grad_norm=41.110, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.998e-04, train_time=0.447
+[gpuc02:0/16] 2024-01-15 10:24:24,848 (trainer:737) INFO: 26epoch:train:501-600batch: iter_time=1.534e-04, forward_time=0.104, loss_ctc=49.995, loss_att=47.436, acc=0.744, loss=48.204, backward_time=0.098, grad_norm=43.779, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.997e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 10:25:08,660 (trainer:737) INFO: 26epoch:train:601-700batch: iter_time=1.643e-04, forward_time=0.105, loss_ctc=47.521, loss_att=60.071, acc=0.705, loss=56.306, backward_time=0.098, grad_norm=42.355, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.997e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 10:25:50,792 (trainer:737) INFO: 26epoch:train:701-800batch: iter_time=1.458e-04, forward_time=0.106, loss_ctc=42.163, loss_att=50.265, acc=0.710, loss=47.834, backward_time=0.097, grad_norm=41.757, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.996e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 10:26:32,650 (trainer:737) INFO: 26epoch:train:801-900batch: iter_time=1.287e-04, forward_time=0.104, loss_ctc=47.068, loss_att=44.770, acc=0.730, loss=45.459, backward_time=0.097, grad_norm=47.222, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.996e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-15 10:26:35,150 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 10:27:14,559 (trainer:737) INFO: 26epoch:train:901-1000batch: iter_time=9.916e-05, forward_time=0.104, loss_ctc=56.679, loss_att=65.686, acc=0.702, loss=62.984, backward_time=0.097, grad_norm=53.075, clip=100.000, loss_scale=2.182e+34, optim_step_time=0.041, optim0_lr0=3.995e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 10:28:03,430 (trainer:737) INFO: 26epoch:train:1001-1100batch: iter_time=1.120e-04, forward_time=0.144, loss_ctc=50.174, loss_att=57.666, acc=0.700, loss=55.418, backward_time=0.102, grad_norm=51.748, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=3.995e-04, train_time=0.488
+[gpuc02:0/16] 2024-01-15 10:28:48,969 (trainer:737) INFO: 26epoch:train:1101-1200batch: iter_time=1.097e-04, forward_time=0.107, loss_ctc=58.956, loss_att=70.754, acc=0.688, loss=67.214, backward_time=0.099, grad_norm=45.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.994e-04, train_time=0.455
+[gpuc02:0/16] 2024-01-15 10:29:33,828 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 10:29:53,143 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 10:29:56,733 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e35747100>)
+[gpuc02:0/16] 2024-01-15 10:29:56,733 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 10:29:56,736 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 10:36:14,699 (trainer:737) INFO: 26epoch:train:1201-1300batch: iter_time=3.715, forward_time=0.103, loss_ctc=37.801, loss_att=47.984, acc=0.727, loss=44.929, backward_time=0.097, grad_norm=35.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.994e-04, train_time=4.457
+[gpuc02:0/16] 2024-01-15 10:36:56,942 (trainer:737) INFO: 26epoch:train:1301-1400batch: iter_time=1.376e-04, forward_time=0.104, loss_ctc=43.930, loss_att=46.508, acc=0.732, loss=45.735, backward_time=0.097, grad_norm=41.699, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.993e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:37:39,340 (trainer:737) INFO: 26epoch:train:1401-1500batch: iter_time=1.479e-04, forward_time=0.103, loss_ctc=41.517, loss_att=45.372, acc=0.725, loss=44.215, backward_time=0.097, grad_norm=36.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.993e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 10:38:21,832 (trainer:737) INFO: 26epoch:train:1501-1600batch: iter_time=1.294e-04, forward_time=0.105, loss_ctc=43.508, loss_att=47.490, acc=0.742, loss=46.296, backward_time=0.097, grad_norm=41.365, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.992e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 10:39:04,084 (trainer:737) INFO: 26epoch:train:1601-1700batch: iter_time=1.547e-04, forward_time=0.105, loss_ctc=43.227, loss_att=57.469, acc=0.719, loss=53.197, backward_time=0.097, grad_norm=39.692, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.992e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:39:46,340 (trainer:737) INFO: 26epoch:train:1701-1800batch: iter_time=1.510e-04, forward_time=0.104, loss_ctc=50.762, loss_att=48.519, acc=0.734, loss=49.192, backward_time=0.097, grad_norm=42.735, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.991e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:40:28,659 (trainer:737) INFO: 26epoch:train:1801-1900batch: iter_time=1.321e-04, forward_time=0.104, loss_ctc=46.930, loss_att=50.002, acc=0.738, loss=49.081, backward_time=0.098, grad_norm=41.065, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.991e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 10:41:10,836 (trainer:737) INFO: 26epoch:train:1901-2000batch: iter_time=1.467e-04, forward_time=0.104, loss_ctc=41.666, loss_att=54.169, acc=0.719, loss=50.418, backward_time=0.097, grad_norm=42.017, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.990e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:41:53,224 (trainer:737) INFO: 26epoch:train:2001-2100batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=42.771, loss_att=47.459, acc=0.717, loss=46.053, backward_time=0.097, grad_norm=43.740, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.989e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 10:42:35,765 (trainer:737) INFO: 26epoch:train:2101-2200batch: iter_time=1.372e-04, forward_time=0.104, loss_ctc=50.706, loss_att=53.921, acc=0.718, loss=52.956, backward_time=0.098, grad_norm=48.302, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.989e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 10:43:18,048 (trainer:737) INFO: 26epoch:train:2201-2300batch: iter_time=1.302e-04, forward_time=0.103, loss_ctc=46.655, loss_att=52.500, acc=0.709, loss=50.747, backward_time=0.097, grad_norm=45.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.988e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 10:44:00,485 (trainer:737) INFO: 26epoch:train:2301-2400batch: iter_time=1.169e-04, forward_time=0.105, loss_ctc=54.423, loss_att=71.857, acc=0.687, loss=66.627, backward_time=0.098, grad_norm=49.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.988e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 10:44:42,786 (trainer:737) INFO: 26epoch:train:2401-2500batch: iter_time=1.112e-04, forward_time=0.104, loss_ctc=50.355, loss_att=61.319, acc=0.710, loss=58.030, backward_time=0.097, grad_norm=39.938, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.987e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 10:44:47,322 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 10:45:06,614 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 10:45:10,453 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e44b53bb0>)
+[gpuc02:0/16] 2024-01-15 10:45:10,453 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 10:45:10,456 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 10:50:24,625 (trainer:737) INFO: 26epoch:train:2501-2600batch: iter_time=2.644, forward_time=0.107, loss_ctc=38.484, loss_att=41.361, acc=0.746, loss=40.498, backward_time=0.098, grad_norm=35.020, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.987e-04, train_time=3.418
+[gpuc02:0/16] 2024-01-15 10:51:07,339 (trainer:737) INFO: 26epoch:train:2601-2700batch: iter_time=1.327e-04, forward_time=0.104, loss_ctc=43.471, loss_att=47.069, acc=0.714, loss=45.990, backward_time=0.097, grad_norm=39.211, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.986e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 10:51:49,556 (trainer:737) INFO: 26epoch:train:2701-2800batch: iter_time=1.214e-04, forward_time=0.104, loss_ctc=40.953, loss_att=43.654, acc=0.742, loss=42.844, backward_time=0.097, grad_norm=40.362, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.986e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:52:32,552 (trainer:737) INFO: 26epoch:train:2801-2900batch: iter_time=1.317e-04, forward_time=0.104, loss_ctc=44.503, loss_att=60.607, acc=0.723, loss=55.776, backward_time=0.097, grad_norm=40.324, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.985e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 10:53:15,087 (trainer:737) INFO: 26epoch:train:2901-3000batch: iter_time=1.243e-04, forward_time=0.104, loss_ctc=44.180, loss_att=45.664, acc=0.738, loss=45.219, backward_time=0.096, grad_norm=39.555, clip=100.000, loss_scale=4.029e+34, optim_step_time=0.041, optim0_lr0=3.985e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 10:53:57,233 (trainer:737) INFO: 26epoch:train:3001-3100batch: iter_time=1.187e-04, forward_time=0.103, loss_ctc=48.479, loss_att=45.995, acc=0.748, loss=46.740, backward_time=0.096, grad_norm=42.332, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=3.984e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 10:54:39,740 (trainer:737) INFO: 26epoch:train:3101-3200batch: iter_time=1.272e-04, forward_time=0.104, loss_ctc=45.970, loss_att=59.210, acc=0.709, loss=55.238, backward_time=0.097, grad_norm=42.200, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.984e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 10:55:21,866 (trainer:737) INFO: 26epoch:train:3201-3300batch: iter_time=1.476e-04, forward_time=0.103, loss_ctc=41.929, loss_att=50.015, acc=0.712, loss=47.589, backward_time=0.097, grad_norm=45.253, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.983e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 10:56:04,096 (trainer:737) INFO: 26epoch:train:3301-3400batch: iter_time=1.422e-04, forward_time=0.103, loss_ctc=44.170, loss_att=43.285, acc=0.737, loss=43.550, backward_time=0.097, grad_norm=43.321, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.983e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 10:56:46,577 (trainer:737) INFO: 26epoch:train:3401-3500batch: iter_time=1.267e-04, forward_time=0.104, loss_ctc=54.821, loss_att=64.776, acc=0.704, loss=61.790, backward_time=0.098, grad_norm=51.349, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.982e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 10:57:28,909 (trainer:737) INFO: 26epoch:train:3501-3600batch: iter_time=1.440e-04, forward_time=0.104, loss_ctc=47.715, loss_att=56.875, acc=0.703, loss=54.127, backward_time=0.097, grad_norm=51.154, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.982e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 10:58:12,021 (trainer:737) INFO: 26epoch:train:3601-3700batch: iter_time=1.481e-04, forward_time=0.107, loss_ctc=57.450, loss_att=69.513, acc=0.692, loss=65.894, backward_time=0.098, grad_norm=45.381, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.981e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 10:58:38,210 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 10:58:57,325 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 10:59:00,845 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f243844a1d0>)
+[gpuc02:0/16] 2024-01-15 10:59:00,846 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 10:59:00,849 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 11:03:50,502 (trainer:737) INFO: 26epoch:train:3701-3800batch: iter_time=2.498, forward_time=0.106, loss_ctc=36.858, loss_att=49.395, acc=0.732, loss=45.634, backward_time=0.097, grad_norm=35.337, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.980e-04, train_time=3.385
+[gpuc02:0/16] 2024-01-15 11:04:32,235 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 11:04:32,654 (trainer:737) INFO: 26epoch:train:3801-3900batch: iter_time=1.165e-04, forward_time=0.103, loss_ctc=43.001, loss_att=48.421, acc=0.732, loss=46.795, backward_time=0.097, grad_norm=40.369, clip=100.000, loss_scale=4.133e+34, optim_step_time=0.041, optim0_lr0=3.980e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 11:05:14,745 (trainer:737) INFO: 26epoch:train:3901-4000batch: iter_time=1.154e-04, forward_time=0.103, loss_ctc=40.325, loss_att=45.992, acc=0.732, loss=44.292, backward_time=0.096, grad_norm=38.218, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.979e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 11:05:57,086 (trainer:737) INFO: 26epoch:train:4001-4100batch: iter_time=1.185e-04, forward_time=0.103, loss_ctc=42.304, loss_att=48.361, acc=0.748, loss=46.544, backward_time=0.097, grad_norm=38.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.979e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:06:39,612 (trainer:737) INFO: 26epoch:train:4101-4200batch: iter_time=1.086e-04, forward_time=0.103, loss_ctc=42.658, loss_att=58.015, acc=0.727, loss=53.408, backward_time=0.097, grad_norm=41.793, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.978e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:07:22,685 (trainer:737) INFO: 26epoch:train:4201-4300batch: iter_time=1.207e-04, forward_time=0.103, loss_ctc=49.689, loss_att=51.074, acc=0.735, loss=50.658, backward_time=0.097, grad_norm=42.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.978e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 11:08:05,037 (trainer:737) INFO: 26epoch:train:4301-4400batch: iter_time=1.164e-04, forward_time=0.104, loss_ctc=45.425, loss_att=50.802, acc=0.741, loss=49.189, backward_time=0.097, grad_norm=40.961, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.977e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:08:47,576 (trainer:737) INFO: 26epoch:train:4401-4500batch: iter_time=1.050e-04, forward_time=0.103, loss_ctc=41.154, loss_att=54.467, acc=0.733, loss=50.473, backward_time=0.097, grad_norm=38.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.977e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:09:30,308 (trainer:737) INFO: 26epoch:train:4501-4600batch: iter_time=1.387e-04, forward_time=0.103, loss_ctc=42.367, loss_att=50.818, acc=0.725, loss=48.283, backward_time=0.097, grad_norm=44.046, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.976e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 11:10:12,793 (trainer:737) INFO: 26epoch:train:4601-4700batch: iter_time=1.178e-04, forward_time=0.104, loss_ctc=49.323, loss_att=54.051, acc=0.724, loss=52.633, backward_time=0.097, grad_norm=47.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.976e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:10:55,007 (trainer:737) INFO: 26epoch:train:4701-4800batch: iter_time=1.139e-04, forward_time=0.103, loss_ctc=45.970, loss_att=54.852, acc=0.723, loss=52.187, backward_time=0.097, grad_norm=44.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.975e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 11:11:37,801 (trainer:737) INFO: 26epoch:train:4801-4900batch: iter_time=1.060e-04, forward_time=0.105, loss_ctc=53.215, loss_att=75.705, acc=0.683, loss=68.958, backward_time=0.098, grad_norm=49.592, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.975e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 11:12:20,168 (trainer:737) INFO: 26epoch:train:4901-5000batch: iter_time=9.972e-05, forward_time=0.104, loss_ctc=50.624, loss_att=60.727, acc=0.722, loss=57.696, backward_time=0.098, grad_norm=41.393, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.974e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:12:27,095 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 11:12:46,150 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 11:12:49,667 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e44b88f70>)
+[gpuc02:0/16] 2024-01-15 11:12:49,667 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 11:12:49,671 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 11:17:47,687 (trainer:737) INFO: 26epoch:train:5001-5100batch: iter_time=2.495, forward_time=0.124, loss_ctc=38.385, loss_att=42.402, acc=0.745, loss=41.197, backward_time=0.102, grad_norm=35.137, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.974e-04, train_time=3.275
+[gpuc02:0/16] 2024-01-15 11:18:29,844 (trainer:737) INFO: 26epoch:train:5101-5200batch: iter_time=1.027e-04, forward_time=0.103, loss_ctc=43.296, loss_att=48.901, acc=0.711, loss=47.220, backward_time=0.096, grad_norm=43.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.973e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 11:19:12,213 (trainer:737) INFO: 26epoch:train:5201-5300batch: iter_time=9.765e-05, forward_time=0.103, loss_ctc=40.222, loss_att=43.748, acc=0.742, loss=42.690, backward_time=0.096, grad_norm=36.324, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.973e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:19:54,522 (trainer:737) INFO: 26epoch:train:5301-5400batch: iter_time=1.053e-04, forward_time=0.104, loss_ctc=43.571, loss_att=61.360, acc=0.719, loss=56.023, backward_time=0.097, grad_norm=40.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.972e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:20:36,692 (trainer:737) INFO: 26epoch:train:5401-5500batch: iter_time=1.142e-04, forward_time=0.104, loss_ctc=43.995, loss_att=46.481, acc=0.739, loss=45.735, backward_time=0.096, grad_norm=38.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.972e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 11:21:19,567 (trainer:737) INFO: 26epoch:train:5501-5600batch: iter_time=1.169e-04, forward_time=0.103, loss_ctc=47.253, loss_att=45.985, acc=0.751, loss=46.366, backward_time=0.097, grad_norm=40.269, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.971e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 11:22:02,024 (trainer:737) INFO: 26epoch:train:5601-5700batch: iter_time=1.125e-04, forward_time=0.104, loss_ctc=44.777, loss_att=59.109, acc=0.710, loss=54.809, backward_time=0.097, grad_norm=40.158, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.971e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 11:22:44,264 (trainer:737) INFO: 26epoch:train:5701-5800batch: iter_time=9.848e-05, forward_time=0.103, loss_ctc=41.052, loss_att=50.566, acc=0.712, loss=47.712, backward_time=0.097, grad_norm=41.476, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.970e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 11:23:26,861 (trainer:737) INFO: 26epoch:train:5801-5900batch: iter_time=1.034e-04, forward_time=0.107, loss_ctc=43.336, loss_att=43.637, acc=0.735, loss=43.546, backward_time=0.097, grad_norm=42.952, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.041, optim0_lr0=3.970e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 11:24:09,716 (trainer:737) INFO: 26epoch:train:5901-6000batch: iter_time=9.190e-05, forward_time=0.105, loss_ctc=53.442, loss_att=64.495, acc=0.706, loss=61.179, backward_time=0.098, grad_norm=47.828, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.969e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 11:24:52,896 (trainer:737) INFO: 26epoch:train:6001-6100batch: iter_time=9.741e-05, forward_time=0.105, loss_ctc=46.825, loss_att=56.828, acc=0.705, loss=53.828, backward_time=0.097, grad_norm=50.001, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.968e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 11:25:35,468 (trainer:737) INFO: 26epoch:train:6101-6200batch: iter_time=1.173e-04, forward_time=0.106, loss_ctc=57.764, loss_att=69.602, acc=0.691, loss=66.050, backward_time=0.097, grad_norm=45.962, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.968e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:25:59,391 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 11:26:19,082 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 11:26:22,752 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ea47ea530>)
+[gpuc02:0/16] 2024-01-15 11:26:22,752 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 11:26:22,775 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 11:30:54,087 (trainer:737) INFO: 26epoch:train:6201-6300batch: iter_time=2.429, forward_time=0.103, loss_ctc=36.715, loss_att=48.458, acc=0.733, loss=44.935, backward_time=0.097, grad_norm=34.648, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.967e-04, train_time=3.186
+[gpuc02:0/16] 2024-01-15 11:31:36,449 (trainer:737) INFO: 26epoch:train:6301-6400batch: iter_time=1.096e-04, forward_time=0.103, loss_ctc=42.723, loss_att=48.316, acc=0.733, loss=46.638, backward_time=0.097, grad_norm=41.612, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.967e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:32:18,714 (trainer:737) INFO: 26epoch:train:6401-6500batch: iter_time=1.194e-04, forward_time=0.103, loss_ctc=39.790, loss_att=45.623, acc=0.731, loss=43.873, backward_time=0.096, grad_norm=35.979, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.966e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 11:33:01,273 (trainer:737) INFO: 26epoch:train:6501-6600batch: iter_time=1.187e-04, forward_time=0.104, loss_ctc=42.038, loss_att=47.856, acc=0.750, loss=46.111, backward_time=0.097, grad_norm=38.935, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.966e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:33:43,750 (trainer:737) INFO: 26epoch:train:6601-6700batch: iter_time=1.292e-04, forward_time=0.103, loss_ctc=42.352, loss_att=57.259, acc=0.729, loss=52.787, backward_time=0.097, grad_norm=39.744, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.965e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:34:26,031 (trainer:737) INFO: 26epoch:train:6701-6800batch: iter_time=1.361e-04, forward_time=0.103, loss_ctc=49.506, loss_att=50.503, acc=0.737, loss=50.203, backward_time=0.096, grad_norm=43.260, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.965e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:35:08,515 (trainer:737) INFO: 26epoch:train:6801-6900batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=44.878, loss_att=51.174, acc=0.739, loss=49.285, backward_time=0.097, grad_norm=40.754, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.964e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:35:50,803 (trainer:737) INFO: 26epoch:train:6901-7000batch: iter_time=1.425e-04, forward_time=0.104, loss_ctc=40.799, loss_att=54.347, acc=0.735, loss=50.282, backward_time=0.097, grad_norm=38.660, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.964e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:36:33,067 (trainer:737) INFO: 26epoch:train:7001-7100batch: iter_time=1.344e-04, forward_time=0.104, loss_ctc=41.262, loss_att=49.999, acc=0.727, loss=47.378, backward_time=0.097, grad_norm=43.336, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.963e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 11:37:16,079 (trainer:737) INFO: 26epoch:train:7101-7200batch: iter_time=1.236e-04, forward_time=0.103, loss_ctc=48.751, loss_att=53.323, acc=0.727, loss=51.951, backward_time=0.097, grad_norm=47.565, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.963e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 11:37:58,444 (trainer:737) INFO: 26epoch:train:7201-7300batch: iter_time=1.157e-04, forward_time=0.103, loss_ctc=45.553, loss_att=54.450, acc=0.722, loss=51.781, backward_time=0.097, grad_norm=43.372, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.962e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:38:41,340 (trainer:737) INFO: 26epoch:train:7301-7400batch: iter_time=1.265e-04, forward_time=0.104, loss_ctc=52.760, loss_att=75.592, acc=0.685, loss=68.742, backward_time=0.098, grad_norm=48.324, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.962e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 11:39:24,188 (trainer:737) INFO: 26epoch:train:7401-7500batch: iter_time=1.199e-04, forward_time=0.103, loss_ctc=50.415, loss_att=60.462, acc=0.723, loss=57.448, backward_time=0.097, grad_norm=40.325, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.961e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 11:39:28,996 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 11:39:47,939 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 11:39:51,577 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f24489f3cd0>)
+[gpuc02:0/16] 2024-01-15 11:39:51,577 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 11:39:51,580 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 11:44:45,726 (trainer:737) INFO: 26epoch:train:7501-7600batch: iter_time=2.452, forward_time=0.103, loss_ctc=38.195, loss_att=41.520, acc=0.749, loss=40.523, backward_time=0.096, grad_norm=33.133, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.961e-04, train_time=3.215
+[gpuc02:0/16] 2024-01-15 11:45:28,266 (trainer:737) INFO: 26epoch:train:7601-7700batch: iter_time=9.819e-05, forward_time=0.104, loss_ctc=42.928, loss_att=47.328, acc=0.723, loss=46.008, backward_time=0.096, grad_norm=42.455, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.960e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 11:46:10,574 (trainer:737) INFO: 26epoch:train:7701-7800batch: iter_time=1.036e-04, forward_time=0.103, loss_ctc=39.982, loss_att=43.085, acc=0.752, loss=42.154, backward_time=0.096, grad_norm=37.103, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.960e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:46:53,531 (trainer:737) INFO: 26epoch:train:7801-7900batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=43.070, loss_att=60.026, acc=0.726, loss=54.939, backward_time=0.097, grad_norm=39.119, clip=100.000, loss_scale=4.195e+34, optim_step_time=0.042, optim0_lr0=3.959e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 11:47:35,805 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 11:47:36,235 (trainer:737) INFO: 26epoch:train:7901-8000batch: iter_time=1.071e-04, forward_time=0.103, loss_ctc=43.837, loss_att=46.094, acc=0.753, loss=45.417, backward_time=0.096, grad_norm=38.733, clip=100.000, loss_scale=8.266e+34, optim_step_time=0.042, optim0_lr0=3.959e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 11:47:39,151 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 11:48:18,575 (trainer:737) INFO: 26epoch:train:8001-8100batch: iter_time=1.019e-04, forward_time=0.103, loss_ctc=47.144, loss_att=48.244, acc=0.749, loss=47.914, backward_time=0.097, grad_norm=42.341, clip=100.000, loss_scale=2.203e+34, optim_step_time=0.042, optim0_lr0=3.958e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 11:49:00,961 (trainer:737) INFO: 26epoch:train:8101-8200batch: iter_time=1.002e-04, forward_time=0.104, loss_ctc=44.853, loss_att=59.294, acc=0.715, loss=54.962, backward_time=0.097, grad_norm=41.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.958e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 11:49:43,217 (trainer:737) INFO: 26epoch:train:8201-8300batch: iter_time=1.051e-04, forward_time=0.103, loss_ctc=42.044, loss_att=53.058, acc=0.720, loss=49.754, backward_time=0.096, grad_norm=42.247, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.957e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 11:50:26,603 (trainer:737) INFO: 26epoch:train:8301-8400batch: iter_time=1.052e-04, forward_time=0.104, loss_ctc=43.492, loss_att=43.937, acc=0.744, loss=43.803, backward_time=0.097, grad_norm=42.270, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.957e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 11:51:09,377 (trainer:737) INFO: 26epoch:train:8401-8500batch: iter_time=9.858e-05, forward_time=0.105, loss_ctc=52.720, loss_att=64.506, acc=0.722, loss=60.970, backward_time=0.097, grad_norm=47.420, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.956e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 11:51:52,142 (trainer:737) INFO: 26epoch:train:8501-8600batch: iter_time=1.010e-04, forward_time=0.105, loss_ctc=46.238, loss_att=58.707, acc=0.714, loss=54.967, backward_time=0.097, grad_norm=48.065, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.956e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 11:52:36,167 (trainer:737) INFO: 26epoch:train:8601-8700batch: iter_time=1.024e-04, forward_time=0.105, loss_ctc=57.968, loss_att=71.149, acc=0.695, loss=67.195, backward_time=0.098, grad_norm=47.707, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.955e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-15 11:52:59,774 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 11:53:20,215 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 11:53:23,921 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8bdafdc0>)
+[gpuc02:0/16] 2024-01-15 11:53:23,921 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 11:53:23,925 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 11:58:37,212 (trainer:737) INFO: 26epoch:train:8701-8800batch: iter_time=2.474, forward_time=0.103, loss_ctc=36.468, loss_att=47.575, acc=0.739, loss=44.243, backward_time=0.096, grad_norm=35.790, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.954e-04, train_time=3.610
+[gpuc02:0/16] 2024-01-15 11:59:19,417 (trainer:737) INFO: 26epoch:train:8801-8900batch: iter_time=1.166e-04, forward_time=0.103, loss_ctc=41.953, loss_att=46.973, acc=0.734, loss=45.467, backward_time=0.096, grad_norm=39.278, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.954e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 12:00:01,503 (trainer:737) INFO: 26epoch:train:8901-9000batch: iter_time=1.205e-04, forward_time=0.102, loss_ctc=39.728, loss_att=45.477, acc=0.727, loss=43.752, backward_time=0.096, grad_norm=36.531, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.953e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:00:43,999 (trainer:737) INFO: 26epoch:train:9001-9100batch: iter_time=1.331e-04, forward_time=0.103, loss_ctc=41.876, loss_att=47.786, acc=0.745, loss=46.013, backward_time=0.097, grad_norm=40.237, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.953e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:01:26,097 (trainer:737) INFO: 26epoch:train:9101-9200batch: iter_time=1.277e-04, forward_time=0.102, loss_ctc=41.693, loss_att=57.352, acc=0.722, loss=52.654, backward_time=0.096, grad_norm=37.902, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.952e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:02:08,512 (trainer:737) INFO: 26epoch:train:9201-9300batch: iter_time=1.208e-04, forward_time=0.102, loss_ctc=49.149, loss_att=48.413, acc=0.737, loss=48.634, backward_time=0.097, grad_norm=42.091, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.952e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:02:51,001 (trainer:737) INFO: 26epoch:train:9301-9400batch: iter_time=1.238e-04, forward_time=0.103, loss_ctc=44.492, loss_att=50.249, acc=0.740, loss=48.522, backward_time=0.097, grad_norm=40.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.951e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:03:33,684 (trainer:737) INFO: 26epoch:train:9401-9500batch: iter_time=1.283e-04, forward_time=0.105, loss_ctc=40.135, loss_att=53.927, acc=0.722, loss=49.789, backward_time=0.096, grad_norm=39.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.951e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 12:04:15,813 (trainer:737) INFO: 26epoch:train:9501-9600batch: iter_time=1.215e-04, forward_time=0.102, loss_ctc=41.491, loss_att=48.338, acc=0.717, loss=46.284, backward_time=0.096, grad_norm=43.098, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.950e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:04:59,194 (trainer:737) INFO: 26epoch:train:9601-9700batch: iter_time=1.292e-04, forward_time=0.103, loss_ctc=47.986, loss_att=53.267, acc=0.722, loss=51.683, backward_time=0.097, grad_norm=46.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.950e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 12:05:41,786 (trainer:737) INFO: 26epoch:train:9701-9800batch: iter_time=1.396e-04, forward_time=0.103, loss_ctc=45.049, loss_att=52.606, acc=0.711, loss=50.339, backward_time=0.096, grad_norm=42.640, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.949e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 12:06:24,283 (trainer:737) INFO: 26epoch:train:9801-9900batch: iter_time=1.742e-04, forward_time=0.105, loss_ctc=52.361, loss_att=71.713, acc=0.689, loss=65.907, backward_time=0.098, grad_norm=46.096, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.949e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:07:06,696 (trainer:737) INFO: 26epoch:train:9901-10000batch: iter_time=1.736e-04, forward_time=0.105, loss_ctc=49.698, loss_att=60.927, acc=0.712, loss=57.558, backward_time=0.098, grad_norm=42.553, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.948e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:07:13,226 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 12:07:32,416 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 12:07:36,022 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1eb3a978e0>)
+[gpuc02:0/16] 2024-01-15 12:07:36,022 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 12:07:36,026 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 12:12:30,708 (trainer:737) INFO: 26epoch:train:10001-10100batch: iter_time=2.496, forward_time=0.104, loss_ctc=37.652, loss_att=42.390, acc=0.748, loss=40.969, backward_time=0.097, grad_norm=34.604, clip=100.000, loss_scale=4.008e+34, optim_step_time=0.042, optim0_lr0=3.948e-04, train_time=3.240
+[gpuc02:0/16] 2024-01-15 12:13:12,884 (trainer:737) INFO: 26epoch:train:10101-10200batch: iter_time=1.544e-04, forward_time=0.104, loss_ctc=42.630, loss_att=48.870, acc=0.720, loss=46.998, backward_time=0.097, grad_norm=39.490, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.947e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 12:13:55,051 (trainer:737) INFO: 26epoch:train:10201-10300batch: iter_time=1.499e-04, forward_time=0.104, loss_ctc=39.753, loss_att=43.251, acc=0.752, loss=42.202, backward_time=0.097, grad_norm=35.993, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.947e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:14:37,486 (trainer:737) INFO: 26epoch:train:10301-10400batch: iter_time=1.746e-04, forward_time=0.105, loss_ctc=43.165, loss_att=61.167, acc=0.724, loss=55.766, backward_time=0.098, grad_norm=40.148, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.946e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:15:20,006 (trainer:737) INFO: 26epoch:train:10401-10500batch: iter_time=1.646e-04, forward_time=0.104, loss_ctc=43.565, loss_att=46.086, acc=0.754, loss=45.330, backward_time=0.097, grad_norm=39.582, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.946e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:16:02,355 (trainer:737) INFO: 26epoch:train:10501-10600batch: iter_time=1.612e-04, forward_time=0.105, loss_ctc=48.306, loss_att=48.466, acc=0.750, loss=48.418, backward_time=0.098, grad_norm=44.033, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.945e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:16:44,851 (trainer:737) INFO: 26epoch:train:10601-10700batch: iter_time=1.367e-04, forward_time=0.105, loss_ctc=44.506, loss_att=60.034, acc=0.717, loss=55.376, backward_time=0.098, grad_norm=41.768, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.945e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:17:27,153 (trainer:737) INFO: 26epoch:train:10701-10800batch: iter_time=1.792e-04, forward_time=0.104, loss_ctc=41.009, loss_att=53.275, acc=0.720, loss=49.595, backward_time=0.097, grad_norm=43.247, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.944e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:17:32,601 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 12:18:09,405 (trainer:737) INFO: 26epoch:train:10801-10900batch: iter_time=1.644e-04, forward_time=0.103, loss_ctc=43.185, loss_att=43.551, acc=0.745, loss=43.441, backward_time=0.098, grad_norm=43.734, clip=100.000, loss_scale=2.329e+34, optim_step_time=0.042, optim0_lr0=3.944e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 12:18:52,284 (trainer:737) INFO: 26epoch:train:10901-11000batch: iter_time=1.590e-04, forward_time=0.105, loss_ctc=51.945, loss_att=65.068, acc=0.721, loss=61.131, backward_time=0.099, grad_norm=46.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.943e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 12:19:35,005 (trainer:737) INFO: 26epoch:train:11001-11100batch: iter_time=1.694e-04, forward_time=0.105, loss_ctc=45.851, loss_att=58.023, acc=0.714, loss=54.371, backward_time=0.098, grad_norm=46.558, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.943e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 12:20:17,556 (trainer:737) INFO: 26epoch:train:11101-11200batch: iter_time=1.623e-04, forward_time=0.105, loss_ctc=57.468, loss_att=71.098, acc=0.694, loss=67.009, backward_time=0.098, grad_norm=46.180, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.942e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:20:44,784 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 12:21:04,768 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 12:21:08,409 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8fc76f20>)
+[gpuc02:0/16] 2024-01-15 12:21:08,409 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 12:21:08,412 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 12:25:35,856 (trainer:737) INFO: 26epoch:train:11201-11300batch: iter_time=2.486, forward_time=0.104, loss_ctc=35.851, loss_att=45.979, acc=0.746, loss=42.940, backward_time=0.097, grad_norm=33.552, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.942e-04, train_time=3.183
+[gpuc02:0/16] 2024-01-15 12:26:18,190 (trainer:737) INFO: 26epoch:train:11301-11400batch: iter_time=1.413e-04, forward_time=0.105, loss_ctc=42.626, loss_att=47.004, acc=0.736, loss=45.691, backward_time=0.097, grad_norm=38.173, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.941e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:27:00,768 (trainer:737) INFO: 26epoch:train:11401-11500batch: iter_time=1.438e-04, forward_time=0.105, loss_ctc=39.273, loss_att=43.650, acc=0.741, loss=42.337, backward_time=0.097, grad_norm=34.181, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.941e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 12:27:43,142 (trainer:737) INFO: 26epoch:train:11501-11600batch: iter_time=1.604e-04, forward_time=0.105, loss_ctc=41.651, loss_att=46.879, acc=0.752, loss=45.311, backward_time=0.098, grad_norm=36.121, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.940e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:28:25,500 (trainer:737) INFO: 26epoch:train:11601-11700batch: iter_time=1.687e-04, forward_time=0.105, loss_ctc=41.788, loss_att=56.113, acc=0.734, loss=51.816, backward_time=0.097, grad_norm=38.777, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.940e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:29:07,807 (trainer:737) INFO: 26epoch:train:11701-11800batch: iter_time=1.692e-04, forward_time=0.106, loss_ctc=48.442, loss_att=50.436, acc=0.739, loss=49.838, backward_time=0.097, grad_norm=43.602, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.939e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:29:50,151 (trainer:737) INFO: 26epoch:train:11801-11900batch: iter_time=1.732e-04, forward_time=0.106, loss_ctc=44.025, loss_att=50.543, acc=0.741, loss=48.588, backward_time=0.098, grad_norm=40.776, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.939e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:30:32,528 (trainer:737) INFO: 26epoch:train:11901-12000batch: iter_time=1.779e-04, forward_time=0.105, loss_ctc=40.326, loss_att=53.796, acc=0.734, loss=49.755, backward_time=0.097, grad_norm=38.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.938e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:31:14,768 (trainer:737) INFO: 26epoch:train:12001-12100batch: iter_time=1.577e-04, forward_time=0.105, loss_ctc=41.071, loss_att=48.832, acc=0.732, loss=46.504, backward_time=0.097, grad_norm=43.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.938e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 12:31:57,570 (trainer:737) INFO: 26epoch:train:12101-12200batch: iter_time=1.713e-04, forward_time=0.105, loss_ctc=47.952, loss_att=51.884, acc=0.732, loss=50.704, backward_time=0.097, grad_norm=45.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.937e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 12:32:40,028 (trainer:737) INFO: 26epoch:train:12201-12300batch: iter_time=1.593e-04, forward_time=0.105, loss_ctc=45.351, loss_att=52.950, acc=0.730, loss=50.670, backward_time=0.097, grad_norm=42.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.937e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:33:23,014 (trainer:737) INFO: 26epoch:train:12301-12400batch: iter_time=1.568e-04, forward_time=0.107, loss_ctc=51.868, loss_att=73.788, acc=0.689, loss=67.212, backward_time=0.098, grad_norm=49.575, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.936e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 12:34:05,592 (trainer:737) INFO: 26epoch:train:12401-12500batch: iter_time=1.319e-04, forward_time=0.105, loss_ctc=49.789, loss_att=59.737, acc=0.727, loss=56.753, backward_time=0.098, grad_norm=42.104, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.936e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 12:34:12,197 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 12:34:31,483 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 12:34:35,024 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1eb445e170>)
+[gpuc02:0/16] 2024-01-15 12:34:35,024 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 12:34:35,027 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 12:39:27,489 (trainer:737) INFO: 26epoch:train:12501-12600batch: iter_time=2.476, forward_time=0.102, loss_ctc=37.856, loss_att=43.376, acc=0.745, loss=41.720, backward_time=0.096, grad_norm=34.188, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.935e-04, train_time=3.219
+[gpuc02:0/16] 2024-01-15 12:40:09,650 (trainer:737) INFO: 26epoch:train:12601-12700batch: iter_time=9.403e-05, forward_time=0.103, loss_ctc=42.640, loss_att=48.953, acc=0.711, loss=47.059, backward_time=0.096, grad_norm=43.720, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.935e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:40:52,217 (trainer:737) INFO: 26epoch:train:12701-12800batch: iter_time=1.056e-04, forward_time=0.103, loss_ctc=40.168, loss_att=44.157, acc=0.743, loss=42.961, backward_time=0.097, grad_norm=35.793, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.934e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 12:41:34,554 (trainer:737) INFO: 26epoch:train:12801-12900batch: iter_time=1.037e-04, forward_time=0.104, loss_ctc=42.845, loss_att=60.202, acc=0.725, loss=54.995, backward_time=0.098, grad_norm=40.750, clip=100.000, loss_scale=3.884e+34, optim_step_time=0.042, optim0_lr0=3.934e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:42:16,793 (trainer:737) INFO: 26epoch:train:12901-13000batch: iter_time=1.082e-04, forward_time=0.103, loss_ctc=43.009, loss_att=45.943, acc=0.741, loss=45.063, backward_time=0.097, grad_norm=37.880, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.933e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 12:42:59,392 (trainer:737) INFO: 26epoch:train:13001-13100batch: iter_time=1.316e-04, forward_time=0.104, loss_ctc=46.471, loss_att=45.942, acc=0.749, loss=46.101, backward_time=0.097, grad_norm=41.037, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.933e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 12:43:42,301 (trainer:737) INFO: 26epoch:train:13101-13200batch: iter_time=1.405e-04, forward_time=0.104, loss_ctc=43.704, loss_att=59.956, acc=0.708, loss=55.081, backward_time=0.097, grad_norm=41.906, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.932e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 12:44:24,449 (trainer:737) INFO: 26epoch:train:13201-13300batch: iter_time=1.531e-04, forward_time=0.102, loss_ctc=41.078, loss_att=50.791, acc=0.713, loss=47.877, backward_time=0.096, grad_norm=41.186, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.932e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 12:45:07,327 (trainer:737) INFO: 26epoch:train:13301-13400batch: iter_time=1.381e-04, forward_time=0.103, loss_ctc=42.830, loss_att=43.014, acc=0.739, loss=42.959, backward_time=0.097, grad_norm=42.802, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.931e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 12:45:49,901 (trainer:737) INFO: 26epoch:train:13401-13500batch: iter_time=1.367e-04, forward_time=0.104, loss_ctc=51.924, loss_att=63.807, acc=0.706, loss=60.242, backward_time=0.097, grad_norm=47.941, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.930e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 12:46:32,586 (trainer:737) INFO: 26epoch:train:13501-13600batch: iter_time=1.117e-04, forward_time=0.104, loss_ctc=45.212, loss_att=56.238, acc=0.706, loss=52.930, backward_time=0.097, grad_norm=47.053, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.930e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 12:47:15,932 (trainer:737) INFO: 26epoch:train:13601-13700batch: iter_time=1.056e-04, forward_time=0.104, loss_ctc=56.893, loss_att=69.537, acc=0.693, loss=65.744, backward_time=0.098, grad_norm=45.800, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.929e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 12:47:39,486 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 12:47:59,900 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 12:48:03,982 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e5c6c3f70>)
+[gpuc02:0/16] 2024-01-15 12:48:03,982 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 12:48:03,985 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 12:52:34,920 (trainer:737) INFO: 26epoch:train:13701-13800batch: iter_time=2.480, forward_time=0.103, loss_ctc=36.049, loss_att=47.760, acc=0.736, loss=44.247, backward_time=0.097, grad_norm=35.320, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.929e-04, train_time=3.190
+[gpuc02:0/16] 2024-01-15 12:53:17,658 (trainer:737) INFO: 26epoch:train:13801-13900batch: iter_time=1.264e-04, forward_time=0.105, loss_ctc=41.496, loss_att=47.565, acc=0.735, loss=45.745, backward_time=0.097, grad_norm=37.176, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.928e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 12:53:43,031 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 12:53:59,980 (trainer:737) INFO: 26epoch:train:13901-14000batch: iter_time=1.498e-04, forward_time=0.105, loss_ctc=39.338, loss_att=44.208, acc=0.737, loss=42.747, backward_time=0.097, grad_norm=36.191, clip=100.000, loss_scale=3.315e+34, optim_step_time=0.042, optim0_lr0=3.928e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 12:54:42,439 (trainer:737) INFO: 26epoch:train:14001-14100batch: iter_time=1.188e-04, forward_time=0.104, loss_ctc=41.989, loss_att=47.470, acc=0.751, loss=45.825, backward_time=0.097, grad_norm=39.696, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.927e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:55:25,113 (trainer:737) INFO: 26epoch:train:14101-14200batch: iter_time=1.481e-04, forward_time=0.104, loss_ctc=42.469, loss_att=57.552, acc=0.731, loss=53.027, backward_time=0.098, grad_norm=39.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.927e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 12:56:07,553 (trainer:737) INFO: 26epoch:train:14201-14300batch: iter_time=1.487e-04, forward_time=0.104, loss_ctc=49.103, loss_att=49.879, acc=0.739, loss=49.646, backward_time=0.097, grad_norm=44.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.926e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:56:50,020 (trainer:737) INFO: 26epoch:train:14301-14400batch: iter_time=1.439e-04, forward_time=0.105, loss_ctc=44.049, loss_att=50.449, acc=0.742, loss=48.529, backward_time=0.098, grad_norm=41.234, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.926e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:57:32,463 (trainer:737) INFO: 26epoch:train:14401-14500batch: iter_time=1.449e-04, forward_time=0.105, loss_ctc=40.405, loss_att=53.417, acc=0.737, loss=49.513, backward_time=0.098, grad_norm=38.647, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.925e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 12:58:15,861 (trainer:737) INFO: 26epoch:train:14501-14600batch: iter_time=1.265e-04, forward_time=0.106, loss_ctc=41.088, loss_att=49.478, acc=0.730, loss=46.961, backward_time=0.098, grad_norm=43.152, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.925e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 12:59:00,152 (trainer:737) INFO: 26epoch:train:14601-14700batch: iter_time=1.258e-04, forward_time=0.104, loss_ctc=47.892, loss_att=53.386, acc=0.727, loss=51.738, backward_time=0.098, grad_norm=43.667, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.924e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-15 12:59:42,599 (trainer:737) INFO: 26epoch:train:14701-14800batch: iter_time=1.339e-04, forward_time=0.104, loss_ctc=45.003, loss_att=53.819, acc=0.726, loss=51.174, backward_time=0.098, grad_norm=42.775, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.924e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 13:00:25,349 (trainer:737) INFO: 26epoch:train:14801-14900batch: iter_time=1.369e-04, forward_time=0.106, loss_ctc=51.964, loss_att=74.820, acc=0.686, loss=67.963, backward_time=0.099, grad_norm=47.073, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.923e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 13:01:07,876 (trainer:737) INFO: 26epoch:train:14901-15000batch: iter_time=1.181e-04, forward_time=0.104, loss_ctc=49.217, loss_att=59.713, acc=0.726, loss=56.564, backward_time=0.098, grad_norm=40.246, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.923e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 13:21:27,547 (trainer:343) INFO: 26epoch results: [train] iter_time=0.208, forward_time=0.105, loss_ctc=45.000, loss_att=53.012, acc=0.726, loss=50.608, backward_time=0.097, grad_norm=41.811, clip=100.000, loss_scale=2.929e+34, optim_step_time=0.042, optim0_lr0=3.961e-04, train_time=0.662, time=2 hours, 45 minutes and 49.08 seconds, total_count=390000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=51.615, cer_ctc=0.267, loss_att=50.866, acc=0.605, cer=0.341, wer=0.998, loss=51.091, time=20 minutes and 3.78 seconds, total_count=121446, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 13:21:32,551 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-15 13:21:32,581 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/21epoch.pth
+[gpuc02:0/16] 2024-01-15 13:21:32,581 (trainer:272) INFO: 27/45epoch started. Estimated time to finish: 2 days, 10 hours and 10 minutes
+[gpuc02:0/16] 2024-01-15 13:21:32,591 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 13:21:51,963 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 13:21:55,732 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e898ac0>)
+[gpuc02:0/16] 2024-01-15 13:21:55,732 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 13:21:55,736 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 13:26:34,800 (trainer:737) INFO: 27epoch:train:1-100batch: iter_time=2.360, forward_time=0.104, loss_ctc=52.190, loss_att=65.152, acc=0.683, loss=61.263, backward_time=0.097, grad_norm=46.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.922e-04, train_time=3.022
+[gpuc02:0/16] 2024-01-15 13:27:17,380 (trainer:737) INFO: 27epoch:train:101-200batch: iter_time=1.074e-04, forward_time=0.105, loss_ctc=47.671, loss_att=67.700, acc=0.705, loss=61.691, backward_time=0.098, grad_norm=45.390, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.922e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 13:27:59,301 (trainer:737) INFO: 27epoch:train:201-300batch: iter_time=1.078e-04, forward_time=0.103, loss_ctc=45.946, loss_att=47.617, acc=0.750, loss=47.116, backward_time=0.097, grad_norm=44.532, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.921e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 13:28:43,155 (trainer:737) INFO: 27epoch:train:301-400batch: iter_time=1.139e-04, forward_time=0.117, loss_ctc=42.686, loss_att=45.080, acc=0.769, loss=44.362, backward_time=0.098, grad_norm=38.912, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.921e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 13:29:25,766 (trainer:737) INFO: 27epoch:train:401-500batch: iter_time=1.163e-04, forward_time=0.103, loss_ctc=41.863, loss_att=45.814, acc=0.751, loss=44.629, backward_time=0.097, grad_norm=37.160, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.920e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 13:30:09,958 (trainer:737) INFO: 27epoch:train:501-600batch: iter_time=1.193e-04, forward_time=0.120, loss_ctc=39.365, loss_att=38.861, acc=0.746, loss=39.012, backward_time=0.099, grad_norm=35.218, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.920e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-15 13:30:54,150 (trainer:737) INFO: 27epoch:train:601-700batch: iter_time=1.162e-04, forward_time=0.103, loss_ctc=45.871, loss_att=43.193, acc=0.736, loss=43.996, backward_time=0.101, grad_norm=42.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.919e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-15 13:31:39,133 (trainer:737) INFO: 27epoch:train:701-800batch: iter_time=1.182e-04, forward_time=0.103, loss_ctc=46.754, loss_att=54.034, acc=0.728, loss=51.850, backward_time=0.099, grad_norm=44.546, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.919e-04, train_time=0.450
+[gpuc02:0/16] 2024-01-15 13:32:21,270 (trainer:737) INFO: 27epoch:train:801-900batch: iter_time=1.151e-04, forward_time=0.103, loss_ctc=37.092, loss_att=47.397, acc=0.732, loss=44.306, backward_time=0.096, grad_norm=39.461, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.918e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 13:33:05,119 (trainer:737) INFO: 27epoch:train:901-1000batch: iter_time=1.195e-04, forward_time=0.103, loss_ctc=49.423, loss_att=56.095, acc=0.698, loss=54.093, backward_time=0.097, grad_norm=45.287, clip=100.000, loss_scale=2.908e+34, optim_step_time=0.041, optim0_lr0=3.918e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 13:33:46,985 (trainer:737) INFO: 27epoch:train:1001-1100batch: iter_time=1.101e-04, forward_time=0.103, loss_ctc=48.818, loss_att=46.374, acc=0.742, loss=47.107, backward_time=0.097, grad_norm=47.768, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.917e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-15 13:33:49,021 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 13:34:29,873 (trainer:737) INFO: 27epoch:train:1101-1200batch: iter_time=1.169e-04, forward_time=0.104, loss_ctc=45.880, loss_att=54.169, acc=0.726, loss=51.683, backward_time=0.098, grad_norm=37.958, clip=100.000, loss_scale=2.161e+34, optim_step_time=0.041, optim0_lr0=3.917e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 13:35:03,684 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 13:35:23,025 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 13:35:26,679 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d9fae59c0>)
+[gpuc02:0/16] 2024-01-15 13:35:26,679 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 13:35:26,682 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 13:40:43,731 (trainer:737) INFO: 27epoch:train:1201-1300batch: iter_time=3.008, forward_time=0.124, loss_ctc=48.530, loss_att=57.077, acc=0.699, loss=54.513, backward_time=0.100, grad_norm=48.431, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.916e-04, train_time=3.738
+[gpuc02:0/16] 2024-01-15 13:41:26,013 (trainer:737) INFO: 27epoch:train:1301-1400batch: iter_time=1.151e-04, forward_time=0.105, loss_ctc=53.392, loss_att=70.470, acc=0.684, loss=65.346, backward_time=0.097, grad_norm=48.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.916e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 13:42:09,106 (trainer:737) INFO: 27epoch:train:1401-1500batch: iter_time=1.193e-04, forward_time=0.105, loss_ctc=45.338, loss_att=56.750, acc=0.750, loss=53.327, backward_time=0.098, grad_norm=40.665, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.915e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 13:42:51,397 (trainer:737) INFO: 27epoch:train:1501-1600batch: iter_time=1.155e-04, forward_time=0.103, loss_ctc=40.651, loss_att=45.260, acc=0.752, loss=43.877, backward_time=0.097, grad_norm=40.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.915e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 13:43:33,798 (trainer:737) INFO: 27epoch:train:1601-1700batch: iter_time=1.168e-04, forward_time=0.104, loss_ctc=43.633, loss_att=45.250, acc=0.757, loss=44.765, backward_time=0.097, grad_norm=39.926, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.914e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 13:44:15,779 (trainer:737) INFO: 27epoch:train:1701-1800batch: iter_time=1.229e-04, forward_time=0.103, loss_ctc=38.574, loss_att=41.046, acc=0.757, loss=40.304, backward_time=0.097, grad_norm=35.698, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.914e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 13:44:57,805 (trainer:737) INFO: 27epoch:train:1801-1900batch: iter_time=1.060e-04, forward_time=0.103, loss_ctc=41.270, loss_att=38.127, acc=0.765, loss=39.070, backward_time=0.097, grad_norm=35.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.913e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 13:45:40,135 (trainer:737) INFO: 27epoch:train:1901-2000batch: iter_time=1.187e-04, forward_time=0.104, loss_ctc=47.197, loss_att=53.853, acc=0.720, loss=51.856, backward_time=0.098, grad_norm=43.074, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.913e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 13:46:22,586 (trainer:737) INFO: 27epoch:train:2001-2100batch: iter_time=1.157e-04, forward_time=0.103, loss_ctc=43.865, loss_att=52.140, acc=0.737, loss=49.658, backward_time=0.098, grad_norm=43.707, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.912e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 13:47:04,672 (trainer:737) INFO: 27epoch:train:2101-2200batch: iter_time=9.826e-05, forward_time=0.104, loss_ctc=41.685, loss_att=54.594, acc=0.730, loss=50.722, backward_time=0.098, grad_norm=40.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.912e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 13:47:46,659 (trainer:737) INFO: 27epoch:train:2201-2300batch: iter_time=1.039e-04, forward_time=0.103, loss_ctc=47.028, loss_att=48.280, acc=0.721, loss=47.905, backward_time=0.097, grad_norm=43.469, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.911e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 13:48:29,065 (trainer:737) INFO: 27epoch:train:2301-2400batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=46.228, loss_att=48.231, acc=0.736, loss=47.630, backward_time=0.098, grad_norm=40.907, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.911e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 13:49:13,228 (trainer:737) INFO: 27epoch:train:2401-2500batch: iter_time=9.642e-05, forward_time=0.104, loss_ctc=49.198, loss_att=57.581, acc=0.713, loss=55.066, backward_time=0.098, grad_norm=44.683, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.910e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-15 13:49:15,922 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 13:49:35,134 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 13:49:38,623 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d97dd4910>)
+[gpuc02:0/16] 2024-01-15 13:49:38,623 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 13:49:38,626 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 13:56:48,017 (trainer:737) INFO: 27epoch:train:2501-2600batch: iter_time=2.576, forward_time=0.114, loss_ctc=50.530, loss_att=62.714, acc=0.695, loss=59.059, backward_time=0.099, grad_norm=44.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.910e-04, train_time=4.548
+[gpuc02:0/16] 2024-01-15 13:57:30,690 (trainer:737) INFO: 27epoch:train:2601-2700batch: iter_time=1.644e-04, forward_time=0.106, loss_ctc=46.895, loss_att=66.121, acc=0.715, loss=60.353, backward_time=0.098, grad_norm=47.227, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.909e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 13:58:12,791 (trainer:737) INFO: 27epoch:train:2701-2800batch: iter_time=1.713e-04, forward_time=0.105, loss_ctc=44.875, loss_att=46.709, acc=0.757, loss=46.159, backward_time=0.097, grad_norm=42.952, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.909e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 13:58:55,417 (trainer:737) INFO: 27epoch:train:2801-2900batch: iter_time=1.620e-04, forward_time=0.104, loss_ctc=41.984, loss_att=44.624, acc=0.775, loss=43.832, backward_time=0.097, grad_norm=38.211, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.908e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 13:59:38,561 (trainer:737) INFO: 27epoch:train:2901-3000batch: iter_time=1.764e-04, forward_time=0.104, loss_ctc=40.604, loss_att=45.527, acc=0.756, loss=44.050, backward_time=0.097, grad_norm=37.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.908e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 14:00:20,979 (trainer:737) INFO: 27epoch:train:3001-3100batch: iter_time=1.978e-04, forward_time=0.103, loss_ctc=39.006, loss_att=39.193, acc=0.747, loss=39.137, backward_time=0.096, grad_norm=36.994, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.907e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:01:04,117 (trainer:737) INFO: 27epoch:train:3101-3200batch: iter_time=1.655e-04, forward_time=0.107, loss_ctc=44.620, loss_att=42.173, acc=0.749, loss=42.907, backward_time=0.097, grad_norm=40.247, clip=100.000, loss_scale=4.050e+34, optim_step_time=0.042, optim0_lr0=3.907e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 14:01:46,606 (trainer:737) INFO: 27epoch:train:3201-3300batch: iter_time=1.387e-04, forward_time=0.104, loss_ctc=45.888, loss_att=52.624, acc=0.739, loss=50.603, backward_time=0.097, grad_norm=44.578, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.906e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:02:28,902 (trainer:737) INFO: 27epoch:train:3301-3400batch: iter_time=1.463e-04, forward_time=0.103, loss_ctc=36.088, loss_att=47.153, acc=0.742, loss=43.834, backward_time=0.097, grad_norm=38.560, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.906e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:03:11,383 (trainer:737) INFO: 27epoch:train:3401-3500batch: iter_time=1.324e-04, forward_time=0.104, loss_ctc=48.047, loss_att=56.357, acc=0.703, loss=53.864, backward_time=0.098, grad_norm=45.216, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.905e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:03:54,098 (trainer:737) INFO: 27epoch:train:3501-3600batch: iter_time=1.262e-04, forward_time=0.104, loss_ctc=47.602, loss_att=47.511, acc=0.748, loss=47.539, backward_time=0.098, grad_norm=44.960, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.905e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 14:04:36,968 (trainer:737) INFO: 27epoch:train:3601-3700batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=44.662, loss_att=53.946, acc=0.731, loss=51.160, backward_time=0.098, grad_norm=38.415, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.904e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 14:05:06,264 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 14:05:25,321 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 14:05:28,795 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f247e24be80>)
+[gpuc02:0/16] 2024-01-15 14:05:28,795 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 14:05:28,799 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 14:09:53,349 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 14:10:06,631 (trainer:737) INFO: 27epoch:train:3701-3800batch: iter_time=2.527, forward_time=0.104, loss_ctc=46.795, loss_att=56.408, acc=0.698, loss=53.524, backward_time=0.097, grad_norm=46.660, clip=100.000, loss_scale=3.503e+34, optim_step_time=0.042, optim0_lr0=3.904e-04, train_time=3.296
+[gpuc02:0/16] 2024-01-15 14:10:49,147 (trainer:737) INFO: 27epoch:train:3801-3900batch: iter_time=1.072e-04, forward_time=0.104, loss_ctc=53.294, loss_att=70.599, acc=0.679, loss=65.407, backward_time=0.098, grad_norm=50.577, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.903e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:11:31,682 (trainer:737) INFO: 27epoch:train:3901-4000batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=44.808, loss_att=57.431, acc=0.744, loss=53.644, backward_time=0.098, grad_norm=41.186, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.903e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:12:13,905 (trainer:737) INFO: 27epoch:train:4001-4100batch: iter_time=1.320e-04, forward_time=0.104, loss_ctc=40.494, loss_att=44.664, acc=0.753, loss=43.413, backward_time=0.096, grad_norm=41.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.902e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 14:12:57,406 (trainer:737) INFO: 27epoch:train:4101-4200batch: iter_time=1.112e-04, forward_time=0.105, loss_ctc=44.196, loss_att=45.076, acc=0.751, loss=44.812, backward_time=0.097, grad_norm=39.959, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.902e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-15 14:13:39,936 (trainer:737) INFO: 27epoch:train:4201-4300batch: iter_time=1.085e-04, forward_time=0.104, loss_ctc=37.736, loss_att=39.836, acc=0.764, loss=39.206, backward_time=0.096, grad_norm=33.421, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.901e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:14:22,365 (trainer:737) INFO: 27epoch:train:4301-4400batch: iter_time=1.064e-04, forward_time=0.104, loss_ctc=40.966, loss_att=38.133, acc=0.762, loss=38.983, backward_time=0.096, grad_norm=36.866, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.901e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:15:04,937 (trainer:737) INFO: 27epoch:train:4401-4500batch: iter_time=1.194e-04, forward_time=0.103, loss_ctc=46.971, loss_att=52.253, acc=0.716, loss=50.668, backward_time=0.097, grad_norm=45.042, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.900e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:15:47,186 (trainer:737) INFO: 27epoch:train:4501-4600batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=42.962, loss_att=51.338, acc=0.729, loss=48.825, backward_time=0.097, grad_norm=41.552, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.900e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 14:16:30,252 (trainer:737) INFO: 27epoch:train:4601-4700batch: iter_time=1.107e-04, forward_time=0.104, loss_ctc=41.171, loss_att=55.470, acc=0.715, loss=51.181, backward_time=0.097, grad_norm=39.446, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.900e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 14:17:12,887 (trainer:737) INFO: 27epoch:train:4701-4800batch: iter_time=1.090e-04, forward_time=0.103, loss_ctc=46.141, loss_att=44.940, acc=0.726, loss=45.300, backward_time=0.097, grad_norm=44.873, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.899e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 14:17:57,716 (trainer:737) INFO: 27epoch:train:4801-4900batch: iter_time=1.130e-04, forward_time=0.104, loss_ctc=45.370, loss_att=47.581, acc=0.731, loss=46.918, backward_time=0.097, grad_norm=41.096, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.899e-04, train_time=0.448
+[gpuc02:0/16] 2024-01-15 14:18:46,133 (trainer:737) INFO: 27epoch:train:4901-5000batch: iter_time=9.311e-05, forward_time=0.104, loss_ctc=48.558, loss_att=58.022, acc=0.704, loss=55.183, backward_time=0.098, grad_norm=48.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.898e-04, train_time=0.484
+[gpuc02:0/16] 2024-01-15 14:18:54,789 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 14:19:14,775 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 14:19:18,788 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d9a05f7f0>)
+[gpuc02:0/16] 2024-01-15 14:19:18,788 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 14:19:18,792 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 14:26:07,694 (trainer:737) INFO: 27epoch:train:5001-5100batch: iter_time=3.532, forward_time=0.107, loss_ctc=50.288, loss_att=64.257, acc=0.694, loss=60.066, backward_time=0.098, grad_norm=48.199, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.898e-04, train_time=4.414
+[gpuc02:0/16] 2024-01-15 14:26:51,344 (trainer:737) INFO: 27epoch:train:5101-5200batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=46.195, loss_att=66.059, acc=0.720, loss=60.100, backward_time=0.099, grad_norm=45.718, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.897e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 14:27:33,837 (trainer:737) INFO: 27epoch:train:5201-5300batch: iter_time=1.034e-04, forward_time=0.104, loss_ctc=44.787, loss_att=47.273, acc=0.756, loss=46.527, backward_time=0.098, grad_norm=42.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.897e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:28:16,285 (trainer:737) INFO: 27epoch:train:5301-5400batch: iter_time=1.063e-04, forward_time=0.104, loss_ctc=41.598, loss_att=44.548, acc=0.777, loss=43.663, backward_time=0.098, grad_norm=35.584, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.896e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:28:58,677 (trainer:737) INFO: 27epoch:train:5401-5500batch: iter_time=1.391e-04, forward_time=0.103, loss_ctc=40.652, loss_att=46.220, acc=0.754, loss=44.550, backward_time=0.098, grad_norm=37.217, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.896e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:29:40,845 (trainer:737) INFO: 27epoch:train:5501-5600batch: iter_time=1.046e-04, forward_time=0.103, loss_ctc=38.979, loss_att=39.066, acc=0.749, loss=39.040, backward_time=0.097, grad_norm=35.387, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.895e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 14:30:23,142 (trainer:737) INFO: 27epoch:train:5601-5700batch: iter_time=1.109e-04, forward_time=0.104, loss_ctc=44.407, loss_att=42.341, acc=0.749, loss=42.961, backward_time=0.097, grad_norm=39.504, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.895e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:31:07,366 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 14:31:07,809 (trainer:737) INFO: 27epoch:train:5701-5800batch: iter_time=0.011, forward_time=0.104, loss_ctc=45.720, loss_att=53.514, acc=0.737, loss=51.176, backward_time=0.097, grad_norm=44.414, clip=100.000, loss_scale=2.685e+34, optim_step_time=0.042, optim0_lr0=3.894e-04, train_time=0.446
+[gpuc02:0/16] 2024-01-15 14:31:50,560 (trainer:737) INFO: 27epoch:train:5801-5900batch: iter_time=1.061e-04, forward_time=0.103, loss_ctc=36.116, loss_att=46.826, acc=0.744, loss=43.613, backward_time=0.097, grad_norm=37.711, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.894e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 14:32:33,651 (trainer:737) INFO: 27epoch:train:5901-6000batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=48.072, loss_att=55.372, acc=0.707, loss=53.182, backward_time=0.097, grad_norm=44.988, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.893e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 14:33:15,994 (trainer:737) INFO: 27epoch:train:6001-6100batch: iter_time=1.161e-04, forward_time=0.104, loss_ctc=46.567, loss_att=47.276, acc=0.748, loss=47.063, backward_time=0.098, grad_norm=42.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.893e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:33:58,667 (trainer:737) INFO: 27epoch:train:6101-6200batch: iter_time=1.058e-04, forward_time=0.105, loss_ctc=44.669, loss_att=53.834, acc=0.736, loss=51.084, backward_time=0.098, grad_norm=37.852, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.892e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 14:34:24,425 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 14:34:44,239 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 14:34:47,920 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ee65139d0>)
+[gpuc02:0/16] 2024-01-15 14:34:47,921 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 14:34:47,924 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 14:39:25,033 (trainer:737) INFO: 27epoch:train:6201-6300batch: iter_time=2.542, forward_time=0.105, loss_ctc=46.711, loss_att=54.333, acc=0.708, loss=52.046, backward_time=0.098, grad_norm=46.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.892e-04, train_time=3.263
+[gpuc02:0/16] 2024-01-15 14:40:07,523 (trainer:737) INFO: 27epoch:train:6301-6400batch: iter_time=1.224e-04, forward_time=0.105, loss_ctc=52.671, loss_att=68.861, acc=0.688, loss=64.004, backward_time=0.098, grad_norm=49.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.891e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 14:40:50,765 (trainer:737) INFO: 27epoch:train:6401-6500batch: iter_time=1.374e-04, forward_time=0.108, loss_ctc=44.304, loss_att=55.758, acc=0.752, loss=52.322, backward_time=0.098, grad_norm=40.047, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.891e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 14:41:33,021 (trainer:737) INFO: 27epoch:train:6501-6600batch: iter_time=1.327e-04, forward_time=0.103, loss_ctc=40.266, loss_att=44.347, acc=0.759, loss=43.123, backward_time=0.096, grad_norm=43.293, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.890e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 14:42:15,454 (trainer:737) INFO: 27epoch:train:6601-6700batch: iter_time=1.256e-04, forward_time=0.105, loss_ctc=43.352, loss_att=44.158, acc=0.761, loss=43.917, backward_time=0.097, grad_norm=39.521, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.890e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:42:57,749 (trainer:737) INFO: 27epoch:train:6701-6800batch: iter_time=1.449e-04, forward_time=0.104, loss_ctc=37.770, loss_att=40.836, acc=0.760, loss=39.916, backward_time=0.096, grad_norm=33.555, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.889e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:43:40,121 (trainer:737) INFO: 27epoch:train:6801-6900batch: iter_time=1.472e-04, forward_time=0.103, loss_ctc=40.757, loss_att=37.707, acc=0.769, loss=38.622, backward_time=0.097, grad_norm=35.245, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.889e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:44:22,563 (trainer:737) INFO: 27epoch:train:6901-7000batch: iter_time=1.305e-04, forward_time=0.105, loss_ctc=46.491, loss_att=52.092, acc=0.727, loss=50.411, backward_time=0.097, grad_norm=42.482, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.888e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:45:04,971 (trainer:737) INFO: 27epoch:train:7001-7100batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=42.738, loss_att=51.002, acc=0.741, loss=48.523, backward_time=0.097, grad_norm=42.566, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.888e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:45:47,422 (trainer:737) INFO: 27epoch:train:7101-7200batch: iter_time=1.513e-04, forward_time=0.105, loss_ctc=41.250, loss_att=55.644, acc=0.725, loss=51.326, backward_time=0.097, grad_norm=39.383, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.887e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 14:46:29,658 (trainer:737) INFO: 27epoch:train:7201-7300batch: iter_time=1.525e-04, forward_time=0.104, loss_ctc=45.674, loss_att=47.409, acc=0.724, loss=46.888, backward_time=0.097, grad_norm=45.325, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.887e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 14:47:11,971 (trainer:737) INFO: 27epoch:train:7301-7400batch: iter_time=1.376e-04, forward_time=0.105, loss_ctc=44.800, loss_att=47.170, acc=0.739, loss=46.459, backward_time=0.097, grad_norm=39.341, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.886e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:47:54,245 (trainer:737) INFO: 27epoch:train:7401-7500batch: iter_time=1.224e-04, forward_time=0.104, loss_ctc=48.608, loss_att=57.776, acc=0.712, loss=55.025, backward_time=0.097, grad_norm=42.671, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.886e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:48:00,032 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 14:48:20,186 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 14:48:23,859 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2545076a70>)
+[gpuc02:0/16] 2024-01-15 14:48:23,859 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 14:48:23,862 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 14:53:15,456 (trainer:737) INFO: 27epoch:train:7501-7600batch: iter_time=2.472, forward_time=0.104, loss_ctc=50.052, loss_att=64.589, acc=0.688, loss=60.228, backward_time=0.097, grad_norm=46.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.885e-04, train_time=3.212
+[gpuc02:0/16] 2024-01-15 14:53:58,248 (trainer:737) INFO: 27epoch:train:7601-7700batch: iter_time=1.044e-04, forward_time=0.105, loss_ctc=45.968, loss_att=66.837, acc=0.708, loss=60.576, backward_time=0.097, grad_norm=45.619, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.885e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 14:54:41,150 (trainer:737) INFO: 27epoch:train:7701-7800batch: iter_time=1.138e-04, forward_time=0.106, loss_ctc=44.201, loss_att=46.752, acc=0.755, loss=45.987, backward_time=0.097, grad_norm=40.528, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.042, optim0_lr0=3.884e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 14:55:24,627 (trainer:737) INFO: 27epoch:train:7801-7900batch: iter_time=1.220e-04, forward_time=0.105, loss_ctc=41.654, loss_att=45.064, acc=0.771, loss=44.041, backward_time=0.097, grad_norm=37.664, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.884e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-15 14:56:06,967 (trainer:737) INFO: 27epoch:train:7901-8000batch: iter_time=1.173e-04, forward_time=0.104, loss_ctc=39.941, loss_att=45.358, acc=0.755, loss=43.733, backward_time=0.097, grad_norm=37.174, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.883e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:56:49,060 (trainer:737) INFO: 27epoch:train:8001-8100batch: iter_time=1.183e-04, forward_time=0.102, loss_ctc=38.473, loss_att=38.226, acc=0.750, loss=38.300, backward_time=0.097, grad_norm=35.890, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.883e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 14:57:31,327 (trainer:737) INFO: 27epoch:train:8101-8200batch: iter_time=1.334e-04, forward_time=0.103, loss_ctc=43.843, loss_att=42.342, acc=0.741, loss=42.792, backward_time=0.097, grad_norm=38.630, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.882e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 14:58:13,673 (trainer:737) INFO: 27epoch:train:8201-8300batch: iter_time=1.372e-04, forward_time=0.104, loss_ctc=45.180, loss_att=52.691, acc=0.733, loss=50.438, backward_time=0.097, grad_norm=45.022, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.882e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:58:56,030 (trainer:737) INFO: 27epoch:train:8301-8400batch: iter_time=1.267e-04, forward_time=0.103, loss_ctc=35.634, loss_att=46.855, acc=0.735, loss=43.488, backward_time=0.097, grad_norm=39.132, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.881e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 14:59:38,510 (trainer:737) INFO: 27epoch:train:8401-8500batch: iter_time=1.217e-04, forward_time=0.104, loss_ctc=47.171, loss_att=54.998, acc=0.703, loss=52.650, backward_time=0.097, grad_norm=44.657, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.881e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:00:20,764 (trainer:737) INFO: 27epoch:train:8501-8600batch: iter_time=1.193e-04, forward_time=0.103, loss_ctc=46.008, loss_att=45.374, acc=0.745, loss=45.564, backward_time=0.096, grad_norm=42.272, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.880e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:01:03,195 (trainer:737) INFO: 27epoch:train:8601-8700batch: iter_time=1.198e-04, forward_time=0.104, loss_ctc=44.401, loss_att=53.730, acc=0.729, loss=50.931, backward_time=0.097, grad_norm=39.024, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.880e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:01:31,408 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 15:01:50,744 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 15:01:54,615 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2559b1f700>)
+[gpuc02:0/16] 2024-01-15 15:01:54,615 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 15:01:54,618 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 15:06:34,488 (trainer:737) INFO: 27epoch:train:8701-8800batch: iter_time=2.547, forward_time=0.121, loss_ctc=46.586, loss_att=53.942, acc=0.701, loss=51.735, backward_time=0.100, grad_norm=47.557, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.879e-04, train_time=3.313
+[gpuc02:0/16] 2024-01-15 15:07:17,115 (trainer:737) INFO: 27epoch:train:8801-8900batch: iter_time=1.230e-04, forward_time=0.105, loss_ctc=52.338, loss_att=67.974, acc=0.686, loss=63.283, backward_time=0.098, grad_norm=49.235, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.879e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 15:07:59,750 (trainer:737) INFO: 27epoch:train:8901-9000batch: iter_time=1.464e-04, forward_time=0.107, loss_ctc=44.427, loss_att=56.240, acc=0.746, loss=52.696, backward_time=0.098, grad_norm=40.902, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.878e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 15:08:42,598 (trainer:737) INFO: 27epoch:train:9001-9100batch: iter_time=1.559e-04, forward_time=0.105, loss_ctc=40.337, loss_att=44.071, acc=0.757, loss=42.951, backward_time=0.097, grad_norm=38.487, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.878e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 15:08:50,443 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 15:09:25,461 (trainer:737) INFO: 27epoch:train:9101-9200batch: iter_time=1.531e-04, forward_time=0.106, loss_ctc=42.370, loss_att=43.760, acc=0.755, loss=43.343, backward_time=0.097, grad_norm=38.945, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.042, optim0_lr0=3.877e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 15:10:07,737 (trainer:737) INFO: 27epoch:train:9201-9300batch: iter_time=1.395e-04, forward_time=0.103, loss_ctc=37.172, loss_att=39.598, acc=0.766, loss=38.870, backward_time=0.096, grad_norm=34.934, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.877e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:10:50,371 (trainer:737) INFO: 27epoch:train:9301-9400batch: iter_time=1.409e-04, forward_time=0.103, loss_ctc=40.605, loss_att=37.605, acc=0.763, loss=38.505, backward_time=0.096, grad_norm=36.498, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.876e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 15:11:32,765 (trainer:737) INFO: 27epoch:train:9401-9500batch: iter_time=1.267e-04, forward_time=0.104, loss_ctc=45.908, loss_att=51.175, acc=0.719, loss=49.595, backward_time=0.096, grad_norm=44.662, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.876e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:12:15,317 (trainer:737) INFO: 27epoch:train:9501-9600batch: iter_time=1.178e-04, forward_time=0.103, loss_ctc=42.598, loss_att=50.482, acc=0.732, loss=48.117, backward_time=0.096, grad_norm=43.194, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.876e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:12:57,877 (trainer:737) INFO: 27epoch:train:9601-9700batch: iter_time=1.208e-04, forward_time=0.103, loss_ctc=40.432, loss_att=54.896, acc=0.716, loss=50.556, backward_time=0.096, grad_norm=39.507, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.875e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:13:40,327 (trainer:737) INFO: 27epoch:train:9701-9800batch: iter_time=1.131e-04, forward_time=0.103, loss_ctc=45.277, loss_att=44.693, acc=0.727, loss=44.868, backward_time=0.096, grad_norm=42.312, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.875e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:14:23,762 (trainer:737) INFO: 27epoch:train:9801-9900batch: iter_time=1.182e-04, forward_time=0.103, loss_ctc=44.241, loss_att=46.839, acc=0.734, loss=46.059, backward_time=0.097, grad_norm=39.297, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.874e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 15:15:06,152 (trainer:737) INFO: 27epoch:train:9901-10000batch: iter_time=1.128e-04, forward_time=0.105, loss_ctc=47.855, loss_att=57.610, acc=0.706, loss=54.683, backward_time=0.098, grad_norm=44.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.874e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:15:08,894 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 15:15:28,937 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 15:15:32,636 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1f0a14bfa0>)
+[gpuc02:0/16] 2024-01-15 15:15:32,636 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 15:15:32,639 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 15:20:26,244 (trainer:737) INFO: 27epoch:train:10001-10100batch: iter_time=2.453, forward_time=0.119, loss_ctc=49.369, loss_att=64.504, acc=0.696, loss=59.963, backward_time=0.098, grad_norm=45.617, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.873e-04, train_time=3.201
+[gpuc02:0/16] 2024-01-15 15:21:09,010 (trainer:737) INFO: 27epoch:train:10101-10200batch: iter_time=9.314e-05, forward_time=0.105, loss_ctc=46.113, loss_att=66.783, acc=0.718, loss=60.582, backward_time=0.099, grad_norm=44.675, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.873e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 15:21:51,761 (trainer:737) INFO: 27epoch:train:10201-10300batch: iter_time=1.050e-04, forward_time=0.104, loss_ctc=44.015, loss_att=47.079, acc=0.758, loss=46.160, backward_time=0.097, grad_norm=42.001, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.872e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 15:22:34,241 (trainer:737) INFO: 27epoch:train:10301-10400batch: iter_time=1.048e-04, forward_time=0.105, loss_ctc=41.282, loss_att=44.064, acc=0.777, loss=43.229, backward_time=0.098, grad_norm=36.604, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.872e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:23:17,014 (trainer:737) INFO: 27epoch:train:10401-10500batch: iter_time=1.062e-04, forward_time=0.108, loss_ctc=40.143, loss_att=46.085, acc=0.755, loss=44.302, backward_time=0.098, grad_norm=36.063, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.871e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 15:23:59,331 (trainer:737) INFO: 27epoch:train:10501-10600batch: iter_time=1.073e-04, forward_time=0.104, loss_ctc=38.574, loss_att=39.129, acc=0.750, loss=38.963, backward_time=0.097, grad_norm=35.791, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.871e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:24:41,669 (trainer:737) INFO: 27epoch:train:10601-10700batch: iter_time=1.047e-04, forward_time=0.105, loss_ctc=44.120, loss_att=42.819, acc=0.748, loss=43.209, backward_time=0.097, grad_norm=39.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.870e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:25:24,190 (trainer:737) INFO: 27epoch:train:10701-10800batch: iter_time=1.026e-04, forward_time=0.105, loss_ctc=45.373, loss_att=53.584, acc=0.738, loss=51.121, backward_time=0.098, grad_norm=44.916, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.870e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:26:06,613 (trainer:737) INFO: 27epoch:train:10801-10900batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=35.519, loss_att=46.767, acc=0.746, loss=43.393, backward_time=0.098, grad_norm=37.173, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.869e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:26:49,067 (trainer:737) INFO: 27epoch:train:10901-11000batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=47.040, loss_att=55.461, acc=0.710, loss=52.935, backward_time=0.098, grad_norm=45.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.869e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:27:31,453 (trainer:737) INFO: 27epoch:train:11001-11100batch: iter_time=1.068e-04, forward_time=0.105, loss_ctc=46.081, loss_att=47.364, acc=0.748, loss=46.979, backward_time=0.097, grad_norm=42.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.868e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 15:28:14,017 (trainer:737) INFO: 27epoch:train:11101-11200batch: iter_time=1.120e-04, forward_time=0.105, loss_ctc=43.947, loss_att=53.106, acc=0.735, loss=50.358, backward_time=0.098, grad_norm=38.694, clip=100.000, loss_scale=3.780e+34, optim_step_time=0.042, optim0_lr0=3.868e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:28:37,887 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 15:28:57,910 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 15:29:01,812 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2559da1960>)
+[gpuc02:0/16] 2024-01-15 15:29:01,812 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 15:29:01,833 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 15:33:33,776 (trainer:737) INFO: 27epoch:train:11201-11300batch: iter_time=2.450, forward_time=0.105, loss_ctc=46.450, loss_att=55.472, acc=0.704, loss=52.765, backward_time=0.097, grad_norm=46.579, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.867e-04, train_time=3.197
+[gpuc02:0/16] 2024-01-15 15:34:16,771 (trainer:737) INFO: 27epoch:train:11301-11400batch: iter_time=1.167e-04, forward_time=0.104, loss_ctc=52.388, loss_att=69.439, acc=0.682, loss=64.324, backward_time=0.098, grad_norm=48.299, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.867e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 15:34:59,273 (trainer:737) INFO: 27epoch:train:11401-11500batch: iter_time=1.247e-04, forward_time=0.105, loss_ctc=44.028, loss_att=56.098, acc=0.749, loss=52.477, backward_time=0.098, grad_norm=36.948, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.866e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:35:41,500 (trainer:737) INFO: 27epoch:train:11501-11600batch: iter_time=1.309e-04, forward_time=0.103, loss_ctc=39.699, loss_att=44.077, acc=0.758, loss=42.763, backward_time=0.096, grad_norm=39.198, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.866e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:36:23,789 (trainer:737) INFO: 27epoch:train:11601-11700batch: iter_time=1.356e-04, forward_time=0.104, loss_ctc=43.019, loss_att=44.101, acc=0.756, loss=43.776, backward_time=0.096, grad_norm=38.754, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.865e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:37:05,954 (trainer:737) INFO: 27epoch:train:11701-11800batch: iter_time=1.400e-04, forward_time=0.104, loss_ctc=37.071, loss_att=40.096, acc=0.763, loss=39.189, backward_time=0.096, grad_norm=34.111, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.865e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 15:37:48,106 (trainer:737) INFO: 27epoch:train:11801-11900batch: iter_time=1.118e-04, forward_time=0.103, loss_ctc=40.312, loss_att=37.382, acc=0.764, loss=38.261, backward_time=0.096, grad_norm=34.178, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.864e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 15:38:30,378 (trainer:737) INFO: 27epoch:train:11901-12000batch: iter_time=1.332e-04, forward_time=0.104, loss_ctc=45.964, loss_att=52.399, acc=0.716, loss=50.469, backward_time=0.096, grad_norm=48.859, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.864e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:39:13,221 (trainer:737) INFO: 27epoch:train:12001-12100batch: iter_time=1.305e-04, forward_time=0.104, loss_ctc=42.412, loss_att=49.645, acc=0.736, loss=47.475, backward_time=0.097, grad_norm=42.699, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.863e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 15:39:55,520 (trainer:737) INFO: 27epoch:train:12101-12200batch: iter_time=1.225e-04, forward_time=0.104, loss_ctc=40.631, loss_att=54.191, acc=0.720, loss=50.123, backward_time=0.097, grad_norm=39.348, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.863e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:40:14,318 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 15:40:37,709 (trainer:737) INFO: 27epoch:train:12201-12300batch: iter_time=1.307e-04, forward_time=0.104, loss_ctc=44.592, loss_att=44.558, acc=0.729, loss=44.569, backward_time=0.096, grad_norm=40.697, clip=100.000, loss_scale=3.000e+34, optim_step_time=0.042, optim0_lr0=3.862e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:41:19,953 (trainer:737) INFO: 27epoch:train:12301-12400batch: iter_time=1.100e-04, forward_time=0.104, loss_ctc=44.755, loss_att=47.348, acc=0.732, loss=46.570, backward_time=0.096, grad_norm=40.798, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.862e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:42:02,229 (trainer:737) INFO: 27epoch:train:12401-12500batch: iter_time=1.035e-04, forward_time=0.104, loss_ctc=47.627, loss_att=57.127, acc=0.708, loss=54.277, backward_time=0.097, grad_norm=42.897, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.862e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 15:42:04,840 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 15:42:25,146 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 15:42:28,832 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1f0b5db7c0>)
+[gpuc02:0/16] 2024-01-15 15:42:28,832 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 15:42:28,836 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 15:47:22,906 (trainer:737) INFO: 27epoch:train:12501-12600batch: iter_time=2.507, forward_time=0.105, loss_ctc=49.577, loss_att=60.801, acc=0.697, loss=57.433, backward_time=0.097, grad_norm=47.418, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.861e-04, train_time=3.207
+[gpuc02:0/16] 2024-01-15 15:48:05,433 (trainer:737) INFO: 27epoch:train:12601-12700batch: iter_time=9.752e-05, forward_time=0.106, loss_ctc=45.771, loss_att=65.853, acc=0.710, loss=59.829, backward_time=0.098, grad_norm=46.792, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.861e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:48:48,395 (trainer:737) INFO: 27epoch:train:12701-12800batch: iter_time=1.067e-04, forward_time=0.104, loss_ctc=43.823, loss_att=46.167, acc=0.756, loss=45.464, backward_time=0.097, grad_norm=40.240, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.860e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 15:49:31,445 (trainer:737) INFO: 27epoch:train:12801-12900batch: iter_time=1.197e-04, forward_time=0.105, loss_ctc=41.387, loss_att=44.508, acc=0.772, loss=43.571, backward_time=0.097, grad_norm=38.104, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.860e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 15:50:14,296 (trainer:737) INFO: 27epoch:train:12901-13000batch: iter_time=1.127e-04, forward_time=0.104, loss_ctc=39.989, loss_att=44.785, acc=0.755, loss=43.346, backward_time=0.097, grad_norm=38.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.859e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 15:50:56,344 (trainer:737) INFO: 27epoch:train:13001-13100batch: iter_time=1.027e-04, forward_time=0.103, loss_ctc=38.603, loss_att=37.677, acc=0.754, loss=37.955, backward_time=0.096, grad_norm=36.354, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.859e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 15:51:38,461 (trainer:737) INFO: 27epoch:train:13101-13200batch: iter_time=1.036e-04, forward_time=0.103, loss_ctc=43.637, loss_att=41.426, acc=0.745, loss=42.089, backward_time=0.096, grad_norm=40.258, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.858e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 15:52:20,703 (trainer:737) INFO: 27epoch:train:13201-13300batch: iter_time=1.038e-04, forward_time=0.103, loss_ctc=44.968, loss_att=51.946, acc=0.736, loss=49.853, backward_time=0.096, grad_norm=43.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.858e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 15:53:02,849 (trainer:737) INFO: 27epoch:train:13301-13400batch: iter_time=1.086e-04, forward_time=0.103, loss_ctc=35.502, loss_att=46.629, acc=0.733, loss=43.291, backward_time=0.096, grad_norm=38.218, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.857e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 15:53:46,794 (trainer:737) INFO: 27epoch:train:13401-13500batch: iter_time=9.671e-05, forward_time=0.115, loss_ctc=46.539, loss_att=54.840, acc=0.704, loss=52.350, backward_time=0.098, grad_norm=44.879, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.857e-04, train_time=0.439
+[gpuc02:0/16] 2024-01-15 15:54:29,327 (trainer:737) INFO: 27epoch:train:13501-13600batch: iter_time=1.014e-04, forward_time=0.103, loss_ctc=45.432, loss_att=45.086, acc=0.745, loss=45.190, backward_time=0.096, grad_norm=41.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.856e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 15:55:12,358 (trainer:737) INFO: 27epoch:train:13601-13700batch: iter_time=1.039e-04, forward_time=0.104, loss_ctc=44.278, loss_att=53.896, acc=0.730, loss=51.011, backward_time=0.097, grad_norm=39.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.856e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 15:55:35,957 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 15:55:56,243 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 15:55:59,945 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ebefeff70>)
+[gpuc02:0/16] 2024-01-15 15:55:59,945 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 15:55:59,948 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 16:00:37,797 (trainer:737) INFO: 27epoch:train:13701-13800batch: iter_time=2.458, forward_time=0.146, loss_ctc=46.419, loss_att=56.831, acc=0.700, loss=53.708, backward_time=0.102, grad_norm=45.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.855e-04, train_time=3.254
+[gpuc02:0/16] 2024-01-15 16:01:20,251 (trainer:737) INFO: 27epoch:train:13801-13900batch: iter_time=1.209e-04, forward_time=0.105, loss_ctc=52.388, loss_att=71.477, acc=0.685, loss=65.750, backward_time=0.098, grad_norm=49.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.855e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 16:02:02,522 (trainer:737) INFO: 27epoch:train:13901-14000batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=43.776, loss_att=56.221, acc=0.752, loss=52.488, backward_time=0.098, grad_norm=38.224, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.854e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 16:02:44,535 (trainer:737) INFO: 27epoch:train:14001-14100batch: iter_time=1.231e-04, forward_time=0.104, loss_ctc=39.733, loss_att=44.859, acc=0.757, loss=43.321, backward_time=0.097, grad_norm=38.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.854e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 16:03:26,596 (trainer:737) INFO: 27epoch:train:14101-14200batch: iter_time=1.250e-04, forward_time=0.104, loss_ctc=42.872, loss_att=44.345, acc=0.762, loss=43.903, backward_time=0.098, grad_norm=40.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.853e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 16:04:08,637 (trainer:737) INFO: 27epoch:train:14201-14300batch: iter_time=1.256e-04, forward_time=0.104, loss_ctc=36.874, loss_att=40.407, acc=0.761, loss=39.347, backward_time=0.097, grad_norm=34.165, clip=100.000, loss_scale=3.219e+34, optim_step_time=0.042, optim0_lr0=3.853e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 16:04:50,668 (trainer:737) INFO: 27epoch:train:14301-14400batch: iter_time=1.221e-04, forward_time=0.104, loss_ctc=40.486, loss_att=37.800, acc=0.770, loss=38.606, backward_time=0.097, grad_norm=36.145, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.852e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 16:05:32,883 (trainer:737) INFO: 27epoch:train:14401-14500batch: iter_time=1.151e-04, forward_time=0.104, loss_ctc=45.685, loss_att=52.567, acc=0.727, loss=50.502, backward_time=0.097, grad_norm=44.386, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.852e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 16:06:15,040 (trainer:737) INFO: 27epoch:train:14501-14600batch: iter_time=1.158e-04, forward_time=0.105, loss_ctc=42.098, loss_att=50.805, acc=0.742, loss=48.193, backward_time=0.098, grad_norm=42.528, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.852e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 16:06:57,229 (trainer:737) INFO: 27epoch:train:14601-14700batch: iter_time=1.168e-04, forward_time=0.105, loss_ctc=40.362, loss_att=55.437, acc=0.727, loss=50.915, backward_time=0.097, grad_norm=39.860, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.851e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 16:07:39,480 (trainer:737) INFO: 27epoch:train:14701-14800batch: iter_time=1.172e-04, forward_time=0.104, loss_ctc=45.103, loss_att=48.088, acc=0.725, loss=47.193, backward_time=0.096, grad_norm=44.792, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.851e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 16:07:56,764 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 16:08:21,765 (trainer:737) INFO: 27epoch:train:14801-14900batch: iter_time=1.223e-04, forward_time=0.105, loss_ctc=44.252, loss_att=47.264, acc=0.742, loss=46.360, backward_time=0.097, grad_norm=41.404, clip=100.000, loss_scale=2.916e+34, optim_step_time=0.042, optim0_lr0=3.850e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 16:09:04,147 (trainer:737) INFO: 27epoch:train:14901-15000batch: iter_time=1.149e-04, forward_time=0.105, loss_ctc=47.887, loss_att=58.028, acc=0.713, loss=54.986, backward_time=0.097, grad_norm=45.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.850e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 16:29:12,228 (trainer:343) INFO: 27epoch results: [train] iter_time=0.210, forward_time=0.105, loss_ctc=44.097, loss_att=50.522, acc=0.734, loss=48.595, backward_time=0.097, grad_norm=41.471, clip=100.000, loss_scale=2.614e+34, optim_step_time=0.042, optim0_lr0=3.886e-04, train_time=0.670, time=2 hours, 47 minutes and 42.05 seconds, total_count=405000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=54.108, cer_ctc=0.273, loss_att=54.143, acc=0.590, cer=0.342, wer=0.998, loss=54.133, time=19 minutes and 57.3 seconds, total_count=126117, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 16:29:17,748 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-15 16:29:17,796 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/22epoch.pth
+[gpuc02:0/16] 2024-01-15 16:29:17,796 (trainer:272) INFO: 28/45epoch started. Estimated time to finish: 2 days, 7 hours and 14 minutes
+[gpuc02:0/16] 2024-01-15 16:29:17,806 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 16:29:36,588 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 16:29:39,992 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e8985b0>)
+[gpuc02:0/16] 2024-01-15 16:29:39,992 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 16:29:39,995 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 16:34:21,904 (trainer:737) INFO: 28epoch:train:1-100batch: iter_time=2.419, forward_time=0.110, loss_ctc=44.170, loss_att=61.330, acc=0.708, loss=56.182, backward_time=0.100, grad_norm=47.093, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.849e-04, train_time=3.041
+[gpuc02:0/16] 2024-01-15 16:35:04,425 (trainer:737) INFO: 28epoch:train:101-200batch: iter_time=1.093e-04, forward_time=0.104, loss_ctc=48.869, loss_att=58.935, acc=0.709, loss=55.915, backward_time=0.099, grad_norm=48.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.849e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 16:35:46,868 (trainer:737) INFO: 28epoch:train:201-300batch: iter_time=1.124e-04, forward_time=0.105, loss_ctc=55.805, loss_att=67.227, acc=0.717, loss=63.800, backward_time=0.099, grad_norm=57.323, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.848e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 16:36:29,127 (trainer:737) INFO: 28epoch:train:301-400batch: iter_time=1.228e-04, forward_time=0.104, loss_ctc=54.754, loss_att=56.373, acc=0.717, loss=55.887, backward_time=0.098, grad_norm=54.775, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.848e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 16:37:11,618 (trainer:737) INFO: 28epoch:train:401-500batch: iter_time=1.080e-04, forward_time=0.105, loss_ctc=49.469, loss_att=55.642, acc=0.716, loss=53.790, backward_time=0.098, grad_norm=44.045, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.847e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 16:37:53,923 (trainer:737) INFO: 28epoch:train:501-600batch: iter_time=1.095e-04, forward_time=0.103, loss_ctc=38.033, loss_att=43.537, acc=0.736, loss=41.886, backward_time=0.097, grad_norm=38.494, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.847e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 16:38:35,823 (trainer:737) INFO: 28epoch:train:601-700batch: iter_time=1.070e-04, forward_time=0.104, loss_ctc=45.643, loss_att=51.647, acc=0.701, loss=49.845, backward_time=0.097, grad_norm=47.739, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.846e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 16:39:17,750 (trainer:737) INFO: 28epoch:train:701-800batch: iter_time=1.005e-04, forward_time=0.104, loss_ctc=46.078, loss_att=45.396, acc=0.739, loss=45.601, backward_time=0.097, grad_norm=40.702, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.846e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 16:39:59,659 (trainer:737) INFO: 28epoch:train:801-900batch: iter_time=1.182e-04, forward_time=0.104, loss_ctc=53.465, loss_att=47.301, acc=0.729, loss=49.151, backward_time=0.097, grad_norm=49.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.845e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 16:40:43,233 (trainer:737) INFO: 28epoch:train:901-1000batch: iter_time=1.118e-04, forward_time=0.105, loss_ctc=45.324, loss_att=56.132, acc=0.702, loss=52.889, backward_time=0.102, grad_norm=43.841, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.845e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-15 16:41:25,819 (trainer:737) INFO: 28epoch:train:1001-1100batch: iter_time=1.015e-04, forward_time=0.104, loss_ctc=44.450, loss_att=56.101, acc=0.704, loss=52.605, backward_time=0.098, grad_norm=41.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.844e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 16:42:11,651 (trainer:737) INFO: 28epoch:train:1101-1200batch: iter_time=1.134e-04, forward_time=0.104, loss_ctc=51.817, loss_att=55.347, acc=0.713, loss=54.288, backward_time=0.098, grad_norm=43.538, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.844e-04, train_time=0.458
+[gpuc02:0/16] 2024-01-15 16:42:40,019 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 16:42:59,663 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 16:43:03,308 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f252df90550>)
+[gpuc02:0/16] 2024-01-15 16:43:03,308 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 16:43:03,311 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 16:48:56,704 (trainer:737) INFO: 28epoch:train:1201-1300batch: iter_time=2.906, forward_time=0.110, loss_ctc=43.118, loss_att=55.393, acc=0.724, loss=51.710, backward_time=0.098, grad_norm=38.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.843e-04, train_time=4.050
+[gpuc02:0/16] 2024-01-15 16:49:39,239 (trainer:737) INFO: 28epoch:train:1301-1400batch: iter_time=1.037e-04, forward_time=0.106, loss_ctc=45.373, loss_att=62.270, acc=0.714, loss=57.201, backward_time=0.098, grad_norm=46.264, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.843e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 16:50:21,657 (trainer:737) INFO: 28epoch:train:1401-1500batch: iter_time=1.247e-04, forward_time=0.105, loss_ctc=58.154, loss_att=66.645, acc=0.721, loss=64.098, backward_time=0.098, grad_norm=64.025, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.842e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 16:51:04,021 (trainer:737) INFO: 28epoch:train:1501-1600batch: iter_time=1.316e-04, forward_time=0.105, loss_ctc=47.900, loss_att=53.294, acc=0.739, loss=51.676, backward_time=0.098, grad_norm=48.988, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.842e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 16:51:47,381 (trainer:737) INFO: 28epoch:train:1601-1700batch: iter_time=1.268e-04, forward_time=0.104, loss_ctc=48.821, loss_att=55.609, acc=0.736, loss=53.573, backward_time=0.097, grad_norm=43.595, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.842e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 16:52:30,038 (trainer:737) INFO: 28epoch:train:1701-1800batch: iter_time=1.349e-04, forward_time=0.107, loss_ctc=45.319, loss_att=58.554, acc=0.714, loss=54.584, backward_time=0.097, grad_norm=41.287, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.841e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 16:53:12,339 (trainer:737) INFO: 28epoch:train:1801-1900batch: iter_time=1.180e-04, forward_time=0.104, loss_ctc=43.346, loss_att=47.250, acc=0.738, loss=46.079, backward_time=0.097, grad_norm=45.538, clip=100.000, loss_scale=3.302e+34, optim_step_time=0.041, optim0_lr0=3.841e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 16:53:54,949 (trainer:737) INFO: 28epoch:train:1901-2000batch: iter_time=1.432e-04, forward_time=0.103, loss_ctc=40.178, loss_att=39.563, acc=0.742, loss=39.747, backward_time=0.096, grad_norm=39.260, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.840e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 16:54:22,738 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 16:54:37,537 (trainer:737) INFO: 28epoch:train:2001-2100batch: iter_time=1.369e-04, forward_time=0.104, loss_ctc=43.096, loss_att=44.565, acc=0.740, loss=44.125, backward_time=0.097, grad_norm=39.170, clip=100.000, loss_scale=3.441e+34, optim_step_time=0.042, optim0_lr0=3.840e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 16:55:19,666 (trainer:737) INFO: 28epoch:train:2101-2200batch: iter_time=1.402e-04, forward_time=0.104, loss_ctc=50.602, loss_att=51.044, acc=0.724, loss=50.911, backward_time=0.097, grad_norm=52.054, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.839e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 16:56:01,946 (trainer:737) INFO: 28epoch:train:2201-2300batch: iter_time=1.300e-04, forward_time=0.104, loss_ctc=45.196, loss_att=57.309, acc=0.725, loss=53.675, backward_time=0.098, grad_norm=41.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.839e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 16:56:51,143 (trainer:737) INFO: 28epoch:train:2301-2400batch: iter_time=1.530e-04, forward_time=0.140, loss_ctc=49.657, loss_att=59.833, acc=0.700, loss=56.780, backward_time=0.110, grad_norm=44.661, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.052, optim0_lr0=3.838e-04, train_time=0.492
+[gpuc02:0/16] 2024-01-15 16:57:34,131 (trainer:737) INFO: 28epoch:train:2401-2500batch: iter_time=1.103e-04, forward_time=0.109, loss_ctc=47.018, loss_att=55.379, acc=0.737, loss=52.871, backward_time=0.100, grad_norm=42.091, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.838e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 16:58:04,688 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 16:58:25,006 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 16:58:28,782 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d997828f0>)
+[gpuc02:0/16] 2024-01-15 16:58:28,782 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 16:58:28,785 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 17:06:18,738 (trainer:737) INFO: 28epoch:train:2501-2600batch: iter_time=4.384, forward_time=0.126, loss_ctc=42.889, loss_att=59.757, acc=0.715, loss=54.697, backward_time=0.099, grad_norm=44.280, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.837e-04, train_time=5.246
+[gpuc02:0/16] 2024-01-15 17:07:01,162 (trainer:737) INFO: 28epoch:train:2601-2700batch: iter_time=1.292e-04, forward_time=0.106, loss_ctc=46.969, loss_att=56.605, acc=0.713, loss=53.714, backward_time=0.098, grad_norm=49.079, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.837e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 17:07:43,731 (trainer:737) INFO: 28epoch:train:2701-2800batch: iter_time=1.270e-04, forward_time=0.106, loss_ctc=51.652, loss_att=64.655, acc=0.719, loss=60.754, backward_time=0.098, grad_norm=56.136, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.836e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:08:26,528 (trainer:737) INFO: 28epoch:train:2801-2900batch: iter_time=1.254e-04, forward_time=0.104, loss_ctc=51.599, loss_att=54.359, acc=0.722, loss=53.531, backward_time=0.098, grad_norm=52.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.836e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 17:09:09,052 (trainer:737) INFO: 28epoch:train:2901-3000batch: iter_time=1.353e-04, forward_time=0.104, loss_ctc=48.787, loss_att=53.870, acc=0.721, loss=52.345, backward_time=0.097, grad_norm=43.576, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.835e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:09:51,398 (trainer:737) INFO: 28epoch:train:3001-3100batch: iter_time=1.189e-04, forward_time=0.103, loss_ctc=37.196, loss_att=42.710, acc=0.739, loss=41.056, backward_time=0.096, grad_norm=40.607, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.835e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:10:33,536 (trainer:737) INFO: 28epoch:train:3101-3200batch: iter_time=1.233e-04, forward_time=0.103, loss_ctc=44.335, loss_att=51.113, acc=0.705, loss=49.080, backward_time=0.096, grad_norm=45.137, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.834e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 17:11:15,863 (trainer:737) INFO: 28epoch:train:3201-3300batch: iter_time=1.317e-04, forward_time=0.103, loss_ctc=44.796, loss_att=44.783, acc=0.739, loss=44.787, backward_time=0.097, grad_norm=41.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.834e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:11:58,233 (trainer:737) INFO: 28epoch:train:3301-3400batch: iter_time=1.390e-04, forward_time=0.104, loss_ctc=51.119, loss_att=46.336, acc=0.731, loss=47.771, backward_time=0.097, grad_norm=48.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.834e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:12:40,632 (trainer:737) INFO: 28epoch:train:3401-3500batch: iter_time=1.154e-04, forward_time=0.104, loss_ctc=43.965, loss_att=56.015, acc=0.705, loss=52.400, backward_time=0.096, grad_norm=43.520, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.833e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 17:13:23,695 (trainer:737) INFO: 28epoch:train:3501-3600batch: iter_time=1.009e-04, forward_time=0.107, loss_ctc=43.162, loss_att=54.726, acc=0.710, loss=51.257, backward_time=0.097, grad_norm=40.000, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.833e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 17:14:06,297 (trainer:737) INFO: 28epoch:train:3601-3700batch: iter_time=1.129e-04, forward_time=0.104, loss_ctc=51.740, loss_att=55.165, acc=0.715, loss=54.137, backward_time=0.097, grad_norm=44.411, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.832e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 17:14:33,895 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 17:14:53,522 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 17:14:57,029 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f243c3a6fb0>)
+[gpuc02:0/16] 2024-01-15 17:14:57,029 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 17:14:57,033 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 17:21:13,320 (trainer:737) INFO: 28epoch:train:3701-3800batch: iter_time=2.537, forward_time=0.106, loss_ctc=42.155, loss_att=54.392, acc=0.721, loss=50.721, backward_time=0.097, grad_norm=39.815, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.832e-04, train_time=4.270
+[gpuc02:0/16] 2024-01-15 17:21:55,683 (trainer:737) INFO: 28epoch:train:3801-3900batch: iter_time=1.072e-04, forward_time=0.106, loss_ctc=44.769, loss_att=59.694, acc=0.705, loss=55.217, backward_time=0.098, grad_norm=47.437, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.831e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:22:38,001 (trainer:737) INFO: 28epoch:train:3901-4000batch: iter_time=1.309e-04, forward_time=0.106, loss_ctc=56.760, loss_att=64.913, acc=0.719, loss=62.467, backward_time=0.097, grad_norm=59.596, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.831e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:23:20,283 (trainer:737) INFO: 28epoch:train:4001-4100batch: iter_time=1.363e-04, forward_time=0.106, loss_ctc=47.231, loss_att=51.389, acc=0.734, loss=50.142, backward_time=0.096, grad_norm=46.720, clip=100.000, loss_scale=2.783e+34, optim_step_time=0.041, optim0_lr0=3.830e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:24:03,172 (trainer:737) INFO: 28epoch:train:4101-4200batch: iter_time=1.423e-04, forward_time=0.106, loss_ctc=48.540, loss_att=55.114, acc=0.726, loss=53.142, backward_time=0.097, grad_norm=45.992, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.830e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 17:24:45,373 (trainer:737) INFO: 28epoch:train:4201-4300batch: iter_time=1.430e-04, forward_time=0.105, loss_ctc=45.166, loss_att=56.101, acc=0.707, loss=52.820, backward_time=0.096, grad_norm=42.706, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.829e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 17:25:27,857 (trainer:737) INFO: 28epoch:train:4301-4400batch: iter_time=1.624e-04, forward_time=0.104, loss_ctc=43.093, loss_att=47.324, acc=0.724, loss=46.054, backward_time=0.096, grad_norm=44.671, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.829e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:25:37,921 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 17:26:10,142 (trainer:737) INFO: 28epoch:train:4401-4500batch: iter_time=1.348e-04, forward_time=0.104, loss_ctc=39.256, loss_att=38.610, acc=0.744, loss=38.804, backward_time=0.096, grad_norm=36.696, clip=100.000, loss_scale=2.559e+34, optim_step_time=0.041, optim0_lr0=3.828e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:26:52,889 (trainer:737) INFO: 28epoch:train:4501-4600batch: iter_time=1.455e-04, forward_time=0.105, loss_ctc=42.683, loss_att=44.006, acc=0.734, loss=43.609, backward_time=0.096, grad_norm=39.790, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.828e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 17:27:35,419 (trainer:737) INFO: 28epoch:train:4601-4700batch: iter_time=1.289e-04, forward_time=0.105, loss_ctc=49.354, loss_att=49.669, acc=0.720, loss=49.574, backward_time=0.096, grad_norm=49.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.827e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:28:17,782 (trainer:737) INFO: 28epoch:train:4701-4800batch: iter_time=1.344e-04, forward_time=0.106, loss_ctc=44.569, loss_att=55.167, acc=0.723, loss=51.987, backward_time=0.097, grad_norm=38.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.827e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:29:01,569 (trainer:737) INFO: 28epoch:train:4801-4900batch: iter_time=1.478e-04, forward_time=0.113, loss_ctc=49.252, loss_att=57.940, acc=0.693, loss=55.334, backward_time=0.103, grad_norm=43.970, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.827e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 17:29:44,327 (trainer:737) INFO: 28epoch:train:4901-5000batch: iter_time=1.264e-04, forward_time=0.104, loss_ctc=46.663, loss_att=55.021, acc=0.733, loss=52.514, backward_time=0.098, grad_norm=40.392, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.826e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 17:29:50,508 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 17:30:09,588 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 17:30:13,046 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1dc2ee7370>)
+[gpuc02:0/16] 2024-01-15 17:30:13,046 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 17:30:13,049 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 17:35:05,958 (trainer:737) INFO: 28epoch:train:5001-5100batch: iter_time=2.650, forward_time=0.162, loss_ctc=42.324, loss_att=58.031, acc=0.719, loss=53.319, backward_time=0.103, grad_norm=41.988, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.826e-04, train_time=3.216
+[gpuc02:0/16] 2024-01-15 17:35:48,362 (trainer:737) INFO: 28epoch:train:5101-5200batch: iter_time=1.455e-04, forward_time=0.105, loss_ctc=46.415, loss_att=56.581, acc=0.714, loss=53.531, backward_time=0.098, grad_norm=49.078, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.825e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 17:36:30,848 (trainer:737) INFO: 28epoch:train:5201-5300batch: iter_time=1.415e-04, forward_time=0.106, loss_ctc=49.915, loss_att=63.148, acc=0.724, loss=59.178, backward_time=0.098, grad_norm=50.685, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.825e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:37:13,128 (trainer:737) INFO: 28epoch:train:5301-5400batch: iter_time=1.520e-04, forward_time=0.104, loss_ctc=51.197, loss_att=54.100, acc=0.724, loss=53.229, backward_time=0.097, grad_norm=54.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.824e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:37:55,466 (trainer:737) INFO: 28epoch:train:5401-5500batch: iter_time=1.583e-04, forward_time=0.105, loss_ctc=47.546, loss_att=53.580, acc=0.723, loss=51.770, backward_time=0.097, grad_norm=41.297, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.824e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:38:37,616 (trainer:737) INFO: 28epoch:train:5501-5600batch: iter_time=1.657e-04, forward_time=0.103, loss_ctc=37.117, loss_att=41.760, acc=0.740, loss=40.367, backward_time=0.097, grad_norm=39.916, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.823e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 17:39:19,798 (trainer:737) INFO: 28epoch:train:5601-5700batch: iter_time=1.377e-04, forward_time=0.103, loss_ctc=43.337, loss_att=49.899, acc=0.707, loss=47.930, backward_time=0.096, grad_norm=43.927, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.823e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 17:40:02,007 (trainer:737) INFO: 28epoch:train:5701-5800batch: iter_time=1.469e-04, forward_time=0.104, loss_ctc=44.277, loss_att=43.622, acc=0.742, loss=43.818, backward_time=0.097, grad_norm=37.982, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.822e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 17:40:44,204 (trainer:737) INFO: 28epoch:train:5801-5900batch: iter_time=1.364e-04, forward_time=0.104, loss_ctc=50.072, loss_att=45.590, acc=0.736, loss=46.934, backward_time=0.097, grad_norm=44.463, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.822e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 17:41:26,881 (trainer:737) INFO: 28epoch:train:5901-6000batch: iter_time=1.561e-04, forward_time=0.104, loss_ctc=43.663, loss_att=54.685, acc=0.707, loss=51.379, backward_time=0.097, grad_norm=42.416, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.821e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 17:42:09,218 (trainer:737) INFO: 28epoch:train:6001-6100batch: iter_time=1.412e-04, forward_time=0.105, loss_ctc=43.068, loss_att=54.729, acc=0.710, loss=51.231, backward_time=0.098, grad_norm=40.767, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.821e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 17:42:51,801 (trainer:737) INFO: 28epoch:train:6101-6200batch: iter_time=1.666e-04, forward_time=0.104, loss_ctc=50.905, loss_att=54.777, acc=0.716, loss=53.615, backward_time=0.098, grad_norm=42.477, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.820e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 17:43:18,440 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 17:43:38,700 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 17:43:42,454 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8c46f5e0>)
+[gpuc02:0/16] 2024-01-15 17:43:42,454 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 17:43:42,457 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 17:48:16,771 (trainer:737) INFO: 28epoch:train:6201-6300batch: iter_time=2.614, forward_time=0.103, loss_ctc=41.566, loss_att=53.627, acc=0.725, loss=50.009, backward_time=0.101, grad_norm=40.705, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.820e-04, train_time=3.249
+[gpuc02:0/16] 2024-01-15 17:48:59,283 (trainer:737) INFO: 28epoch:train:6301-6400batch: iter_time=1.034e-04, forward_time=0.103, loss_ctc=44.640, loss_att=59.346, acc=0.707, loss=54.934, backward_time=0.097, grad_norm=46.377, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.820e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:49:41,832 (trainer:737) INFO: 28epoch:train:6401-6500batch: iter_time=1.154e-04, forward_time=0.104, loss_ctc=53.759, loss_att=64.445, acc=0.721, loss=61.240, backward_time=0.098, grad_norm=60.707, clip=100.000, loss_scale=3.655e+34, optim_step_time=0.042, optim0_lr0=3.819e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:50:24,325 (trainer:737) INFO: 28epoch:train:6501-6600batch: iter_time=1.109e-04, forward_time=0.104, loss_ctc=46.365, loss_att=50.196, acc=0.736, loss=49.047, backward_time=0.098, grad_norm=47.578, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.819e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:51:06,766 (trainer:737) INFO: 28epoch:train:6601-6700batch: iter_time=1.163e-04, forward_time=0.104, loss_ctc=47.602, loss_att=54.577, acc=0.730, loss=52.484, backward_time=0.098, grad_norm=42.626, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.818e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 17:51:48,788 (trainer:737) INFO: 28epoch:train:6701-6800batch: iter_time=1.227e-04, forward_time=0.104, loss_ctc=44.072, loss_att=55.547, acc=0.710, loss=52.105, backward_time=0.098, grad_norm=41.088, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.818e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 17:52:30,735 (trainer:737) INFO: 28epoch:train:6801-6900batch: iter_time=1.190e-04, forward_time=0.103, loss_ctc=42.065, loss_att=47.162, acc=0.725, loss=45.633, backward_time=0.097, grad_norm=44.713, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.817e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 17:53:12,714 (trainer:737) INFO: 28epoch:train:6901-7000batch: iter_time=1.747e-04, forward_time=0.103, loss_ctc=39.559, loss_att=38.817, acc=0.742, loss=39.040, backward_time=0.097, grad_norm=37.460, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.817e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 17:53:54,931 (trainer:737) INFO: 28epoch:train:7001-7100batch: iter_time=1.977e-04, forward_time=0.104, loss_ctc=42.376, loss_att=43.584, acc=0.735, loss=43.222, backward_time=0.097, grad_norm=39.881, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.816e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 17:54:37,429 (trainer:737) INFO: 28epoch:train:7101-7200batch: iter_time=1.782e-04, forward_time=0.107, loss_ctc=48.338, loss_att=49.106, acc=0.721, loss=48.875, backward_time=0.097, grad_norm=48.532, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.816e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:55:19,929 (trainer:737) INFO: 28epoch:train:7201-7300batch: iter_time=2.180e-04, forward_time=0.106, loss_ctc=44.398, loss_att=55.129, acc=0.724, loss=51.909, backward_time=0.099, grad_norm=39.281, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.815e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:56:02,452 (trainer:737) INFO: 28epoch:train:7301-7400batch: iter_time=2.615e-04, forward_time=0.106, loss_ctc=48.764, loss_att=57.055, acc=0.695, loss=54.568, backward_time=0.099, grad_norm=46.336, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.815e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 17:56:44,898 (trainer:737) INFO: 28epoch:train:7401-7500batch: iter_time=1.946e-04, forward_time=0.106, loss_ctc=46.327, loss_att=54.583, acc=0.734, loss=52.107, backward_time=0.099, grad_norm=39.622, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.814e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 17:56:49,699 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 17:57:09,765 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 17:57:13,551 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f244a30f1f0>)
+[gpuc02:0/16] 2024-01-15 17:57:13,551 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 17:57:13,554 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 18:02:11,610 (trainer:737) INFO: 28epoch:train:7501-7600batch: iter_time=2.607, forward_time=0.104, loss_ctc=41.748, loss_att=56.985, acc=0.721, loss=52.414, backward_time=0.097, grad_norm=42.890, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.814e-04, train_time=3.267
+[gpuc02:0/16] 2024-01-15 18:02:53,971 (trainer:737) INFO: 28epoch:train:7601-7700batch: iter_time=1.253e-04, forward_time=0.104, loss_ctc=45.579, loss_att=55.468, acc=0.717, loss=52.501, backward_time=0.097, grad_norm=46.050, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.814e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:03:34,510 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 18:03:36,237 (trainer:737) INFO: 28epoch:train:7701-7800batch: iter_time=1.275e-04, forward_time=0.105, loss_ctc=50.065, loss_att=63.537, acc=0.726, loss=59.496, backward_time=0.098, grad_norm=53.050, clip=100.000, loss_scale=4.070e+34, optim_step_time=0.042, optim0_lr0=3.813e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:04:18,567 (trainer:737) INFO: 28epoch:train:7801-7900batch: iter_time=1.377e-04, forward_time=0.104, loss_ctc=50.566, loss_att=53.660, acc=0.725, loss=52.732, backward_time=0.097, grad_norm=51.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.813e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:05:00,688 (trainer:737) INFO: 28epoch:train:7901-8000batch: iter_time=1.309e-04, forward_time=0.105, loss_ctc=47.238, loss_att=53.044, acc=0.725, loss=51.303, backward_time=0.097, grad_norm=40.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.812e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 18:05:42,794 (trainer:737) INFO: 28epoch:train:8001-8100batch: iter_time=1.383e-04, forward_time=0.103, loss_ctc=36.464, loss_att=42.078, acc=0.741, loss=40.394, backward_time=0.097, grad_norm=38.438, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.812e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 18:06:24,754 (trainer:737) INFO: 28epoch:train:8101-8200batch: iter_time=1.591e-04, forward_time=0.104, loss_ctc=43.355, loss_att=49.658, acc=0.707, loss=47.767, backward_time=0.097, grad_norm=44.697, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.811e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 18:07:06,982 (trainer:737) INFO: 28epoch:train:8201-8300batch: iter_time=1.427e-04, forward_time=0.105, loss_ctc=44.112, loss_att=43.907, acc=0.742, loss=43.969, backward_time=0.098, grad_norm=39.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.811e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:07:49,179 (trainer:737) INFO: 28epoch:train:8301-8400batch: iter_time=1.539e-04, forward_time=0.104, loss_ctc=49.045, loss_att=45.558, acc=0.736, loss=46.604, backward_time=0.098, grad_norm=42.775, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.810e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:08:31,753 (trainer:737) INFO: 28epoch:train:8401-8500batch: iter_time=1.421e-04, forward_time=0.104, loss_ctc=43.713, loss_att=55.083, acc=0.709, loss=51.672, backward_time=0.097, grad_norm=41.042, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.810e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:09:13,929 (trainer:737) INFO: 28epoch:train:8501-8600batch: iter_time=1.362e-04, forward_time=0.105, loss_ctc=42.882, loss_att=54.522, acc=0.713, loss=51.030, backward_time=0.098, grad_norm=40.446, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.809e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:09:56,481 (trainer:737) INFO: 28epoch:train:8601-8700batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=50.573, loss_att=54.344, acc=0.717, loss=53.213, backward_time=0.098, grad_norm=42.502, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.809e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:10:24,086 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 18:10:43,284 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 18:10:46,907 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ecbab6e00>)
+[gpuc02:0/16] 2024-01-15 18:10:46,907 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 18:10:46,910 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 18:15:21,359 (trainer:737) INFO: 28epoch:train:8701-8800batch: iter_time=2.825, forward_time=0.105, loss_ctc=41.501, loss_att=54.746, acc=0.727, loss=50.773, backward_time=0.097, grad_norm=38.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.808e-04, train_time=3.249
+[gpuc02:0/16] 2024-01-15 18:16:03,959 (trainer:737) INFO: 28epoch:train:8801-8900batch: iter_time=1.603e-04, forward_time=0.105, loss_ctc=44.211, loss_att=62.613, acc=0.715, loss=57.092, backward_time=0.098, grad_norm=45.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.808e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:16:46,363 (trainer:737) INFO: 28epoch:train:8901-9000batch: iter_time=1.735e-04, forward_time=0.106, loss_ctc=53.596, loss_att=65.229, acc=0.727, loss=61.739, backward_time=0.098, grad_norm=57.477, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.808e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:17:28,917 (trainer:737) INFO: 28epoch:train:9001-9100batch: iter_time=1.687e-04, forward_time=0.106, loss_ctc=46.041, loss_att=52.621, acc=0.742, loss=50.647, backward_time=0.098, grad_norm=43.780, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.807e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:18:11,306 (trainer:737) INFO: 28epoch:train:9101-9200batch: iter_time=1.599e-04, forward_time=0.105, loss_ctc=47.378, loss_att=55.691, acc=0.737, loss=53.197, backward_time=0.098, grad_norm=42.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.807e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:18:53,947 (trainer:737) INFO: 28epoch:train:9201-9300batch: iter_time=1.818e-04, forward_time=0.105, loss_ctc=44.316, loss_att=57.859, acc=0.720, loss=53.796, backward_time=0.098, grad_norm=42.363, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.806e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:19:36,212 (trainer:737) INFO: 28epoch:train:9301-9400batch: iter_time=1.737e-04, forward_time=0.105, loss_ctc=42.341, loss_att=47.633, acc=0.737, loss=46.046, backward_time=0.098, grad_norm=43.971, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.806e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:20:18,597 (trainer:737) INFO: 28epoch:train:9401-9500batch: iter_time=1.885e-04, forward_time=0.105, loss_ctc=39.118, loss_att=39.197, acc=0.745, loss=39.173, backward_time=0.097, grad_norm=37.046, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.805e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:21:01,346 (trainer:737) INFO: 28epoch:train:9501-9600batch: iter_time=1.717e-04, forward_time=0.105, loss_ctc=41.974, loss_att=44.318, acc=0.740, loss=43.615, backward_time=0.098, grad_norm=38.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.805e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:21:44,165 (trainer:737) INFO: 28epoch:train:9601-9700batch: iter_time=1.588e-04, forward_time=0.105, loss_ctc=48.359, loss_att=51.496, acc=0.726, loss=50.555, backward_time=0.097, grad_norm=50.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.804e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 18:22:26,443 (trainer:737) INFO: 28epoch:train:9701-9800batch: iter_time=1.459e-04, forward_time=0.105, loss_ctc=43.759, loss_att=57.279, acc=0.727, loss=53.223, backward_time=0.098, grad_norm=38.477, clip=100.000, loss_scale=2.160e+34, optim_step_time=0.041, optim0_lr0=3.804e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:23:09,140 (trainer:737) INFO: 28epoch:train:9801-9900batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=48.866, loss_att=60.447, acc=0.698, loss=56.973, backward_time=0.098, grad_norm=45.645, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.803e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:23:51,513 (trainer:737) INFO: 28epoch:train:9901-10000batch: iter_time=1.412e-04, forward_time=0.105, loss_ctc=46.356, loss_att=54.363, acc=0.742, loss=51.961, backward_time=0.097, grad_norm=40.078, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.803e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:23:57,997 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 18:24:17,271 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 18:24:20,914 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1eea5f79d0>)
+[gpuc02:0/16] 2024-01-15 18:24:20,914 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 18:24:20,917 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 18:29:08,885 (trainer:737) INFO: 28epoch:train:10001-10100batch: iter_time=2.745, forward_time=0.105, loss_ctc=41.694, loss_att=57.010, acc=0.730, loss=52.415, backward_time=0.098, grad_norm=40.682, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.802e-04, train_time=3.173
+[gpuc02:0/16] 2024-01-15 18:29:28,410 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 18:29:51,403 (trainer:737) INFO: 28epoch:train:10101-10200batch: iter_time=1.558e-04, forward_time=0.104, loss_ctc=45.500, loss_att=57.084, acc=0.722, loss=53.609, backward_time=0.098, grad_norm=47.053, clip=100.000, loss_scale=3.021e+34, optim_step_time=0.041, optim0_lr0=3.802e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:30:34,136 (trainer:737) INFO: 28epoch:train:10201-10300batch: iter_time=1.505e-04, forward_time=0.105, loss_ctc=49.053, loss_att=63.275, acc=0.738, loss=59.009, backward_time=0.098, grad_norm=55.259, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.802e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:31:16,614 (trainer:737) INFO: 28epoch:train:10301-10400batch: iter_time=1.742e-04, forward_time=0.105, loss_ctc=50.169, loss_att=53.638, acc=0.737, loss=52.598, backward_time=0.098, grad_norm=51.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.801e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:31:59,085 (trainer:737) INFO: 28epoch:train:10401-10500batch: iter_time=1.752e-04, forward_time=0.104, loss_ctc=47.579, loss_att=55.676, acc=0.730, loss=53.247, backward_time=0.098, grad_norm=41.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.801e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:32:41,311 (trainer:737) INFO: 28epoch:train:10501-10600batch: iter_time=1.646e-04, forward_time=0.103, loss_ctc=36.481, loss_att=40.982, acc=0.756, loss=39.632, backward_time=0.096, grad_norm=36.679, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.800e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 18:33:23,613 (trainer:737) INFO: 28epoch:train:10601-10700batch: iter_time=1.441e-04, forward_time=0.104, loss_ctc=43.008, loss_att=50.056, acc=0.720, loss=47.942, backward_time=0.097, grad_norm=45.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.800e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:34:05,945 (trainer:737) INFO: 28epoch:train:10701-10800batch: iter_time=1.597e-04, forward_time=0.104, loss_ctc=43.676, loss_att=44.108, acc=0.748, loss=43.978, backward_time=0.097, grad_norm=38.412, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.799e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:34:48,265 (trainer:737) INFO: 28epoch:train:10801-10900batch: iter_time=1.705e-04, forward_time=0.104, loss_ctc=48.943, loss_att=46.223, acc=0.739, loss=47.039, backward_time=0.097, grad_norm=42.163, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.799e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:35:30,605 (trainer:737) INFO: 28epoch:train:10901-11000batch: iter_time=1.704e-04, forward_time=0.104, loss_ctc=43.673, loss_att=56.405, acc=0.716, loss=52.586, backward_time=0.097, grad_norm=42.531, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.798e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:36:13,381 (trainer:737) INFO: 28epoch:train:11001-11100batch: iter_time=1.918e-04, forward_time=0.107, loss_ctc=42.183, loss_att=55.904, acc=0.717, loss=51.788, backward_time=0.098, grad_norm=40.726, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.798e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 18:36:55,887 (trainer:737) INFO: 28epoch:train:11101-11200batch: iter_time=1.839e-04, forward_time=0.105, loss_ctc=50.197, loss_att=56.912, acc=0.721, loss=54.897, backward_time=0.097, grad_norm=42.046, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.797e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:37:24,566 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 18:37:43,572 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 18:37:47,107 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2559bebfa0>)
+[gpuc02:0/16] 2024-01-15 18:37:47,107 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 18:37:47,110 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 18:42:16,481 (trainer:737) INFO: 28epoch:train:11201-11300batch: iter_time=2.743, forward_time=0.106, loss_ctc=41.621, loss_att=52.181, acc=0.738, loss=49.013, backward_time=0.098, grad_norm=37.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.797e-04, train_time=3.206
+[gpuc02:0/16] 2024-01-15 18:42:59,063 (trainer:737) INFO: 28epoch:train:11301-11400batch: iter_time=1.482e-04, forward_time=0.106, loss_ctc=44.080, loss_att=59.653, acc=0.721, loss=54.981, backward_time=0.098, grad_norm=43.593, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.797e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:43:41,613 (trainer:737) INFO: 28epoch:train:11401-11500batch: iter_time=1.717e-04, forward_time=0.105, loss_ctc=53.588, loss_att=64.350, acc=0.729, loss=61.121, backward_time=0.097, grad_norm=57.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.796e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:44:24,104 (trainer:737) INFO: 28epoch:train:11501-11600batch: iter_time=1.669e-04, forward_time=0.105, loss_ctc=46.446, loss_att=52.322, acc=0.744, loss=50.559, backward_time=0.097, grad_norm=44.972, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.796e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:45:06,943 (trainer:737) INFO: 28epoch:train:11601-11700batch: iter_time=2.021e-04, forward_time=0.106, loss_ctc=47.602, loss_att=54.127, acc=0.741, loss=52.170, backward_time=0.098, grad_norm=45.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.795e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 18:45:49,264 (trainer:737) INFO: 28epoch:train:11701-11800batch: iter_time=1.708e-04, forward_time=0.105, loss_ctc=44.230, loss_att=56.535, acc=0.723, loss=52.844, backward_time=0.097, grad_norm=39.342, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.795e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:46:31,864 (trainer:737) INFO: 28epoch:train:11801-11900batch: iter_time=2.006e-04, forward_time=0.104, loss_ctc=42.011, loss_att=46.183, acc=0.740, loss=44.932, backward_time=0.097, grad_norm=42.613, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.794e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:47:14,600 (trainer:737) INFO: 28epoch:train:11901-12000batch: iter_time=2.224e-04, forward_time=0.105, loss_ctc=39.196, loss_att=39.166, acc=0.744, loss=39.175, backward_time=0.097, grad_norm=36.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.794e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:47:57,233 (trainer:737) INFO: 28epoch:train:12001-12100batch: iter_time=1.920e-04, forward_time=0.105, loss_ctc=41.590, loss_att=43.628, acc=0.744, loss=43.017, backward_time=0.097, grad_norm=37.060, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.793e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 18:48:39,566 (trainer:737) INFO: 28epoch:train:12101-12200batch: iter_time=1.958e-04, forward_time=0.105, loss_ctc=47.348, loss_att=50.107, acc=0.728, loss=49.279, backward_time=0.097, grad_norm=47.552, clip=100.000, loss_scale=3.198e+34, optim_step_time=0.041, optim0_lr0=3.793e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 18:49:21,988 (trainer:737) INFO: 28epoch:train:12201-12300batch: iter_time=1.773e-04, forward_time=0.105, loss_ctc=43.553, loss_att=56.229, acc=0.728, loss=52.426, backward_time=0.097, grad_norm=39.110, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.792e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:50:04,471 (trainer:737) INFO: 28epoch:train:12301-12400batch: iter_time=1.940e-04, forward_time=0.106, loss_ctc=48.593, loss_att=59.771, acc=0.700, loss=56.417, backward_time=0.097, grad_norm=44.041, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.792e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 18:50:47,173 (trainer:737) INFO: 28epoch:train:12401-12500batch: iter_time=1.600e-04, forward_time=0.106, loss_ctc=46.063, loss_att=53.977, acc=0.742, loss=51.603, backward_time=0.097, grad_norm=39.717, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.792e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:50:53,645 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 18:51:12,903 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 18:51:16,605 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e72fa3fa0>)
+[gpuc02:0/16] 2024-01-15 18:51:16,605 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 18:51:16,609 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 18:56:09,299 (trainer:737) INFO: 28epoch:train:12501-12600batch: iter_time=2.793, forward_time=0.106, loss_ctc=41.821, loss_att=56.511, acc=0.735, loss=52.104, backward_time=0.097, grad_norm=41.137, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.791e-04, train_time=3.221
+[gpuc02:0/16] 2024-01-15 18:56:52,036 (trainer:737) INFO: 28epoch:train:12601-12700batch: iter_time=1.719e-04, forward_time=0.108, loss_ctc=44.911, loss_att=56.518, acc=0.723, loss=53.036, backward_time=0.097, grad_norm=46.899, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.791e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:57:34,900 (trainer:737) INFO: 28epoch:train:12701-12800batch: iter_time=1.913e-04, forward_time=0.107, loss_ctc=49.083, loss_att=63.093, acc=0.738, loss=58.890, backward_time=0.099, grad_norm=51.334, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.790e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 18:58:17,300 (trainer:737) INFO: 28epoch:train:12801-12900batch: iter_time=1.932e-04, forward_time=0.106, loss_ctc=50.264, loss_att=53.747, acc=0.737, loss=52.702, backward_time=0.097, grad_norm=50.827, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.790e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 18:59:00,005 (trainer:737) INFO: 28epoch:train:12901-13000batch: iter_time=1.859e-04, forward_time=0.106, loss_ctc=46.883, loss_att=54.726, acc=0.733, loss=52.373, backward_time=0.098, grad_norm=40.474, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.789e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 18:59:42,329 (trainer:737) INFO: 28epoch:train:13001-13100batch: iter_time=2.017e-04, forward_time=0.105, loss_ctc=36.403, loss_att=41.037, acc=0.752, loss=39.647, backward_time=0.097, grad_norm=39.140, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.789e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 19:00:24,658 (trainer:737) INFO: 28epoch:train:13101-13200batch: iter_time=2.173e-04, forward_time=0.106, loss_ctc=42.822, loss_att=49.048, acc=0.723, loss=47.180, backward_time=0.097, grad_norm=42.529, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.788e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 19:01:07,627 (trainer:737) INFO: 28epoch:train:13201-13300batch: iter_time=2.110e-04, forward_time=0.105, loss_ctc=43.881, loss_att=43.843, acc=0.749, loss=43.854, backward_time=0.098, grad_norm=36.857, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.788e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 19:01:50,328 (trainer:737) INFO: 28epoch:train:13301-13400batch: iter_time=2.091e-04, forward_time=0.105, loss_ctc=48.572, loss_att=45.616, acc=0.741, loss=46.502, backward_time=0.097, grad_norm=43.341, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.787e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 19:02:33,078 (trainer:737) INFO: 28epoch:train:13401-13500batch: iter_time=2.044e-04, forward_time=0.105, loss_ctc=43.374, loss_att=56.103, acc=0.718, loss=52.285, backward_time=0.098, grad_norm=42.371, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.787e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 19:03:16,622 (trainer:737) INFO: 28epoch:train:13501-13600batch: iter_time=1.914e-04, forward_time=0.106, loss_ctc=42.009, loss_att=55.210, acc=0.722, loss=51.250, backward_time=0.098, grad_norm=39.573, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.787e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-15 19:03:59,696 (trainer:737) INFO: 28epoch:train:13601-13700batch: iter_time=1.732e-04, forward_time=0.105, loss_ctc=49.559, loss_att=56.572, acc=0.722, loss=54.468, backward_time=0.097, grad_norm=41.199, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.786e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 19:04:27,102 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 19:04:47,038 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 19:04:50,698 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e98ffbf70>)
+[gpuc02:0/16] 2024-01-15 19:04:50,699 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 19:04:50,702 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 19:09:54,136 (trainer:737) INFO: 28epoch:train:13701-13800batch: iter_time=2.965, forward_time=0.105, loss_ctc=40.975, loss_att=52.402, acc=0.739, loss=48.974, backward_time=0.097, grad_norm=37.999, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.786e-04, train_time=3.544
+[gpuc02:0/16] 2024-01-15 19:10:36,644 (trainer:737) INFO: 28epoch:train:13801-13900batch: iter_time=1.175e-04, forward_time=0.104, loss_ctc=43.893, loss_att=58.916, acc=0.723, loss=54.409, backward_time=0.098, grad_norm=44.787, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.785e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 19:11:19,576 (trainer:737) INFO: 28epoch:train:13901-14000batch: iter_time=1.205e-04, forward_time=0.104, loss_ctc=52.694, loss_att=64.211, acc=0.729, loss=60.756, backward_time=0.098, grad_norm=56.588, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.785e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 19:12:02,014 (trainer:737) INFO: 28epoch:train:14001-14100batch: iter_time=1.348e-04, forward_time=0.105, loss_ctc=45.352, loss_att=50.574, acc=0.747, loss=49.007, backward_time=0.099, grad_norm=47.082, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.784e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 19:12:23,257 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 19:12:44,603 (trainer:737) INFO: 28epoch:train:14101-14200batch: iter_time=1.436e-04, forward_time=0.105, loss_ctc=46.778, loss_att=53.708, acc=0.744, loss=51.629, backward_time=0.099, grad_norm=43.119, clip=100.000, loss_scale=4.280e+34, optim_step_time=0.042, optim0_lr0=3.784e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 19:13:26,969 (trainer:737) INFO: 28epoch:train:14201-14300batch: iter_time=1.395e-04, forward_time=0.105, loss_ctc=43.986, loss_att=56.943, acc=0.719, loss=53.056, backward_time=0.098, grad_norm=40.590, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.783e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 19:14:09,233 (trainer:737) INFO: 28epoch:train:14301-14400batch: iter_time=1.393e-04, forward_time=0.104, loss_ctc=42.167, loss_att=45.643, acc=0.742, loss=44.600, backward_time=0.098, grad_norm=43.676, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.783e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 19:14:51,216 (trainer:737) INFO: 28epoch:train:14401-14500batch: iter_time=1.399e-04, forward_time=0.104, loss_ctc=38.726, loss_att=38.420, acc=0.747, loss=38.512, backward_time=0.097, grad_norm=36.321, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.782e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 19:15:33,377 (trainer:737) INFO: 28epoch:train:14501-14600batch: iter_time=1.354e-04, forward_time=0.104, loss_ctc=41.497, loss_att=43.500, acc=0.745, loss=42.899, backward_time=0.098, grad_norm=37.397, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.782e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 19:16:15,564 (trainer:737) INFO: 28epoch:train:14601-14700batch: iter_time=1.087e-04, forward_time=0.104, loss_ctc=48.049, loss_att=49.650, acc=0.730, loss=49.170, backward_time=0.097, grad_norm=47.734, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.782e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 19:16:57,857 (trainer:737) INFO: 28epoch:train:14701-14800batch: iter_time=1.188e-04, forward_time=0.105, loss_ctc=43.754, loss_att=55.695, acc=0.730, loss=52.113, backward_time=0.097, grad_norm=38.856, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.781e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 19:17:40,234 (trainer:737) INFO: 28epoch:train:14801-14900batch: iter_time=1.242e-04, forward_time=0.105, loss_ctc=47.891, loss_att=58.469, acc=0.706, loss=55.296, backward_time=0.098, grad_norm=42.226, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.781e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 19:18:22,476 (trainer:737) INFO: 28epoch:train:14901-15000batch: iter_time=1.220e-04, forward_time=0.104, loss_ctc=45.818, loss_att=53.831, acc=0.742, loss=51.427, backward_time=0.097, grad_norm=41.368, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.780e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 19:38:35,909 (trainer:343) INFO: 28epoch results: [train] iter_time=0.228, forward_time=0.106, loss_ctc=45.769, loss_att=53.171, acc=0.726, loss=50.950, backward_time=0.098, grad_norm=44.090, clip=100.000, loss_scale=2.792e+34, optim_step_time=0.042, optim0_lr0=3.814e-04, train_time=0.676, time=2 hours, 49 minutes and 17.03 seconds, total_count=420000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=54.180, cer_ctc=0.272, loss_att=52.266, acc=0.596, cer=0.381, wer=0.998, loss=52.840, time=20 minutes and 0.81 seconds, total_count=130788, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 19:38:41,828 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-15 19:38:41,848 (trainer:272) INFO: 29/45epoch started. Estimated time to finish: 2 days, 4 hours and 19 minutes
+[gpuc02:0/16] 2024-01-15 19:38:41,857 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 19:39:00,841 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 19:39:04,273 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d5806cc40>)
+[gpuc02:0/16] 2024-01-15 19:39:04,273 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-15 19:39:04,276 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 19:43:45,403 (trainer:737) INFO: 29epoch:train:1-100batch: iter_time=2.483, forward_time=0.106, loss_ctc=42.373, loss_att=38.067, acc=0.775, loss=39.359, backward_time=0.098, grad_norm=41.020, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.780e-04, train_time=3.035
+[gpuc02:0/16] 2024-01-15 19:44:31,576 (trainer:737) INFO: 29epoch:train:101-200batch: iter_time=1.301e-04, forward_time=0.104, loss_ctc=49.722, loss_att=56.746, acc=0.720, loss=54.639, backward_time=0.098, grad_norm=50.115, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.779e-04, train_time=0.462
+[gpuc02:0/16] 2024-01-15 19:45:18,342 (trainer:737) INFO: 29epoch:train:201-300batch: iter_time=1.242e-04, forward_time=0.107, loss_ctc=48.799, loss_att=53.732, acc=0.735, loss=52.252, backward_time=0.098, grad_norm=44.185, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.779e-04, train_time=0.467
+[gpuc02:0/16] 2024-01-15 19:46:00,684 (trainer:737) INFO: 29epoch:train:301-400batch: iter_time=1.293e-04, forward_time=0.105, loss_ctc=59.670, loss_att=57.657, acc=0.714, loss=58.261, backward_time=0.098, grad_norm=56.693, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.778e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 19:46:46,493 (trainer:737) INFO: 29epoch:train:401-500batch: iter_time=1.347e-04, forward_time=0.113, loss_ctc=41.858, loss_att=52.848, acc=0.711, loss=49.551, backward_time=0.098, grad_norm=43.769, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.778e-04, train_time=0.458
+[gpuc02:0/16] 2024-01-15 19:46:57,405 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 19:47:35,864 (trainer:737) INFO: 29epoch:train:501-600batch: iter_time=1.430e-04, forward_time=0.134, loss_ctc=46.485, loss_att=48.573, acc=0.724, loss=47.946, backward_time=0.112, grad_norm=50.341, clip=100.000, loss_scale=2.601e+34, optim_step_time=0.048, optim0_lr0=3.778e-04, train_time=0.493
+[gpuc02:0/16] 2024-01-15 19:48:22,576 (trainer:737) INFO: 29epoch:train:601-700batch: iter_time=1.255e-04, forward_time=0.122, loss_ctc=50.538, loss_att=52.621, acc=0.734, loss=51.996, backward_time=0.108, grad_norm=44.455, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.046, optim0_lr0=3.777e-04, train_time=0.467
+[gpuc02:0/16] 2024-01-15 19:49:08,778 (trainer:737) INFO: 29epoch:train:701-800batch: iter_time=1.387e-04, forward_time=0.130, loss_ctc=51.424, loss_att=57.775, acc=0.708, loss=55.870, backward_time=0.104, grad_norm=48.466, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.045, optim0_lr0=3.777e-04, train_time=0.462
+[gpuc02:0/16] 2024-01-15 19:50:02,411 (trainer:737) INFO: 29epoch:train:801-900batch: iter_time=1.184e-04, forward_time=0.122, loss_ctc=59.682, loss_att=57.403, acc=0.732, loss=58.086, backward_time=0.115, grad_norm=59.119, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.776e-04, train_time=0.536
+[gpuc02:0/16] 2024-01-15 19:50:46,498 (trainer:737) INFO: 29epoch:train:901-1000batch: iter_time=1.090e-04, forward_time=0.121, loss_ctc=43.843, loss_att=45.772, acc=0.732, loss=45.193, backward_time=0.100, grad_norm=40.661, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.776e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-15 19:51:33,753 (trainer:737) INFO: 29epoch:train:1001-1100batch: iter_time=1.132e-04, forward_time=0.104, loss_ctc=43.951, loss_att=50.000, acc=0.741, loss=48.185, backward_time=0.097, grad_norm=39.912, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.775e-04, train_time=0.472
+[gpuc02:0/16] 2024-01-15 19:52:18,336 (trainer:737) INFO: 29epoch:train:1101-1200batch: iter_time=1.127e-04, forward_time=0.103, loss_ctc=48.893, loss_att=55.535, acc=0.722, loss=53.542, backward_time=0.097, grad_norm=53.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.775e-04, train_time=0.446
+[gpuc02:0/16] 2024-01-15 19:53:12,668 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 19:53:31,409 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 19:53:34,896 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d3597b0>)
+[gpuc02:0/16] 2024-01-15 19:53:34,896 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-15 19:53:34,899 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 20:01:22,973 (trainer:737) INFO: 29epoch:train:1201-1300batch: iter_time=4.721, forward_time=0.103, loss_ctc=38.990, loss_att=48.295, acc=0.738, loss=45.504, backward_time=0.097, grad_norm=38.062, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.774e-04, train_time=5.446
+[gpuc02:0/16] 2024-01-15 20:02:05,502 (trainer:737) INFO: 29epoch:train:1301-1400batch: iter_time=1.069e-04, forward_time=0.104, loss_ctc=43.818, loss_att=47.009, acc=0.739, loss=46.051, backward_time=0.096, grad_norm=44.449, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.774e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:02:48,323 (trainer:737) INFO: 29epoch:train:1401-1500batch: iter_time=1.225e-04, forward_time=0.103, loss_ctc=54.850, loss_att=57.620, acc=0.729, loss=56.789, backward_time=0.097, grad_norm=49.799, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.774e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 20:03:30,909 (trainer:737) INFO: 29epoch:train:1501-1600batch: iter_time=1.297e-04, forward_time=0.104, loss_ctc=44.462, loss_att=47.614, acc=0.737, loss=46.669, backward_time=0.097, grad_norm=43.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.773e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 20:04:13,448 (trainer:737) INFO: 29epoch:train:1601-1700batch: iter_time=1.292e-04, forward_time=0.103, loss_ctc=54.241, loss_att=60.551, acc=0.701, loss=58.658, backward_time=0.097, grad_norm=52.449, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.773e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:04:55,755 (trainer:737) INFO: 29epoch:train:1701-1800batch: iter_time=1.314e-04, forward_time=0.103, loss_ctc=37.289, loss_att=36.687, acc=0.750, loss=36.868, backward_time=0.097, grad_norm=44.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.772e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:05:38,453 (trainer:737) INFO: 29epoch:train:1801-1900batch: iter_time=1.327e-04, forward_time=0.104, loss_ctc=51.504, loss_att=53.565, acc=0.734, loss=52.946, backward_time=0.098, grad_norm=44.164, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.772e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 20:06:20,831 (trainer:737) INFO: 29epoch:train:1901-2000batch: iter_time=1.426e-04, forward_time=0.104, loss_ctc=44.957, loss_att=53.937, acc=0.704, loss=51.243, backward_time=0.097, grad_norm=43.534, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.771e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:07:03,418 (trainer:737) INFO: 29epoch:train:2001-2100batch: iter_time=1.079e-04, forward_time=0.106, loss_ctc=54.969, loss_att=51.612, acc=0.737, loss=52.619, backward_time=0.097, grad_norm=47.592, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.771e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 20:07:45,783 (trainer:737) INFO: 29epoch:train:2101-2200batch: iter_time=1.235e-04, forward_time=0.104, loss_ctc=53.166, loss_att=59.083, acc=0.716, loss=57.308, backward_time=0.097, grad_norm=45.659, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.770e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:08:28,026 (trainer:737) INFO: 29epoch:train:2201-2300batch: iter_time=1.119e-04, forward_time=0.103, loss_ctc=41.362, loss_att=47.560, acc=0.738, loss=45.701, backward_time=0.096, grad_norm=40.023, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.770e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:09:10,365 (trainer:737) INFO: 29epoch:train:2301-2400batch: iter_time=1.178e-04, forward_time=0.104, loss_ctc=50.051, loss_att=55.292, acc=0.732, loss=53.720, backward_time=0.097, grad_norm=48.705, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.769e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:09:52,573 (trainer:737) INFO: 29epoch:train:2401-2500batch: iter_time=1.179e-04, forward_time=0.103, loss_ctc=38.923, loss_att=50.948, acc=0.707, loss=47.340, backward_time=0.097, grad_norm=39.484, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.769e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:09:57,811 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 20:10:17,467 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 20:10:21,072 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8c1baa10>)
+[gpuc02:0/16] 2024-01-15 20:10:21,072 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 20:10:21,075 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 20:15:19,817 (trainer:737) INFO: 29epoch:train:2501-2600batch: iter_time=2.552, forward_time=0.104, loss_ctc=41.024, loss_att=38.863, acc=0.778, loss=39.511, backward_time=0.098, grad_norm=39.953, clip=100.000, loss_scale=3.614e+34, optim_step_time=0.042, optim0_lr0=3.769e-04, train_time=3.272
+[gpuc02:0/16] 2024-01-15 20:16:02,852 (trainer:737) INFO: 29epoch:train:2601-2700batch: iter_time=1.342e-04, forward_time=0.106, loss_ctc=47.892, loss_att=59.926, acc=0.719, loss=56.316, backward_time=0.098, grad_norm=49.368, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.768e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 20:16:45,688 (trainer:737) INFO: 29epoch:train:2701-2800batch: iter_time=1.257e-04, forward_time=0.105, loss_ctc=47.236, loss_att=51.823, acc=0.748, loss=50.447, backward_time=0.098, grad_norm=43.134, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.768e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 20:17:28,017 (trainer:737) INFO: 29epoch:train:2801-2900batch: iter_time=1.214e-04, forward_time=0.106, loss_ctc=54.796, loss_att=56.895, acc=0.722, loss=56.265, backward_time=0.098, grad_norm=53.844, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.767e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:18:11,231 (trainer:737) INFO: 29epoch:train:2901-3000batch: iter_time=1.233e-04, forward_time=0.105, loss_ctc=40.675, loss_att=51.593, acc=0.730, loss=48.317, backward_time=0.098, grad_norm=40.496, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.767e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 20:18:53,798 (trainer:737) INFO: 29epoch:train:3001-3100batch: iter_time=1.205e-04, forward_time=0.106, loss_ctc=43.766, loss_att=46.875, acc=0.742, loss=45.942, backward_time=0.098, grad_norm=44.155, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.766e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:19:36,489 (trainer:737) INFO: 29epoch:train:3101-3200batch: iter_time=1.176e-04, forward_time=0.106, loss_ctc=48.628, loss_att=52.649, acc=0.740, loss=51.443, backward_time=0.099, grad_norm=42.938, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.766e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 20:20:18,692 (trainer:737) INFO: 29epoch:train:3201-3300batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=50.695, loss_att=59.914, acc=0.711, loss=57.148, backward_time=0.098, grad_norm=42.169, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.765e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:21:01,233 (trainer:737) INFO: 29epoch:train:3301-3400batch: iter_time=1.310e-04, forward_time=0.104, loss_ctc=55.946, loss_att=55.933, acc=0.744, loss=55.937, backward_time=0.098, grad_norm=52.516, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.765e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:21:28,420 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 20:21:43,552 (trainer:737) INFO: 29epoch:train:3401-3500batch: iter_time=1.351e-04, forward_time=0.105, loss_ctc=42.819, loss_att=44.854, acc=0.741, loss=44.243, backward_time=0.098, grad_norm=38.455, clip=100.000, loss_scale=3.399e+34, optim_step_time=0.042, optim0_lr0=3.765e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:22:25,775 (trainer:737) INFO: 29epoch:train:3501-3600batch: iter_time=1.368e-04, forward_time=0.104, loss_ctc=43.271, loss_att=48.799, acc=0.753, loss=47.141, backward_time=0.098, grad_norm=37.708, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.764e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:23:08,100 (trainer:737) INFO: 29epoch:train:3601-3700batch: iter_time=1.224e-04, forward_time=0.105, loss_ctc=46.601, loss_att=56.294, acc=0.727, loss=53.386, backward_time=0.099, grad_norm=48.724, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.764e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:23:34,860 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 20:23:53,784 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 20:23:57,535 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f248a897d00>)
+[gpuc02:0/16] 2024-01-15 20:23:57,535 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 20:23:57,538 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 20:28:25,918 (trainer:737) INFO: 29epoch:train:3701-3800batch: iter_time=2.602, forward_time=0.104, loss_ctc=38.168, loss_att=47.486, acc=0.749, loss=44.691, backward_time=0.098, grad_norm=36.653, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.763e-04, train_time=3.178
+[gpuc02:0/16] 2024-01-15 20:29:08,261 (trainer:737) INFO: 29epoch:train:3801-3900batch: iter_time=1.203e-04, forward_time=0.104, loss_ctc=42.478, loss_att=50.111, acc=0.736, loss=47.821, backward_time=0.097, grad_norm=44.024, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.763e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:29:50,635 (trainer:737) INFO: 29epoch:train:3901-4000batch: iter_time=1.247e-04, forward_time=0.104, loss_ctc=52.904, loss_att=57.122, acc=0.742, loss=55.857, backward_time=0.097, grad_norm=47.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.762e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:30:33,032 (trainer:737) INFO: 29epoch:train:4001-4100batch: iter_time=1.228e-04, forward_time=0.104, loss_ctc=43.256, loss_att=46.828, acc=0.745, loss=45.757, backward_time=0.097, grad_norm=41.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.762e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:31:15,570 (trainer:737) INFO: 29epoch:train:4101-4200batch: iter_time=1.483e-04, forward_time=0.105, loss_ctc=53.688, loss_att=60.676, acc=0.712, loss=58.579, backward_time=0.098, grad_norm=50.128, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.761e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:31:58,636 (trainer:737) INFO: 29epoch:train:4201-4300batch: iter_time=1.548e-04, forward_time=0.104, loss_ctc=36.020, loss_att=37.348, acc=0.756, loss=36.950, backward_time=0.097, grad_norm=42.495, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.761e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 20:32:41,384 (trainer:737) INFO: 29epoch:train:4301-4400batch: iter_time=1.369e-04, forward_time=0.106, loss_ctc=50.704, loss_att=53.177, acc=0.746, loss=52.435, backward_time=0.099, grad_norm=44.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.761e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 20:33:24,012 (trainer:737) INFO: 29epoch:train:4401-4500batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=44.065, loss_att=56.590, acc=0.708, loss=52.832, backward_time=0.097, grad_norm=42.322, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.760e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 20:34:06,847 (trainer:737) INFO: 29epoch:train:4501-4600batch: iter_time=1.253e-04, forward_time=0.104, loss_ctc=54.163, loss_att=51.392, acc=0.745, loss=52.223, backward_time=0.097, grad_norm=47.479, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.760e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 20:34:49,537 (trainer:737) INFO: 29epoch:train:4601-4700batch: iter_time=1.252e-04, forward_time=0.105, loss_ctc=52.001, loss_att=58.478, acc=0.729, loss=56.535, backward_time=0.097, grad_norm=44.652, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.759e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 20:35:32,078 (trainer:737) INFO: 29epoch:train:4701-4800batch: iter_time=1.176e-04, forward_time=0.103, loss_ctc=40.533, loss_att=48.101, acc=0.741, loss=45.830, backward_time=0.096, grad_norm=38.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.759e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:36:14,710 (trainer:737) INFO: 29epoch:train:4801-4900batch: iter_time=1.062e-04, forward_time=0.105, loss_ctc=48.626, loss_att=54.404, acc=0.747, loss=52.671, backward_time=0.097, grad_norm=46.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.758e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 20:36:56,935 (trainer:737) INFO: 29epoch:train:4901-5000batch: iter_time=1.029e-04, forward_time=0.104, loss_ctc=38.152, loss_att=50.392, acc=0.724, loss=46.720, backward_time=0.097, grad_norm=37.946, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.758e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:37:00,909 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 20:37:20,960 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 20:37:24,974 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2539e2b850>)
+[gpuc02:0/16] 2024-01-15 20:37:24,974 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 20:37:24,977 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 20:42:10,613 (trainer:737) INFO: 29epoch:train:5001-5100batch: iter_time=2.552, forward_time=0.104, loss_ctc=40.097, loss_att=37.828, acc=0.782, loss=38.509, backward_time=0.098, grad_norm=40.435, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.757e-04, train_time=3.137
+[gpuc02:0/16] 2024-01-15 20:42:53,436 (trainer:737) INFO: 29epoch:train:5101-5200batch: iter_time=1.350e-04, forward_time=0.108, loss_ctc=47.500, loss_att=56.736, acc=0.725, loss=53.965, backward_time=0.098, grad_norm=44.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.757e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 20:43:35,813 (trainer:737) INFO: 29epoch:train:5201-5300batch: iter_time=1.314e-04, forward_time=0.106, loss_ctc=45.895, loss_att=50.653, acc=0.750, loss=49.226, backward_time=0.098, grad_norm=42.175, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.757e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:44:18,582 (trainer:737) INFO: 29epoch:train:5301-5400batch: iter_time=1.474e-04, forward_time=0.106, loss_ctc=53.493, loss_att=56.270, acc=0.724, loss=55.437, backward_time=0.098, grad_norm=52.918, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.756e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 20:44:46,581 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 20:45:01,078 (trainer:737) INFO: 29epoch:train:5401-5500batch: iter_time=1.382e-04, forward_time=0.105, loss_ctc=40.560, loss_att=50.666, acc=0.733, loss=47.634, backward_time=0.098, grad_norm=40.239, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.042, optim0_lr0=3.756e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:45:43,599 (trainer:737) INFO: 29epoch:train:5501-5600batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=43.297, loss_att=46.380, acc=0.743, loss=45.455, backward_time=0.098, grad_norm=43.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.755e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:46:26,105 (trainer:737) INFO: 29epoch:train:5601-5700batch: iter_time=1.405e-04, forward_time=0.106, loss_ctc=47.991, loss_att=51.922, acc=0.741, loss=50.743, backward_time=0.099, grad_norm=41.004, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.755e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:47:08,423 (trainer:737) INFO: 29epoch:train:5701-5800batch: iter_time=1.659e-04, forward_time=0.105, loss_ctc=50.058, loss_att=59.605, acc=0.712, loss=56.741, backward_time=0.098, grad_norm=42.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.754e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:47:50,841 (trainer:737) INFO: 29epoch:train:5801-5900batch: iter_time=1.605e-04, forward_time=0.105, loss_ctc=53.771, loss_att=55.254, acc=0.746, loss=54.809, backward_time=0.098, grad_norm=48.559, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.754e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:48:33,093 (trainer:737) INFO: 29epoch:train:5901-6000batch: iter_time=1.340e-04, forward_time=0.105, loss_ctc=42.603, loss_att=44.319, acc=0.742, loss=43.804, backward_time=0.098, grad_norm=38.538, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.754e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 20:49:16,248 (trainer:737) INFO: 29epoch:train:6001-6100batch: iter_time=1.417e-04, forward_time=0.105, loss_ctc=42.697, loss_att=48.206, acc=0.756, loss=46.553, backward_time=0.098, grad_norm=39.458, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.753e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-15 20:49:59,620 (trainer:737) INFO: 29epoch:train:6101-6200batch: iter_time=1.580e-04, forward_time=0.104, loss_ctc=46.094, loss_att=55.906, acc=0.728, loss=52.963, backward_time=0.097, grad_norm=49.182, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.753e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 20:50:26,866 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 20:50:46,028 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 20:50:49,621 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ea5892b00>)
+[gpuc02:0/16] 2024-01-15 20:50:49,621 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 20:50:49,625 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 20:55:04,485 (trainer:737) INFO: 29epoch:train:6201-6300batch: iter_time=2.558, forward_time=0.105, loss_ctc=38.083, loss_att=46.984, acc=0.750, loss=44.314, backward_time=0.097, grad_norm=35.332, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.752e-04, train_time=3.048
+[gpuc02:0/16] 2024-01-15 20:55:46,822 (trainer:737) INFO: 29epoch:train:6301-6400batch: iter_time=1.125e-04, forward_time=0.104, loss_ctc=42.335, loss_att=49.676, acc=0.739, loss=47.474, backward_time=0.097, grad_norm=43.415, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.752e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 20:56:29,307 (trainer:737) INFO: 29epoch:train:6401-6500batch: iter_time=1.187e-04, forward_time=0.104, loss_ctc=51.847, loss_att=56.354, acc=0.744, loss=55.002, backward_time=0.097, grad_norm=48.384, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.751e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:57:11,729 (trainer:737) INFO: 29epoch:train:6501-6600batch: iter_time=1.194e-04, forward_time=0.104, loss_ctc=43.211, loss_att=46.922, acc=0.746, loss=45.808, backward_time=0.097, grad_norm=41.910, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.751e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 20:57:54,227 (trainer:737) INFO: 29epoch:train:6601-6700batch: iter_time=1.257e-04, forward_time=0.104, loss_ctc=52.541, loss_att=59.649, acc=0.715, loss=57.517, backward_time=0.098, grad_norm=48.137, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.750e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 20:58:36,221 (trainer:737) INFO: 29epoch:train:6701-6800batch: iter_time=1.082e-04, forward_time=0.103, loss_ctc=35.276, loss_att=36.903, acc=0.758, loss=36.415, backward_time=0.096, grad_norm=42.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.750e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-15 20:59:18,709 (trainer:737) INFO: 29epoch:train:6801-6900batch: iter_time=1.214e-04, forward_time=0.104, loss_ctc=50.703, loss_att=52.529, acc=0.746, loss=51.982, backward_time=0.098, grad_norm=45.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.750e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:00:01,018 (trainer:737) INFO: 29epoch:train:6901-7000batch: iter_time=1.306e-04, forward_time=0.103, loss_ctc=44.231, loss_att=56.720, acc=0.709, loss=52.973, backward_time=0.097, grad_norm=41.309, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.749e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:00:43,376 (trainer:737) INFO: 29epoch:train:7001-7100batch: iter_time=1.340e-04, forward_time=0.104, loss_ctc=52.830, loss_att=50.952, acc=0.747, loss=51.515, backward_time=0.097, grad_norm=46.201, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.749e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:01:26,048 (trainer:737) INFO: 29epoch:train:7101-7200batch: iter_time=1.289e-04, forward_time=0.107, loss_ctc=51.614, loss_att=57.783, acc=0.730, loss=55.933, backward_time=0.098, grad_norm=46.171, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.748e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 21:02:08,552 (trainer:737) INFO: 29epoch:train:7201-7300batch: iter_time=9.903e-05, forward_time=0.104, loss_ctc=40.865, loss_att=48.181, acc=0.741, loss=45.986, backward_time=0.097, grad_norm=39.643, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.748e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:02:51,417 (trainer:737) INFO: 29epoch:train:7301-7400batch: iter_time=9.676e-05, forward_time=0.104, loss_ctc=49.016, loss_att=54.995, acc=0.747, loss=53.201, backward_time=0.097, grad_norm=48.759, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.747e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 21:03:33,637 (trainer:737) INFO: 29epoch:train:7401-7500batch: iter_time=1.033e-04, forward_time=0.104, loss_ctc=37.956, loss_att=50.224, acc=0.725, loss=46.543, backward_time=0.097, grad_norm=39.238, clip=100.000, loss_scale=2.783e+34, optim_step_time=0.041, optim0_lr0=3.747e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:03:38,992 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-15 21:03:58,494 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 21:04:02,094 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f254f58ed40>)
+[gpuc02:0/16] 2024-01-15 21:04:02,094 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 21:04:02,098 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 21:08:54,411 (trainer:737) INFO: 29epoch:train:7501-7600batch: iter_time=2.557, forward_time=0.105, loss_ctc=40.566, loss_att=39.673, acc=0.775, loss=39.941, backward_time=0.097, grad_norm=41.454, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.746e-04, train_time=3.208
+[gpuc02:0/16] 2024-01-15 21:09:36,952 (trainer:737) INFO: 29epoch:train:7601-7700batch: iter_time=1.241e-04, forward_time=0.104, loss_ctc=46.888, loss_att=57.643, acc=0.718, loss=54.417, backward_time=0.098, grad_norm=46.544, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.746e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:10:19,565 (trainer:737) INFO: 29epoch:train:7701-7800batch: iter_time=1.268e-04, forward_time=0.105, loss_ctc=45.093, loss_att=52.732, acc=0.737, loss=50.440, backward_time=0.098, grad_norm=43.481, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.746e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 21:11:02,035 (trainer:737) INFO: 29epoch:train:7801-7900batch: iter_time=1.438e-04, forward_time=0.105, loss_ctc=53.616, loss_att=56.218, acc=0.722, loss=55.438, backward_time=0.098, grad_norm=51.802, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.745e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:11:44,264 (trainer:737) INFO: 29epoch:train:7901-8000batch: iter_time=1.379e-04, forward_time=0.104, loss_ctc=40.050, loss_att=52.087, acc=0.716, loss=48.476, backward_time=0.098, grad_norm=40.264, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.745e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:12:26,555 (trainer:737) INFO: 29epoch:train:8001-8100batch: iter_time=1.343e-04, forward_time=0.104, loss_ctc=42.854, loss_att=47.378, acc=0.730, loss=46.021, backward_time=0.098, grad_norm=47.256, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.744e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:13:09,312 (trainer:737) INFO: 29epoch:train:8101-8200batch: iter_time=1.291e-04, forward_time=0.106, loss_ctc=47.908, loss_att=52.038, acc=0.734, loss=50.799, backward_time=0.098, grad_norm=43.402, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.744e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 21:13:51,965 (trainer:737) INFO: 29epoch:train:8201-8300batch: iter_time=1.264e-04, forward_time=0.105, loss_ctc=49.734, loss_att=57.164, acc=0.714, loss=54.935, backward_time=0.098, grad_norm=43.378, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.743e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 21:14:34,425 (trainer:737) INFO: 29epoch:train:8301-8400batch: iter_time=1.557e-04, forward_time=0.105, loss_ctc=54.010, loss_att=56.431, acc=0.735, loss=55.705, backward_time=0.098, grad_norm=51.488, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.743e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:15:16,669 (trainer:737) INFO: 29epoch:train:8401-8500batch: iter_time=1.597e-04, forward_time=0.105, loss_ctc=42.395, loss_att=45.108, acc=0.736, loss=44.294, backward_time=0.097, grad_norm=40.037, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.743e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:15:59,016 (trainer:737) INFO: 29epoch:train:8501-8600batch: iter_time=1.591e-04, forward_time=0.105, loss_ctc=41.933, loss_att=49.166, acc=0.744, loss=46.996, backward_time=0.098, grad_norm=36.533, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.742e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:16:41,380 (trainer:737) INFO: 29epoch:train:8601-8700batch: iter_time=1.620e-04, forward_time=0.105, loss_ctc=45.146, loss_att=55.281, acc=0.720, loss=52.241, backward_time=0.098, grad_norm=49.313, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.742e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:17:02,022 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 21:17:06,599 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-15 21:17:26,125 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 21:17:29,698 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2540aebe20>)
+[gpuc02:0/16] 2024-01-15 21:17:29,698 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-15 21:17:29,701 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 21:21:53,540 (trainer:737) INFO: 29epoch:train:8701-8800batch: iter_time=2.546, forward_time=0.105, loss_ctc=37.355, loss_att=47.609, acc=0.744, loss=44.533, backward_time=0.097, grad_norm=34.955, clip=100.000, loss_scale=3.084e+34, optim_step_time=0.042, optim0_lr0=3.741e-04, train_time=3.121
+[gpuc02:0/16] 2024-01-15 21:22:35,968 (trainer:737) INFO: 29epoch:train:8801-8900batch: iter_time=9.792e-05, forward_time=0.104, loss_ctc=41.968, loss_att=51.592, acc=0.736, loss=48.705, backward_time=0.097, grad_norm=44.422, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.741e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:23:18,731 (trainer:737) INFO: 29epoch:train:8901-9000batch: iter_time=1.024e-04, forward_time=0.105, loss_ctc=51.910, loss_att=57.812, acc=0.744, loss=56.041, backward_time=0.098, grad_norm=47.734, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.740e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 21:24:01,220 (trainer:737) INFO: 29epoch:train:9001-9100batch: iter_time=1.021e-04, forward_time=0.105, loss_ctc=42.360, loss_att=46.379, acc=0.746, loss=45.173, backward_time=0.097, grad_norm=41.886, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.740e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:24:44,107 (trainer:737) INFO: 29epoch:train:9101-9200batch: iter_time=1.041e-04, forward_time=0.105, loss_ctc=52.249, loss_att=59.541, acc=0.717, loss=57.353, backward_time=0.098, grad_norm=48.331, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.740e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 21:25:26,586 (trainer:737) INFO: 29epoch:train:9201-9300batch: iter_time=1.126e-04, forward_time=0.103, loss_ctc=35.238, loss_att=37.408, acc=0.757, loss=36.757, backward_time=0.096, grad_norm=40.794, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.739e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:26:10,246 (trainer:737) INFO: 29epoch:train:9301-9400batch: iter_time=1.019e-04, forward_time=0.105, loss_ctc=49.945, loss_att=51.936, acc=0.749, loss=51.339, backward_time=0.097, grad_norm=45.313, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.739e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-15 21:26:52,646 (trainer:737) INFO: 29epoch:train:9401-9500batch: iter_time=1.106e-04, forward_time=0.105, loss_ctc=43.443, loss_att=56.443, acc=0.708, loss=52.543, backward_time=0.097, grad_norm=40.166, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.738e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:27:35,547 (trainer:737) INFO: 29epoch:train:9501-9600batch: iter_time=9.943e-05, forward_time=0.105, loss_ctc=53.069, loss_att=51.082, acc=0.746, loss=51.678, backward_time=0.097, grad_norm=47.669, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.738e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 21:28:18,813 (trainer:737) INFO: 29epoch:train:9601-9700batch: iter_time=1.169e-04, forward_time=0.106, loss_ctc=51.285, loss_att=56.777, acc=0.734, loss=55.129, backward_time=0.098, grad_norm=47.748, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.737e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-15 21:29:02,621 (trainer:737) INFO: 29epoch:train:9701-9800batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=40.322, loss_att=47.955, acc=0.744, loss=45.665, backward_time=0.097, grad_norm=38.960, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.737e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 21:29:45,318 (trainer:737) INFO: 29epoch:train:9801-9900batch: iter_time=1.157e-04, forward_time=0.105, loss_ctc=48.522, loss_att=54.816, acc=0.746, loss=52.928, backward_time=0.097, grad_norm=48.235, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.736e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 21:30:27,582 (trainer:737) INFO: 29epoch:train:9901-10000batch: iter_time=1.007e-04, forward_time=0.105, loss_ctc=37.788, loss_att=51.465, acc=0.721, loss=47.362, backward_time=0.097, grad_norm=38.953, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.736e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:30:30,033 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-15 21:30:50,156 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 21:30:53,837 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e80402fb0>)
+[gpuc02:0/16] 2024-01-15 21:30:53,838 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-15 21:30:53,841 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 21:35:50,113 (trainer:737) INFO: 29epoch:train:10001-10100batch: iter_time=2.499, forward_time=0.103, loss_ctc=39.631, loss_att=38.771, acc=0.778, loss=39.029, backward_time=0.096, grad_norm=39.711, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.736e-04, train_time=3.225
+[gpuc02:0/16] 2024-01-15 21:36:32,338 (trainer:737) INFO: 29epoch:train:10101-10200batch: iter_time=1.053e-04, forward_time=0.104, loss_ctc=46.578, loss_att=56.271, acc=0.723, loss=53.363, backward_time=0.097, grad_norm=47.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.735e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:37:14,517 (trainer:737) INFO: 29epoch:train:10201-10300batch: iter_time=1.229e-04, forward_time=0.104, loss_ctc=44.397, loss_att=52.304, acc=0.738, loss=49.932, backward_time=0.097, grad_norm=40.986, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.735e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:37:56,733 (trainer:737) INFO: 29epoch:train:10301-10400batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=52.465, loss_att=55.184, acc=0.723, loss=54.368, backward_time=0.097, grad_norm=54.524, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.734e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:38:38,825 (trainer:737) INFO: 29epoch:train:10401-10500batch: iter_time=1.523e-04, forward_time=0.103, loss_ctc=39.597, loss_att=51.077, acc=0.718, loss=47.633, backward_time=0.096, grad_norm=40.765, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.734e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-15 21:39:21,165 (trainer:737) INFO: 29epoch:train:10501-10600batch: iter_time=1.289e-04, forward_time=0.104, loss_ctc=42.635, loss_att=47.012, acc=0.732, loss=45.699, backward_time=0.096, grad_norm=45.490, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.733e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:40:03,737 (trainer:737) INFO: 29epoch:train:10601-10700batch: iter_time=1.318e-04, forward_time=0.105, loss_ctc=47.507, loss_att=51.490, acc=0.735, loss=50.295, backward_time=0.097, grad_norm=42.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.733e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:40:46,402 (trainer:737) INFO: 29epoch:train:10701-10800batch: iter_time=1.064e-04, forward_time=0.104, loss_ctc=49.673, loss_att=56.681, acc=0.716, loss=54.578, backward_time=0.096, grad_norm=43.049, clip=100.000, loss_scale=3.136e+34, optim_step_time=0.041, optim0_lr0=3.733e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 21:41:29,314 (trainer:737) INFO: 29epoch:train:10801-10900batch: iter_time=1.065e-04, forward_time=0.104, loss_ctc=53.308, loss_att=55.748, acc=0.737, loss=55.016, backward_time=0.097, grad_norm=49.993, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.732e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 21:41:50,908 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 21:42:11,580 (trainer:737) INFO: 29epoch:train:10901-11000batch: iter_time=1.012e-04, forward_time=0.105, loss_ctc=42.948, loss_att=45.320, acc=0.735, loss=44.608, backward_time=0.097, grad_norm=39.352, clip=100.000, loss_scale=3.126e+34, optim_step_time=0.042, optim0_lr0=3.732e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:42:53,989 (trainer:737) INFO: 29epoch:train:11001-11100batch: iter_time=1.080e-04, forward_time=0.104, loss_ctc=42.519, loss_att=48.831, acc=0.745, loss=46.937, backward_time=0.098, grad_norm=39.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.731e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:43:36,279 (trainer:737) INFO: 29epoch:train:11101-11200batch: iter_time=1.080e-04, forward_time=0.104, loss_ctc=46.028, loss_att=55.300, acc=0.721, loss=52.518, backward_time=0.097, grad_norm=49.883, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.731e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:43:59,920 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-15 21:44:20,839 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 21:44:25,020 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e7286efe0>)
+[gpuc02:0/16] 2024-01-15 21:44:25,020 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-15 21:44:25,024 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 21:48:43,689 (trainer:737) INFO: 29epoch:train:11201-11300batch: iter_time=2.573, forward_time=0.104, loss_ctc=37.420, loss_att=47.609, acc=0.743, loss=44.552, backward_time=0.097, grad_norm=35.965, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.730e-04, train_time=3.074
+[gpuc02:0/16] 2024-01-15 21:49:26,026 (trainer:737) INFO: 29epoch:train:11301-11400batch: iter_time=1.323e-04, forward_time=0.105, loss_ctc=41.374, loss_att=50.794, acc=0.739, loss=47.968, backward_time=0.098, grad_norm=44.130, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.730e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:50:08,526 (trainer:737) INFO: 29epoch:train:11401-11500batch: iter_time=1.398e-04, forward_time=0.105, loss_ctc=50.828, loss_att=57.407, acc=0.744, loss=55.433, backward_time=0.098, grad_norm=47.049, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.730e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:50:51,301 (trainer:737) INFO: 29epoch:train:11501-11600batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=42.461, loss_att=46.846, acc=0.746, loss=45.531, backward_time=0.098, grad_norm=41.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.729e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 21:51:34,044 (trainer:737) INFO: 29epoch:train:11601-11700batch: iter_time=1.188e-04, forward_time=0.105, loss_ctc=51.440, loss_att=60.339, acc=0.715, loss=57.669, backward_time=0.098, grad_norm=48.723, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.729e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 21:52:16,235 (trainer:737) INFO: 29epoch:train:11701-11800batch: iter_time=1.351e-04, forward_time=0.104, loss_ctc=34.438, loss_att=37.035, acc=0.758, loss=36.256, backward_time=0.096, grad_norm=38.764, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.728e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 21:52:58,782 (trainer:737) INFO: 29epoch:train:11801-11900batch: iter_time=1.041e-04, forward_time=0.106, loss_ctc=50.282, loss_att=52.882, acc=0.746, loss=52.102, backward_time=0.098, grad_norm=43.964, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.728e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 21:53:41,187 (trainer:737) INFO: 29epoch:train:11901-12000batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=43.248, loss_att=56.343, acc=0.710, loss=52.415, backward_time=0.097, grad_norm=41.697, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.727e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:54:23,653 (trainer:737) INFO: 29epoch:train:12001-12100batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=52.267, loss_att=51.173, acc=0.747, loss=51.501, backward_time=0.098, grad_norm=45.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.727e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:55:06,092 (trainer:737) INFO: 29epoch:train:12101-12200batch: iter_time=1.036e-04, forward_time=0.105, loss_ctc=51.337, loss_att=58.362, acc=0.730, loss=56.255, backward_time=0.097, grad_norm=45.271, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.727e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:55:49,827 (trainer:737) INFO: 29epoch:train:12201-12300batch: iter_time=1.011e-04, forward_time=0.113, loss_ctc=40.211, loss_att=48.092, acc=0.742, loss=45.728, backward_time=0.100, grad_norm=37.083, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.726e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-15 21:56:32,291 (trainer:737) INFO: 29epoch:train:12301-12400batch: iter_time=1.048e-04, forward_time=0.104, loss_ctc=46.844, loss_att=53.371, acc=0.749, loss=51.413, backward_time=0.097, grad_norm=44.634, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.726e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 21:57:14,589 (trainer:737) INFO: 29epoch:train:12401-12500batch: iter_time=1.028e-04, forward_time=0.104, loss_ctc=37.784, loss_att=50.780, acc=0.725, loss=46.881, backward_time=0.097, grad_norm=38.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.725e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 21:57:17,114 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-15 21:57:37,375 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 21:57:41,085 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e7c5ef820>)
+[gpuc02:0/16] 2024-01-15 21:57:41,085 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-15 21:57:41,088 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 22:02:26,281 (trainer:737) INFO: 29epoch:train:12501-12600batch: iter_time=2.524, forward_time=0.103, loss_ctc=39.627, loss_att=38.215, acc=0.780, loss=38.639, backward_time=0.097, grad_norm=40.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.725e-04, train_time=3.117
+[gpuc02:0/16] 2024-01-15 22:03:08,616 (trainer:737) INFO: 29epoch:train:12601-12700batch: iter_time=1.382e-04, forward_time=0.104, loss_ctc=46.397, loss_att=55.425, acc=0.724, loss=52.717, backward_time=0.097, grad_norm=46.476, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.724e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 22:03:51,264 (trainer:737) INFO: 29epoch:train:12701-12800batch: iter_time=1.165e-04, forward_time=0.104, loss_ctc=44.953, loss_att=51.993, acc=0.741, loss=49.881, backward_time=0.097, grad_norm=42.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.724e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 22:04:33,786 (trainer:737) INFO: 29epoch:train:12801-12900batch: iter_time=1.217e-04, forward_time=0.104, loss_ctc=52.467, loss_att=56.198, acc=0.719, loss=55.079, backward_time=0.097, grad_norm=53.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.723e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 22:05:16,448 (trainer:737) INFO: 29epoch:train:12901-13000batch: iter_time=1.192e-04, forward_time=0.103, loss_ctc=39.930, loss_att=51.407, acc=0.716, loss=47.964, backward_time=0.097, grad_norm=40.002, clip=100.000, loss_scale=3.095e+34, optim_step_time=0.041, optim0_lr0=3.723e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 22:05:46,436 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 22:05:59,495 (trainer:737) INFO: 29epoch:train:13001-13100batch: iter_time=1.344e-04, forward_time=0.107, loss_ctc=41.980, loss_att=46.551, acc=0.732, loss=45.180, backward_time=0.097, grad_norm=43.320, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.041, optim0_lr0=3.723e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 22:06:42,030 (trainer:737) INFO: 29epoch:train:13101-13200batch: iter_time=1.326e-04, forward_time=0.105, loss_ctc=47.378, loss_att=50.520, acc=0.740, loss=49.578, backward_time=0.098, grad_norm=41.651, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.722e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 22:07:24,702 (trainer:737) INFO: 29epoch:train:13201-13300batch: iter_time=1.864e-04, forward_time=0.105, loss_ctc=49.066, loss_att=56.457, acc=0.715, loss=54.240, backward_time=0.098, grad_norm=43.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.722e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 22:08:07,719 (trainer:737) INFO: 29epoch:train:13301-13400batch: iter_time=1.602e-04, forward_time=0.105, loss_ctc=52.717, loss_att=55.521, acc=0.737, loss=54.680, backward_time=0.098, grad_norm=51.822, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.721e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 22:08:50,418 (trainer:737) INFO: 29epoch:train:13401-13500batch: iter_time=1.649e-04, forward_time=0.105, loss_ctc=42.180, loss_att=44.666, acc=0.737, loss=43.920, backward_time=0.097, grad_norm=37.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.721e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 22:09:33,204 (trainer:737) INFO: 29epoch:train:13501-13600batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=42.757, loss_att=48.509, acc=0.746, loss=46.783, backward_time=0.097, grad_norm=39.599, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.720e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 22:10:15,968 (trainer:737) INFO: 29epoch:train:13601-13700batch: iter_time=1.799e-04, forward_time=0.105, loss_ctc=45.463, loss_att=54.315, acc=0.728, loss=51.659, backward_time=0.098, grad_norm=48.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.720e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 22:10:41,446 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-15 22:11:01,220 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 22:11:04,817 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e6b473c10>)
+[gpuc02:0/16] 2024-01-15 22:11:04,817 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 22:11:04,820 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 22:15:24,256 (trainer:737) INFO: 29epoch:train:13701-13800batch: iter_time=2.535, forward_time=0.104, loss_ctc=37.129, loss_att=46.647, acc=0.743, loss=43.792, backward_time=0.097, grad_norm=35.384, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.720e-04, train_time=3.083
+[gpuc02:0/16] 2024-01-15 22:16:06,470 (trainer:737) INFO: 29epoch:train:13801-13900batch: iter_time=1.170e-04, forward_time=0.104, loss_ctc=41.341, loss_att=46.076, acc=0.745, loss=44.656, backward_time=0.097, grad_norm=43.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.719e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 22:16:48,850 (trainer:737) INFO: 29epoch:train:13901-14000batch: iter_time=1.117e-04, forward_time=0.104, loss_ctc=50.663, loss_att=56.491, acc=0.736, loss=54.742, backward_time=0.098, grad_norm=46.829, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.719e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 22:17:31,182 (trainer:737) INFO: 29epoch:train:14001-14100batch: iter_time=1.185e-04, forward_time=0.105, loss_ctc=42.029, loss_att=45.822, acc=0.746, loss=44.684, backward_time=0.097, grad_norm=41.186, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.718e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 22:18:13,533 (trainer:737) INFO: 29epoch:train:14101-14200batch: iter_time=1.214e-04, forward_time=0.104, loss_ctc=51.916, loss_att=60.368, acc=0.701, loss=57.832, backward_time=0.097, grad_norm=51.511, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.718e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 22:18:55,744 (trainer:737) INFO: 29epoch:train:14201-14300batch: iter_time=1.454e-04, forward_time=0.104, loss_ctc=34.799, loss_att=35.896, acc=0.753, loss=35.567, backward_time=0.097, grad_norm=39.176, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.717e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 22:19:38,376 (trainer:737) INFO: 29epoch:train:14301-14400batch: iter_time=1.341e-04, forward_time=0.106, loss_ctc=49.986, loss_att=52.038, acc=0.739, loss=51.422, backward_time=0.098, grad_norm=43.235, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.717e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 22:20:20,752 (trainer:737) INFO: 29epoch:train:14401-14500batch: iter_time=1.153e-04, forward_time=0.104, loss_ctc=43.078, loss_att=53.070, acc=0.710, loss=50.072, backward_time=0.097, grad_norm=39.001, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.717e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 22:21:03,177 (trainer:737) INFO: 29epoch:train:14501-14600batch: iter_time=1.296e-04, forward_time=0.105, loss_ctc=52.003, loss_att=50.675, acc=0.741, loss=51.073, backward_time=0.098, grad_norm=46.290, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.716e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 22:21:45,908 (trainer:737) INFO: 29epoch:train:14601-14700batch: iter_time=1.042e-04, forward_time=0.105, loss_ctc=50.525, loss_att=58.208, acc=0.720, loss=55.903, backward_time=0.098, grad_norm=45.514, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.716e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 22:22:28,273 (trainer:737) INFO: 29epoch:train:14701-14800batch: iter_time=1.028e-04, forward_time=0.105, loss_ctc=40.404, loss_att=47.002, acc=0.743, loss=45.023, backward_time=0.097, grad_norm=37.656, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.715e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 22:23:10,726 (trainer:737) INFO: 29epoch:train:14801-14900batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=46.946, loss_att=53.861, acc=0.741, loss=51.787, backward_time=0.098, grad_norm=47.994, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.715e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 22:23:53,580 (trainer:737) INFO: 29epoch:train:14901-15000batch: iter_time=9.721e-05, forward_time=0.104, loss_ctc=37.837, loss_att=50.549, acc=0.711, loss=46.735, backward_time=0.097, grad_norm=37.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.714e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 22:44:04,519 (trainer:343) INFO: 29epoch results: [train] iter_time=0.218, forward_time=0.105, loss_ctc=45.900, loss_att=51.453, acc=0.735, loss=49.787, backward_time=0.098, grad_norm=44.077, clip=100.000, loss_scale=2.501e+34, optim_step_time=0.042, optim0_lr0=3.747e-04, train_time=0.661, time=2 hours, 45 minutes and 24.93 seconds, total_count=435000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=51.657, cer_ctc=0.265, loss_att=52.854, acc=0.597, cer=0.350, wer=0.998, loss=52.495, time=19 minutes and 57.54 seconds, total_count=135459, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-15 22:44:09,588 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-15 22:44:09,596 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/23epoch.pth, exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/24epoch.pth
+[gpuc02:0/16] 2024-01-15 22:44:09,596 (trainer:272) INFO: 30/45epoch started. Estimated time to finish: 2 days, 1 hour and 15 minutes
+[gpuc02:0/16] 2024-01-15 22:44:09,605 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-15 22:44:30,148 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 22:44:33,681 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d790adbd0>)
+[gpuc02:0/16] 2024-01-15 22:44:33,682 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-15 22:44:33,685 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 22:49:09,537 (trainer:737) INFO: 30epoch:train:1-100batch: iter_time=2.412, forward_time=0.143, loss_ctc=43.943, loss_att=48.617, acc=0.748, loss=47.214, backward_time=0.103, grad_norm=39.053, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.041, optim0_lr0=3.714e-04, train_time=2.999
+[gpuc02:0/16] 2024-01-15 22:49:55,458 (trainer:737) INFO: 30epoch:train:101-200batch: iter_time=1.099e-04, forward_time=0.104, loss_ctc=45.684, loss_att=54.408, acc=0.748, loss=51.791, backward_time=0.098, grad_norm=46.572, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.714e-04, train_time=0.459
+[gpuc02:0/16] 2024-01-15 22:50:23,126 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 22:50:42,799 (trainer:737) INFO: 30epoch:train:201-300batch: iter_time=1.034e-04, forward_time=0.109, loss_ctc=45.824, loss_att=46.207, acc=0.725, loss=46.092, backward_time=0.128, grad_norm=42.925, clip=100.000, loss_scale=3.210e+34, optim_step_time=0.042, optim0_lr0=3.713e-04, train_time=0.473
+[gpuc02:0/16] 2024-01-15 22:51:27,863 (trainer:737) INFO: 30epoch:train:301-400batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=40.564, loss_att=45.528, acc=0.738, loss=44.039, backward_time=0.097, grad_norm=41.216, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.713e-04, train_time=0.450
+[gpuc02:0/16] 2024-01-15 22:52:11,670 (trainer:737) INFO: 30epoch:train:401-500batch: iter_time=1.143e-04, forward_time=0.118, loss_ctc=42.267, loss_att=50.535, acc=0.744, loss=48.055, backward_time=0.101, grad_norm=38.564, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.712e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 22:53:00,966 (trainer:737) INFO: 30epoch:train:501-600batch: iter_time=1.188e-04, forward_time=0.104, loss_ctc=48.915, loss_att=46.568, acc=0.745, loss=47.272, backward_time=0.098, grad_norm=45.105, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.712e-04, train_time=0.493
+[gpuc02:0/16] 2024-01-15 22:53:47,266 (trainer:737) INFO: 30epoch:train:601-700batch: iter_time=1.095e-04, forward_time=0.113, loss_ctc=51.459, loss_att=49.163, acc=0.724, loss=49.852, backward_time=0.122, grad_norm=45.348, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.712e-04, train_time=0.463
+[gpuc02:0/16] 2024-01-15 22:54:31,075 (trainer:737) INFO: 30epoch:train:701-800batch: iter_time=1.075e-04, forward_time=0.119, loss_ctc=47.305, loss_att=52.288, acc=0.729, loss=50.793, backward_time=0.102, grad_norm=47.184, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.711e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-15 22:55:17,936 (trainer:737) INFO: 30epoch:train:801-900batch: iter_time=9.400e-05, forward_time=0.117, loss_ctc=41.292, loss_att=46.695, acc=0.757, loss=45.074, backward_time=0.104, grad_norm=39.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.046, optim0_lr0=3.711e-04, train_time=0.468
+[gpuc02:0/16] 2024-01-15 22:56:00,836 (trainer:737) INFO: 30epoch:train:901-1000batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=53.510, loss_att=59.513, acc=0.719, loss=57.712, backward_time=0.098, grad_norm=50.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.710e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 22:56:49,503 (trainer:737) INFO: 30epoch:train:1001-1100batch: iter_time=1.192e-04, forward_time=0.104, loss_ctc=45.529, loss_att=50.717, acc=0.711, loss=49.161, backward_time=0.097, grad_norm=41.823, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.710e-04, train_time=0.486
+[gpuc02:0/16] 2024-01-15 22:57:31,474 (trainer:737) INFO: 30epoch:train:1101-1200batch: iter_time=1.149e-04, forward_time=0.104, loss_ctc=44.553, loss_att=44.602, acc=0.745, loss=44.587, backward_time=0.098, grad_norm=42.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.709e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-15 22:58:23,813 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-15 22:58:43,452 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 22:58:47,109 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d32abf0>)
+[gpuc02:0/16] 2024-01-15 22:58:47,109 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-15 22:58:47,134 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 23:05:53,398 (trainer:737) INFO: 30epoch:train:1201-1300batch: iter_time=4.284, forward_time=0.105, loss_ctc=45.565, loss_att=49.496, acc=0.760, loss=48.316, backward_time=0.098, grad_norm=40.108, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.709e-04, train_time=5.019
+[gpuc02:0/16] 2024-01-15 23:06:37,605 (trainer:737) INFO: 30epoch:train:1301-1400batch: iter_time=1.244e-04, forward_time=0.104, loss_ctc=42.644, loss_att=54.666, acc=0.749, loss=51.059, backward_time=0.098, grad_norm=43.357, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.709e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-15 23:07:20,531 (trainer:737) INFO: 30epoch:train:1401-1500batch: iter_time=1.252e-04, forward_time=0.106, loss_ctc=46.269, loss_att=52.463, acc=0.737, loss=50.605, backward_time=0.099, grad_norm=40.040, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.708e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-15 23:08:03,829 (trainer:737) INFO: 30epoch:train:1501-1600batch: iter_time=1.300e-04, forward_time=0.104, loss_ctc=40.726, loss_att=43.018, acc=0.752, loss=42.330, backward_time=0.098, grad_norm=39.988, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.708e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-15 23:08:46,435 (trainer:737) INFO: 30epoch:train:1601-1700batch: iter_time=1.451e-04, forward_time=0.105, loss_ctc=41.629, loss_att=50.404, acc=0.727, loss=47.771, backward_time=0.098, grad_norm=41.473, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.707e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:09:29,103 (trainer:737) INFO: 30epoch:train:1701-1800batch: iter_time=1.276e-04, forward_time=0.105, loss_ctc=44.330, loss_att=49.798, acc=0.760, loss=48.157, backward_time=0.098, grad_norm=38.682, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.707e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:10:11,468 (trainer:737) INFO: 30epoch:train:1801-1900batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=47.759, loss_att=46.452, acc=0.742, loss=46.844, backward_time=0.098, grad_norm=43.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.706e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:10:53,919 (trainer:737) INFO: 30epoch:train:1901-2000batch: iter_time=1.168e-04, forward_time=0.106, loss_ctc=46.856, loss_att=52.075, acc=0.728, loss=50.509, backward_time=0.098, grad_norm=43.342, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.706e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:11:36,225 (trainer:737) INFO: 30epoch:train:2001-2100batch: iter_time=1.057e-04, forward_time=0.105, loss_ctc=42.679, loss_att=48.488, acc=0.754, loss=46.745, backward_time=0.098, grad_norm=41.534, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.706e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:12:19,006 (trainer:737) INFO: 30epoch:train:2101-2200batch: iter_time=1.085e-04, forward_time=0.106, loss_ctc=51.550, loss_att=59.967, acc=0.729, loss=57.442, backward_time=0.099, grad_norm=47.865, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.705e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:13:01,184 (trainer:737) INFO: 30epoch:train:2201-2300batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=47.266, loss_att=48.328, acc=0.735, loss=48.009, backward_time=0.097, grad_norm=40.580, clip=100.000, loss_scale=3.012e+34, optim_step_time=0.041, optim0_lr0=3.705e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-15 23:13:43,726 (trainer:737) INFO: 30epoch:train:2301-2400batch: iter_time=1.288e-04, forward_time=0.105, loss_ctc=44.398, loss_att=49.615, acc=0.754, loss=48.050, backward_time=0.097, grad_norm=41.656, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.704e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:14:26,309 (trainer:737) INFO: 30epoch:train:2401-2500batch: iter_time=1.102e-04, forward_time=0.107, loss_ctc=45.520, loss_att=45.115, acc=0.756, loss=45.237, backward_time=0.097, grad_norm=42.853, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.704e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:14:31,384 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-15 23:14:50,252 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 23:14:53,821 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d97d4b160>)
+[gpuc02:0/16] 2024-01-15 23:14:53,822 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-15 23:14:53,825 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 23:19:40,882 (trainer:737) INFO: 30epoch:train:2501-2600batch: iter_time=2.463, forward_time=0.106, loss_ctc=43.251, loss_att=47.416, acc=0.755, loss=46.166, backward_time=0.098, grad_norm=38.366, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.703e-04, train_time=3.145
+[gpuc02:0/16] 2024-01-15 23:20:23,671 (trainer:737) INFO: 30epoch:train:2601-2700batch: iter_time=1.104e-04, forward_time=0.106, loss_ctc=45.556, loss_att=57.095, acc=0.753, loss=53.634, backward_time=0.098, grad_norm=44.946, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.703e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:21:05,998 (trainer:737) INFO: 30epoch:train:2701-2800batch: iter_time=1.272e-04, forward_time=0.105, loss_ctc=44.554, loss_att=45.108, acc=0.740, loss=44.942, backward_time=0.098, grad_norm=40.519, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.703e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:21:48,501 (trainer:737) INFO: 30epoch:train:2801-2900batch: iter_time=1.330e-04, forward_time=0.105, loss_ctc=39.463, loss_att=45.238, acc=0.743, loss=43.506, backward_time=0.097, grad_norm=40.132, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.702e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:22:31,371 (trainer:737) INFO: 30epoch:train:2901-3000batch: iter_time=1.469e-04, forward_time=0.106, loss_ctc=41.326, loss_att=50.999, acc=0.748, loss=48.097, backward_time=0.098, grad_norm=38.209, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.702e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:23:14,786 (trainer:737) INFO: 30epoch:train:3001-3100batch: iter_time=1.357e-04, forward_time=0.107, loss_ctc=47.310, loss_att=47.425, acc=0.751, loss=47.390, backward_time=0.098, grad_norm=43.427, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.701e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-15 23:23:57,181 (trainer:737) INFO: 30epoch:train:3101-3200batch: iter_time=1.308e-04, forward_time=0.105, loss_ctc=48.957, loss_att=48.128, acc=0.730, loss=48.376, backward_time=0.098, grad_norm=43.636, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.701e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:24:39,609 (trainer:737) INFO: 30epoch:train:3201-3300batch: iter_time=1.391e-04, forward_time=0.106, loss_ctc=45.942, loss_att=52.152, acc=0.742, loss=50.289, backward_time=0.099, grad_norm=44.547, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.700e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:25:09,512 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 23:25:22,340 (trainer:737) INFO: 30epoch:train:3301-3400batch: iter_time=1.312e-04, forward_time=0.106, loss_ctc=40.678, loss_att=46.444, acc=0.765, loss=44.714, backward_time=0.099, grad_norm=37.769, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.042, optim0_lr0=3.700e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 23:26:04,841 (trainer:737) INFO: 30epoch:train:3401-3500batch: iter_time=1.278e-04, forward_time=0.107, loss_ctc=52.175, loss_att=59.562, acc=0.723, loss=57.346, backward_time=0.098, grad_norm=48.118, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.700e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:26:47,219 (trainer:737) INFO: 30epoch:train:3501-3600batch: iter_time=1.366e-04, forward_time=0.106, loss_ctc=44.524, loss_att=49.322, acc=0.730, loss=47.883, backward_time=0.098, grad_norm=41.881, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.699e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:27:29,717 (trainer:737) INFO: 30epoch:train:3601-3700batch: iter_time=1.357e-04, forward_time=0.105, loss_ctc=43.484, loss_att=44.874, acc=0.755, loss=44.457, backward_time=0.098, grad_norm=40.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.699e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:27:53,831 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-15 23:28:13,880 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 23:28:17,624 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f248603a9e0>)
+[gpuc02:0/16] 2024-01-15 23:28:17,624 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-15 23:28:17,628 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 23:32:43,415 (trainer:737) INFO: 30epoch:train:3701-3800batch: iter_time=2.529, forward_time=0.129, loss_ctc=44.703, loss_att=47.288, acc=0.766, loss=46.512, backward_time=0.103, grad_norm=39.510, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.698e-04, train_time=3.137
+[gpuc02:0/16] 2024-01-15 23:33:26,053 (trainer:737) INFO: 30epoch:train:3801-3900batch: iter_time=1.173e-04, forward_time=0.106, loss_ctc=42.205, loss_att=53.657, acc=0.751, loss=50.221, backward_time=0.099, grad_norm=43.518, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.698e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:34:08,525 (trainer:737) INFO: 30epoch:train:3901-4000batch: iter_time=1.363e-04, forward_time=0.106, loss_ctc=45.459, loss_att=50.248, acc=0.743, loss=48.812, backward_time=0.098, grad_norm=40.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.698e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:34:51,062 (trainer:737) INFO: 30epoch:train:4001-4100batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=40.377, loss_att=41.866, acc=0.755, loss=41.420, backward_time=0.097, grad_norm=40.033, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.697e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:35:33,803 (trainer:737) INFO: 30epoch:train:4101-4200batch: iter_time=1.148e-04, forward_time=0.106, loss_ctc=40.788, loss_att=48.776, acc=0.732, loss=46.380, backward_time=0.098, grad_norm=38.347, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.697e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 23:36:16,472 (trainer:737) INFO: 30epoch:train:4201-4300batch: iter_time=1.223e-04, forward_time=0.106, loss_ctc=43.294, loss_att=48.682, acc=0.763, loss=47.066, backward_time=0.098, grad_norm=39.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.696e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:36:58,826 (trainer:737) INFO: 30epoch:train:4301-4400batch: iter_time=1.142e-04, forward_time=0.106, loss_ctc=46.839, loss_att=45.767, acc=0.745, loss=46.089, backward_time=0.098, grad_norm=43.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.696e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:37:41,308 (trainer:737) INFO: 30epoch:train:4401-4500batch: iter_time=1.359e-04, forward_time=0.106, loss_ctc=46.193, loss_att=51.164, acc=0.731, loss=49.673, backward_time=0.098, grad_norm=44.975, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.695e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:38:24,117 (trainer:737) INFO: 30epoch:train:4501-4600batch: iter_time=1.162e-04, forward_time=0.105, loss_ctc=41.695, loss_att=47.454, acc=0.755, loss=45.726, backward_time=0.098, grad_norm=39.156, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.695e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:39:07,180 (trainer:737) INFO: 30epoch:train:4601-4700batch: iter_time=1.153e-04, forward_time=0.107, loss_ctc=50.472, loss_att=58.499, acc=0.734, loss=56.091, backward_time=0.099, grad_norm=46.389, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.695e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 23:39:50,187 (trainer:737) INFO: 30epoch:train:4701-4800batch: iter_time=1.129e-04, forward_time=0.104, loss_ctc=45.628, loss_att=47.565, acc=0.739, loss=46.984, backward_time=0.097, grad_norm=40.688, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.694e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-15 23:40:32,952 (trainer:737) INFO: 30epoch:train:4801-4900batch: iter_time=1.080e-04, forward_time=0.105, loss_ctc=44.021, loss_att=49.374, acc=0.755, loss=47.768, backward_time=0.098, grad_norm=41.829, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.694e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 23:41:15,368 (trainer:737) INFO: 30epoch:train:4901-5000batch: iter_time=1.061e-04, forward_time=0.106, loss_ctc=43.982, loss_att=44.440, acc=0.756, loss=44.303, backward_time=0.098, grad_norm=42.353, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.693e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:41:27,174 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-15 23:41:46,489 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 23:41:50,125 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e9a167f40>)
+[gpuc02:0/16] 2024-01-15 23:41:50,125 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-15 23:41:50,128 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 23:46:51,430 (trainer:737) INFO: 30epoch:train:5001-5100batch: iter_time=2.777, forward_time=0.125, loss_ctc=42.979, loss_att=48.125, acc=0.753, loss=46.581, backward_time=0.102, grad_norm=37.856, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.693e-04, train_time=3.360
+[gpuc02:0/16] 2024-01-15 23:47:33,917 (trainer:737) INFO: 30epoch:train:5101-5200batch: iter_time=9.159e-05, forward_time=0.106, loss_ctc=45.621, loss_att=55.902, acc=0.758, loss=52.818, backward_time=0.099, grad_norm=45.519, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.692e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-15 23:48:16,217 (trainer:737) INFO: 30epoch:train:5201-5300batch: iter_time=9.704e-05, forward_time=0.105, loss_ctc=43.532, loss_att=44.561, acc=0.744, loss=44.252, backward_time=0.098, grad_norm=41.007, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.692e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:48:58,525 (trainer:737) INFO: 30epoch:train:5301-5400batch: iter_time=1.071e-04, forward_time=0.105, loss_ctc=39.106, loss_att=44.948, acc=0.743, loss=43.195, backward_time=0.098, grad_norm=39.470, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.042, optim0_lr0=3.692e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:49:30,680 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-15 23:49:40,897 (trainer:737) INFO: 30epoch:train:5401-5500batch: iter_time=9.988e-05, forward_time=0.105, loss_ctc=41.091, loss_att=50.108, acc=0.750, loss=47.403, backward_time=0.097, grad_norm=39.103, clip=100.000, loss_scale=3.650e+34, optim_step_time=0.042, optim0_lr0=3.691e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:50:23,732 (trainer:737) INFO: 30epoch:train:5501-5600batch: iter_time=1.099e-04, forward_time=0.108, loss_ctc=46.978, loss_att=46.960, acc=0.752, loss=46.965, backward_time=0.098, grad_norm=41.186, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.691e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:51:06,087 (trainer:737) INFO: 30epoch:train:5601-5700batch: iter_time=8.847e-05, forward_time=0.105, loss_ctc=48.052, loss_att=47.657, acc=0.732, loss=47.776, backward_time=0.098, grad_norm=43.311, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.690e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:51:48,514 (trainer:737) INFO: 30epoch:train:5701-5800batch: iter_time=1.088e-04, forward_time=0.106, loss_ctc=45.015, loss_att=51.726, acc=0.743, loss=49.713, backward_time=0.098, grad_norm=44.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.690e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-15 23:52:31,220 (trainer:737) INFO: 30epoch:train:5801-5900batch: iter_time=1.040e-04, forward_time=0.105, loss_ctc=40.678, loss_att=45.988, acc=0.768, loss=44.395, backward_time=0.098, grad_norm=37.191, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.690e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-15 23:53:14,000 (trainer:737) INFO: 30epoch:train:5901-6000batch: iter_time=1.012e-04, forward_time=0.106, loss_ctc=50.785, loss_att=58.593, acc=0.726, loss=56.250, backward_time=0.098, grad_norm=47.441, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.689e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-15 23:53:56,599 (trainer:737) INFO: 30epoch:train:6001-6100batch: iter_time=1.067e-04, forward_time=0.104, loss_ctc=43.592, loss_att=50.036, acc=0.729, loss=48.103, backward_time=0.098, grad_norm=41.034, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.689e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-15 23:54:38,967 (trainer:737) INFO: 30epoch:train:6101-6200batch: iter_time=1.039e-04, forward_time=0.105, loss_ctc=43.387, loss_att=45.076, acc=0.754, loss=44.569, backward_time=0.098, grad_norm=42.310, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.688e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-15 23:55:02,878 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-15 23:55:23,494 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-15 23:55:27,223 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e949e7040>)
+[gpuc02:0/16] 2024-01-15 23:55:27,223 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-15 23:55:27,226 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-15 23:59:56,373 (trainer:737) INFO: 30epoch:train:6201-6300batch: iter_time=2.546, forward_time=0.154, loss_ctc=44.865, loss_att=49.342, acc=0.759, loss=47.999, backward_time=0.105, grad_norm=39.512, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.688e-04, train_time=3.174
+[gpuc02:0/16] 2024-01-16 00:00:38,483 (trainer:737) INFO: 30epoch:train:6301-6400batch: iter_time=1.148e-04, forward_time=0.103, loss_ctc=41.185, loss_att=54.205, acc=0.751, loss=50.299, backward_time=0.097, grad_norm=43.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.687e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:01:20,651 (trainer:737) INFO: 30epoch:train:6401-6500batch: iter_time=1.149e-04, forward_time=0.104, loss_ctc=45.555, loss_att=50.384, acc=0.728, loss=48.935, backward_time=0.097, grad_norm=40.738, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.687e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:02:02,784 (trainer:737) INFO: 30epoch:train:6501-6600batch: iter_time=1.157e-04, forward_time=0.104, loss_ctc=39.889, loss_att=42.567, acc=0.752, loss=41.764, backward_time=0.097, grad_norm=38.233, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.687e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:02:44,760 (trainer:737) INFO: 30epoch:train:6601-6700batch: iter_time=1.075e-04, forward_time=0.103, loss_ctc=39.976, loss_att=49.145, acc=0.728, loss=46.394, backward_time=0.097, grad_norm=38.719, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.686e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 00:03:26,865 (trainer:737) INFO: 30epoch:train:6701-6800batch: iter_time=1.147e-04, forward_time=0.105, loss_ctc=42.862, loss_att=48.866, acc=0.760, loss=47.064, backward_time=0.098, grad_norm=39.442, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.686e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:04:08,929 (trainer:737) INFO: 30epoch:train:6801-6900batch: iter_time=1.174e-04, forward_time=0.105, loss_ctc=46.184, loss_att=44.896, acc=0.744, loss=45.282, backward_time=0.097, grad_norm=43.871, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.685e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 00:04:51,423 (trainer:737) INFO: 30epoch:train:6901-7000batch: iter_time=1.072e-04, forward_time=0.104, loss_ctc=45.720, loss_att=50.897, acc=0.729, loss=49.344, backward_time=0.097, grad_norm=44.955, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.685e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:05:33,730 (trainer:737) INFO: 30epoch:train:7001-7100batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=41.609, loss_att=48.314, acc=0.746, loss=46.302, backward_time=0.097, grad_norm=41.682, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.685e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:06:16,247 (trainer:737) INFO: 30epoch:train:7101-7200batch: iter_time=1.336e-04, forward_time=0.105, loss_ctc=49.985, loss_att=58.998, acc=0.728, loss=56.294, backward_time=0.097, grad_norm=47.174, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.684e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:06:58,521 (trainer:737) INFO: 30epoch:train:7201-7300batch: iter_time=1.190e-04, forward_time=0.104, loss_ctc=45.124, loss_att=47.232, acc=0.735, loss=46.600, backward_time=0.096, grad_norm=39.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.684e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:07:40,843 (trainer:737) INFO: 30epoch:train:7301-7400batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=43.392, loss_att=48.134, acc=0.751, loss=46.711, backward_time=0.097, grad_norm=42.262, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.683e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:08:23,157 (trainer:737) INFO: 30epoch:train:7401-7500batch: iter_time=9.967e-05, forward_time=0.104, loss_ctc=43.856, loss_att=44.228, acc=0.754, loss=44.116, backward_time=0.097, grad_norm=43.152, clip=100.000, loss_scale=2.575e+34, optim_step_time=0.041, optim0_lr0=3.683e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:08:27,442 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-16 00:08:46,387 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 00:08:50,253 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e9a130ca0>)
+[gpuc02:0/16] 2024-01-16 00:08:50,253 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-16 00:08:50,256 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 00:13:24,649 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 00:13:32,691 (trainer:737) INFO: 30epoch:train:7501-7600batch: iter_time=2.477, forward_time=0.104, loss_ctc=42.694, loss_att=49.057, acc=0.756, loss=47.148, backward_time=0.098, grad_norm=35.959, clip=100.000, loss_scale=3.755e+34, optim_step_time=0.042, optim0_lr0=3.682e-04, train_time=3.095
+[gpuc02:0/16] 2024-01-16 00:14:16,510 (trainer:737) INFO: 30epoch:train:7601-7700batch: iter_time=1.096e-04, forward_time=0.105, loss_ctc=44.975, loss_att=57.171, acc=0.755, loss=53.512, backward_time=0.099, grad_norm=44.746, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.682e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-16 00:14:59,409 (trainer:737) INFO: 30epoch:train:7701-7800batch: iter_time=1.119e-04, forward_time=0.104, loss_ctc=43.205, loss_att=44.686, acc=0.744, loss=44.241, backward_time=0.098, grad_norm=39.189, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.682e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 00:15:41,713 (trainer:737) INFO: 30epoch:train:7801-7900batch: iter_time=1.106e-04, forward_time=0.103, loss_ctc=38.616, loss_att=44.950, acc=0.746, loss=43.050, backward_time=0.098, grad_norm=38.101, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.681e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:16:24,036 (trainer:737) INFO: 30epoch:train:7901-8000batch: iter_time=1.017e-04, forward_time=0.104, loss_ctc=40.633, loss_att=50.693, acc=0.749, loss=47.675, backward_time=0.098, grad_norm=38.943, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.681e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:17:06,451 (trainer:737) INFO: 30epoch:train:8001-8100batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=46.588, loss_att=47.401, acc=0.752, loss=47.157, backward_time=0.098, grad_norm=42.751, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.680e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:17:49,200 (trainer:737) INFO: 30epoch:train:8101-8200batch: iter_time=1.074e-04, forward_time=0.104, loss_ctc=47.378, loss_att=47.065, acc=0.734, loss=47.159, backward_time=0.098, grad_norm=42.666, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.680e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 00:18:33,550 (trainer:737) INFO: 30epoch:train:8201-8300batch: iter_time=1.178e-04, forward_time=0.104, loss_ctc=44.746, loss_att=52.150, acc=0.743, loss=49.929, backward_time=0.098, grad_norm=44.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.680e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-16 00:19:16,711 (trainer:737) INFO: 30epoch:train:8301-8400batch: iter_time=1.251e-04, forward_time=0.105, loss_ctc=39.977, loss_att=46.047, acc=0.766, loss=44.226, backward_time=0.098, grad_norm=37.746, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.679e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 00:19:59,192 (trainer:737) INFO: 30epoch:train:8401-8500batch: iter_time=1.255e-04, forward_time=0.105, loss_ctc=50.434, loss_att=58.459, acc=0.726, loss=56.052, backward_time=0.098, grad_norm=46.840, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.679e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:20:41,578 (trainer:737) INFO: 30epoch:train:8501-8600batch: iter_time=1.353e-04, forward_time=0.104, loss_ctc=43.751, loss_att=49.277, acc=0.732, loss=47.619, backward_time=0.098, grad_norm=41.383, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.678e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:21:24,249 (trainer:737) INFO: 30epoch:train:8601-8700batch: iter_time=1.372e-04, forward_time=0.105, loss_ctc=43.476, loss_att=44.728, acc=0.756, loss=44.352, backward_time=0.098, grad_norm=40.895, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.678e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 00:21:49,074 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-16 00:22:08,202 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 00:22:11,724 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e968eada0>)
+[gpuc02:0/16] 2024-01-16 00:22:11,725 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-16 00:22:11,728 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 00:26:36,913 (trainer:737) INFO: 30epoch:train:8701-8800batch: iter_time=2.473, forward_time=0.105, loss_ctc=44.412, loss_att=48.567, acc=0.761, loss=47.320, backward_time=0.098, grad_norm=41.183, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.677e-04, train_time=3.126
+[gpuc02:0/16] 2024-01-16 00:27:19,452 (trainer:737) INFO: 30epoch:train:8801-8900batch: iter_time=9.674e-05, forward_time=0.105, loss_ctc=41.477, loss_att=52.993, acc=0.751, loss=49.538, backward_time=0.098, grad_norm=42.476, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.677e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:28:02,196 (trainer:737) INFO: 30epoch:train:8901-9000batch: iter_time=1.015e-04, forward_time=0.105, loss_ctc=45.193, loss_att=49.679, acc=0.731, loss=48.334, backward_time=0.098, grad_norm=39.422, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.677e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 00:28:44,371 (trainer:737) INFO: 30epoch:train:9001-9100batch: iter_time=1.080e-04, forward_time=0.104, loss_ctc=39.415, loss_att=42.129, acc=0.753, loss=41.315, backward_time=0.097, grad_norm=39.366, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.676e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:29:26,846 (trainer:737) INFO: 30epoch:train:9101-9200batch: iter_time=1.132e-04, forward_time=0.105, loss_ctc=39.703, loss_att=48.458, acc=0.728, loss=45.831, backward_time=0.098, grad_norm=37.823, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.676e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:30:09,835 (trainer:737) INFO: 30epoch:train:9201-9300batch: iter_time=9.276e-05, forward_time=0.109, loss_ctc=43.306, loss_att=48.804, acc=0.760, loss=47.155, backward_time=0.098, grad_norm=39.227, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.675e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 00:30:52,087 (trainer:737) INFO: 30epoch:train:9301-9400batch: iter_time=1.024e-04, forward_time=0.105, loss_ctc=46.100, loss_att=44.778, acc=0.744, loss=45.174, backward_time=0.098, grad_norm=41.900, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.675e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:31:34,686 (trainer:737) INFO: 30epoch:train:9401-9500batch: iter_time=1.093e-04, forward_time=0.105, loss_ctc=45.553, loss_att=50.657, acc=0.729, loss=49.126, backward_time=0.098, grad_norm=44.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.675e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 00:32:17,322 (trainer:737) INFO: 30epoch:train:9501-9600batch: iter_time=1.022e-04, forward_time=0.105, loss_ctc=41.570, loss_att=47.949, acc=0.747, loss=46.035, backward_time=0.098, grad_norm=40.179, clip=100.000, loss_scale=2.472e+34, optim_step_time=0.042, optim0_lr0=3.674e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 00:33:01,210 (trainer:737) INFO: 30epoch:train:9601-9700batch: iter_time=9.917e-05, forward_time=0.106, loss_ctc=49.977, loss_att=58.710, acc=0.729, loss=56.090, backward_time=0.098, grad_norm=50.855, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.674e-04, train_time=0.439
+[gpuc02:0/16] 2024-01-16 00:33:43,511 (trainer:737) INFO: 30epoch:train:9701-9800batch: iter_time=9.928e-05, forward_time=0.105, loss_ctc=45.295, loss_att=47.557, acc=0.735, loss=46.878, backward_time=0.097, grad_norm=39.342, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.673e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:34:10,305 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 00:34:26,435 (trainer:737) INFO: 30epoch:train:9801-9900batch: iter_time=1.008e-04, forward_time=0.105, loss_ctc=43.231, loss_att=47.862, acc=0.752, loss=46.473, backward_time=0.097, grad_norm=41.112, clip=100.000, loss_scale=3.357e+34, optim_step_time=0.042, optim0_lr0=3.673e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 00:35:08,857 (trainer:737) INFO: 30epoch:train:9901-10000batch: iter_time=1.006e-04, forward_time=0.104, loss_ctc=43.693, loss_att=44.040, acc=0.755, loss=43.936, backward_time=0.097, grad_norm=42.382, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.673e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:35:11,433 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-16 00:35:31,461 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 00:35:35,175 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f254f256b90>)
+[gpuc02:0/16] 2024-01-16 00:35:35,176 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-16 00:35:35,179 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 00:40:18,875 (trainer:737) INFO: 30epoch:train:10001-10100batch: iter_time=2.505, forward_time=0.105, loss_ctc=42.615, loss_att=47.032, acc=0.754, loss=45.707, backward_time=0.097, grad_norm=38.076, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.672e-04, train_time=3.100
+[gpuc02:0/16] 2024-01-16 00:41:01,675 (trainer:737) INFO: 30epoch:train:10101-10200batch: iter_time=1.106e-04, forward_time=0.105, loss_ctc=44.446, loss_att=54.034, acc=0.754, loss=51.157, backward_time=0.098, grad_norm=43.864, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.672e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 00:41:44,010 (trainer:737) INFO: 30epoch:train:10201-10300batch: iter_time=1.082e-04, forward_time=0.105, loss_ctc=42.307, loss_att=44.129, acc=0.734, loss=43.582, backward_time=0.097, grad_norm=40.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.671e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:42:26,205 (trainer:737) INFO: 30epoch:train:10301-10400batch: iter_time=1.179e-04, forward_time=0.105, loss_ctc=38.648, loss_att=44.051, acc=0.744, loss=42.430, backward_time=0.097, grad_norm=39.629, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.671e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:43:08,540 (trainer:737) INFO: 30epoch:train:10401-10500batch: iter_time=1.039e-04, forward_time=0.105, loss_ctc=40.486, loss_att=49.697, acc=0.749, loss=46.934, backward_time=0.097, grad_norm=37.074, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.670e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:43:51,054 (trainer:737) INFO: 30epoch:train:10501-10600batch: iter_time=1.225e-04, forward_time=0.106, loss_ctc=46.717, loss_att=46.010, acc=0.750, loss=46.222, backward_time=0.097, grad_norm=40.617, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.670e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:44:33,389 (trainer:737) INFO: 30epoch:train:10601-10700batch: iter_time=1.161e-04, forward_time=0.106, loss_ctc=46.779, loss_att=46.690, acc=0.732, loss=46.717, backward_time=0.097, grad_norm=42.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.670e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:45:15,786 (trainer:737) INFO: 30epoch:train:10701-10800batch: iter_time=1.025e-04, forward_time=0.105, loss_ctc=44.710, loss_att=51.607, acc=0.733, loss=49.538, backward_time=0.097, grad_norm=45.110, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.669e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:45:58,310 (trainer:737) INFO: 30epoch:train:10801-10900batch: iter_time=1.269e-04, forward_time=0.106, loss_ctc=39.887, loss_att=46.068, acc=0.760, loss=44.214, backward_time=0.097, grad_norm=38.015, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.669e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:46:41,372 (trainer:737) INFO: 30epoch:train:10901-11000batch: iter_time=1.259e-04, forward_time=0.106, loss_ctc=49.668, loss_att=57.878, acc=0.726, loss=55.415, backward_time=0.097, grad_norm=46.948, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.668e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 00:47:23,945 (trainer:737) INFO: 30epoch:train:11001-11100batch: iter_time=1.150e-04, forward_time=0.105, loss_ctc=43.589, loss_att=49.979, acc=0.718, loss=48.062, backward_time=0.097, grad_norm=40.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.668e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 00:48:06,345 (trainer:737) INFO: 30epoch:train:11101-11200batch: iter_time=1.073e-04, forward_time=0.106, loss_ctc=42.600, loss_att=44.094, acc=0.750, loss=43.646, backward_time=0.097, grad_norm=41.882, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.668e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:48:31,936 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-16 00:48:51,532 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 00:48:55,132 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e78d277c0>)
+[gpuc02:0/16] 2024-01-16 00:48:55,132 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-16 00:48:55,135 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 00:53:17,631 (trainer:737) INFO: 30epoch:train:11201-11300batch: iter_time=2.497, forward_time=0.105, loss_ctc=44.165, loss_att=47.371, acc=0.762, loss=46.409, backward_time=0.097, grad_norm=39.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.667e-04, train_time=3.113
+[gpuc02:0/16] 2024-01-16 00:53:59,871 (trainer:737) INFO: 30epoch:train:11301-11400batch: iter_time=9.966e-05, forward_time=0.105, loss_ctc=41.319, loss_att=51.491, acc=0.754, loss=48.439, backward_time=0.097, grad_norm=42.828, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.667e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:54:42,195 (trainer:737) INFO: 30epoch:train:11401-11500batch: iter_time=1.099e-04, forward_time=0.105, loss_ctc=45.303, loss_att=48.958, acc=0.731, loss=47.861, backward_time=0.098, grad_norm=41.003, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.666e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 00:55:24,273 (trainer:737) INFO: 30epoch:train:11501-11600batch: iter_time=1.322e-04, forward_time=0.104, loss_ctc=39.195, loss_att=41.685, acc=0.755, loss=40.938, backward_time=0.097, grad_norm=36.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.666e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:56:06,705 (trainer:737) INFO: 30epoch:train:11601-11700batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=39.765, loss_att=47.329, acc=0.734, loss=45.060, backward_time=0.097, grad_norm=37.755, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.666e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 00:56:48,963 (trainer:737) INFO: 30epoch:train:11701-11800batch: iter_time=1.004e-04, forward_time=0.105, loss_ctc=42.783, loss_att=47.935, acc=0.761, loss=46.390, backward_time=0.097, grad_norm=39.175, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.665e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:57:31,099 (trainer:737) INFO: 30epoch:train:11801-11900batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=45.953, loss_att=44.185, acc=0.744, loss=44.715, backward_time=0.097, grad_norm=41.225, clip=100.000, loss_scale=2.866e+34, optim_step_time=0.041, optim0_lr0=3.665e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 00:58:13,346 (trainer:737) INFO: 30epoch:train:11901-12000batch: iter_time=1.132e-04, forward_time=0.105, loss_ctc=45.557, loss_att=50.204, acc=0.730, loss=48.810, backward_time=0.097, grad_norm=44.032, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.664e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:58:55,544 (trainer:737) INFO: 30epoch:train:12001-12100batch: iter_time=1.195e-04, forward_time=0.105, loss_ctc=40.836, loss_att=47.481, acc=0.746, loss=45.488, backward_time=0.098, grad_norm=41.106, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.664e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 00:59:38,219 (trainer:737) INFO: 30epoch:train:12101-12200batch: iter_time=1.006e-04, forward_time=0.106, loss_ctc=49.070, loss_att=57.909, acc=0.729, loss=55.257, backward_time=0.098, grad_norm=47.594, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.663e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 01:00:20,325 (trainer:737) INFO: 30epoch:train:12201-12300batch: iter_time=1.044e-04, forward_time=0.105, loss_ctc=44.709, loss_att=47.397, acc=0.735, loss=46.590, backward_time=0.096, grad_norm=38.653, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.663e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 01:01:02,686 (trainer:737) INFO: 30epoch:train:12301-12400batch: iter_time=9.691e-05, forward_time=0.105, loss_ctc=43.189, loss_att=48.337, acc=0.749, loss=46.792, backward_time=0.097, grad_norm=41.844, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.663e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 01:01:45,199 (trainer:737) INFO: 30epoch:train:12401-12500batch: iter_time=8.949e-05, forward_time=0.104, loss_ctc=43.580, loss_att=44.243, acc=0.752, loss=44.044, backward_time=0.097, grad_norm=43.598, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.662e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 01:01:51,142 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-16 01:02:10,360 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 01:02:13,870 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ec3e5be80>)
+[gpuc02:0/16] 2024-01-16 01:02:13,870 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-16 01:02:13,874 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 01:06:56,567 (trainer:737) INFO: 30epoch:train:12501-12600batch: iter_time=2.618, forward_time=0.106, loss_ctc=42.514, loss_att=46.889, acc=0.756, loss=45.576, backward_time=0.097, grad_norm=37.565, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.662e-04, train_time=3.113
+[gpuc02:0/16] 2024-01-16 01:07:39,091 (trainer:737) INFO: 30epoch:train:12601-12700batch: iter_time=1.192e-04, forward_time=0.106, loss_ctc=44.053, loss_att=52.622, acc=0.760, loss=50.051, backward_time=0.098, grad_norm=42.707, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.661e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 01:07:53,418 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 01:08:21,731 (trainer:737) INFO: 30epoch:train:12701-12800batch: iter_time=1.186e-04, forward_time=0.104, loss_ctc=42.352, loss_att=44.144, acc=0.737, loss=43.607, backward_time=0.098, grad_norm=38.616, clip=100.000, loss_scale=2.769e+34, optim_step_time=0.041, optim0_lr0=3.661e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 01:09:04,638 (trainer:737) INFO: 30epoch:train:12801-12900batch: iter_time=1.077e-04, forward_time=0.104, loss_ctc=38.460, loss_att=44.905, acc=0.742, loss=42.972, backward_time=0.098, grad_norm=39.618, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.661e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 01:09:47,102 (trainer:737) INFO: 30epoch:train:12901-13000batch: iter_time=1.236e-04, forward_time=0.104, loss_ctc=40.013, loss_att=49.711, acc=0.749, loss=46.802, backward_time=0.098, grad_norm=38.289, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.660e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 01:10:29,506 (trainer:737) INFO: 30epoch:train:13001-13100batch: iter_time=1.245e-04, forward_time=0.104, loss_ctc=46.193, loss_att=45.366, acc=0.751, loss=45.614, backward_time=0.098, grad_norm=41.481, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.660e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 01:11:12,688 (trainer:737) INFO: 30epoch:train:13101-13200batch: iter_time=1.275e-04, forward_time=0.104, loss_ctc=47.322, loss_att=46.645, acc=0.735, loss=46.848, backward_time=0.098, grad_norm=42.223, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.659e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-16 01:11:55,476 (trainer:737) INFO: 30epoch:train:13201-13300batch: iter_time=1.363e-04, forward_time=0.105, loss_ctc=44.635, loss_att=50.875, acc=0.735, loss=49.003, backward_time=0.098, grad_norm=44.962, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.659e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 01:12:38,757 (trainer:737) INFO: 30epoch:train:13301-13400batch: iter_time=1.276e-04, forward_time=0.105, loss_ctc=39.632, loss_att=45.586, acc=0.763, loss=43.800, backward_time=0.098, grad_norm=39.014, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.659e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 01:13:21,269 (trainer:737) INFO: 30epoch:train:13401-13500batch: iter_time=1.159e-04, forward_time=0.105, loss_ctc=50.062, loss_att=57.697, acc=0.727, loss=55.406, backward_time=0.097, grad_norm=48.633, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.658e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 01:14:04,256 (trainer:737) INFO: 30epoch:train:13501-13600batch: iter_time=1.028e-04, forward_time=0.107, loss_ctc=42.898, loss_att=48.618, acc=0.722, loss=46.902, backward_time=0.097, grad_norm=40.549, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.658e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 01:14:46,958 (trainer:737) INFO: 30epoch:train:13601-13700batch: iter_time=1.129e-04, forward_time=0.105, loss_ctc=42.437, loss_att=43.669, acc=0.752, loss=43.299, backward_time=0.097, grad_norm=38.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.657e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 01:15:14,384 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-16 01:15:33,411 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 01:15:36,983 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e6841baf0>)
+[gpuc02:0/16] 2024-01-16 01:15:36,983 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-16 01:15:36,986 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 01:20:14,066 (trainer:737) INFO: 30epoch:train:13701-13800batch: iter_time=2.545, forward_time=0.105, loss_ctc=44.014, loss_att=49.069, acc=0.763, loss=47.553, backward_time=0.098, grad_norm=39.595, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.657e-04, train_time=3.271
+[gpuc02:0/16] 2024-01-16 01:20:56,831 (trainer:737) INFO: 30epoch:train:13801-13900batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=41.030, loss_att=54.107, acc=0.751, loss=50.184, backward_time=0.098, grad_norm=46.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.657e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 01:21:39,521 (trainer:737) INFO: 30epoch:train:13901-14000batch: iter_time=1.358e-04, forward_time=0.106, loss_ctc=44.915, loss_att=51.826, acc=0.742, loss=49.753, backward_time=0.098, grad_norm=40.707, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.656e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 01:22:21,798 (trainer:737) INFO: 30epoch:train:14001-14100batch: iter_time=1.474e-04, forward_time=0.105, loss_ctc=39.469, loss_att=41.715, acc=0.757, loss=41.041, backward_time=0.098, grad_norm=38.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.656e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 01:23:04,114 (trainer:737) INFO: 30epoch:train:14101-14200batch: iter_time=1.341e-04, forward_time=0.106, loss_ctc=39.369, loss_att=48.761, acc=0.734, loss=45.944, backward_time=0.098, grad_norm=38.649, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.655e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 01:23:46,621 (trainer:737) INFO: 30epoch:train:14201-14300batch: iter_time=1.204e-04, forward_time=0.106, loss_ctc=42.671, loss_att=49.315, acc=0.764, loss=47.322, backward_time=0.098, grad_norm=38.348, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.655e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 01:24:29,037 (trainer:737) INFO: 30epoch:train:14301-14400batch: iter_time=1.269e-04, forward_time=0.105, loss_ctc=45.151, loss_att=45.074, acc=0.749, loss=45.097, backward_time=0.098, grad_norm=40.361, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.655e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 01:25:11,489 (trainer:737) INFO: 30epoch:train:14401-14500batch: iter_time=1.211e-04, forward_time=0.105, loss_ctc=45.571, loss_att=51.237, acc=0.734, loss=49.537, backward_time=0.098, grad_norm=46.906, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.654e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 01:25:53,691 (trainer:737) INFO: 30epoch:train:14501-14600batch: iter_time=1.244e-04, forward_time=0.105, loss_ctc=41.093, loss_att=47.970, acc=0.755, loss=45.907, backward_time=0.098, grad_norm=42.176, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.654e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 01:26:36,354 (trainer:737) INFO: 30epoch:train:14601-14700batch: iter_time=1.183e-04, forward_time=0.106, loss_ctc=48.998, loss_att=58.876, acc=0.735, loss=55.913, backward_time=0.099, grad_norm=46.213, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.653e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 01:27:18,605 (trainer:737) INFO: 30epoch:train:14701-14800batch: iter_time=1.171e-04, forward_time=0.105, loss_ctc=44.505, loss_att=46.607, acc=0.743, loss=45.977, backward_time=0.097, grad_norm=38.056, clip=100.000, loss_scale=3.448e+34, optim_step_time=0.042, optim0_lr0=3.653e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 01:27:39,293 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 01:28:00,922 (trainer:737) INFO: 30epoch:train:14801-14900batch: iter_time=1.496e-04, forward_time=0.105, loss_ctc=43.048, loss_att=49.260, acc=0.758, loss=47.396, backward_time=0.098, grad_norm=39.912, clip=100.000, loss_scale=3.084e+34, optim_step_time=0.042, optim0_lr0=3.652e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 01:28:43,616 (trainer:737) INFO: 30epoch:train:14901-15000batch: iter_time=1.073e-04, forward_time=0.105, loss_ctc=43.503, loss_att=45.084, acc=0.757, loss=44.610, backward_time=0.098, grad_norm=43.672, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.652e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 01:48:54,937 (trainer:343) INFO: 30epoch results: [train] iter_time=0.214, forward_time=0.106, loss_ctc=44.127, loss_att=48.970, acc=0.745, loss=47.517, backward_time=0.098, grad_norm=41.646, clip=100.000, loss_scale=2.461e+34, optim_step_time=0.042, optim0_lr0=3.683e-04, train_time=0.658, time=2 hours, 44 minutes and 45.95 seconds, total_count=450000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=49.820, cer_ctc=0.265, loss_att=51.214, acc=0.592, cer=0.376, wer=0.999, loss=50.796, time=19 minutes and 59.22 seconds, total_count=140130, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-16 01:48:59,987 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-16 01:49:00,021 (trainer:272) INFO: 31/45epoch started. Estimated time to finish: 1 day, 22 hours and 11 minutes
+[gpuc02:0/16] 2024-01-16 01:49:00,031 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-16 01:49:19,275 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 01:49:22,722 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e89bc10>)
+[gpuc02:0/16] 2024-01-16 01:49:22,722 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-16 01:49:22,725 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 01:54:10,199 (trainer:737) INFO: 31epoch:train:1-100batch: iter_time=2.415, forward_time=0.104, loss_ctc=36.160, loss_att=41.235, acc=0.718, loss=39.712, backward_time=0.097, grad_norm=39.923, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.652e-04, train_time=3.101
+[gpuc02:0/16] 2024-01-16 01:54:52,578 (trainer:737) INFO: 31epoch:train:101-200batch: iter_time=1.057e-04, forward_time=0.104, loss_ctc=49.965, loss_att=48.512, acc=0.731, loss=48.948, backward_time=0.098, grad_norm=43.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.651e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 01:55:36,356 (trainer:737) INFO: 31epoch:train:201-300batch: iter_time=9.932e-05, forward_time=0.112, loss_ctc=45.743, loss_att=55.753, acc=0.717, loss=52.750, backward_time=0.099, grad_norm=43.282, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.651e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-16 01:56:20,029 (trainer:737) INFO: 31epoch:train:301-400batch: iter_time=1.150e-04, forward_time=0.110, loss_ctc=50.433, loss_att=53.357, acc=0.723, loss=52.480, backward_time=0.103, grad_norm=47.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.650e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-16 01:57:02,571 (trainer:737) INFO: 31epoch:train:401-500batch: iter_time=1.334e-04, forward_time=0.106, loss_ctc=47.181, loss_att=58.988, acc=0.726, loss=55.446, backward_time=0.098, grad_norm=46.093, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.650e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 01:57:50,456 (trainer:737) INFO: 31epoch:train:501-600batch: iter_time=3.389e-04, forward_time=0.115, loss_ctc=43.838, loss_att=47.672, acc=0.743, loss=46.522, backward_time=0.104, grad_norm=44.752, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.650e-04, train_time=0.479
+[gpuc02:0/16] 2024-01-16 01:58:19,478 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 01:58:33,315 (trainer:737) INFO: 31epoch:train:601-700batch: iter_time=1.168e-04, forward_time=0.110, loss_ctc=53.443, loss_att=49.156, acc=0.737, loss=50.442, backward_time=0.099, grad_norm=57.776, clip=100.000, loss_scale=1.752e+34, optim_step_time=0.042, optim0_lr0=3.649e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 01:59:16,717 (trainer:737) INFO: 31epoch:train:701-800batch: iter_time=1.093e-04, forward_time=0.104, loss_ctc=41.746, loss_att=39.089, acc=0.743, loss=39.886, backward_time=0.097, grad_norm=39.272, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.649e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-16 02:00:02,452 (trainer:737) INFO: 31epoch:train:801-900batch: iter_time=1.070e-04, forward_time=0.104, loss_ctc=42.749, loss_att=38.918, acc=0.762, loss=40.067, backward_time=0.097, grad_norm=41.121, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.648e-04, train_time=0.457
+[gpuc02:0/16] 2024-01-16 02:00:44,595 (trainer:737) INFO: 31epoch:train:901-1000batch: iter_time=1.075e-04, forward_time=0.103, loss_ctc=40.626, loss_att=44.226, acc=0.727, loss=43.146, backward_time=0.097, grad_norm=40.488, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.648e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 02:01:28,638 (trainer:737) INFO: 31epoch:train:1001-1100batch: iter_time=1.171e-04, forward_time=0.112, loss_ctc=49.565, loss_att=65.675, acc=0.714, loss=60.842, backward_time=0.103, grad_norm=44.040, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.648e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-16 02:02:12,725 (trainer:737) INFO: 31epoch:train:1101-1200batch: iter_time=1.095e-04, forward_time=0.105, loss_ctc=56.384, loss_att=61.519, acc=0.706, loss=59.978, backward_time=0.100, grad_norm=55.616, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.647e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-16 02:03:01,232 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-16 02:03:20,428 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 02:03:24,380 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d58084e20>)
+[gpuc02:0/16] 2024-01-16 02:03:24,380 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-16 02:03:24,383 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 02:09:38,765 (trainer:737) INFO: 31epoch:train:1201-1300batch: iter_time=3.910, forward_time=0.108, loss_ctc=39.182, loss_att=42.694, acc=0.728, loss=41.640, backward_time=0.101, grad_norm=37.975, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.647e-04, train_time=4.460
+[gpuc02:0/16] 2024-01-16 02:10:21,207 (trainer:737) INFO: 31epoch:train:1301-1400batch: iter_time=9.231e-05, forward_time=0.104, loss_ctc=48.459, loss_att=49.790, acc=0.719, loss=49.391, backward_time=0.098, grad_norm=47.669, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.646e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:11:03,690 (trainer:737) INFO: 31epoch:train:1401-1500batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=44.611, loss_att=48.787, acc=0.726, loss=47.534, backward_time=0.098, grad_norm=41.400, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.646e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 02:11:46,403 (trainer:737) INFO: 31epoch:train:1501-1600batch: iter_time=1.023e-04, forward_time=0.104, loss_ctc=37.411, loss_att=42.151, acc=0.739, loss=40.729, backward_time=0.097, grad_norm=36.392, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.646e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 02:12:28,693 (trainer:737) INFO: 31epoch:train:1601-1700batch: iter_time=1.122e-04, forward_time=0.105, loss_ctc=53.001, loss_att=58.302, acc=0.736, loss=56.712, backward_time=0.098, grad_norm=49.290, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.645e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:13:11,786 (trainer:737) INFO: 31epoch:train:1701-1800batch: iter_time=1.034e-04, forward_time=0.105, loss_ctc=49.259, loss_att=55.465, acc=0.729, loss=53.604, backward_time=0.098, grad_norm=48.595, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.645e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 02:13:54,187 (trainer:737) INFO: 31epoch:train:1801-1900batch: iter_time=1.045e-04, forward_time=0.104, loss_ctc=46.781, loss_att=52.798, acc=0.728, loss=50.993, backward_time=0.098, grad_norm=47.371, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.644e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:14:36,387 (trainer:737) INFO: 31epoch:train:1901-2000batch: iter_time=1.079e-04, forward_time=0.105, loss_ctc=47.593, loss_att=42.894, acc=0.746, loss=44.304, backward_time=0.097, grad_norm=42.295, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.644e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:15:18,612 (trainer:737) INFO: 31epoch:train:2001-2100batch: iter_time=1.213e-04, forward_time=0.104, loss_ctc=41.779, loss_att=36.240, acc=0.765, loss=37.902, backward_time=0.097, grad_norm=40.304, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.644e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:16:00,724 (trainer:737) INFO: 31epoch:train:2101-2200batch: iter_time=1.196e-04, forward_time=0.104, loss_ctc=39.752, loss_att=44.301, acc=0.732, loss=42.936, backward_time=0.096, grad_norm=38.742, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.643e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 02:16:42,907 (trainer:737) INFO: 31epoch:train:2201-2300batch: iter_time=1.145e-04, forward_time=0.104, loss_ctc=40.605, loss_att=49.575, acc=0.740, loss=46.884, backward_time=0.097, grad_norm=38.300, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.643e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:17:25,119 (trainer:737) INFO: 31epoch:train:2301-2400batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=46.987, loss_att=61.259, acc=0.710, loss=56.978, backward_time=0.097, grad_norm=45.619, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.642e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:18:07,553 (trainer:737) INFO: 31epoch:train:2401-2500batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=57.542, loss_att=59.194, acc=0.711, loss=58.698, backward_time=0.097, grad_norm=52.805, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.642e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:18:09,975 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-16 02:18:29,959 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 02:18:33,648 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e6847b4c0>)
+[gpuc02:0/16] 2024-01-16 02:18:33,648 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-16 02:18:33,651 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 02:23:18,484 (trainer:737) INFO: 31epoch:train:2501-2600batch: iter_time=2.491, forward_time=0.105, loss_ctc=34.412, loss_att=39.609, acc=0.738, loss=38.050, backward_time=0.096, grad_norm=36.926, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.642e-04, train_time=3.109
+[gpuc02:0/16] 2024-01-16 02:24:00,840 (trainer:737) INFO: 31epoch:train:2601-2700batch: iter_time=1.211e-04, forward_time=0.105, loss_ctc=47.897, loss_att=48.313, acc=0.747, loss=48.189, backward_time=0.097, grad_norm=43.537, clip=100.000, loss_scale=1.360e+34, optim_step_time=0.042, optim0_lr0=3.641e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:24:43,202 (trainer:737) INFO: 31epoch:train:2701-2800batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=44.545, loss_att=56.297, acc=0.724, loss=52.771, backward_time=0.098, grad_norm=45.822, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.641e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:25:25,961 (trainer:737) INFO: 31epoch:train:2801-2900batch: iter_time=1.119e-04, forward_time=0.105, loss_ctc=48.080, loss_att=53.590, acc=0.731, loss=51.937, backward_time=0.098, grad_norm=44.773, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.640e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 02:26:08,759 (trainer:737) INFO: 31epoch:train:2901-3000batch: iter_time=1.130e-04, forward_time=0.105, loss_ctc=46.017, loss_att=62.431, acc=0.730, loss=57.507, backward_time=0.099, grad_norm=44.496, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.640e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 02:26:51,332 (trainer:737) INFO: 31epoch:train:3001-3100batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=43.994, loss_att=48.404, acc=0.753, loss=47.081, backward_time=0.098, grad_norm=45.114, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.640e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 02:27:34,526 (trainer:737) INFO: 31epoch:train:3101-3200batch: iter_time=1.225e-04, forward_time=0.105, loss_ctc=51.649, loss_att=51.230, acc=0.740, loss=51.355, backward_time=0.098, grad_norm=57.281, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.639e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-16 02:28:16,729 (trainer:737) INFO: 31epoch:train:3201-3300batch: iter_time=1.433e-04, forward_time=0.105, loss_ctc=40.565, loss_att=37.974, acc=0.755, loss=38.751, backward_time=0.097, grad_norm=39.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.639e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:28:59,474 (trainer:737) INFO: 31epoch:train:3301-3400batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=41.090, loss_att=38.360, acc=0.769, loss=39.179, backward_time=0.097, grad_norm=39.246, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.638e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 02:29:41,825 (trainer:737) INFO: 31epoch:train:3401-3500batch: iter_time=1.359e-04, forward_time=0.104, loss_ctc=39.655, loss_att=42.831, acc=0.739, loss=41.878, backward_time=0.097, grad_norm=38.937, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.638e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:30:24,780 (trainer:737) INFO: 31epoch:train:3501-3600batch: iter_time=1.200e-04, forward_time=0.106, loss_ctc=47.840, loss_att=70.118, acc=0.716, loss=63.435, backward_time=0.098, grad_norm=46.096, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.638e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 02:31:07,134 (trainer:737) INFO: 31epoch:train:3601-3700batch: iter_time=1.412e-04, forward_time=0.105, loss_ctc=52.590, loss_att=60.290, acc=0.717, loss=57.980, backward_time=0.098, grad_norm=52.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.637e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:31:31,957 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-16 02:31:51,253 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 02:31:54,885 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2482d00040>)
+[gpuc02:0/16] 2024-01-16 02:31:54,885 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-16 02:31:54,888 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 02:36:23,450 (trainer:737) INFO: 31epoch:train:3701-3800batch: iter_time=2.494, forward_time=0.105, loss_ctc=38.207, loss_att=42.924, acc=0.731, loss=41.509, backward_time=0.096, grad_norm=37.538, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.637e-04, train_time=3.163
+[gpuc02:0/16] 2024-01-16 02:37:05,718 (trainer:737) INFO: 31epoch:train:3801-3900batch: iter_time=1.046e-04, forward_time=0.105, loss_ctc=47.422, loss_att=51.173, acc=0.716, loss=50.048, backward_time=0.097, grad_norm=47.043, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.636e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:37:47,932 (trainer:737) INFO: 31epoch:train:3901-4000batch: iter_time=1.051e-04, forward_time=0.104, loss_ctc=44.078, loss_att=49.421, acc=0.728, loss=47.818, backward_time=0.097, grad_norm=43.690, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.636e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:38:30,045 (trainer:737) INFO: 31epoch:train:4001-4100batch: iter_time=1.053e-04, forward_time=0.104, loss_ctc=37.260, loss_att=41.902, acc=0.740, loss=40.509, backward_time=0.096, grad_norm=38.074, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.636e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 02:39:12,423 (trainer:737) INFO: 31epoch:train:4101-4200batch: iter_time=1.056e-04, forward_time=0.105, loss_ctc=52.412, loss_att=58.796, acc=0.737, loss=56.881, backward_time=0.097, grad_norm=50.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.635e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:39:55,464 (trainer:737) INFO: 31epoch:train:4201-4300batch: iter_time=9.898e-05, forward_time=0.105, loss_ctc=48.564, loss_att=55.213, acc=0.733, loss=53.218, backward_time=0.097, grad_norm=46.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.635e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 02:40:37,685 (trainer:737) INFO: 31epoch:train:4301-4400batch: iter_time=1.007e-04, forward_time=0.104, loss_ctc=46.182, loss_att=53.231, acc=0.727, loss=51.117, backward_time=0.096, grad_norm=49.010, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.634e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:41:19,936 (trainer:737) INFO: 31epoch:train:4401-4500batch: iter_time=1.090e-04, forward_time=0.104, loss_ctc=47.335, loss_att=42.752, acc=0.747, loss=44.127, backward_time=0.096, grad_norm=42.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.634e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:42:02,869 (trainer:737) INFO: 31epoch:train:4501-4600batch: iter_time=9.441e-05, forward_time=0.108, loss_ctc=41.386, loss_att=36.267, acc=0.766, loss=37.803, backward_time=0.097, grad_norm=40.147, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.634e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 02:42:45,348 (trainer:737) INFO: 31epoch:train:4601-4700batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=39.727, loss_att=44.670, acc=0.732, loss=43.187, backward_time=0.097, grad_norm=38.192, clip=100.000, loss_scale=2.721e+34, optim_step_time=0.042, optim0_lr0=3.633e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 02:43:27,549 (trainer:737) INFO: 31epoch:train:4701-4800batch: iter_time=1.017e-04, forward_time=0.104, loss_ctc=40.223, loss_att=49.766, acc=0.742, loss=46.903, backward_time=0.097, grad_norm=37.066, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.633e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:44:10,556 (trainer:737) INFO: 31epoch:train:4801-4900batch: iter_time=1.034e-04, forward_time=0.105, loss_ctc=47.188, loss_att=61.205, acc=0.709, loss=57.000, backward_time=0.097, grad_norm=46.460, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.632e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 02:44:27,701 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 02:44:53,039 (trainer:737) INFO: 31epoch:train:4901-5000batch: iter_time=9.859e-05, forward_time=0.105, loss_ctc=55.954, loss_att=60.208, acc=0.712, loss=58.932, backward_time=0.097, grad_norm=52.860, clip=100.000, loss_scale=2.895e+34, optim_step_time=0.041, optim0_lr0=3.632e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 02:44:55,825 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-16 02:45:15,730 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 02:45:19,459 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e990873a0>)
+[gpuc02:0/16] 2024-01-16 02:45:19,460 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-16 02:45:19,493 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 02:50:08,463 (trainer:737) INFO: 31epoch:train:5001-5100batch: iter_time=2.622, forward_time=0.127, loss_ctc=34.393, loss_att=38.689, acc=0.729, loss=37.400, backward_time=0.101, grad_norm=36.434, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.632e-04, train_time=3.154
+[gpuc02:0/16] 2024-01-16 02:50:50,846 (trainer:737) INFO: 31epoch:train:5101-5200batch: iter_time=1.319e-04, forward_time=0.105, loss_ctc=47.652, loss_att=46.826, acc=0.739, loss=47.074, backward_time=0.098, grad_norm=43.654, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.631e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:51:33,159 (trainer:737) INFO: 31epoch:train:5201-5300batch: iter_time=1.212e-04, forward_time=0.104, loss_ctc=44.270, loss_att=54.038, acc=0.721, loss=51.108, backward_time=0.097, grad_norm=43.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.631e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:52:15,426 (trainer:737) INFO: 31epoch:train:5301-5400batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=47.586, loss_att=51.991, acc=0.729, loss=50.669, backward_time=0.097, grad_norm=44.406, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.630e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:52:57,950 (trainer:737) INFO: 31epoch:train:5401-5500batch: iter_time=1.172e-04, forward_time=0.106, loss_ctc=45.506, loss_att=58.021, acc=0.730, loss=54.266, backward_time=0.098, grad_norm=45.270, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.630e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 02:53:40,229 (trainer:737) INFO: 31epoch:train:5501-5600batch: iter_time=1.138e-04, forward_time=0.106, loss_ctc=42.903, loss_att=46.526, acc=0.750, loss=45.439, backward_time=0.097, grad_norm=43.947, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.630e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:54:22,518 (trainer:737) INFO: 31epoch:train:5601-5700batch: iter_time=1.250e-04, forward_time=0.105, loss_ctc=51.753, loss_att=48.165, acc=0.741, loss=49.241, backward_time=0.096, grad_norm=51.294, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.629e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:55:04,714 (trainer:737) INFO: 31epoch:train:5701-5800batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=40.515, loss_att=38.050, acc=0.750, loss=38.790, backward_time=0.096, grad_norm=38.750, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.629e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 02:55:47,119 (trainer:737) INFO: 31epoch:train:5801-5900batch: iter_time=1.385e-04, forward_time=0.105, loss_ctc=40.481, loss_att=37.194, acc=0.768, loss=38.180, backward_time=0.097, grad_norm=40.504, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.628e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 02:56:29,483 (trainer:737) INFO: 31epoch:train:5901-6000batch: iter_time=1.247e-04, forward_time=0.105, loss_ctc=39.681, loss_att=42.946, acc=0.735, loss=41.966, backward_time=0.096, grad_norm=38.516, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.628e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 02:57:12,232 (trainer:737) INFO: 31epoch:train:6001-6100batch: iter_time=1.289e-04, forward_time=0.106, loss_ctc=47.982, loss_att=64.487, acc=0.721, loss=59.536, backward_time=0.097, grad_norm=46.527, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.628e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 02:57:54,757 (trainer:737) INFO: 31epoch:train:6101-6200batch: iter_time=1.240e-04, forward_time=0.106, loss_ctc=52.190, loss_att=59.517, acc=0.714, loss=57.319, backward_time=0.097, grad_norm=51.527, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.627e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 02:58:20,534 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-16 02:58:39,321 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 02:58:42,835 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2482d2cd00>)
+[gpuc02:0/16] 2024-01-16 02:58:42,836 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-16 02:58:42,839 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 03:03:11,150 (trainer:737) INFO: 31epoch:train:6201-6300batch: iter_time=2.588, forward_time=0.105, loss_ctc=37.934, loss_att=42.535, acc=0.737, loss=41.155, backward_time=0.096, grad_norm=37.974, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.627e-04, train_time=3.164
+[gpuc02:0/16] 2024-01-16 03:03:53,542 (trainer:737) INFO: 31epoch:train:6301-6400batch: iter_time=1.286e-04, forward_time=0.105, loss_ctc=46.260, loss_att=50.115, acc=0.733, loss=48.958, backward_time=0.097, grad_norm=45.437, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.626e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:04:36,251 (trainer:737) INFO: 31epoch:train:6401-6500batch: iter_time=1.310e-04, forward_time=0.106, loss_ctc=43.514, loss_att=51.438, acc=0.734, loss=49.061, backward_time=0.097, grad_norm=43.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.626e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 03:05:18,498 (trainer:737) INFO: 31epoch:train:6501-6600batch: iter_time=1.342e-04, forward_time=0.104, loss_ctc=36.906, loss_att=43.663, acc=0.740, loss=41.636, backward_time=0.097, grad_norm=36.099, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.626e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:06:01,032 (trainer:737) INFO: 31epoch:train:6601-6700batch: iter_time=1.330e-04, forward_time=0.106, loss_ctc=52.687, loss_att=58.912, acc=0.745, loss=57.045, backward_time=0.098, grad_norm=49.896, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.625e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:06:43,598 (trainer:737) INFO: 31epoch:train:6701-6800batch: iter_time=1.335e-04, forward_time=0.107, loss_ctc=48.350, loss_att=59.267, acc=0.731, loss=55.992, backward_time=0.098, grad_norm=47.210, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.625e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:07:25,950 (trainer:737) INFO: 31epoch:train:6801-6900batch: iter_time=1.474e-04, forward_time=0.105, loss_ctc=45.415, loss_att=55.082, acc=0.737, loss=52.182, backward_time=0.097, grad_norm=48.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.624e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:08:08,472 (trainer:737) INFO: 31epoch:train:6901-7000batch: iter_time=1.479e-04, forward_time=0.105, loss_ctc=46.617, loss_att=42.947, acc=0.751, loss=44.048, backward_time=0.097, grad_norm=40.896, clip=100.000, loss_scale=3.323e+34, optim_step_time=0.042, optim0_lr0=3.624e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:08:50,732 (trainer:737) INFO: 31epoch:train:7001-7100batch: iter_time=1.444e-04, forward_time=0.105, loss_ctc=40.646, loss_att=36.136, acc=0.774, loss=37.489, backward_time=0.097, grad_norm=38.856, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.624e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:09:32,931 (trainer:737) INFO: 31epoch:train:7101-7200batch: iter_time=1.539e-04, forward_time=0.105, loss_ctc=38.564, loss_att=43.799, acc=0.742, loss=42.229, backward_time=0.096, grad_norm=38.293, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.623e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:10:15,374 (trainer:737) INFO: 31epoch:train:7201-7300batch: iter_time=1.277e-04, forward_time=0.106, loss_ctc=39.972, loss_att=52.428, acc=0.745, loss=48.691, backward_time=0.098, grad_norm=38.112, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.623e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:10:57,850 (trainer:737) INFO: 31epoch:train:7301-7400batch: iter_time=1.284e-04, forward_time=0.106, loss_ctc=46.239, loss_att=63.599, acc=0.715, loss=58.391, backward_time=0.098, grad_norm=44.449, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.622e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:11:16,953 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 03:11:40,248 (trainer:737) INFO: 31epoch:train:7401-7500batch: iter_time=1.168e-04, forward_time=0.106, loss_ctc=55.895, loss_att=61.768, acc=0.713, loss=60.006, backward_time=0.097, grad_norm=52.585, clip=100.000, loss_scale=3.000e+34, optim_step_time=0.041, optim0_lr0=3.622e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:11:44,936 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-16 03:12:04,270 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 03:12:07,760 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2548ccb940>)
+[gpuc02:0/16] 2024-01-16 03:12:07,761 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-16 03:12:07,764 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 03:16:56,083 (trainer:737) INFO: 31epoch:train:7501-7600batch: iter_time=2.534, forward_time=0.104, loss_ctc=34.363, loss_att=39.852, acc=0.728, loss=38.206, backward_time=0.097, grad_norm=37.425, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.622e-04, train_time=3.158
+[gpuc02:0/16] 2024-01-16 03:17:38,810 (trainer:737) INFO: 31epoch:train:7601-7700batch: iter_time=1.374e-04, forward_time=0.104, loss_ctc=46.743, loss_att=47.456, acc=0.737, loss=47.242, backward_time=0.098, grad_norm=42.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.621e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 03:18:21,616 (trainer:737) INFO: 31epoch:train:7701-7800batch: iter_time=1.323e-04, forward_time=0.104, loss_ctc=44.279, loss_att=54.839, acc=0.722, loss=51.671, backward_time=0.097, grad_norm=43.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.621e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 03:19:03,888 (trainer:737) INFO: 31epoch:train:7801-7900batch: iter_time=1.210e-04, forward_time=0.104, loss_ctc=47.136, loss_att=51.800, acc=0.729, loss=50.401, backward_time=0.096, grad_norm=43.688, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.620e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:19:46,557 (trainer:737) INFO: 31epoch:train:7901-8000batch: iter_time=1.132e-04, forward_time=0.106, loss_ctc=45.551, loss_att=58.186, acc=0.732, loss=54.395, backward_time=0.097, grad_norm=43.945, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.620e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 03:20:29,086 (trainer:737) INFO: 31epoch:train:8001-8100batch: iter_time=1.119e-04, forward_time=0.104, loss_ctc=42.100, loss_att=46.839, acc=0.748, loss=45.417, backward_time=0.096, grad_norm=41.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.620e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:21:11,943 (trainer:737) INFO: 31epoch:train:8101-8200batch: iter_time=1.075e-04, forward_time=0.108, loss_ctc=50.848, loss_att=48.519, acc=0.741, loss=49.218, backward_time=0.097, grad_norm=55.810, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.619e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 03:21:54,386 (trainer:737) INFO: 31epoch:train:8201-8300batch: iter_time=1.066e-04, forward_time=0.104, loss_ctc=40.411, loss_att=38.336, acc=0.748, loss=38.958, backward_time=0.096, grad_norm=47.310, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.619e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:22:35,788 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 03:22:36,649 (trainer:737) INFO: 31epoch:train:8301-8400batch: iter_time=1.010e-04, forward_time=0.104, loss_ctc=40.694, loss_att=37.706, acc=0.767, loss=38.603, backward_time=0.097, grad_norm=38.599, clip=100.000, loss_scale=2.056e+34, optim_step_time=0.042, optim0_lr0=3.618e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:23:19,165 (trainer:737) INFO: 31epoch:train:8401-8500batch: iter_time=1.100e-04, forward_time=0.103, loss_ctc=39.234, loss_att=43.391, acc=0.732, loss=42.144, backward_time=0.097, grad_norm=36.879, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.618e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:24:01,723 (trainer:737) INFO: 31epoch:train:8501-8600batch: iter_time=1.117e-04, forward_time=0.105, loss_ctc=47.314, loss_att=65.093, acc=0.718, loss=59.759, backward_time=0.098, grad_norm=45.204, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.618e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:24:44,205 (trainer:737) INFO: 31epoch:train:8601-8700batch: iter_time=1.107e-04, forward_time=0.105, loss_ctc=52.018, loss_att=60.471, acc=0.710, loss=57.935, backward_time=0.098, grad_norm=52.305, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.617e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:25:08,600 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-16 03:25:28,099 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 03:25:31,940 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e9309db70>)
+[gpuc02:0/16] 2024-01-16 03:25:31,940 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-16 03:25:31,943 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 03:29:57,661 (trainer:737) INFO: 31epoch:train:8701-8800batch: iter_time=2.547, forward_time=0.115, loss_ctc=38.045, loss_att=41.447, acc=0.733, loss=40.427, backward_time=0.099, grad_norm=36.789, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.617e-04, train_time=3.134
+[gpuc02:0/16] 2024-01-16 03:30:40,090 (trainer:737) INFO: 31epoch:train:8801-8900batch: iter_time=1.005e-04, forward_time=0.105, loss_ctc=46.105, loss_att=48.826, acc=0.723, loss=48.010, backward_time=0.097, grad_norm=44.845, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.616e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:31:22,393 (trainer:737) INFO: 31epoch:train:8901-9000batch: iter_time=1.157e-04, forward_time=0.105, loss_ctc=42.911, loss_att=48.346, acc=0.730, loss=46.716, backward_time=0.097, grad_norm=43.071, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.616e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:32:04,639 (trainer:737) INFO: 31epoch:train:9001-9100batch: iter_time=1.185e-04, forward_time=0.105, loss_ctc=37.263, loss_att=42.259, acc=0.741, loss=40.760, backward_time=0.097, grad_norm=36.262, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.616e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:32:47,432 (trainer:737) INFO: 31epoch:train:9101-9200batch: iter_time=1.118e-04, forward_time=0.106, loss_ctc=51.920, loss_att=57.927, acc=0.738, loss=56.125, backward_time=0.098, grad_norm=51.760, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.615e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 03:33:29,953 (trainer:737) INFO: 31epoch:train:9201-9300batch: iter_time=1.078e-04, forward_time=0.106, loss_ctc=47.464, loss_att=55.077, acc=0.732, loss=52.793, backward_time=0.098, grad_norm=47.257, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.615e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:34:12,837 (trainer:737) INFO: 31epoch:train:9301-9400batch: iter_time=1.029e-04, forward_time=0.105, loss_ctc=45.801, loss_att=52.634, acc=0.731, loss=50.584, backward_time=0.097, grad_norm=47.792, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.615e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 03:34:55,765 (trainer:737) INFO: 31epoch:train:9401-9500batch: iter_time=1.065e-04, forward_time=0.106, loss_ctc=46.548, loss_att=42.472, acc=0.747, loss=43.695, backward_time=0.101, grad_norm=43.505, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.614e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 03:35:38,222 (trainer:737) INFO: 31epoch:train:9501-9600batch: iter_time=1.049e-04, forward_time=0.105, loss_ctc=40.407, loss_att=35.365, acc=0.769, loss=36.878, backward_time=0.097, grad_norm=39.578, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.614e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:36:22,113 (trainer:737) INFO: 31epoch:train:9601-9700batch: iter_time=1.107e-04, forward_time=0.106, loss_ctc=38.920, loss_att=43.695, acc=0.736, loss=42.263, backward_time=0.099, grad_norm=37.919, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.613e-04, train_time=0.439
+[gpuc02:0/16] 2024-01-16 03:37:04,888 (trainer:737) INFO: 31epoch:train:9701-9800batch: iter_time=1.069e-04, forward_time=0.105, loss_ctc=40.035, loss_att=49.258, acc=0.744, loss=46.491, backward_time=0.097, grad_norm=38.313, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.613e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 03:37:48,042 (trainer:737) INFO: 31epoch:train:9801-9900batch: iter_time=1.084e-04, forward_time=0.105, loss_ctc=45.776, loss_att=59.637, acc=0.714, loss=55.479, backward_time=0.098, grad_norm=43.310, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.613e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 03:38:30,482 (trainer:737) INFO: 31epoch:train:9901-10000batch: iter_time=9.707e-05, forward_time=0.105, loss_ctc=54.159, loss_att=59.070, acc=0.714, loss=57.596, backward_time=0.098, grad_norm=51.305, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.612e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:38:33,130 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-16 03:38:53,016 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 03:38:56,761 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2547a78a90>)
+[gpuc02:0/16] 2024-01-16 03:38:56,761 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-16 03:38:56,764 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 03:43:40,722 (trainer:737) INFO: 31epoch:train:10001-10100batch: iter_time=2.553, forward_time=0.104, loss_ctc=33.781, loss_att=39.438, acc=0.740, loss=37.741, backward_time=0.096, grad_norm=37.414, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.612e-04, train_time=3.102
+[gpuc02:0/16] 2024-01-16 03:44:22,994 (trainer:737) INFO: 31epoch:train:10101-10200batch: iter_time=1.174e-04, forward_time=0.105, loss_ctc=46.191, loss_att=48.157, acc=0.747, loss=47.567, backward_time=0.097, grad_norm=42.180, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.611e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:45:05,360 (trainer:737) INFO: 31epoch:train:10201-10300batch: iter_time=1.073e-04, forward_time=0.104, loss_ctc=43.980, loss_att=56.352, acc=0.725, loss=52.640, backward_time=0.097, grad_norm=44.002, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.611e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:45:47,555 (trainer:737) INFO: 31epoch:train:10301-10400batch: iter_time=1.188e-04, forward_time=0.105, loss_ctc=46.491, loss_att=52.664, acc=0.733, loss=50.812, backward_time=0.097, grad_norm=43.315, clip=100.000, loss_scale=1.059e+34, optim_step_time=0.041, optim0_lr0=3.611e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 03:46:30,344 (trainer:737) INFO: 31epoch:train:10401-10500batch: iter_time=1.130e-04, forward_time=0.106, loss_ctc=45.039, loss_att=61.308, acc=0.732, loss=56.427, backward_time=0.098, grad_norm=43.243, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.610e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 03:47:12,670 (trainer:737) INFO: 31epoch:train:10501-10600batch: iter_time=1.209e-04, forward_time=0.105, loss_ctc=43.055, loss_att=47.629, acc=0.756, loss=46.257, backward_time=0.097, grad_norm=43.159, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.610e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:47:55,025 (trainer:737) INFO: 31epoch:train:10601-10700batch: iter_time=1.102e-04, forward_time=0.105, loss_ctc=50.746, loss_att=50.941, acc=0.745, loss=50.883, backward_time=0.097, grad_norm=51.871, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.609e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:48:37,124 (trainer:737) INFO: 31epoch:train:10701-10800batch: iter_time=1.236e-04, forward_time=0.104, loss_ctc=40.269, loss_att=38.100, acc=0.755, loss=38.751, backward_time=0.096, grad_norm=38.369, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.609e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 03:49:19,429 (trainer:737) INFO: 31epoch:train:10801-10900batch: iter_time=1.149e-04, forward_time=0.105, loss_ctc=40.321, loss_att=37.650, acc=0.773, loss=38.451, backward_time=0.097, grad_norm=38.941, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.609e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:50:01,722 (trainer:737) INFO: 31epoch:train:10901-11000batch: iter_time=1.225e-04, forward_time=0.104, loss_ctc=39.079, loss_att=42.702, acc=0.742, loss=41.615, backward_time=0.096, grad_norm=38.815, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.608e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 03:50:44,287 (trainer:737) INFO: 31epoch:train:11001-11100batch: iter_time=1.262e-04, forward_time=0.106, loss_ctc=47.795, loss_att=69.361, acc=0.719, loss=62.891, backward_time=0.098, grad_norm=47.864, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.608e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:51:26,764 (trainer:737) INFO: 31epoch:train:11101-11200batch: iter_time=1.217e-04, forward_time=0.106, loss_ctc=50.850, loss_att=59.270, acc=0.721, loss=56.744, backward_time=0.098, grad_norm=53.217, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.607e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:51:52,973 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-16 03:52:12,285 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 03:52:15,850 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e69c3ae00>)
+[gpuc02:0/16] 2024-01-16 03:52:15,850 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-16 03:52:15,854 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 03:56:37,243 (trainer:737) INFO: 31epoch:train:11201-11300batch: iter_time=2.550, forward_time=0.109, loss_ctc=37.680, loss_att=41.424, acc=0.742, loss=40.301, backward_time=0.097, grad_norm=37.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.607e-04, train_time=3.105
+[gpuc02:0/16] 2024-01-16 03:57:19,760 (trainer:737) INFO: 31epoch:train:11301-11400batch: iter_time=1.035e-04, forward_time=0.105, loss_ctc=46.647, loss_att=48.502, acc=0.737, loss=47.945, backward_time=0.099, grad_norm=46.735, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.607e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 03:58:02,371 (trainer:737) INFO: 31epoch:train:11401-11500batch: iter_time=1.051e-04, forward_time=0.105, loss_ctc=43.133, loss_att=49.529, acc=0.736, loss=47.611, backward_time=0.098, grad_norm=42.729, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.606e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 03:58:44,784 (trainer:737) INFO: 31epoch:train:11501-11600batch: iter_time=1.167e-04, forward_time=0.104, loss_ctc=36.906, loss_att=42.712, acc=0.744, loss=40.971, backward_time=0.097, grad_norm=36.959, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.606e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 03:59:27,524 (trainer:737) INFO: 31epoch:train:11601-11700batch: iter_time=1.173e-04, forward_time=0.106, loss_ctc=51.185, loss_att=58.665, acc=0.746, loss=56.421, backward_time=0.098, grad_norm=46.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.606e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 04:00:10,531 (trainer:737) INFO: 31epoch:train:11701-11800batch: iter_time=1.169e-04, forward_time=0.105, loss_ctc=47.110, loss_att=58.242, acc=0.736, loss=54.902, backward_time=0.098, grad_norm=46.627, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.605e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 04:00:52,917 (trainer:737) INFO: 31epoch:train:11801-11900batch: iter_time=1.117e-04, forward_time=0.105, loss_ctc=45.419, loss_att=55.805, acc=0.735, loss=52.689, backward_time=0.098, grad_norm=49.435, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.605e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:01:35,239 (trainer:737) INFO: 31epoch:train:11901-12000batch: iter_time=1.111e-04, forward_time=0.105, loss_ctc=46.215, loss_att=42.616, acc=0.751, loss=43.695, backward_time=0.098, grad_norm=41.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.604e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 04:02:17,809 (trainer:737) INFO: 31epoch:train:12001-12100batch: iter_time=1.171e-04, forward_time=0.104, loss_ctc=39.971, loss_att=34.802, acc=0.778, loss=36.353, backward_time=0.097, grad_norm=39.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.604e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:02:59,991 (trainer:737) INFO: 31epoch:train:12101-12200batch: iter_time=9.968e-05, forward_time=0.104, loss_ctc=38.697, loss_att=43.260, acc=0.744, loss=41.891, backward_time=0.097, grad_norm=37.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.604e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 04:03:42,609 (trainer:737) INFO: 31epoch:train:12201-12300batch: iter_time=1.027e-04, forward_time=0.107, loss_ctc=39.781, loss_att=51.131, acc=0.745, loss=47.726, backward_time=0.097, grad_norm=38.763, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.603e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 04:04:25,034 (trainer:737) INFO: 31epoch:train:12301-12400batch: iter_time=9.883e-05, forward_time=0.104, loss_ctc=45.834, loss_att=62.304, acc=0.718, loss=57.363, backward_time=0.097, grad_norm=45.933, clip=100.000, loss_scale=2.118e+34, optim_step_time=0.041, optim0_lr0=3.603e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:05:07,482 (trainer:737) INFO: 31epoch:train:12401-12500batch: iter_time=8.948e-05, forward_time=0.105, loss_ctc=54.362, loss_att=59.715, acc=0.720, loss=58.109, backward_time=0.098, grad_norm=50.668, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.602e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:05:10,037 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-16 04:05:29,772 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 04:05:33,448 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ec64631c0>)
+[gpuc02:0/16] 2024-01-16 04:05:33,448 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-16 04:05:33,451 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 04:10:22,100 (trainer:737) INFO: 31epoch:train:12501-12600batch: iter_time=2.535, forward_time=0.104, loss_ctc=33.394, loss_att=37.776, acc=0.745, loss=36.462, backward_time=0.096, grad_norm=35.940, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.602e-04, train_time=3.146
+[gpuc02:0/16] 2024-01-16 04:10:27,584 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 04:11:04,525 (trainer:737) INFO: 31epoch:train:12601-12700batch: iter_time=1.218e-04, forward_time=0.105, loss_ctc=46.079, loss_att=46.178, acc=0.753, loss=46.148, backward_time=0.097, grad_norm=41.884, clip=100.000, loss_scale=2.329e+34, optim_step_time=0.041, optim0_lr0=3.602e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:11:46,842 (trainer:737) INFO: 31epoch:train:12701-12800batch: iter_time=1.198e-04, forward_time=0.104, loss_ctc=43.570, loss_att=54.797, acc=0.730, loss=51.429, backward_time=0.097, grad_norm=44.038, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.601e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 04:12:29,757 (trainer:737) INFO: 31epoch:train:12801-12900batch: iter_time=1.160e-04, forward_time=0.104, loss_ctc=46.187, loss_att=51.958, acc=0.734, loss=50.226, backward_time=0.097, grad_norm=43.786, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.601e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 04:13:12,724 (trainer:737) INFO: 31epoch:train:12901-13000batch: iter_time=1.223e-04, forward_time=0.106, loss_ctc=45.858, loss_att=61.400, acc=0.733, loss=56.738, backward_time=0.098, grad_norm=43.363, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.600e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 04:13:55,125 (trainer:737) INFO: 31epoch:train:13001-13100batch: iter_time=1.241e-04, forward_time=0.106, loss_ctc=41.886, loss_att=46.582, acc=0.758, loss=45.173, backward_time=0.097, grad_norm=42.166, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.600e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:14:37,829 (trainer:737) INFO: 31epoch:train:13101-13200batch: iter_time=1.143e-04, forward_time=0.106, loss_ctc=50.294, loss_att=50.114, acc=0.746, loss=50.168, backward_time=0.097, grad_norm=52.889, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.600e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 04:15:20,096 (trainer:737) INFO: 31epoch:train:13201-13300batch: iter_time=1.223e-04, forward_time=0.106, loss_ctc=39.812, loss_att=38.179, acc=0.754, loss=38.669, backward_time=0.096, grad_norm=38.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.599e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 04:16:02,438 (trainer:737) INFO: 31epoch:train:13301-13400batch: iter_time=1.116e-04, forward_time=0.106, loss_ctc=40.301, loss_att=37.161, acc=0.774, loss=38.103, backward_time=0.097, grad_norm=39.585, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.599e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 04:16:45,386 (trainer:737) INFO: 31epoch:train:13401-13500batch: iter_time=1.142e-04, forward_time=0.106, loss_ctc=39.190, loss_att=43.456, acc=0.737, loss=42.176, backward_time=0.096, grad_norm=36.937, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.598e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 04:17:29,181 (trainer:737) INFO: 31epoch:train:13501-13600batch: iter_time=1.128e-04, forward_time=0.111, loss_ctc=47.141, loss_att=69.010, acc=0.721, loss=62.449, backward_time=0.098, grad_norm=45.387, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.598e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-16 04:18:12,231 (trainer:737) INFO: 31epoch:train:13601-13700batch: iter_time=1.232e-04, forward_time=0.106, loss_ctc=51.362, loss_att=60.848, acc=0.717, loss=58.002, backward_time=0.097, grad_norm=52.762, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.598e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 04:18:38,482 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-16 04:18:58,235 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 04:19:01,983 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e58d02b00>)
+[gpuc02:0/16] 2024-01-16 04:19:01,984 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-16 04:19:01,987 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 04:23:57,599 (trainer:737) INFO: 31epoch:train:13701-13800batch: iter_time=2.645, forward_time=0.105, loss_ctc=37.376, loss_att=41.756, acc=0.741, loss=40.442, backward_time=0.096, grad_norm=35.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.597e-04, train_time=3.453
+[gpuc02:0/16] 2024-01-16 04:24:39,995 (trainer:737) INFO: 31epoch:train:13801-13900batch: iter_time=1.291e-04, forward_time=0.107, loss_ctc=46.098, loss_att=48.236, acc=0.737, loss=47.594, backward_time=0.097, grad_norm=45.418, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.597e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:25:22,297 (trainer:737) INFO: 31epoch:train:13901-14000batch: iter_time=1.357e-04, forward_time=0.106, loss_ctc=43.091, loss_att=48.857, acc=0.738, loss=47.127, backward_time=0.097, grad_norm=42.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.597e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 04:26:04,772 (trainer:737) INFO: 31epoch:train:14001-14100batch: iter_time=1.209e-04, forward_time=0.109, loss_ctc=36.294, loss_att=42.383, acc=0.745, loss=40.557, backward_time=0.097, grad_norm=38.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.596e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:26:47,281 (trainer:737) INFO: 31epoch:train:14101-14200batch: iter_time=1.274e-04, forward_time=0.107, loss_ctc=51.896, loss_att=57.254, acc=0.749, loss=55.647, backward_time=0.098, grad_norm=47.824, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.596e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:27:29,820 (trainer:737) INFO: 31epoch:train:14201-14300batch: iter_time=1.460e-04, forward_time=0.107, loss_ctc=47.416, loss_att=58.187, acc=0.735, loss=54.955, backward_time=0.098, grad_norm=46.387, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.595e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:28:12,526 (trainer:737) INFO: 31epoch:train:14301-14400batch: iter_time=1.469e-04, forward_time=0.106, loss_ctc=44.960, loss_att=54.791, acc=0.739, loss=51.842, backward_time=0.097, grad_norm=49.006, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.595e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 04:28:54,901 (trainer:737) INFO: 31epoch:train:14401-14500batch: iter_time=1.433e-04, forward_time=0.106, loss_ctc=46.167, loss_att=42.184, acc=0.754, loss=43.379, backward_time=0.097, grad_norm=40.335, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.595e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:29:37,288 (trainer:737) INFO: 31epoch:train:14501-14600batch: iter_time=1.478e-04, forward_time=0.106, loss_ctc=40.623, loss_att=35.813, acc=0.775, loss=37.256, backward_time=0.097, grad_norm=39.498, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.594e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 04:30:19,606 (trainer:737) INFO: 31epoch:train:14601-14700batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=38.814, loss_att=43.629, acc=0.742, loss=42.185, backward_time=0.096, grad_norm=38.550, clip=100.000, loss_scale=3.884e+34, optim_step_time=0.042, optim0_lr0=3.594e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 04:31:02,095 (trainer:737) INFO: 31epoch:train:14701-14800batch: iter_time=1.299e-04, forward_time=0.106, loss_ctc=39.943, loss_att=51.136, acc=0.747, loss=47.778, backward_time=0.097, grad_norm=37.508, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.593e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:31:44,580 (trainer:737) INFO: 31epoch:train:14801-14900batch: iter_time=1.375e-04, forward_time=0.106, loss_ctc=46.211, loss_att=63.047, acc=0.715, loss=57.996, backward_time=0.097, grad_norm=46.247, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.593e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:32:27,098 (trainer:737) INFO: 31epoch:train:14901-15000batch: iter_time=1.201e-04, forward_time=0.107, loss_ctc=53.329, loss_att=59.933, acc=0.715, loss=57.952, backward_time=0.097, grad_norm=50.520, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.593e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:52:40,122 (trainer:343) INFO: 31epoch results: [train] iter_time=0.213, forward_time=0.106, loss_ctc=44.619, loss_att=49.854, acc=0.737, loss=48.284, backward_time=0.097, grad_norm=43.643, clip=100.000, loss_scale=1.990e+34, optim_step_time=0.042, optim0_lr0=3.622e-04, train_time=0.654, time=2 hours, 43 minutes and 37.18 seconds, total_count=465000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=50.029, cer_ctc=0.261, loss_att=50.580, acc=0.612, cer=0.350, wer=0.997, loss=50.415, time=20 minutes and 2.7 seconds, total_count=144801, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-16 04:52:45,100 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-16 04:52:45,126 (trainer:272) INFO: 32/45epoch started. Estimated time to finish: 1 day, 19 hours and 5 minutes
+[gpuc02:0/16] 2024-01-16 04:52:45,136 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-16 04:53:04,154 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 04:53:07,824 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d6e88d210>)
+[gpuc02:0/16] 2024-01-16 04:53:07,824 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-16 04:53:07,827 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 04:57:50,818 (trainer:737) INFO: 32epoch:train:1-100batch: iter_time=2.610, forward_time=0.109, loss_ctc=47.222, loss_att=55.321, acc=0.731, loss=52.891, backward_time=0.097, grad_norm=47.953, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.592e-04, train_time=3.057
+[gpuc02:0/16] 2024-01-16 04:58:33,316 (trainer:737) INFO: 32epoch:train:101-200batch: iter_time=1.299e-04, forward_time=0.105, loss_ctc=47.647, loss_att=52.941, acc=0.734, loss=51.353, backward_time=0.098, grad_norm=44.457, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.592e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 04:59:16,169 (trainer:737) INFO: 32epoch:train:201-300batch: iter_time=1.371e-04, forward_time=0.107, loss_ctc=43.627, loss_att=51.628, acc=0.745, loss=49.228, backward_time=0.098, grad_norm=41.325, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.592e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 04:59:59,211 (trainer:737) INFO: 32epoch:train:301-400batch: iter_time=1.380e-04, forward_time=0.112, loss_ctc=51.070, loss_att=56.715, acc=0.726, loss=55.022, backward_time=0.098, grad_norm=45.997, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.591e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 05:00:41,439 (trainer:737) INFO: 32epoch:train:401-500batch: iter_time=1.463e-04, forward_time=0.105, loss_ctc=41.243, loss_att=39.625, acc=0.761, loss=40.111, backward_time=0.097, grad_norm=41.414, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.591e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 05:01:23,796 (trainer:737) INFO: 32epoch:train:501-600batch: iter_time=1.438e-04, forward_time=0.106, loss_ctc=38.094, loss_att=46.247, acc=0.737, loss=43.801, backward_time=0.098, grad_norm=36.262, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.590e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:02:05,861 (trainer:737) INFO: 32epoch:train:601-700batch: iter_time=1.272e-04, forward_time=0.106, loss_ctc=42.346, loss_att=44.261, acc=0.745, loss=43.686, backward_time=0.097, grad_norm=37.850, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.590e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 05:02:45,001 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 05:02:49,179 (trainer:737) INFO: 32epoch:train:701-800batch: iter_time=1.295e-04, forward_time=0.106, loss_ctc=48.549, loss_att=54.806, acc=0.716, loss=52.929, backward_time=0.097, grad_norm=48.331, clip=100.000, loss_scale=3.944e+34, optim_step_time=0.041, optim0_lr0=3.590e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 05:03:33,530 (trainer:737) INFO: 32epoch:train:801-900batch: iter_time=1.294e-04, forward_time=0.107, loss_ctc=48.971, loss_att=63.286, acc=0.717, loss=58.992, backward_time=0.098, grad_norm=47.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.589e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-16 05:04:22,162 (trainer:737) INFO: 32epoch:train:901-1000batch: iter_time=1.345e-04, forward_time=0.105, loss_ctc=43.190, loss_att=45.437, acc=0.753, loss=44.763, backward_time=0.097, grad_norm=41.699, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.589e-04, train_time=0.486
+[gpuc02:0/16] 2024-01-16 05:05:10,090 (trainer:737) INFO: 32epoch:train:1001-1100batch: iter_time=1.304e-04, forward_time=0.128, loss_ctc=59.693, loss_att=63.650, acc=0.731, loss=62.463, backward_time=0.117, grad_norm=55.025, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.588e-04, train_time=0.479
+[gpuc02:0/16] 2024-01-16 05:05:53,474 (trainer:737) INFO: 32epoch:train:1101-1200batch: iter_time=1.344e-04, forward_time=0.113, loss_ctc=49.211, loss_att=55.452, acc=0.735, loss=53.580, backward_time=0.099, grad_norm=47.203, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.588e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-16 05:06:20,277 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-16 05:06:39,579 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 05:06:43,184 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ec0e0a2c0>)
+[gpuc02:0/16] 2024-01-16 05:06:43,184 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-16 05:06:43,187 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 05:11:45,193 (trainer:737) INFO: 32epoch:train:1201-1300batch: iter_time=2.839, forward_time=0.107, loss_ctc=44.114, loss_att=53.015, acc=0.736, loss=50.345, backward_time=0.098, grad_norm=44.038, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.588e-04, train_time=3.517
+[gpuc02:0/16] 2024-01-16 05:12:27,474 (trainer:737) INFO: 32epoch:train:1301-1400batch: iter_time=1.669e-04, forward_time=0.106, loss_ctc=44.640, loss_att=47.805, acc=0.723, loss=46.855, backward_time=0.097, grad_norm=46.193, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.587e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:13:09,856 (trainer:737) INFO: 32epoch:train:1401-1500batch: iter_time=1.636e-04, forward_time=0.105, loss_ctc=47.101, loss_att=52.284, acc=0.735, loss=50.729, backward_time=0.098, grad_norm=43.888, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.587e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 05:13:52,426 (trainer:737) INFO: 32epoch:train:1501-1600batch: iter_time=1.622e-04, forward_time=0.106, loss_ctc=46.115, loss_att=49.491, acc=0.751, loss=48.478, backward_time=0.098, grad_norm=39.321, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.587e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:14:34,787 (trainer:737) INFO: 32epoch:train:1601-1700batch: iter_time=1.623e-04, forward_time=0.105, loss_ctc=47.276, loss_att=51.894, acc=0.732, loss=50.509, backward_time=0.098, grad_norm=44.512, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.586e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:15:17,953 (trainer:737) INFO: 32epoch:train:1701-1800batch: iter_time=1.507e-04, forward_time=0.109, loss_ctc=41.731, loss_att=47.092, acc=0.737, loss=45.484, backward_time=0.098, grad_norm=43.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.586e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 05:16:01,289 (trainer:737) INFO: 32epoch:train:1801-1900batch: iter_time=1.510e-04, forward_time=0.112, loss_ctc=37.805, loss_att=40.716, acc=0.746, loss=39.843, backward_time=0.098, grad_norm=35.724, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.585e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 05:16:43,944 (trainer:737) INFO: 32epoch:train:1901-2000batch: iter_time=1.838e-04, forward_time=0.105, loss_ctc=39.333, loss_att=43.401, acc=0.744, loss=42.181, backward_time=0.097, grad_norm=38.128, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.585e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 05:17:26,114 (trainer:737) INFO: 32epoch:train:2001-2100batch: iter_time=1.590e-04, forward_time=0.105, loss_ctc=49.634, loss_att=60.910, acc=0.687, loss=57.527, backward_time=0.097, grad_norm=47.794, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.585e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 05:18:08,441 (trainer:737) INFO: 32epoch:train:2101-2200batch: iter_time=1.633e-04, forward_time=0.105, loss_ctc=46.136, loss_att=55.442, acc=0.723, loss=52.650, backward_time=0.097, grad_norm=45.109, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.584e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:18:52,663 (trainer:737) INFO: 32epoch:train:2201-2300batch: iter_time=1.545e-04, forward_time=0.111, loss_ctc=49.317, loss_att=54.732, acc=0.735, loss=53.108, backward_time=0.108, grad_norm=46.023, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.584e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-16 05:19:35,197 (trainer:737) INFO: 32epoch:train:2301-2400batch: iter_time=1.639e-04, forward_time=0.105, loss_ctc=53.636, loss_att=58.655, acc=0.722, loss=57.149, backward_time=0.097, grad_norm=52.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.583e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:20:17,451 (trainer:737) INFO: 32epoch:train:2401-2500batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=49.077, loss_att=50.317, acc=0.744, loss=49.945, backward_time=0.097, grad_norm=49.382, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.583e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 05:20:26,867 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-16 05:20:46,128 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 05:20:49,712 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da523f520>)
+[gpuc02:0/16] 2024-01-16 05:20:49,713 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-16 05:20:49,716 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 05:27:32,987 (trainer:737) INFO: 32epoch:train:2501-2600batch: iter_time=3.848, forward_time=0.164, loss_ctc=45.352, loss_att=53.242, acc=0.726, loss=50.875, backward_time=0.106, grad_norm=46.365, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.583e-04, train_time=4.355
+[gpuc02:0/16] 2024-01-16 05:28:15,622 (trainer:737) INFO: 32epoch:train:2601-2700batch: iter_time=1.278e-04, forward_time=0.105, loss_ctc=45.341, loss_att=49.588, acc=0.733, loss=48.314, backward_time=0.097, grad_norm=40.890, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.582e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 05:28:58,398 (trainer:737) INFO: 32epoch:train:2701-2800batch: iter_time=1.320e-04, forward_time=0.106, loss_ctc=42.938, loss_att=50.244, acc=0.747, loss=48.052, backward_time=0.098, grad_norm=39.395, clip=100.000, loss_scale=2.285e+34, optim_step_time=0.042, optim0_lr0=3.582e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 05:29:41,688 (trainer:737) INFO: 32epoch:train:2801-2900batch: iter_time=1.445e-04, forward_time=0.106, loss_ctc=49.018, loss_att=52.892, acc=0.730, loss=51.730, backward_time=0.098, grad_norm=43.868, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.582e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 05:30:23,999 (trainer:737) INFO: 32epoch:train:2901-3000batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=41.041, loss_att=39.801, acc=0.761, loss=40.173, backward_time=0.097, grad_norm=41.406, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.581e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:31:06,238 (trainer:737) INFO: 32epoch:train:3001-3100batch: iter_time=1.516e-04, forward_time=0.104, loss_ctc=37.244, loss_att=40.974, acc=0.742, loss=39.855, backward_time=0.096, grad_norm=36.761, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.581e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 05:31:48,814 (trainer:737) INFO: 32epoch:train:3101-3200batch: iter_time=1.402e-04, forward_time=0.105, loss_ctc=40.952, loss_att=43.435, acc=0.744, loss=42.690, backward_time=0.097, grad_norm=37.178, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.580e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 05:32:31,138 (trainer:737) INFO: 32epoch:train:3201-3300batch: iter_time=1.371e-04, forward_time=0.106, loss_ctc=46.531, loss_att=53.980, acc=0.716, loss=51.745, backward_time=0.097, grad_norm=43.909, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.580e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:32:43,913 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 05:33:13,900 (trainer:737) INFO: 32epoch:train:3301-3400batch: iter_time=1.380e-04, forward_time=0.107, loss_ctc=48.153, loss_att=62.605, acc=0.706, loss=58.269, backward_time=0.097, grad_norm=48.768, clip=100.000, loss_scale=2.685e+34, optim_step_time=0.042, optim0_lr0=3.580e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:33:56,432 (trainer:737) INFO: 32epoch:train:3401-3500batch: iter_time=1.310e-04, forward_time=0.106, loss_ctc=42.720, loss_att=46.211, acc=0.739, loss=45.164, backward_time=0.097, grad_norm=40.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.579e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:34:39,336 (trainer:737) INFO: 32epoch:train:3501-3600batch: iter_time=1.317e-04, forward_time=0.107, loss_ctc=57.704, loss_att=62.383, acc=0.726, loss=60.980, backward_time=0.098, grad_norm=56.670, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.579e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 05:35:22,243 (trainer:737) INFO: 32epoch:train:3601-3700batch: iter_time=1.171e-04, forward_time=0.109, loss_ctc=48.156, loss_att=54.914, acc=0.729, loss=52.887, backward_time=0.097, grad_norm=45.913, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.578e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 05:35:59,960 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-16 05:36:19,996 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 05:36:23,866 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f2447b35ab0>)
+[gpuc02:0/16] 2024-01-16 05:36:23,866 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-16 05:36:23,869 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 05:40:49,501 (trainer:737) INFO: 32epoch:train:3701-3800batch: iter_time=2.761, forward_time=0.105, loss_ctc=42.100, loss_att=51.303, acc=0.743, loss=48.542, backward_time=0.097, grad_norm=44.664, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.578e-04, train_time=3.272
+[gpuc02:0/16] 2024-01-16 05:41:31,746 (trainer:737) INFO: 32epoch:train:3801-3900batch: iter_time=1.173e-04, forward_time=0.104, loss_ctc=43.180, loss_att=50.806, acc=0.732, loss=48.518, backward_time=0.097, grad_norm=44.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.578e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 05:42:14,221 (trainer:737) INFO: 32epoch:train:3901-4000batch: iter_time=1.420e-04, forward_time=0.104, loss_ctc=46.709, loss_att=52.338, acc=0.737, loss=50.649, backward_time=0.097, grad_norm=42.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.577e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:42:56,615 (trainer:737) INFO: 32epoch:train:4001-4100batch: iter_time=1.205e-04, forward_time=0.105, loss_ctc=44.960, loss_att=50.010, acc=0.758, loss=48.495, backward_time=0.097, grad_norm=38.495, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.577e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 05:43:38,907 (trainer:737) INFO: 32epoch:train:4101-4200batch: iter_time=1.240e-04, forward_time=0.104, loss_ctc=46.412, loss_att=51.240, acc=0.735, loss=49.791, backward_time=0.097, grad_norm=43.946, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.577e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:44:21,259 (trainer:737) INFO: 32epoch:train:4201-4300batch: iter_time=1.281e-04, forward_time=0.105, loss_ctc=40.888, loss_att=51.012, acc=0.739, loss=47.975, backward_time=0.097, grad_norm=42.809, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.576e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:45:03,970 (trainer:737) INFO: 32epoch:train:4301-4400batch: iter_time=1.228e-04, forward_time=0.103, loss_ctc=37.977, loss_att=40.274, acc=0.750, loss=39.585, backward_time=0.096, grad_norm=34.798, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.576e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:45:46,343 (trainer:737) INFO: 32epoch:train:4401-4500batch: iter_time=1.390e-04, forward_time=0.104, loss_ctc=38.716, loss_att=42.456, acc=0.752, loss=41.334, backward_time=0.097, grad_norm=39.241, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.575e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:46:29,137 (trainer:737) INFO: 32epoch:train:4501-4600batch: iter_time=1.396e-04, forward_time=0.105, loss_ctc=48.732, loss_att=60.570, acc=0.701, loss=57.019, backward_time=0.097, grad_norm=45.619, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.575e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 05:47:11,694 (trainer:737) INFO: 32epoch:train:4601-4700batch: iter_time=1.321e-04, forward_time=0.105, loss_ctc=45.514, loss_att=54.515, acc=0.741, loss=51.815, backward_time=0.097, grad_norm=44.511, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.575e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:47:54,207 (trainer:737) INFO: 32epoch:train:4701-4800batch: iter_time=1.380e-04, forward_time=0.105, loss_ctc=48.373, loss_att=52.963, acc=0.751, loss=51.586, backward_time=0.098, grad_norm=44.158, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.574e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:48:36,904 (trainer:737) INFO: 32epoch:train:4801-4900batch: iter_time=1.305e-04, forward_time=0.104, loss_ctc=51.932, loss_att=58.422, acc=0.732, loss=56.475, backward_time=0.097, grad_norm=51.210, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.574e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:49:19,602 (trainer:737) INFO: 32epoch:train:4901-5000batch: iter_time=1.248e-04, forward_time=0.104, loss_ctc=47.166, loss_att=51.852, acc=0.749, loss=50.446, backward_time=0.097, grad_norm=47.435, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.574e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:49:24,330 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-16 05:49:44,475 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 05:49:48,351 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1da5298700>)
+[gpuc02:0/16] 2024-01-16 05:49:48,351 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-16 05:49:48,354 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 05:54:41,962 (trainer:737) INFO: 32epoch:train:5001-5100batch: iter_time=2.635, forward_time=0.105, loss_ctc=43.797, loss_att=53.907, acc=0.726, loss=50.874, backward_time=0.098, grad_norm=47.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.573e-04, train_time=3.223
+[gpuc02:0/16] 2024-01-16 05:55:24,395 (trainer:737) INFO: 32epoch:train:5101-5200batch: iter_time=1.251e-04, forward_time=0.105, loss_ctc=45.211, loss_att=49.865, acc=0.734, loss=48.469, backward_time=0.098, grad_norm=41.208, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.573e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 05:56:07,155 (trainer:737) INFO: 32epoch:train:5201-5300batch: iter_time=1.537e-04, forward_time=0.105, loss_ctc=42.668, loss_att=50.915, acc=0.747, loss=48.441, backward_time=0.098, grad_norm=40.676, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.572e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:56:49,869 (trainer:737) INFO: 32epoch:train:5301-5400batch: iter_time=1.416e-04, forward_time=0.106, loss_ctc=48.754, loss_att=53.202, acc=0.729, loss=51.867, backward_time=0.098, grad_norm=42.370, clip=100.000, loss_scale=3.531e+34, optim_step_time=0.042, optim0_lr0=3.572e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 05:57:32,162 (trainer:737) INFO: 32epoch:train:5401-5500batch: iter_time=1.394e-04, forward_time=0.105, loss_ctc=40.726, loss_att=39.544, acc=0.763, loss=39.899, backward_time=0.097, grad_norm=43.326, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.572e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:58:14,660 (trainer:737) INFO: 32epoch:train:5501-5600batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=37.026, loss_att=41.583, acc=0.739, loss=40.216, backward_time=0.097, grad_norm=35.918, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.571e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 05:58:56,950 (trainer:737) INFO: 32epoch:train:5601-5700batch: iter_time=1.384e-04, forward_time=0.105, loss_ctc=41.053, loss_att=43.847, acc=0.744, loss=43.009, backward_time=0.098, grad_norm=38.182, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.571e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 05:59:39,597 (trainer:737) INFO: 32epoch:train:5701-5800batch: iter_time=1.384e-04, forward_time=0.105, loss_ctc=45.958, loss_att=54.170, acc=0.717, loss=51.706, backward_time=0.097, grad_norm=43.509, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.570e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:00:22,086 (trainer:737) INFO: 32epoch:train:5801-5900batch: iter_time=1.271e-04, forward_time=0.106, loss_ctc=47.718, loss_att=62.621, acc=0.707, loss=58.150, backward_time=0.098, grad_norm=44.681, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.570e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:01:04,399 (trainer:737) INFO: 32epoch:train:5901-6000batch: iter_time=1.205e-04, forward_time=0.105, loss_ctc=42.611, loss_att=46.091, acc=0.740, loss=45.047, backward_time=0.097, grad_norm=39.708, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.570e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 06:01:47,287 (trainer:737) INFO: 32epoch:train:6001-6100batch: iter_time=1.229e-04, forward_time=0.106, loss_ctc=56.573, loss_att=62.145, acc=0.729, loss=60.474, backward_time=0.098, grad_norm=53.759, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.569e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 06:02:30,001 (trainer:737) INFO: 32epoch:train:6101-6200batch: iter_time=1.294e-04, forward_time=0.105, loss_ctc=47.496, loss_att=54.045, acc=0.732, loss=52.080, backward_time=0.098, grad_norm=45.816, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.569e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:02:55,338 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-16 06:03:14,836 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 06:03:18,753 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e8554a9b0>)
+[gpuc02:0/16] 2024-01-16 06:03:18,753 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-16 06:03:18,757 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 06:07:46,947 (trainer:737) INFO: 32epoch:train:6201-6300batch: iter_time=2.623, forward_time=0.105, loss_ctc=40.815, loss_att=50.949, acc=0.744, loss=47.909, backward_time=0.097, grad_norm=43.520, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.569e-04, train_time=3.169
+[gpuc02:0/16] 2024-01-16 06:08:29,640 (trainer:737) INFO: 32epoch:train:6301-6400batch: iter_time=1.286e-04, forward_time=0.105, loss_ctc=41.994, loss_att=49.398, acc=0.734, loss=47.177, backward_time=0.097, grad_norm=42.301, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.568e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:09:12,116 (trainer:737) INFO: 32epoch:train:6401-6500batch: iter_time=1.481e-04, forward_time=0.106, loss_ctc=46.553, loss_att=53.006, acc=0.736, loss=51.070, backward_time=0.097, grad_norm=43.109, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.568e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:09:49,541 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 06:09:54,651 (trainer:737) INFO: 32epoch:train:6501-6600batch: iter_time=1.316e-04, forward_time=0.106, loss_ctc=45.201, loss_att=51.079, acc=0.756, loss=49.316, backward_time=0.097, grad_norm=39.278, clip=100.000, loss_scale=3.902e+34, optim_step_time=0.042, optim0_lr0=3.567e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:10:37,109 (trainer:737) INFO: 32epoch:train:6601-6700batch: iter_time=1.477e-04, forward_time=0.106, loss_ctc=46.135, loss_att=51.208, acc=0.736, loss=49.686, backward_time=0.097, grad_norm=44.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.567e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:11:19,498 (trainer:737) INFO: 32epoch:train:6701-6800batch: iter_time=1.365e-04, forward_time=0.106, loss_ctc=40.662, loss_att=50.597, acc=0.742, loss=47.616, backward_time=0.097, grad_norm=41.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.567e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:12:01,896 (trainer:737) INFO: 32epoch:train:6801-6900batch: iter_time=1.629e-04, forward_time=0.105, loss_ctc=37.332, loss_att=40.172, acc=0.750, loss=39.320, backward_time=0.096, grad_norm=35.042, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.566e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:12:44,262 (trainer:737) INFO: 32epoch:train:6901-7000batch: iter_time=1.548e-04, forward_time=0.105, loss_ctc=38.389, loss_att=42.339, acc=0.752, loss=41.154, backward_time=0.097, grad_norm=39.160, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.566e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 06:13:27,013 (trainer:737) INFO: 32epoch:train:7001-7100batch: iter_time=1.351e-04, forward_time=0.108, loss_ctc=48.870, loss_att=61.177, acc=0.702, loss=57.485, backward_time=0.097, grad_norm=47.955, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.566e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:14:09,767 (trainer:737) INFO: 32epoch:train:7101-7200batch: iter_time=1.431e-04, forward_time=0.107, loss_ctc=45.285, loss_att=54.308, acc=0.742, loss=51.601, backward_time=0.097, grad_norm=43.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.565e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:14:52,456 (trainer:737) INFO: 32epoch:train:7201-7300batch: iter_time=1.191e-04, forward_time=0.107, loss_ctc=48.356, loss_att=52.639, acc=0.751, loss=51.354, backward_time=0.098, grad_norm=46.418, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.565e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:15:34,950 (trainer:737) INFO: 32epoch:train:7301-7400batch: iter_time=1.376e-04, forward_time=0.106, loss_ctc=51.144, loss_att=58.897, acc=0.730, loss=56.571, backward_time=0.097, grad_norm=52.951, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.564e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:16:17,677 (trainer:737) INFO: 32epoch:train:7401-7500batch: iter_time=1.302e-04, forward_time=0.106, loss_ctc=46.400, loss_att=51.825, acc=0.749, loss=50.197, backward_time=0.097, grad_norm=48.666, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.564e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:16:22,139 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-16 06:16:41,629 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 06:16:45,632 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f254c126e60>)
+[gpuc02:0/16] 2024-01-16 06:16:45,632 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-16 06:16:45,636 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 06:21:35,789 (trainer:737) INFO: 32epoch:train:7501-7600batch: iter_time=2.616, forward_time=0.105, loss_ctc=43.823, loss_att=53.239, acc=0.736, loss=50.414, backward_time=0.097, grad_norm=44.828, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.564e-04, train_time=3.181
+[gpuc02:0/16] 2024-01-16 06:22:18,385 (trainer:737) INFO: 32epoch:train:7601-7700batch: iter_time=1.247e-04, forward_time=0.105, loss_ctc=44.669, loss_att=51.256, acc=0.740, loss=49.280, backward_time=0.097, grad_norm=41.444, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.563e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:23:01,293 (trainer:737) INFO: 32epoch:train:7701-7800batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=42.214, loss_att=50.789, acc=0.751, loss=48.217, backward_time=0.097, grad_norm=37.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.563e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 06:23:43,892 (trainer:737) INFO: 32epoch:train:7801-7900batch: iter_time=1.132e-04, forward_time=0.106, loss_ctc=48.662, loss_att=55.809, acc=0.731, loss=53.665, backward_time=0.097, grad_norm=42.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.563e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:24:26,543 (trainer:737) INFO: 32epoch:train:7901-8000batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=40.505, loss_att=39.330, acc=0.765, loss=39.682, backward_time=0.096, grad_norm=42.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.562e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:25:08,991 (trainer:737) INFO: 32epoch:train:8001-8100batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=36.870, loss_att=45.035, acc=0.742, loss=42.586, backward_time=0.096, grad_norm=37.365, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.562e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:25:51,712 (trainer:737) INFO: 32epoch:train:8101-8200batch: iter_time=1.066e-04, forward_time=0.104, loss_ctc=40.477, loss_att=43.132, acc=0.750, loss=42.336, backward_time=0.097, grad_norm=37.244, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.561e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:26:34,963 (trainer:737) INFO: 32epoch:train:8201-8300batch: iter_time=1.135e-04, forward_time=0.105, loss_ctc=46.024, loss_att=54.433, acc=0.719, loss=51.911, backward_time=0.096, grad_norm=43.967, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.561e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-16 06:27:17,610 (trainer:737) INFO: 32epoch:train:8301-8400batch: iter_time=1.192e-04, forward_time=0.106, loss_ctc=47.959, loss_att=62.936, acc=0.722, loss=58.443, backward_time=0.097, grad_norm=46.574, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.561e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:28:00,202 (trainer:737) INFO: 32epoch:train:8401-8500batch: iter_time=1.107e-04, forward_time=0.105, loss_ctc=42.001, loss_att=44.943, acc=0.757, loss=44.060, backward_time=0.097, grad_norm=38.720, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.560e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:28:43,027 (trainer:737) INFO: 32epoch:train:8501-8600batch: iter_time=1.189e-04, forward_time=0.107, loss_ctc=55.291, loss_att=62.199, acc=0.735, loss=60.126, backward_time=0.098, grad_norm=56.658, clip=100.000, loss_scale=2.326e+34, optim_step_time=0.041, optim0_lr0=3.560e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 06:29:25,540 (trainer:737) INFO: 32epoch:train:8601-8700batch: iter_time=1.187e-04, forward_time=0.105, loss_ctc=47.249, loss_att=54.892, acc=0.740, loss=52.599, backward_time=0.097, grad_norm=47.020, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.560e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:29:51,478 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-16 06:30:10,976 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 06:30:14,577 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f25552989d0>)
+[gpuc02:0/16] 2024-01-16 06:30:14,577 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-16 06:30:14,580 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 06:34:41,087 (trainer:737) INFO: 32epoch:train:8701-8800batch: iter_time=2.618, forward_time=0.105, loss_ctc=39.920, loss_att=51.749, acc=0.741, loss=48.200, backward_time=0.097, grad_norm=44.070, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.559e-04, train_time=3.155
+[gpuc02:0/16] 2024-01-16 06:35:23,456 (trainer:737) INFO: 32epoch:train:8801-8900batch: iter_time=1.315e-04, forward_time=0.104, loss_ctc=42.498, loss_att=45.806, acc=0.730, loss=44.813, backward_time=0.096, grad_norm=43.646, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.559e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 06:36:06,101 (trainer:737) INFO: 32epoch:train:8901-9000batch: iter_time=1.316e-04, forward_time=0.105, loss_ctc=45.932, loss_att=51.704, acc=0.738, loss=49.972, backward_time=0.097, grad_norm=41.408, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.558e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:36:48,518 (trainer:737) INFO: 32epoch:train:9001-9100batch: iter_time=1.246e-04, forward_time=0.106, loss_ctc=44.996, loss_att=48.482, acc=0.755, loss=47.436, backward_time=0.097, grad_norm=39.696, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.558e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:37:30,903 (trainer:737) INFO: 32epoch:train:9101-9200batch: iter_time=1.478e-04, forward_time=0.106, loss_ctc=46.477, loss_att=51.004, acc=0.734, loss=49.646, backward_time=0.097, grad_norm=44.641, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.558e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:38:13,524 (trainer:737) INFO: 32epoch:train:9201-9300batch: iter_time=1.240e-04, forward_time=0.106, loss_ctc=41.060, loss_att=46.246, acc=0.740, loss=44.691, backward_time=0.097, grad_norm=40.994, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.557e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:38:55,621 (trainer:737) INFO: 32epoch:train:9301-9400batch: iter_time=1.324e-04, forward_time=0.104, loss_ctc=36.949, loss_att=39.893, acc=0.748, loss=39.010, backward_time=0.096, grad_norm=34.870, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.557e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 06:39:38,228 (trainer:737) INFO: 32epoch:train:9401-9500batch: iter_time=1.372e-04, forward_time=0.105, loss_ctc=38.064, loss_att=42.827, acc=0.748, loss=41.398, backward_time=0.097, grad_norm=37.612, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.557e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:40:21,051 (trainer:737) INFO: 32epoch:train:9501-9600batch: iter_time=1.462e-04, forward_time=0.105, loss_ctc=48.843, loss_att=60.835, acc=0.689, loss=57.237, backward_time=0.096, grad_norm=48.110, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.556e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 06:40:32,224 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 06:41:03,510 (trainer:737) INFO: 32epoch:train:9601-9700batch: iter_time=1.402e-04, forward_time=0.105, loss_ctc=45.103, loss_att=54.444, acc=0.727, loss=51.641, backward_time=0.097, grad_norm=42.980, clip=100.000, loss_scale=2.601e+34, optim_step_time=0.042, optim0_lr0=3.556e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:41:45,936 (trainer:737) INFO: 32epoch:train:9701-9800batch: iter_time=1.404e-04, forward_time=0.105, loss_ctc=47.946, loss_att=53.884, acc=0.738, loss=52.103, backward_time=0.097, grad_norm=44.144, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.555e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:42:28,651 (trainer:737) INFO: 32epoch:train:9801-9900batch: iter_time=1.337e-04, forward_time=0.106, loss_ctc=50.298, loss_att=59.077, acc=0.725, loss=56.443, backward_time=0.097, grad_norm=49.535, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.555e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 06:43:11,164 (trainer:737) INFO: 32epoch:train:9901-10000batch: iter_time=1.318e-04, forward_time=0.105, loss_ctc=45.591, loss_att=48.733, acc=0.748, loss=47.790, backward_time=0.097, grad_norm=45.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.555e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:43:16,525 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-16 06:43:35,862 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 06:43:39,492 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1eb5696b60>)
+[gpuc02:0/16] 2024-01-16 06:43:39,492 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-16 06:43:39,495 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 06:48:37,353 (trainer:737) INFO: 32epoch:train:10001-10100batch: iter_time=2.539, forward_time=0.106, loss_ctc=43.785, loss_att=54.851, acc=0.735, loss=51.531, backward_time=0.097, grad_norm=46.675, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.554e-04, train_time=3.262
+[gpuc02:0/16] 2024-01-16 06:49:19,928 (trainer:737) INFO: 32epoch:train:10101-10200batch: iter_time=1.301e-04, forward_time=0.106, loss_ctc=44.055, loss_att=51.501, acc=0.740, loss=49.267, backward_time=0.097, grad_norm=41.396, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.554e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:50:03,218 (trainer:737) INFO: 32epoch:train:10201-10300batch: iter_time=1.317e-04, forward_time=0.107, loss_ctc=42.242, loss_att=51.309, acc=0.750, loss=48.589, backward_time=0.098, grad_norm=40.464, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.554e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 06:50:45,814 (trainer:737) INFO: 32epoch:train:10301-10400batch: iter_time=1.437e-04, forward_time=0.107, loss_ctc=48.162, loss_att=55.670, acc=0.732, loss=53.418, backward_time=0.097, grad_norm=43.263, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.553e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 06:51:28,207 (trainer:737) INFO: 32epoch:train:10401-10500batch: iter_time=1.417e-04, forward_time=0.105, loss_ctc=40.963, loss_att=39.510, acc=0.764, loss=39.946, backward_time=0.096, grad_norm=44.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.553e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:52:11,154 (trainer:737) INFO: 32epoch:train:10501-10600batch: iter_time=1.414e-04, forward_time=0.104, loss_ctc=36.760, loss_att=45.213, acc=0.744, loss=42.677, backward_time=0.097, grad_norm=37.027, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.552e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 06:52:53,581 (trainer:737) INFO: 32epoch:train:10601-10700batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=40.337, loss_att=43.260, acc=0.752, loss=42.383, backward_time=0.097, grad_norm=36.255, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.552e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:53:36,051 (trainer:737) INFO: 32epoch:train:10701-10800batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=45.413, loss_att=53.504, acc=0.722, loss=51.077, backward_time=0.097, grad_norm=42.339, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.552e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:54:19,123 (trainer:737) INFO: 32epoch:train:10801-10900batch: iter_time=1.441e-04, forward_time=0.106, loss_ctc=47.173, loss_att=63.010, acc=0.723, loss=58.259, backward_time=0.098, grad_norm=45.307, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.551e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 06:55:01,523 (trainer:737) INFO: 32epoch:train:10901-11000batch: iter_time=1.250e-04, forward_time=0.105, loss_ctc=42.247, loss_att=44.709, acc=0.758, loss=43.970, backward_time=0.097, grad_norm=39.840, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.551e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 06:55:45,196 (trainer:737) INFO: 32epoch:train:11001-11100batch: iter_time=1.350e-04, forward_time=0.109, loss_ctc=54.835, loss_att=62.031, acc=0.737, loss=59.872, backward_time=0.099, grad_norm=58.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.551e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-16 06:56:27,711 (trainer:737) INFO: 32epoch:train:11101-11200batch: iter_time=1.319e-04, forward_time=0.105, loss_ctc=47.334, loss_att=55.272, acc=0.738, loss=52.891, backward_time=0.098, grad_norm=45.108, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.550e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 06:56:52,509 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-16 06:57:12,492 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 06:57:16,226 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e68c96d40>)
+[gpuc02:0/16] 2024-01-16 06:57:16,226 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-16 06:57:16,229 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 07:01:37,613 (trainer:737) INFO: 32epoch:train:11201-11300batch: iter_time=2.552, forward_time=0.104, loss_ctc=39.829, loss_att=50.725, acc=0.747, loss=47.456, backward_time=0.098, grad_norm=43.501, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.550e-04, train_time=3.099
+[gpuc02:0/16] 2024-01-16 07:02:20,263 (trainer:737) INFO: 32epoch:train:11301-11400batch: iter_time=1.272e-04, forward_time=0.105, loss_ctc=42.732, loss_att=48.384, acc=0.736, loss=46.688, backward_time=0.097, grad_norm=43.465, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.549e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:03:02,772 (trainer:737) INFO: 32epoch:train:11401-11500batch: iter_time=1.253e-04, forward_time=0.105, loss_ctc=45.534, loss_att=50.944, acc=0.739, loss=49.321, backward_time=0.098, grad_norm=41.380, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.549e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:03:45,357 (trainer:737) INFO: 32epoch:train:11501-11600batch: iter_time=1.147e-04, forward_time=0.105, loss_ctc=45.226, loss_att=49.651, acc=0.759, loss=48.324, backward_time=0.098, grad_norm=39.853, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.549e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:04:22,956 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 07:04:27,992 (trainer:737) INFO: 32epoch:train:11601-11700batch: iter_time=1.101e-04, forward_time=0.105, loss_ctc=46.117, loss_att=50.145, acc=0.738, loss=48.937, backward_time=0.098, grad_norm=41.420, clip=100.000, loss_scale=3.357e+34, optim_step_time=0.042, optim0_lr0=3.548e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:05:10,663 (trainer:737) INFO: 32epoch:train:11701-11800batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=40.682, loss_att=49.796, acc=0.743, loss=47.062, backward_time=0.097, grad_norm=40.300, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.548e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:05:53,411 (trainer:737) INFO: 32epoch:train:11801-11900batch: iter_time=1.125e-04, forward_time=0.104, loss_ctc=36.970, loss_att=39.661, acc=0.753, loss=38.854, backward_time=0.097, grad_norm=34.611, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.548e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 07:06:35,772 (trainer:737) INFO: 32epoch:train:11901-12000batch: iter_time=1.146e-04, forward_time=0.105, loss_ctc=37.779, loss_att=41.935, acc=0.754, loss=40.688, backward_time=0.097, grad_norm=39.521, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.547e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 07:07:18,118 (trainer:737) INFO: 32epoch:train:12001-12100batch: iter_time=1.195e-04, forward_time=0.105, loss_ctc=47.995, loss_att=60.195, acc=0.704, loss=56.535, backward_time=0.097, grad_norm=45.040, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.547e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 07:08:00,612 (trainer:737) INFO: 32epoch:train:12101-12200batch: iter_time=1.266e-04, forward_time=0.105, loss_ctc=44.707, loss_att=53.993, acc=0.743, loss=51.207, backward_time=0.098, grad_norm=42.549, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.546e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:08:43,125 (trainer:737) INFO: 32epoch:train:12201-12300batch: iter_time=1.200e-04, forward_time=0.106, loss_ctc=48.143, loss_att=52.765, acc=0.753, loss=51.378, backward_time=0.098, grad_norm=45.197, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.546e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:09:25,604 (trainer:737) INFO: 32epoch:train:12301-12400batch: iter_time=1.225e-04, forward_time=0.105, loss_ctc=49.972, loss_att=58.156, acc=0.733, loss=55.701, backward_time=0.098, grad_norm=52.174, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.546e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:10:08,088 (trainer:737) INFO: 32epoch:train:12401-12500batch: iter_time=1.119e-04, forward_time=0.105, loss_ctc=45.280, loss_att=50.777, acc=0.751, loss=49.128, backward_time=0.098, grad_norm=44.576, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.545e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:10:12,757 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-16 07:10:33,179 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 07:10:36,925 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1eb5665a80>)
+[gpuc02:0/16] 2024-01-16 07:10:36,925 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-16 07:10:36,929 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 07:15:20,988 (trainer:737) INFO: 32epoch:train:12501-12600batch: iter_time=2.519, forward_time=0.105, loss_ctc=43.872, loss_att=54.189, acc=0.730, loss=51.094, backward_time=0.097, grad_norm=44.327, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.545e-04, train_time=3.129
+[gpuc02:0/16] 2024-01-16 07:16:03,786 (trainer:737) INFO: 32epoch:train:12601-12700batch: iter_time=1.175e-04, forward_time=0.109, loss_ctc=44.101, loss_att=50.151, acc=0.734, loss=48.336, backward_time=0.097, grad_norm=41.286, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.545e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 07:16:46,188 (trainer:737) INFO: 32epoch:train:12701-12800batch: iter_time=1.173e-04, forward_time=0.105, loss_ctc=42.023, loss_att=51.107, acc=0.745, loss=48.382, backward_time=0.097, grad_norm=38.700, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.544e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:17:28,650 (trainer:737) INFO: 32epoch:train:12801-12900batch: iter_time=1.206e-04, forward_time=0.105, loss_ctc=47.999, loss_att=53.596, acc=0.728, loss=51.917, backward_time=0.097, grad_norm=41.855, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.544e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:18:10,914 (trainer:737) INFO: 32epoch:train:12901-13000batch: iter_time=1.237e-04, forward_time=0.104, loss_ctc=40.018, loss_att=39.124, acc=0.765, loss=39.392, backward_time=0.096, grad_norm=39.389, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.543e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 07:18:53,166 (trainer:737) INFO: 32epoch:train:13001-13100batch: iter_time=1.219e-04, forward_time=0.104, loss_ctc=36.595, loss_att=41.581, acc=0.740, loss=40.085, backward_time=0.096, grad_norm=37.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.543e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 07:19:36,058 (trainer:737) INFO: 32epoch:train:13101-13200batch: iter_time=1.275e-04, forward_time=0.104, loss_ctc=40.270, loss_att=44.088, acc=0.744, loss=42.943, backward_time=0.097, grad_norm=37.709, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.543e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 07:20:18,748 (trainer:737) INFO: 32epoch:train:13201-13300batch: iter_time=1.248e-04, forward_time=0.105, loss_ctc=45.167, loss_att=54.641, acc=0.714, loss=51.798, backward_time=0.097, grad_norm=44.095, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.542e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 07:21:01,226 (trainer:737) INFO: 32epoch:train:13301-13400batch: iter_time=1.284e-04, forward_time=0.105, loss_ctc=46.695, loss_att=62.305, acc=0.709, loss=57.622, backward_time=0.097, grad_norm=46.109, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.542e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:21:43,889 (trainer:737) INFO: 32epoch:train:13401-13500batch: iter_time=1.202e-04, forward_time=0.105, loss_ctc=41.387, loss_att=45.556, acc=0.742, loss=44.306, backward_time=0.097, grad_norm=40.125, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.542e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:22:26,705 (trainer:737) INFO: 32epoch:train:13501-13600batch: iter_time=1.288e-04, forward_time=0.106, loss_ctc=54.467, loss_att=62.095, acc=0.727, loss=59.806, backward_time=0.098, grad_norm=56.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.541e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 07:23:09,214 (trainer:737) INFO: 32epoch:train:13601-13700batch: iter_time=1.218e-04, forward_time=0.105, loss_ctc=47.325, loss_att=53.643, acc=0.736, loss=51.748, backward_time=0.097, grad_norm=45.015, clip=100.000, loss_scale=2.326e+34, optim_step_time=0.042, optim0_lr0=3.541e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:23:33,333 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-16 07:23:54,539 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 07:23:58,211 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e531abe80>)
+[gpuc02:0/16] 2024-01-16 07:23:58,211 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-16 07:23:58,214 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 07:28:18,545 (trainer:737) INFO: 32epoch:train:13701-13800batch: iter_time=2.502, forward_time=0.105, loss_ctc=39.701, loss_att=49.944, acc=0.743, loss=46.871, backward_time=0.097, grad_norm=44.981, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.541e-04, train_time=3.093
+[gpuc02:0/16] 2024-01-16 07:28:27,846 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 07:29:00,856 (trainer:737) INFO: 32epoch:train:13801-13900batch: iter_time=9.885e-05, forward_time=0.104, loss_ctc=42.337, loss_att=44.786, acc=0.730, loss=44.052, backward_time=0.096, grad_norm=43.238, clip=100.000, loss_scale=2.517e+34, optim_step_time=0.041, optim0_lr0=3.540e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 07:29:43,325 (trainer:737) INFO: 32epoch:train:13901-14000batch: iter_time=9.802e-05, forward_time=0.104, loss_ctc=46.113, loss_att=50.767, acc=0.740, loss=49.370, backward_time=0.097, grad_norm=42.325, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.540e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:30:25,829 (trainer:737) INFO: 32epoch:train:14001-14100batch: iter_time=9.791e-05, forward_time=0.104, loss_ctc=44.788, loss_att=47.387, acc=0.761, loss=46.608, backward_time=0.097, grad_norm=39.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.539e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:31:08,408 (trainer:737) INFO: 32epoch:train:14101-14200batch: iter_time=9.981e-05, forward_time=0.104, loss_ctc=46.534, loss_att=50.343, acc=0.736, loss=49.200, backward_time=0.097, grad_norm=44.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.539e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:31:50,709 (trainer:737) INFO: 32epoch:train:14201-14300batch: iter_time=1.075e-04, forward_time=0.104, loss_ctc=40.435, loss_att=45.808, acc=0.742, loss=44.196, backward_time=0.097, grad_norm=42.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.539e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 07:32:32,787 (trainer:737) INFO: 32epoch:train:14301-14400batch: iter_time=1.305e-04, forward_time=0.103, loss_ctc=37.036, loss_att=39.672, acc=0.750, loss=38.881, backward_time=0.096, grad_norm=35.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.538e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 07:33:15,353 (trainer:737) INFO: 32epoch:train:14401-14500batch: iter_time=1.350e-04, forward_time=0.104, loss_ctc=37.513, loss_att=41.905, acc=0.751, loss=40.587, backward_time=0.097, grad_norm=38.273, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.538e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 07:33:57,807 (trainer:737) INFO: 32epoch:train:14501-14600batch: iter_time=1.367e-04, forward_time=0.104, loss_ctc=47.984, loss_att=59.608, acc=0.692, loss=56.121, backward_time=0.096, grad_norm=47.124, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.538e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:34:40,250 (trainer:737) INFO: 32epoch:train:14601-14700batch: iter_time=1.271e-04, forward_time=0.105, loss_ctc=44.938, loss_att=54.591, acc=0.726, loss=51.695, backward_time=0.097, grad_norm=42.710, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.537e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:35:22,687 (trainer:737) INFO: 32epoch:train:14701-14800batch: iter_time=1.332e-04, forward_time=0.105, loss_ctc=47.337, loss_att=52.997, acc=0.739, loss=51.299, backward_time=0.097, grad_norm=45.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.537e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:36:05,086 (trainer:737) INFO: 32epoch:train:14801-14900batch: iter_time=1.382e-04, forward_time=0.105, loss_ctc=49.684, loss_att=58.162, acc=0.727, loss=55.619, backward_time=0.097, grad_norm=51.756, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.536e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 07:36:47,718 (trainer:737) INFO: 32epoch:train:14901-15000batch: iter_time=1.270e-04, forward_time=0.104, loss_ctc=45.148, loss_att=49.559, acc=0.746, loss=48.235, backward_time=0.097, grad_norm=45.644, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.536e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 07:56:52,391 (trainer:343) INFO: 32epoch results: [train] iter_time=0.218, forward_time=0.106, loss_ctc=44.763, loss_att=51.066, acc=0.737, loss=49.175, backward_time=0.097, grad_norm=43.353, clip=100.000, loss_scale=2.605e+34, optim_step_time=0.042, optim0_lr0=3.564e-04, train_time=0.656, time=2 hours, 44 minutes and 12.93 seconds, total_count=480000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=49.298, cer_ctc=0.261, loss_att=49.292, acc=0.618, cer=0.332, wer=0.997, loss=49.294, time=19 minutes and 54.17 seconds, total_count=149472, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-16 07:56:57,090 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-16 07:56:57,098 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/27epoch.pth
+[gpuc02:0/16] 2024-01-16 07:56:57,098 (trainer:272) INFO: 33/45epoch started. Estimated time to finish: 1 day, 16 hours and 22.12 seconds
+[gpuc02:0/16] 2024-01-16 07:56:57,107 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-16 07:57:17,034 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 07:57:20,536 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96e77550>)
+[gpuc02:0/16] 2024-01-16 07:57:20,536 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-16 07:57:20,540 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 08:01:56,626 (trainer:737) INFO: 33epoch:train:1-100batch: iter_time=2.398, forward_time=0.105, loss_ctc=52.346, loss_att=59.699, acc=0.727, loss=57.493, backward_time=0.099, grad_norm=58.499, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.536e-04, train_time=2.995
+[gpuc02:0/16] 2024-01-16 08:02:39,066 (trainer:737) INFO: 33epoch:train:101-200batch: iter_time=1.048e-04, forward_time=0.105, loss_ctc=39.887, loss_att=49.194, acc=0.761, loss=46.402, backward_time=0.099, grad_norm=39.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.535e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:03:21,338 (trainer:737) INFO: 33epoch:train:201-300batch: iter_time=1.152e-04, forward_time=0.103, loss_ctc=34.969, loss_att=42.071, acc=0.740, loss=39.940, backward_time=0.098, grad_norm=34.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.535e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:04:03,477 (trainer:737) INFO: 33epoch:train:301-400batch: iter_time=1.150e-04, forward_time=0.104, loss_ctc=42.613, loss_att=48.903, acc=0.745, loss=47.016, backward_time=0.099, grad_norm=49.179, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.535e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 08:04:45,916 (trainer:737) INFO: 33epoch:train:401-500batch: iter_time=1.073e-04, forward_time=0.104, loss_ctc=41.458, loss_att=43.599, acc=0.740, loss=42.957, backward_time=0.098, grad_norm=44.146, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.534e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:05:29,279 (trainer:737) INFO: 33epoch:train:501-600batch: iter_time=1.107e-04, forward_time=0.108, loss_ctc=49.179, loss_att=55.784, acc=0.720, loss=53.802, backward_time=0.098, grad_norm=46.826, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.534e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 08:06:12,343 (trainer:737) INFO: 33epoch:train:601-700batch: iter_time=1.156e-04, forward_time=0.104, loss_ctc=40.622, loss_att=44.911, acc=0.752, loss=43.624, backward_time=0.098, grad_norm=37.530, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.534e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 08:06:56,427 (trainer:737) INFO: 33epoch:train:701-800batch: iter_time=1.277e-04, forward_time=0.105, loss_ctc=48.472, loss_att=55.825, acc=0.716, loss=53.619, backward_time=0.098, grad_norm=45.049, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.533e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-16 08:07:38,497 (trainer:737) INFO: 33epoch:train:801-900batch: iter_time=1.177e-04, forward_time=0.103, loss_ctc=40.340, loss_att=42.466, acc=0.738, loss=41.828, backward_time=0.096, grad_norm=39.430, clip=100.000, loss_scale=3.697e+34, optim_step_time=0.041, optim0_lr0=3.533e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 08:08:22,515 (trainer:737) INFO: 33epoch:train:901-1000batch: iter_time=1.175e-04, forward_time=0.109, loss_ctc=47.469, loss_att=49.460, acc=0.746, loss=48.863, backward_time=0.097, grad_norm=48.672, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.532e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-16 08:09:05,348 (trainer:737) INFO: 33epoch:train:1001-1100batch: iter_time=1.186e-04, forward_time=0.104, loss_ctc=45.751, loss_att=61.555, acc=0.711, loss=56.814, backward_time=0.097, grad_norm=47.966, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.532e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 08:09:47,545 (trainer:737) INFO: 33epoch:train:1101-1200batch: iter_time=1.171e-04, forward_time=0.104, loss_ctc=40.563, loss_att=48.955, acc=0.736, loss=46.437, backward_time=0.097, grad_norm=40.749, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.532e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:10:11,267 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-16 08:10:30,945 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 08:10:34,618 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d97d6ff10>)
+[gpuc02:0/16] 2024-01-16 08:10:34,618 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-16 08:10:34,621 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 08:17:08,374 (trainer:737) INFO: 33epoch:train:1201-1300batch: iter_time=2.606, forward_time=0.105, loss_ctc=44.884, loss_att=51.320, acc=0.751, loss=49.389, backward_time=0.098, grad_norm=42.641, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.531e-04, train_time=4.408
+[gpuc02:0/16] 2024-01-16 08:17:50,620 (trainer:737) INFO: 33epoch:train:1301-1400batch: iter_time=1.165e-04, forward_time=0.105, loss_ctc=40.229, loss_att=45.743, acc=0.753, loss=44.089, backward_time=0.097, grad_norm=46.822, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.531e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:18:33,569 (trainer:737) INFO: 33epoch:train:1401-1500batch: iter_time=1.080e-04, forward_time=0.105, loss_ctc=40.203, loss_att=48.901, acc=0.755, loss=46.291, backward_time=0.098, grad_norm=36.277, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.531e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 08:19:15,958 (trainer:737) INFO: 33epoch:train:1501-1600batch: iter_time=1.307e-04, forward_time=0.104, loss_ctc=39.279, loss_att=43.269, acc=0.736, loss=42.072, backward_time=0.097, grad_norm=37.064, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.530e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:19:58,985 (trainer:737) INFO: 33epoch:train:1601-1700batch: iter_time=1.230e-04, forward_time=0.109, loss_ctc=40.310, loss_att=48.129, acc=0.727, loss=45.784, backward_time=0.098, grad_norm=48.016, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=3.530e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 08:20:38,612 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 08:20:41,580 (trainer:737) INFO: 33epoch:train:1701-1800batch: iter_time=1.351e-04, forward_time=0.106, loss_ctc=48.303, loss_att=49.066, acc=0.738, loss=48.837, backward_time=0.098, grad_norm=44.958, clip=100.000, loss_scale=4.007e+34, optim_step_time=0.042, optim0_lr0=3.529e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 08:21:23,868 (trainer:737) INFO: 33epoch:train:1801-1900batch: iter_time=1.343e-04, forward_time=0.105, loss_ctc=42.990, loss_att=47.602, acc=0.724, loss=46.218, backward_time=0.097, grad_norm=40.875, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.529e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 08:22:08,389 (trainer:737) INFO: 33epoch:train:1901-2000batch: iter_time=2.397e-04, forward_time=0.113, loss_ctc=42.951, loss_att=53.862, acc=0.715, loss=50.589, backward_time=0.098, grad_norm=42.737, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.529e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-16 08:22:50,582 (trainer:737) INFO: 33epoch:train:2001-2100batch: iter_time=1.217e-04, forward_time=0.105, loss_ctc=41.698, loss_att=42.826, acc=0.737, loss=42.487, backward_time=0.097, grad_norm=39.274, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.528e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:23:32,861 (trainer:737) INFO: 33epoch:train:2101-2200batch: iter_time=1.055e-04, forward_time=0.105, loss_ctc=50.832, loss_att=55.884, acc=0.703, loss=54.368, backward_time=0.097, grad_norm=51.389, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.528e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 08:24:15,258 (trainer:737) INFO: 33epoch:train:2201-2300batch: iter_time=1.029e-04, forward_time=0.105, loss_ctc=40.924, loss_att=46.922, acc=0.748, loss=45.123, backward_time=0.096, grad_norm=39.267, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.528e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:24:58,046 (trainer:737) INFO: 33epoch:train:2301-2400batch: iter_time=1.227e-04, forward_time=0.110, loss_ctc=45.151, loss_att=59.842, acc=0.709, loss=55.435, backward_time=0.097, grad_norm=43.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.527e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 08:25:40,185 (trainer:737) INFO: 33epoch:train:2401-2500batch: iter_time=9.607e-05, forward_time=0.105, loss_ctc=38.890, loss_att=43.002, acc=0.745, loss=41.769, backward_time=0.096, grad_norm=37.115, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.527e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 08:25:44,811 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-16 08:26:04,997 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 08:26:08,745 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f289d394e20>)
+[gpuc02:0/16] 2024-01-16 08:26:08,745 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-16 08:26:08,749 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 08:31:00,601 (trainer:737) INFO: 33epoch:train:2501-2600batch: iter_time=2.677, forward_time=0.126, loss_ctc=48.250, loss_att=57.715, acc=0.730, loss=54.875, backward_time=0.102, grad_norm=56.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.527e-04, train_time=3.204
+[gpuc02:0/16] 2024-01-16 08:31:43,086 (trainer:737) INFO: 33epoch:train:2601-2700batch: iter_time=1.312e-04, forward_time=0.105, loss_ctc=38.513, loss_att=48.026, acc=0.765, loss=45.172, backward_time=0.097, grad_norm=35.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.526e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 08:32:25,311 (trainer:737) INFO: 33epoch:train:2701-2800batch: iter_time=1.329e-04, forward_time=0.104, loss_ctc=34.136, loss_att=41.384, acc=0.745, loss=39.210, backward_time=0.097, grad_norm=35.715, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.526e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:33:07,948 (trainer:737) INFO: 33epoch:train:2801-2900batch: iter_time=1.516e-04, forward_time=0.105, loss_ctc=41.322, loss_att=47.212, acc=0.752, loss=45.445, backward_time=0.097, grad_norm=47.596, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.525e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 08:33:50,480 (trainer:737) INFO: 33epoch:train:2901-3000batch: iter_time=1.441e-04, forward_time=0.105, loss_ctc=40.221, loss_att=42.362, acc=0.747, loss=41.720, backward_time=0.097, grad_norm=41.815, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.525e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 08:34:33,875 (trainer:737) INFO: 33epoch:train:3001-3100batch: iter_time=1.260e-04, forward_time=0.105, loss_ctc=47.934, loss_att=54.456, acc=0.722, loss=52.499, backward_time=0.097, grad_norm=45.827, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.525e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-16 08:34:34,248 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 08:35:16,364 (trainer:737) INFO: 33epoch:train:3101-3200batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=39.612, loss_att=43.879, acc=0.756, loss=42.599, backward_time=0.097, grad_norm=35.799, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.524e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 08:35:58,990 (trainer:737) INFO: 33epoch:train:3201-3300batch: iter_time=1.439e-04, forward_time=0.105, loss_ctc=46.661, loss_att=54.678, acc=0.718, loss=52.273, backward_time=0.097, grad_norm=42.593, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.524e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 08:36:41,120 (trainer:737) INFO: 33epoch:train:3301-3400batch: iter_time=1.402e-04, forward_time=0.104, loss_ctc=39.058, loss_att=42.998, acc=0.735, loss=41.816, backward_time=0.096, grad_norm=39.205, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.524e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 08:37:23,470 (trainer:737) INFO: 33epoch:train:3401-3500batch: iter_time=1.424e-04, forward_time=0.105, loss_ctc=47.307, loss_att=49.436, acc=0.746, loss=48.797, backward_time=0.097, grad_norm=48.163, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.523e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 08:38:06,086 (trainer:737) INFO: 33epoch:train:3501-3600batch: iter_time=1.512e-04, forward_time=0.105, loss_ctc=45.234, loss_att=61.332, acc=0.712, loss=56.503, backward_time=0.097, grad_norm=46.555, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.523e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 08:38:48,351 (trainer:737) INFO: 33epoch:train:3601-3700batch: iter_time=1.419e-04, forward_time=0.104, loss_ctc=39.516, loss_att=48.067, acc=0.738, loss=45.502, backward_time=0.097, grad_norm=37.827, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.523e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:39:14,294 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-16 08:39:34,438 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 08:39:38,111 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e575bad40>)
+[gpuc02:0/16] 2024-01-16 08:39:38,111 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-16 08:39:38,115 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 08:44:08,479 (trainer:737) INFO: 33epoch:train:3701-3800batch: iter_time=2.548, forward_time=0.114, loss_ctc=44.413, loss_att=51.005, acc=0.752, loss=49.027, backward_time=0.099, grad_norm=40.201, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.522e-04, train_time=3.201
+[gpuc02:0/16] 2024-01-16 08:44:50,673 (trainer:737) INFO: 33epoch:train:3801-3900batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=38.398, loss_att=45.644, acc=0.755, loss=43.470, backward_time=0.097, grad_norm=47.586, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.522e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:45:33,318 (trainer:737) INFO: 33epoch:train:3901-4000batch: iter_time=1.486e-04, forward_time=0.106, loss_ctc=39.536, loss_att=49.181, acc=0.756, loss=46.288, backward_time=0.098, grad_norm=35.987, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.521e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 08:46:15,561 (trainer:737) INFO: 33epoch:train:4001-4100batch: iter_time=1.503e-04, forward_time=0.105, loss_ctc=38.873, loss_att=44.183, acc=0.736, loss=42.590, backward_time=0.097, grad_norm=37.666, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.521e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:46:57,809 (trainer:737) INFO: 33epoch:train:4101-4200batch: iter_time=1.429e-04, forward_time=0.105, loss_ctc=39.977, loss_att=49.115, acc=0.725, loss=46.374, backward_time=0.097, grad_norm=48.101, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.521e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 08:47:40,249 (trainer:737) INFO: 33epoch:train:4201-4300batch: iter_time=1.141e-04, forward_time=0.105, loss_ctc=47.833, loss_att=48.963, acc=0.739, loss=48.624, backward_time=0.097, grad_norm=45.263, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.520e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:48:22,729 (trainer:737) INFO: 33epoch:train:4301-4400batch: iter_time=1.251e-04, forward_time=0.104, loss_ctc=42.550, loss_att=47.696, acc=0.727, loss=46.152, backward_time=0.096, grad_norm=41.065, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.520e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 08:49:05,143 (trainer:737) INFO: 33epoch:train:4401-4500batch: iter_time=1.489e-04, forward_time=0.106, loss_ctc=42.337, loss_att=53.642, acc=0.716, loss=50.251, backward_time=0.097, grad_norm=40.155, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.520e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 08:49:47,697 (trainer:737) INFO: 33epoch:train:4501-4600batch: iter_time=1.669e-04, forward_time=0.106, loss_ctc=41.273, loss_att=42.796, acc=0.740, loss=42.339, backward_time=0.097, grad_norm=40.142, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.519e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 08:50:30,497 (trainer:737) INFO: 33epoch:train:4601-4700batch: iter_time=1.686e-04, forward_time=0.106, loss_ctc=49.050, loss_att=54.685, acc=0.705, loss=52.994, backward_time=0.097, grad_norm=51.319, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.519e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 08:51:13,297 (trainer:737) INFO: 33epoch:train:4701-4800batch: iter_time=1.503e-04, forward_time=0.105, loss_ctc=40.291, loss_att=46.264, acc=0.750, loss=44.472, backward_time=0.097, grad_norm=40.227, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.519e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 08:51:56,306 (trainer:737) INFO: 33epoch:train:4801-4900batch: iter_time=1.461e-04, forward_time=0.107, loss_ctc=45.181, loss_att=59.253, acc=0.711, loss=55.031, backward_time=0.098, grad_norm=43.116, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.518e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 08:52:38,606 (trainer:737) INFO: 33epoch:train:4901-5000batch: iter_time=1.452e-04, forward_time=0.106, loss_ctc=38.687, loss_att=42.101, acc=0.751, loss=41.077, backward_time=0.097, grad_norm=37.770, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.518e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 08:52:45,379 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-16 08:53:04,578 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 08:53:08,133 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f24931de740>)
+[gpuc02:0/16] 2024-01-16 08:53:08,133 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-16 08:53:08,136 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 08:58:15,256 (trainer:737) INFO: 33epoch:train:5001-5100batch: iter_time=2.885, forward_time=0.115, loss_ctc=47.339, loss_att=54.411, acc=0.729, loss=52.289, backward_time=0.099, grad_norm=53.329, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.517e-04, train_time=3.366
+[gpuc02:0/16] 2024-01-16 08:58:57,620 (trainer:737) INFO: 33epoch:train:5101-5200batch: iter_time=1.201e-04, forward_time=0.104, loss_ctc=38.434, loss_att=46.893, acc=0.768, loss=44.355, backward_time=0.097, grad_norm=37.073, clip=100.000, loss_scale=2.067e+34, optim_step_time=0.041, optim0_lr0=3.517e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 08:59:39,790 (trainer:737) INFO: 33epoch:train:5201-5300batch: iter_time=1.250e-04, forward_time=0.103, loss_ctc=33.742, loss_att=38.106, acc=0.750, loss=36.796, backward_time=0.096, grad_norm=33.131, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.517e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 09:00:22,109 (trainer:737) INFO: 33epoch:train:5301-5400batch: iter_time=1.342e-04, forward_time=0.104, loss_ctc=40.880, loss_att=45.220, acc=0.750, loss=43.918, backward_time=0.097, grad_norm=44.011, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.516e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:01:04,507 (trainer:737) INFO: 33epoch:train:5401-5500batch: iter_time=1.420e-04, forward_time=0.104, loss_ctc=40.034, loss_att=42.555, acc=0.734, loss=41.799, backward_time=0.096, grad_norm=43.169, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.516e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:01:47,668 (trainer:737) INFO: 33epoch:train:5501-5600batch: iter_time=1.213e-04, forward_time=0.107, loss_ctc=47.371, loss_att=53.702, acc=0.722, loss=51.803, backward_time=0.097, grad_norm=45.569, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.516e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 09:02:30,302 (trainer:737) INFO: 33epoch:train:5601-5700batch: iter_time=1.270e-04, forward_time=0.105, loss_ctc=39.491, loss_att=44.150, acc=0.753, loss=42.752, backward_time=0.096, grad_norm=36.028, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.515e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:03:12,966 (trainer:737) INFO: 33epoch:train:5701-5800batch: iter_time=1.486e-04, forward_time=0.105, loss_ctc=47.191, loss_att=55.439, acc=0.707, loss=52.965, backward_time=0.097, grad_norm=42.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.515e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:03:55,162 (trainer:737) INFO: 33epoch:train:5801-5900batch: iter_time=1.388e-04, forward_time=0.104, loss_ctc=38.519, loss_att=42.859, acc=0.727, loss=41.557, backward_time=0.096, grad_norm=38.827, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.515e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:04:37,598 (trainer:737) INFO: 33epoch:train:5901-6000batch: iter_time=1.359e-04, forward_time=0.105, loss_ctc=47.164, loss_att=48.879, acc=0.739, loss=48.364, backward_time=0.096, grad_norm=47.745, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.514e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:05:03,803 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 09:05:19,920 (trainer:737) INFO: 33epoch:train:6001-6100batch: iter_time=1.535e-04, forward_time=0.105, loss_ctc=44.761, loss_att=58.846, acc=0.705, loss=54.620, backward_time=0.096, grad_norm=44.554, clip=100.000, loss_scale=1.678e+34, optim_step_time=0.041, optim0_lr0=3.514e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:06:02,433 (trainer:737) INFO: 33epoch:train:6101-6200batch: iter_time=1.453e-04, forward_time=0.104, loss_ctc=39.460, loss_att=46.684, acc=0.737, loss=44.517, backward_time=0.096, grad_norm=39.222, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.513e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:06:30,392 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-16 09:06:49,914 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 09:06:53,576 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e937afcd0>)
+[gpuc02:0/16] 2024-01-16 09:06:53,576 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-16 09:06:53,580 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 09:11:22,499 (trainer:737) INFO: 33epoch:train:6201-6300batch: iter_time=2.498, forward_time=0.105, loss_ctc=43.704, loss_att=52.784, acc=0.749, loss=50.060, backward_time=0.097, grad_norm=41.449, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.513e-04, train_time=3.200
+[gpuc02:0/16] 2024-01-16 09:12:04,797 (trainer:737) INFO: 33epoch:train:6301-6400batch: iter_time=1.048e-04, forward_time=0.104, loss_ctc=39.293, loss_att=46.005, acc=0.761, loss=43.991, backward_time=0.097, grad_norm=47.223, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.513e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:12:47,199 (trainer:737) INFO: 33epoch:train:6401-6500batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=38.771, loss_att=48.896, acc=0.763, loss=45.858, backward_time=0.097, grad_norm=35.474, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.512e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:13:29,810 (trainer:737) INFO: 33epoch:train:6501-6600batch: iter_time=1.065e-04, forward_time=0.104, loss_ctc=38.875, loss_att=48.120, acc=0.731, loss=45.347, backward_time=0.097, grad_norm=38.491, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.512e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:14:12,149 (trainer:737) INFO: 33epoch:train:6601-6700batch: iter_time=1.185e-04, forward_time=0.104, loss_ctc=39.622, loss_att=48.005, acc=0.745, loss=45.490, backward_time=0.097, grad_norm=43.751, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.512e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:14:54,640 (trainer:737) INFO: 33epoch:train:6701-6800batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=47.574, loss_att=48.652, acc=0.746, loss=48.329, backward_time=0.097, grad_norm=44.914, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.511e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:15:36,948 (trainer:737) INFO: 33epoch:train:6801-6900batch: iter_time=1.188e-04, forward_time=0.104, loss_ctc=42.305, loss_att=48.091, acc=0.733, loss=46.355, backward_time=0.097, grad_norm=41.303, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.511e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:16:19,513 (trainer:737) INFO: 33epoch:train:6901-7000batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=41.940, loss_att=53.245, acc=0.725, loss=49.853, backward_time=0.097, grad_norm=40.919, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.511e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:17:01,754 (trainer:737) INFO: 33epoch:train:7001-7100batch: iter_time=1.167e-04, forward_time=0.104, loss_ctc=41.024, loss_att=42.777, acc=0.744, loss=42.251, backward_time=0.097, grad_norm=41.111, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.510e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:17:44,077 (trainer:737) INFO: 33epoch:train:7101-7200batch: iter_time=1.090e-04, forward_time=0.104, loss_ctc=48.887, loss_att=53.547, acc=0.719, loss=52.149, backward_time=0.098, grad_norm=51.111, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.510e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:18:26,421 (trainer:737) INFO: 33epoch:train:7201-7300batch: iter_time=1.105e-04, forward_time=0.104, loss_ctc=40.217, loss_att=48.808, acc=0.752, loss=46.231, backward_time=0.098, grad_norm=41.553, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.510e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:19:08,859 (trainer:737) INFO: 33epoch:train:7301-7400batch: iter_time=1.090e-04, forward_time=0.105, loss_ctc=43.991, loss_att=59.892, acc=0.722, loss=55.122, backward_time=0.098, grad_norm=41.626, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.509e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:19:51,863 (trainer:737) INFO: 33epoch:train:7401-7500batch: iter_time=9.823e-05, forward_time=0.104, loss_ctc=38.469, loss_att=42.538, acc=0.748, loss=41.317, backward_time=0.097, grad_norm=37.421, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.509e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 09:19:54,304 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-16 09:20:14,267 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 09:20:18,242 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1e928fba60>)
+[gpuc02:0/16] 2024-01-16 09:20:18,242 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-16 09:20:18,245 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 09:25:03,671 (trainer:737) INFO: 33epoch:train:7501-7600batch: iter_time=2.518, forward_time=0.105, loss_ctc=47.433, loss_att=56.584, acc=0.725, loss=53.839, backward_time=0.097, grad_norm=56.084, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.508e-04, train_time=3.118
+[gpuc02:0/16] 2024-01-16 09:25:46,792 (trainer:737) INFO: 33epoch:train:7601-7700batch: iter_time=1.470e-04, forward_time=0.104, loss_ctc=38.094, loss_att=47.790, acc=0.764, loss=44.881, backward_time=0.097, grad_norm=36.419, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.508e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-16 09:26:29,196 (trainer:737) INFO: 33epoch:train:7701-7800batch: iter_time=1.440e-04, forward_time=0.104, loss_ctc=33.730, loss_att=38.279, acc=0.750, loss=36.914, backward_time=0.097, grad_norm=34.441, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.508e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:27:11,817 (trainer:737) INFO: 33epoch:train:7801-7900batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=40.570, loss_att=45.967, acc=0.748, loss=44.348, backward_time=0.097, grad_norm=44.128, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.507e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:27:54,590 (trainer:737) INFO: 33epoch:train:7901-8000batch: iter_time=1.357e-04, forward_time=0.105, loss_ctc=40.063, loss_att=43.482, acc=0.731, loss=42.456, backward_time=0.097, grad_norm=44.652, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.507e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 09:28:37,498 (trainer:737) INFO: 33epoch:train:8001-8100batch: iter_time=1.472e-04, forward_time=0.105, loss_ctc=46.874, loss_att=53.032, acc=0.725, loss=51.185, backward_time=0.097, grad_norm=44.689, clip=100.000, loss_scale=1.433e+34, optim_step_time=0.042, optim0_lr0=3.507e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-16 09:29:19,816 (trainer:737) INFO: 33epoch:train:8101-8200batch: iter_time=1.327e-04, forward_time=0.104, loss_ctc=38.910, loss_att=43.618, acc=0.753, loss=42.206, backward_time=0.097, grad_norm=36.477, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.506e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:30:02,114 (trainer:737) INFO: 33epoch:train:8201-8300batch: iter_time=1.438e-04, forward_time=0.105, loss_ctc=46.106, loss_att=54.773, acc=0.708, loss=52.173, backward_time=0.097, grad_norm=42.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.506e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:30:44,171 (trainer:737) INFO: 33epoch:train:8301-8400batch: iter_time=1.426e-04, forward_time=0.103, loss_ctc=38.282, loss_att=43.002, acc=0.727, loss=41.586, backward_time=0.097, grad_norm=39.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.506e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 09:31:26,784 (trainer:737) INFO: 33epoch:train:8401-8500batch: iter_time=1.416e-04, forward_time=0.105, loss_ctc=47.402, loss_att=48.766, acc=0.740, loss=48.357, backward_time=0.097, grad_norm=45.637, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.505e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:32:09,040 (trainer:737) INFO: 33epoch:train:8501-8600batch: iter_time=1.434e-04, forward_time=0.104, loss_ctc=44.113, loss_att=58.729, acc=0.706, loss=54.344, backward_time=0.097, grad_norm=45.708, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.505e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:32:51,249 (trainer:737) INFO: 33epoch:train:8601-8700batch: iter_time=1.407e-04, forward_time=0.104, loss_ctc=39.034, loss_att=45.623, acc=0.741, loss=43.646, backward_time=0.097, grad_norm=39.303, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.504e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:33:16,301 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-16 09:33:35,512 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 09:33:39,104 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f256cb43f40>)
+[gpuc02:0/16] 2024-01-16 09:33:39,104 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-16 09:33:39,107 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 09:38:07,322 (trainer:737) INFO: 33epoch:train:8701-8800batch: iter_time=2.509, forward_time=0.105, loss_ctc=43.809, loss_att=52.803, acc=0.750, loss=50.105, backward_time=0.098, grad_norm=41.180, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.504e-04, train_time=3.161
+[gpuc02:0/16] 2024-01-16 09:38:49,963 (trainer:737) INFO: 33epoch:train:8801-8900batch: iter_time=9.181e-05, forward_time=0.104, loss_ctc=37.342, loss_att=44.998, acc=0.762, loss=42.701, backward_time=0.097, grad_norm=46.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.504e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:39:32,330 (trainer:737) INFO: 33epoch:train:8901-9000batch: iter_time=9.512e-05, forward_time=0.105, loss_ctc=39.130, loss_att=48.813, acc=0.762, loss=45.908, backward_time=0.098, grad_norm=35.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.503e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:40:14,670 (trainer:737) INFO: 33epoch:train:9001-9100batch: iter_time=1.055e-04, forward_time=0.105, loss_ctc=38.143, loss_att=47.767, acc=0.731, loss=44.880, backward_time=0.097, grad_norm=37.670, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.503e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:40:57,083 (trainer:737) INFO: 33epoch:train:9101-9200batch: iter_time=1.354e-04, forward_time=0.106, loss_ctc=39.371, loss_att=47.912, acc=0.745, loss=45.349, backward_time=0.098, grad_norm=42.133, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.503e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:41:39,587 (trainer:737) INFO: 33epoch:train:9201-9300batch: iter_time=1.432e-04, forward_time=0.106, loss_ctc=47.216, loss_att=48.035, acc=0.747, loss=47.789, backward_time=0.098, grad_norm=44.771, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.502e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:42:22,250 (trainer:737) INFO: 33epoch:train:9301-9400batch: iter_time=1.006e-04, forward_time=0.108, loss_ctc=41.619, loss_att=47.342, acc=0.735, loss=45.625, backward_time=0.097, grad_norm=40.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.502e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 09:43:04,970 (trainer:737) INFO: 33epoch:train:9401-9500batch: iter_time=1.128e-04, forward_time=0.105, loss_ctc=41.733, loss_att=53.293, acc=0.725, loss=49.825, backward_time=0.098, grad_norm=38.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.502e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 09:43:47,200 (trainer:737) INFO: 33epoch:train:9501-9600batch: iter_time=1.021e-04, forward_time=0.105, loss_ctc=40.796, loss_att=42.261, acc=0.745, loss=41.821, backward_time=0.097, grad_norm=40.023, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.501e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:44:29,504 (trainer:737) INFO: 33epoch:train:9601-9700batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=48.082, loss_att=53.365, acc=0.719, loss=51.780, backward_time=0.097, grad_norm=50.806, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.501e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:45:12,488 (trainer:737) INFO: 33epoch:train:9701-9800batch: iter_time=9.883e-05, forward_time=0.104, loss_ctc=39.760, loss_att=48.416, acc=0.754, loss=45.819, backward_time=0.097, grad_norm=41.177, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.501e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 09:45:54,883 (trainer:737) INFO: 33epoch:train:9801-9900batch: iter_time=1.260e-04, forward_time=0.105, loss_ctc=44.472, loss_att=60.249, acc=0.722, loss=55.516, backward_time=0.097, grad_norm=43.763, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.500e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:46:37,384 (trainer:737) INFO: 33epoch:train:9901-10000batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=38.120, loss_att=42.819, acc=0.750, loss=41.409, backward_time=0.096, grad_norm=37.372, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.500e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:46:40,043 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-16 09:47:00,058 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 09:47:03,751 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ec49744f0>)
+[gpuc02:0/16] 2024-01-16 09:47:03,751 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-16 09:47:03,755 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 09:51:57,948 (trainer:737) INFO: 33epoch:train:10001-10100batch: iter_time=2.657, forward_time=0.104, loss_ctc=46.608, loss_att=56.316, acc=0.726, loss=53.404, backward_time=0.097, grad_norm=53.926, clip=100.000, loss_scale=2.866e+34, optim_step_time=0.042, optim0_lr0=3.499e-04, train_time=3.205
+[gpuc02:0/16] 2024-01-16 09:52:40,369 (trainer:737) INFO: 33epoch:train:10101-10200batch: iter_time=1.286e-04, forward_time=0.105, loss_ctc=38.159, loss_att=47.203, acc=0.769, loss=44.490, backward_time=0.097, grad_norm=39.055, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.499e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:52:54,977 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 09:53:22,878 (trainer:737) INFO: 33epoch:train:10201-10300batch: iter_time=1.239e-04, forward_time=0.104, loss_ctc=33.676, loss_att=38.785, acc=0.749, loss=37.252, backward_time=0.097, grad_norm=33.471, clip=100.000, loss_scale=2.769e+34, optim_step_time=0.041, optim0_lr0=3.499e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:54:05,264 (trainer:737) INFO: 33epoch:train:10301-10400batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=40.593, loss_att=45.526, acc=0.750, loss=44.046, backward_time=0.097, grad_norm=40.250, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.498e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:54:47,563 (trainer:737) INFO: 33epoch:train:10401-10500batch: iter_time=1.352e-04, forward_time=0.104, loss_ctc=39.845, loss_att=42.888, acc=0.735, loss=41.975, backward_time=0.096, grad_norm=44.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.498e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 09:55:30,003 (trainer:737) INFO: 33epoch:train:10501-10600batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=46.751, loss_att=53.522, acc=0.723, loss=51.491, backward_time=0.097, grad_norm=44.878, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.498e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:56:12,423 (trainer:737) INFO: 33epoch:train:10601-10700batch: iter_time=1.187e-04, forward_time=0.103, loss_ctc=38.981, loss_att=43.775, acc=0.754, loss=42.337, backward_time=0.097, grad_norm=36.041, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.497e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 09:56:54,971 (trainer:737) INFO: 33epoch:train:10701-10800batch: iter_time=1.351e-04, forward_time=0.105, loss_ctc=45.950, loss_att=55.252, acc=0.708, loss=52.462, backward_time=0.097, grad_norm=43.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.497e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 09:57:37,151 (trainer:737) INFO: 33epoch:train:10801-10900batch: iter_time=1.294e-04, forward_time=0.103, loss_ctc=38.489, loss_att=42.898, acc=0.730, loss=41.576, backward_time=0.096, grad_norm=42.027, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.497e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:58:20,465 (trainer:737) INFO: 33epoch:train:10901-11000batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=45.771, loss_att=48.074, acc=0.742, loss=47.383, backward_time=0.096, grad_norm=47.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.496e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-16 09:59:02,715 (trainer:737) INFO: 33epoch:train:11001-11100batch: iter_time=1.273e-04, forward_time=0.105, loss_ctc=44.465, loss_att=59.160, acc=0.704, loss=54.751, backward_time=0.097, grad_norm=44.801, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.496e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 09:59:45,023 (trainer:737) INFO: 33epoch:train:11101-11200batch: iter_time=1.500e-04, forward_time=0.105, loss_ctc=39.001, loss_att=46.791, acc=0.735, loss=44.454, backward_time=0.097, grad_norm=38.766, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.496e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:00:10,164 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-16 10:00:30,663 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 10:00:34,389 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ec88737f0>)
+[gpuc02:0/16] 2024-01-16 10:00:34,389 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-16 10:00:34,393 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 10:05:35,372 (trainer:737) INFO: 33epoch:train:11201-11300batch: iter_time=2.525, forward_time=0.106, loss_ctc=43.918, loss_att=50.266, acc=0.750, loss=48.362, backward_time=0.097, grad_norm=39.868, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.495e-04, train_time=3.503
+[gpuc02:0/16] 2024-01-16 10:06:19,640 (trainer:737) INFO: 33epoch:train:11301-11400batch: iter_time=1.411e-04, forward_time=0.114, loss_ctc=37.720, loss_att=44.118, acc=0.758, loss=42.199, backward_time=0.101, grad_norm=48.063, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.495e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-16 10:07:02,615 (trainer:737) INFO: 33epoch:train:11401-11500batch: iter_time=1.413e-04, forward_time=0.104, loss_ctc=38.884, loss_att=48.094, acc=0.760, loss=45.331, backward_time=0.097, grad_norm=35.518, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.494e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-16 10:07:45,120 (trainer:737) INFO: 33epoch:train:11501-11600batch: iter_time=1.424e-04, forward_time=0.104, loss_ctc=38.152, loss_att=43.725, acc=0.739, loss=42.053, backward_time=0.096, grad_norm=38.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.494e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 10:08:27,418 (trainer:737) INFO: 33epoch:train:11601-11700batch: iter_time=1.390e-04, forward_time=0.105, loss_ctc=39.687, loss_att=47.937, acc=0.730, loss=45.462, backward_time=0.096, grad_norm=46.712, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.494e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:09:10,154 (trainer:737) INFO: 33epoch:train:11701-11800batch: iter_time=1.433e-04, forward_time=0.106, loss_ctc=47.038, loss_att=48.643, acc=0.742, loss=48.161, backward_time=0.097, grad_norm=46.033, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.493e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 10:09:52,440 (trainer:737) INFO: 33epoch:train:11801-11900batch: iter_time=1.310e-04, forward_time=0.105, loss_ctc=41.698, loss_att=46.771, acc=0.730, loss=45.249, backward_time=0.096, grad_norm=39.447, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.493e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:10:34,790 (trainer:737) INFO: 33epoch:train:11901-12000batch: iter_time=1.299e-04, forward_time=0.105, loss_ctc=41.606, loss_att=53.506, acc=0.717, loss=49.936, backward_time=0.096, grad_norm=40.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.493e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:11:17,057 (trainer:737) INFO: 33epoch:train:12001-12100batch: iter_time=1.451e-04, forward_time=0.105, loss_ctc=40.189, loss_att=42.244, acc=0.742, loss=41.627, backward_time=0.096, grad_norm=39.540, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.492e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 10:12:00,537 (trainer:737) INFO: 33epoch:train:12101-12200batch: iter_time=1.323e-04, forward_time=0.105, loss_ctc=47.713, loss_att=53.354, acc=0.709, loss=51.662, backward_time=0.096, grad_norm=48.450, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.492e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-16 10:12:42,871 (trainer:737) INFO: 33epoch:train:12201-12300batch: iter_time=1.381e-04, forward_time=0.104, loss_ctc=39.608, loss_att=45.559, acc=0.753, loss=43.774, backward_time=0.096, grad_norm=41.600, clip=100.000, loss_scale=3.448e+34, optim_step_time=0.041, optim0_lr0=3.492e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:13:25,729 (trainer:737) INFO: 33epoch:train:12301-12400batch: iter_time=1.468e-04, forward_time=0.106, loss_ctc=44.115, loss_att=58.160, acc=0.714, loss=53.946, backward_time=0.097, grad_norm=41.772, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.491e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 10:13:42,082 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 10:14:07,820 (trainer:737) INFO: 33epoch:train:12401-12500batch: iter_time=1.339e-04, forward_time=0.105, loss_ctc=38.325, loss_att=41.708, acc=0.752, loss=40.693, backward_time=0.096, grad_norm=36.758, clip=100.000, loss_scale=2.874e+34, optim_step_time=0.041, optim0_lr0=3.491e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 10:14:13,929 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-16 10:14:34,723 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 10:14:38,501 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ebd85a950>)
+[gpuc02:0/16] 2024-01-16 10:14:38,501 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-16 10:14:38,504 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 10:19:31,703 (trainer:737) INFO: 33epoch:train:12501-12600batch: iter_time=2.754, forward_time=0.105, loss_ctc=47.606, loss_att=57.879, acc=0.734, loss=54.797, backward_time=0.098, grad_norm=52.748, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.491e-04, train_time=3.239
+[gpuc02:0/16] 2024-01-16 10:20:14,140 (trainer:737) INFO: 33epoch:train:12601-12700batch: iter_time=1.078e-04, forward_time=0.104, loss_ctc=37.570, loss_att=47.687, acc=0.768, loss=44.652, backward_time=0.098, grad_norm=36.479, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.490e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 10:20:56,083 (trainer:737) INFO: 33epoch:train:12701-12800batch: iter_time=1.045e-04, forward_time=0.104, loss_ctc=33.614, loss_att=41.528, acc=0.745, loss=39.154, backward_time=0.097, grad_norm=35.080, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.490e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-16 10:21:38,262 (trainer:737) INFO: 33epoch:train:12801-12900batch: iter_time=1.147e-04, forward_time=0.105, loss_ctc=40.403, loss_att=48.082, acc=0.752, loss=45.779, backward_time=0.098, grad_norm=39.768, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.490e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 10:22:20,357 (trainer:737) INFO: 33epoch:train:12901-13000batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=39.603, loss_att=42.760, acc=0.747, loss=41.813, backward_time=0.098, grad_norm=43.085, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.489e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 10:23:02,983 (trainer:737) INFO: 33epoch:train:13001-13100batch: iter_time=1.337e-04, forward_time=0.105, loss_ctc=46.304, loss_att=54.140, acc=0.725, loss=51.789, backward_time=0.098, grad_norm=43.794, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.489e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 10:23:45,330 (trainer:737) INFO: 33epoch:train:13101-13200batch: iter_time=1.284e-04, forward_time=0.104, loss_ctc=39.192, loss_att=43.750, acc=0.757, loss=42.382, backward_time=0.098, grad_norm=36.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.488e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:24:27,995 (trainer:737) INFO: 33epoch:train:13201-13300batch: iter_time=1.332e-04, forward_time=0.106, loss_ctc=46.028, loss_att=54.660, acc=0.721, loss=52.071, backward_time=0.098, grad_norm=41.993, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.488e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 10:25:09,996 (trainer:737) INFO: 33epoch:train:13301-13400batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=38.030, loss_att=41.591, acc=0.744, loss=40.522, backward_time=0.097, grad_norm=38.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.488e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 10:25:52,546 (trainer:737) INFO: 33epoch:train:13401-13500batch: iter_time=1.371e-04, forward_time=0.105, loss_ctc=45.879, loss_att=47.951, acc=0.751, loss=47.329, backward_time=0.097, grad_norm=47.553, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.487e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 10:26:34,819 (trainer:737) INFO: 33epoch:train:13501-13600batch: iter_time=1.415e-04, forward_time=0.105, loss_ctc=43.738, loss_att=60.772, acc=0.715, loss=55.662, backward_time=0.097, grad_norm=44.971, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.487e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 10:27:17,610 (trainer:737) INFO: 33epoch:train:13601-13700batch: iter_time=1.452e-04, forward_time=0.105, loss_ctc=38.956, loss_att=47.657, acc=0.742, loss=45.047, backward_time=0.097, grad_norm=40.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.487e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-16 10:27:42,253 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-16 10:28:01,532 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 10:28:05,692 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1ebe8abfd0>)
+[gpuc02:0/16] 2024-01-16 10:28:05,692 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-16 10:28:05,696 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 10:32:27,036 (trainer:737) INFO: 33epoch:train:13701-13800batch: iter_time=2.598, forward_time=0.106, loss_ctc=43.599, loss_att=51.884, acc=0.755, loss=49.398, backward_time=0.098, grad_norm=39.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.486e-04, train_time=3.094
+[gpuc02:0/16] 2024-01-16 10:33:09,345 (trainer:737) INFO: 33epoch:train:13801-13900batch: iter_time=1.197e-04, forward_time=0.105, loss_ctc=38.329, loss_att=43.332, acc=0.767, loss=41.831, backward_time=0.097, grad_norm=47.728, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.486e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:33:51,758 (trainer:737) INFO: 33epoch:train:13901-14000batch: iter_time=1.330e-04, forward_time=0.106, loss_ctc=38.555, loss_att=48.154, acc=0.764, loss=45.274, backward_time=0.097, grad_norm=35.789, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.486e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 10:34:34,315 (trainer:737) INFO: 33epoch:train:14001-14100batch: iter_time=1.354e-04, forward_time=0.105, loss_ctc=38.216, loss_att=48.058, acc=0.730, loss=45.105, backward_time=0.097, grad_norm=38.375, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.485e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 10:35:16,838 (trainer:737) INFO: 33epoch:train:14101-14200batch: iter_time=1.459e-04, forward_time=0.105, loss_ctc=39.034, loss_att=46.932, acc=0.748, loss=44.562, backward_time=0.097, grad_norm=43.803, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.485e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 10:35:59,248 (trainer:737) INFO: 33epoch:train:14201-14300batch: iter_time=1.416e-04, forward_time=0.107, loss_ctc=46.849, loss_att=48.066, acc=0.745, loss=47.701, backward_time=0.097, grad_norm=44.249, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.485e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 10:36:41,510 (trainer:737) INFO: 33epoch:train:14301-14400batch: iter_time=1.283e-04, forward_time=0.106, loss_ctc=41.845, loss_att=47.386, acc=0.736, loss=45.723, backward_time=0.097, grad_norm=38.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.484e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-16 10:37:23,811 (trainer:737) INFO: 33epoch:train:14401-14500batch: iter_time=1.372e-04, forward_time=0.106, loss_ctc=41.854, loss_att=53.731, acc=0.725, loss=50.168, backward_time=0.097, grad_norm=40.432, clip=100.000, loss_scale=3.344e+34, optim_step_time=0.041, optim0_lr0=3.484e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:38:06,128 (trainer:737) INFO: 33epoch:train:14501-14600batch: iter_time=1.550e-04, forward_time=0.104, loss_ctc=40.093, loss_att=41.257, acc=0.751, loss=40.908, backward_time=0.097, grad_norm=39.310, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.484e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:38:49,838 (trainer:737) INFO: 33epoch:train:14601-14700batch: iter_time=1.400e-04, forward_time=0.104, loss_ctc=47.257, loss_att=52.333, acc=0.722, loss=50.810, backward_time=0.097, grad_norm=49.484, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.483e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-16 10:39:32,143 (trainer:737) INFO: 33epoch:train:14701-14800batch: iter_time=1.486e-04, forward_time=0.105, loss_ctc=39.335, loss_att=47.921, acc=0.757, loss=45.345, backward_time=0.097, grad_norm=39.544, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.483e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 10:40:14,618 (trainer:737) INFO: 33epoch:train:14801-14900batch: iter_time=1.365e-04, forward_time=0.105, loss_ctc=44.304, loss_att=59.390, acc=0.728, loss=54.864, backward_time=0.098, grad_norm=41.630, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=3.482e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 10:40:55,387 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-16 10:40:57,088 (trainer:737) INFO: 33epoch:train:14901-15000batch: iter_time=1.331e-04, forward_time=0.104, loss_ctc=38.257, loss_att=42.905, acc=0.751, loss=41.510, backward_time=0.097, grad_norm=36.891, clip=100.000, loss_scale=4.070e+34, optim_step_time=0.042, optim0_lr0=3.482e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-16 11:01:04,649 (trainer:343) INFO: 33epoch results: [train] iter_time=0.208, forward_time=0.105, loss_ctc=41.981, loss_att=48.838, acc=0.738, loss=46.781, backward_time=0.097, grad_norm=42.210, clip=100.000, loss_scale=2.063e+34, optim_step_time=0.042, optim0_lr0=3.509e-04, train_time=0.656, time=2 hours, 44 minutes and 10.47 seconds, total_count=495000, gpu_max_cached_mem_GB=26.281, [valid] loss_ctc=50.677, cer_ctc=0.258, loss_att=51.548, acc=0.595, cer=0.379, wer=0.999, loss=51.287, time=19 minutes and 56.92 seconds, total_count=154143, gpu_max_cached_mem_GB=26.281
+[gpuc02:0/16] 2024-01-16 11:01:09,311 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-16 11:01:09,319 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/28epoch.pth
+[gpuc02:0/16] 2024-01-16 11:01:09,319 (trainer:272) INFO: 34/45epoch started. Estimated time to finish: 1 day, 12 hours and 55 minutes
+[gpuc02:0/16] 2024-01-16 11:01:09,329 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-16 11:01:29,171 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 11:01:32,864 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96e836d0>)
+[gpuc02:0/16] 2024-01-16 11:01:32,864 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-16 11:01:32,868 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 11:06:08,927 (trainer:737) INFO: 34epoch:train:1-100batch: iter_time=2.427, forward_time=0.105, loss_ctc=57.960, loss_att=64.362, acc=0.700, loss=62.442, backward_time=0.098, grad_norm=55.033, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.482e-04, train_time=2.996
+[gpuc02:0/16] 2024-01-16 11:06:51,100 (trainer:737) INFO: 34epoch:train:101-200batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=49.624, loss_att=50.300, acc=0.727, loss=50.097, backward_time=0.098, grad_norm=46.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.481e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 11:07:33,594 (trainer:737) INFO: 34epoch:train:201-300batch: iter_time=1.010e-04, forward_time=0.104, loss_ctc=36.944, loss_att=39.587, acc=0.743, loss=38.794, backward_time=0.097, grad_norm=38.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.481e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 11:08:15,718 (trainer:737) INFO: 34epoch:train:301-400batch: iter_time=1.107e-04, forward_time=0.105, loss_ctc=44.884, loss_att=56.701, acc=0.720, loss=53.156, backward_time=0.098, grad_norm=44.377, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.481e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 11:08:58,410 (trainer:737) INFO: 34epoch:train:401-500batch: iter_time=1.089e-04, forward_time=0.105, loss_ctc=44.239, loss_att=48.151, acc=0.724, loss=46.978, backward_time=0.097, grad_norm=44.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.480e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-16 11:09:40,548 (trainer:737) INFO: 34epoch:train:501-600batch: iter_time=1.153e-04, forward_time=0.105, loss_ctc=44.264, loss_att=52.698, acc=0.733, loss=50.168, backward_time=0.098, grad_norm=48.671, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.480e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-16 11:10:22,854 (trainer:737) INFO: 34epoch:train:601-700batch: iter_time=9.879e-05, forward_time=0.105, loss_ctc=39.980, loss_att=51.123, acc=0.749, loss=47.780, backward_time=0.098, grad_norm=39.592, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.480e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-16 11:11:05,408 (trainer:737) INFO: 34epoch:train:701-800batch: iter_time=1.116e-04, forward_time=0.104, loss_ctc=34.069, loss_att=38.372, acc=0.772, loss=37.081, backward_time=0.097, grad_norm=35.800, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.479e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-16 11:11:47,407 (trainer:737) INFO: 34epoch:train:801-900batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=44.564, loss_att=46.173, acc=0.736, loss=45.691, backward_time=0.097, grad_norm=45.710, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.479e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-16 11:12:30,074 (trainer:737) INFO: 34epoch:train:901-1000batch: iter_time=1.122e-04, forward_time=0.105, loss_ctc=50.113, loss_att=56.389, acc=0.728, loss=54.506, backward_time=0.098, grad_norm=52.223, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.479e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-16 11:13:17,793 (trainer:737) INFO: 34epoch:train:1001-1100batch: iter_time=1.147e-04, forward_time=0.113, loss_ctc=43.241, loss_att=43.335, acc=0.748, loss=43.307, backward_time=0.104, grad_norm=44.968, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.478e-04, train_time=0.477
+[gpuc02:0/16] 2024-01-16 11:14:02,725 (trainer:737) INFO: 34epoch:train:1101-1200batch: iter_time=1.216e-04, forward_time=0.105, loss_ctc=43.926, loss_att=44.727, acc=0.729, loss=44.486, backward_time=0.098, grad_norm=42.585, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.478e-04, train_time=0.449
+[gpuc02:0/16] 2024-01-16 11:14:37,482 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-16 11:14:57,228 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-16 11:15:00,853 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f1d96e3b160>)
+[gpuc02:0/16] 2024-01-16 11:15:00,854 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-16 11:15:00,857 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-16 11:20:21,282 (trainer:737) INFO: 34epoch:train:1201-1300batch: iter_time=3.102, forward_time=0.118, loss_ctc=45.367, loss_att=54.449, acc=0.728, loss=51.724, backward_time=0.099, grad_norm=44.193, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.478e-04, train_time=3.785
+[gpuc02:0/16] 2024-01-16 11:21:03,563 (trainer:737) INFO: 34epoch:train:1301-1400batch: iter_time=1.215e-04, forward_time=0.106, loss_ctc=51.649, loss_att=56.931, acc=0.734, loss=55.346, backward_time=0.097, grad_norm=50.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.477e-04, train_time=0.423
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.
+slurmstepd: error: *** STEP 2858112.0 ON gpuc02 CANCELLED AT 2024-01-16T11:21:41 ***
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.2.log b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.2.log
new file mode 100644
index 0000000000000000000000000000000000000000..1fc199751648f71c9e3795daca31a11d6ad9c98a
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.2.log
@@ -0,0 +1,2367 @@
+# Running on gpuc02.delta.ncsa.illinois.edu
+# Started at Sat Jan 13 23:45:40 CST 2024
+# SLURMD_NODENAME=gpuc02
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=128
+# SLURM_CPUS_PER_TASK=128
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GPUS_ON_NODE=8
+# SLURM_GTIDS=0
+# SLURM_JOBID=2857828
+# SLURM_JOB_ACCOUNT=bbjs-delta-gpu
+# SLURM_JOB_CPUS_PER_NODE='128(x2)'
+# SLURM_JOB_END_TIME=1705383918
+# SLURM_JOB_GID=202
+# SLURM_JOB_GPUS=0,1,2,3,4,5,6,7
+# SLURM_JOB_ID=2857828
+# SLURM_JOB_NAME=exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
+# SLURM_JOB_NODELIST='gpuc[02,04]'
+# SLURM_JOB_NUM_NODES=2
+# SLURM_JOB_PARTITION=gpuA100x8
+# SLURM_JOB_QOS=bbjs-delta-gpu
+# SLURM_JOB_START_TIME=1705211118
+# SLURM_JOB_UID=68077
+# SLURM_JOB_USER=peng6
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_NODE=2000000
+# SLURM_NNODES=2
+# SLURM_NODEID=0
+# SLURM_NODELIST='gpuc[02,04]'
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1
+# SLURM_SUBMIT_HOST=dt-login01.delta.ncsa.illinois.edu
+# SLURM_TASKS_PER_NODE='1(x2)'
+# SLURM_TASK_PID=1245766
+# SLURM_TOPOLOGY_ADDR=ss00.ss05.gpuc02
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9984:109
+# srun --export=ALL python3 -m espnet2.bin.s2t_train --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_68891fd4-62b2-4f8e-90c8-ad36169b65e2 
+/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/spe/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8ech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_68891fd4-62b2-4f8e-90c8-ad36169b65e2
+ --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_68891fd4-62b2-4f8e-90c8-ad36169b65e2
+[gpuc02:0/16] 2024-01-13 23:48:29,700 (distributed_c10d:319) INFO: Added key: store_based_barrier_key:1 to store for rank: 0
+[gpuc02:0/16] 2024-01-13 23:48:30,232 (distributed_c10d:353) INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
+[gpuc02:0/16] 2024-01-13 23:48:30,289 (s2t:464) INFO: Vocabulary size: 50002
+[gpuc02:0/16] 2024-01-13 23:48:38,862 (abs_task:1231) INFO: pytorch.version=1.13.1, cuda.available=True, cudnn.version=8500, cudnn.benchmark=False, cudnn.deterministic=True
+[gpuc02:0/16] 2024-01-13 23:48:38,867 (abs_task:1232) INFO: Model structure:
+ESPnetS2TModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=512, win_length=400, hop_length=160, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=512, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=1, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=1, axis=time)
+  )
+  (normalize): GlobalMVN(stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz, norm_means=True, norm_vars=True)
+  (encoder): EBranchformerEncoder(
+    (embed): Conv2dSubsampling(
+      (conv): Sequential(
+        (0): Conv2d(1, 384, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=7296, out_features=384, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.05, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (1): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (2): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (3): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (4): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (5): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+  )
+  (decoder): TransformerDecoder(
+    (embed): Sequential(
+      (0): Embedding(50002, 384)
+      (1): PositionalEncoding(
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+    (output_layer): Linear(in_features=384, out_features=50002, bias=True)
+    (decoders): MultiSequential(
+      (0): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (1): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (2): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (3): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (4): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (5): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=384, out_features=50002, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetS2TModel
+    Total Number of model parameters: 101.18 M
+    Number of trainable parameters: 101.18 M (100.0%)
+    Size: 404.73 MB
+    Type: torch.float32
+[gpuc02:0/16] 2024-01-13 23:48:38,868 (abs_task:1235) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.9, 0.98]
+    capturable: False
+    eps: 1e-06
+    foreach: None
+    initial_lr: 0.001
+    lr: 3.3333333333333334e-09
+    maximize: False
+    weight_decay: 0.0
+)
+[gpuc02:0/16] 2024-01-13 23:48:38,868 (abs_task:1236) INFO: Scheduler: PiecewiseLinearWarmupLR(warmup_steps_list=[0, 30000, 60000], warmup_lr_list=[0.0, 0.0001, 0.001])
+[gpuc02:0/16] 2024-01-13 23:48:38,885 (abs_task:1245) INFO: Saving the configuration in exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
+[gpuc02:0/16] 2024-01-13 23:48:44,774 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 23:48:45,741 (abs_task:1616) INFO: [valid] dataset:
+ESPnetDataset(
+  speech: {"path": "dump/raw/dev_v3/wav.scp", "type": "kaldi_ark"}
+  text_prev: {"path": "dump/raw/dev_v3/text.prev", "type": "text"}
+  text_ctc: {"path": "dump/raw/dev_v3/text.ctc", "type": "text"}
+  text: {"path": "dump/raw/dev_v3/text", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f69c7457940>)
+[gpuc02:0/16] 2024-01-13 23:48:45,741 (abs_task:1617) INFO: [valid] Batch sampler: UnsortedBatchSampler(N-batch=4671, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/valid/speech_shape, 
+[gpuc02:0/16] 2024-01-13 23:48:45,742 (abs_task:1618) INFO: [valid] mini-batch sizes summary: N-batch=4671, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 23:48:50,861 (trainer:159) INFO: The training was resumed using exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/checkpoint.pth
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+[gpuc02:0/16] 2024-01-13 23:48:57,735 (trainer:284) INFO: 16/45epoch started
+[gpuc02:0/16] 2024-01-13 23:48:57,782 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 23:49:17,572 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 23:49:21,214 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f6908bc8c70>)
+[gpuc02:0/16] 2024-01-13 23:49:21,214 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 23:49:21,217 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
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+[gpuc02:0/16] 2024-01-13 23:53:28,523 (distributed:1027) INFO: Reducer buckets have been rebuilt in this iteration.
+[gpuc02:0/16] 2024-01-13 23:54:11,410 (trainer:737) INFO: 16epoch:train:1-100batch: iter_time=2.348, forward_time=0.186, loss_ctc=45.535, loss_att=50.003, acc=0.718, loss=48.662, backward_time=0.106, grad_norm=33.021, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=5.164e-04, train_time=3.136
+[gpuc02:0/16] 2024-01-13 23:54:56,543 (trainer:737) INFO: 16epoch:train:101-200batch: iter_time=1.050e-04, forward_time=0.105, loss_ctc=54.103, loss_att=60.293, acc=0.700, loss=58.436, backward_time=0.097, grad_norm=48.018, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.162e-04, train_time=0.452
+[gpuc02:0/16] 2024-01-13 23:55:38,807 (trainer:737) INFO: 16epoch:train:201-300batch: iter_time=1.022e-04, forward_time=0.105, loss_ctc=55.703, loss_att=59.403, acc=0.699, loss=58.293, backward_time=0.097, grad_norm=45.596, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.161e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 23:56:20,825 (trainer:737) INFO: 16epoch:train:301-400batch: iter_time=1.110e-04, forward_time=0.104, loss_ctc=44.816, loss_att=56.487, acc=0.714, loss=52.986, backward_time=0.097, grad_norm=33.769, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.160e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 23:57:02,992 (trainer:737) INFO: 16epoch:train:401-500batch: iter_time=1.036e-04, forward_time=0.105, loss_ctc=52.016, loss_att=59.999, acc=0.725, loss=57.604, backward_time=0.098, grad_norm=37.559, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.159e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-13 23:57:44,944 (trainer:737) INFO: 16epoch:train:501-600batch: iter_time=1.055e-04, forward_time=0.104, loss_ctc=42.965, loss_att=51.458, acc=0.728, loss=48.910, backward_time=0.096, grad_norm=32.326, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.158e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 23:58:27,188 (trainer:737) INFO: 16epoch:train:601-700batch: iter_time=1.176e-04, forward_time=0.105, loss_ctc=50.893, loss_att=57.418, acc=0.730, loss=55.460, backward_time=0.098, grad_norm=37.693, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.157e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 23:59:09,136 (trainer:737) INFO: 16epoch:train:701-800batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=47.303, loss_att=49.293, acc=0.717, loss=48.696, backward_time=0.096, grad_norm=35.642, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.156e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 23:59:51,072 (trainer:737) INFO: 16epoch:train:801-900batch: iter_time=1.129e-04, forward_time=0.104, loss_ctc=43.754, loss_att=48.996, acc=0.712, loss=47.423, backward_time=0.096, grad_norm=33.813, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.154e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 00:00:42,014 (trainer:737) INFO: 16epoch:train:901-1000batch: iter_time=1.016e-04, forward_time=0.103, loss_ctc=42.880, loss_att=48.924, acc=0.709, loss=47.111, backward_time=0.096, grad_norm=36.645, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.153e-04, train_time=0.509
+[gpuc02:0/16] 2024-01-14 00:01:33,794 (trainer:737) INFO: 16epoch:train:1001-1100batch: iter_time=1.057e-04, forward_time=0.107, loss_ctc=46.041, loss_att=48.284, acc=0.732, loss=47.611, backward_time=0.097, grad_norm=34.587, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.152e-04, train_time=0.518
+[gpuc02:0/16] 2024-01-14 00:01:42,710 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 00:02:15,933 (trainer:737) INFO: 16epoch:train:1101-1200batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=51.393, loss_att=52.755, acc=0.722, loss=52.346, backward_time=0.097, grad_norm=36.961, clip=100.000, loss_scale=2.496e+34, optim_step_time=0.040, optim0_lr0=5.151e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 00:02:50,659 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 00:03:10,553 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 00:03:14,237 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f690731d510>)
+[gpuc02:0/16] 2024-01-14 00:03:14,237 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 00:03:14,241 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 00:10:46,290 (trainer:737) INFO: 16epoch:train:1201-1300batch: iter_time=3.152, forward_time=0.109, loss_ctc=54.815, loss_att=64.636, acc=0.709, loss=61.690, backward_time=0.098, grad_norm=45.004, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.150e-04, train_time=5.103
+[gpuc02:0/16] 2024-01-14 00:11:28,709 (trainer:737) INFO: 16epoch:train:1301-1400batch: iter_time=1.624e-04, forward_time=0.104, loss_ctc=53.626, loss_att=56.313, acc=0.710, loss=55.507, backward_time=0.098, grad_norm=46.133, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=5.149e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:12:11,440 (trainer:737) INFO: 16epoch:train:1401-1500batch: iter_time=1.359e-04, forward_time=0.102, loss_ctc=46.505, loss_att=55.576, acc=0.698, loss=52.854, backward_time=0.098, grad_norm=40.636, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.148e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 00:12:57,472 (trainer:737) INFO: 16epoch:train:1501-1600batch: iter_time=1.377e-04, forward_time=0.101, loss_ctc=50.750, loss_att=60.397, acc=0.724, loss=57.503, backward_time=0.098, grad_norm=40.335, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.146e-04, train_time=0.460
+[gpuc02:0/16] 2024-01-14 00:13:39,987 (trainer:737) INFO: 16epoch:train:1601-1700batch: iter_time=1.299e-04, forward_time=0.104, loss_ctc=47.680, loss_att=57.004, acc=0.724, loss=54.207, backward_time=0.098, grad_norm=34.025, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.145e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:14:22,526 (trainer:737) INFO: 16epoch:train:1701-1800batch: iter_time=1.433e-04, forward_time=0.104, loss_ctc=50.163, loss_att=62.536, acc=0.730, loss=58.824, backward_time=0.098, grad_norm=34.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.144e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:15:04,985 (trainer:737) INFO: 16epoch:train:1801-1900batch: iter_time=1.649e-04, forward_time=0.104, loss_ctc=46.647, loss_att=55.997, acc=0.732, loss=53.192, backward_time=0.098, grad_norm=35.401, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.143e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:15:47,408 (trainer:737) INFO: 16epoch:train:1901-2000batch: iter_time=1.400e-04, forward_time=0.102, loss_ctc=46.179, loss_att=52.824, acc=0.738, loss=50.830, backward_time=0.098, grad_norm=35.712, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.142e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:16:29,623 (trainer:737) INFO: 16epoch:train:2001-2100batch: iter_time=1.429e-04, forward_time=0.101, loss_ctc=42.702, loss_att=45.621, acc=0.728, loss=44.745, backward_time=0.097, grad_norm=33.220, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.141e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 00:17:11,934 (trainer:737) INFO: 16epoch:train:2101-2200batch: iter_time=1.348e-04, forward_time=0.102, loss_ctc=44.469, loss_att=54.812, acc=0.706, loss=51.709, backward_time=0.097, grad_norm=33.520, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.140e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:17:54,319 (trainer:737) INFO: 16epoch:train:2201-2300batch: iter_time=1.435e-04, forward_time=0.102, loss_ctc=45.783, loss_att=47.720, acc=0.744, loss=47.139, backward_time=0.097, grad_norm=35.147, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.138e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:18:36,705 (trainer:737) INFO: 16epoch:train:2301-2400batch: iter_time=1.372e-04, forward_time=0.103, loss_ctc=47.915, loss_att=52.800, acc=0.733, loss=51.334, backward_time=0.098, grad_norm=35.645, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.137e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:19:19,023 (trainer:737) INFO: 16epoch:train:2401-2500batch: iter_time=1.086e-04, forward_time=0.103, loss_ctc=55.789, loss_att=56.301, acc=0.722, loss=56.147, backward_time=0.098, grad_norm=43.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.136e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:19:26,787 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 00:19:46,630 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 00:19:50,610 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f6908bcb5e0>)
+[gpuc02:0/16] 2024-01-14 00:19:50,610 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 00:19:50,614 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 00:24:15,655 (trainer:737) INFO: 16epoch:train:2501-2600batch: iter_time=2.399, forward_time=0.104, loss_ctc=44.423, loss_att=48.360, acc=0.730, loss=47.179, backward_time=0.097, grad_norm=32.501, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.135e-04, train_time=2.966
+[gpuc02:0/16] 2024-01-14 00:24:58,158 (trainer:737) INFO: 16epoch:train:2601-2700batch: iter_time=1.382e-04, forward_time=0.105, loss_ctc=51.885, loss_att=61.417, acc=0.702, loss=58.558, backward_time=0.098, grad_norm=44.014, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.134e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:25:40,614 (trainer:737) INFO: 16epoch:train:2701-2800batch: iter_time=1.339e-04, forward_time=0.105, loss_ctc=52.669, loss_att=59.525, acc=0.700, loss=57.468, backward_time=0.098, grad_norm=44.940, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.133e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:26:22,930 (trainer:737) INFO: 16epoch:train:2801-2900batch: iter_time=1.599e-04, forward_time=0.104, loss_ctc=44.313, loss_att=57.041, acc=0.722, loss=53.222, backward_time=0.098, grad_norm=33.375, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.132e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:27:05,506 (trainer:737) INFO: 16epoch:train:2901-3000batch: iter_time=1.559e-04, forward_time=0.106, loss_ctc=51.193, loss_att=59.461, acc=0.739, loss=56.980, backward_time=0.098, grad_norm=36.150, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.131e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 00:27:50,068 (trainer:737) INFO: 16epoch:train:3001-3100batch: iter_time=1.437e-04, forward_time=0.104, loss_ctc=42.359, loss_att=54.123, acc=0.732, loss=50.594, backward_time=0.097, grad_norm=32.218, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.129e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-14 00:28:33,435 (trainer:737) INFO: 16epoch:train:3101-3200batch: iter_time=1.486e-04, forward_time=0.105, loss_ctc=49.449, loss_att=57.121, acc=0.738, loss=54.819, backward_time=0.098, grad_norm=33.850, clip=100.000, loss_scale=3.718e+34, optim_step_time=0.041, optim0_lr0=5.128e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 00:29:15,880 (trainer:737) INFO: 16epoch:train:3201-3300batch: iter_time=1.401e-04, forward_time=0.103, loss_ctc=46.385, loss_att=49.188, acc=0.726, loss=48.347, backward_time=0.097, grad_norm=35.556, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.127e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:29:58,241 (trainer:737) INFO: 16epoch:train:3301-3400batch: iter_time=1.246e-04, forward_time=0.104, loss_ctc=43.420, loss_att=50.618, acc=0.725, loss=48.459, backward_time=0.097, grad_norm=33.572, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.126e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:30:40,516 (trainer:737) INFO: 16epoch:train:3401-3500batch: iter_time=1.437e-04, forward_time=0.104, loss_ctc=41.947, loss_att=47.326, acc=0.730, loss=45.713, backward_time=0.097, grad_norm=34.335, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.125e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:31:22,879 (trainer:737) INFO: 16epoch:train:3501-3600batch: iter_time=1.373e-04, forward_time=0.105, loss_ctc=44.301, loss_att=46.151, acc=0.756, loss=45.596, backward_time=0.098, grad_norm=32.600, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.124e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:32:05,286 (trainer:737) INFO: 16epoch:train:3601-3700batch: iter_time=1.460e-04, forward_time=0.105, loss_ctc=50.050, loss_att=51.916, acc=0.727, loss=51.356, backward_time=0.098, grad_norm=34.893, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.123e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:32:29,673 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 00:32:50,008 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 00:32:53,868 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f69c2b6b640>)
+[gpuc02:0/16] 2024-01-14 00:32:53,868 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 00:32:53,871 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 00:36:57,317 (trainer:737) INFO: 16epoch:train:3701-3800batch: iter_time=2.494, forward_time=0.105, loss_ctc=53.171, loss_att=61.503, acc=0.715, loss=59.004, backward_time=0.097, grad_norm=43.592, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.122e-04, train_time=2.920
+[gpuc02:0/16] 2024-01-14 00:37:39,794 (trainer:737) INFO: 16epoch:train:3801-3900batch: iter_time=1.662e-04, forward_time=0.106, loss_ctc=53.165, loss_att=54.909, acc=0.715, loss=54.386, backward_time=0.097, grad_norm=43.986, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.120e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:38:22,080 (trainer:737) INFO: 16epoch:train:3901-4000batch: iter_time=1.543e-04, forward_time=0.106, loss_ctc=45.225, loss_att=53.260, acc=0.702, loss=50.849, backward_time=0.097, grad_norm=39.321, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.119e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:39:04,573 (trainer:737) INFO: 16epoch:train:4001-4100batch: iter_time=1.456e-04, forward_time=0.107, loss_ctc=49.568, loss_att=58.696, acc=0.728, loss=55.958, backward_time=0.098, grad_norm=40.556, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.118e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:39:46,888 (trainer:737) INFO: 16epoch:train:4101-4200batch: iter_time=1.334e-04, forward_time=0.106, loss_ctc=47.022, loss_att=56.110, acc=0.727, loss=53.384, backward_time=0.097, grad_norm=33.928, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.117e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:40:29,359 (trainer:737) INFO: 16epoch:train:4201-4300batch: iter_time=1.615e-04, forward_time=0.106, loss_ctc=49.155, loss_att=61.364, acc=0.734, loss=57.701, backward_time=0.098, grad_norm=33.642, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.116e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:40:58,983 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 00:41:11,758 (trainer:737) INFO: 16epoch:train:4301-4400batch: iter_time=1.453e-04, forward_time=0.106, loss_ctc=46.360, loss_att=55.012, acc=0.735, loss=52.416, backward_time=0.098, grad_norm=34.949, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.040, optim0_lr0=5.115e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:41:54,119 (trainer:737) INFO: 16epoch:train:4401-4500batch: iter_time=1.365e-04, forward_time=0.105, loss_ctc=45.359, loss_att=51.949, acc=0.740, loss=49.972, backward_time=0.098, grad_norm=34.411, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.114e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:42:36,299 (trainer:737) INFO: 16epoch:train:4501-4600batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=43.352, loss_att=45.900, acc=0.729, loss=45.136, backward_time=0.097, grad_norm=34.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.113e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 00:43:18,586 (trainer:737) INFO: 16epoch:train:4601-4700batch: iter_time=1.383e-04, forward_time=0.105, loss_ctc=43.697, loss_att=53.328, acc=0.710, loss=50.439, backward_time=0.097, grad_norm=33.997, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.112e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:44:03,665 (trainer:737) INFO: 16epoch:train:4701-4800batch: iter_time=1.485e-04, forward_time=0.105, loss_ctc=44.840, loss_att=47.093, acc=0.746, loss=46.417, backward_time=0.097, grad_norm=32.313, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.110e-04, train_time=0.451
+[gpuc02:0/16] 2024-01-14 00:44:46,549 (trainer:737) INFO: 16epoch:train:4801-4900batch: iter_time=1.199e-04, forward_time=0.105, loss_ctc=46.975, loss_att=52.783, acc=0.735, loss=51.040, backward_time=0.097, grad_norm=33.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.109e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 00:45:28,876 (trainer:737) INFO: 16epoch:train:4901-5000batch: iter_time=1.194e-04, forward_time=0.105, loss_ctc=54.140, loss_att=56.569, acc=0.724, loss=55.840, backward_time=0.097, grad_norm=42.219, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.108e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:45:33,309 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 00:45:54,121 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 00:45:57,827 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f68da6ca2f0>)
+[gpuc02:0/16] 2024-01-14 00:45:57,827 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 00:45:57,830 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 00:50:42,538 (trainer:737) INFO: 16epoch:train:5001-5100batch: iter_time=2.374, forward_time=0.199, loss_ctc=44.050, loss_att=48.122, acc=0.732, loss=46.901, backward_time=0.109, grad_norm=32.424, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=5.107e-04, train_time=3.136
+[gpuc02:0/16] 2024-01-14 00:51:26,785 (trainer:737) INFO: 16epoch:train:5101-5200batch: iter_time=1.280e-04, forward_time=0.117, loss_ctc=50.736, loss_att=59.538, acc=0.707, loss=56.897, backward_time=0.100, grad_norm=45.953, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.106e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-14 00:52:09,495 (trainer:737) INFO: 16epoch:train:5201-5300batch: iter_time=1.351e-04, forward_time=0.106, loss_ctc=52.245, loss_att=58.011, acc=0.707, loss=56.282, backward_time=0.098, grad_norm=42.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.105e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 00:52:51,811 (trainer:737) INFO: 16epoch:train:5301-5400batch: iter_time=1.628e-04, forward_time=0.105, loss_ctc=43.873, loss_att=56.462, acc=0.723, loss=52.685, backward_time=0.097, grad_norm=31.943, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.104e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:53:34,367 (trainer:737) INFO: 16epoch:train:5401-5500batch: iter_time=1.444e-04, forward_time=0.106, loss_ctc=50.425, loss_att=59.211, acc=0.742, loss=56.575, backward_time=0.098, grad_norm=36.657, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.103e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 00:54:16,630 (trainer:737) INFO: 16epoch:train:5501-5600batch: iter_time=1.053e-04, forward_time=0.105, loss_ctc=41.317, loss_att=52.952, acc=0.735, loss=49.461, backward_time=0.097, grad_norm=31.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.102e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 00:54:59,240 (trainer:737) INFO: 16epoch:train:5601-5700batch: iter_time=1.198e-04, forward_time=0.107, loss_ctc=49.102, loss_att=56.501, acc=0.742, loss=54.282, backward_time=0.099, grad_norm=34.054, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.100e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 00:55:41,487 (trainer:737) INFO: 16epoch:train:5701-5800batch: iter_time=1.217e-04, forward_time=0.106, loss_ctc=46.074, loss_att=48.428, acc=0.730, loss=47.722, backward_time=0.097, grad_norm=34.637, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.099e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 00:56:23,733 (trainer:737) INFO: 16epoch:train:5801-5900batch: iter_time=1.113e-04, forward_time=0.106, loss_ctc=42.785, loss_att=49.357, acc=0.729, loss=47.385, backward_time=0.097, grad_norm=32.993, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.098e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 00:57:06,013 (trainer:737) INFO: 16epoch:train:5901-6000batch: iter_time=1.216e-04, forward_time=0.105, loss_ctc=41.203, loss_att=46.768, acc=0.732, loss=45.098, backward_time=0.097, grad_norm=33.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.097e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 00:57:48,467 (trainer:737) INFO: 16epoch:train:6001-6100batch: iter_time=1.086e-04, forward_time=0.106, loss_ctc=44.316, loss_att=46.456, acc=0.755, loss=45.814, backward_time=0.098, grad_norm=32.346, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.096e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:58:30,870 (trainer:737) INFO: 16epoch:train:6101-6200batch: iter_time=1.031e-04, forward_time=0.106, loss_ctc=49.949, loss_att=52.904, acc=0.724, loss=52.017, backward_time=0.097, grad_norm=35.978, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.095e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 00:59:07,392 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 00:59:28,158 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 00:59:31,928 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f66d6bbfc70>)
+[gpuc02:0/16] 2024-01-14 00:59:31,928 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 00:59:31,932 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 01:05:11,305 (trainer:737) INFO: 16epoch:train:6201-6300batch: iter_time=3.331, forward_time=0.105, loss_ctc=51.706, loss_att=63.223, acc=0.710, loss=59.768, backward_time=0.097, grad_norm=43.235, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.094e-04, train_time=4.004
+[gpuc02:0/16] 2024-01-14 01:05:53,728 (trainer:737) INFO: 16epoch:train:6301-6400batch: iter_time=9.335e-05, forward_time=0.105, loss_ctc=53.092, loss_att=56.493, acc=0.712, loss=55.473, backward_time=0.097, grad_norm=42.008, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.041, optim0_lr0=5.093e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:06:35,892 (trainer:737) INFO: 16epoch:train:6401-6500batch: iter_time=1.040e-04, forward_time=0.105, loss_ctc=45.134, loss_att=54.828, acc=0.699, loss=51.920, backward_time=0.097, grad_norm=38.080, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.092e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 01:07:18,304 (trainer:737) INFO: 16epoch:train:6501-6600batch: iter_time=1.081e-04, forward_time=0.105, loss_ctc=48.784, loss_att=58.676, acc=0.728, loss=55.709, backward_time=0.098, grad_norm=38.856, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.091e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:08:00,685 (trainer:737) INFO: 16epoch:train:6601-6700batch: iter_time=1.098e-04, forward_time=0.105, loss_ctc=46.819, loss_att=56.880, acc=0.718, loss=53.861, backward_time=0.098, grad_norm=35.125, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.089e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:08:43,134 (trainer:737) INFO: 16epoch:train:6701-6800batch: iter_time=1.105e-04, forward_time=0.105, loss_ctc=48.777, loss_att=61.543, acc=0.728, loss=57.713, backward_time=0.098, grad_norm=33.907, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.088e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:09:25,502 (trainer:737) INFO: 16epoch:train:6801-6900batch: iter_time=1.009e-04, forward_time=0.106, loss_ctc=45.849, loss_att=52.944, acc=0.730, loss=50.815, backward_time=0.098, grad_norm=34.766, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.087e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:10:08,034 (trainer:737) INFO: 16epoch:train:6901-7000batch: iter_time=1.102e-04, forward_time=0.108, loss_ctc=44.887, loss_att=51.297, acc=0.736, loss=49.374, backward_time=0.097, grad_norm=34.198, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.086e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:10:50,226 (trainer:737) INFO: 16epoch:train:7001-7100batch: iter_time=1.179e-04, forward_time=0.104, loss_ctc=42.639, loss_att=43.517, acc=0.728, loss=43.253, backward_time=0.096, grad_norm=33.972, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.085e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:11:32,393 (trainer:737) INFO: 16epoch:train:7101-7200batch: iter_time=1.122e-04, forward_time=0.105, loss_ctc=43.528, loss_att=55.591, acc=0.689, loss=51.972, backward_time=0.097, grad_norm=35.572, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.084e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 01:12:14,897 (trainer:737) INFO: 16epoch:train:7201-7300batch: iter_time=1.200e-04, forward_time=0.105, loss_ctc=44.522, loss_att=47.721, acc=0.734, loss=46.761, backward_time=0.098, grad_norm=32.370, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.083e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:13:00,302 (trainer:737) INFO: 16epoch:train:7301-7400batch: iter_time=1.053e-04, forward_time=0.122, loss_ctc=46.720, loss_att=53.000, acc=0.727, loss=51.116, backward_time=0.105, grad_norm=34.566, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=5.082e-04, train_time=0.454
+[gpuc02:0/16] 2024-01-14 01:13:42,755 (trainer:737) INFO: 16epoch:train:7401-7500batch: iter_time=1.023e-04, forward_time=0.104, loss_ctc=53.830, loss_att=56.060, acc=0.725, loss=55.391, backward_time=0.097, grad_norm=41.920, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.081e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:13:50,788 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 01:14:11,243 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 01:14:15,012 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5e354c2f80>)
+[gpuc02:0/16] 2024-01-14 01:14:15,012 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 01:14:15,015 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 01:19:01,869 (trainer:737) INFO: 16epoch:train:7501-7600batch: iter_time=2.731, forward_time=0.105, loss_ctc=44.020, loss_att=49.865, acc=0.731, loss=48.112, backward_time=0.098, grad_norm=33.349, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.080e-04, train_time=3.191
+[gpuc02:0/16] 2024-01-14 01:19:44,726 (trainer:737) INFO: 16epoch:train:7601-7700batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=50.182, loss_att=60.093, acc=0.707, loss=57.119, backward_time=0.098, grad_norm=46.401, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.078e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 01:20:28,440 (trainer:737) INFO: 16epoch:train:7701-7800batch: iter_time=1.078e-04, forward_time=0.105, loss_ctc=51.652, loss_att=58.208, acc=0.708, loss=56.241, backward_time=0.098, grad_norm=44.256, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.077e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-14 01:21:12,632 (trainer:737) INFO: 16epoch:train:7801-7900batch: iter_time=1.386e-04, forward_time=0.105, loss_ctc=43.558, loss_att=56.810, acc=0.723, loss=52.835, backward_time=0.097, grad_norm=33.050, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.076e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-14 01:21:55,136 (trainer:737) INFO: 16epoch:train:7901-8000batch: iter_time=1.354e-04, forward_time=0.106, loss_ctc=50.332, loss_att=59.252, acc=0.742, loss=56.576, backward_time=0.098, grad_norm=35.210, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.075e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:22:37,456 (trainer:737) INFO: 16epoch:train:8001-8100batch: iter_time=1.401e-04, forward_time=0.105, loss_ctc=41.502, loss_att=53.341, acc=0.736, loss=49.789, backward_time=0.097, grad_norm=32.327, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.074e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:23:20,058 (trainer:737) INFO: 16epoch:train:8101-8200batch: iter_time=1.321e-04, forward_time=0.106, loss_ctc=49.056, loss_att=56.557, acc=0.742, loss=54.307, backward_time=0.098, grad_norm=34.593, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.073e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 01:24:02,290 (trainer:737) INFO: 16epoch:train:8201-8300batch: iter_time=1.291e-04, forward_time=0.104, loss_ctc=44.826, loss_att=48.106, acc=0.731, loss=47.122, backward_time=0.097, grad_norm=33.898, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.072e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:24:44,507 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 01:24:44,514 (trainer:737) INFO: 16epoch:train:8301-8400batch: iter_time=1.328e-04, forward_time=0.105, loss_ctc=42.344, loss_att=49.457, acc=0.730, loss=47.323, backward_time=0.097, grad_norm=35.161, clip=100.000, loss_scale=5.371e+34, optim_step_time=0.040, optim0_lr0=5.071e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:25:26,851 (trainer:737) INFO: 16epoch:train:8401-8500batch: iter_time=1.107e-04, forward_time=0.105, loss_ctc=41.246, loss_att=47.267, acc=0.732, loss=45.461, backward_time=0.097, grad_norm=32.323, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.070e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:26:09,234 (trainer:737) INFO: 16epoch:train:8501-8600batch: iter_time=1.296e-04, forward_time=0.106, loss_ctc=43.774, loss_att=46.482, acc=0.755, loss=45.669, backward_time=0.097, grad_norm=32.168, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.069e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:26:51,529 (trainer:737) INFO: 16epoch:train:8601-8700batch: iter_time=1.235e-04, forward_time=0.106, loss_ctc=49.589, loss_att=52.815, acc=0.725, loss=51.847, backward_time=0.097, grad_norm=36.156, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.068e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:27:17,115 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 01:27:37,357 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 01:27:41,518 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f66c968ece0>)
+[gpuc02:0/16] 2024-01-14 01:27:41,518 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 01:27:41,521 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 01:31:56,290 (trainer:737) INFO: 16epoch:train:8701-8800batch: iter_time=2.467, forward_time=0.106, loss_ctc=51.208, loss_att=62.001, acc=0.710, loss=58.763, backward_time=0.098, grad_norm=41.118, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.067e-04, train_time=3.047
+[gpuc02:0/16] 2024-01-14 01:32:38,638 (trainer:737) INFO: 16epoch:train:8801-8900batch: iter_time=1.268e-04, forward_time=0.105, loss_ctc=50.587, loss_att=53.825, acc=0.716, loss=52.854, backward_time=0.097, grad_norm=41.405, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.065e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:33:20,987 (trainer:737) INFO: 16epoch:train:8901-9000batch: iter_time=1.139e-04, forward_time=0.105, loss_ctc=45.025, loss_att=54.106, acc=0.700, loss=51.382, backward_time=0.097, grad_norm=36.608, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.064e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:34:03,372 (trainer:737) INFO: 16epoch:train:9001-9100batch: iter_time=1.078e-04, forward_time=0.106, loss_ctc=48.239, loss_att=57.818, acc=0.731, loss=54.945, backward_time=0.098, grad_norm=40.041, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.063e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:34:45,637 (trainer:737) INFO: 16epoch:train:9101-9200batch: iter_time=1.031e-04, forward_time=0.106, loss_ctc=46.600, loss_att=56.451, acc=0.717, loss=53.495, backward_time=0.097, grad_norm=34.497, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.062e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:35:28,104 (trainer:737) INFO: 16epoch:train:9201-9300batch: iter_time=1.139e-04, forward_time=0.106, loss_ctc=49.130, loss_att=61.605, acc=0.730, loss=57.863, backward_time=0.098, grad_norm=34.974, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.061e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:36:10,584 (trainer:737) INFO: 16epoch:train:9301-9400batch: iter_time=1.060e-04, forward_time=0.106, loss_ctc=45.696, loss_att=53.236, acc=0.729, loss=50.974, backward_time=0.098, grad_norm=34.863, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.060e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:36:52,966 (trainer:737) INFO: 16epoch:train:9401-9500batch: iter_time=1.264e-04, forward_time=0.105, loss_ctc=44.604, loss_att=51.014, acc=0.738, loss=49.091, backward_time=0.097, grad_norm=36.543, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.059e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:37:35,188 (trainer:737) INFO: 16epoch:train:9501-9600batch: iter_time=9.897e-05, forward_time=0.105, loss_ctc=42.472, loss_att=43.502, acc=0.727, loss=43.193, backward_time=0.097, grad_norm=33.699, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.058e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:38:17,905 (trainer:737) INFO: 16epoch:train:9601-9700batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=43.195, loss_att=54.321, acc=0.691, loss=50.983, backward_time=0.097, grad_norm=37.754, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.057e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 01:39:01,182 (trainer:737) INFO: 16epoch:train:9701-9800batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=44.125, loss_att=47.177, acc=0.736, loss=46.261, backward_time=0.097, grad_norm=33.079, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.056e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 01:39:19,983 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 01:39:45,161 (trainer:737) INFO: 16epoch:train:9801-9900batch: iter_time=1.054e-04, forward_time=0.106, loss_ctc=46.436, loss_att=52.508, acc=0.726, loss=50.686, backward_time=0.097, grad_norm=36.553, clip=100.000, loss_scale=2.958e+34, optim_step_time=0.040, optim0_lr0=5.055e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-14 01:40:28,802 (trainer:737) INFO: 16epoch:train:9901-10000batch: iter_time=1.120e-04, forward_time=0.105, loss_ctc=53.414, loss_att=57.534, acc=0.721, loss=56.298, backward_time=0.097, grad_norm=43.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.054e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-14 01:40:35,013 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 01:40:55,321 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 01:40:59,000 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5e30113f10>)
+[gpuc02:0/16] 2024-01-14 01:40:59,000 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 01:40:59,003 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 01:45:31,891 (trainer:737) INFO: 16epoch:train:10001-10100batch: iter_time=2.561, forward_time=0.104, loss_ctc=43.520, loss_att=47.054, acc=0.728, loss=45.994, backward_time=0.097, grad_norm=31.078, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.053e-04, train_time=3.031
+[gpuc02:0/16] 2024-01-14 01:46:14,422 (trainer:737) INFO: 16epoch:train:10101-10200batch: iter_time=1.221e-04, forward_time=0.105, loss_ctc=49.436, loss_att=58.086, acc=0.707, loss=55.491, backward_time=0.097, grad_norm=44.663, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.051e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:46:56,902 (trainer:737) INFO: 16epoch:train:10201-10300batch: iter_time=1.170e-04, forward_time=0.106, loss_ctc=51.537, loss_att=57.654, acc=0.706, loss=55.819, backward_time=0.097, grad_norm=44.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.050e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:47:39,529 (trainer:737) INFO: 16epoch:train:10301-10400batch: iter_time=1.567e-04, forward_time=0.107, loss_ctc=43.172, loss_att=55.459, acc=0.720, loss=51.773, backward_time=0.096, grad_norm=33.396, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.049e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 01:48:22,019 (trainer:737) INFO: 16epoch:train:10401-10500batch: iter_time=1.580e-04, forward_time=0.106, loss_ctc=49.905, loss_att=59.090, acc=0.730, loss=56.335, backward_time=0.098, grad_norm=33.860, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.048e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 01:49:04,328 (trainer:737) INFO: 16epoch:train:10501-10600batch: iter_time=1.692e-04, forward_time=0.105, loss_ctc=41.028, loss_att=50.401, acc=0.733, loss=47.589, backward_time=0.097, grad_norm=33.181, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.047e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:49:46,973 (trainer:737) INFO: 16epoch:train:10601-10700batch: iter_time=1.801e-04, forward_time=0.106, loss_ctc=48.290, loss_att=56.021, acc=0.738, loss=53.702, backward_time=0.098, grad_norm=35.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.046e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 01:50:29,179 (trainer:737) INFO: 16epoch:train:10701-10800batch: iter_time=1.626e-04, forward_time=0.105, loss_ctc=45.099, loss_att=47.603, acc=0.726, loss=46.852, backward_time=0.097, grad_norm=34.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.045e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:51:11,429 (trainer:737) INFO: 16epoch:train:10801-10900batch: iter_time=1.716e-04, forward_time=0.104, loss_ctc=42.561, loss_att=47.977, acc=0.718, loss=46.352, backward_time=0.096, grad_norm=34.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.044e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 01:51:53,721 (trainer:737) INFO: 16epoch:train:10901-11000batch: iter_time=1.395e-04, forward_time=0.105, loss_ctc=40.970, loss_att=47.446, acc=0.717, loss=45.503, backward_time=0.097, grad_norm=33.119, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.043e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 01:52:36,104 (trainer:737) INFO: 16epoch:train:11001-11100batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=43.766, loss_att=46.907, acc=0.740, loss=45.965, backward_time=0.097, grad_norm=34.554, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.042e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:53:18,554 (trainer:737) INFO: 16epoch:train:11101-11200batch: iter_time=1.623e-04, forward_time=0.105, loss_ctc=49.921, loss_att=52.267, acc=0.728, loss=51.563, backward_time=0.097, grad_norm=49.535, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.041e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:53:46,176 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 01:54:06,444 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 01:54:10,203 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5dfda6b040>)
+[gpuc02:0/16] 2024-01-14 01:54:10,203 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 01:54:10,207 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 01:58:27,610 (trainer:737) INFO: 16epoch:train:11201-11300batch: iter_time=2.650, forward_time=0.105, loss_ctc=50.439, loss_att=60.538, acc=0.714, loss=57.508, backward_time=0.097, grad_norm=41.537, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.040e-04, train_time=3.090
+[gpuc02:0/16] 2024-01-14 01:59:10,038 (trainer:737) INFO: 16epoch:train:11301-11400batch: iter_time=2.005e-04, forward_time=0.107, loss_ctc=51.399, loss_att=53.064, acc=0.720, loss=52.565, backward_time=0.098, grad_norm=44.134, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.039e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 01:59:52,414 (trainer:737) INFO: 16epoch:train:11401-11500batch: iter_time=1.742e-04, forward_time=0.105, loss_ctc=44.336, loss_att=52.208, acc=0.704, loss=49.846, backward_time=0.097, grad_norm=37.655, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.038e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:00:34,995 (trainer:737) INFO: 16epoch:train:11501-11600batch: iter_time=1.319e-04, forward_time=0.107, loss_ctc=47.380, loss_att=56.282, acc=0.732, loss=53.611, backward_time=0.098, grad_norm=39.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.036e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 02:01:17,545 (trainer:737) INFO: 16epoch:train:11601-11700batch: iter_time=1.455e-04, forward_time=0.105, loss_ctc=46.565, loss_att=55.594, acc=0.719, loss=52.885, backward_time=0.097, grad_norm=34.650, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.035e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 02:02:00,209 (trainer:737) INFO: 16epoch:train:11701-11800batch: iter_time=1.418e-04, forward_time=0.106, loss_ctc=49.003, loss_att=61.017, acc=0.731, loss=57.413, backward_time=0.098, grad_norm=36.180, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.034e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 02:02:42,651 (trainer:737) INFO: 16epoch:train:11801-11900batch: iter_time=1.475e-04, forward_time=0.106, loss_ctc=45.041, loss_att=51.759, acc=0.731, loss=49.744, backward_time=0.097, grad_norm=34.914, clip=100.000, loss_scale=3.261e+34, optim_step_time=0.040, optim0_lr0=5.033e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:03:25,070 (trainer:737) INFO: 16epoch:train:11901-12000batch: iter_time=1.486e-04, forward_time=0.106, loss_ctc=44.993, loss_att=51.388, acc=0.738, loss=49.469, backward_time=0.097, grad_norm=34.513, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.032e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:04:07,370 (trainer:737) INFO: 16epoch:train:12001-12100batch: iter_time=1.598e-04, forward_time=0.105, loss_ctc=42.174, loss_att=43.024, acc=0.728, loss=42.769, backward_time=0.096, grad_norm=33.332, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=5.031e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 02:04:49,651 (trainer:737) INFO: 16epoch:train:12101-12200batch: iter_time=1.379e-04, forward_time=0.105, loss_ctc=42.948, loss_att=53.389, acc=0.695, loss=50.257, backward_time=0.096, grad_norm=35.396, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.030e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 02:05:32,094 (trainer:737) INFO: 16epoch:train:12201-12300batch: iter_time=1.303e-04, forward_time=0.105, loss_ctc=44.461, loss_att=47.171, acc=0.736, loss=46.358, backward_time=0.097, grad_norm=32.868, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=5.029e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:06:04,421 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 02:06:14,589 (trainer:737) INFO: 16epoch:train:12301-12400batch: iter_time=1.244e-04, forward_time=0.106, loss_ctc=46.550, loss_att=52.158, acc=0.729, loss=50.475, backward_time=0.098, grad_norm=35.799, clip=100.000, loss_scale=3.650e+34, optim_step_time=0.041, optim0_lr0=5.028e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 02:06:56,944 (trainer:737) INFO: 16epoch:train:12401-12500batch: iter_time=1.233e-04, forward_time=0.105, loss_ctc=52.971, loss_att=56.636, acc=0.724, loss=55.536, backward_time=0.097, grad_norm=43.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.027e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 02:07:02,507 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 02:07:22,831 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 02:07:26,911 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5def6e7c10>)
+[gpuc02:0/16] 2024-01-14 02:07:26,911 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 02:07:26,914 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 02:12:12,476 (trainer:737) INFO: 16epoch:train:12501-12600batch: iter_time=2.694, forward_time=0.109, loss_ctc=43.361, loss_att=51.426, acc=0.729, loss=49.007, backward_time=0.098, grad_norm=32.132, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.026e-04, train_time=3.155
+[gpuc02:0/16] 2024-01-14 02:12:54,981 (trainer:737) INFO: 16epoch:train:12601-12700batch: iter_time=1.552e-04, forward_time=0.106, loss_ctc=49.300, loss_att=60.035, acc=0.709, loss=56.815, backward_time=0.097, grad_norm=42.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.025e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 02:13:37,557 (trainer:737) INFO: 16epoch:train:12701-12800batch: iter_time=1.614e-04, forward_time=0.105, loss_ctc=51.053, loss_att=58.041, acc=0.709, loss=55.945, backward_time=0.098, grad_norm=45.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.024e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 02:14:19,994 (trainer:737) INFO: 16epoch:train:12801-12900batch: iter_time=1.628e-04, forward_time=0.105, loss_ctc=43.473, loss_att=57.388, acc=0.723, loss=53.214, backward_time=0.098, grad_norm=34.682, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.023e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:15:02,781 (trainer:737) INFO: 16epoch:train:12901-13000batch: iter_time=1.121e-04, forward_time=0.107, loss_ctc=49.837, loss_att=59.278, acc=0.742, loss=56.446, backward_time=0.099, grad_norm=35.991, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.022e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 02:15:45,220 (trainer:737) INFO: 16epoch:train:13001-13100batch: iter_time=1.211e-04, forward_time=0.106, loss_ctc=41.016, loss_att=53.098, acc=0.736, loss=49.473, backward_time=0.098, grad_norm=31.935, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.021e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:16:27,969 (trainer:737) INFO: 16epoch:train:13101-13200batch: iter_time=1.377e-04, forward_time=0.107, loss_ctc=48.215, loss_att=56.553, acc=0.744, loss=54.052, backward_time=0.099, grad_norm=33.837, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.020e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 02:17:10,450 (trainer:737) INFO: 16epoch:train:13201-13300batch: iter_time=1.331e-04, forward_time=0.105, loss_ctc=45.140, loss_att=48.599, acc=0.729, loss=47.561, backward_time=0.098, grad_norm=35.788, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.018e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 02:17:52,886 (trainer:737) INFO: 16epoch:train:13301-13400batch: iter_time=1.422e-04, forward_time=0.106, loss_ctc=43.179, loss_att=50.592, acc=0.729, loss=48.368, backward_time=0.097, grad_norm=32.563, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=5.017e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:18:35,260 (trainer:737) INFO: 16epoch:train:13401-13500batch: iter_time=1.389e-04, forward_time=0.106, loss_ctc=40.986, loss_att=47.220, acc=0.733, loss=45.350, backward_time=0.097, grad_norm=31.882, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.016e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:19:19,342 (trainer:737) INFO: 16epoch:train:13501-13600batch: iter_time=1.533e-04, forward_time=0.106, loss_ctc=43.707, loss_att=46.603, acc=0.756, loss=45.734, backward_time=0.098, grad_norm=32.579, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.015e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-14 02:20:05,156 (trainer:737) INFO: 16epoch:train:13601-13700batch: iter_time=1.475e-04, forward_time=0.108, loss_ctc=49.495, loss_att=52.284, acc=0.727, loss=51.447, backward_time=0.098, grad_norm=35.690, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.014e-04, train_time=0.458
+[gpuc02:0/16] 2024-01-14 02:20:36,876 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 02:20:57,395 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 02:21:01,186 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f66647fb820>)
+[gpuc02:0/16] 2024-01-14 02:21:01,186 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 02:21:01,189 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 02:25:08,692 (trainer:737) INFO: 16epoch:train:13701-13800batch: iter_time=2.605, forward_time=0.110, loss_ctc=50.490, loss_att=62.478, acc=0.712, loss=58.882, backward_time=0.098, grad_norm=44.122, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.013e-04, train_time=3.035
+[gpuc02:0/16] 2024-01-14 02:25:51,073 (trainer:737) INFO: 16epoch:train:13801-13900batch: iter_time=1.900e-04, forward_time=0.104, loss_ctc=51.026, loss_att=53.779, acc=0.720, loss=52.953, backward_time=0.097, grad_norm=43.302, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.012e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:26:33,478 (trainer:737) INFO: 16epoch:train:13901-14000batch: iter_time=1.865e-04, forward_time=0.104, loss_ctc=43.924, loss_att=53.232, acc=0.704, loss=50.440, backward_time=0.097, grad_norm=36.302, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.011e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:27:16,018 (trainer:737) INFO: 16epoch:train:14001-14100batch: iter_time=1.817e-04, forward_time=0.105, loss_ctc=47.975, loss_att=57.575, acc=0.731, loss=54.695, backward_time=0.098, grad_norm=40.863, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.010e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 02:27:58,470 (trainer:737) INFO: 16epoch:train:14101-14200batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=46.409, loss_att=56.128, acc=0.719, loss=53.213, backward_time=0.098, grad_norm=35.027, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.009e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:28:41,407 (trainer:737) INFO: 16epoch:train:14201-14300batch: iter_time=1.566e-04, forward_time=0.109, loss_ctc=48.300, loss_att=60.772, acc=0.732, loss=57.030, backward_time=0.098, grad_norm=33.590, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.008e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 02:29:23,983 (trainer:737) INFO: 16epoch:train:14301-14400batch: iter_time=1.514e-04, forward_time=0.105, loss_ctc=44.868, loss_att=52.414, acc=0.731, loss=50.150, backward_time=0.098, grad_norm=35.399, clip=100.000, loss_scale=2.575e+34, optim_step_time=0.040, optim0_lr0=5.007e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 02:29:27,774 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 02:30:06,389 (trainer:737) INFO: 16epoch:train:14401-14500batch: iter_time=2.022e-04, forward_time=0.105, loss_ctc=44.252, loss_att=50.985, acc=0.739, loss=48.965, backward_time=0.098, grad_norm=36.856, clip=100.000, loss_scale=2.245e+34, optim_step_time=0.040, optim0_lr0=5.006e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:30:48,701 (trainer:737) INFO: 16epoch:train:14501-14600batch: iter_time=2.015e-04, forward_time=0.105, loss_ctc=42.933, loss_att=43.710, acc=0.728, loss=43.477, backward_time=0.097, grad_norm=34.539, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.005e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 02:31:31,073 (trainer:737) INFO: 16epoch:train:14601-14700batch: iter_time=1.929e-04, forward_time=0.104, loss_ctc=42.937, loss_att=53.548, acc=0.694, loss=50.364, backward_time=0.097, grad_norm=35.464, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.004e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 02:32:13,520 (trainer:737) INFO: 16epoch:train:14701-14800batch: iter_time=1.959e-04, forward_time=0.105, loss_ctc=43.851, loss_att=46.835, acc=0.737, loss=45.940, backward_time=0.097, grad_norm=33.891, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.003e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:32:56,748 (trainer:737) INFO: 16epoch:train:14801-14900batch: iter_time=2.141e-04, forward_time=0.105, loss_ctc=46.276, loss_att=51.298, acc=0.730, loss=49.791, backward_time=0.098, grad_norm=36.321, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.002e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-14 02:33:39,155 (trainer:737) INFO: 16epoch:train:14901-15000batch: iter_time=1.733e-04, forward_time=0.105, loss_ctc=53.053, loss_att=57.331, acc=0.722, loss=56.047, backward_time=0.098, grad_norm=41.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=5.001e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 02:53:28,517 (trainer:343) INFO: 16epoch results: [train] iter_time=0.212, forward_time=0.106, loss_ctc=46.950, loss_att=53.785, acc=0.725, loss=51.735, backward_time=0.098, grad_norm=36.679, clip=100.000, loss_scale=2.972e+34, optim_step_time=0.040, optim0_lr0=5.081e-04, train_time=0.659, time=2 hours, 44 minutes and 49.7 seconds, total_count=240000, gpu_max_cached_mem_GB=27.297, [valid] loss_ctc=57.539, cer_ctc=0.290, loss_att=60.703, acc=0.563, cer=0.406, wer=1.000, loss=59.754, time=19 minutes and 40.8 seconds, total_count=74736, gpu_max_cached_mem_GB=27.297
+[gpuc02:0/16] 2024-01-14 02:53:38,427 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-14 02:53:38,438 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/11epoch.pth
+[gpuc02:0/16] 2024-01-14 02:53:38,438 (trainer:272) INFO: 17/45epoch started. Estimated time to finish: 3 days, 17 hours and 15 minutes
+[gpuc02:0/16] 2024-01-14 02:53:38,447 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 02:53:58,066 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 02:54:01,612 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5decd3b850>)
+[gpuc02:0/16] 2024-01-14 02:54:01,612 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 02:54:01,615 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 02:58:38,019 (trainer:737) INFO: 17epoch:train:1-100batch: iter_time=2.432, forward_time=0.134, loss_ctc=47.381, loss_att=57.474, acc=0.697, loss=54.446, backward_time=0.104, grad_norm=41.464, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=5.000e-04, train_time=2.995
+[gpuc02:0/16] 2024-01-14 02:59:20,573 (trainer:737) INFO: 17epoch:train:101-200batch: iter_time=1.090e-04, forward_time=0.105, loss_ctc=53.382, loss_att=54.733, acc=0.703, loss=54.327, backward_time=0.099, grad_norm=40.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.999e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 03:00:02,978 (trainer:737) INFO: 17epoch:train:201-300batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=48.697, loss_att=60.592, acc=0.719, loss=57.024, backward_time=0.100, grad_norm=40.296, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.998e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:00:45,387 (trainer:737) INFO: 17epoch:train:301-400batch: iter_time=1.092e-04, forward_time=0.106, loss_ctc=51.935, loss_att=59.255, acc=0.717, loss=57.059, backward_time=0.100, grad_norm=38.937, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.997e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:01:29,544 (trainer:737) INFO: 17epoch:train:401-500batch: iter_time=1.160e-04, forward_time=0.116, loss_ctc=47.971, loss_att=48.873, acc=0.707, loss=48.602, backward_time=0.098, grad_norm=39.384, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.995e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-14 03:02:14,976 (trainer:737) INFO: 17epoch:train:501-600batch: iter_time=1.152e-04, forward_time=0.105, loss_ctc=52.029, loss_att=52.888, acc=0.742, loss=52.631, backward_time=0.099, grad_norm=48.038, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.994e-04, train_time=0.454
+[gpuc02:0/16] 2024-01-14 03:02:57,299 (trainer:737) INFO: 17epoch:train:601-700batch: iter_time=1.034e-04, forward_time=0.105, loss_ctc=51.199, loss_att=55.099, acc=0.714, loss=53.929, backward_time=0.098, grad_norm=41.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.993e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:03:44,965 (trainer:737) INFO: 17epoch:train:701-800batch: iter_time=9.871e-05, forward_time=0.107, loss_ctc=45.940, loss_att=44.223, acc=0.739, loss=44.738, backward_time=0.098, grad_norm=37.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.992e-04, train_time=0.476
+[gpuc02:0/16] 2024-01-14 03:04:34,533 (trainer:737) INFO: 17epoch:train:801-900batch: iter_time=1.128e-04, forward_time=0.106, loss_ctc=46.651, loss_att=59.087, acc=0.730, loss=55.356, backward_time=0.099, grad_norm=33.614, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.991e-04, train_time=0.495
+[gpuc02:0/16] 2024-01-14 03:05:17,963 (trainer:737) INFO: 17epoch:train:901-1000batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=42.764, loss_att=45.611, acc=0.738, loss=44.757, backward_time=0.098, grad_norm=35.386, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.990e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 03:06:00,048 (trainer:737) INFO: 17epoch:train:1001-1100batch: iter_time=1.064e-04, forward_time=0.104, loss_ctc=47.407, loss_att=52.613, acc=0.721, loss=51.052, backward_time=0.097, grad_norm=37.956, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.989e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 03:06:44,718 (trainer:737) INFO: 17epoch:train:1101-1200batch: iter_time=1.126e-04, forward_time=0.116, loss_ctc=59.793, loss_att=66.081, acc=0.710, loss=64.195, backward_time=0.109, grad_norm=49.368, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.988e-04, train_time=0.446
+[gpuc02:0/16] 2024-01-14 03:07:26,759 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 03:07:47,072 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 03:07:50,722 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f653f7eab30>)
+[gpuc02:0/16] 2024-01-14 03:07:50,722 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 03:07:50,725 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 03:14:59,335 (trainer:737) INFO: 17epoch:train:1201-1300batch: iter_time=3.645, forward_time=0.110, loss_ctc=46.647, loss_att=50.942, acc=0.731, loss=49.654, backward_time=0.098, grad_norm=38.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.987e-04, train_time=4.946
+[gpuc02:0/16] 2024-01-14 03:15:42,201 (trainer:737) INFO: 17epoch:train:1301-1400batch: iter_time=1.576e-04, forward_time=0.104, loss_ctc=49.836, loss_att=56.319, acc=0.675, loss=54.374, backward_time=0.097, grad_norm=39.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.986e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 03:16:26,924 (trainer:737) INFO: 17epoch:train:1401-1500batch: iter_time=1.688e-04, forward_time=0.105, loss_ctc=51.823, loss_att=66.757, acc=0.695, loss=62.277, backward_time=0.098, grad_norm=39.771, clip=100.000, loss_scale=3.967e+34, optim_step_time=0.041, optim0_lr0=4.985e-04, train_time=0.447
+[gpuc02:0/16] 2024-01-14 03:17:09,548 (trainer:737) INFO: 17epoch:train:1501-1600batch: iter_time=1.618e-04, forward_time=0.105, loss_ctc=45.948, loss_att=51.489, acc=0.733, loss=49.827, backward_time=0.098, grad_norm=35.682, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.984e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 03:17:51,824 (trainer:737) INFO: 17epoch:train:1601-1700batch: iter_time=1.658e-04, forward_time=0.105, loss_ctc=48.030, loss_att=52.205, acc=0.697, loss=50.952, backward_time=0.097, grad_norm=37.733, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.983e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:18:34,133 (trainer:737) INFO: 17epoch:train:1701-1800batch: iter_time=1.763e-04, forward_time=0.106, loss_ctc=48.005, loss_att=46.803, acc=0.738, loss=47.163, backward_time=0.097, grad_norm=36.988, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.982e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:19:16,485 (trainer:737) INFO: 17epoch:train:1801-1900batch: iter_time=1.291e-04, forward_time=0.106, loss_ctc=52.721, loss_att=51.128, acc=0.719, loss=51.606, backward_time=0.097, grad_norm=45.941, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.981e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:19:58,631 (trainer:737) INFO: 17epoch:train:1901-2000batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=46.199, loss_att=51.991, acc=0.702, loss=50.254, backward_time=0.096, grad_norm=36.865, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.980e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 03:20:40,951 (trainer:737) INFO: 17epoch:train:2001-2100batch: iter_time=1.754e-04, forward_time=0.105, loss_ctc=47.385, loss_att=47.230, acc=0.748, loss=47.276, backward_time=0.097, grad_norm=33.888, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.979e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:21:23,313 (trainer:737) INFO: 17epoch:train:2101-2200batch: iter_time=1.563e-04, forward_time=0.105, loss_ctc=44.103, loss_att=56.614, acc=0.707, loss=52.861, backward_time=0.098, grad_norm=38.042, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.978e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:22:05,498 (trainer:737) INFO: 17epoch:train:2201-2300batch: iter_time=1.659e-04, forward_time=0.105, loss_ctc=40.762, loss_att=42.036, acc=0.744, loss=41.654, backward_time=0.097, grad_norm=33.630, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.977e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 03:22:47,884 (trainer:737) INFO: 17epoch:train:2301-2400batch: iter_time=1.675e-04, forward_time=0.106, loss_ctc=61.123, loss_att=72.633, acc=0.670, loss=69.180, backward_time=0.098, grad_norm=52.477, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.976e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:23:30,193 (trainer:737) INFO: 17epoch:train:2401-2500batch: iter_time=1.529e-04, forward_time=0.105, loss_ctc=48.188, loss_att=43.813, acc=0.753, loss=45.126, backward_time=0.097, grad_norm=36.128, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.975e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:23:37,867 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 03:23:58,312 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 03:24:02,098 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5e07f969b0>)
+[gpuc02:0/16] 2024-01-14 03:24:02,098 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 03:24:02,101 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 03:29:39,664 (trainer:737) INFO: 17epoch:train:2501-2600batch: iter_time=3.207, forward_time=0.146, loss_ctc=44.903, loss_att=53.814, acc=0.691, loss=51.141, backward_time=0.110, grad_norm=36.818, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.974e-04, train_time=3.694
+[gpuc02:0/16] 2024-01-14 03:30:21,896 (trainer:737) INFO: 17epoch:train:2601-2700batch: iter_time=2.218e-04, forward_time=0.105, loss_ctc=50.840, loss_att=53.431, acc=0.702, loss=52.654, backward_time=0.096, grad_norm=37.485, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.973e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 03:31:04,255 (trainer:737) INFO: 17epoch:train:2701-2800batch: iter_time=2.460e-04, forward_time=0.105, loss_ctc=46.471, loss_att=58.427, acc=0.718, loss=54.840, backward_time=0.097, grad_norm=35.305, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.972e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:31:46,643 (trainer:737) INFO: 17epoch:train:2801-2900batch: iter_time=1.972e-04, forward_time=0.106, loss_ctc=50.176, loss_att=57.990, acc=0.713, loss=55.646, backward_time=0.097, grad_norm=39.264, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.971e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:32:30,085 (trainer:737) INFO: 17epoch:train:2901-3000batch: iter_time=1.966e-04, forward_time=0.104, loss_ctc=46.526, loss_att=49.060, acc=0.697, loss=48.300, backward_time=0.096, grad_norm=37.457, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.970e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 03:33:14,212 (trainer:737) INFO: 17epoch:train:3001-3100batch: iter_time=1.854e-04, forward_time=0.106, loss_ctc=49.508, loss_att=49.837, acc=0.742, loss=49.738, backward_time=0.097, grad_norm=41.622, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.969e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-14 03:33:56,998 (trainer:737) INFO: 17epoch:train:3101-3200batch: iter_time=1.762e-04, forward_time=0.105, loss_ctc=49.295, loss_att=51.890, acc=0.720, loss=51.112, backward_time=0.097, grad_norm=36.391, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.968e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 03:34:39,249 (trainer:737) INFO: 17epoch:train:3201-3300batch: iter_time=1.719e-04, forward_time=0.105, loss_ctc=45.209, loss_att=44.461, acc=0.731, loss=44.686, backward_time=0.096, grad_norm=35.170, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.967e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 03:35:13,486 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 03:35:21,643 (trainer:737) INFO: 17epoch:train:3301-3400batch: iter_time=1.594e-04, forward_time=0.107, loss_ctc=45.945, loss_att=58.030, acc=0.721, loss=54.405, backward_time=0.097, grad_norm=34.157, clip=100.000, loss_scale=3.755e+34, optim_step_time=0.041, optim0_lr0=4.966e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:36:03,950 (trainer:737) INFO: 17epoch:train:3401-3500batch: iter_time=1.686e-04, forward_time=0.105, loss_ctc=41.480, loss_att=44.281, acc=0.734, loss=43.441, backward_time=0.097, grad_norm=33.595, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.965e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:36:46,128 (trainer:737) INFO: 17epoch:train:3501-3600batch: iter_time=1.805e-04, forward_time=0.105, loss_ctc=46.868, loss_att=52.255, acc=0.716, loss=50.639, backward_time=0.096, grad_norm=35.470, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.964e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 03:37:28,481 (trainer:737) INFO: 17epoch:train:3601-3700batch: iter_time=1.862e-04, forward_time=0.106, loss_ctc=57.433, loss_att=64.012, acc=0.698, loss=62.038, backward_time=0.097, grad_norm=46.644, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.963e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:37:59,928 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 03:38:19,989 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 03:38:23,739 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5ee52050f0>)
+[gpuc02:0/16] 2024-01-14 03:38:23,739 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 03:38:23,742 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 03:42:56,589 (trainer:737) INFO: 17epoch:train:3701-3800batch: iter_time=2.725, forward_time=0.110, loss_ctc=44.764, loss_att=47.997, acc=0.743, loss=47.027, backward_time=0.098, grad_norm=34.098, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.962e-04, train_time=3.281
+[gpuc02:0/16] 2024-01-14 03:43:38,978 (trainer:737) INFO: 17epoch:train:3801-3900batch: iter_time=1.779e-04, forward_time=0.106, loss_ctc=48.797, loss_att=56.566, acc=0.685, loss=54.235, backward_time=0.098, grad_norm=39.518, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.961e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:44:21,513 (trainer:737) INFO: 17epoch:train:3901-4000batch: iter_time=1.829e-04, forward_time=0.106, loss_ctc=50.279, loss_att=66.966, acc=0.702, loss=61.959, backward_time=0.098, grad_norm=40.150, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.960e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 03:45:04,177 (trainer:737) INFO: 17epoch:train:4001-4100batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=44.606, loss_att=49.946, acc=0.746, loss=48.344, backward_time=0.098, grad_norm=34.162, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.959e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 03:45:46,538 (trainer:737) INFO: 17epoch:train:4101-4200batch: iter_time=1.520e-04, forward_time=0.105, loss_ctc=47.258, loss_att=52.227, acc=0.706, loss=50.736, backward_time=0.097, grad_norm=36.807, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.957e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:46:29,126 (trainer:737) INFO: 17epoch:train:4201-4300batch: iter_time=1.475e-04, forward_time=0.107, loss_ctc=47.665, loss_att=45.990, acc=0.753, loss=46.492, backward_time=0.097, grad_norm=36.261, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.956e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 03:47:11,461 (trainer:737) INFO: 17epoch:train:4301-4400batch: iter_time=1.408e-04, forward_time=0.105, loss_ctc=49.897, loss_att=55.209, acc=0.716, loss=53.615, backward_time=0.097, grad_norm=45.244, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.955e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:47:53,725 (trainer:737) INFO: 17epoch:train:4401-4500batch: iter_time=1.558e-04, forward_time=0.104, loss_ctc=45.392, loss_att=50.635, acc=0.715, loss=49.062, backward_time=0.097, grad_norm=37.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.954e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 03:48:36,168 (trainer:737) INFO: 17epoch:train:4501-4600batch: iter_time=1.573e-04, forward_time=0.105, loss_ctc=46.426, loss_att=46.625, acc=0.760, loss=46.566, backward_time=0.097, grad_norm=33.450, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.953e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 03:49:19,615 (trainer:737) INFO: 17epoch:train:4601-4700batch: iter_time=1.564e-04, forward_time=0.105, loss_ctc=43.050, loss_att=56.354, acc=0.725, loss=52.363, backward_time=0.097, grad_norm=34.942, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.952e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 03:50:03,931 (trainer:737) INFO: 17epoch:train:4701-4800batch: iter_time=1.540e-04, forward_time=0.105, loss_ctc=40.164, loss_att=41.395, acc=0.756, loss=41.025, backward_time=0.097, grad_norm=31.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.951e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-14 03:50:47,021 (trainer:737) INFO: 17epoch:train:4801-4900batch: iter_time=1.469e-04, forward_time=0.106, loss_ctc=60.098, loss_att=73.531, acc=0.687, loss=69.501, backward_time=0.098, grad_norm=51.200, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.950e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 03:51:29,377 (trainer:737) INFO: 17epoch:train:4901-5000batch: iter_time=1.125e-04, forward_time=0.105, loss_ctc=47.463, loss_att=44.923, acc=0.761, loss=45.685, backward_time=0.097, grad_norm=34.026, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.949e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:51:34,320 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 03:51:54,285 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 03:51:58,062 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5e238eff40>)
+[gpuc02:0/16] 2024-01-14 03:51:58,062 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 03:51:58,065 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 03:56:35,531 (trainer:737) INFO: 17epoch:train:5001-5100batch: iter_time=2.514, forward_time=0.107, loss_ctc=44.329, loss_att=53.024, acc=0.705, loss=50.415, backward_time=0.099, grad_norm=36.605, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.948e-04, train_time=3.061
+[gpuc02:0/16] 2024-01-14 03:57:17,874 (trainer:737) INFO: 17epoch:train:5101-5200batch: iter_time=1.739e-04, forward_time=0.106, loss_ctc=50.285, loss_att=53.312, acc=0.710, loss=52.404, backward_time=0.098, grad_norm=38.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.947e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 03:58:00,380 (trainer:737) INFO: 17epoch:train:5201-5300batch: iter_time=1.594e-04, forward_time=0.105, loss_ctc=45.272, loss_att=57.215, acc=0.729, loss=53.632, backward_time=0.098, grad_norm=34.424, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.946e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 03:58:42,938 (trainer:737) INFO: 17epoch:train:5301-5400batch: iter_time=1.696e-04, forward_time=0.105, loss_ctc=48.866, loss_att=57.321, acc=0.723, loss=54.785, backward_time=0.098, grad_norm=37.263, clip=100.000, loss_scale=2.472e+34, optim_step_time=0.041, optim0_lr0=4.945e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 03:59:25,118 (trainer:737) INFO: 17epoch:train:5401-5500batch: iter_time=1.956e-04, forward_time=0.104, loss_ctc=45.001, loss_att=47.399, acc=0.713, loss=46.680, backward_time=0.097, grad_norm=34.068, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.944e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:00:07,562 (trainer:737) INFO: 17epoch:train:5501-5600batch: iter_time=1.688e-04, forward_time=0.105, loss_ctc=47.948, loss_att=51.877, acc=0.747, loss=50.699, backward_time=0.098, grad_norm=41.400, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.943e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:00:49,876 (trainer:737) INFO: 17epoch:train:5601-5700batch: iter_time=1.670e-04, forward_time=0.105, loss_ctc=49.016, loss_att=53.454, acc=0.723, loss=52.122, backward_time=0.098, grad_norm=36.923, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.942e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:01:32,114 (trainer:737) INFO: 17epoch:train:5701-5800batch: iter_time=1.704e-04, forward_time=0.105, loss_ctc=44.236, loss_att=43.543, acc=0.743, loss=43.751, backward_time=0.097, grad_norm=35.268, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.941e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:02:14,655 (trainer:737) INFO: 17epoch:train:5801-5900batch: iter_time=1.680e-04, forward_time=0.106, loss_ctc=45.415, loss_att=58.613, acc=0.735, loss=54.653, backward_time=0.098, grad_norm=32.849, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.940e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:02:56,967 (trainer:737) INFO: 17epoch:train:5901-6000batch: iter_time=1.804e-04, forward_time=0.105, loss_ctc=41.104, loss_att=44.106, acc=0.745, loss=43.206, backward_time=0.098, grad_norm=34.099, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.939e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:03:39,205 (trainer:737) INFO: 17epoch:train:6001-6100batch: iter_time=1.861e-04, forward_time=0.105, loss_ctc=45.656, loss_att=51.246, acc=0.726, loss=49.569, backward_time=0.097, grad_norm=36.900, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.938e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:03:51,870 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 04:04:21,681 (trainer:737) INFO: 17epoch:train:6101-6200batch: iter_time=1.887e-04, forward_time=0.106, loss_ctc=55.825, loss_att=63.288, acc=0.716, loss=61.049, backward_time=0.098, grad_norm=44.276, clip=100.000, loss_scale=2.685e+34, optim_step_time=0.042, optim0_lr0=4.937e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:04:47,303 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 04:05:07,492 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 04:05:11,600 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f1a697bb0>)
+[gpuc02:0/16] 2024-01-14 04:05:11,600 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 04:05:11,603 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 04:09:37,992 (trainer:737) INFO: 17epoch:train:6201-6300batch: iter_time=2.548, forward_time=0.106, loss_ctc=43.622, loss_att=47.732, acc=0.743, loss=46.499, backward_time=0.098, grad_norm=33.933, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.936e-04, train_time=3.163
+[gpuc02:0/16] 2024-01-14 04:10:20,492 (trainer:737) INFO: 17epoch:train:6301-6400batch: iter_time=1.968e-04, forward_time=0.106, loss_ctc=47.841, loss_att=53.519, acc=0.692, loss=51.816, backward_time=0.097, grad_norm=36.757, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.935e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:11:03,340 (trainer:737) INFO: 17epoch:train:6401-6500batch: iter_time=1.593e-04, forward_time=0.106, loss_ctc=49.947, loss_att=65.459, acc=0.706, loss=60.806, backward_time=0.098, grad_norm=39.503, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.934e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 04:11:45,948 (trainer:737) INFO: 17epoch:train:6501-6600batch: iter_time=1.728e-04, forward_time=0.105, loss_ctc=44.319, loss_att=49.237, acc=0.748, loss=47.761, backward_time=0.097, grad_norm=34.042, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.933e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 04:12:28,298 (trainer:737) INFO: 17epoch:train:6601-6700batch: iter_time=1.981e-04, forward_time=0.105, loss_ctc=46.295, loss_att=51.410, acc=0.708, loss=49.875, backward_time=0.097, grad_norm=35.049, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.932e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:13:10,723 (trainer:737) INFO: 17epoch:train:6701-6800batch: iter_time=1.666e-04, forward_time=0.104, loss_ctc=46.490, loss_att=45.028, acc=0.754, loss=45.467, backward_time=0.097, grad_norm=34.413, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.931e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:13:53,247 (trainer:737) INFO: 17epoch:train:6801-6900batch: iter_time=1.458e-04, forward_time=0.105, loss_ctc=48.552, loss_att=53.874, acc=0.718, loss=52.278, backward_time=0.097, grad_norm=46.242, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.930e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:14:35,585 (trainer:737) INFO: 17epoch:train:6901-7000batch: iter_time=1.268e-04, forward_time=0.104, loss_ctc=44.822, loss_att=50.129, acc=0.716, loss=48.537, backward_time=0.096, grad_norm=36.360, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.929e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:15:18,095 (trainer:737) INFO: 17epoch:train:7001-7100batch: iter_time=1.715e-04, forward_time=0.105, loss_ctc=45.897, loss_att=45.540, acc=0.763, loss=45.647, backward_time=0.098, grad_norm=32.098, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.928e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:16:00,638 (trainer:737) INFO: 17epoch:train:7101-7200batch: iter_time=1.658e-04, forward_time=0.106, loss_ctc=42.531, loss_att=55.169, acc=0.728, loss=51.378, backward_time=0.098, grad_norm=36.014, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.927e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:16:42,956 (trainer:737) INFO: 17epoch:train:7201-7300batch: iter_time=1.973e-04, forward_time=0.105, loss_ctc=40.031, loss_att=40.893, acc=0.758, loss=40.634, backward_time=0.097, grad_norm=32.725, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.926e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:17:25,501 (trainer:737) INFO: 17epoch:train:7301-7400batch: iter_time=2.073e-04, forward_time=0.106, loss_ctc=58.698, loss_att=71.747, acc=0.690, loss=67.832, backward_time=0.098, grad_norm=48.510, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.925e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:18:07,972 (trainer:737) INFO: 17epoch:train:7401-7500batch: iter_time=1.780e-04, forward_time=0.105, loss_ctc=46.814, loss_att=44.176, acc=0.763, loss=44.968, backward_time=0.098, grad_norm=35.891, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.924e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:18:13,389 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 04:18:33,867 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 04:18:37,627 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f655e0db910>)
+[gpuc02:0/16] 2024-01-14 04:18:37,627 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 04:18:37,630 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 04:23:44,840 (trainer:737) INFO: 17epoch:train:7501-7600batch: iter_time=2.460, forward_time=0.105, loss_ctc=44.449, loss_att=57.904, acc=0.686, loss=53.867, backward_time=0.097, grad_norm=36.974, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.923e-04, train_time=3.368
+[gpuc02:0/16] 2024-01-14 04:24:27,834 (trainer:737) INFO: 17epoch:train:7601-7700batch: iter_time=1.289e-04, forward_time=0.107, loss_ctc=49.977, loss_att=53.889, acc=0.705, loss=52.716, backward_time=0.097, grad_norm=40.613, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.922e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 04:25:11,572 (trainer:737) INFO: 17epoch:train:7701-7800batch: iter_time=1.463e-04, forward_time=0.105, loss_ctc=45.724, loss_att=59.013, acc=0.720, loss=55.026, backward_time=0.097, grad_norm=37.909, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.921e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-14 04:25:55,941 (trainer:737) INFO: 17epoch:train:7801-7900batch: iter_time=1.649e-04, forward_time=0.105, loss_ctc=49.047, loss_att=58.010, acc=0.712, loss=55.321, backward_time=0.097, grad_norm=37.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.920e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-14 04:26:38,049 (trainer:737) INFO: 17epoch:train:7901-8000batch: iter_time=1.807e-04, forward_time=0.103, loss_ctc=44.949, loss_att=48.600, acc=0.699, loss=47.505, backward_time=0.096, grad_norm=34.004, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.919e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 04:27:20,685 (trainer:737) INFO: 17epoch:train:8001-8100batch: iter_time=1.521e-04, forward_time=0.104, loss_ctc=46.458, loss_att=49.429, acc=0.744, loss=48.537, backward_time=0.097, grad_norm=39.742, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.918e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 04:28:03,004 (trainer:737) INFO: 17epoch:train:8101-8200batch: iter_time=1.619e-04, forward_time=0.104, loss_ctc=48.160, loss_att=51.700, acc=0.725, loss=50.638, backward_time=0.097, grad_norm=36.873, clip=100.000, loss_scale=3.531e+34, optim_step_time=0.041, optim0_lr0=4.917e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:28:45,296 (trainer:737) INFO: 17epoch:train:8201-8300batch: iter_time=1.697e-04, forward_time=0.105, loss_ctc=44.152, loss_att=44.553, acc=0.732, loss=44.433, backward_time=0.097, grad_norm=34.665, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.916e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:29:27,790 (trainer:737) INFO: 17epoch:train:8301-8400batch: iter_time=1.533e-04, forward_time=0.105, loss_ctc=45.487, loss_att=58.508, acc=0.721, loss=54.601, backward_time=0.097, grad_norm=33.780, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.915e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:30:10,051 (trainer:737) INFO: 17epoch:train:8401-8500batch: iter_time=1.656e-04, forward_time=0.104, loss_ctc=40.504, loss_att=44.271, acc=0.736, loss=43.141, backward_time=0.097, grad_norm=34.529, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.914e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:30:52,269 (trainer:737) INFO: 17epoch:train:8501-8600batch: iter_time=1.389e-04, forward_time=0.105, loss_ctc=45.144, loss_att=51.930, acc=0.718, loss=49.894, backward_time=0.096, grad_norm=36.276, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.913e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:31:34,686 (trainer:737) INFO: 17epoch:train:8601-8700batch: iter_time=1.523e-04, forward_time=0.105, loss_ctc=56.172, loss_att=63.892, acc=0.699, loss=61.576, backward_time=0.097, grad_norm=46.177, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.912e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:32:01,781 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 04:32:22,368 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 04:32:26,140 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f1d4a7fd0>)
+[gpuc02:0/16] 2024-01-14 04:32:26,140 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 04:32:26,143 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 04:36:58,304 (trainer:737) INFO: 17epoch:train:8701-8800batch: iter_time=2.488, forward_time=0.105, loss_ctc=44.114, loss_att=46.682, acc=0.747, loss=45.911, backward_time=0.097, grad_norm=34.779, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.911e-04, train_time=3.236
+[gpuc02:0/16] 2024-01-14 04:37:40,608 (trainer:737) INFO: 17epoch:train:8801-8900batch: iter_time=1.406e-04, forward_time=0.105, loss_ctc=47.523, loss_att=54.257, acc=0.691, loss=52.237, backward_time=0.097, grad_norm=37.179, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.910e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:38:23,142 (trainer:737) INFO: 17epoch:train:8901-9000batch: iter_time=1.720e-04, forward_time=0.106, loss_ctc=49.706, loss_att=66.810, acc=0.704, loss=61.679, backward_time=0.098, grad_norm=39.544, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.909e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:39:05,510 (trainer:737) INFO: 17epoch:train:9001-9100batch: iter_time=1.669e-04, forward_time=0.106, loss_ctc=44.434, loss_att=49.587, acc=0.747, loss=48.041, backward_time=0.098, grad_norm=33.391, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.908e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:39:47,826 (trainer:737) INFO: 17epoch:train:9101-9200batch: iter_time=1.785e-04, forward_time=0.105, loss_ctc=45.886, loss_att=51.657, acc=0.710, loss=49.925, backward_time=0.098, grad_norm=35.275, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.908e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:39:57,096 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 04:40:30,036 (trainer:737) INFO: 17epoch:train:9201-9300batch: iter_time=1.703e-04, forward_time=0.105, loss_ctc=46.602, loss_att=45.204, acc=0.754, loss=45.623, backward_time=0.097, grad_norm=34.681, clip=100.000, loss_scale=2.517e+34, optim_step_time=0.041, optim0_lr0=4.907e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:41:12,336 (trainer:737) INFO: 17epoch:train:9301-9400batch: iter_time=1.484e-04, forward_time=0.106, loss_ctc=47.925, loss_att=53.122, acc=0.724, loss=51.563, backward_time=0.098, grad_norm=43.645, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.906e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:41:54,550 (trainer:737) INFO: 17epoch:train:9401-9500batch: iter_time=1.350e-04, forward_time=0.104, loss_ctc=44.510, loss_att=49.647, acc=0.720, loss=48.106, backward_time=0.097, grad_norm=34.384, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.905e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:42:36,923 (trainer:737) INFO: 17epoch:train:9501-9600batch: iter_time=1.579e-04, forward_time=0.106, loss_ctc=45.920, loss_att=45.807, acc=0.763, loss=45.841, backward_time=0.097, grad_norm=32.509, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.904e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:43:19,981 (trainer:737) INFO: 17epoch:train:9601-9700batch: iter_time=1.289e-04, forward_time=0.106, loss_ctc=42.490, loss_att=55.249, acc=0.727, loss=51.421, backward_time=0.098, grad_norm=34.884, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.903e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 04:44:02,541 (trainer:737) INFO: 17epoch:train:9701-9800batch: iter_time=1.295e-04, forward_time=0.105, loss_ctc=39.802, loss_att=41.336, acc=0.756, loss=40.876, backward_time=0.098, grad_norm=33.026, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.902e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:44:45,639 (trainer:737) INFO: 17epoch:train:9801-9900batch: iter_time=1.187e-04, forward_time=0.106, loss_ctc=57.658, loss_att=72.037, acc=0.692, loss=67.723, backward_time=0.098, grad_norm=55.230, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.901e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 04:45:30,188 (trainer:737) INFO: 17epoch:train:9901-10000batch: iter_time=1.046e-04, forward_time=0.105, loss_ctc=45.971, loss_att=44.755, acc=0.761, loss=45.120, backward_time=0.098, grad_norm=34.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.900e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-14 04:45:37,731 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 04:45:58,190 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 04:46:02,222 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5efc0ff760>)
+[gpuc02:0/16] 2024-01-14 04:46:02,222 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 04:46:02,225 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 04:51:00,102 (trainer:737) INFO: 17epoch:train:10001-10100batch: iter_time=2.524, forward_time=0.104, loss_ctc=43.801, loss_att=52.896, acc=0.706, loss=50.168, backward_time=0.097, grad_norm=37.520, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.899e-04, train_time=3.299
+[gpuc02:0/16] 2024-01-14 04:51:42,477 (trainer:737) INFO: 17epoch:train:10101-10200batch: iter_time=1.155e-04, forward_time=0.104, loss_ctc=49.360, loss_att=52.322, acc=0.713, loss=51.433, backward_time=0.098, grad_norm=38.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.898e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:52:25,099 (trainer:737) INFO: 17epoch:train:10201-10300batch: iter_time=1.016e-04, forward_time=0.106, loss_ctc=45.110, loss_att=56.802, acc=0.732, loss=53.294, backward_time=0.098, grad_norm=34.446, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.897e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 04:53:07,626 (trainer:737) INFO: 17epoch:train:10301-10400batch: iter_time=1.164e-04, forward_time=0.106, loss_ctc=48.287, loss_att=56.460, acc=0.728, loss=54.008, backward_time=0.099, grad_norm=34.557, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.896e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:53:50,166 (trainer:737) INFO: 17epoch:train:10401-10500batch: iter_time=1.106e-04, forward_time=0.105, loss_ctc=44.537, loss_att=47.256, acc=0.714, loss=46.440, backward_time=0.097, grad_norm=35.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.895e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:54:32,632 (trainer:737) INFO: 17epoch:train:10501-10600batch: iter_time=1.238e-04, forward_time=0.105, loss_ctc=46.045, loss_att=51.813, acc=0.743, loss=50.082, backward_time=0.098, grad_norm=39.533, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.894e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:55:15,044 (trainer:737) INFO: 17epoch:train:10601-10700batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=48.599, loss_att=52.532, acc=0.727, loss=51.352, backward_time=0.097, grad_norm=38.479, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.893e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 04:55:57,274 (trainer:737) INFO: 17epoch:train:10701-10800batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=43.159, loss_att=42.850, acc=0.746, loss=42.943, backward_time=0.098, grad_norm=36.650, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.892e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:56:39,834 (trainer:737) INFO: 17epoch:train:10801-10900batch: iter_time=1.244e-04, forward_time=0.106, loss_ctc=45.338, loss_att=57.975, acc=0.736, loss=54.184, backward_time=0.099, grad_norm=33.601, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.891e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:57:22,171 (trainer:737) INFO: 17epoch:train:10901-11000batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=40.239, loss_att=43.041, acc=0.750, loss=42.200, backward_time=0.098, grad_norm=32.989, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.890e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 04:58:04,435 (trainer:737) INFO: 17epoch:train:11001-11100batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=44.856, loss_att=51.365, acc=0.728, loss=49.412, backward_time=0.098, grad_norm=35.030, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.889e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 04:58:46,937 (trainer:737) INFO: 17epoch:train:11101-11200batch: iter_time=1.294e-04, forward_time=0.106, loss_ctc=55.489, loss_att=63.675, acc=0.719, loss=61.219, backward_time=0.099, grad_norm=46.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.888e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 04:59:14,842 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 04:59:35,320 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 04:59:39,103 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f1d464f40>)
+[gpuc02:0/16] 2024-01-14 04:59:39,103 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 04:59:39,106 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 05:04:11,164 (trainer:737) INFO: 17epoch:train:11201-11300batch: iter_time=2.439, forward_time=0.105, loss_ctc=43.568, loss_att=48.544, acc=0.741, loss=47.051, backward_time=0.097, grad_norm=34.508, clip=100.000, loss_scale=3.697e+34, optim_step_time=0.041, optim0_lr0=4.887e-04, train_time=3.242
+[gpuc02:0/16] 2024-01-14 05:04:53,302 (trainer:737) INFO: 17epoch:train:11301-11400batch: iter_time=1.245e-04, forward_time=0.104, loss_ctc=47.569, loss_att=55.914, acc=0.681, loss=53.411, backward_time=0.096, grad_norm=38.387, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.886e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:05:35,665 (trainer:737) INFO: 17epoch:train:11401-11500batch: iter_time=1.282e-04, forward_time=0.105, loss_ctc=49.286, loss_att=65.646, acc=0.703, loss=60.738, backward_time=0.097, grad_norm=39.131, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.885e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:06:18,253 (trainer:737) INFO: 17epoch:train:11501-11600batch: iter_time=1.023e-04, forward_time=0.106, loss_ctc=44.018, loss_att=51.032, acc=0.739, loss=48.928, backward_time=0.097, grad_norm=32.768, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.884e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 05:07:00,711 (trainer:737) INFO: 17epoch:train:11601-11700batch: iter_time=1.130e-04, forward_time=0.108, loss_ctc=45.747, loss_att=51.126, acc=0.702, loss=49.513, backward_time=0.096, grad_norm=33.823, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.883e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 05:07:42,803 (trainer:737) INFO: 17epoch:train:11701-11800batch: iter_time=9.997e-05, forward_time=0.104, loss_ctc=46.331, loss_att=46.000, acc=0.742, loss=46.100, backward_time=0.096, grad_norm=35.735, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.882e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:08:05,551 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 05:08:24,948 (trainer:737) INFO: 17epoch:train:11801-11900batch: iter_time=1.184e-04, forward_time=0.104, loss_ctc=47.824, loss_att=50.100, acc=0.728, loss=49.417, backward_time=0.096, grad_norm=44.781, clip=100.000, loss_scale=3.189e+34, optim_step_time=0.041, optim0_lr0=4.881e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:09:07,013 (trainer:737) INFO: 17epoch:train:11901-12000batch: iter_time=1.026e-04, forward_time=0.103, loss_ctc=44.472, loss_att=51.511, acc=0.707, loss=49.399, backward_time=0.096, grad_norm=37.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.880e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 05:09:49,263 (trainer:737) INFO: 17epoch:train:12001-12100batch: iter_time=1.101e-04, forward_time=0.105, loss_ctc=45.482, loss_att=46.101, acc=0.752, loss=45.915, backward_time=0.097, grad_norm=33.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.879e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:10:31,577 (trainer:737) INFO: 17epoch:train:12101-12200batch: iter_time=1.003e-04, forward_time=0.104, loss_ctc=42.205, loss_att=55.337, acc=0.715, loss=51.397, backward_time=0.097, grad_norm=34.319, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.878e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:11:13,736 (trainer:737) INFO: 17epoch:train:12201-12300batch: iter_time=1.123e-04, forward_time=0.102, loss_ctc=39.699, loss_att=41.549, acc=0.748, loss=40.994, backward_time=0.096, grad_norm=32.228, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.877e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:11:56,121 (trainer:737) INFO: 17epoch:train:12301-12400batch: iter_time=1.131e-04, forward_time=0.105, loss_ctc=58.614, loss_att=72.202, acc=0.674, loss=68.126, backward_time=0.097, grad_norm=53.448, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.876e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 05:12:38,356 (trainer:737) INFO: 17epoch:train:12401-12500batch: iter_time=1.042e-04, forward_time=0.104, loss_ctc=46.052, loss_att=43.151, acc=0.758, loss=44.021, backward_time=0.097, grad_norm=35.275, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.875e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:12:41,929 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 05:13:02,358 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 05:13:06,090 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f3c8e3e20>)
+[gpuc02:0/16] 2024-01-14 05:13:06,091 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 05:13:06,094 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 05:18:04,147 (trainer:737) INFO: 17epoch:train:12501-12600batch: iter_time=2.435, forward_time=0.105, loss_ctc=43.358, loss_att=52.683, acc=0.695, loss=49.885, backward_time=0.097, grad_norm=35.946, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.874e-04, train_time=3.258
+[gpuc02:0/16] 2024-01-14 05:18:46,436 (trainer:737) INFO: 17epoch:train:12601-12700batch: iter_time=1.323e-04, forward_time=0.105, loss_ctc=49.178, loss_att=52.313, acc=0.708, loss=51.372, backward_time=0.097, grad_norm=38.289, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.873e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:19:28,716 (trainer:737) INFO: 17epoch:train:12701-12800batch: iter_time=1.496e-04, forward_time=0.106, loss_ctc=44.948, loss_att=57.374, acc=0.724, loss=53.646, backward_time=0.098, grad_norm=37.215, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.872e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:20:10,986 (trainer:737) INFO: 17epoch:train:12801-12900batch: iter_time=1.564e-04, forward_time=0.106, loss_ctc=47.699, loss_att=56.179, acc=0.716, loss=53.635, backward_time=0.098, grad_norm=35.736, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.871e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:20:53,004 (trainer:737) INFO: 17epoch:train:12901-13000batch: iter_time=1.523e-04, forward_time=0.105, loss_ctc=44.050, loss_att=47.474, acc=0.703, loss=46.447, backward_time=0.097, grad_norm=34.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.871e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 05:21:35,228 (trainer:737) INFO: 17epoch:train:13001-13100batch: iter_time=1.684e-04, forward_time=0.105, loss_ctc=46.512, loss_att=49.721, acc=0.745, loss=48.758, backward_time=0.097, grad_norm=41.942, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.870e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:22:17,449 (trainer:737) INFO: 17epoch:train:13101-13200batch: iter_time=1.430e-04, forward_time=0.105, loss_ctc=48.456, loss_att=51.213, acc=0.726, loss=50.386, backward_time=0.097, grad_norm=36.782, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.869e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:22:59,591 (trainer:737) INFO: 17epoch:train:13201-13300batch: iter_time=1.460e-04, forward_time=0.105, loss_ctc=43.128, loss_att=43.779, acc=0.734, loss=43.584, backward_time=0.097, grad_norm=36.764, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.868e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:23:41,917 (trainer:737) INFO: 17epoch:train:13301-13400batch: iter_time=1.515e-04, forward_time=0.106, loss_ctc=45.168, loss_att=57.908, acc=0.723, loss=54.086, backward_time=0.098, grad_norm=34.078, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.867e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:24:24,403 (trainer:737) INFO: 17epoch:train:13401-13500batch: iter_time=1.431e-04, forward_time=0.105, loss_ctc=40.150, loss_att=43.557, acc=0.740, loss=42.535, backward_time=0.097, grad_norm=33.453, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.866e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 05:25:07,739 (trainer:737) INFO: 17epoch:train:13501-13600batch: iter_time=1.610e-04, forward_time=0.105, loss_ctc=44.733, loss_att=50.859, acc=0.720, loss=49.021, backward_time=0.097, grad_norm=35.951, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.865e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-14 05:25:52,287 (trainer:737) INFO: 17epoch:train:13601-13700batch: iter_time=1.651e-04, forward_time=0.105, loss_ctc=55.602, loss_att=63.632, acc=0.701, loss=61.223, backward_time=0.098, grad_norm=45.961, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.864e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-14 05:26:16,534 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 05:26:36,899 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 05:26:40,608 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f65cd4bea70>)
+[gpuc02:0/16] 2024-01-14 05:26:40,608 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 05:26:40,611 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 05:31:25,133 (trainer:737) INFO: 17epoch:train:13701-13800batch: iter_time=2.453, forward_time=0.105, loss_ctc=43.297, loss_att=45.479, acc=0.745, loss=44.824, backward_time=0.098, grad_norm=34.198, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.863e-04, train_time=3.328
+[gpuc02:0/16] 2024-01-14 05:32:07,221 (trainer:737) INFO: 17epoch:train:13801-13900batch: iter_time=1.109e-04, forward_time=0.104, loss_ctc=47.749, loss_att=53.174, acc=0.686, loss=51.546, backward_time=0.097, grad_norm=39.618, clip=100.000, loss_scale=3.032e+34, optim_step_time=0.042, optim0_lr0=4.862e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:32:49,628 (trainer:737) INFO: 17epoch:train:13901-14000batch: iter_time=1.182e-04, forward_time=0.106, loss_ctc=49.010, loss_att=63.434, acc=0.706, loss=59.107, backward_time=0.098, grad_norm=39.910, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.861e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 05:33:32,004 (trainer:737) INFO: 17epoch:train:14001-14100batch: iter_time=1.324e-04, forward_time=0.105, loss_ctc=43.765, loss_att=49.449, acc=0.741, loss=47.744, backward_time=0.098, grad_norm=32.601, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.860e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 05:34:14,222 (trainer:737) INFO: 17epoch:train:14101-14200batch: iter_time=1.143e-04, forward_time=0.104, loss_ctc=45.261, loss_att=50.386, acc=0.705, loss=48.848, backward_time=0.097, grad_norm=34.832, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.859e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:34:56,308 (trainer:737) INFO: 17epoch:train:14201-14300batch: iter_time=1.153e-04, forward_time=0.104, loss_ctc=46.219, loss_att=45.344, acc=0.743, loss=45.606, backward_time=0.097, grad_norm=35.430, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.858e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:35:38,544 (trainer:737) INFO: 17epoch:train:14301-14400batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=46.779, loss_att=48.702, acc=0.730, loss=48.125, backward_time=0.097, grad_norm=42.369, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.857e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 05:36:20,606 (trainer:737) INFO: 17epoch:train:14401-14500batch: iter_time=1.263e-04, forward_time=0.104, loss_ctc=44.549, loss_att=50.740, acc=0.709, loss=48.883, backward_time=0.097, grad_norm=36.871, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.856e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 05:37:02,893 (trainer:737) INFO: 17epoch:train:14501-14600batch: iter_time=1.025e-04, forward_time=0.106, loss_ctc=45.315, loss_att=45.869, acc=0.753, loss=45.703, backward_time=0.098, grad_norm=32.565, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.855e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:37:45,195 (trainer:737) INFO: 17epoch:train:14601-14700batch: iter_time=1.076e-04, forward_time=0.106, loss_ctc=41.852, loss_att=54.299, acc=0.716, loss=50.565, backward_time=0.098, grad_norm=34.277, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.854e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:38:27,338 (trainer:737) INFO: 17epoch:train:14701-14800batch: iter_time=1.160e-04, forward_time=0.105, loss_ctc=39.403, loss_att=40.992, acc=0.750, loss=40.515, backward_time=0.097, grad_norm=33.251, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.853e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 05:39:09,651 (trainer:737) INFO: 17epoch:train:14801-14900batch: iter_time=1.065e-04, forward_time=0.105, loss_ctc=57.861, loss_att=72.256, acc=0.675, loss=67.938, backward_time=0.098, grad_norm=51.801, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.852e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 05:39:51,886 (trainer:737) INFO: 17epoch:train:14901-15000batch: iter_time=1.069e-04, forward_time=0.105, loss_ctc=45.613, loss_att=42.443, acc=0.760, loss=43.394, backward_time=0.097, grad_norm=33.788, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.851e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:00:00,866 (trainer:343) INFO: 17epoch results: [train] iter_time=0.213, forward_time=0.106, loss_ctc=47.022, loss_att=52.633, acc=0.723, loss=50.950, backward_time=0.098, grad_norm=37.599, clip=100.000, loss_scale=2.851e+34, optim_step_time=0.041, optim0_lr0=4.924e-04, train_time=0.665, time=2 hours, 46 minutes and 23.16 seconds, total_count=255000, gpu_max_cached_mem_GB=27.297, [valid] loss_ctc=58.479, cer_ctc=0.301, loss_att=56.389, acc=0.575, cer=0.366, wer=0.999, loss=57.016, time=19 minutes and 59.04 seconds, total_count=79407, gpu_max_cached_mem_GB=27.297
+[gpuc02:0/16] 2024-01-14 06:00:05,910 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-14 06:00:05,914 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/12epoch.pth
+[gpuc02:0/16] 2024-01-14 06:00:05,914 (trainer:272) INFO: 18/45epoch started. Estimated time to finish: 3 days, 14 hours and 35 minutes
+[gpuc02:0/16] 2024-01-14 06:00:05,925 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 06:00:25,564 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 06:00:29,135 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f6664895de0>)
+[gpuc02:0/16] 2024-01-14 06:00:29,136 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 06:00:29,139 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 06:05:15,159 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 06:05:21,119 (trainer:737) INFO: 18epoch:train:1-100batch: iter_time=2.329, forward_time=0.156, loss_ctc=41.843, loss_att=51.365, acc=0.728, loss=48.508, backward_time=0.103, grad_norm=34.585, clip=100.000, loss_scale=3.860e+34, optim_step_time=0.043, optim0_lr0=4.850e-04, train_time=3.152
+[gpuc02:0/16] 2024-01-14 06:06:07,177 (trainer:737) INFO: 18epoch:train:101-200batch: iter_time=9.038e-05, forward_time=0.104, loss_ctc=51.631, loss_att=56.485, acc=0.711, loss=55.029, backward_time=0.099, grad_norm=37.144, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.849e-04, train_time=0.460
+[gpuc02:0/16] 2024-01-14 06:06:49,519 (trainer:737) INFO: 18epoch:train:201-300batch: iter_time=8.924e-05, forward_time=0.103, loss_ctc=42.058, loss_att=42.768, acc=0.716, loss=42.555, backward_time=0.098, grad_norm=32.957, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.849e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 06:07:34,905 (trainer:737) INFO: 18epoch:train:301-400batch: iter_time=1.001e-04, forward_time=0.104, loss_ctc=49.984, loss_att=56.145, acc=0.712, loss=54.297, backward_time=0.099, grad_norm=39.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.848e-04, train_time=0.454
+[gpuc02:0/16] 2024-01-14 06:08:21,019 (trainer:737) INFO: 18epoch:train:401-500batch: iter_time=9.777e-05, forward_time=0.104, loss_ctc=59.863, loss_att=58.065, acc=0.716, loss=58.604, backward_time=0.098, grad_norm=55.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.847e-04, train_time=0.461
+[gpuc02:0/16] 2024-01-14 06:09:07,631 (trainer:737) INFO: 18epoch:train:501-600batch: iter_time=1.051e-04, forward_time=0.104, loss_ctc=48.178, loss_att=63.580, acc=0.694, loss=58.959, backward_time=0.098, grad_norm=38.679, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.846e-04, train_time=0.466
+[gpuc02:0/16] 2024-01-14 06:09:51,406 (trainer:737) INFO: 18epoch:train:601-700batch: iter_time=9.399e-05, forward_time=0.104, loss_ctc=59.247, loss_att=54.661, acc=0.722, loss=56.037, backward_time=0.098, grad_norm=45.045, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.845e-04, train_time=0.438
+[gpuc02:0/16] 2024-01-14 06:10:34,436 (trainer:737) INFO: 18epoch:train:701-800batch: iter_time=9.257e-05, forward_time=0.104, loss_ctc=46.259, loss_att=54.185, acc=0.716, loss=51.807, backward_time=0.097, grad_norm=35.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.844e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 06:11:16,837 (trainer:737) INFO: 18epoch:train:801-900batch: iter_time=9.244e-05, forward_time=0.105, loss_ctc=56.534, loss_att=56.749, acc=0.720, loss=56.685, backward_time=0.099, grad_norm=40.423, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.843e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 06:11:59,628 (trainer:737) INFO: 18epoch:train:901-1000batch: iter_time=9.692e-05, forward_time=0.104, loss_ctc=46.430, loss_att=52.844, acc=0.733, loss=50.920, backward_time=0.098, grad_norm=36.448, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.842e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 06:12:41,850 (trainer:737) INFO: 18epoch:train:1001-1100batch: iter_time=9.833e-05, forward_time=0.105, loss_ctc=53.546, loss_att=60.393, acc=0.715, loss=58.339, backward_time=0.098, grad_norm=43.408, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.841e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:13:26,448 (trainer:737) INFO: 18epoch:train:1101-1200batch: iter_time=9.786e-05, forward_time=0.124, loss_ctc=45.276, loss_att=48.439, acc=0.702, loss=47.490, backward_time=0.098, grad_norm=39.954, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.840e-04, train_time=0.446
+[gpuc02:0/16] 2024-01-14 06:13:54,455 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 06:14:14,845 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 06:14:18,636 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f6545df3eb0>)
+[gpuc02:0/16] 2024-01-14 06:14:18,637 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 06:14:18,640 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 06:21:48,600 (trainer:737) INFO: 18epoch:train:1201-1300batch: iter_time=2.573, forward_time=0.110, loss_ctc=41.627, loss_att=51.464, acc=0.710, loss=48.513, backward_time=0.098, grad_norm=34.833, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.839e-04, train_time=5.021
+[gpuc02:0/16] 2024-01-14 06:22:31,247 (trainer:737) INFO: 18epoch:train:1301-1400batch: iter_time=9.741e-05, forward_time=0.109, loss_ctc=48.627, loss_att=52.014, acc=0.733, loss=50.998, backward_time=0.099, grad_norm=33.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.838e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 06:23:13,942 (trainer:737) INFO: 18epoch:train:1401-1500batch: iter_time=1.077e-04, forward_time=0.105, loss_ctc=44.234, loss_att=47.063, acc=0.731, loss=46.215, backward_time=0.098, grad_norm=31.932, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.837e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 06:23:56,339 (trainer:737) INFO: 18epoch:train:1501-1600batch: iter_time=1.144e-04, forward_time=0.104, loss_ctc=41.861, loss_att=42.250, acc=0.708, loss=42.133, backward_time=0.099, grad_norm=36.263, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.836e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 06:24:39,808 (trainer:737) INFO: 18epoch:train:1601-1700batch: iter_time=1.273e-04, forward_time=0.107, loss_ctc=53.582, loss_att=61.682, acc=0.713, loss=59.252, backward_time=0.099, grad_norm=49.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.835e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 06:25:22,656 (trainer:737) INFO: 18epoch:train:1701-1800batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=54.350, loss_att=55.191, acc=0.728, loss=54.939, backward_time=0.098, grad_norm=38.788, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.834e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 06:26:05,619 (trainer:737) INFO: 18epoch:train:1801-1900batch: iter_time=1.053e-04, forward_time=0.107, loss_ctc=48.689, loss_att=54.765, acc=0.719, loss=52.942, backward_time=0.099, grad_norm=40.527, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.833e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 06:26:48,134 (trainer:737) INFO: 18epoch:train:1901-2000batch: iter_time=1.045e-04, forward_time=0.104, loss_ctc=53.582, loss_att=53.016, acc=0.716, loss=53.186, backward_time=0.098, grad_norm=43.266, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.832e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 06:27:30,422 (trainer:737) INFO: 18epoch:train:2001-2100batch: iter_time=1.527e-04, forward_time=0.105, loss_ctc=52.773, loss_att=60.495, acc=0.722, loss=58.178, backward_time=0.098, grad_norm=41.728, clip=100.000, loss_scale=2.368e+34, optim_step_time=0.042, optim0_lr0=4.832e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 06:28:12,928 (trainer:737) INFO: 18epoch:train:2101-2200batch: iter_time=1.411e-04, forward_time=0.105, loss_ctc=46.871, loss_att=51.326, acc=0.718, loss=49.990, backward_time=0.098, grad_norm=33.725, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.831e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 06:28:55,533 (trainer:737) INFO: 18epoch:train:2201-2300batch: iter_time=1.428e-04, forward_time=0.104, loss_ctc=51.339, loss_att=53.593, acc=0.745, loss=52.916, backward_time=0.098, grad_norm=36.341, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.830e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 06:29:37,888 (trainer:737) INFO: 18epoch:train:2301-2400batch: iter_time=1.433e-04, forward_time=0.105, loss_ctc=50.775, loss_att=60.245, acc=0.693, loss=57.404, backward_time=0.099, grad_norm=46.824, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.829e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 06:29:38,696 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 06:30:21,608 (trainer:737) INFO: 18epoch:train:2401-2500batch: iter_time=1.311e-04, forward_time=0.109, loss_ctc=41.268, loss_att=45.701, acc=0.715, loss=44.371, backward_time=0.099, grad_norm=33.536, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.049, optim0_lr0=4.828e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-14 06:30:42,587 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 06:31:03,278 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 06:31:07,018 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f1d4bcd90>)
+[gpuc02:0/16] 2024-01-14 06:31:07,018 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 06:31:07,021 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 06:37:25,651 (trainer:737) INFO: 18epoch:train:2501-2600batch: iter_time=3.501, forward_time=0.110, loss_ctc=40.349, loss_att=51.770, acc=0.721, loss=48.344, backward_time=0.098, grad_norm=34.247, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.827e-04, train_time=4.240
+[gpuc02:0/16] 2024-01-14 06:38:07,872 (trainer:737) INFO: 18epoch:train:2601-2700batch: iter_time=8.926e-05, forward_time=0.105, loss_ctc=49.904, loss_att=54.218, acc=0.702, loss=52.924, backward_time=0.097, grad_norm=34.964, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.826e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:38:49,887 (trainer:737) INFO: 18epoch:train:2701-2800batch: iter_time=9.942e-05, forward_time=0.105, loss_ctc=39.815, loss_att=41.344, acc=0.721, loss=40.886, backward_time=0.096, grad_norm=33.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.825e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 06:39:32,086 (trainer:737) INFO: 18epoch:train:2801-2900batch: iter_time=1.254e-04, forward_time=0.104, loss_ctc=48.617, loss_att=55.711, acc=0.705, loss=53.583, backward_time=0.097, grad_norm=38.710, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.824e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:40:14,312 (trainer:737) INFO: 18epoch:train:2901-3000batch: iter_time=1.372e-04, forward_time=0.105, loss_ctc=55.255, loss_att=55.468, acc=0.713, loss=55.404, backward_time=0.098, grad_norm=49.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.823e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:40:57,212 (trainer:737) INFO: 18epoch:train:3001-3100batch: iter_time=1.094e-04, forward_time=0.105, loss_ctc=46.756, loss_att=62.615, acc=0.687, loss=57.858, backward_time=0.098, grad_norm=37.823, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.822e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 06:41:40,687 (trainer:737) INFO: 18epoch:train:3101-3200batch: iter_time=1.022e-04, forward_time=0.111, loss_ctc=57.895, loss_att=51.588, acc=0.725, loss=53.480, backward_time=0.098, grad_norm=43.613, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.821e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-14 06:42:23,235 (trainer:737) INFO: 18epoch:train:3201-3300batch: iter_time=1.001e-04, forward_time=0.107, loss_ctc=44.342, loss_att=50.769, acc=0.708, loss=48.841, backward_time=0.097, grad_norm=36.117, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.820e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 06:43:07,317 (trainer:737) INFO: 18epoch:train:3301-3400batch: iter_time=1.049e-04, forward_time=0.114, loss_ctc=54.950, loss_att=55.669, acc=0.715, loss=55.453, backward_time=0.098, grad_norm=40.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.819e-04, train_time=0.441
+[gpuc02:0/16] 2024-01-14 06:43:52,724 (trainer:737) INFO: 18epoch:train:3401-3500batch: iter_time=9.994e-05, forward_time=0.112, loss_ctc=44.999, loss_att=49.887, acc=0.729, loss=48.421, backward_time=0.112, grad_norm=36.727, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.818e-04, train_time=0.454
+[gpuc02:0/16] 2024-01-14 06:44:35,503 (trainer:737) INFO: 18epoch:train:3501-3600batch: iter_time=1.001e-04, forward_time=0.106, loss_ctc=51.675, loss_att=59.535, acc=0.712, loss=57.177, backward_time=0.098, grad_norm=41.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.817e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 06:45:18,091 (trainer:737) INFO: 18epoch:train:3601-3700batch: iter_time=1.013e-04, forward_time=0.107, loss_ctc=43.752, loss_att=47.202, acc=0.699, loss=46.167, backward_time=0.097, grad_norm=39.550, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.817e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 06:45:52,077 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 06:46:12,064 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 06:46:15,764 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f65432b77c0>)
+[gpuc02:0/16] 2024-01-14 06:46:15,764 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 06:46:15,768 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 06:51:10,964 (trainer:737) INFO: 18epoch:train:3701-3800batch: iter_time=2.769, forward_time=0.105, loss_ctc=40.989, loss_att=52.270, acc=0.694, loss=48.886, backward_time=0.097, grad_norm=34.333, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.816e-04, train_time=3.529
+[gpuc02:0/16] 2024-01-14 06:51:53,295 (trainer:737) INFO: 18epoch:train:3801-3900batch: iter_time=1.081e-04, forward_time=0.105, loss_ctc=47.907, loss_att=49.835, acc=0.724, loss=49.257, backward_time=0.098, grad_norm=35.972, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.815e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 06:52:35,449 (trainer:737) INFO: 18epoch:train:3901-4000batch: iter_time=1.186e-04, forward_time=0.104, loss_ctc=44.147, loss_att=46.992, acc=0.722, loss=46.139, backward_time=0.097, grad_norm=33.547, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.814e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 06:53:17,331 (trainer:737) INFO: 18epoch:train:4001-4100batch: iter_time=1.079e-04, forward_time=0.103, loss_ctc=41.197, loss_att=42.916, acc=0.698, loss=42.400, backward_time=0.096, grad_norm=34.812, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.813e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 06:53:59,552 (trainer:737) INFO: 18epoch:train:4101-4200batch: iter_time=1.100e-04, forward_time=0.105, loss_ctc=52.517, loss_att=59.627, acc=0.708, loss=57.494, backward_time=0.097, grad_norm=47.779, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.812e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:54:41,725 (trainer:737) INFO: 18epoch:train:4201-4300batch: iter_time=1.096e-04, forward_time=0.105, loss_ctc=53.361, loss_att=54.923, acc=0.722, loss=54.454, backward_time=0.097, grad_norm=40.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.811e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 06:55:23,881 (trainer:737) INFO: 18epoch:train:4301-4400batch: iter_time=1.135e-04, forward_time=0.105, loss_ctc=47.525, loss_att=53.964, acc=0.711, loss=52.032, backward_time=0.097, grad_norm=36.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.810e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 06:56:05,938 (trainer:737) INFO: 18epoch:train:4401-4500batch: iter_time=1.090e-04, forward_time=0.104, loss_ctc=52.045, loss_att=51.317, acc=0.709, loss=51.536, backward_time=0.097, grad_norm=41.544, clip=100.000, loss_scale=4.112e+34, optim_step_time=0.041, optim0_lr0=4.809e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 06:56:48,049 (trainer:737) INFO: 18epoch:train:4501-4600batch: iter_time=1.053e-04, forward_time=0.105, loss_ctc=52.047, loss_att=58.197, acc=0.718, loss=56.352, backward_time=0.097, grad_norm=38.596, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.808e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 06:57:30,058 (trainer:737) INFO: 18epoch:train:4601-4700batch: iter_time=9.782e-05, forward_time=0.104, loss_ctc=45.723, loss_att=50.292, acc=0.706, loss=48.921, backward_time=0.096, grad_norm=35.778, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.807e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 06:58:12,461 (trainer:737) INFO: 18epoch:train:4701-4800batch: iter_time=1.033e-04, forward_time=0.105, loss_ctc=51.243, loss_att=51.810, acc=0.746, loss=51.640, backward_time=0.097, grad_norm=37.097, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.806e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 06:58:54,855 (trainer:737) INFO: 18epoch:train:4801-4900batch: iter_time=1.019e-04, forward_time=0.105, loss_ctc=49.848, loss_att=58.885, acc=0.687, loss=56.174, backward_time=0.097, grad_norm=44.919, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.805e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 06:59:37,825 (trainer:737) INFO: 18epoch:train:4901-5000batch: iter_time=9.017e-05, forward_time=0.104, loss_ctc=40.464, loss_att=46.665, acc=0.696, loss=44.805, backward_time=0.096, grad_norm=34.568, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.805e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 06:59:40,938 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 07:00:01,012 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 07:00:05,051 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f1d402530>)
+[gpuc02:0/16] 2024-01-14 07:00:05,051 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 07:00:05,054 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 07:04:52,829 (trainer:737) INFO: 18epoch:train:5001-5100batch: iter_time=2.385, forward_time=0.118, loss_ctc=39.867, loss_att=49.521, acc=0.734, loss=46.625, backward_time=0.099, grad_norm=34.514, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.804e-04, train_time=3.150
+[gpuc02:0/16] 2024-01-14 07:05:35,159 (trainer:737) INFO: 18epoch:train:5101-5200batch: iter_time=1.089e-04, forward_time=0.105, loss_ctc=49.257, loss_att=54.802, acc=0.718, loss=53.138, backward_time=0.098, grad_norm=35.442, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.803e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:06:17,113 (trainer:737) INFO: 18epoch:train:5201-5300batch: iter_time=1.374e-04, forward_time=0.103, loss_ctc=39.450, loss_att=40.113, acc=0.726, loss=39.914, backward_time=0.097, grad_norm=32.504, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.802e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 07:06:59,358 (trainer:737) INFO: 18epoch:train:5301-5400batch: iter_time=1.644e-04, forward_time=0.105, loss_ctc=48.242, loss_att=55.180, acc=0.718, loss=53.099, backward_time=0.098, grad_norm=38.049, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.801e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:07:41,597 (trainer:737) INFO: 18epoch:train:5401-5500batch: iter_time=1.400e-04, forward_time=0.106, loss_ctc=55.228, loss_att=55.436, acc=0.722, loss=55.373, backward_time=0.098, grad_norm=49.055, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.800e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:08:23,858 (trainer:737) INFO: 18epoch:train:5501-5600batch: iter_time=1.130e-04, forward_time=0.105, loss_ctc=46.563, loss_att=62.165, acc=0.701, loss=57.484, backward_time=0.098, grad_norm=38.648, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.799e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:09:06,156 (trainer:737) INFO: 18epoch:train:5601-5700batch: iter_time=1.122e-04, forward_time=0.106, loss_ctc=56.222, loss_att=53.020, acc=0.727, loss=53.981, backward_time=0.098, grad_norm=42.676, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.798e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:09:48,373 (trainer:737) INFO: 18epoch:train:5701-5800batch: iter_time=1.112e-04, forward_time=0.105, loss_ctc=44.390, loss_att=52.565, acc=0.724, loss=50.112, backward_time=0.098, grad_norm=34.532, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.797e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:10:30,570 (trainer:737) INFO: 18epoch:train:5801-5900batch: iter_time=1.042e-04, forward_time=0.106, loss_ctc=54.666, loss_att=55.757, acc=0.725, loss=55.430, backward_time=0.098, grad_norm=39.874, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.796e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:11:12,856 (trainer:737) INFO: 18epoch:train:5901-6000batch: iter_time=1.039e-04, forward_time=0.105, loss_ctc=44.801, loss_att=51.728, acc=0.739, loss=49.650, backward_time=0.098, grad_norm=35.183, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.795e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:11:55,187 (trainer:737) INFO: 18epoch:train:6001-6100batch: iter_time=1.147e-04, forward_time=0.106, loss_ctc=51.333, loss_att=59.979, acc=0.717, loss=57.385, backward_time=0.098, grad_norm=43.228, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.794e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:12:37,348 (trainer:737) INFO: 18epoch:train:6101-6200batch: iter_time=1.085e-04, forward_time=0.105, loss_ctc=42.726, loss_att=48.051, acc=0.707, loss=46.454, backward_time=0.097, grad_norm=37.849, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.793e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:13:02,984 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 07:13:23,150 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 07:13:26,864 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f58276f20>)
+[gpuc02:0/16] 2024-01-14 07:13:26,865 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 07:13:26,868 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 07:18:01,015 (trainer:737) INFO: 18epoch:train:6201-6300batch: iter_time=2.413, forward_time=0.139, loss_ctc=40.370, loss_att=52.156, acc=0.707, loss=48.620, backward_time=0.100, grad_norm=34.572, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.793e-04, train_time=3.236
+[gpuc02:0/16] 2024-01-14 07:18:44,068 (trainer:737) INFO: 18epoch:train:6301-6400batch: iter_time=1.197e-04, forward_time=0.109, loss_ctc=47.683, loss_att=50.825, acc=0.737, loss=49.883, backward_time=0.099, grad_norm=34.635, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.792e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 07:19:11,463 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 07:19:26,334 (trainer:737) INFO: 18epoch:train:6401-6500batch: iter_time=1.192e-04, forward_time=0.104, loss_ctc=44.164, loss_att=46.477, acc=0.733, loss=45.783, backward_time=0.098, grad_norm=32.298, clip=100.000, loss_scale=6.755e+34, optim_step_time=0.041, optim0_lr0=4.791e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:20:08,400 (trainer:737) INFO: 18epoch:train:6501-6600batch: iter_time=1.438e-04, forward_time=0.103, loss_ctc=40.657, loss_att=41.035, acc=0.713, loss=40.921, backward_time=0.097, grad_norm=36.216, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.790e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 07:20:50,870 (trainer:737) INFO: 18epoch:train:6601-6700batch: iter_time=1.782e-04, forward_time=0.106, loss_ctc=51.978, loss_att=60.723, acc=0.715, loss=58.099, backward_time=0.098, grad_norm=48.161, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.789e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 07:21:33,296 (trainer:737) INFO: 18epoch:train:6701-6800batch: iter_time=1.378e-04, forward_time=0.105, loss_ctc=52.628, loss_att=54.679, acc=0.729, loss=54.064, backward_time=0.098, grad_norm=38.379, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.788e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 07:22:16,012 (trainer:737) INFO: 18epoch:train:6801-6900batch: iter_time=1.248e-04, forward_time=0.105, loss_ctc=46.092, loss_att=53.361, acc=0.723, loss=51.181, backward_time=0.098, grad_norm=37.506, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.787e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 07:22:58,300 (trainer:737) INFO: 18epoch:train:6901-7000batch: iter_time=1.267e-04, forward_time=0.105, loss_ctc=51.288, loss_att=52.332, acc=0.718, loss=52.019, backward_time=0.097, grad_norm=41.091, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.786e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:23:40,959 (trainer:737) INFO: 18epoch:train:7001-7100batch: iter_time=1.268e-04, forward_time=0.106, loss_ctc=50.979, loss_att=59.485, acc=0.727, loss=56.933, backward_time=0.098, grad_norm=41.605, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.785e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 07:24:23,280 (trainer:737) INFO: 18epoch:train:7101-7200batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=46.155, loss_att=50.715, acc=0.720, loss=49.347, backward_time=0.097, grad_norm=34.760, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.784e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:25:05,671 (trainer:737) INFO: 18epoch:train:7201-7300batch: iter_time=1.270e-04, forward_time=0.105, loss_ctc=50.877, loss_att=53.485, acc=0.747, loss=52.703, backward_time=0.098, grad_norm=37.836, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.783e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 07:25:48,123 (trainer:737) INFO: 18epoch:train:7301-7400batch: iter_time=1.129e-04, forward_time=0.105, loss_ctc=49.331, loss_att=59.935, acc=0.694, loss=56.754, backward_time=0.098, grad_norm=45.824, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.782e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 07:26:30,283 (trainer:737) INFO: 18epoch:train:7401-7500batch: iter_time=1.134e-04, forward_time=0.104, loss_ctc=40.077, loss_att=46.034, acc=0.715, loss=44.247, backward_time=0.097, grad_norm=33.772, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.782e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:26:33,335 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 07:26:53,475 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 07:26:57,180 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f5e34e860>)
+[gpuc02:0/16] 2024-01-14 07:26:57,180 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 07:26:57,183 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 07:31:56,609 (trainer:737) INFO: 18epoch:train:7501-7600batch: iter_time=2.401, forward_time=0.105, loss_ctc=40.444, loss_att=51.503, acc=0.722, loss=48.185, backward_time=0.098, grad_norm=34.811, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.781e-04, train_time=3.263
+[gpuc02:0/16] 2024-01-14 07:32:38,947 (trainer:737) INFO: 18epoch:train:7601-7700batch: iter_time=1.533e-04, forward_time=0.105, loss_ctc=49.352, loss_att=53.939, acc=0.703, loss=52.562, backward_time=0.097, grad_norm=35.450, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.780e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:32:50,241 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 07:33:20,946 (trainer:737) INFO: 18epoch:train:7701-7800batch: iter_time=1.624e-04, forward_time=0.103, loss_ctc=39.272, loss_att=40.270, acc=0.728, loss=39.971, backward_time=0.097, grad_norm=32.855, clip=100.000, loss_scale=2.622e+34, optim_step_time=0.042, optim0_lr0=4.779e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 07:34:03,178 (trainer:737) INFO: 18epoch:train:7801-7900batch: iter_time=1.577e-04, forward_time=0.105, loss_ctc=47.737, loss_att=55.025, acc=0.707, loss=52.839, backward_time=0.097, grad_norm=39.448, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.778e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:34:46,183 (trainer:737) INFO: 18epoch:train:7901-8000batch: iter_time=1.509e-04, forward_time=0.105, loss_ctc=54.425, loss_att=55.428, acc=0.714, loss=55.127, backward_time=0.098, grad_norm=51.592, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.777e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 07:35:28,867 (trainer:737) INFO: 18epoch:train:8001-8100batch: iter_time=1.525e-04, forward_time=0.105, loss_ctc=46.709, loss_att=62.245, acc=0.688, loss=57.584, backward_time=0.097, grad_norm=40.012, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.776e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 07:36:11,431 (trainer:737) INFO: 18epoch:train:8101-8200batch: iter_time=1.821e-04, forward_time=0.106, loss_ctc=55.304, loss_att=50.907, acc=0.729, loss=52.226, backward_time=0.098, grad_norm=42.076, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.775e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 07:36:54,976 (trainer:737) INFO: 18epoch:train:8201-8300batch: iter_time=1.480e-04, forward_time=0.105, loss_ctc=44.214, loss_att=50.602, acc=0.710, loss=48.686, backward_time=0.097, grad_norm=34.053, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.774e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-14 07:37:37,755 (trainer:737) INFO: 18epoch:train:8301-8400batch: iter_time=1.294e-04, forward_time=0.105, loss_ctc=54.549, loss_att=55.605, acc=0.720, loss=55.288, backward_time=0.097, grad_norm=39.652, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.773e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 07:38:19,917 (trainer:737) INFO: 18epoch:train:8401-8500batch: iter_time=1.303e-04, forward_time=0.105, loss_ctc=44.712, loss_att=50.067, acc=0.728, loss=48.461, backward_time=0.097, grad_norm=36.035, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.772e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:39:02,144 (trainer:737) INFO: 18epoch:train:8501-8600batch: iter_time=1.373e-04, forward_time=0.105, loss_ctc=51.045, loss_att=59.278, acc=0.716, loss=56.808, backward_time=0.098, grad_norm=40.366, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.772e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:39:44,477 (trainer:737) INFO: 18epoch:train:8601-8700batch: iter_time=1.394e-04, forward_time=0.108, loss_ctc=42.714, loss_att=47.852, acc=0.695, loss=46.311, backward_time=0.097, grad_norm=39.030, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.771e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:40:10,203 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 07:40:29,934 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 07:40:33,563 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f582e04c0>)
+[gpuc02:0/16] 2024-01-14 07:40:33,563 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 07:40:33,567 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 07:45:08,330 (trainer:737) INFO: 18epoch:train:8701-8800batch: iter_time=2.413, forward_time=0.104, loss_ctc=40.005, loss_att=51.395, acc=0.701, loss=47.978, backward_time=0.097, grad_norm=33.438, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.770e-04, train_time=3.238
+[gpuc02:0/16] 2024-01-14 07:45:50,616 (trainer:737) INFO: 18epoch:train:8801-8900batch: iter_time=1.121e-04, forward_time=0.106, loss_ctc=47.135, loss_att=49.197, acc=0.728, loss=48.579, backward_time=0.099, grad_norm=35.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.769e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 07:46:32,772 (trainer:737) INFO: 18epoch:train:8901-9000batch: iter_time=1.291e-04, forward_time=0.105, loss_ctc=43.067, loss_att=46.208, acc=0.724, loss=45.266, backward_time=0.098, grad_norm=52.887, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.768e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:47:14,660 (trainer:737) INFO: 18epoch:train:9001-9100batch: iter_time=1.615e-04, forward_time=0.103, loss_ctc=40.305, loss_att=41.561, acc=0.705, loss=41.184, backward_time=0.096, grad_norm=35.607, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.767e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 07:47:56,896 (trainer:737) INFO: 18epoch:train:9101-9200batch: iter_time=1.896e-04, forward_time=0.104, loss_ctc=52.538, loss_att=62.035, acc=0.709, loss=59.186, backward_time=0.098, grad_norm=46.767, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.766e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:48:39,073 (trainer:737) INFO: 18epoch:train:9201-9300batch: iter_time=1.591e-04, forward_time=0.104, loss_ctc=52.026, loss_att=55.052, acc=0.724, loss=54.144, backward_time=0.097, grad_norm=37.447, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.765e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:49:21,226 (trainer:737) INFO: 18epoch:train:9301-9400batch: iter_time=1.347e-04, forward_time=0.104, loss_ctc=46.959, loss_att=53.409, acc=0.715, loss=51.474, backward_time=0.097, grad_norm=38.942, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.764e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:50:03,278 (trainer:737) INFO: 18epoch:train:9401-9500batch: iter_time=1.026e-04, forward_time=0.104, loss_ctc=51.490, loss_att=50.849, acc=0.711, loss=51.041, backward_time=0.096, grad_norm=41.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.763e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 07:50:45,443 (trainer:737) INFO: 18epoch:train:9501-9600batch: iter_time=1.036e-04, forward_time=0.104, loss_ctc=50.918, loss_att=56.995, acc=0.722, loss=55.172, backward_time=0.097, grad_norm=39.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.763e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 07:51:27,476 (trainer:737) INFO: 18epoch:train:9601-9700batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=46.118, loss_att=50.775, acc=0.707, loss=49.378, backward_time=0.097, grad_norm=36.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.762e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 07:52:09,687 (trainer:737) INFO: 18epoch:train:9701-9800batch: iter_time=1.139e-04, forward_time=0.105, loss_ctc=50.094, loss_att=50.675, acc=0.750, loss=50.501, backward_time=0.097, grad_norm=36.329, clip=100.000, loss_scale=3.593e+34, optim_step_time=0.042, optim0_lr0=4.761e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:52:51,885 (trainer:737) INFO: 18epoch:train:9801-9900batch: iter_time=1.059e-04, forward_time=0.105, loss_ctc=48.980, loss_att=59.025, acc=0.687, loss=56.011, backward_time=0.097, grad_norm=45.253, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.760e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 07:53:33,852 (trainer:737) INFO: 18epoch:train:9901-10000batch: iter_time=9.533e-05, forward_time=0.103, loss_ctc=39.509, loss_att=46.072, acc=0.702, loss=44.103, backward_time=0.096, grad_norm=33.106, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.759e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 07:53:39,495 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 07:53:59,957 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 07:54:03,718 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f70f4d180>)
+[gpuc02:0/16] 2024-01-14 07:54:03,718 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 07:54:03,721 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 07:58:59,751 (trainer:737) INFO: 18epoch:train:10001-10100batch: iter_time=2.451, forward_time=0.106, loss_ctc=40.319, loss_att=49.479, acc=0.737, loss=46.731, backward_time=0.097, grad_norm=34.932, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.758e-04, train_time=3.259
+[gpuc02:0/16] 2024-01-14 07:59:42,627 (trainer:737) INFO: 18epoch:train:10101-10200batch: iter_time=1.122e-04, forward_time=0.108, loss_ctc=49.289, loss_att=54.526, acc=0.720, loss=52.955, backward_time=0.097, grad_norm=35.729, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.757e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 07:59:48,044 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 08:00:24,589 (trainer:737) INFO: 18epoch:train:10201-10300batch: iter_time=1.214e-04, forward_time=0.102, loss_ctc=38.941, loss_att=40.678, acc=0.725, loss=40.157, backward_time=0.096, grad_norm=32.411, clip=100.000, loss_scale=2.329e+34, optim_step_time=0.041, optim0_lr0=4.756e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 08:01:06,892 (trainer:737) INFO: 18epoch:train:10301-10400batch: iter_time=1.037e-04, forward_time=0.104, loss_ctc=47.812, loss_att=54.513, acc=0.720, loss=52.503, backward_time=0.097, grad_norm=39.638, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.755e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:01:49,154 (trainer:737) INFO: 18epoch:train:10401-10500batch: iter_time=1.050e-04, forward_time=0.105, loss_ctc=56.087, loss_att=58.207, acc=0.724, loss=57.571, backward_time=0.097, grad_norm=49.502, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.754e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:02:31,622 (trainer:737) INFO: 18epoch:train:10501-10600batch: iter_time=9.724e-05, forward_time=0.104, loss_ctc=45.792, loss_att=62.105, acc=0.703, loss=57.211, backward_time=0.097, grad_norm=37.233, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.754e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 08:03:13,886 (trainer:737) INFO: 18epoch:train:10601-10700batch: iter_time=1.031e-04, forward_time=0.104, loss_ctc=56.285, loss_att=53.298, acc=0.730, loss=54.194, backward_time=0.098, grad_norm=45.867, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.753e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:03:56,124 (trainer:737) INFO: 18epoch:train:10701-10800batch: iter_time=1.016e-04, forward_time=0.106, loss_ctc=43.757, loss_att=52.445, acc=0.725, loss=49.839, backward_time=0.097, grad_norm=33.024, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.752e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:04:38,303 (trainer:737) INFO: 18epoch:train:10801-10900batch: iter_time=9.753e-05, forward_time=0.106, loss_ctc=54.384, loss_att=55.829, acc=0.726, loss=55.396, backward_time=0.097, grad_norm=40.814, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.751e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:05:20,497 (trainer:737) INFO: 18epoch:train:10901-11000batch: iter_time=8.991e-05, forward_time=0.105, loss_ctc=44.587, loss_att=51.241, acc=0.740, loss=49.245, backward_time=0.097, grad_norm=35.618, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.750e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:06:02,796 (trainer:737) INFO: 18epoch:train:11001-11100batch: iter_time=1.005e-04, forward_time=0.104, loss_ctc=50.716, loss_att=59.030, acc=0.724, loss=56.536, backward_time=0.097, grad_norm=40.695, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.749e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:06:44,903 (trainer:737) INFO: 18epoch:train:11101-11200batch: iter_time=9.854e-05, forward_time=0.105, loss_ctc=42.290, loss_att=47.068, acc=0.712, loss=45.634, backward_time=0.097, grad_norm=38.147, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.748e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 08:07:09,428 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 08:07:29,809 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 08:07:33,484 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f4a10ec80>)
+[gpuc02:0/16] 2024-01-14 08:07:33,485 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 08:07:33,488 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 08:12:00,609 (trainer:737) INFO: 18epoch:train:11201-11300batch: iter_time=2.404, forward_time=0.104, loss_ctc=39.873, loss_att=52.239, acc=0.708, loss=48.529, backward_time=0.097, grad_norm=35.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.747e-04, train_time=3.157
+[gpuc02:0/16] 2024-01-14 08:12:42,940 (trainer:737) INFO: 18epoch:train:11301-11400batch: iter_time=1.072e-04, forward_time=0.104, loss_ctc=47.066, loss_att=50.551, acc=0.726, loss=49.506, backward_time=0.098, grad_norm=34.514, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.746e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:13:25,713 (trainer:737) INFO: 18epoch:train:11401-11500batch: iter_time=1.291e-04, forward_time=0.104, loss_ctc=43.667, loss_att=47.248, acc=0.724, loss=46.174, backward_time=0.097, grad_norm=33.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.746e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 08:14:08,231 (trainer:737) INFO: 18epoch:train:11501-11600batch: iter_time=1.399e-04, forward_time=0.103, loss_ctc=40.371, loss_att=41.611, acc=0.706, loss=41.239, backward_time=0.096, grad_norm=36.425, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.745e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 08:14:50,727 (trainer:737) INFO: 18epoch:train:11601-11700batch: iter_time=1.506e-04, forward_time=0.104, loss_ctc=51.959, loss_att=60.516, acc=0.708, loss=57.949, backward_time=0.097, grad_norm=48.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.744e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 08:15:34,168 (trainer:737) INFO: 18epoch:train:11701-11800batch: iter_time=1.208e-04, forward_time=0.105, loss_ctc=51.960, loss_att=54.770, acc=0.723, loss=53.927, backward_time=0.097, grad_norm=38.722, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.743e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-14 08:16:16,872 (trainer:737) INFO: 18epoch:train:11801-11900batch: iter_time=1.108e-04, forward_time=0.104, loss_ctc=46.219, loss_att=53.193, acc=0.716, loss=51.101, backward_time=0.097, grad_norm=37.850, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.742e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 08:16:59,803 (trainer:737) INFO: 18epoch:train:11901-12000batch: iter_time=9.767e-05, forward_time=0.104, loss_ctc=51.289, loss_att=50.727, acc=0.713, loss=50.896, backward_time=0.096, grad_norm=40.822, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.741e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 08:17:42,201 (trainer:737) INFO: 18epoch:train:12001-12100batch: iter_time=1.162e-04, forward_time=0.104, loss_ctc=50.619, loss_att=57.188, acc=0.721, loss=55.217, backward_time=0.096, grad_norm=38.801, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.740e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 08:18:24,172 (trainer:737) INFO: 18epoch:train:12101-12200batch: iter_time=1.183e-04, forward_time=0.104, loss_ctc=45.915, loss_att=50.897, acc=0.708, loss=49.403, backward_time=0.096, grad_norm=34.593, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.739e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 08:19:06,326 (trainer:737) INFO: 18epoch:train:12201-12300batch: iter_time=1.374e-04, forward_time=0.105, loss_ctc=50.216, loss_att=51.025, acc=0.751, loss=50.782, backward_time=0.097, grad_norm=36.743, clip=100.000, loss_scale=3.884e+34, optim_step_time=0.041, optim0_lr0=4.738e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 08:19:48,483 (trainer:737) INFO: 18epoch:train:12301-12400batch: iter_time=1.238e-04, forward_time=0.105, loss_ctc=48.524, loss_att=58.840, acc=0.687, loss=55.745, backward_time=0.097, grad_norm=44.527, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.738e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 08:20:30,673 (trainer:737) INFO: 18epoch:train:12401-12500batch: iter_time=1.099e-04, forward_time=0.103, loss_ctc=39.520, loss_att=45.963, acc=0.701, loss=44.030, backward_time=0.096, grad_norm=33.636, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.737e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:20:34,124 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 08:20:54,696 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 08:20:58,746 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f70f34310>)
+[gpuc02:0/16] 2024-01-14 08:20:58,747 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 08:20:58,750 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 08:25:43,619 (trainer:737) INFO: 18epoch:train:12501-12600batch: iter_time=2.386, forward_time=0.106, loss_ctc=40.165, loss_att=48.773, acc=0.737, loss=46.190, backward_time=0.098, grad_norm=33.681, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.736e-04, train_time=3.129
+[gpuc02:0/16] 2024-01-14 08:26:25,906 (trainer:737) INFO: 18epoch:train:12601-12700batch: iter_time=1.256e-04, forward_time=0.105, loss_ctc=49.248, loss_att=54.488, acc=0.720, loss=52.916, backward_time=0.100, grad_norm=35.954, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.735e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:27:07,930 (trainer:737) INFO: 18epoch:train:12701-12800batch: iter_time=1.333e-04, forward_time=0.104, loss_ctc=38.910, loss_att=39.823, acc=0.728, loss=39.549, backward_time=0.097, grad_norm=32.439, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.734e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 08:27:50,237 (trainer:737) INFO: 18epoch:train:12801-12900batch: iter_time=1.064e-04, forward_time=0.105, loss_ctc=46.419, loss_att=54.195, acc=0.721, loss=51.862, backward_time=0.098, grad_norm=36.430, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.733e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:28:32,471 (trainer:737) INFO: 18epoch:train:12901-13000batch: iter_time=1.218e-04, forward_time=0.105, loss_ctc=54.000, loss_att=54.600, acc=0.727, loss=54.420, backward_time=0.097, grad_norm=48.938, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.732e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:29:14,714 (trainer:737) INFO: 18epoch:train:13001-13100batch: iter_time=1.241e-04, forward_time=0.105, loss_ctc=45.811, loss_att=61.754, acc=0.704, loss=56.971, backward_time=0.098, grad_norm=39.151, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.731e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:29:57,014 (trainer:737) INFO: 18epoch:train:13101-13200batch: iter_time=1.371e-04, forward_time=0.107, loss_ctc=54.955, loss_att=52.342, acc=0.731, loss=53.126, backward_time=0.099, grad_norm=44.079, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.730e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:30:39,279 (trainer:737) INFO: 18epoch:train:13201-13300batch: iter_time=1.166e-04, forward_time=0.106, loss_ctc=43.505, loss_att=51.607, acc=0.726, loss=49.177, backward_time=0.098, grad_norm=34.662, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.730e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:31:21,622 (trainer:737) INFO: 18epoch:train:13301-13400batch: iter_time=1.039e-04, forward_time=0.105, loss_ctc=53.510, loss_att=55.209, acc=0.728, loss=54.700, backward_time=0.098, grad_norm=38.945, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.729e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:32:04,142 (trainer:737) INFO: 18epoch:train:13401-13500batch: iter_time=1.097e-04, forward_time=0.105, loss_ctc=44.040, loss_att=50.985, acc=0.740, loss=48.901, backward_time=0.099, grad_norm=34.548, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.728e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 08:32:46,571 (trainer:737) INFO: 18epoch:train:13501-13600batch: iter_time=1.148e-04, forward_time=0.107, loss_ctc=50.235, loss_att=59.223, acc=0.722, loss=56.526, backward_time=0.099, grad_norm=42.240, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.727e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 08:33:28,746 (trainer:737) INFO: 18epoch:train:13601-13700batch: iter_time=1.041e-04, forward_time=0.106, loss_ctc=41.583, loss_att=47.550, acc=0.710, loss=45.760, backward_time=0.098, grad_norm=36.057, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.726e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:33:54,073 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 08:34:14,013 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 08:34:17,727 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f291d6ef0>)
+[gpuc02:0/16] 2024-01-14 08:34:17,727 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 08:34:17,731 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 08:38:50,881 (trainer:737) INFO: 18epoch:train:13701-13800batch: iter_time=2.437, forward_time=0.106, loss_ctc=39.493, loss_att=52.912, acc=0.703, loss=48.886, backward_time=0.098, grad_norm=34.033, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.725e-04, train_time=3.221
+[gpuc02:0/16] 2024-01-14 08:39:33,184 (trainer:737) INFO: 18epoch:train:13801-13900batch: iter_time=1.380e-04, forward_time=0.105, loss_ctc=46.873, loss_att=50.857, acc=0.725, loss=49.662, backward_time=0.097, grad_norm=34.849, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.724e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 08:40:15,402 (trainer:737) INFO: 18epoch:train:13901-14000batch: iter_time=1.494e-04, forward_time=0.105, loss_ctc=42.986, loss_att=47.368, acc=0.724, loss=46.053, backward_time=0.097, grad_norm=32.366, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.723e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:40:57,643 (trainer:737) INFO: 18epoch:train:14001-14100batch: iter_time=1.142e-04, forward_time=0.104, loss_ctc=39.910, loss_att=42.254, acc=0.703, loss=41.551, backward_time=0.096, grad_norm=36.964, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.723e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:41:27,189 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 08:41:39,812 (trainer:737) INFO: 18epoch:train:14101-14200batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=51.705, loss_att=60.310, acc=0.709, loss=57.728, backward_time=0.097, grad_norm=46.851, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.041, optim0_lr0=4.722e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 08:42:22,033 (trainer:737) INFO: 18epoch:train:14201-14300batch: iter_time=1.694e-04, forward_time=0.106, loss_ctc=51.814, loss_att=53.894, acc=0.726, loss=53.270, backward_time=0.098, grad_norm=39.283, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.721e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:43:04,176 (trainer:737) INFO: 18epoch:train:14301-14400batch: iter_time=1.477e-04, forward_time=0.105, loss_ctc=45.913, loss_att=53.266, acc=0.715, loss=51.060, backward_time=0.097, grad_norm=37.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.720e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 08:43:46,656 (trainer:737) INFO: 18epoch:train:14401-14500batch: iter_time=1.485e-04, forward_time=0.109, loss_ctc=50.427, loss_att=50.532, acc=0.713, loss=50.501, backward_time=0.097, grad_norm=41.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.719e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 08:44:28,890 (trainer:737) INFO: 18epoch:train:14501-14600batch: iter_time=1.248e-04, forward_time=0.105, loss_ctc=50.543, loss_att=56.985, acc=0.720, loss=55.052, backward_time=0.097, grad_norm=38.083, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.718e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:45:10,953 (trainer:737) INFO: 18epoch:train:14601-14700batch: iter_time=1.328e-04, forward_time=0.105, loss_ctc=45.931, loss_att=50.515, acc=0.708, loss=49.140, backward_time=0.097, grad_norm=35.808, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.717e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 08:45:53,493 (trainer:737) INFO: 18epoch:train:14701-14800batch: iter_time=1.306e-04, forward_time=0.106, loss_ctc=50.056, loss_att=50.778, acc=0.751, loss=50.562, backward_time=0.097, grad_norm=37.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.716e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 08:46:35,754 (trainer:737) INFO: 18epoch:train:14801-14900batch: iter_time=1.255e-04, forward_time=0.106, loss_ctc=48.715, loss_att=58.714, acc=0.688, loss=55.715, backward_time=0.098, grad_norm=45.763, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.716e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 08:47:17,762 (trainer:737) INFO: 18epoch:train:14901-15000batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=39.574, loss_att=45.948, acc=0.702, loss=44.036, backward_time=0.097, grad_norm=33.523, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.715e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 09:07:36,009 (trainer:343) INFO: 18epoch results: [train] iter_time=0.203, forward_time=0.106, loss_ctc=47.574, loss_att=52.677, acc=0.717, loss=51.146, backward_time=0.098, grad_norm=38.584, clip=100.000, loss_scale=2.948e+34, optim_step_time=0.041, optim0_lr0=4.782e-04, train_time=0.669, time=2 hours, 47 minutes and 21.34 seconds, total_count=270000, gpu_max_cached_mem_GB=27.297, [valid] loss_ctc=56.238, cer_ctc=0.293, loss_att=56.327, acc=0.571, cer=0.355, wer=0.999, loss=56.300, time=20 minutes and 8.53 seconds, total_count=84078, gpu_max_cached_mem_GB=27.297
+[gpuc02:0/16] 2024-01-14 09:07:41,177 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-14 09:07:41,181 (trainer:272) INFO: 19/45epoch started. Estimated time to finish: 3 days, 11 hours and 48 minutes
+[gpuc02:0/16] 2024-01-14 09:07:41,190 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-14 09:07:59,749 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 09:08:03,256 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5df8e87bb0>)
+[gpuc02:0/16] 2024-01-14 09:08:03,257 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-14 09:08:03,260 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 09:12:45,083 (trainer:737) INFO: 19epoch:train:1-100batch: iter_time=2.302, forward_time=0.106, loss_ctc=42.671, loss_att=46.521, acc=0.743, loss=45.366, backward_time=0.098, grad_norm=35.559, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.714e-04, train_time=3.039
+[gpuc02:0/16] 2024-01-14 09:13:29,448 (trainer:737) INFO: 19epoch:train:101-200batch: iter_time=1.026e-04, forward_time=0.108, loss_ctc=63.166, loss_att=72.129, acc=0.705, loss=69.440, backward_time=0.099, grad_norm=50.473, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.713e-04, train_time=0.443
+[gpuc02:0/16] 2024-01-14 09:14:12,310 (trainer:737) INFO: 19epoch:train:201-300batch: iter_time=9.566e-05, forward_time=0.106, loss_ctc=49.093, loss_att=55.553, acc=0.721, loss=53.615, backward_time=0.099, grad_norm=36.010, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.712e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 09:14:56,561 (trainer:737) INFO: 19epoch:train:301-400batch: iter_time=1.018e-04, forward_time=0.106, loss_ctc=43.778, loss_att=44.546, acc=0.741, loss=44.315, backward_time=0.098, grad_norm=33.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.711e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-14 09:15:39,104 (trainer:737) INFO: 19epoch:train:401-500batch: iter_time=1.011e-04, forward_time=0.106, loss_ctc=55.434, loss_att=63.956, acc=0.694, loss=61.400, backward_time=0.099, grad_norm=44.233, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.710e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 09:16:24,205 (trainer:737) INFO: 19epoch:train:501-600batch: iter_time=1.035e-04, forward_time=0.106, loss_ctc=55.635, loss_att=54.879, acc=0.716, loss=55.106, backward_time=0.099, grad_norm=45.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.709e-04, train_time=0.451
+[gpuc02:0/16] 2024-01-14 09:17:11,348 (trainer:737) INFO: 19epoch:train:601-700batch: iter_time=9.139e-05, forward_time=0.109, loss_ctc=47.278, loss_att=52.014, acc=0.733, loss=50.593, backward_time=0.098, grad_norm=35.941, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.709e-04, train_time=0.471
+[gpuc02:0/16] 2024-01-14 09:17:57,192 (trainer:737) INFO: 19epoch:train:701-800batch: iter_time=9.560e-05, forward_time=0.106, loss_ctc=42.167, loss_att=44.086, acc=0.742, loss=43.510, backward_time=0.098, grad_norm=33.940, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.708e-04, train_time=0.458
+[gpuc02:0/16] 2024-01-14 09:18:39,779 (trainer:737) INFO: 19epoch:train:801-900batch: iter_time=9.534e-05, forward_time=0.106, loss_ctc=61.963, loss_att=62.098, acc=0.701, loss=62.057, backward_time=0.098, grad_norm=62.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.707e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 09:19:22,468 (trainer:737) INFO: 19epoch:train:901-1000batch: iter_time=8.814e-05, forward_time=0.106, loss_ctc=49.709, loss_att=53.026, acc=0.740, loss=52.031, backward_time=0.099, grad_norm=39.234, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.706e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 09:20:05,258 (trainer:737) INFO: 19epoch:train:1001-1100batch: iter_time=9.760e-05, forward_time=0.105, loss_ctc=39.916, loss_att=47.738, acc=0.727, loss=45.391, backward_time=0.098, grad_norm=32.103, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.705e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 09:20:47,941 (trainer:737) INFO: 19epoch:train:1101-1200batch: iter_time=9.619e-05, forward_time=0.105, loss_ctc=41.138, loss_att=47.049, acc=0.728, loss=45.276, backward_time=0.098, grad_norm=36.718, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.041, optim0_lr0=4.704e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 09:21:13,920 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-14 09:21:33,840 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 09:21:37,646 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f6549fd76a0>)
+[gpuc02:0/16] 2024-01-14 09:21:37,646 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-14 09:21:37,649 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 09:26:02,257 (trainer:737) INFO: 19epoch:train:1201-1300batch: iter_time=2.615, forward_time=0.107, loss_ctc=51.051, loss_att=52.945, acc=0.726, loss=52.377, backward_time=0.098, grad_norm=42.378, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.703e-04, train_time=3.143
+[gpuc02:0/16] 2024-01-14 09:26:44,699 (trainer:737) INFO: 19epoch:train:1301-1400batch: iter_time=1.263e-04, forward_time=0.107, loss_ctc=57.910, loss_att=61.138, acc=0.731, loss=60.169, backward_time=0.099, grad_norm=41.900, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.703e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 09:27:26,937 (trainer:737) INFO: 19epoch:train:1401-1500batch: iter_time=1.378e-04, forward_time=0.105, loss_ctc=43.900, loss_att=58.746, acc=0.723, loss=54.292, backward_time=0.098, grad_norm=36.689, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.702e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:28:09,494 (trainer:737) INFO: 19epoch:train:1501-1600batch: iter_time=1.366e-04, forward_time=0.106, loss_ctc=49.152, loss_att=47.865, acc=0.732, loss=48.251, backward_time=0.099, grad_norm=35.963, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.701e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 09:28:52,124 (trainer:737) INFO: 19epoch:train:1601-1700batch: iter_time=1.202e-04, forward_time=0.107, loss_ctc=47.820, loss_att=58.394, acc=0.710, loss=55.222, backward_time=0.100, grad_norm=37.758, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.700e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 09:29:34,304 (trainer:737) INFO: 19epoch:train:1701-1800batch: iter_time=1.240e-04, forward_time=0.104, loss_ctc=54.707, loss_att=51.816, acc=0.714, loss=52.683, backward_time=0.097, grad_norm=45.988, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.699e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:30:16,602 (trainer:737) INFO: 19epoch:train:1801-1900batch: iter_time=1.236e-04, forward_time=0.105, loss_ctc=47.715, loss_att=54.050, acc=0.738, loss=52.150, backward_time=0.098, grad_norm=36.094, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.698e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 09:30:58,762 (trainer:737) INFO: 19epoch:train:1901-2000batch: iter_time=1.437e-04, forward_time=0.104, loss_ctc=42.600, loss_att=44.331, acc=0.734, loss=43.812, backward_time=0.097, grad_norm=33.975, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.697e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 09:31:41,355 (trainer:737) INFO: 19epoch:train:2001-2100batch: iter_time=1.209e-04, forward_time=0.105, loss_ctc=50.440, loss_att=55.721, acc=0.716, loss=54.136, backward_time=0.097, grad_norm=40.173, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.696e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 09:32:23,966 (trainer:737) INFO: 19epoch:train:2101-2200batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=58.845, loss_att=63.069, acc=0.722, loss=61.802, backward_time=0.098, grad_norm=52.612, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.696e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 09:33:06,140 (trainer:737) INFO: 19epoch:train:2201-2300batch: iter_time=1.339e-04, forward_time=0.105, loss_ctc=39.602, loss_att=48.203, acc=0.746, loss=45.623, backward_time=0.098, grad_norm=30.231, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.695e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:33:48,399 (trainer:737) INFO: 19epoch:train:2301-2400batch: iter_time=1.277e-04, forward_time=0.105, loss_ctc=41.209, loss_att=47.909, acc=0.727, loss=45.899, backward_time=0.097, grad_norm=33.453, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.694e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:34:31,204 (trainer:737) INFO: 19epoch:train:2401-2500batch: iter_time=1.187e-04, forward_time=0.107, loss_ctc=48.496, loss_att=49.412, acc=0.723, loss=49.137, backward_time=0.097, grad_norm=43.622, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.693e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-14 09:34:37,942 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-14 09:34:58,922 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 09:35:02,733 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f29114610>)
+[gpuc02:0/16] 2024-01-14 09:35:02,733 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-14 09:35:02,737 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 09:41:13,739 (trainer:737) INFO: 19epoch:train:2501-2600batch: iter_time=3.205, forward_time=0.129, loss_ctc=41.839, loss_att=47.482, acc=0.735, loss=45.790, backward_time=0.103, grad_norm=35.483, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.692e-04, train_time=4.025
+[gpuc02:0/16] 2024-01-14 09:41:56,175 (trainer:737) INFO: 19epoch:train:2601-2700batch: iter_time=1.458e-04, forward_time=0.107, loss_ctc=58.400, loss_att=70.726, acc=0.705, loss=67.029, backward_time=0.098, grad_norm=44.632, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.691e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 09:42:38,350 (trainer:737) INFO: 19epoch:train:2701-2800batch: iter_time=1.439e-04, forward_time=0.106, loss_ctc=47.858, loss_att=56.675, acc=0.711, loss=54.030, backward_time=0.098, grad_norm=36.748, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.690e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:43:20,518 (trainer:737) INFO: 19epoch:train:2801-2900batch: iter_time=1.534e-04, forward_time=0.106, loss_ctc=42.714, loss_att=44.618, acc=0.729, loss=44.047, backward_time=0.098, grad_norm=33.357, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.690e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 09:44:02,744 (trainer:737) INFO: 19epoch:train:2901-3000batch: iter_time=1.480e-04, forward_time=0.105, loss_ctc=53.322, loss_att=64.172, acc=0.690, loss=60.917, backward_time=0.098, grad_norm=42.988, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.689e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:44:16,172 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 09:44:45,078 (trainer:737) INFO: 19epoch:train:3001-3100batch: iter_time=1.551e-04, forward_time=0.105, loss_ctc=49.466, loss_att=51.903, acc=0.716, loss=51.171, backward_time=0.097, grad_norm=39.451, clip=100.000, loss_scale=2.727e+34, optim_step_time=0.041, optim0_lr0=4.688e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 09:45:27,256 (trainer:737) INFO: 19epoch:train:3101-3200batch: iter_time=1.558e-04, forward_time=0.106, loss_ctc=46.185, loss_att=48.030, acc=0.736, loss=47.476, backward_time=0.098, grad_norm=35.071, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.687e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:46:09,345 (trainer:737) INFO: 19epoch:train:3201-3300batch: iter_time=1.379e-04, forward_time=0.105, loss_ctc=41.460, loss_att=43.689, acc=0.735, loss=43.020, backward_time=0.097, grad_norm=34.469, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.686e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 09:46:51,549 (trainer:737) INFO: 19epoch:train:3301-3400batch: iter_time=1.250e-04, forward_time=0.106, loss_ctc=60.864, loss_att=63.100, acc=0.702, loss=62.429, backward_time=0.098, grad_norm=57.550, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.685e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:47:33,789 (trainer:737) INFO: 19epoch:train:3401-3500batch: iter_time=1.246e-04, forward_time=0.106, loss_ctc=48.670, loss_att=51.597, acc=0.732, loss=50.719, backward_time=0.098, grad_norm=37.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.684e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:48:15,812 (trainer:737) INFO: 19epoch:train:3501-3600batch: iter_time=1.291e-04, forward_time=0.105, loss_ctc=38.937, loss_att=46.951, acc=0.725, loss=44.546, backward_time=0.097, grad_norm=31.661, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.684e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 09:48:57,849 (trainer:737) INFO: 19epoch:train:3601-3700batch: iter_time=1.277e-04, forward_time=0.105, loss_ctc=39.932, loss_att=47.177, acc=0.723, loss=45.003, backward_time=0.097, grad_norm=35.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.683e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 09:49:28,923 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-14 09:49:49,394 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 09:49:53,227 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f65a7786fb0>)
+[gpuc02:0/16] 2024-01-14 09:49:53,227 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-14 09:49:53,230 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 09:56:12,543 (trainer:737) INFO: 19epoch:train:3701-3800batch: iter_time=3.884, forward_time=0.138, loss_ctc=50.366, loss_att=52.628, acc=0.722, loss=51.950, backward_time=0.103, grad_norm=43.766, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.682e-04, train_time=4.347
+[gpuc02:0/16] 2024-01-14 09:57:19,342 (trainer:737) INFO: 19epoch:train:3801-3900batch: iter_time=1.786e-04, forward_time=0.109, loss_ctc=55.651, loss_att=62.203, acc=0.714, loss=60.237, backward_time=0.099, grad_norm=43.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.681e-04, train_time=0.668
+[gpuc02:0/16] 2024-01-14 09:58:01,555 (trainer:737) INFO: 19epoch:train:3901-4000batch: iter_time=1.862e-04, forward_time=0.107, loss_ctc=42.849, loss_att=57.614, acc=0.721, loss=53.185, backward_time=0.099, grad_norm=35.167, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.680e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:58:43,816 (trainer:737) INFO: 19epoch:train:4001-4100batch: iter_time=1.748e-04, forward_time=0.108, loss_ctc=48.496, loss_att=47.875, acc=0.722, loss=48.061, backward_time=0.100, grad_norm=36.250, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.679e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 09:59:26,300 (trainer:737) INFO: 19epoch:train:4101-4200batch: iter_time=1.934e-04, forward_time=0.108, loss_ctc=47.228, loss_att=58.421, acc=0.697, loss=55.063, backward_time=0.098, grad_norm=39.011, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.678e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 10:00:08,867 (trainer:737) INFO: 19epoch:train:4201-4300batch: iter_time=1.567e-04, forward_time=0.107, loss_ctc=51.195, loss_att=49.492, acc=0.722, loss=50.003, backward_time=0.097, grad_norm=42.593, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.678e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 10:00:50,922 (trainer:737) INFO: 19epoch:train:4301-4400batch: iter_time=1.366e-04, forward_time=0.106, loss_ctc=47.122, loss_att=51.385, acc=0.733, loss=50.106, backward_time=0.097, grad_norm=35.801, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.677e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 10:01:33,036 (trainer:737) INFO: 19epoch:train:4401-4500batch: iter_time=1.572e-04, forward_time=0.107, loss_ctc=42.132, loss_att=42.422, acc=0.736, loss=42.335, backward_time=0.098, grad_norm=33.118, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.676e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:02:15,114 (trainer:737) INFO: 19epoch:train:4501-4600batch: iter_time=1.604e-04, forward_time=0.107, loss_ctc=49.241, loss_att=54.043, acc=0.711, loss=52.602, backward_time=0.097, grad_norm=40.178, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.675e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:02:57,315 (trainer:737) INFO: 19epoch:train:4601-4700batch: iter_time=1.517e-04, forward_time=0.107, loss_ctc=57.256, loss_att=61.930, acc=0.712, loss=60.528, backward_time=0.097, grad_norm=55.089, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.674e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:03:39,418 (trainer:737) INFO: 19epoch:train:4701-4800batch: iter_time=1.433e-04, forward_time=0.107, loss_ctc=39.047, loss_att=47.996, acc=0.743, loss=45.311, backward_time=0.097, grad_norm=30.004, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.673e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:04:21,627 (trainer:737) INFO: 19epoch:train:4801-4900batch: iter_time=1.435e-04, forward_time=0.107, loss_ctc=40.218, loss_att=46.974, acc=0.725, loss=44.947, backward_time=0.097, grad_norm=33.073, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.672e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:05:03,662 (trainer:737) INFO: 19epoch:train:4901-5000batch: iter_time=1.276e-04, forward_time=0.106, loss_ctc=47.736, loss_att=49.588, acc=0.714, loss=49.033, backward_time=0.097, grad_norm=44.607, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.672e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 10:05:29,178 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-14 10:05:49,369 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 10:05:53,079 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f7a46ffd0>)
+[gpuc02:0/16] 2024-01-14 10:05:53,079 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-14 10:05:53,083 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 10:11:18,776 (trainer:737) INFO: 19epoch:train:5001-5100batch: iter_time=3.255, forward_time=0.166, loss_ctc=41.441, loss_att=43.457, acc=0.746, loss=42.852, backward_time=0.102, grad_norm=33.516, clip=100.000, loss_scale=3.489e+34, optim_step_time=0.044, optim0_lr0=4.671e-04, train_time=3.751
+[gpuc02:0/16] 2024-01-14 10:12:01,525 (trainer:737) INFO: 19epoch:train:5101-5200batch: iter_time=1.879e-04, forward_time=0.109, loss_ctc=57.688, loss_att=69.765, acc=0.708, loss=66.142, backward_time=0.100, grad_norm=43.245, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.043, optim0_lr0=4.670e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 10:12:22,396 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 10:12:43,928 (trainer:737) INFO: 19epoch:train:5201-5300batch: iter_time=1.987e-04, forward_time=0.107, loss_ctc=47.661, loss_att=53.847, acc=0.719, loss=51.991, backward_time=0.099, grad_norm=34.379, clip=100.000, loss_scale=3.084e+34, optim_step_time=0.042, optim0_lr0=4.669e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:13:26,334 (trainer:737) INFO: 19epoch:train:5301-5400batch: iter_time=2.167e-04, forward_time=0.107, loss_ctc=42.372, loss_att=42.647, acc=0.735, loss=42.564, backward_time=0.098, grad_norm=32.888, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.668e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:14:08,557 (trainer:737) INFO: 19epoch:train:5401-5500batch: iter_time=1.995e-04, forward_time=0.106, loss_ctc=52.120, loss_att=61.253, acc=0.698, loss=58.513, backward_time=0.098, grad_norm=40.367, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.667e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:14:51,227 (trainer:737) INFO: 19epoch:train:5501-5600batch: iter_time=1.875e-04, forward_time=0.105, loss_ctc=47.414, loss_att=50.913, acc=0.717, loss=49.863, backward_time=0.097, grad_norm=42.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.667e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 10:15:34,340 (trainer:737) INFO: 19epoch:train:5601-5700batch: iter_time=1.876e-04, forward_time=0.106, loss_ctc=45.986, loss_att=47.515, acc=0.738, loss=47.056, backward_time=0.098, grad_norm=37.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.666e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-14 10:16:17,112 (trainer:737) INFO: 19epoch:train:5701-5800batch: iter_time=1.792e-04, forward_time=0.113, loss_ctc=41.158, loss_att=43.856, acc=0.735, loss=43.046, backward_time=0.097, grad_norm=32.402, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.665e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 10:16:59,450 (trainer:737) INFO: 19epoch:train:5801-5900batch: iter_time=1.626e-04, forward_time=0.106, loss_ctc=57.898, loss_att=59.100, acc=0.704, loss=58.739, backward_time=0.096, grad_norm=60.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.664e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:17:41,547 (trainer:737) INFO: 19epoch:train:5901-6000batch: iter_time=1.661e-04, forward_time=0.107, loss_ctc=47.499, loss_att=50.873, acc=0.734, loss=49.861, backward_time=0.097, grad_norm=37.807, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.663e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:18:23,887 (trainer:737) INFO: 19epoch:train:6001-6100batch: iter_time=1.595e-04, forward_time=0.106, loss_ctc=38.608, loss_att=47.119, acc=0.728, loss=44.565, backward_time=0.098, grad_norm=31.240, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=4.662e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:19:05,805 (trainer:737) INFO: 19epoch:train:6101-6200batch: iter_time=1.472e-04, forward_time=0.105, loss_ctc=39.119, loss_att=47.041, acc=0.724, loss=44.664, backward_time=0.096, grad_norm=34.725, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.661e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-14 10:19:36,025 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-14 10:19:56,243 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 10:19:59,934 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f7b095c00>)
+[gpuc02:0/16] 2024-01-14 10:19:59,934 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-14 10:19:59,938 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 10:24:29,033 (trainer:737) INFO: 19epoch:train:6201-6300batch: iter_time=2.808, forward_time=0.107, loss_ctc=49.203, loss_att=52.284, acc=0.724, loss=51.360, backward_time=0.097, grad_norm=41.852, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.661e-04, train_time=3.232
+[gpuc02:0/16] 2024-01-14 10:25:11,426 (trainer:737) INFO: 19epoch:train:6301-6400batch: iter_time=1.437e-04, forward_time=0.108, loss_ctc=54.862, loss_att=61.541, acc=0.716, loss=59.538, backward_time=0.098, grad_norm=43.496, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.660e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:25:53,785 (trainer:737) INFO: 19epoch:train:6401-6500batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=43.024, loss_att=58.320, acc=0.720, loss=53.731, backward_time=0.099, grad_norm=33.739, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.659e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:26:36,059 (trainer:737) INFO: 19epoch:train:6501-6600batch: iter_time=1.437e-04, forward_time=0.106, loss_ctc=48.482, loss_att=47.723, acc=0.723, loss=47.951, backward_time=0.099, grad_norm=34.750, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.658e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:27:18,571 (trainer:737) INFO: 19epoch:train:6601-6700batch: iter_time=1.409e-04, forward_time=0.106, loss_ctc=46.775, loss_att=58.502, acc=0.700, loss=54.984, backward_time=0.098, grad_norm=36.992, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.657e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 10:28:00,695 (trainer:737) INFO: 19epoch:train:6701-6800batch: iter_time=1.401e-04, forward_time=0.106, loss_ctc=49.132, loss_att=48.722, acc=0.724, loss=48.845, backward_time=0.097, grad_norm=42.559, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.656e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:28:43,725 (trainer:737) INFO: 19epoch:train:6801-6900batch: iter_time=1.296e-04, forward_time=0.106, loss_ctc=47.169, loss_att=50.659, acc=0.734, loss=49.612, backward_time=0.098, grad_norm=37.492, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.656e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 10:29:25,900 (trainer:737) INFO: 19epoch:train:6901-7000batch: iter_time=1.320e-04, forward_time=0.106, loss_ctc=42.240, loss_att=42.345, acc=0.735, loss=42.314, backward_time=0.097, grad_norm=32.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.655e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:30:08,453 (trainer:737) INFO: 19epoch:train:7001-7100batch: iter_time=1.456e-04, forward_time=0.106, loss_ctc=49.137, loss_att=53.337, acc=0.714, loss=52.077, backward_time=0.098, grad_norm=41.480, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.654e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 10:30:51,161 (trainer:737) INFO: 19epoch:train:7101-7200batch: iter_time=1.429e-04, forward_time=0.106, loss_ctc=55.687, loss_att=58.795, acc=0.716, loss=57.863, backward_time=0.098, grad_norm=52.852, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.653e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 10:31:33,610 (trainer:737) INFO: 19epoch:train:7201-7300batch: iter_time=1.428e-04, forward_time=0.106, loss_ctc=38.908, loss_att=47.443, acc=0.745, loss=44.882, backward_time=0.097, grad_norm=31.322, clip=100.000, loss_scale=3.136e+34, optim_step_time=0.041, optim0_lr0=4.652e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:32:15,652 (trainer:737) INFO: 19epoch:train:7301-7400batch: iter_time=1.502e-04, forward_time=0.105, loss_ctc=40.213, loss_att=47.465, acc=0.723, loss=45.289, backward_time=0.097, grad_norm=33.291, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.651e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 10:32:57,943 (trainer:737) INFO: 19epoch:train:7401-7500batch: iter_time=1.346e-04, forward_time=0.104, loss_ctc=46.844, loss_att=48.737, acc=0.716, loss=48.169, backward_time=0.097, grad_norm=43.190, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.651e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:33:02,393 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-14 10:33:22,656 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 10:33:26,487 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f7a439150>)
+[gpuc02:0/16] 2024-01-14 10:33:26,487 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-14 10:33:26,491 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 10:38:23,738 (trainer:737) INFO: 19epoch:train:7501-7600batch: iter_time=2.534, forward_time=0.117, loss_ctc=41.099, loss_att=45.983, acc=0.748, loss=44.518, backward_time=0.102, grad_norm=35.080, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.650e-04, train_time=3.258
+[gpuc02:0/16] 2024-01-14 10:39:06,117 (trainer:737) INFO: 19epoch:train:7601-7700batch: iter_time=1.421e-04, forward_time=0.106, loss_ctc=56.114, loss_att=69.881, acc=0.717, loss=65.751, backward_time=0.100, grad_norm=43.758, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.649e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:39:48,575 (trainer:737) INFO: 19epoch:train:7701-7800batch: iter_time=1.471e-04, forward_time=0.105, loss_ctc=47.466, loss_att=56.051, acc=0.724, loss=53.475, backward_time=0.099, grad_norm=35.509, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.648e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:40:30,701 (trainer:737) INFO: 19epoch:train:7801-7900batch: iter_time=1.551e-04, forward_time=0.105, loss_ctc=42.323, loss_att=44.688, acc=0.743, loss=43.978, backward_time=0.099, grad_norm=33.211, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.647e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:41:12,928 (trainer:737) INFO: 19epoch:train:7901-8000batch: iter_time=1.507e-04, forward_time=0.105, loss_ctc=51.805, loss_att=62.323, acc=0.701, loss=59.168, backward_time=0.098, grad_norm=44.206, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.646e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:41:55,161 (trainer:737) INFO: 19epoch:train:8001-8100batch: iter_time=1.769e-04, forward_time=0.104, loss_ctc=46.836, loss_att=53.233, acc=0.718, loss=51.314, backward_time=0.099, grad_norm=42.161, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.646e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:42:37,470 (trainer:737) INFO: 19epoch:train:8101-8200batch: iter_time=1.709e-04, forward_time=0.106, loss_ctc=45.890, loss_att=52.032, acc=0.737, loss=50.189, backward_time=0.099, grad_norm=36.351, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.645e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:43:19,597 (trainer:737) INFO: 19epoch:train:8201-8300batch: iter_time=1.585e-04, forward_time=0.105, loss_ctc=40.846, loss_att=43.568, acc=0.745, loss=42.751, backward_time=0.098, grad_norm=32.653, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.644e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:44:01,802 (trainer:737) INFO: 19epoch:train:8301-8400batch: iter_time=1.835e-04, forward_time=0.105, loss_ctc=57.504, loss_att=60.704, acc=0.706, loss=59.744, backward_time=0.099, grad_norm=54.555, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.643e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:44:44,054 (trainer:737) INFO: 19epoch:train:8401-8500batch: iter_time=1.556e-04, forward_time=0.106, loss_ctc=47.470, loss_att=52.841, acc=0.741, loss=51.230, backward_time=0.099, grad_norm=37.784, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.642e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:45:26,122 (trainer:737) INFO: 19epoch:train:8501-8600batch: iter_time=1.362e-04, forward_time=0.105, loss_ctc=37.813, loss_att=47.552, acc=0.732, loss=44.630, backward_time=0.098, grad_norm=30.899, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.641e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 10:46:08,432 (trainer:737) INFO: 19epoch:train:8601-8700batch: iter_time=1.405e-04, forward_time=0.105, loss_ctc=39.202, loss_att=46.764, acc=0.733, loss=44.495, backward_time=0.098, grad_norm=33.789, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.641e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:46:26,555 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 10:46:34,671 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-14 10:46:54,141 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 10:46:57,741 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f65d7f430>)
+[gpuc02:0/16] 2024-01-14 10:46:57,741 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-14 10:46:57,744 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 10:51:18,464 (trainer:737) INFO: 19epoch:train:8701-8800batch: iter_time=2.587, forward_time=0.106, loss_ctc=47.968, loss_att=51.176, acc=0.736, loss=50.214, backward_time=0.098, grad_norm=40.033, clip=100.000, loss_scale=2.958e+34, optim_step_time=0.042, optim0_lr0=4.640e-04, train_time=3.100
+[gpuc02:0/16] 2024-01-14 10:52:01,140 (trainer:737) INFO: 19epoch:train:8801-8900batch: iter_time=1.616e-04, forward_time=0.106, loss_ctc=54.498, loss_att=62.874, acc=0.716, loss=60.361, backward_time=0.098, grad_norm=43.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.639e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-14 10:52:43,321 (trainer:737) INFO: 19epoch:train:8901-9000batch: iter_time=1.414e-04, forward_time=0.104, loss_ctc=42.271, loss_att=58.077, acc=0.722, loss=53.335, backward_time=0.097, grad_norm=33.369, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.638e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:53:25,467 (trainer:737) INFO: 19epoch:train:9001-9100batch: iter_time=1.441e-04, forward_time=0.104, loss_ctc=48.236, loss_att=48.650, acc=0.723, loss=48.526, backward_time=0.097, grad_norm=35.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.637e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:54:07,623 (trainer:737) INFO: 19epoch:train:9101-9200batch: iter_time=1.451e-04, forward_time=0.106, loss_ctc=46.564, loss_att=58.287, acc=0.701, loss=54.770, backward_time=0.097, grad_norm=41.182, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.636e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:54:50,272 (trainer:737) INFO: 19epoch:train:9201-9300batch: iter_time=1.332e-04, forward_time=0.105, loss_ctc=48.542, loss_att=49.144, acc=0.721, loss=48.964, backward_time=0.096, grad_norm=45.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.636e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 10:55:32,535 (trainer:737) INFO: 19epoch:train:9301-9400batch: iter_time=1.230e-04, forward_time=0.106, loss_ctc=46.937, loss_att=51.031, acc=0.734, loss=49.803, backward_time=0.097, grad_norm=37.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.635e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:56:14,952 (trainer:737) INFO: 19epoch:train:9401-9500batch: iter_time=1.172e-04, forward_time=0.108, loss_ctc=42.380, loss_att=42.963, acc=0.735, loss=42.788, backward_time=0.097, grad_norm=33.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.634e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 10:56:57,211 (trainer:737) INFO: 19epoch:train:9501-9600batch: iter_time=1.379e-04, forward_time=0.106, loss_ctc=48.169, loss_att=53.264, acc=0.714, loss=51.735, backward_time=0.098, grad_norm=41.942, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.633e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 10:57:39,493 (trainer:737) INFO: 19epoch:train:9601-9700batch: iter_time=1.175e-04, forward_time=0.106, loss_ctc=54.929, loss_att=60.606, acc=0.714, loss=58.903, backward_time=0.098, grad_norm=53.685, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.632e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 10:58:22,024 (trainer:737) INFO: 19epoch:train:9701-9800batch: iter_time=1.126e-04, forward_time=0.106, loss_ctc=38.741, loss_att=47.140, acc=0.746, loss=44.620, backward_time=0.097, grad_norm=31.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.631e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 10:59:04,130 (trainer:737) INFO: 19epoch:train:9801-9900batch: iter_time=1.171e-04, forward_time=0.105, loss_ctc=40.116, loss_att=47.414, acc=0.724, loss=45.225, backward_time=0.097, grad_norm=33.545, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.631e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 10:59:46,183 (trainer:737) INFO: 19epoch:train:9901-10000batch: iter_time=1.205e-04, forward_time=0.105, loss_ctc=46.029, loss_att=49.176, acc=0.712, loss=48.232, backward_time=0.097, grad_norm=39.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.630e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 10:59:50,487 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-14 11:00:09,442 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 11:00:13,029 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f654d4dbb50>)
+[gpuc02:0/16] 2024-01-14 11:00:13,030 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-14 11:00:13,033 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 11:04:53,954 (trainer:737) INFO: 19epoch:train:10001-10100batch: iter_time=2.542, forward_time=0.105, loss_ctc=41.616, loss_att=45.326, acc=0.751, loss=44.213, backward_time=0.099, grad_norm=33.596, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.629e-04, train_time=3.077
+[gpuc02:0/16] 2024-01-14 11:05:37,024 (trainer:737) INFO: 19epoch:train:10101-10200batch: iter_time=1.183e-04, forward_time=0.106, loss_ctc=56.119, loss_att=68.734, acc=0.717, loss=64.950, backward_time=0.101, grad_norm=42.735, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.628e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-14 11:06:19,301 (trainer:737) INFO: 19epoch:train:10201-10300batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=46.851, loss_att=54.085, acc=0.729, loss=51.915, backward_time=0.099, grad_norm=34.210, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.627e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:07:01,799 (trainer:737) INFO: 19epoch:train:10301-10400batch: iter_time=1.204e-04, forward_time=0.105, loss_ctc=41.832, loss_att=43.441, acc=0.747, loss=42.958, backward_time=0.099, grad_norm=33.440, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.626e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:07:44,439 (trainer:737) INFO: 19epoch:train:10401-10500batch: iter_time=1.493e-04, forward_time=0.105, loss_ctc=51.644, loss_att=61.955, acc=0.702, loss=58.862, backward_time=0.100, grad_norm=42.288, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.626e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 11:08:26,950 (trainer:737) INFO: 19epoch:train:10501-10600batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=45.757, loss_att=52.462, acc=0.720, loss=50.451, backward_time=0.099, grad_norm=44.142, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.625e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:09:09,472 (trainer:737) INFO: 19epoch:train:10601-10700batch: iter_time=1.349e-04, forward_time=0.106, loss_ctc=45.405, loss_att=50.944, acc=0.740, loss=49.282, backward_time=0.098, grad_norm=35.474, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.624e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:09:51,512 (trainer:737) INFO: 19epoch:train:10701-10800batch: iter_time=1.258e-04, forward_time=0.104, loss_ctc=40.696, loss_att=43.335, acc=0.747, loss=42.543, backward_time=0.097, grad_norm=33.029, clip=100.000, loss_scale=3.261e+34, optim_step_time=0.041, optim0_lr0=4.623e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 11:10:18,969 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 11:10:33,965 (trainer:737) INFO: 19epoch:train:10801-10900batch: iter_time=1.265e-04, forward_time=0.105, loss_ctc=58.593, loss_att=62.558, acc=0.708, loss=61.369, backward_time=0.098, grad_norm=53.990, clip=100.000, loss_scale=3.420e+34, optim_step_time=0.041, optim0_lr0=4.622e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 11:11:16,932 (trainer:737) INFO: 19epoch:train:10901-11000batch: iter_time=1.279e-04, forward_time=0.105, loss_ctc=46.980, loss_att=51.614, acc=0.745, loss=50.224, backward_time=0.098, grad_norm=37.377, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.622e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 11:11:59,547 (trainer:737) INFO: 19epoch:train:11001-11100batch: iter_time=1.295e-04, forward_time=0.104, loss_ctc=38.029, loss_att=46.943, acc=0.733, loss=44.269, backward_time=0.097, grad_norm=32.133, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.621e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 11:12:41,546 (trainer:737) INFO: 19epoch:train:11101-11200batch: iter_time=1.276e-04, forward_time=0.104, loss_ctc=38.883, loss_att=46.225, acc=0.733, loss=44.022, backward_time=0.097, grad_norm=33.457, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.620e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 11:13:09,390 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-14 11:13:28,819 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 11:13:32,517 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f663306ee90>)
+[gpuc02:0/16] 2024-01-14 11:13:32,518 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-14 11:13:32,521 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 11:17:52,786 (trainer:737) INFO: 19epoch:train:11201-11300batch: iter_time=2.535, forward_time=0.119, loss_ctc=48.313, loss_att=52.097, acc=0.732, loss=50.962, backward_time=0.099, grad_norm=40.719, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=4.619e-04, train_time=3.112
+[gpuc02:0/16] 2024-01-14 11:18:35,383 (trainer:737) INFO: 19epoch:train:11301-11400batch: iter_time=1.255e-04, forward_time=0.106, loss_ctc=54.939, loss_att=59.677, acc=0.734, loss=58.255, backward_time=0.099, grad_norm=42.109, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.618e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 11:19:17,625 (trainer:737) INFO: 19epoch:train:11401-11500batch: iter_time=1.626e-04, forward_time=0.106, loss_ctc=42.188, loss_att=57.379, acc=0.728, loss=52.821, backward_time=0.098, grad_norm=34.091, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.617e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:19:59,910 (trainer:737) INFO: 19epoch:train:11501-11600batch: iter_time=1.666e-04, forward_time=0.107, loss_ctc=47.833, loss_att=48.488, acc=0.734, loss=48.291, backward_time=0.098, grad_norm=35.765, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.617e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:20:42,120 (trainer:737) INFO: 19epoch:train:11601-11700batch: iter_time=1.556e-04, forward_time=0.106, loss_ctc=46.161, loss_att=57.333, acc=0.714, loss=53.981, backward_time=0.098, grad_norm=37.634, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.616e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:21:24,408 (trainer:737) INFO: 19epoch:train:11701-11800batch: iter_time=1.588e-04, forward_time=0.105, loss_ctc=47.972, loss_att=50.184, acc=0.718, loss=49.520, backward_time=0.097, grad_norm=43.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.615e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:22:06,736 (trainer:737) INFO: 19epoch:train:11801-11900batch: iter_time=1.485e-04, forward_time=0.106, loss_ctc=46.847, loss_att=53.595, acc=0.742, loss=51.570, backward_time=0.099, grad_norm=37.101, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.614e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:22:48,948 (trainer:737) INFO: 19epoch:train:11901-12000batch: iter_time=1.263e-04, forward_time=0.104, loss_ctc=42.093, loss_att=44.896, acc=0.736, loss=44.055, backward_time=0.099, grad_norm=33.857, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.613e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:23:31,234 (trainer:737) INFO: 19epoch:train:12001-12100batch: iter_time=1.347e-04, forward_time=0.105, loss_ctc=48.374, loss_att=54.390, acc=0.718, loss=52.585, backward_time=0.099, grad_norm=37.655, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.612e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:24:13,599 (trainer:737) INFO: 19epoch:train:12101-12200batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=55.534, loss_att=60.037, acc=0.725, loss=58.686, backward_time=0.099, grad_norm=54.203, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.612e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:24:56,142 (trainer:737) INFO: 19epoch:train:12201-12300batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=38.264, loss_att=47.943, acc=0.748, loss=45.040, backward_time=0.099, grad_norm=30.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.611e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:25:38,261 (trainer:737) INFO: 19epoch:train:12301-12400batch: iter_time=1.247e-04, forward_time=0.104, loss_ctc=39.856, loss_att=47.124, acc=0.733, loss=44.944, backward_time=0.098, grad_norm=31.701, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.610e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 11:26:20,878 (trainer:737) INFO: 19epoch:train:12401-12500batch: iter_time=1.158e-04, forward_time=0.105, loss_ctc=46.114, loss_att=48.159, acc=0.727, loss=47.546, backward_time=0.098, grad_norm=40.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.609e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-14 11:26:27,987 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-14 11:26:47,002 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 11:26:50,604 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f70cbb880>)
+[gpuc02:0/16] 2024-01-14 11:26:50,604 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-14 11:26:50,608 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 11:31:36,683 (trainer:737) INFO: 19epoch:train:12501-12600batch: iter_time=2.592, forward_time=0.106, loss_ctc=40.771, loss_att=43.137, acc=0.752, loss=42.427, backward_time=0.098, grad_norm=33.756, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.608e-04, train_time=3.158
+[gpuc02:0/16] 2024-01-14 11:32:19,151 (trainer:737) INFO: 19epoch:train:12601-12700batch: iter_time=1.430e-04, forward_time=0.106, loss_ctc=55.914, loss_att=67.745, acc=0.720, loss=64.195, backward_time=0.099, grad_norm=42.311, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.608e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-14 11:33:01,420 (trainer:737) INFO: 19epoch:train:12701-12800batch: iter_time=1.392e-04, forward_time=0.104, loss_ctc=46.894, loss_att=54.452, acc=0.729, loss=52.185, backward_time=0.098, grad_norm=33.956, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.607e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:33:43,933 (trainer:737) INFO: 19epoch:train:12801-12900batch: iter_time=1.457e-04, forward_time=0.104, loss_ctc=42.263, loss_att=43.327, acc=0.746, loss=43.008, backward_time=0.098, grad_norm=33.542, clip=100.000, loss_scale=2.804e+34, optim_step_time=0.041, optim0_lr0=4.606e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:34:26,192 (trainer:737) INFO: 19epoch:train:12901-13000batch: iter_time=1.624e-04, forward_time=0.105, loss_ctc=51.335, loss_att=61.480, acc=0.704, loss=58.437, backward_time=0.098, grad_norm=41.403, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.605e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:35:08,335 (trainer:737) INFO: 19epoch:train:13001-13100batch: iter_time=1.419e-04, forward_time=0.104, loss_ctc=45.808, loss_att=51.407, acc=0.722, loss=49.728, backward_time=0.098, grad_norm=42.763, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.604e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 11:35:50,891 (trainer:737) INFO: 19epoch:train:13101-13200batch: iter_time=1.423e-04, forward_time=0.104, loss_ctc=45.392, loss_att=51.292, acc=0.738, loss=49.522, backward_time=0.098, grad_norm=35.667, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.604e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-14 11:36:33,036 (trainer:737) INFO: 19epoch:train:13201-13300batch: iter_time=1.495e-04, forward_time=0.104, loss_ctc=40.784, loss_att=42.890, acc=0.747, loss=42.258, backward_time=0.098, grad_norm=33.439, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.603e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 11:37:15,308 (trainer:737) INFO: 19epoch:train:13301-13400batch: iter_time=1.804e-04, forward_time=0.105, loss_ctc=56.720, loss_att=59.911, acc=0.709, loss=58.954, backward_time=0.098, grad_norm=53.775, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.602e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:37:57,614 (trainer:737) INFO: 19epoch:train:13401-13500batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=46.957, loss_att=51.355, acc=0.747, loss=50.036, backward_time=0.098, grad_norm=37.793, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.601e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:38:39,967 (trainer:737) INFO: 19epoch:train:13501-13600batch: iter_time=1.271e-04, forward_time=0.103, loss_ctc=37.828, loss_att=46.530, acc=0.735, loss=43.919, backward_time=0.097, grad_norm=31.855, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.600e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-14 11:39:21,954 (trainer:737) INFO: 19epoch:train:13601-13700batch: iter_time=1.408e-04, forward_time=0.102, loss_ctc=38.736, loss_att=46.022, acc=0.736, loss=43.836, backward_time=0.097, grad_norm=34.225, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.599e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-14 11:39:48,894 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-14 11:40:08,126 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-14 11:40:12,076 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f5f3fdb2c20>)
+[gpuc02:0/16] 2024-01-14 11:40:12,077 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-14 11:40:12,080 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-14 11:44:41,199 (trainer:737) INFO: 19epoch:train:13701-13800batch: iter_time=2.539, forward_time=0.105, loss_ctc=46.994, loss_att=50.726, acc=0.738, loss=49.607, backward_time=0.098, grad_norm=38.425, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.042, optim0_lr0=4.599e-04, train_time=3.192
+[gpuc02:0/16] 2024-01-14 11:45:24,125 (trainer:737) INFO: 19epoch:train:13801-13900batch: iter_time=1.478e-04, forward_time=0.106, loss_ctc=53.670, loss_att=64.293, acc=0.715, loss=61.106, backward_time=0.097, grad_norm=42.088, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.598e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-14 11:46:06,307 (trainer:737) INFO: 19epoch:train:13901-14000batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=42.212, loss_att=57.966, acc=0.723, loss=53.240, backward_time=0.097, grad_norm=35.134, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=4.597e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-14 11:46:14,241 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc02:0/16] 2024-01-14 11:46:48,456 (trainer:737) INFO: 19epoch:train:14001-14100batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=47.587, loss_att=48.780, acc=0.723, loss=48.422, backward_time=0.097, grad_norm=35.758, clip=100.000, loss_scale=2.455e+34, optim_step_time=0.041, optim0_lr0=4.596e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-14 11:47:30,638 (trainer:737) INFO: 19epoch:train:14101-14200batch: iter_time=1.471e-04, forward_time=0.104, loss_ctc=46.218, loss_att=58.441, acc=0.703, loss=54.774, backward_time=0.097, grad_norm=37.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=4.595e-04, train_time=0.422
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245918:1246033 [0] transport/net_ib.cc:1237 NCCL WARN NET/IB : Got completion from peer 172.28.23.204<46958> with error 12, opcode 129, len 16, vendor err 129
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port error
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245923:1246017 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245918:1246019 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245919:1246018 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245922:1246021 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245924:1246016 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245920:1246020 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245925:1246023 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc02:1245921:1246022 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : port active
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854595:1854688 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854597:1854692 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854601:1854691 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854602:1854693 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854598:1854687 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854596:1854694 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854599:1854690 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+
+gpuc04:1854600:1854689 [0] transport/net_ib.cc:93 NCCL WARN NET/IB : Got async event : GID table change
+gpuc02:1245918:1246033 [0] NCCL INFO include/net.h:35 -> 6
+gpuc02:1245918:1246033 [0] NCCL INFO transport/net.cc:1034 -> 6
+gpuc02:1245918:1246033 [0] NCCL INFO proxy.cc:520 -> 6
+gpuc02:1245918:1246033 [0] NCCL INFO proxy.cc:684 -> 6 [Proxy Thread]
+Process SpawnProcess-1:
+Traceback (most recent call last):
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
+    self.run()
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/multiprocessing/process.py", line 108, in run
+    self._target(*self._args, **self._kwargs)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/tasks/abs_task.py", line 1393, in main_worker
+    cls.trainer.run(
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/train/trainer.py", line 290, in run
+    all_steps_are_invalid = cls.train_one_epoch(
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/train/trainer.py", line 615, in train_one_epoch
+    stats, weight = recursive_average(stats, weight, distributed)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/torch_utils/recursive_op.py", line 41, in recursive_average
+    obj = recursive_sum(obj, weight, distributed)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/torch_utils/recursive_op.py", line 13, in recursive_sum
+    return {k: recursive_sum(v, weight, distributed) for k, v in obj.items()}
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/torch_utils/recursive_op.py", line 13, in <dictcomp>
+    return {k: recursive_sum(v, weight, distributed) for k, v in obj.items()}
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/torch_utils/recursive_op.py", line 18, in recursive_sum
+    torch.distributed.all_reduce(obj, op=ReduceOp.SUM)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1541, in all_reduce
+    work.wait()
+RuntimeError: NCCL error: remote process exited or there was a network error, NCCL version 2.14.3
+ncclRemoteError: A call failed possibly due to a network error or a remote process exiting prematurely.
+Last error:
+NET/IB : Got async event : GID table change
+gpuc02:1245918:1245918 [0] NCCL INFO comm 0x543ab2d0 rank 0 nranks 16 cudaDev 0 busId 7000 - Abort COMPLETE
+Traceback (most recent call last):
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py", line 23, in <module>
+    main()
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py", line 19, in main
+    S2TTask.main(cmd=cmd)
+  File "/scratch/bbjs/peng6/espnet-whisper-public/espnet2/tasks/abs_task.py", line 1134, in main
+    while not ProcessContext(processes, error_queues).join():
+  File "/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 149, in join
+    raise ProcessExitedException(
+torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with exit code 1
+srun: error: gpuc02: task 0: Exited with exit code 1
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.3.log b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.3.log
new file mode 100644
index 0000000000000000000000000000000000000000..1b1bb4bfcd7195aad6464567ad1fbba7fc720c03
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.3.log
@@ -0,0 +1,5431 @@
+# Running on gpuc02.delta.ncsa.illinois.edu
+# Started at Thu Jan 11 23:24:30 CST 2024
+# SLURMD_NODENAME=gpuc02
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=128
+# SLURM_CPUS_PER_TASK=128
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GPUS_ON_NODE=8
+# SLURM_GTIDS=0
+# SLURM_JOBID=2855139
+# SLURM_JOB_ACCOUNT=bbjs-delta-gpu
+# SLURM_JOB_CPUS_PER_NODE='128(x2)'
+# SLURM_JOB_END_TIME=1705209848
+# SLURM_JOB_GID=202
+# SLURM_JOB_GPUS=0,1,2,3,4,5,6,7
+# SLURM_JOB_ID=2855139
+# SLURM_JOB_NAME=exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
+# SLURM_JOB_NODELIST='gpuc[02,05]'
+# SLURM_JOB_NUM_NODES=2
+# SLURM_JOB_PARTITION=gpuA100x8
+# SLURM_JOB_QOS=bbjs-delta-gpu
+# SLURM_JOB_START_TIME=1705037048
+# SLURM_JOB_UID=68077
+# SLURM_JOB_USER=peng6
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_NODE=2000000
+# SLURM_NNODES=2
+# SLURM_NODEID=0
+# SLURM_NODELIST='gpuc[02,05]'
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1
+# SLURM_SUBMIT_HOST=dt-login01.delta.ncsa.illinois.edu
+# SLURM_TASKS_PER_NODE='1(x2)'
+# SLURM_TASK_PID=191473
+# SLURM_TOPOLOGY_ADDR=ss00.ss05.gpuc02
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9984:109
+# srun --export=ALL python3 -m espnet2.bin.s2t_train --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_76e4f837-19da-4a99-b112-53c1402d3c83 
+/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/spe/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_76e4f837-19da-4a99-b112-53c1402d3c83
+ech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_76e4f837-19da-4a99-b112-53c1402d3c83
+[gpuc02:0/16] 2024-01-11 23:27:36,412 (distributed_c10d:319) INFO: Added key: store_based_barrier_key:1 to store for rank: 0
+[gpuc02:0/16] 2024-01-11 23:27:36,586 (distributed_c10d:353) INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
+[gpuc02:0/16] 2024-01-11 23:27:36,731 (s2t:464) INFO: Vocabulary size: 50002
+[gpuc02:0/16] 2024-01-11 23:27:45,044 (abs_task:1231) INFO: pytorch.version=1.13.1, cuda.available=True, cudnn.version=8500, cudnn.benchmark=False, cudnn.deterministic=True
+[gpuc02:0/16] 2024-01-11 23:27:45,050 (abs_task:1232) INFO: Model structure:
+ESPnetS2TModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=512, win_length=400, hop_length=160, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=512, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=1, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=1, axis=time)
+  )
+  (normalize): GlobalMVN(stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz, norm_means=True, norm_vars=True)
+  (encoder): EBranchformerEncoder(
+    (embed): Conv2dSubsampling(
+      (conv): Sequential(
+        (0): Conv2d(1, 384, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=7296, out_features=384, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.05, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (1): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (2): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (3): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (4): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (5): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+  )
+  (decoder): TransformerDecoder(
+    (embed): Sequential(
+      (0): Embedding(50002, 384)
+      (1): PositionalEncoding(
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+    (output_layer): Linear(in_features=384, out_features=50002, bias=True)
+    (decoders): MultiSequential(
+      (0): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (1): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (2): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (3): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (4): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (5): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=384, out_features=50002, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetS2TModel
+    Total Number of model parameters: 101.18 M
+    Number of trainable parameters: 101.18 M (100.0%)
+    Size: 404.73 MB
+    Type: torch.float32
+[gpuc02:0/16] 2024-01-11 23:27:45,050 (abs_task:1235) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.9, 0.98]
+    capturable: False
+    eps: 1e-06
+    foreach: None
+    initial_lr: 0.001
+    lr: 3.3333333333333334e-09
+    maximize: False
+    weight_decay: 0.0
+)
+[gpuc02:0/16] 2024-01-11 23:27:45,050 (abs_task:1236) INFO: Scheduler: PiecewiseLinearWarmupLR(warmup_steps_list=[0, 30000, 60000], warmup_lr_list=[0.0, 0.0001, 0.001])
+[gpuc02:0/16] 2024-01-11 23:27:45,051 (abs_task:1245) INFO: Saving the configuration in exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
+[gpuc02:0/16] 2024-01-11 23:27:51,640 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-11 23:27:52,607 (abs_task:1616) INFO: [valid] dataset:
+ESPnetDataset(
+  speech: {"path": "dump/raw/dev_v3/wav.scp", "type": "kaldi_ark"}
+  text_prev: {"path": "dump/raw/dev_v3/text.prev", "type": "text"}
+  text_ctc: {"path": "dump/raw/dev_v3/text.ctc", "type": "text"}
+  text: {"path": "dump/raw/dev_v3/text", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f43b1c7f940>)
+[gpuc02:0/16] 2024-01-11 23:27:52,607 (abs_task:1617) INFO: [valid] Batch sampler: UnsortedBatchSampler(N-batch=4671, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/valid/speech_shape, 
+[gpuc02:0/16] 2024-01-11 23:27:52,609 (abs_task:1618) INFO: [valid] mini-batch sizes summary: N-batch=4671, mean=256.0, min=256, max=257
+gpuc02:191629:191629 [0] NCCL INFO Bootstrap : Using eth0:172.28.23.202<0>
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+NCCL version 2.14.3+cuda11.7
+[gpuc02:0/16] 2024-01-11 23:28:00,310 (trainer:284) INFO: 1/45epoch started
+[gpuc02:0/16] 2024-01-11 23:28:00,359 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-11 23:28:19,766 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-11 23:28:23,614 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42df3eea40>)
+[gpuc02:0/16] 2024-01-11 23:28:23,614 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-11 23:28:23,618 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
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+gpuc02:191633:191721 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3
+gpuc02:191633:191721 [4] NCCL INFO Channel 00/0 : 4[88000] -> 5[8b000] via P2P/IPC/read
+gpuc02:191633:191721 [4] NCCL INFO Channel 01/0 : 4[88000] -> 5[8b000] via P2P/IPC/read
+gpuc02:191633:191721 [4] NCCL INFO Connected all rings
+gpuc02:191633:191721 [4] NCCL INFO Channel 00/0 : 4[88000] -> 3[4c000] via P2P/IPC/read
+gpuc02:191633:191721 [4] NCCL INFO Channel 01/0 : 4[88000] -> 3[4c000] via P2P/IPC/read
+gpuc02:191633:191721 [4] NCCL INFO Connected all trees
+gpuc02:191633:191721 [4] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+gpuc02:191633:191721 [4] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
+gpuc02:191633:191721 [4] NCCL INFO comm 0x1125adf0 rank 4 nranks 16 cudaDev 4 busId 88000 - Init COMPLETE
+[gpuc02:0/16] 2024-01-11 23:32:21,974 (distributed:1027) INFO: Reducer buckets have been rebuilt in this iteration.
+[gpuc02:0/16] 2024-01-11 23:33:03,745 (trainer:737) INFO: 1epoch:train:1-100batch: iter_time=2.391, forward_time=0.183, loss_ctc=7.457e+03, loss_att=391.280, acc=3.121e-05, loss=2.511e+03, backward_time=0.108, grad_norm=4.391e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.030, optim0_lr0=1.717e-07, train_time=3.033
+[gpuc02:0/16] 2024-01-11 23:33:44,873 (trainer:737) INFO: 1epoch:train:101-200batch: iter_time=1.106e-04, forward_time=0.103, loss_ctc=6.975e+03, loss_att=397.791, acc=2.337e-05, loss=2.371e+03, backward_time=0.098, grad_norm=8.599e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=5.050e-07, train_time=0.412
+[gpuc02:0/16] 2024-01-11 23:34:26,073 (trainer:737) INFO: 1epoch:train:201-300batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=4.555e+03, loss_att=431.826, acc=3.918e-05, loss=1.669e+03, backward_time=0.099, grad_norm=1.058e+04, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=8.383e-07, train_time=0.412
+[gpuc02:0/16] 2024-01-11 23:35:08,222 (trainer:737) INFO: 1epoch:train:301-400batch: iter_time=1.026e-04, forward_time=0.103, loss_ctc=1.454e+03, loss_att=373.331, acc=2.875e-05, loss=697.422, backward_time=0.097, grad_norm=4.358e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=1.172e-06, train_time=0.421
+[gpuc02:0/16] 2024-01-11 23:35:51,519 (trainer:737) INFO: 1epoch:train:401-500batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=804.950, loss_att=399.800, acc=2.928e-05, loss=521.345, backward_time=0.097, grad_norm=2.016e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=1.505e-06, train_time=0.433
+[gpuc02:0/16] 2024-01-11 23:36:32,432 (trainer:737) INFO: 1epoch:train:501-600batch: iter_time=1.198e-04, forward_time=0.104, loss_ctc=712.421, loss_att=398.908, acc=4.393e-05, loss=492.962, backward_time=0.098, grad_norm=1.678e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=1.838e-06, train_time=0.409
+[gpuc02:0/16] 2024-01-11 23:37:13,339 (trainer:737) INFO: 1epoch:train:601-700batch: iter_time=1.115e-04, forward_time=0.104, loss_ctc=635.638, loss_att=394.027, acc=6.123e-05, loss=466.510, backward_time=0.098, grad_norm=1.494e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=2.172e-06, train_time=0.409
+[gpuc02:0/16] 2024-01-11 23:37:54,079 (trainer:737) INFO: 1epoch:train:701-800batch: iter_time=1.131e-04, forward_time=0.104, loss_ctc=556.085, loss_att=344.927, acc=6.498e-05, loss=408.274, backward_time=0.096, grad_norm=1.338e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=2.505e-06, train_time=0.407
+[gpuc02:0/16] 2024-01-11 23:38:34,913 (trainer:737) INFO: 1epoch:train:801-900batch: iter_time=1.114e-04, forward_time=0.104, loss_ctc=566.535, loss_att=405.032, acc=1.454e-04, loss=453.483, backward_time=0.097, grad_norm=1.130e+03, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=2.838e-06, train_time=0.408
+[gpuc02:0/16] 2024-01-11 23:39:15,664 (trainer:737) INFO: 1epoch:train:901-1000batch: iter_time=1.176e-04, forward_time=0.104, loss_ctc=507.043, loss_att=375.760, acc=3.569e-04, loss=415.145, backward_time=0.097, grad_norm=972.188, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.028, optim0_lr0=3.172e-06, train_time=0.407
+[gpuc02:0/16] 2024-01-11 23:39:56,479 (trainer:737) INFO: 1epoch:train:1001-1100batch: iter_time=1.188e-04, forward_time=0.104, loss_ctc=486.487, loss_att=389.476, acc=0.004, loss=418.579, backward_time=0.097, grad_norm=795.188, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=3.505e-06, train_time=0.408
+[gpuc02:0/16] 2024-01-11 23:40:37,367 (trainer:737) INFO: 1epoch:train:1101-1200batch: iter_time=1.075e-04, forward_time=0.104, loss_ctc=471.975, loss_att=392.589, acc=0.021, loss=416.405, backward_time=0.097, grad_norm=622.823, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=3.838e-06, train_time=0.409
+[gpuc02:0/16] 2024-01-11 23:41:12,859 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-11 23:41:32,927 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-11 23:41:36,867 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42df3ed1e0>)
+[gpuc02:0/16] 2024-01-11 23:41:36,867 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-11 23:41:36,870 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-11 23:45:55,227 (trainer:737) INFO: 1epoch:train:1201-1300batch: iter_time=2.438, forward_time=0.104, loss_ctc=424.154, loss_att=375.678, acc=0.040, loss=390.221, backward_time=0.097, grad_norm=489.343, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=4.172e-06, train_time=3.178
+[gpuc02:0/16] 2024-01-11 23:46:36,810 (trainer:737) INFO: 1epoch:train:1301-1400batch: iter_time=1.171e-04, forward_time=0.106, loss_ctc=424.779, loss_att=425.098, acc=0.048, loss=425.002, backward_time=0.099, grad_norm=353.790, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=4.505e-06, train_time=0.416
+[gpuc02:0/16] 2024-01-11 23:47:18,393 (trainer:737) INFO: 1epoch:train:1401-1500batch: iter_time=1.242e-04, forward_time=0.106, loss_ctc=398.299, loss_att=406.543, acc=0.062, loss=404.070, backward_time=0.099, grad_norm=277.497, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=4.838e-06, train_time=0.416
+[gpuc02:0/16] 2024-01-11 23:47:59,698 (trainer:737) INFO: 1epoch:train:1501-1600batch: iter_time=1.139e-04, forward_time=0.104, loss_ctc=356.666, loss_att=373.747, acc=0.056, loss=368.623, backward_time=0.098, grad_norm=215.948, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=5.172e-06, train_time=0.413
+[gpuc02:0/16] 2024-01-11 23:48:41,037 (trainer:737) INFO: 1epoch:train:1601-1700batch: iter_time=1.191e-04, forward_time=0.105, loss_ctc=372.536, loss_att=387.160, acc=0.051, loss=382.773, backward_time=0.099, grad_norm=161.251, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=5.505e-06, train_time=0.413
+[gpuc02:0/16] 2024-01-11 23:49:22,295 (trainer:737) INFO: 1epoch:train:1701-1800batch: iter_time=1.206e-04, forward_time=0.105, loss_ctc=333.016, loss_att=356.025, acc=0.046, loss=349.122, backward_time=0.098, grad_norm=132.581, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=5.838e-06, train_time=0.412
+[gpuc02:0/16] 2024-01-11 23:50:05,396 (trainer:737) INFO: 1epoch:train:1801-1900batch: iter_time=1.306e-04, forward_time=0.105, loss_ctc=339.241, loss_att=363.112, acc=0.051, loss=355.951, backward_time=0.098, grad_norm=118.765, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.030, optim0_lr0=6.172e-06, train_time=0.431
+[gpuc02:0/16] 2024-01-11 23:50:49,382 (trainer:737) INFO: 1epoch:train:1901-2000batch: iter_time=1.241e-04, forward_time=0.106, loss_ctc=332.506, loss_att=360.393, acc=0.046, loss=352.026, backward_time=0.099, grad_norm=98.719, clip=100.000, loss_scale=6.554e+04, optim_step_time=0.029, optim0_lr0=6.505e-06, train_time=0.440
+[gpuc02:0/16] 2024-01-11 23:51:31,026 (trainer:737) INFO: 1epoch:train:2001-2100batch: iter_time=1.311e-04, forward_time=0.105, loss_ctc=319.152, loss_att=340.797, acc=0.048, loss=334.304, backward_time=0.098, grad_norm=87.736, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=6.838e-06, train_time=0.416
+[gpuc02:0/16] 2024-01-11 23:52:12,417 (trainer:737) INFO: 1epoch:train:2101-2200batch: iter_time=1.397e-04, forward_time=0.106, loss_ctc=317.378, loss_att=334.206, acc=0.053, loss=329.157, backward_time=0.098, grad_norm=80.259, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.030, optim0_lr0=7.172e-06, train_time=0.414
+[gpuc02:0/16] 2024-01-11 23:52:53,725 (trainer:737) INFO: 1epoch:train:2201-2300batch: iter_time=1.331e-04, forward_time=0.105, loss_ctc=307.266, loss_att=332.630, acc=0.048, loss=325.021, backward_time=0.098, grad_norm=72.657, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.030, optim0_lr0=7.505e-06, train_time=0.413
+[gpuc02:0/16] 2024-01-11 23:53:35,252 (trainer:737) INFO: 1epoch:train:2301-2400batch: iter_time=1.325e-04, forward_time=0.107, loss_ctc=337.023, loss_att=360.536, acc=0.059, loss=353.482, backward_time=0.099, grad_norm=84.253, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=7.838e-06, train_time=0.415
+[gpuc02:0/16] 2024-01-11 23:54:16,710 (trainer:737) INFO: 1epoch:train:2401-2500batch: iter_time=1.176e-04, forward_time=0.106, loss_ctc=326.057, loss_att=342.688, acc=0.067, loss=337.699, backward_time=0.099, grad_norm=75.545, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=8.172e-06, train_time=0.414
+[gpuc02:0/16] 2024-01-11 23:54:19,201 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-11 23:54:39,081 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-11 23:54:42,742 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42df3eed40>)
+[gpuc02:0/16] 2024-01-11 23:54:42,742 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-11 23:54:42,745 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-11 23:59:13,438 (trainer:737) INFO: 1epoch:train:2501-2600batch: iter_time=2.373, forward_time=0.106, loss_ctc=305.572, loss_att=313.952, acc=0.084, loss=311.438, backward_time=0.098, grad_norm=77.911, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=8.505e-06, train_time=2.967
+[gpuc02:0/16] 2024-01-11 23:59:54,888 (trainer:737) INFO: 1epoch:train:2601-2700batch: iter_time=1.114e-04, forward_time=0.105, loss_ctc=301.482, loss_att=317.152, acc=0.088, loss=312.451, backward_time=0.097, grad_norm=66.634, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=8.838e-06, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:00:36,608 (trainer:737) INFO: 1epoch:train:2701-2800batch: iter_time=1.088e-04, forward_time=0.106, loss_ctc=319.267, loss_att=337.943, acc=0.098, loss=332.340, backward_time=0.098, grad_norm=70.096, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=9.172e-06, train_time=0.417
+[gpuc02:0/16] 2024-01-12 00:01:17,978 (trainer:737) INFO: 1epoch:train:2801-2900batch: iter_time=1.437e-04, forward_time=0.105, loss_ctc=280.464, loss_att=288.349, acc=0.102, loss=285.983, backward_time=0.097, grad_norm=61.955, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=9.505e-06, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:01:59,297 (trainer:737) INFO: 1epoch:train:2901-3000batch: iter_time=1.512e-04, forward_time=0.106, loss_ctc=300.902, loss_att=312.886, acc=0.098, loss=309.291, backward_time=0.097, grad_norm=71.501, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=9.838e-06, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:02:40,767 (trainer:737) INFO: 1epoch:train:3001-3100batch: iter_time=1.630e-04, forward_time=0.106, loss_ctc=303.073, loss_att=308.899, acc=0.099, loss=307.151, backward_time=0.097, grad_norm=67.366, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.017e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:03:22,186 (trainer:737) INFO: 1epoch:train:3101-3200batch: iter_time=1.593e-04, forward_time=0.104, loss_ctc=290.971, loss_att=305.513, acc=0.108, loss=301.151, backward_time=0.097, grad_norm=77.801, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.050e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:04:03,233 (trainer:737) INFO: 1epoch:train:3201-3300batch: iter_time=1.471e-04, forward_time=0.104, loss_ctc=256.728, loss_att=267.192, acc=0.109, loss=264.053, backward_time=0.096, grad_norm=58.156, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.084e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 00:04:44,887 (trainer:737) INFO: 1epoch:train:3301-3400batch: iter_time=1.519e-04, forward_time=0.106, loss_ctc=302.670, loss_att=311.523, acc=0.103, loss=308.867, backward_time=0.098, grad_norm=68.632, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.117e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 00:05:27,553 (trainer:737) INFO: 1epoch:train:3401-3500batch: iter_time=1.319e-04, forward_time=0.105, loss_ctc=279.956, loss_att=288.233, acc=0.109, loss=285.750, backward_time=0.097, grad_norm=61.635, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.151e-05, train_time=0.426
+[gpuc02:0/16] 2024-01-12 00:06:10,921 (trainer:737) INFO: 1epoch:train:3501-3600batch: iter_time=1.507e-04, forward_time=0.106, loss_ctc=284.730, loss_att=298.079, acc=0.110, loss=294.075, backward_time=0.097, grad_norm=64.143, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.184e-05, train_time=0.433
+[gpuc02:0/16] 2024-01-12 00:06:52,434 (trainer:737) INFO: 1epoch:train:3601-3700batch: iter_time=1.345e-04, forward_time=0.106, loss_ctc=301.571, loss_att=302.401, acc=0.108, loss=302.152, backward_time=0.097, grad_norm=68.867, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.029, optim0_lr0=1.217e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 00:07:16,122 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 00:07:35,167 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 00:07:38,818 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40b00228c0>)
+[gpuc02:0/16] 2024-01-12 00:07:38,819 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 00:07:38,822 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 00:11:44,356 (trainer:737) INFO: 1epoch:train:3701-3800batch: iter_time=2.327, forward_time=0.105, loss_ctc=290.290, loss_att=290.519, acc=0.115, loss=290.450, backward_time=0.097, grad_norm=70.353, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.028, optim0_lr0=1.251e-05, train_time=2.919
+[gpuc02:0/16] 2024-01-12 00:12:26,207 (trainer:737) INFO: 1epoch:train:3801-3900batch: iter_time=8.860e-05, forward_time=0.106, loss_ctc=301.305, loss_att=335.660, acc=0.103, loss=325.354, backward_time=0.098, grad_norm=74.580, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.028, optim0_lr0=1.284e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 00:13:07,770 (trainer:737) INFO: 1epoch:train:3901-4000batch: iter_time=1.004e-04, forward_time=0.106, loss_ctc=297.353, loss_att=318.866, acc=0.109, loss=312.412, backward_time=0.098, grad_norm=71.092, clip=100.000, loss_scale=1.311e+05, optim_step_time=0.028, optim0_lr0=1.317e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 00:13:49,241 (trainer:737) INFO: 1epoch:train:4001-4100batch: iter_time=1.059e-04, forward_time=0.104, loss_ctc=267.328, loss_att=292.179, acc=0.115, loss=284.724, backward_time=0.097, grad_norm=65.389, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.350e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:14:30,591 (trainer:737) INFO: 1epoch:train:4101-4200batch: iter_time=9.962e-05, forward_time=0.105, loss_ctc=299.345, loss_att=313.846, acc=0.112, loss=309.496, backward_time=0.097, grad_norm=77.511, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.384e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:15:11,982 (trainer:737) INFO: 1epoch:train:4201-4300batch: iter_time=9.379e-05, forward_time=0.107, loss_ctc=265.865, loss_att=290.893, acc=0.112, loss=283.385, backward_time=0.097, grad_norm=70.585, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.417e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:15:53,319 (trainer:737) INFO: 1epoch:train:4301-4400batch: iter_time=1.036e-04, forward_time=0.105, loss_ctc=287.046, loss_att=306.420, acc=0.120, loss=300.608, backward_time=0.098, grad_norm=79.057, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.451e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:16:34,688 (trainer:737) INFO: 1epoch:train:4401-4500batch: iter_time=9.689e-05, forward_time=0.105, loss_ctc=279.985, loss_att=305.752, acc=0.115, loss=298.022, backward_time=0.098, grad_norm=77.380, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.484e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:17:15,972 (trainer:737) INFO: 1epoch:train:4501-4600batch: iter_time=1.032e-04, forward_time=0.105, loss_ctc=278.390, loss_att=295.806, acc=0.120, loss=290.581, backward_time=0.097, grad_norm=82.742, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.517e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:17:57,115 (trainer:737) INFO: 1epoch:train:4601-4700batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=281.668, loss_att=292.218, acc=0.121, loss=289.053, backward_time=0.097, grad_norm=78.880, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.551e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 00:18:38,328 (trainer:737) INFO: 1epoch:train:4701-4800batch: iter_time=9.680e-05, forward_time=0.104, loss_ctc=270.332, loss_att=292.751, acc=0.115, loss=286.025, backward_time=0.097, grad_norm=74.419, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.584e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:19:19,766 (trainer:737) INFO: 1epoch:train:4801-4900batch: iter_time=9.923e-05, forward_time=0.105, loss_ctc=300.938, loss_att=322.525, acc=0.112, loss=316.049, backward_time=0.098, grad_norm=90.088, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.617e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:20:00,997 (trainer:737) INFO: 1epoch:train:4901-5000batch: iter_time=9.882e-05, forward_time=0.105, loss_ctc=291.876, loss_att=306.462, acc=0.119, loss=302.086, backward_time=0.098, grad_norm=81.905, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.650e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:20:03,930 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 00:20:23,189 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 00:20:26,873 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42d05b2ef0>)
+[gpuc02:0/16] 2024-01-12 00:20:26,873 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 00:20:26,876 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 00:24:46,867 (trainer:737) INFO: 1epoch:train:5001-5100batch: iter_time=2.420, forward_time=0.104, loss_ctc=280.728, loss_att=288.688, acc=0.130, loss=286.300, backward_time=0.097, grad_norm=78.423, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.684e-05, train_time=2.858
+[gpuc02:0/16] 2024-01-12 00:25:28,407 (trainer:737) INFO: 1epoch:train:5101-5200batch: iter_time=1.073e-04, forward_time=0.104, loss_ctc=281.317, loss_att=294.482, acc=0.117, loss=290.533, backward_time=0.097, grad_norm=72.719, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.717e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 00:26:10,000 (trainer:737) INFO: 1epoch:train:5201-5300batch: iter_time=1.020e-04, forward_time=0.104, loss_ctc=295.537, loss_att=311.284, acc=0.121, loss=306.560, backward_time=0.098, grad_norm=78.115, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.751e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 00:26:51,269 (trainer:737) INFO: 1epoch:train:5301-5400batch: iter_time=1.100e-04, forward_time=0.103, loss_ctc=262.903, loss_att=268.381, acc=0.133, loss=266.738, backward_time=0.097, grad_norm=76.070, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.784e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:27:32,598 (trainer:737) INFO: 1epoch:train:5401-5500batch: iter_time=1.097e-04, forward_time=0.103, loss_ctc=285.521, loss_att=294.414, acc=0.126, loss=291.746, backward_time=0.097, grad_norm=80.355, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.817e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:28:13,932 (trainer:737) INFO: 1epoch:train:5501-5600batch: iter_time=1.020e-04, forward_time=0.104, loss_ctc=291.887, loss_att=293.730, acc=0.132, loss=293.177, backward_time=0.097, grad_norm=103.555, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.850e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:28:55,211 (trainer:737) INFO: 1epoch:train:5601-5700batch: iter_time=1.041e-04, forward_time=0.103, loss_ctc=280.958, loss_att=290.747, acc=0.132, loss=287.811, backward_time=0.097, grad_norm=82.859, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.884e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:29:40,410 (trainer:737) INFO: 1epoch:train:5701-5800batch: iter_time=1.009e-04, forward_time=0.126, loss_ctc=249.403, loss_att=255.738, acc=0.139, loss=253.837, backward_time=0.101, grad_norm=83.337, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.917e-05, train_time=0.451
+[gpuc02:0/16] 2024-01-12 00:30:21,945 (trainer:737) INFO: 1epoch:train:5801-5900batch: iter_time=1.045e-04, forward_time=0.103, loss_ctc=293.258, loss_att=296.392, acc=0.132, loss=295.452, backward_time=0.097, grad_norm=100.952, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.951e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 00:31:03,047 (trainer:737) INFO: 1epoch:train:5901-6000batch: iter_time=1.007e-04, forward_time=0.103, loss_ctc=271.899, loss_att=275.249, acc=0.133, loss=274.244, backward_time=0.097, grad_norm=101.109, clip=100.000, loss_scale=2.621e+05, optim_step_time=0.028, optim0_lr0=1.984e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 00:31:45,714 (trainer:737) INFO: 1epoch:train:6001-6100batch: iter_time=1.010e-04, forward_time=0.118, loss_ctc=275.676, loss_att=281.359, acc=0.133, loss=279.654, backward_time=0.097, grad_norm=101.605, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.028, optim0_lr0=2.017e-05, train_time=0.426
+[gpuc02:0/16] 2024-01-12 00:32:27,167 (trainer:737) INFO: 1epoch:train:6101-6200batch: iter_time=1.033e-04, forward_time=0.105, loss_ctc=293.458, loss_att=285.191, acc=0.136, loss=287.671, backward_time=0.098, grad_norm=107.615, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.028, optim0_lr0=2.051e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:33:02,510 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 00:33:21,588 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 00:33:25,322 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f412390bd30>)
+[gpuc02:0/16] 2024-01-12 00:33:25,323 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 00:33:25,326 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 00:38:47,825 (trainer:737) INFO: 1epoch:train:6201-6300batch: iter_time=3.368, forward_time=0.104, loss_ctc=285.711, loss_att=275.120, acc=0.141, loss=278.297, backward_time=0.098, grad_norm=103.901, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.028, optim0_lr0=2.084e-05, train_time=3.806
+[gpuc02:0/16] 2024-01-12 00:39:29,169 (trainer:737) INFO: 1epoch:train:6301-6400batch: iter_time=1.585e-04, forward_time=0.106, loss_ctc=294.820, loss_att=297.920, acc=0.126, loss=296.990, backward_time=0.099, grad_norm=106.253, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.117e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:40:10,973 (trainer:737) INFO: 1epoch:train:6401-6500batch: iter_time=1.508e-04, forward_time=0.105, loss_ctc=287.768, loss_att=295.420, acc=0.133, loss=293.125, backward_time=0.098, grad_norm=94.944, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.151e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 00:40:52,210 (trainer:737) INFO: 1epoch:train:6501-6600batch: iter_time=1.883e-04, forward_time=0.104, loss_ctc=260.030, loss_att=257.944, acc=0.140, loss=258.570, backward_time=0.097, grad_norm=83.671, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.184e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:41:33,489 (trainer:737) INFO: 1epoch:train:6601-6700batch: iter_time=1.729e-04, forward_time=0.104, loss_ctc=292.357, loss_att=282.796, acc=0.137, loss=285.664, backward_time=0.098, grad_norm=127.516, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.217e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:42:14,588 (trainer:737) INFO: 1epoch:train:6701-6800batch: iter_time=1.774e-04, forward_time=0.104, loss_ctc=259.730, loss_att=253.738, acc=0.140, loss=255.535, backward_time=0.097, grad_norm=94.522, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.250e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 00:42:55,870 (trainer:737) INFO: 1epoch:train:6801-6900batch: iter_time=1.586e-04, forward_time=0.105, loss_ctc=281.242, loss_att=276.079, acc=0.144, loss=277.628, backward_time=0.098, grad_norm=92.883, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.030, optim0_lr0=2.284e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:43:37,132 (trainer:737) INFO: 1epoch:train:6901-7000batch: iter_time=1.551e-04, forward_time=0.106, loss_ctc=275.886, loss_att=273.169, acc=0.136, loss=273.984, backward_time=0.098, grad_norm=102.797, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.317e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:44:18,324 (trainer:737) INFO: 1epoch:train:7001-7100batch: iter_time=1.598e-04, forward_time=0.105, loss_ctc=272.623, loss_att=265.621, acc=0.141, loss=267.722, backward_time=0.098, grad_norm=96.272, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.350e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:44:59,515 (trainer:737) INFO: 1epoch:train:7101-7200batch: iter_time=2.307e-04, forward_time=0.105, loss_ctc=277.458, loss_att=267.807, acc=0.143, loss=270.702, backward_time=0.098, grad_norm=116.341, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.384e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:45:40,684 (trainer:737) INFO: 1epoch:train:7201-7300batch: iter_time=1.622e-04, forward_time=0.105, loss_ctc=264.623, loss_att=262.487, acc=0.141, loss=263.127, backward_time=0.098, grad_norm=100.119, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.417e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 00:46:22,016 (trainer:737) INFO: 1epoch:train:7301-7400batch: iter_time=1.548e-04, forward_time=0.106, loss_ctc=294.646, loss_att=278.253, acc=0.139, loss=283.171, backward_time=0.098, grad_norm=83.912, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.451e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:47:03,278 (trainer:737) INFO: 1epoch:train:7401-7500batch: iter_time=1.451e-04, forward_time=0.106, loss_ctc=287.432, loss_att=271.125, acc=0.142, loss=276.017, backward_time=0.098, grad_norm=102.301, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.484e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 00:47:08,327 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 00:47:27,175 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 00:47:30,834 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42dd4c9c00>)
+[gpuc02:0/16] 2024-01-12 00:47:30,834 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 00:47:30,837 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 00:51:56,767 (trainer:737) INFO: 1epoch:train:7501-7600batch: iter_time=2.438, forward_time=0.108, loss_ctc=278.482, loss_att=282.749, acc=0.147, loss=281.469, backward_time=0.098, grad_norm=92.389, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.517e-05, train_time=2.935
+[gpuc02:0/16] 2024-01-12 00:52:38,231 (trainer:737) INFO: 1epoch:train:7601-7700batch: iter_time=1.955e-04, forward_time=0.106, loss_ctc=278.398, loss_att=284.266, acc=0.135, loss=282.505, backward_time=0.098, grad_norm=112.606, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.550e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:53:19,717 (trainer:737) INFO: 1epoch:train:7701-7800batch: iter_time=1.605e-04, forward_time=0.106, loss_ctc=290.556, loss_att=291.752, acc=0.143, loss=291.393, backward_time=0.098, grad_norm=96.282, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.584e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 00:54:01,054 (trainer:737) INFO: 1epoch:train:7801-7900batch: iter_time=1.665e-04, forward_time=0.104, loss_ctc=258.037, loss_att=262.649, acc=0.151, loss=261.265, backward_time=0.097, grad_norm=88.896, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.617e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:54:42,401 (trainer:737) INFO: 1epoch:train:7901-8000batch: iter_time=2.752e-04, forward_time=0.105, loss_ctc=282.479, loss_att=284.821, acc=0.141, loss=284.118, backward_time=0.098, grad_norm=102.899, clip=100.000, loss_scale=5.243e+05, optim_step_time=0.029, optim0_lr0=2.650e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:55:23,839 (trainer:737) INFO: 1epoch:train:8001-8100batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=289.253, loss_att=289.260, acc=0.146, loss=289.258, backward_time=0.098, grad_norm=194.375, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=2.684e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:56:06,628 (trainer:737) INFO: 1epoch:train:8101-8200batch: iter_time=1.434e-04, forward_time=0.105, loss_ctc=277.331, loss_att=275.921, acc=0.147, loss=276.344, backward_time=0.098, grad_norm=113.865, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=2.717e-05, train_time=0.428
+[gpuc02:0/16] 2024-01-12 00:56:48,902 (trainer:737) INFO: 1epoch:train:8201-8300batch: iter_time=1.317e-04, forward_time=0.104, loss_ctc=244.266, loss_att=248.438, acc=0.153, loss=247.186, backward_time=0.096, grad_norm=93.856, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.028, optim0_lr0=2.751e-05, train_time=0.423
+[gpuc02:0/16] 2024-01-12 00:57:30,494 (trainer:737) INFO: 1epoch:train:8301-8400batch: iter_time=1.540e-04, forward_time=0.104, loss_ctc=288.237, loss_att=280.630, acc=0.148, loss=282.912, backward_time=0.097, grad_norm=104.556, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.028, optim0_lr0=2.784e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 00:58:11,780 (trainer:737) INFO: 1epoch:train:8401-8500batch: iter_time=1.509e-04, forward_time=0.104, loss_ctc=266.646, loss_att=263.170, acc=0.148, loss=264.213, backward_time=0.097, grad_norm=105.981, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.028, optim0_lr0=2.817e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:58:53,085 (trainer:737) INFO: 1epoch:train:8501-8600batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=273.472, loss_att=275.829, acc=0.146, loss=275.122, backward_time=0.097, grad_norm=109.310, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.028, optim0_lr0=2.851e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 00:59:34,482 (trainer:737) INFO: 1epoch:train:8601-8700batch: iter_time=1.319e-04, forward_time=0.104, loss_ctc=290.989, loss_att=279.092, acc=0.151, loss=282.661, backward_time=0.097, grad_norm=124.591, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.028, optim0_lr0=2.884e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 00:59:59,040 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 01:00:18,404 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 01:00:22,176 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38b2b0fd90>)
+[gpuc02:0/16] 2024-01-12 01:00:22,176 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 01:00:22,180 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 01:04:21,572 (trainer:737) INFO: 1epoch:train:8701-8800batch: iter_time=2.396, forward_time=0.104, loss_ctc=278.724, loss_att=259.799, acc=0.156, loss=265.476, backward_time=0.097, grad_norm=100.235, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=2.917e-05, train_time=2.871
+[gpuc02:0/16] 2024-01-12 01:05:03,032 (trainer:737) INFO: 1epoch:train:8801-8900batch: iter_time=1.145e-04, forward_time=0.106, loss_ctc=290.384, loss_att=287.665, acc=0.140, loss=288.481, backward_time=0.098, grad_norm=116.333, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=2.950e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:05:44,428 (trainer:737) INFO: 1epoch:train:8901-9000batch: iter_time=1.246e-04, forward_time=0.106, loss_ctc=281.651, loss_att=277.734, acc=0.148, loss=278.909, backward_time=0.098, grad_norm=108.921, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=2.984e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:06:25,678 (trainer:737) INFO: 1epoch:train:9001-9100batch: iter_time=1.191e-04, forward_time=0.106, loss_ctc=256.240, loss_att=251.545, acc=0.158, loss=252.953, backward_time=0.097, grad_norm=92.573, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.017e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:07:07,053 (trainer:737) INFO: 1epoch:train:9101-9200batch: iter_time=1.299e-04, forward_time=0.106, loss_ctc=287.928, loss_att=270.821, acc=0.154, loss=275.953, backward_time=0.098, grad_norm=152.747, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.051e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:07:48,608 (trainer:737) INFO: 1epoch:train:9201-9300batch: iter_time=1.327e-04, forward_time=0.108, loss_ctc=255.779, loss_att=247.448, acc=0.153, loss=249.947, backward_time=0.097, grad_norm=114.839, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.084e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 01:08:29,936 (trainer:737) INFO: 1epoch:train:9301-9400batch: iter_time=1.135e-04, forward_time=0.106, loss_ctc=277.974, loss_att=267.489, acc=0.160, loss=270.635, backward_time=0.097, grad_norm=122.519, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.117e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:09:11,222 (trainer:737) INFO: 1epoch:train:9401-9500batch: iter_time=1.112e-04, forward_time=0.105, loss_ctc=272.352, loss_att=266.974, acc=0.154, loss=268.587, backward_time=0.097, grad_norm=131.039, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.151e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:09:52,501 (trainer:737) INFO: 1epoch:train:9501-9600batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=268.647, loss_att=258.329, acc=0.159, loss=261.424, backward_time=0.097, grad_norm=107.509, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.184e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:10:33,703 (trainer:737) INFO: 1epoch:train:9601-9700batch: iter_time=1.166e-04, forward_time=0.105, loss_ctc=273.335, loss_att=256.685, acc=0.163, loss=261.680, backward_time=0.097, grad_norm=120.771, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.217e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:11:14,887 (trainer:737) INFO: 1epoch:train:9701-9800batch: iter_time=1.299e-04, forward_time=0.105, loss_ctc=258.752, loss_att=252.641, acc=0.160, loss=254.474, backward_time=0.097, grad_norm=105.743, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.250e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:11:56,325 (trainer:737) INFO: 1epoch:train:9801-9900batch: iter_time=1.201e-04, forward_time=0.106, loss_ctc=290.881, loss_att=278.692, acc=0.151, loss=282.349, backward_time=0.098, grad_norm=106.634, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.284e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:12:37,602 (trainer:737) INFO: 1epoch:train:9901-10000batch: iter_time=1.088e-04, forward_time=0.105, loss_ctc=285.279, loss_att=265.374, acc=0.162, loss=271.346, backward_time=0.098, grad_norm=132.964, clip=100.000, loss_scale=1.049e+06, optim_step_time=0.029, optim0_lr0=3.317e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:12:43,784 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 01:13:03,525 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 01:13:07,238 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e99e4040>)
+[gpuc02:0/16] 2024-01-12 01:13:07,239 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 01:13:07,242 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 01:17:29,736 (trainer:737) INFO: 1epoch:train:10001-10100batch: iter_time=2.372, forward_time=0.105, loss_ctc=273.804, loss_att=253.971, acc=0.164, loss=259.921, backward_time=0.097, grad_norm=105.143, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.350e-05, train_time=2.921
+[gpuc02:0/16] 2024-01-12 01:18:10,930 (trainer:737) INFO: 1epoch:train:10101-10200batch: iter_time=1.451e-04, forward_time=0.104, loss_ctc=272.799, loss_att=255.753, acc=0.151, loss=260.867, backward_time=0.097, grad_norm=127.709, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.384e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:18:52,338 (trainer:737) INFO: 1epoch:train:10201-10300batch: iter_time=1.243e-04, forward_time=0.106, loss_ctc=287.815, loss_att=271.142, acc=0.159, loss=276.144, backward_time=0.098, grad_norm=122.303, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.417e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:19:33,430 (trainer:737) INFO: 1epoch:train:10301-10400batch: iter_time=1.160e-04, forward_time=0.104, loss_ctc=252.454, loss_att=230.798, acc=0.169, loss=237.295, backward_time=0.097, grad_norm=112.027, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.451e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:20:14,623 (trainer:737) INFO: 1epoch:train:10401-10500batch: iter_time=1.435e-04, forward_time=0.104, loss_ctc=280.403, loss_att=256.673, acc=0.156, loss=263.792, backward_time=0.097, grad_norm=121.603, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.484e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:20:55,902 (trainer:737) INFO: 1epoch:train:10501-10600batch: iter_time=1.275e-04, forward_time=0.105, loss_ctc=281.870, loss_att=253.941, acc=0.163, loss=262.320, backward_time=0.098, grad_norm=144.833, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.517e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:21:37,076 (trainer:737) INFO: 1epoch:train:10601-10700batch: iter_time=1.163e-04, forward_time=0.104, loss_ctc=275.149, loss_att=254.314, acc=0.160, loss=260.565, backward_time=0.097, grad_norm=193.166, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.550e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:22:18,049 (trainer:737) INFO: 1epoch:train:10701-10800batch: iter_time=1.259e-04, forward_time=0.104, loss_ctc=241.007, loss_att=222.290, acc=0.175, loss=227.905, backward_time=0.096, grad_norm=135.434, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.584e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 01:22:59,285 (trainer:737) INFO: 1epoch:train:10801-10900batch: iter_time=1.265e-04, forward_time=0.104, loss_ctc=285.427, loss_att=260.219, acc=0.163, loss=267.781, backward_time=0.097, grad_norm=120.085, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.028, optim0_lr0=3.617e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:23:40,308 (trainer:737) INFO: 1epoch:train:10901-11000batch: iter_time=1.170e-04, forward_time=0.105, loss_ctc=261.992, loss_att=237.907, acc=0.169, loss=245.133, backward_time=0.097, grad_norm=121.349, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.028, optim0_lr0=3.651e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 01:24:21,505 (trainer:737) INFO: 1epoch:train:11001-11100batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=267.636, loss_att=245.408, acc=0.166, loss=252.076, backward_time=0.097, grad_norm=117.824, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.684e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:25:02,755 (trainer:737) INFO: 1epoch:train:11101-11200batch: iter_time=1.191e-04, forward_time=0.105, loss_ctc=286.555, loss_att=251.192, acc=0.171, loss=261.801, backward_time=0.097, grad_norm=126.014, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.028, optim0_lr0=3.717e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:25:27,831 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 01:25:46,911 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 01:25:50,934 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38ae6bfdf0>)
+[gpuc02:0/16] 2024-01-12 01:25:50,934 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 01:25:50,937 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 01:29:50,996 (trainer:737) INFO: 1epoch:train:11201-11300batch: iter_time=2.400, forward_time=0.105, loss_ctc=278.689, loss_att=241.380, acc=0.177, loss=252.573, backward_time=0.097, grad_norm=147.416, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.751e-05, train_time=2.882
+[gpuc02:0/16] 2024-01-12 01:30:32,287 (trainer:737) INFO: 1epoch:train:11301-11400batch: iter_time=1.296e-04, forward_time=0.106, loss_ctc=287.405, loss_att=259.399, acc=0.159, loss=267.801, backward_time=0.098, grad_norm=128.320, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.784e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:31:13,596 (trainer:737) INFO: 1epoch:train:11401-11500batch: iter_time=1.314e-04, forward_time=0.106, loss_ctc=278.419, loss_att=261.349, acc=0.168, loss=266.470, backward_time=0.098, grad_norm=112.418, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.817e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:31:55,580 (trainer:737) INFO: 1epoch:train:11501-11600batch: iter_time=1.342e-04, forward_time=0.104, loss_ctc=251.571, loss_att=227.059, acc=0.178, loss=234.413, backward_time=0.097, grad_norm=96.197, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.850e-05, train_time=0.420
+[gpuc02:0/16] 2024-01-12 01:32:37,382 (trainer:737) INFO: 1epoch:train:11601-11700batch: iter_time=1.397e-04, forward_time=0.105, loss_ctc=283.843, loss_att=252.022, acc=0.172, loss=261.569, backward_time=0.098, grad_norm=143.770, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.884e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 01:33:19,578 (trainer:737) INFO: 1epoch:train:11701-11800batch: iter_time=1.439e-04, forward_time=0.104, loss_ctc=252.125, loss_att=223.941, acc=0.173, loss=232.396, backward_time=0.097, grad_norm=127.852, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.917e-05, train_time=0.422
+[gpuc02:0/16] 2024-01-12 01:34:01,435 (trainer:737) INFO: 1epoch:train:11801-11900batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=274.163, loss_att=246.577, acc=0.184, loss=254.853, backward_time=0.097, grad_norm=175.772, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.951e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 01:34:42,616 (trainer:737) INFO: 1epoch:train:11901-12000batch: iter_time=1.339e-04, forward_time=0.105, loss_ctc=266.658, loss_att=243.244, acc=0.171, loss=250.268, backward_time=0.097, grad_norm=143.510, clip=100.000, loss_scale=2.097e+06, optim_step_time=0.029, optim0_lr0=3.984e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:35:24,052 (trainer:737) INFO: 1epoch:train:12001-12100batch: iter_time=1.399e-04, forward_time=0.104, loss_ctc=263.761, loss_att=237.348, acc=0.176, loss=245.272, backward_time=0.097, grad_norm=117.073, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.017e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:36:05,167 (trainer:737) INFO: 1epoch:train:12101-12200batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=269.521, loss_att=240.592, acc=0.179, loss=249.271, backward_time=0.097, grad_norm=111.057, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.050e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:36:46,362 (trainer:737) INFO: 1epoch:train:12201-12300batch: iter_time=1.382e-04, forward_time=0.105, loss_ctc=254.805, loss_att=234.866, acc=0.174, loss=240.847, backward_time=0.097, grad_norm=114.156, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.084e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:37:27,631 (trainer:737) INFO: 1epoch:train:12301-12400batch: iter_time=1.447e-04, forward_time=0.106, loss_ctc=287.884, loss_att=249.393, acc=0.171, loss=260.940, backward_time=0.097, grad_norm=114.230, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.030, optim0_lr0=4.117e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:38:08,777 (trainer:737) INFO: 1epoch:train:12401-12500batch: iter_time=1.338e-04, forward_time=0.105, loss_ctc=280.548, loss_att=243.627, acc=0.178, loss=254.703, backward_time=0.098, grad_norm=123.177, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.150e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:38:13,125 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 01:38:31,823 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 01:38:35,501 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f389196ffa0>)
+[gpuc02:0/16] 2024-01-12 01:38:35,501 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 01:38:35,504 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 01:42:53,297 (trainer:737) INFO: 1epoch:train:12501-12600batch: iter_time=2.372, forward_time=0.106, loss_ctc=268.798, loss_att=251.049, acc=0.180, loss=256.374, backward_time=0.097, grad_norm=118.968, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.184e-05, train_time=2.845
+[gpuc02:0/16] 2024-01-12 01:43:34,495 (trainer:737) INFO: 1epoch:train:12601-12700batch: iter_time=1.392e-04, forward_time=0.106, loss_ctc=266.653, loss_att=251.082, acc=0.164, loss=255.753, backward_time=0.097, grad_norm=106.139, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.217e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:44:15,958 (trainer:737) INFO: 1epoch:train:12701-12800batch: iter_time=1.510e-04, forward_time=0.106, loss_ctc=280.727, loss_att=261.745, acc=0.175, loss=267.439, backward_time=0.098, grad_norm=101.009, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.250e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:44:57,162 (trainer:737) INFO: 1epoch:train:12801-12900batch: iter_time=1.606e-04, forward_time=0.106, loss_ctc=249.642, loss_att=234.480, acc=0.184, loss=239.028, backward_time=0.097, grad_norm=99.121, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.284e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:45:38,644 (trainer:737) INFO: 1epoch:train:12901-13000batch: iter_time=1.547e-04, forward_time=0.109, loss_ctc=274.359, loss_att=254.436, acc=0.172, loss=260.413, backward_time=0.097, grad_norm=124.095, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.317e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 01:46:19,985 (trainer:737) INFO: 1epoch:train:13001-13100batch: iter_time=1.423e-04, forward_time=0.107, loss_ctc=276.853, loss_att=256.287, acc=0.180, loss=262.457, backward_time=0.098, grad_norm=130.371, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.351e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:47:01,226 (trainer:737) INFO: 1epoch:train:13101-13200batch: iter_time=1.397e-04, forward_time=0.106, loss_ctc=266.622, loss_att=248.905, acc=0.181, loss=254.220, backward_time=0.097, grad_norm=146.955, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.384e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:47:42,283 (trainer:737) INFO: 1epoch:train:13201-13300batch: iter_time=1.437e-04, forward_time=0.105, loss_ctc=234.981, loss_att=221.447, acc=0.187, loss=225.507, backward_time=0.097, grad_norm=119.279, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.417e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 01:48:23,588 (trainer:737) INFO: 1epoch:train:13301-13400batch: iter_time=1.477e-04, forward_time=0.106, loss_ctc=281.273, loss_att=255.431, acc=0.174, loss=263.184, backward_time=0.098, grad_norm=120.574, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.451e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:49:04,757 (trainer:737) INFO: 1epoch:train:13401-13500batch: iter_time=1.415e-04, forward_time=0.105, loss_ctc=255.290, loss_att=236.452, acc=0.181, loss=242.104, backward_time=0.097, grad_norm=98.408, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.484e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:49:46,022 (trainer:737) INFO: 1epoch:train:13501-13600batch: iter_time=1.371e-04, forward_time=0.106, loss_ctc=263.210, loss_att=248.139, acc=0.175, loss=252.660, backward_time=0.098, grad_norm=120.050, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.517e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:50:27,368 (trainer:737) INFO: 1epoch:train:13601-13700batch: iter_time=1.415e-04, forward_time=0.106, loss_ctc=282.365, loss_att=253.896, acc=0.181, loss=262.437, backward_time=0.098, grad_norm=139.047, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.551e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:50:52,494 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 01:51:11,158 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 01:51:14,910 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38ae623820>)
+[gpuc02:0/16] 2024-01-12 01:51:14,910 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 01:51:14,913 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 01:55:14,646 (trainer:737) INFO: 1epoch:train:13701-13800batch: iter_time=2.401, forward_time=0.103, loss_ctc=272.760, loss_att=239.701, acc=0.187, loss=249.619, backward_time=0.097, grad_norm=108.389, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.584e-05, train_time=2.873
+[gpuc02:0/16] 2024-01-12 01:55:55,993 (trainer:737) INFO: 1epoch:train:13801-13900batch: iter_time=1.001e-04, forward_time=0.105, loss_ctc=283.398, loss_att=264.233, acc=0.167, loss=269.982, backward_time=0.098, grad_norm=120.685, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.617e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 01:56:37,366 (trainer:737) INFO: 1epoch:train:13901-14000batch: iter_time=1.099e-04, forward_time=0.106, loss_ctc=273.264, loss_att=254.902, acc=0.175, loss=260.411, backward_time=0.098, grad_norm=118.746, clip=100.000, loss_scale=4.194e+06, optim_step_time=0.029, optim0_lr0=4.651e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 01:57:18,512 (trainer:737) INFO: 1epoch:train:14001-14100batch: iter_time=1.078e-04, forward_time=0.104, loss_ctc=247.414, loss_att=229.525, acc=0.187, loss=234.892, backward_time=0.097, grad_norm=79.669, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.029, optim0_lr0=4.684e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:57:59,768 (trainer:737) INFO: 1epoch:train:14101-14200batch: iter_time=1.197e-04, forward_time=0.104, loss_ctc=277.219, loss_att=250.240, acc=0.182, loss=258.334, backward_time=0.098, grad_norm=113.930, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.028, optim0_lr0=4.717e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 01:58:40,914 (trainer:737) INFO: 1epoch:train:14201-14300batch: iter_time=1.081e-04, forward_time=0.104, loss_ctc=247.099, loss_att=226.621, acc=0.178, loss=232.764, backward_time=0.097, grad_norm=121.978, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.028, optim0_lr0=4.750e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 01:59:22,122 (trainer:737) INFO: 1epoch:train:14301-14400batch: iter_time=1.120e-04, forward_time=0.106, loss_ctc=266.372, loss_att=245.450, acc=0.196, loss=251.727, backward_time=0.097, grad_norm=114.260, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.028, optim0_lr0=4.784e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:00:03,329 (trainer:737) INFO: 1epoch:train:14401-14500batch: iter_time=1.066e-04, forward_time=0.106, loss_ctc=261.948, loss_att=244.311, acc=0.178, loss=249.602, backward_time=0.097, grad_norm=138.047, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.028, optim0_lr0=4.817e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:00:44,529 (trainer:737) INFO: 1epoch:train:14501-14600batch: iter_time=1.165e-04, forward_time=0.106, loss_ctc=258.505, loss_att=237.270, acc=0.183, loss=243.641, backward_time=0.097, grad_norm=109.054, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.028, optim0_lr0=4.850e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:01:25,761 (trainer:737) INFO: 1epoch:train:14601-14700batch: iter_time=1.113e-04, forward_time=0.106, loss_ctc=264.960, loss_att=235.811, acc=0.187, loss=244.556, backward_time=0.097, grad_norm=105.365, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.029, optim0_lr0=4.884e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:02:06,952 (trainer:737) INFO: 1epoch:train:14701-14800batch: iter_time=1.184e-04, forward_time=0.106, loss_ctc=250.006, loss_att=233.304, acc=0.182, loss=238.315, backward_time=0.097, grad_norm=107.185, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.029, optim0_lr0=4.917e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:02:48,422 (trainer:737) INFO: 1epoch:train:14801-14900batch: iter_time=1.187e-04, forward_time=0.107, loss_ctc=282.505, loss_att=256.037, acc=0.177, loss=263.977, backward_time=0.098, grad_norm=113.226, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.029, optim0_lr0=4.951e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 02:03:29,656 (trainer:737) INFO: 1epoch:train:14901-15000batch: iter_time=1.043e-04, forward_time=0.106, loss_ctc=274.465, loss_att=245.459, acc=0.184, loss=254.161, backward_time=0.098, grad_norm=102.564, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.029, optim0_lr0=4.984e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:24:16,448 (trainer:343) INFO: 1epoch results: [train] iter_time=0.198, forward_time=0.106, loss_ctc=429.065, loss_att=287.419, acc=0.128, loss=329.913, backward_time=0.098, grad_norm=355.591, clip=100.000, loss_scale=1.669e+06, optim_step_time=0.029, optim0_lr0=2.500e-05, train_time=0.622, time=2 hours, 35 minutes and 39.82 seconds, total_count=15000, gpu_max_cached_mem_GB=24.039, [valid] loss_ctc=192.131, cer_ctc=0.982, loss_att=171.938, acc=0.155, cer=0.758, wer=1.000, loss=177.996, time=20 minutes and 35.94 seconds, total_count=4671, gpu_max_cached_mem_GB=24.039
+[gpuc02:0/16] 2024-01-12 02:24:25,633 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 02:24:25,634 (trainer:272) INFO: 2/45epoch started. Estimated time to finish: 5 days, 9 hours and 22 minutes
+[gpuc02:0/16] 2024-01-12 02:24:25,643 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 02:24:45,007 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 02:24:48,514 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38db485bd0>)
+[gpuc02:0/16] 2024-01-12 02:24:48,514 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 02:24:48,518 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 02:29:48,757 (trainer:737) INFO: 2epoch:train:1-100batch: iter_time=2.295, forward_time=0.106, loss_ctc=278.871, loss_att=244.821, acc=0.180, loss=255.036, backward_time=0.102, grad_norm=110.288, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.017e-05, train_time=3.231
+[gpuc02:0/16] 2024-01-12 02:30:29,803 (trainer:737) INFO: 2epoch:train:101-200batch: iter_time=1.120e-04, forward_time=0.104, loss_ctc=255.677, loss_att=221.677, acc=0.193, loss=231.877, backward_time=0.098, grad_norm=89.377, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.051e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 02:31:10,684 (trainer:737) INFO: 2epoch:train:201-300batch: iter_time=1.142e-04, forward_time=0.104, loss_ctc=247.356, loss_att=216.844, acc=0.197, loss=225.998, backward_time=0.098, grad_norm=105.641, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.084e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 02:31:54,030 (trainer:737) INFO: 2epoch:train:301-400batch: iter_time=1.418e-04, forward_time=0.117, loss_ctc=260.134, loss_att=234.865, acc=0.180, loss=242.445, backward_time=0.099, grad_norm=76.449, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.032, optim0_lr0=5.117e-05, train_time=0.433
+[gpuc02:0/16] 2024-01-12 02:32:44,305 (trainer:737) INFO: 2epoch:train:401-500batch: iter_time=1.343e-04, forward_time=0.149, loss_ctc=281.364, loss_att=237.778, acc=0.175, loss=250.853, backward_time=0.130, grad_norm=134.113, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.034, optim0_lr0=5.151e-05, train_time=0.503
+[gpuc02:0/16] 2024-01-12 02:33:26,350 (trainer:737) INFO: 2epoch:train:501-600batch: iter_time=1.270e-04, forward_time=0.105, loss_ctc=244.210, loss_att=215.458, acc=0.188, loss=224.084, backward_time=0.098, grad_norm=114.747, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.184e-05, train_time=0.420
+[gpuc02:0/16] 2024-01-12 02:34:08,516 (trainer:737) INFO: 2epoch:train:601-700batch: iter_time=1.373e-04, forward_time=0.104, loss_ctc=238.223, loss_att=207.743, acc=0.192, loss=216.887, backward_time=0.098, grad_norm=78.840, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.217e-05, train_time=0.421
+[gpuc02:0/16] 2024-01-12 02:34:50,460 (trainer:737) INFO: 2epoch:train:701-800batch: iter_time=1.303e-04, forward_time=0.104, loss_ctc=230.742, loss_att=212.353, acc=0.195, loss=217.869, backward_time=0.098, grad_norm=87.378, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.251e-05, train_time=0.419
+[gpuc02:0/16] 2024-01-12 02:35:31,503 (trainer:737) INFO: 2epoch:train:801-900batch: iter_time=1.329e-04, forward_time=0.105, loss_ctc=267.108, loss_att=228.109, acc=0.194, loss=239.809, backward_time=0.098, grad_norm=87.226, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.284e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 02:36:13,208 (trainer:737) INFO: 2epoch:train:901-1000batch: iter_time=1.332e-04, forward_time=0.104, loss_ctc=224.122, loss_att=200.861, acc=0.196, loss=207.839, backward_time=0.097, grad_norm=80.298, clip=100.000, loss_scale=8.389e+06, optim_step_time=0.030, optim0_lr0=5.317e-05, train_time=0.417
+[gpuc02:0/16] 2024-01-12 02:36:54,643 (trainer:737) INFO: 2epoch:train:1001-1100batch: iter_time=1.349e-04, forward_time=0.104, loss_ctc=259.321, loss_att=232.450, acc=0.181, loss=240.512, backward_time=0.098, grad_norm=97.056, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.350e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 02:37:35,344 (trainer:737) INFO: 2epoch:train:1101-1200batch: iter_time=1.399e-04, forward_time=0.103, loss_ctc=203.257, loss_att=182.060, acc=0.203, loss=188.419, backward_time=0.097, grad_norm=72.341, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.384e-05, train_time=0.407
+[gpuc02:0/16] 2024-01-12 02:38:02,415 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 02:38:21,431 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 02:38:25,375 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38db4e7400>)
+[gpuc02:0/16] 2024-01-12 02:38:25,375 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 02:38:25,379 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 02:44:47,177 (trainer:737) INFO: 2epoch:train:1201-1300batch: iter_time=2.946, forward_time=0.110, loss_ctc=247.664, loss_att=213.068, acc=0.205, loss=223.447, backward_time=0.100, grad_norm=91.380, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.417e-05, train_time=4.318
+[gpuc02:0/16] 2024-01-12 02:45:28,495 (trainer:737) INFO: 2epoch:train:1301-1400batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=296.196, loss_att=254.722, acc=0.179, loss=267.164, backward_time=0.099, grad_norm=114.723, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.450e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 02:46:09,558 (trainer:737) INFO: 2epoch:train:1401-1500batch: iter_time=1.334e-04, forward_time=0.105, loss_ctc=238.577, loss_att=208.560, acc=0.203, loss=217.565, backward_time=0.098, grad_norm=92.473, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.484e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 02:46:50,703 (trainer:737) INFO: 2epoch:train:1501-1600batch: iter_time=1.264e-04, forward_time=0.105, loss_ctc=230.946, loss_att=207.195, acc=0.198, loss=214.320, backward_time=0.098, grad_norm=75.449, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.517e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 02:47:31,986 (trainer:737) INFO: 2epoch:train:1601-1700batch: iter_time=1.364e-04, forward_time=0.106, loss_ctc=287.728, loss_att=248.302, acc=0.173, loss=260.129, backward_time=0.098, grad_norm=120.557, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.550e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 02:48:13,359 (trainer:737) INFO: 2epoch:train:1701-1800batch: iter_time=2.956e-04, forward_time=0.106, loss_ctc=271.261, loss_att=233.331, acc=0.182, loss=244.710, backward_time=0.098, grad_norm=120.441, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.584e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 02:48:54,276 (trainer:737) INFO: 2epoch:train:1801-1900batch: iter_time=1.162e-04, forward_time=0.104, loss_ctc=199.795, loss_att=178.762, acc=0.210, loss=185.072, backward_time=0.097, grad_norm=69.003, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.617e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 02:49:35,733 (trainer:737) INFO: 2epoch:train:1901-2000batch: iter_time=1.363e-04, forward_time=0.106, loss_ctc=261.494, loss_att=231.555, acc=0.188, loss=240.537, backward_time=0.098, grad_norm=90.960, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.031, optim0_lr0=5.651e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 02:50:17,259 (trainer:737) INFO: 2epoch:train:2001-2100batch: iter_time=1.232e-04, forward_time=0.105, loss_ctc=241.580, loss_att=215.114, acc=0.197, loss=223.054, backward_time=0.098, grad_norm=61.652, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.684e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 02:50:58,885 (trainer:737) INFO: 2epoch:train:2101-2200batch: iter_time=1.204e-04, forward_time=0.105, loss_ctc=245.568, loss_att=214.232, acc=0.200, loss=223.633, backward_time=0.098, grad_norm=84.991, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.717e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 02:51:40,934 (trainer:737) INFO: 2epoch:train:2201-2300batch: iter_time=1.196e-04, forward_time=0.104, loss_ctc=223.517, loss_att=197.060, acc=0.196, loss=204.997, backward_time=0.097, grad_norm=74.752, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.751e-05, train_time=0.420
+[gpuc02:0/16] 2024-01-12 02:52:21,959 (trainer:737) INFO: 2epoch:train:2301-2400batch: iter_time=1.242e-04, forward_time=0.105, loss_ctc=235.510, loss_att=210.934, acc=0.196, loss=218.307, backward_time=0.097, grad_norm=77.062, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.784e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 02:53:03,117 (trainer:737) INFO: 2epoch:train:2401-2500batch: iter_time=1.171e-04, forward_time=0.104, loss_ctc=225.413, loss_att=197.532, acc=0.205, loss=205.896, backward_time=0.097, grad_norm=84.496, clip=98.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.817e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 02:53:13,820 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 02:53:32,702 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 02:53:36,345 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e9902830>)
+[gpuc02:0/16] 2024-01-12 02:53:36,345 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 02:53:36,349 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 02:58:18,492 (trainer:737) INFO: 2epoch:train:2501-2600batch: iter_time=2.723, forward_time=0.111, loss_ctc=268.521, loss_att=244.908, acc=0.189, loss=251.992, backward_time=0.099, grad_norm=89.690, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.031, optim0_lr0=5.851e-05, train_time=3.154
+[gpuc02:0/16] 2024-01-12 02:58:59,678 (trainer:737) INFO: 2epoch:train:2601-2700batch: iter_time=1.365e-04, forward_time=0.105, loss_ctc=247.734, loss_att=218.877, acc=0.199, loss=227.534, backward_time=0.098, grad_norm=87.318, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.884e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 02:59:40,787 (trainer:737) INFO: 2epoch:train:2701-2800batch: iter_time=1.507e-04, forward_time=0.105, loss_ctc=239.961, loss_att=211.370, acc=0.204, loss=219.947, backward_time=0.098, grad_norm=92.520, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.031, optim0_lr0=5.917e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:00:22,084 (trainer:737) INFO: 2epoch:train:2801-2900batch: iter_time=1.490e-04, forward_time=0.106, loss_ctc=251.788, loss_att=236.049, acc=0.187, loss=240.770, backward_time=0.099, grad_norm=70.593, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.951e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:01:03,363 (trainer:737) INFO: 2epoch:train:2901-3000batch: iter_time=1.333e-04, forward_time=0.106, loss_ctc=273.545, loss_att=238.918, acc=0.181, loss=249.306, backward_time=0.099, grad_norm=109.326, clip=100.000, loss_scale=1.678e+07, optim_step_time=0.030, optim0_lr0=5.984e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:01:44,497 (trainer:737) INFO: 2epoch:train:3001-3100batch: iter_time=1.562e-04, forward_time=0.105, loss_ctc=236.409, loss_att=213.914, acc=0.195, loss=220.662, backward_time=0.098, grad_norm=102.597, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.017e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:02:25,575 (trainer:737) INFO: 2epoch:train:3101-3200batch: iter_time=1.253e-04, forward_time=0.104, loss_ctc=232.588, loss_att=205.255, acc=0.199, loss=213.455, backward_time=0.098, grad_norm=74.570, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.050e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:03:06,709 (trainer:737) INFO: 2epoch:train:3201-3300batch: iter_time=1.271e-04, forward_time=0.105, loss_ctc=225.225, loss_att=210.889, acc=0.200, loss=215.190, backward_time=0.098, grad_norm=69.568, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.084e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:03:47,977 (trainer:737) INFO: 2epoch:train:3301-3400batch: iter_time=1.274e-04, forward_time=0.105, loss_ctc=258.428, loss_att=226.284, acc=0.200, loss=235.927, backward_time=0.098, grad_norm=72.606, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.117e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 03:04:28,985 (trainer:737) INFO: 2epoch:train:3401-3500batch: iter_time=1.192e-04, forward_time=0.104, loss_ctc=217.288, loss_att=198.031, acc=0.203, loss=203.808, backward_time=0.098, grad_norm=78.394, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.151e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:05:10,472 (trainer:737) INFO: 2epoch:train:3501-3600batch: iter_time=1.343e-04, forward_time=0.106, loss_ctc=251.572, loss_att=234.680, acc=0.188, loss=239.747, backward_time=0.099, grad_norm=81.193, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.184e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 03:05:51,346 (trainer:737) INFO: 2epoch:train:3601-3700batch: iter_time=1.333e-04, forward_time=0.102, loss_ctc=196.554, loss_att=177.683, acc=0.212, loss=183.345, backward_time=0.097, grad_norm=54.135, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.217e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 03:06:20,750 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 03:06:39,812 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 03:06:43,522 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f466aad40>)
+[gpuc02:0/16] 2024-01-12 03:06:43,523 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 03:06:43,526 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 03:10:43,074 (trainer:737) INFO: 2epoch:train:3701-3800batch: iter_time=2.456, forward_time=0.105, loss_ctc=239.688, loss_att=208.661, acc=0.212, loss=217.969, backward_time=0.098, grad_norm=91.157, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.250e-05, train_time=2.917
+[gpuc02:0/16] 2024-01-12 03:11:24,528 (trainer:737) INFO: 2epoch:train:3801-3900batch: iter_time=1.289e-04, forward_time=0.106, loss_ctc=286.556, loss_att=250.881, acc=0.186, loss=261.584, backward_time=0.099, grad_norm=101.507, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.284e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 03:12:05,672 (trainer:737) INFO: 2epoch:train:3901-4000batch: iter_time=1.214e-04, forward_time=0.105, loss_ctc=229.961, loss_att=201.314, acc=0.210, loss=209.908, backward_time=0.097, grad_norm=84.017, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.317e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:12:46,843 (trainer:737) INFO: 2epoch:train:4001-4100batch: iter_time=1.099e-04, forward_time=0.105, loss_ctc=222.225, loss_att=200.065, acc=0.207, loss=206.713, backward_time=0.097, grad_norm=68.682, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.350e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:13:28,416 (trainer:737) INFO: 2epoch:train:4101-4200batch: iter_time=1.186e-04, forward_time=0.106, loss_ctc=274.698, loss_att=241.306, acc=0.181, loss=251.323, backward_time=0.098, grad_norm=109.707, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.384e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 03:14:09,752 (trainer:737) INFO: 2epoch:train:4201-4300batch: iter_time=1.153e-04, forward_time=0.106, loss_ctc=259.720, loss_att=226.969, acc=0.191, loss=236.795, backward_time=0.098, grad_norm=97.840, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.417e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:14:50,687 (trainer:737) INFO: 2epoch:train:4301-4400batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=193.724, loss_att=174.744, acc=0.215, loss=180.438, backward_time=0.096, grad_norm=67.004, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.451e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 03:15:32,263 (trainer:737) INFO: 2epoch:train:4401-4500batch: iter_time=1.128e-04, forward_time=0.109, loss_ctc=252.057, loss_att=225.959, acc=0.195, loss=233.789, backward_time=0.098, grad_norm=75.787, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.484e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 03:16:13,614 (trainer:737) INFO: 2epoch:train:4501-4600batch: iter_time=1.039e-04, forward_time=0.106, loss_ctc=230.522, loss_att=209.354, acc=0.204, loss=215.704, backward_time=0.098, grad_norm=54.010, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.517e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:16:54,880 (trainer:737) INFO: 2epoch:train:4601-4700batch: iter_time=1.007e-04, forward_time=0.105, loss_ctc=233.830, loss_att=208.707, acc=0.207, loss=216.244, backward_time=0.097, grad_norm=73.631, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.551e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 03:17:35,891 (trainer:737) INFO: 2epoch:train:4701-4800batch: iter_time=1.109e-04, forward_time=0.105, loss_ctc=212.468, loss_att=190.909, acc=0.205, loss=197.377, backward_time=0.097, grad_norm=72.078, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.584e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:18:16,921 (trainer:737) INFO: 2epoch:train:4801-4900batch: iter_time=1.098e-04, forward_time=0.105, loss_ctc=224.464, loss_att=205.908, acc=0.202, loss=211.475, backward_time=0.097, grad_norm=73.096, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.617e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:18:57,878 (trainer:737) INFO: 2epoch:train:4901-5000batch: iter_time=1.161e-04, forward_time=0.105, loss_ctc=211.946, loss_att=191.725, acc=0.213, loss=197.791, backward_time=0.097, grad_norm=82.393, clip=100.000, loss_scale=3.355e+07, optim_step_time=0.030, optim0_lr0=6.650e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 03:19:02,507 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 03:19:21,332 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 03:19:24,868 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a4207e20>)
+[gpuc02:0/16] 2024-01-12 03:19:24,868 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 03:19:24,871 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 03:24:16,182 (trainer:737) INFO: 2epoch:train:5001-5100batch: iter_time=2.470, forward_time=0.106, loss_ctc=255.536, loss_att=239.672, acc=0.196, loss=244.431, backward_time=0.099, grad_norm=89.853, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.684e-05, train_time=3.183
+[gpuc02:0/16] 2024-01-12 03:24:57,309 (trainer:737) INFO: 2epoch:train:5101-5200batch: iter_time=1.523e-04, forward_time=0.104, loss_ctc=231.717, loss_att=212.324, acc=0.207, loss=218.142, backward_time=0.097, grad_norm=93.924, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.717e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:25:38,437 (trainer:737) INFO: 2epoch:train:5201-5300batch: iter_time=1.599e-04, forward_time=0.104, loss_ctc=226.432, loss_att=205.973, acc=0.212, loss=212.111, backward_time=0.097, grad_norm=76.163, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.750e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:26:20,431 (trainer:737) INFO: 2epoch:train:5301-5400batch: iter_time=1.686e-04, forward_time=0.105, loss_ctc=236.877, loss_att=231.574, acc=0.194, loss=233.165, backward_time=0.098, grad_norm=74.591, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.784e-05, train_time=0.420
+[gpuc02:0/16] 2024-01-12 03:27:01,730 (trainer:737) INFO: 2epoch:train:5401-5500batch: iter_time=1.395e-04, forward_time=0.105, loss_ctc=256.281, loss_att=233.674, acc=0.189, loss=240.456, backward_time=0.098, grad_norm=114.007, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.817e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:27:43,022 (trainer:737) INFO: 2epoch:train:5501-5600batch: iter_time=1.324e-04, forward_time=0.105, loss_ctc=220.285, loss_att=207.319, acc=0.204, loss=211.209, backward_time=0.097, grad_norm=93.559, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.850e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:28:24,080 (trainer:737) INFO: 2epoch:train:5601-5700batch: iter_time=1.273e-04, forward_time=0.105, loss_ctc=213.959, loss_att=198.905, acc=0.207, loss=203.421, backward_time=0.097, grad_norm=75.206, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.884e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:29:05,134 (trainer:737) INFO: 2epoch:train:5701-5800batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=209.602, loss_att=205.474, acc=0.207, loss=206.713, backward_time=0.097, grad_norm=67.770, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.917e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:29:46,294 (trainer:737) INFO: 2epoch:train:5801-5900batch: iter_time=1.346e-04, forward_time=0.105, loss_ctc=236.632, loss_att=219.622, acc=0.207, loss=224.725, backward_time=0.098, grad_norm=69.418, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.951e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:30:27,481 (trainer:737) INFO: 2epoch:train:5901-6000batch: iter_time=1.300e-04, forward_time=0.104, loss_ctc=199.987, loss_att=193.676, acc=0.209, loss=195.570, backward_time=0.097, grad_norm=78.798, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=6.984e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 03:31:08,644 (trainer:737) INFO: 2epoch:train:6001-6100batch: iter_time=1.317e-04, forward_time=0.105, loss_ctc=231.416, loss_att=229.287, acc=0.194, loss=229.926, backward_time=0.098, grad_norm=74.844, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.017e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:31:49,405 (trainer:737) INFO: 2epoch:train:6101-6200batch: iter_time=1.191e-04, forward_time=0.104, loss_ctc=179.906, loss_att=173.586, acc=0.218, loss=175.482, backward_time=0.096, grad_norm=59.823, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.051e-05, train_time=0.407
+[gpuc02:0/16] 2024-01-12 03:32:16,697 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 03:32:35,697 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 03:32:39,729 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3892abed40>)
+[gpuc02:0/16] 2024-01-12 03:32:39,729 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 03:32:39,732 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 03:37:00,788 (trainer:737) INFO: 2epoch:train:6201-6300batch: iter_time=2.429, forward_time=0.109, loss_ctc=217.404, loss_att=206.185, acc=0.218, loss=209.551, backward_time=0.097, grad_norm=88.702, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.084e-05, train_time=3.114
+[gpuc02:0/16] 2024-01-12 03:37:42,510 (trainer:737) INFO: 2epoch:train:6301-6400batch: iter_time=2.110e-04, forward_time=0.106, loss_ctc=257.427, loss_att=250.343, acc=0.193, loss=252.468, backward_time=0.098, grad_norm=99.659, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.117e-05, train_time=0.417
+[gpuc02:0/16] 2024-01-12 03:38:23,590 (trainer:737) INFO: 2epoch:train:6401-6500batch: iter_time=2.750e-04, forward_time=0.105, loss_ctc=209.860, loss_att=197.456, acc=0.218, loss=201.177, backward_time=0.097, grad_norm=76.457, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.151e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:39:05,219 (trainer:737) INFO: 2epoch:train:6501-6600batch: iter_time=2.160e-04, forward_time=0.105, loss_ctc=198.760, loss_att=197.869, acc=0.215, loss=198.136, backward_time=0.097, grad_norm=60.298, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.184e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 03:39:46,525 (trainer:737) INFO: 2epoch:train:6601-6700batch: iter_time=2.095e-04, forward_time=0.106, loss_ctc=246.517, loss_att=243.379, acc=0.188, loss=244.321, backward_time=0.098, grad_norm=90.850, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.217e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:40:28,121 (trainer:737) INFO: 2epoch:train:6701-6800batch: iter_time=2.206e-04, forward_time=0.106, loss_ctc=234.254, loss_att=227.726, acc=0.196, loss=229.684, backward_time=0.097, grad_norm=101.772, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.251e-05, train_time=0.416
+[gpuc02:0/16] 2024-01-12 03:41:09,311 (trainer:737) INFO: 2epoch:train:6801-6900batch: iter_time=1.979e-04, forward_time=0.104, loss_ctc=173.358, loss_att=170.722, acc=0.224, loss=171.513, backward_time=0.096, grad_norm=60.973, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.284e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 03:41:51,728 (trainer:737) INFO: 2epoch:train:6901-7000batch: iter_time=2.075e-04, forward_time=0.106, loss_ctc=224.732, loss_att=224.609, acc=0.200, loss=224.646, backward_time=0.097, grad_norm=79.049, clip=100.000, loss_scale=6.711e+07, optim_step_time=0.030, optim0_lr0=7.317e-05, train_time=0.424
+[gpuc02:0/16] 2024-01-12 03:42:32,849 (trainer:737) INFO: 2epoch:train:7001-7100batch: iter_time=1.862e-04, forward_time=0.105, loss_ctc=203.188, loss_att=208.572, acc=0.210, loss=206.957, backward_time=0.097, grad_norm=58.317, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.350e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:43:13,898 (trainer:737) INFO: 2epoch:train:7101-7200batch: iter_time=2.090e-04, forward_time=0.104, loss_ctc=207.941, loss_att=206.806, acc=0.211, loss=207.146, backward_time=0.096, grad_norm=73.653, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.384e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:43:55,334 (trainer:737) INFO: 2epoch:train:7201-7300batch: iter_time=2.220e-04, forward_time=0.104, loss_ctc=190.399, loss_att=190.131, acc=0.212, loss=190.212, backward_time=0.096, grad_norm=64.974, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.417e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 03:44:36,303 (trainer:737) INFO: 2epoch:train:7301-7400batch: iter_time=2.107e-04, forward_time=0.104, loss_ctc=198.775, loss_att=203.956, acc=0.209, loss=202.401, backward_time=0.096, grad_norm=63.348, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.450e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 03:45:17,390 (trainer:737) INFO: 2epoch:train:7401-7500batch: iter_time=1.871e-04, forward_time=0.104, loss_ctc=186.567, loss_att=189.817, acc=0.219, loss=188.842, backward_time=0.096, grad_norm=78.815, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.484e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:45:23,446 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 03:45:42,559 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 03:45:46,253 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f380a04fee0>)
+[gpuc02:0/16] 2024-01-12 03:45:46,253 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 03:45:46,256 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 03:50:12,863 (trainer:737) INFO: 2epoch:train:7501-7600batch: iter_time=2.463, forward_time=0.156, loss_ctc=223.817, loss_att=226.191, acc=0.203, loss=225.479, backward_time=0.107, grad_norm=75.482, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.032, optim0_lr0=7.517e-05, train_time=2.955
+[gpuc02:0/16] 2024-01-12 03:50:53,951 (trainer:737) INFO: 2epoch:train:7601-7700batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=206.346, loss_att=204.293, acc=0.216, loss=204.908, backward_time=0.098, grad_norm=77.726, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.551e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:51:35,138 (trainer:737) INFO: 2epoch:train:7701-7800batch: iter_time=1.254e-04, forward_time=0.105, loss_ctc=201.313, loss_att=198.969, acc=0.221, loss=199.672, backward_time=0.099, grad_norm=67.362, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.031, optim0_lr0=7.584e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 03:52:16,264 (trainer:737) INFO: 2epoch:train:7801-7900batch: iter_time=1.256e-04, forward_time=0.105, loss_ctc=206.853, loss_att=217.143, acc=0.202, loss=214.056, backward_time=0.098, grad_norm=67.187, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.617e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 03:52:57,691 (trainer:737) INFO: 2epoch:train:7901-8000batch: iter_time=1.075e-04, forward_time=0.108, loss_ctc=222.883, loss_att=220.859, acc=0.197, loss=221.466, backward_time=0.099, grad_norm=81.959, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.651e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 03:53:38,720 (trainer:737) INFO: 2epoch:train:8001-8100batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=195.658, loss_att=199.303, acc=0.211, loss=198.209, backward_time=0.098, grad_norm=76.765, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.684e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:54:19,694 (trainer:737) INFO: 2epoch:train:8101-8200batch: iter_time=1.366e-04, forward_time=0.105, loss_ctc=186.123, loss_att=192.041, acc=0.215, loss=190.266, backward_time=0.098, grad_norm=64.035, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.717e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:55:00,696 (trainer:737) INFO: 2epoch:train:8201-8300batch: iter_time=1.613e-04, forward_time=0.106, loss_ctc=186.022, loss_att=196.629, acc=0.215, loss=193.447, backward_time=0.097, grad_norm=57.439, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.751e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 03:55:42,030 (trainer:737) INFO: 2epoch:train:8301-8400batch: iter_time=1.391e-04, forward_time=0.106, loss_ctc=206.127, loss_att=210.498, acc=0.214, loss=209.187, backward_time=0.098, grad_norm=65.291, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.784e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 03:56:22,918 (trainer:737) INFO: 2epoch:train:8401-8500batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=174.586, loss_att=185.775, acc=0.219, loss=182.418, backward_time=0.097, grad_norm=63.540, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.817e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 03:57:04,711 (trainer:737) INFO: 2epoch:train:8501-8600batch: iter_time=1.398e-04, forward_time=0.106, loss_ctc=202.354, loss_att=216.253, acc=0.203, loss=212.083, backward_time=0.098, grad_norm=64.858, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.851e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 03:57:45,425 (trainer:737) INFO: 2epoch:train:8601-8700batch: iter_time=1.164e-04, forward_time=0.104, loss_ctc=158.379, loss_att=168.607, acc=0.222, loss=165.539, backward_time=0.096, grad_norm=49.759, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.884e-05, train_time=0.407
+[gpuc02:0/16] 2024-01-12 03:58:12,082 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 03:58:30,803 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 03:58:34,430 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a38e7be0>)
+[gpuc02:0/16] 2024-01-12 03:58:34,430 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 03:58:34,433 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 04:02:38,900 (trainer:737) INFO: 2epoch:train:8701-8800batch: iter_time=2.482, forward_time=0.105, loss_ctc=190.839, loss_att=202.837, acc=0.225, loss=199.238, backward_time=0.100, grad_norm=72.945, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.031, optim0_lr0=7.917e-05, train_time=2.935
+[gpuc02:0/16] 2024-01-12 04:03:20,274 (trainer:737) INFO: 2epoch:train:8801-8900batch: iter_time=1.109e-04, forward_time=0.106, loss_ctc=225.709, loss_att=247.275, acc=0.198, loss=240.805, backward_time=0.099, grad_norm=90.199, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.950e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:04:01,290 (trainer:737) INFO: 2epoch:train:8901-9000batch: iter_time=1.102e-04, forward_time=0.105, loss_ctc=189.101, loss_att=193.829, acc=0.224, loss=192.411, backward_time=0.098, grad_norm=72.749, clip=100.000, loss_scale=1.342e+08, optim_step_time=0.030, optim0_lr0=7.984e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 04:04:42,661 (trainer:737) INFO: 2epoch:train:9001-9100batch: iter_time=1.057e-04, forward_time=0.104, loss_ctc=172.166, loss_att=192.940, acc=0.223, loss=186.708, backward_time=0.098, grad_norm=51.413, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.017e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:05:24,029 (trainer:737) INFO: 2epoch:train:9101-9200batch: iter_time=1.225e-04, forward_time=0.106, loss_ctc=215.384, loss_att=239.123, acc=0.193, loss=232.001, backward_time=0.099, grad_norm=73.423, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.050e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:06:05,296 (trainer:737) INFO: 2epoch:train:9201-9300batch: iter_time=1.233e-04, forward_time=0.105, loss_ctc=202.961, loss_att=223.016, acc=0.203, loss=216.999, backward_time=0.099, grad_norm=79.951, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.084e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:06:46,135 (trainer:737) INFO: 2epoch:train:9301-9400batch: iter_time=1.158e-04, forward_time=0.103, loss_ctc=151.592, loss_att=165.618, acc=0.232, loss=161.410, backward_time=0.097, grad_norm=60.993, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.117e-05, train_time=0.408
+[gpuc02:0/16] 2024-01-12 04:07:27,327 (trainer:737) INFO: 2epoch:train:9401-9500batch: iter_time=1.109e-04, forward_time=0.105, loss_ctc=196.203, loss_att=219.190, acc=0.209, loss=212.294, backward_time=0.098, grad_norm=67.016, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.151e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:08:08,914 (trainer:737) INFO: 2epoch:train:9501-9600batch: iter_time=1.218e-04, forward_time=0.106, loss_ctc=175.460, loss_att=204.658, acc=0.216, loss=195.899, backward_time=0.097, grad_norm=47.636, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.184e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 04:08:50,065 (trainer:737) INFO: 2epoch:train:9601-9700batch: iter_time=1.365e-04, forward_time=0.106, loss_ctc=181.715, loss_att=201.869, acc=0.219, loss=195.823, backward_time=0.097, grad_norm=61.717, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.217e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:09:31,595 (trainer:737) INFO: 2epoch:train:9701-9800batch: iter_time=1.258e-04, forward_time=0.109, loss_ctc=165.608, loss_att=185.706, acc=0.219, loss=179.677, backward_time=0.098, grad_norm=53.279, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.251e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 04:10:12,543 (trainer:737) INFO: 2epoch:train:9801-9900batch: iter_time=1.148e-04, forward_time=0.105, loss_ctc=174.739, loss_att=200.046, acc=0.217, loss=192.454, backward_time=0.097, grad_norm=59.414, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.284e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 04:10:53,389 (trainer:737) INFO: 2epoch:train:9901-10000batch: iter_time=1.018e-04, forward_time=0.104, loss_ctc=163.763, loss_att=186.775, acc=0.227, loss=179.872, backward_time=0.097, grad_norm=64.857, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.317e-05, train_time=0.408
+[gpuc02:0/16] 2024-01-12 04:10:57,937 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 04:11:17,994 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 04:11:22,049 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f380a0c9120>)
+[gpuc02:0/16] 2024-01-12 04:11:22,049 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 04:11:22,053 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 04:15:48,950 (trainer:737) INFO: 2epoch:train:10001-10100batch: iter_time=2.531, forward_time=0.106, loss_ctc=194.663, loss_att=227.141, acc=0.209, loss=217.398, backward_time=0.099, grad_norm=68.152, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.350e-05, train_time=2.955
+[gpuc02:0/16] 2024-01-12 04:16:31,023 (trainer:737) INFO: 2epoch:train:10101-10200batch: iter_time=1.690e-04, forward_time=0.105, loss_ctc=182.920, loss_att=203.765, acc=0.221, loss=197.512, backward_time=0.097, grad_norm=65.265, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.384e-05, train_time=0.421
+[gpuc02:0/16] 2024-01-12 04:17:12,385 (trainer:737) INFO: 2epoch:train:10201-10300batch: iter_time=1.856e-04, forward_time=0.104, loss_ctc=177.504, loss_att=195.298, acc=0.229, loss=189.960, backward_time=0.097, grad_norm=57.081, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.417e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:17:54,506 (trainer:737) INFO: 2epoch:train:10301-10400batch: iter_time=1.601e-04, forward_time=0.105, loss_ctc=180.036, loss_att=219.039, acc=0.208, loss=207.338, backward_time=0.099, grad_norm=58.278, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.451e-05, train_time=0.421
+[gpuc02:0/16] 2024-01-12 04:18:35,711 (trainer:737) INFO: 2epoch:train:10401-10500batch: iter_time=1.563e-04, forward_time=0.105, loss_ctc=195.362, loss_att=222.740, acc=0.202, loss=214.527, backward_time=0.098, grad_norm=71.659, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.484e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:19:16,953 (trainer:737) INFO: 2epoch:train:10501-10600batch: iter_time=1.628e-04, forward_time=0.104, loss_ctc=171.416, loss_att=198.165, acc=0.217, loss=190.140, backward_time=0.098, grad_norm=63.559, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.517e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:19:58,351 (trainer:737) INFO: 2epoch:train:10601-10700batch: iter_time=1.377e-04, forward_time=0.105, loss_ctc=161.884, loss_att=191.174, acc=0.222, loss=182.387, backward_time=0.098, grad_norm=52.742, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.550e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:20:39,653 (trainer:737) INFO: 2epoch:train:10701-10800batch: iter_time=1.211e-04, forward_time=0.105, loss_ctc=163.587, loss_att=195.337, acc=0.221, loss=185.812, backward_time=0.097, grad_norm=47.150, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.584e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:21:20,835 (trainer:737) INFO: 2epoch:train:10801-10900batch: iter_time=1.196e-04, forward_time=0.105, loss_ctc=179.422, loss_att=210.659, acc=0.219, loss=201.288, backward_time=0.098, grad_norm=55.070, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.617e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:22:01,777 (trainer:737) INFO: 2epoch:train:10901-11000batch: iter_time=1.176e-04, forward_time=0.104, loss_ctc=151.699, loss_att=183.931, acc=0.224, loss=174.261, backward_time=0.097, grad_norm=49.103, clip=100.000, loss_scale=2.684e+08, optim_step_time=0.030, optim0_lr0=8.651e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 04:22:43,300 (trainer:737) INFO: 2epoch:train:11001-11100batch: iter_time=1.238e-04, forward_time=0.105, loss_ctc=177.188, loss_att=219.109, acc=0.207, loss=206.532, backward_time=0.098, grad_norm=59.771, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.684e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 04:23:24,102 (trainer:737) INFO: 2epoch:train:11101-11200batch: iter_time=1.463e-04, forward_time=0.103, loss_ctc=139.366, loss_att=166.311, acc=0.232, loss=158.227, backward_time=0.096, grad_norm=47.988, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.717e-05, train_time=0.408
+[gpuc02:0/16] 2024-01-12 04:23:51,854 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 04:24:10,980 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 04:24:14,720 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3893bb7fa0>)
+[gpuc02:0/16] 2024-01-12 04:24:14,720 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 04:24:14,724 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 04:28:36,455 (trainer:737) INFO: 2epoch:train:11201-11300batch: iter_time=2.666, forward_time=0.112, loss_ctc=167.155, loss_att=196.666, acc=0.232, loss=187.812, backward_time=0.098, grad_norm=66.125, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.750e-05, train_time=3.123
+[gpuc02:0/16] 2024-01-12 04:29:17,869 (trainer:737) INFO: 2epoch:train:11301-11400batch: iter_time=1.335e-04, forward_time=0.106, loss_ctc=196.209, loss_att=236.456, acc=0.206, loss=224.382, backward_time=0.098, grad_norm=65.416, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.784e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:29:58,907 (trainer:737) INFO: 2epoch:train:11401-11500batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=168.831, loss_att=188.265, acc=0.231, loss=182.435, backward_time=0.098, grad_norm=61.994, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.817e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 04:30:40,018 (trainer:737) INFO: 2epoch:train:11501-11600batch: iter_time=1.661e-04, forward_time=0.104, loss_ctc=150.182, loss_att=187.780, acc=0.229, loss=176.501, backward_time=0.098, grad_norm=44.495, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.851e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 04:31:21,314 (trainer:737) INFO: 2epoch:train:11601-11700batch: iter_time=1.502e-04, forward_time=0.106, loss_ctc=190.264, loss_att=229.255, acc=0.198, loss=217.558, backward_time=0.099, grad_norm=61.108, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.884e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:32:02,492 (trainer:737) INFO: 2epoch:train:11701-11800batch: iter_time=1.436e-04, forward_time=0.105, loss_ctc=177.494, loss_att=214.512, acc=0.210, loss=203.407, backward_time=0.098, grad_norm=67.106, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.917e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:32:43,302 (trainer:737) INFO: 2epoch:train:11801-11900batch: iter_time=1.402e-04, forward_time=0.103, loss_ctc=132.769, loss_att=161.685, acc=0.238, loss=153.010, backward_time=0.097, grad_norm=47.172, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.951e-05, train_time=0.408
+[gpuc02:0/16] 2024-01-12 04:33:24,503 (trainer:737) INFO: 2epoch:train:11901-12000batch: iter_time=1.308e-04, forward_time=0.105, loss_ctc=172.856, loss_att=211.555, acc=0.217, loss=199.945, backward_time=0.098, grad_norm=57.701, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=8.984e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:34:05,902 (trainer:737) INFO: 2epoch:train:12001-12100batch: iter_time=1.311e-04, forward_time=0.108, loss_ctc=153.808, loss_att=196.727, acc=0.224, loss=183.851, backward_time=0.098, grad_norm=43.640, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.017e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:34:48,575 (trainer:737) INFO: 2epoch:train:12101-12200batch: iter_time=1.292e-04, forward_time=0.112, loss_ctc=159.499, loss_att=195.646, acc=0.226, loss=184.802, backward_time=0.098, grad_norm=54.066, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.031, optim0_lr0=9.050e-05, train_time=0.426
+[gpuc02:0/16] 2024-01-12 04:35:29,504 (trainer:737) INFO: 2epoch:train:12201-12300batch: iter_time=1.161e-04, forward_time=0.104, loss_ctc=146.271, loss_att=178.498, acc=0.225, loss=168.830, backward_time=0.097, grad_norm=49.195, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.084e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 04:36:10,806 (trainer:737) INFO: 2epoch:train:12301-12400batch: iter_time=1.285e-04, forward_time=0.104, loss_ctc=155.041, loss_att=193.072, acc=0.221, loss=181.662, backward_time=0.097, grad_norm=52.051, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.117e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 04:36:52,260 (trainer:737) INFO: 2epoch:train:12401-12500batch: iter_time=1.153e-04, forward_time=0.104, loss_ctc=143.139, loss_att=181.033, acc=0.234, loss=169.665, backward_time=0.096, grad_norm=53.951, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.151e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:37:03,438 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 04:37:22,580 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 04:37:26,181 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c192fe20>)
+[gpuc02:0/16] 2024-01-12 04:37:26,181 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 04:37:26,185 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 04:42:13,383 (trainer:737) INFO: 2epoch:train:12501-12600batch: iter_time=2.744, forward_time=0.145, loss_ctc=172.151, loss_att=215.567, acc=0.217, loss=202.542, backward_time=0.105, grad_norm=62.385, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.032, optim0_lr0=9.184e-05, train_time=3.211
+[gpuc02:0/16] 2024-01-12 04:42:54,468 (trainer:737) INFO: 2epoch:train:12601-12700batch: iter_time=1.595e-04, forward_time=0.104, loss_ctc=161.287, loss_att=195.986, acc=0.229, loss=185.576, backward_time=0.097, grad_norm=58.219, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.217e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 04:43:35,592 (trainer:737) INFO: 2epoch:train:12701-12800batch: iter_time=1.298e-04, forward_time=0.105, loss_ctc=157.890, loss_att=190.904, acc=0.233, loss=181.000, backward_time=0.098, grad_norm=52.595, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.250e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 04:44:16,790 (trainer:737) INFO: 2epoch:train:12801-12900batch: iter_time=1.257e-04, forward_time=0.105, loss_ctc=157.886, loss_att=207.917, acc=0.216, loss=192.908, backward_time=0.098, grad_norm=51.387, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.284e-05, train_time=0.412
+[gpuc02:0/16] 2024-01-12 04:44:58,254 (trainer:737) INFO: 2epoch:train:12901-13000batch: iter_time=1.322e-04, forward_time=0.108, loss_ctc=173.981, loss_att=215.243, acc=0.209, loss=202.865, backward_time=0.098, grad_norm=61.927, clip=100.000, loss_scale=5.369e+08, optim_step_time=0.030, optim0_lr0=9.317e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:45:39,222 (trainer:737) INFO: 2epoch:train:13001-13100batch: iter_time=1.388e-04, forward_time=0.104, loss_ctc=150.142, loss_att=190.599, acc=0.225, loss=178.462, backward_time=0.097, grad_norm=52.016, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.351e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 04:46:20,203 (trainer:737) INFO: 2epoch:train:13101-13200batch: iter_time=1.233e-04, forward_time=0.104, loss_ctc=142.701, loss_att=185.623, acc=0.229, loss=172.747, backward_time=0.097, grad_norm=48.467, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.384e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 04:47:01,244 (trainer:737) INFO: 2epoch:train:13201-13300batch: iter_time=1.693e-04, forward_time=0.105, loss_ctc=144.940, loss_att=190.396, acc=0.228, loss=176.759, backward_time=0.097, grad_norm=43.237, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.417e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 04:47:43,293 (trainer:737) INFO: 2epoch:train:13301-13400batch: iter_time=1.539e-04, forward_time=0.112, loss_ctc=157.380, loss_att=202.763, acc=0.227, loss=189.148, backward_time=0.098, grad_norm=48.396, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.031, optim0_lr0=9.450e-05, train_time=0.420
+[gpuc02:0/16] 2024-01-12 04:48:24,219 (trainer:737) INFO: 2epoch:train:13401-13500batch: iter_time=1.411e-04, forward_time=0.105, loss_ctc=135.122, loss_att=179.205, acc=0.230, loss=165.981, backward_time=0.097, grad_norm=47.633, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.484e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 04:49:05,292 (trainer:737) INFO: 2epoch:train:13501-13600batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=157.013, loss_att=209.028, acc=0.214, loss=193.424, backward_time=0.098, grad_norm=53.652, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.517e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 04:49:46,042 (trainer:737) INFO: 2epoch:train:13601-13700batch: iter_time=1.274e-04, forward_time=0.103, loss_ctc=123.057, loss_att=161.821, acc=0.236, loss=150.192, backward_time=0.096, grad_norm=43.007, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.551e-05, train_time=0.407
+[gpuc02:0/16] 2024-01-12 04:50:14,433 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 04:50:34,297 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 04:50:38,187 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f388f197670>)
+[gpuc02:0/16] 2024-01-12 04:50:38,187 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 04:50:38,190 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 04:54:57,933 (trainer:737) INFO: 2epoch:train:13701-13800batch: iter_time=2.672, forward_time=0.107, loss_ctc=147.038, loss_att=195.656, acc=0.238, loss=181.071, backward_time=0.098, grad_norm=57.089, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.584e-05, train_time=3.119
+[gpuc02:0/16] 2024-01-12 04:55:40,023 (trainer:737) INFO: 2epoch:train:13801-13900batch: iter_time=1.464e-04, forward_time=0.106, loss_ctc=172.180, loss_att=238.385, acc=0.210, loss=218.523, backward_time=0.099, grad_norm=61.480, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.617e-05, train_time=0.421
+[gpuc02:0/16] 2024-01-12 04:56:21,435 (trainer:737) INFO: 2epoch:train:13901-14000batch: iter_time=1.828e-04, forward_time=0.104, loss_ctc=151.560, loss_att=186.445, acc=0.237, loss=175.979, backward_time=0.097, grad_norm=59.645, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.029, optim0_lr0=9.651e-05, train_time=0.414
+[gpuc02:0/16] 2024-01-12 04:57:02,515 (trainer:737) INFO: 2epoch:train:14001-14100batch: iter_time=1.441e-04, forward_time=0.104, loss_ctc=130.967, loss_att=185.468, acc=0.237, loss=169.117, backward_time=0.097, grad_norm=40.888, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.029, optim0_lr0=9.684e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 04:57:44,324 (trainer:737) INFO: 2epoch:train:14101-14200batch: iter_time=1.357e-04, forward_time=0.106, loss_ctc=168.196, loss_att=232.136, acc=0.204, loss=212.954, backward_time=0.099, grad_norm=55.134, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.717e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 04:58:26,158 (trainer:737) INFO: 2epoch:train:14201-14300batch: iter_time=1.261e-04, forward_time=0.105, loss_ctc=157.494, loss_att=216.181, acc=0.216, loss=198.575, backward_time=0.098, grad_norm=59.007, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.029, optim0_lr0=9.750e-05, train_time=0.418
+[gpuc02:0/16] 2024-01-12 04:59:07,671 (trainer:737) INFO: 2epoch:train:14301-14400batch: iter_time=1.348e-04, forward_time=0.103, loss_ctc=116.808, loss_att=158.429, acc=0.246, loss=145.943, backward_time=0.097, grad_norm=41.084, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.784e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 04:59:48,957 (trainer:737) INFO: 2epoch:train:14401-14500batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=153.929, loss_att=212.920, acc=0.223, loss=195.223, backward_time=0.098, grad_norm=53.532, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.029, optim0_lr0=9.817e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 05:00:30,258 (trainer:737) INFO: 2epoch:train:14501-14600batch: iter_time=1.341e-04, forward_time=0.105, loss_ctc=135.083, loss_att=198.123, acc=0.229, loss=179.211, backward_time=0.098, grad_norm=40.608, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.851e-05, train_time=0.413
+[gpuc02:0/16] 2024-01-12 05:01:11,407 (trainer:737) INFO: 2epoch:train:14601-14700batch: iter_time=1.566e-04, forward_time=0.105, loss_ctc=140.407, loss_att=193.841, acc=0.232, loss=177.811, backward_time=0.098, grad_norm=46.698, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.884e-05, train_time=0.411
+[gpuc02:0/16] 2024-01-12 05:01:52,955 (trainer:737) INFO: 2epoch:train:14701-14800batch: iter_time=1.666e-04, forward_time=0.103, loss_ctc=128.918, loss_att=178.879, acc=0.232, loss=163.891, backward_time=0.097, grad_norm=45.277, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.917e-05, train_time=0.415
+[gpuc02:0/16] 2024-01-12 05:02:34,025 (trainer:737) INFO: 2epoch:train:14801-14900batch: iter_time=1.790e-04, forward_time=0.104, loss_ctc=137.545, loss_att=191.993, acc=0.228, loss=175.659, backward_time=0.098, grad_norm=48.607, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.950e-05, train_time=0.410
+[gpuc02:0/16] 2024-01-12 05:03:14,937 (trainer:737) INFO: 2epoch:train:14901-15000batch: iter_time=1.461e-04, forward_time=0.103, loss_ctc=126.697, loss_att=178.899, acc=0.242, loss=163.238, backward_time=0.097, grad_norm=48.331, clip=100.000, loss_scale=1.074e+09, optim_step_time=0.030, optim0_lr0=9.984e-05, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:23:15,301 (trainer:343) INFO: 2epoch results: [train] iter_time=0.206, forward_time=0.106, loss_ctc=201.864, loss_att=207.221, acc=0.210, loss=205.613, backward_time=0.098, grad_norm=71.047, clip=99.987, loss_scale=2.847e+08, optim_step_time=0.030, optim0_lr0=7.501e-05, train_time=0.635, time=2 hours, 38 minutes and 58.85 seconds, total_count=30000, gpu_max_cached_mem_GB=24.980, [valid] loss_ctc=142.471, cer_ctc=0.700, loss_att=149.777, acc=0.187, cer=0.701, wer=1.000, loss=147.585, time=19 minutes and 50.58 seconds, total_count=9342, gpu_max_cached_mem_GB=24.980
+[gpuc02:0/16] 2024-01-12 05:23:19,950 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 05:23:19,951 (trainer:272) INFO: 3/45epoch started. Estimated time to finish: 5 days, 7 hours and 19 minutes
+[gpuc02:0/16] 2024-01-12 05:23:19,960 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 05:23:38,811 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 05:23:42,371 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e4930070>)
+[gpuc02:0/16] 2024-01-12 05:23:42,372 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 05:23:42,375 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 05:28:20,407 (trainer:737) INFO: 3epoch:train:1-100batch: iter_time=2.380, forward_time=0.104, loss_ctc=146.079, loss_att=199.333, acc=0.225, loss=183.357, backward_time=0.099, grad_norm=58.384, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.015e-04, train_time=3.004
+[gpuc02:0/16] 2024-01-12 05:29:02,284 (trainer:737) INFO: 3epoch:train:101-200batch: iter_time=1.176e-04, forward_time=0.104, loss_ctc=131.489, loss_att=197.503, acc=0.230, loss=177.699, backward_time=0.099, grad_norm=50.723, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.045e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-12 05:29:45,665 (trainer:737) INFO: 3epoch:train:201-300batch: iter_time=1.258e-04, forward_time=0.115, loss_ctc=138.027, loss_att=202.089, acc=0.223, loss=182.870, backward_time=0.108, grad_norm=51.651, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.031, optim0_lr0=1.075e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-12 05:30:26,722 (trainer:737) INFO: 3epoch:train:301-400batch: iter_time=1.213e-04, forward_time=0.105, loss_ctc=141.824, loss_att=206.352, acc=0.222, loss=186.994, backward_time=0.100, grad_norm=51.509, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.105e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 05:31:07,611 (trainer:737) INFO: 3epoch:train:401-500batch: iter_time=1.146e-04, forward_time=0.104, loss_ctc=148.557, loss_att=204.473, acc=0.227, loss=187.698, backward_time=0.098, grad_norm=59.582, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.135e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:31:49,004 (trainer:737) INFO: 3epoch:train:501-600batch: iter_time=1.226e-04, forward_time=0.103, loss_ctc=134.427, loss_att=194.175, acc=0.222, loss=176.251, backward_time=0.098, grad_norm=60.512, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.029, optim0_lr0=1.165e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 05:32:29,943 (trainer:737) INFO: 3epoch:train:601-700batch: iter_time=1.081e-04, forward_time=0.104, loss_ctc=133.011, loss_att=195.570, acc=0.230, loss=176.802, backward_time=0.098, grad_norm=48.451, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.195e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:33:10,922 (trainer:737) INFO: 3epoch:train:701-800batch: iter_time=1.118e-04, forward_time=0.105, loss_ctc=167.908, loss_att=215.800, acc=0.219, loss=201.433, backward_time=0.099, grad_norm=70.883, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.029, optim0_lr0=1.225e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 05:33:52,075 (trainer:737) INFO: 3epoch:train:801-900batch: iter_time=1.238e-04, forward_time=0.106, loss_ctc=135.789, loss_att=213.049, acc=0.221, loss=189.871, backward_time=0.099, grad_norm=50.706, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.255e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 05:34:35,838 (trainer:737) INFO: 3epoch:train:901-1000batch: iter_time=1.120e-04, forward_time=0.105, loss_ctc=133.913, loss_att=211.086, acc=0.223, loss=187.934, backward_time=0.099, grad_norm=57.621, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.029, optim0_lr0=1.285e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-12 05:35:19,589 (trainer:737) INFO: 3epoch:train:1001-1100batch: iter_time=1.000e-04, forward_time=0.105, loss_ctc=131.792, loss_att=178.688, acc=0.237, loss=164.619, backward_time=0.101, grad_norm=54.468, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.315e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-12 05:36:02,164 (trainer:737) INFO: 3epoch:train:1101-1200batch: iter_time=1.052e-04, forward_time=0.106, loss_ctc=142.462, loss_att=209.561, acc=0.221, loss=189.432, backward_time=0.100, grad_norm=51.818, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.345e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-12 05:36:42,809 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 05:37:01,507 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 05:37:05,001 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f56a72980>)
+[gpuc02:0/16] 2024-01-12 05:37:05,001 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 05:37:05,005 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 05:42:41,221 (trainer:737) INFO: 3epoch:train:1201-1300batch: iter_time=3.409, forward_time=0.117, loss_ctc=128.485, loss_att=177.947, acc=0.244, loss=163.108, backward_time=0.099, grad_norm=56.369, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.031, optim0_lr0=1.375e-04, train_time=3.990
+[gpuc02:0/16] 2024-01-12 05:43:22,661 (trainer:737) INFO: 3epoch:train:1301-1400batch: iter_time=1.070e-04, forward_time=0.104, loss_ctc=148.957, loss_att=211.115, acc=0.218, loss=192.468, backward_time=0.099, grad_norm=61.788, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.405e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 05:44:03,766 (trainer:737) INFO: 3epoch:train:1401-1500batch: iter_time=1.085e-04, forward_time=0.104, loss_ctc=133.364, loss_att=207.820, acc=0.230, loss=185.484, backward_time=0.099, grad_norm=49.225, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.435e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 05:44:45,109 (trainer:737) INFO: 3epoch:train:1501-1600batch: iter_time=1.108e-04, forward_time=0.106, loss_ctc=122.835, loss_att=176.515, acc=0.229, loss=160.411, backward_time=0.098, grad_norm=50.127, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.465e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 05:45:26,849 (trainer:737) INFO: 3epoch:train:1601-1700batch: iter_time=1.057e-04, forward_time=0.105, loss_ctc=151.656, loss_att=232.152, acc=0.224, loss=208.003, backward_time=0.099, grad_norm=63.327, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.495e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 05:46:07,725 (trainer:737) INFO: 3epoch:train:1701-1800batch: iter_time=1.135e-04, forward_time=0.104, loss_ctc=127.292, loss_att=188.369, acc=0.225, loss=170.046, backward_time=0.098, grad_norm=53.347, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.525e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:46:48,615 (trainer:737) INFO: 3epoch:train:1801-1900batch: iter_time=1.147e-04, forward_time=0.104, loss_ctc=124.740, loss_att=185.585, acc=0.233, loss=167.331, backward_time=0.098, grad_norm=55.322, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.555e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:47:29,664 (trainer:737) INFO: 3epoch:train:1901-2000batch: iter_time=1.149e-04, forward_time=0.105, loss_ctc=142.255, loss_att=205.126, acc=0.222, loss=186.265, backward_time=0.098, grad_norm=57.104, clip=100.000, loss_scale=2.147e+09, optim_step_time=0.030, optim0_lr0=1.585e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 05:48:10,812 (trainer:737) INFO: 3epoch:train:2001-2100batch: iter_time=1.069e-04, forward_time=0.105, loss_ctc=157.645, loss_att=228.754, acc=0.222, loss=207.422, backward_time=0.098, grad_norm=66.856, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.615e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 05:48:51,853 (trainer:737) INFO: 3epoch:train:2101-2200batch: iter_time=1.060e-04, forward_time=0.104, loss_ctc=112.766, loss_att=189.890, acc=0.231, loss=166.753, backward_time=0.097, grad_norm=45.313, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.645e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 05:49:32,969 (trainer:737) INFO: 3epoch:train:2201-2300batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=143.908, loss_att=211.423, acc=0.231, loss=191.168, backward_time=0.098, grad_norm=58.972, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.675e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 05:50:13,768 (trainer:737) INFO: 3epoch:train:2301-2400batch: iter_time=1.125e-04, forward_time=0.103, loss_ctc=119.313, loss_att=177.872, acc=0.232, loss=160.305, backward_time=0.097, grad_norm=52.982, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.705e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 05:50:54,722 (trainer:737) INFO: 3epoch:train:2401-2500batch: iter_time=9.853e-05, forward_time=0.104, loss_ctc=139.777, loss_att=205.541, acc=0.232, loss=185.812, backward_time=0.098, grad_norm=52.528, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.735e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 05:51:01,370 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 05:51:21,613 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 05:51:25,226 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f541283d0>)
+[gpuc02:0/16] 2024-01-12 05:51:25,226 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 05:51:25,230 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 05:56:05,351 (trainer:737) INFO: 3epoch:train:2501-2600batch: iter_time=2.571, forward_time=0.111, loss_ctc=136.085, loss_att=196.208, acc=0.229, loss=178.171, backward_time=0.099, grad_norm=63.910, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.765e-04, train_time=3.106
+[gpuc02:0/16] 2024-01-12 05:56:46,619 (trainer:737) INFO: 3epoch:train:2601-2700batch: iter_time=1.155e-04, forward_time=0.104, loss_ctc=122.961, loss_att=196.640, acc=0.232, loss=174.536, backward_time=0.097, grad_norm=56.309, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.795e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 05:57:27,904 (trainer:737) INFO: 3epoch:train:2701-2800batch: iter_time=1.185e-04, forward_time=0.105, loss_ctc=126.259, loss_att=197.607, acc=0.226, loss=176.202, backward_time=0.097, grad_norm=54.174, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.029, optim0_lr0=1.825e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 05:58:09,233 (trainer:737) INFO: 3epoch:train:2801-2900batch: iter_time=1.097e-04, forward_time=0.105, loss_ctc=129.212, loss_att=202.502, acc=0.227, loss=180.515, backward_time=0.098, grad_norm=50.286, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.855e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 05:58:50,429 (trainer:737) INFO: 3epoch:train:2901-3000batch: iter_time=1.041e-04, forward_time=0.105, loss_ctc=136.793, loss_att=200.761, acc=0.230, loss=181.571, backward_time=0.097, grad_norm=62.323, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.029, optim0_lr0=1.885e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 05:59:31,543 (trainer:737) INFO: 3epoch:train:3001-3100batch: iter_time=1.023e-04, forward_time=0.104, loss_ctc=122.629, loss_att=191.452, acc=0.228, loss=170.805, backward_time=0.097, grad_norm=55.787, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.915e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:00:12,536 (trainer:737) INFO: 3epoch:train:3101-3200batch: iter_time=1.162e-04, forward_time=0.104, loss_ctc=120.141, loss_att=192.288, acc=0.235, loss=170.644, backward_time=0.097, grad_norm=45.634, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.945e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:00:53,641 (trainer:737) INFO: 3epoch:train:3201-3300batch: iter_time=1.029e-04, forward_time=0.105, loss_ctc=151.828, loss_att=210.041, acc=0.225, loss=192.577, backward_time=0.097, grad_norm=64.624, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=1.975e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:01:34,930 (trainer:737) INFO: 3epoch:train:3301-3400batch: iter_time=9.692e-05, forward_time=0.107, loss_ctc=123.053, loss_att=208.336, acc=0.227, loss=182.751, backward_time=0.097, grad_norm=48.213, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.005e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:02:16,073 (trainer:737) INFO: 3epoch:train:3401-3500batch: iter_time=9.071e-05, forward_time=0.105, loss_ctc=120.791, loss_att=206.531, acc=0.231, loss=180.809, backward_time=0.098, grad_norm=51.919, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.035e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:02:56,903 (trainer:737) INFO: 3epoch:train:3501-3600batch: iter_time=1.013e-04, forward_time=0.104, loss_ctc=120.227, loss_att=176.373, acc=0.241, loss=159.529, backward_time=0.097, grad_norm=54.018, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.065e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:03:38,034 (trainer:737) INFO: 3epoch:train:3601-3700batch: iter_time=1.007e-04, forward_time=0.105, loss_ctc=130.958, loss_att=205.191, acc=0.228, loss=182.921, backward_time=0.098, grad_norm=50.877, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.095e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:04:01,186 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 06:04:21,628 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 06:04:25,275 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f385b86fd00>)
+[gpuc02:0/16] 2024-01-12 06:04:25,275 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 06:04:25,278 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 06:08:59,603 (trainer:737) INFO: 3epoch:train:3701-3800batch: iter_time=2.719, forward_time=0.133, loss_ctc=117.907, loss_att=172.581, acc=0.251, loss=156.179, backward_time=0.100, grad_norm=58.228, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.031, optim0_lr0=2.125e-04, train_time=3.215
+[gpuc02:0/16] 2024-01-12 06:09:40,926 (trainer:737) INFO: 3epoch:train:3801-3900batch: iter_time=1.369e-04, forward_time=0.105, loss_ctc=137.669, loss_att=202.979, acc=0.222, loss=183.386, backward_time=0.098, grad_norm=64.227, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.155e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:10:22,429 (trainer:737) INFO: 3epoch:train:3901-4000batch: iter_time=1.055e-04, forward_time=0.104, loss_ctc=121.468, loss_att=202.108, acc=0.236, loss=177.916, backward_time=0.098, grad_norm=53.508, clip=100.000, loss_scale=4.295e+09, optim_step_time=0.030, optim0_lr0=2.185e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 06:11:03,392 (trainer:737) INFO: 3epoch:train:4001-4100batch: iter_time=1.050e-04, forward_time=0.103, loss_ctc=112.849, loss_att=169.449, acc=0.233, loss=152.469, backward_time=0.097, grad_norm=51.045, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.215e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:11:44,845 (trainer:737) INFO: 3epoch:train:4101-4200batch: iter_time=1.020e-04, forward_time=0.106, loss_ctc=137.690, loss_att=221.579, acc=0.231, loss=196.412, backward_time=0.099, grad_norm=61.006, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.245e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:12:26,173 (trainer:737) INFO: 3epoch:train:4201-4300batch: iter_time=1.051e-04, forward_time=0.103, loss_ctc=116.518, loss_att=181.867, acc=0.235, loss=162.262, backward_time=0.097, grad_norm=54.034, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.275e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:13:07,278 (trainer:737) INFO: 3epoch:train:4301-4400batch: iter_time=1.056e-04, forward_time=0.103, loss_ctc=112.774, loss_att=179.214, acc=0.242, loss=159.282, backward_time=0.097, grad_norm=48.274, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.305e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:13:48,601 (trainer:737) INFO: 3epoch:train:4401-4500batch: iter_time=1.146e-04, forward_time=0.104, loss_ctc=129.653, loss_att=198.023, acc=0.231, loss=177.512, backward_time=0.098, grad_norm=54.567, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.335e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:14:29,993 (trainer:737) INFO: 3epoch:train:4501-4600batch: iter_time=9.911e-05, forward_time=0.106, loss_ctc=141.644, loss_att=221.001, acc=0.231, loss=197.194, backward_time=0.098, grad_norm=60.983, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.365e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:15:10,893 (trainer:737) INFO: 3epoch:train:4601-4700batch: iter_time=1.028e-04, forward_time=0.105, loss_ctc=103.062, loss_att=183.003, acc=0.245, loss=159.021, backward_time=0.097, grad_norm=43.619, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.395e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:15:52,270 (trainer:737) INFO: 3epoch:train:4701-4800batch: iter_time=1.020e-04, forward_time=0.105, loss_ctc=132.164, loss_att=203.577, acc=0.246, loss=182.153, backward_time=0.098, grad_norm=61.612, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.425e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:16:33,177 (trainer:737) INFO: 3epoch:train:4801-4900batch: iter_time=1.091e-04, forward_time=0.104, loss_ctc=107.977, loss_att=171.703, acc=0.241, loss=152.585, backward_time=0.097, grad_norm=44.203, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.455e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:17:14,196 (trainer:737) INFO: 3epoch:train:4901-5000batch: iter_time=9.852e-05, forward_time=0.105, loss_ctc=128.962, loss_att=196.843, acc=0.245, loss=176.479, backward_time=0.098, grad_norm=59.833, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.485e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:17:18,157 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 06:17:38,575 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 06:17:42,722 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a4e525c0>)
+[gpuc02:0/16] 2024-01-12 06:17:42,722 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 06:17:42,725 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 06:22:06,427 (trainer:737) INFO: 3epoch:train:5001-5100batch: iter_time=2.441, forward_time=0.104, loss_ctc=123.827, loss_att=185.975, acc=0.241, loss=167.331, backward_time=0.097, grad_norm=61.380, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.029, optim0_lr0=2.515e-04, train_time=2.922
+[gpuc02:0/16] 2024-01-12 06:22:47,357 (trainer:737) INFO: 3epoch:train:5101-5200batch: iter_time=1.019e-04, forward_time=0.104, loss_ctc=112.269, loss_att=185.073, acc=0.248, loss=163.232, backward_time=0.097, grad_norm=52.631, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.029, optim0_lr0=2.545e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:23:28,250 (trainer:737) INFO: 3epoch:train:5201-5300batch: iter_time=1.016e-04, forward_time=0.104, loss_ctc=115.523, loss_att=188.646, acc=0.243, loss=166.709, backward_time=0.097, grad_norm=55.017, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.575e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:24:09,150 (trainer:737) INFO: 3epoch:train:5301-5400batch: iter_time=1.062e-04, forward_time=0.104, loss_ctc=117.449, loss_att=188.287, acc=0.247, loss=167.035, backward_time=0.097, grad_norm=50.422, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.605e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:24:50,591 (trainer:737) INFO: 3epoch:train:5401-5500batch: iter_time=1.016e-04, forward_time=0.103, loss_ctc=124.760, loss_att=188.979, acc=0.251, loss=169.713, backward_time=0.097, grad_norm=56.253, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.029, optim0_lr0=2.635e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:25:31,650 (trainer:737) INFO: 3epoch:train:5501-5600batch: iter_time=1.082e-04, forward_time=0.103, loss_ctc=111.072, loss_att=179.316, acc=0.249, loss=158.843, backward_time=0.096, grad_norm=55.142, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.665e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:26:13,214 (trainer:737) INFO: 3epoch:train:5601-5700batch: iter_time=9.565e-05, forward_time=0.103, loss_ctc=110.308, loss_att=182.749, acc=0.257, loss=161.017, backward_time=0.097, grad_norm=47.631, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.029, optim0_lr0=2.695e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 06:26:54,764 (trainer:737) INFO: 3epoch:train:5701-5800batch: iter_time=9.756e-05, forward_time=0.104, loss_ctc=140.470, loss_att=201.139, acc=0.242, loss=182.939, backward_time=0.097, grad_norm=60.628, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.725e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 06:27:35,698 (trainer:737) INFO: 3epoch:train:5801-5900batch: iter_time=9.915e-05, forward_time=0.104, loss_ctc=112.575, loss_att=195.099, acc=0.258, loss=170.342, backward_time=0.097, grad_norm=48.444, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.030, optim0_lr0=2.755e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:28:16,994 (trainer:737) INFO: 3epoch:train:5901-6000batch: iter_time=9.728e-05, forward_time=0.104, loss_ctc=110.445, loss_att=190.467, acc=0.263, loss=166.461, backward_time=0.097, grad_norm=54.437, clip=100.000, loss_scale=8.590e+09, optim_step_time=0.029, optim0_lr0=2.785e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:28:58,019 (trainer:737) INFO: 3epoch:train:6001-6100batch: iter_time=9.835e-05, forward_time=0.103, loss_ctc=111.935, loss_att=166.860, acc=0.269, loss=150.382, backward_time=0.096, grad_norm=52.455, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.815e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:29:39,244 (trainer:737) INFO: 3epoch:train:6101-6200batch: iter_time=1.077e-04, forward_time=0.103, loss_ctc=121.218, loss_att=190.988, acc=0.258, loss=170.057, backward_time=0.097, grad_norm=53.428, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.845e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 06:30:03,615 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 06:30:23,089 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 06:30:26,796 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f382e6df6d0>)
+[gpuc02:0/16] 2024-01-12 06:30:26,796 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 06:30:26,799 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 06:34:32,651 (trainer:737) INFO: 3epoch:train:6201-6300batch: iter_time=2.322, forward_time=0.124, loss_ctc=105.706, loss_att=160.726, acc=0.278, loss=144.220, backward_time=0.103, grad_norm=51.898, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.875e-04, train_time=2.934
+[gpuc02:0/16] 2024-01-12 06:35:13,884 (trainer:737) INFO: 3epoch:train:6301-6400batch: iter_time=1.061e-04, forward_time=0.104, loss_ctc=127.160, loss_att=189.607, acc=0.254, loss=170.873, backward_time=0.099, grad_norm=63.074, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.905e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 06:35:54,911 (trainer:737) INFO: 3epoch:train:6401-6500batch: iter_time=1.028e-04, forward_time=0.104, loss_ctc=112.487, loss_att=187.610, acc=0.273, loss=165.073, backward_time=0.099, grad_norm=52.726, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.935e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:36:35,703 (trainer:737) INFO: 3epoch:train:6501-6600batch: iter_time=9.869e-05, forward_time=0.103, loss_ctc=103.806, loss_att=156.646, acc=0.269, loss=140.794, backward_time=0.098, grad_norm=51.494, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.965e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:37:17,135 (trainer:737) INFO: 3epoch:train:6601-6700batch: iter_time=1.131e-04, forward_time=0.107, loss_ctc=126.793, loss_att=203.542, acc=0.273, loss=180.518, backward_time=0.099, grad_norm=60.639, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=2.995e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:37:58,013 (trainer:737) INFO: 3epoch:train:6701-6800batch: iter_time=1.226e-04, forward_time=0.103, loss_ctc=109.383, loss_att=167.986, acc=0.274, loss=150.405, backward_time=0.098, grad_norm=54.654, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.025e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:38:38,857 (trainer:737) INFO: 3epoch:train:6801-6900batch: iter_time=1.418e-04, forward_time=0.104, loss_ctc=102.948, loss_att=164.849, acc=0.281, loss=146.279, backward_time=0.097, grad_norm=49.026, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.055e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:39:19,842 (trainer:737) INFO: 3epoch:train:6901-7000batch: iter_time=1.586e-04, forward_time=0.104, loss_ctc=119.073, loss_att=183.801, acc=0.266, loss=164.383, backward_time=0.098, grad_norm=56.559, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.085e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:40:01,003 (trainer:737) INFO: 3epoch:train:7001-7100batch: iter_time=1.330e-04, forward_time=0.104, loss_ctc=129.786, loss_att=201.224, acc=0.273, loss=179.792, backward_time=0.099, grad_norm=64.719, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.115e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:40:41,875 (trainer:737) INFO: 3epoch:train:7101-7200batch: iter_time=1.332e-04, forward_time=0.103, loss_ctc=94.161, loss_att=160.903, acc=0.300, loss=140.881, backward_time=0.097, grad_norm=49.421, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.145e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:41:23,245 (trainer:737) INFO: 3epoch:train:7201-7300batch: iter_time=1.179e-04, forward_time=0.104, loss_ctc=122.641, loss_att=181.354, acc=0.303, loss=163.740, backward_time=0.098, grad_norm=64.260, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.175e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:42:04,264 (trainer:737) INFO: 3epoch:train:7301-7400batch: iter_time=1.092e-04, forward_time=0.103, loss_ctc=100.578, loss_att=154.449, acc=0.285, loss=138.287, backward_time=0.097, grad_norm=54.113, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.205e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:42:45,792 (trainer:737) INFO: 3epoch:train:7401-7500batch: iter_time=9.830e-05, forward_time=0.104, loss_ctc=119.820, loss_att=174.878, acc=0.302, loss=158.361, backward_time=0.098, grad_norm=67.412, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.235e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 06:42:48,185 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 06:43:08,042 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 06:43:11,659 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3894cc1de0>)
+[gpuc02:0/16] 2024-01-12 06:43:11,660 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 06:43:11,663 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 06:47:32,012 (trainer:737) INFO: 3epoch:train:7501-7600batch: iter_time=2.258, forward_time=0.104, loss_ctc=116.726, loss_att=176.145, acc=0.288, loss=158.319, backward_time=0.098, grad_norm=63.590, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.265e-04, train_time=2.862
+[gpuc02:0/16] 2024-01-12 06:48:13,356 (trainer:737) INFO: 3epoch:train:7601-7700batch: iter_time=1.520e-04, forward_time=0.104, loss_ctc=102.907, loss_att=174.628, acc=0.296, loss=153.112, backward_time=0.098, grad_norm=58.222, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.295e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 06:48:54,757 (trainer:737) INFO: 3epoch:train:7701-7800batch: iter_time=1.437e-04, forward_time=0.104, loss_ctc=106.687, loss_att=171.588, acc=0.300, loss=152.118, backward_time=0.098, grad_norm=59.285, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.029, optim0_lr0=3.325e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 06:49:35,930 (trainer:737) INFO: 3epoch:train:7801-7900batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=109.405, loss_att=176.977, acc=0.295, loss=156.705, backward_time=0.098, grad_norm=57.304, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.030, optim0_lr0=3.355e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 06:50:16,883 (trainer:737) INFO: 3epoch:train:7901-8000batch: iter_time=1.359e-04, forward_time=0.104, loss_ctc=115.635, loss_att=175.772, acc=0.300, loss=157.731, backward_time=0.097, grad_norm=67.405, clip=100.000, loss_scale=1.718e+10, optim_step_time=0.029, optim0_lr0=3.385e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:50:57,719 (trainer:737) INFO: 3epoch:train:8001-8100batch: iter_time=1.709e-04, forward_time=0.104, loss_ctc=104.241, loss_att=164.736, acc=0.295, loss=146.588, backward_time=0.097, grad_norm=56.683, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.415e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:51:38,661 (trainer:737) INFO: 3epoch:train:8101-8200batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=101.489, loss_att=161.658, acc=0.322, loss=143.607, backward_time=0.097, grad_norm=54.513, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.445e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 06:52:19,751 (trainer:737) INFO: 3epoch:train:8201-8300batch: iter_time=1.563e-04, forward_time=0.105, loss_ctc=128.242, loss_att=183.074, acc=0.290, loss=166.624, backward_time=0.097, grad_norm=68.570, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.475e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 06:53:00,786 (trainer:737) INFO: 3epoch:train:8301-8400batch: iter_time=1.571e-04, forward_time=0.105, loss_ctc=105.233, loss_att=170.217, acc=0.324, loss=150.722, backward_time=0.098, grad_norm=58.832, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.505e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:53:41,965 (trainer:737) INFO: 3epoch:train:8401-8500batch: iter_time=1.659e-04, forward_time=0.106, loss_ctc=102.429, loss_att=166.954, acc=0.334, loss=147.597, backward_time=0.099, grad_norm=60.810, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.535e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 06:54:22,803 (trainer:737) INFO: 3epoch:train:8501-8600batch: iter_time=1.826e-04, forward_time=0.104, loss_ctc=103.724, loss_att=146.815, acc=0.328, loss=133.888, backward_time=0.098, grad_norm=61.602, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.565e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 06:55:03,823 (trainer:737) INFO: 3epoch:train:8601-8700batch: iter_time=1.320e-04, forward_time=0.104, loss_ctc=112.655, loss_att=172.003, acc=0.317, loss=154.199, backward_time=0.098, grad_norm=61.163, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.595e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 06:55:26,832 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 06:55:46,462 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 06:55:50,445 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40bf197eb0>)
+[gpuc02:0/16] 2024-01-12 06:55:50,445 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 06:55:50,448 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 07:00:08,783 (trainer:737) INFO: 3epoch:train:8701-8800batch: iter_time=2.335, forward_time=0.104, loss_ctc=101.359, loss_att=146.177, acc=0.335, loss=132.731, backward_time=0.097, grad_norm=60.985, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.625e-04, train_time=3.049
+[gpuc02:0/16] 2024-01-12 07:00:50,510 (trainer:737) INFO: 3epoch:train:8801-8900batch: iter_time=1.310e-04, forward_time=0.106, loss_ctc=121.453, loss_att=175.381, acc=0.309, loss=159.203, backward_time=0.098, grad_norm=74.893, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.655e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 07:01:31,583 (trainer:737) INFO: 3epoch:train:8901-9000batch: iter_time=1.334e-04, forward_time=0.106, loss_ctc=103.919, loss_att=168.262, acc=0.340, loss=148.959, backward_time=0.098, grad_norm=58.798, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.685e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:02:12,362 (trainer:737) INFO: 3epoch:train:9001-9100batch: iter_time=1.271e-04, forward_time=0.104, loss_ctc=96.973, loss_att=139.583, acc=0.330, loss=126.800, backward_time=0.096, grad_norm=56.309, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.715e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:02:53,567 (trainer:737) INFO: 3epoch:train:9101-9200batch: iter_time=1.397e-04, forward_time=0.106, loss_ctc=119.388, loss_att=187.031, acc=0.342, loss=166.738, backward_time=0.098, grad_norm=71.017, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.745e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:03:34,980 (trainer:737) INFO: 3epoch:train:9201-9300batch: iter_time=1.291e-04, forward_time=0.105, loss_ctc=100.682, loss_att=150.445, acc=0.332, loss=135.516, backward_time=0.097, grad_norm=63.435, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.775e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 07:04:16,148 (trainer:737) INFO: 3epoch:train:9301-9400batch: iter_time=1.362e-04, forward_time=0.105, loss_ctc=95.962, loss_att=147.252, acc=0.340, loss=131.865, backward_time=0.097, grad_norm=54.916, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.805e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:04:57,374 (trainer:737) INFO: 3epoch:train:9401-9500batch: iter_time=1.295e-04, forward_time=0.105, loss_ctc=112.322, loss_att=166.600, acc=0.325, loss=150.317, backward_time=0.098, grad_norm=61.226, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.835e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:05:39,413 (trainer:737) INFO: 3epoch:train:9501-9600batch: iter_time=1.316e-04, forward_time=0.106, loss_ctc=123.685, loss_att=181.869, acc=0.338, loss=164.414, backward_time=0.098, grad_norm=71.975, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.865e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-12 07:06:20,344 (trainer:737) INFO: 3epoch:train:9601-9700batch: iter_time=1.339e-04, forward_time=0.105, loss_ctc=88.600, loss_att=139.757, acc=0.372, loss=124.410, backward_time=0.097, grad_norm=60.744, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.895e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:07:01,692 (trainer:737) INFO: 3epoch:train:9701-9800batch: iter_time=1.177e-04, forward_time=0.106, loss_ctc=113.485, loss_att=158.800, acc=0.378, loss=145.205, backward_time=0.098, grad_norm=68.042, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.029, optim0_lr0=3.925e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 07:07:42,467 (trainer:737) INFO: 3epoch:train:9801-9900batch: iter_time=1.019e-04, forward_time=0.105, loss_ctc=93.497, loss_att=135.978, acc=0.352, loss=123.234, backward_time=0.096, grad_norm=54.550, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.955e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:08:23,434 (trainer:737) INFO: 3epoch:train:9901-10000batch: iter_time=8.921e-05, forward_time=0.105, loss_ctc=110.555, loss_att=154.248, acc=0.375, loss=141.140, backward_time=0.098, grad_norm=63.811, clip=100.000, loss_scale=3.436e+10, optim_step_time=0.030, optim0_lr0=3.985e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:08:26,024 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 07:08:45,698 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 07:08:49,328 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38b655b400>)
+[gpuc02:0/16] 2024-01-12 07:08:49,328 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 07:08:49,331 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 07:13:21,920 (trainer:737) INFO: 3epoch:train:10001-10100batch: iter_time=2.293, forward_time=0.104, loss_ctc=111.044, loss_att=151.163, acc=0.344, loss=139.127, backward_time=0.097, grad_norm=74.227, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.015e-04, train_time=2.985
+[gpuc02:0/16] 2024-01-12 07:14:02,834 (trainer:737) INFO: 3epoch:train:10101-10200batch: iter_time=1.012e-04, forward_time=0.104, loss_ctc=96.000, loss_att=145.888, acc=0.368, loss=130.922, backward_time=0.097, grad_norm=59.941, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.045e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:14:43,741 (trainer:737) INFO: 3epoch:train:10201-10300batch: iter_time=1.062e-04, forward_time=0.105, loss_ctc=101.824, loss_att=144.474, acc=0.372, loss=131.679, backward_time=0.097, grad_norm=64.368, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.029, optim0_lr0=4.075e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:15:24,665 (trainer:737) INFO: 3epoch:train:10301-10400batch: iter_time=1.061e-04, forward_time=0.105, loss_ctc=102.593, loss_att=149.938, acc=0.359, loss=135.734, backward_time=0.097, grad_norm=62.177, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.029, optim0_lr0=4.105e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:16:05,834 (trainer:737) INFO: 3epoch:train:10401-10500batch: iter_time=1.193e-04, forward_time=0.107, loss_ctc=108.156, loss_att=149.972, acc=0.368, loss=137.427, backward_time=0.097, grad_norm=64.460, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.029, optim0_lr0=4.135e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:16:46,642 (trainer:737) INFO: 3epoch:train:10501-10600batch: iter_time=9.797e-05, forward_time=0.104, loss_ctc=96.474, loss_att=140.665, acc=0.359, loss=127.408, backward_time=0.097, grad_norm=55.311, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.029, optim0_lr0=4.165e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:17:27,770 (trainer:737) INFO: 3epoch:train:10601-10700batch: iter_time=9.897e-05, forward_time=0.105, loss_ctc=95.447, loss_att=138.755, acc=0.394, loss=125.763, backward_time=0.097, grad_norm=54.906, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.029, optim0_lr0=4.195e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:18:09,108 (trainer:737) INFO: 3epoch:train:10701-10800batch: iter_time=1.062e-04, forward_time=0.106, loss_ctc=119.731, loss_att=158.218, acc=0.357, loss=146.672, backward_time=0.097, grad_norm=66.517, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.225e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 07:18:50,501 (trainer:737) INFO: 3epoch:train:10801-10900batch: iter_time=1.143e-04, forward_time=0.106, loss_ctc=97.603, loss_att=139.271, acc=0.410, loss=126.770, backward_time=0.098, grad_norm=56.811, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.255e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 07:19:32,315 (trainer:737) INFO: 3epoch:train:10901-11000batch: iter_time=1.037e-04, forward_time=0.106, loss_ctc=95.416, loss_att=134.503, acc=0.418, loss=122.777, backward_time=0.098, grad_norm=65.673, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.285e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-12 07:20:13,642 (trainer:737) INFO: 3epoch:train:11001-11100batch: iter_time=1.025e-04, forward_time=0.105, loss_ctc=95.766, loss_att=124.607, acc=0.403, loss=115.955, backward_time=0.097, grad_norm=54.587, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.315e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 07:20:54,587 (trainer:737) INFO: 3epoch:train:11101-11200batch: iter_time=1.022e-04, forward_time=0.106, loss_ctc=106.333, loss_att=145.927, acc=0.385, loss=134.049, backward_time=0.097, grad_norm=62.103, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.345e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:21:17,360 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 07:21:37,355 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 07:21:40,982 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3887c46d40>)
+[gpuc02:0/16] 2024-01-12 07:21:40,982 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 07:21:40,985 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 07:25:48,700 (trainer:737) INFO: 3epoch:train:11201-11300batch: iter_time=2.293, forward_time=0.124, loss_ctc=93.829, loss_att=130.462, acc=0.400, loss=119.472, backward_time=0.101, grad_norm=61.023, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.031, optim0_lr0=4.375e-04, train_time=2.941
+[gpuc02:0/16] 2024-01-12 07:26:29,861 (trainer:737) INFO: 3epoch:train:11301-11400batch: iter_time=1.188e-04, forward_time=0.105, loss_ctc=113.794, loss_att=151.138, acc=0.384, loss=139.934, backward_time=0.098, grad_norm=70.410, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.405e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:27:11,077 (trainer:737) INFO: 3epoch:train:11401-11500batch: iter_time=1.190e-04, forward_time=0.105, loss_ctc=98.793, loss_att=148.900, acc=0.406, loss=133.868, backward_time=0.098, grad_norm=60.773, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.435e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:27:51,957 (trainer:737) INFO: 3epoch:train:11501-11600batch: iter_time=1.552e-04, forward_time=0.103, loss_ctc=90.690, loss_att=122.661, acc=0.387, loss=113.069, backward_time=0.097, grad_norm=57.887, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.465e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:28:33,199 (trainer:737) INFO: 3epoch:train:11601-11700batch: iter_time=1.434e-04, forward_time=0.106, loss_ctc=111.924, loss_att=165.992, acc=0.407, loss=149.772, backward_time=0.099, grad_norm=61.768, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.495e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:29:14,108 (trainer:737) INFO: 3epoch:train:11701-11800batch: iter_time=1.610e-04, forward_time=0.104, loss_ctc=93.580, loss_att=130.513, acc=0.399, loss=119.433, backward_time=0.097, grad_norm=56.823, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.525e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:29:55,024 (trainer:737) INFO: 3epoch:train:11801-11900batch: iter_time=1.488e-04, forward_time=0.104, loss_ctc=88.953, loss_att=126.761, acc=0.414, loss=115.419, backward_time=0.097, grad_norm=51.141, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.555e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:30:35,981 (trainer:737) INFO: 3epoch:train:11901-12000batch: iter_time=1.300e-04, forward_time=0.105, loss_ctc=105.751, loss_att=149.596, acc=0.385, loss=136.443, backward_time=0.098, grad_norm=60.117, clip=100.000, loss_scale=6.872e+10, optim_step_time=0.030, optim0_lr0=4.585e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:31:17,099 (trainer:737) INFO: 3epoch:train:12001-12100batch: iter_time=1.463e-04, forward_time=0.105, loss_ctc=115.378, loss_att=157.407, acc=0.410, loss=144.798, backward_time=0.099, grad_norm=69.149, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.615e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:31:57,977 (trainer:737) INFO: 3epoch:train:12101-12200batch: iter_time=1.532e-04, forward_time=0.104, loss_ctc=82.249, loss_att=114.997, acc=0.452, loss=105.172, backward_time=0.097, grad_norm=58.420, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.645e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:32:39,278 (trainer:737) INFO: 3epoch:train:12201-12300batch: iter_time=1.317e-04, forward_time=0.105, loss_ctc=108.134, loss_att=136.275, acc=0.448, loss=127.833, backward_time=0.098, grad_norm=62.442, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.675e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 07:33:20,132 (trainer:737) INFO: 3epoch:train:12301-12400batch: iter_time=1.371e-04, forward_time=0.104, loss_ctc=89.236, loss_att=120.194, acc=0.412, loss=110.907, backward_time=0.096, grad_norm=55.364, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.705e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:34:01,153 (trainer:737) INFO: 3epoch:train:12401-12500batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=104.449, loss_att=135.700, acc=0.436, loss=126.325, backward_time=0.098, grad_norm=59.813, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.735e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 07:34:03,650 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 07:34:23,827 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 07:34:27,469 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38af87ed40>)
+[gpuc02:0/16] 2024-01-12 07:34:27,469 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 07:34:27,473 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 07:38:57,037 (trainer:737) INFO: 3epoch:train:12501-12600batch: iter_time=2.287, forward_time=0.104, loss_ctc=103.070, loss_att=129.843, acc=0.413, loss=121.811, backward_time=0.097, grad_norm=63.278, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.765e-04, train_time=2.959
+[gpuc02:0/16] 2024-01-12 07:39:37,903 (trainer:737) INFO: 3epoch:train:12601-12700batch: iter_time=1.384e-04, forward_time=0.105, loss_ctc=90.771, loss_att=126.652, acc=0.434, loss=115.888, backward_time=0.097, grad_norm=56.357, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.795e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:40:18,781 (trainer:737) INFO: 3epoch:train:12701-12800batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=95.379, loss_att=128.460, acc=0.428, loss=118.536, backward_time=0.097, grad_norm=64.108, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.825e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:41:00,042 (trainer:737) INFO: 3epoch:train:12801-12900batch: iter_time=1.665e-04, forward_time=0.104, loss_ctc=95.823, loss_att=130.261, acc=0.419, loss=119.929, backward_time=0.097, grad_norm=55.416, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.855e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:41:41,248 (trainer:737) INFO: 3epoch:train:12901-13000batch: iter_time=1.563e-04, forward_time=0.104, loss_ctc=103.440, loss_att=133.062, acc=0.426, loss=124.176, backward_time=0.097, grad_norm=61.866, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.885e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:42:22,112 (trainer:737) INFO: 3epoch:train:13001-13100batch: iter_time=1.076e-04, forward_time=0.105, loss_ctc=90.924, loss_att=125.681, acc=0.414, loss=115.254, backward_time=0.097, grad_norm=51.077, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.915e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:43:03,300 (trainer:737) INFO: 3epoch:train:13101-13200batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=88.514, loss_att=121.507, acc=0.455, loss=111.609, backward_time=0.097, grad_norm=52.105, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.945e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:43:44,998 (trainer:737) INFO: 3epoch:train:13201-13300batch: iter_time=9.858e-05, forward_time=0.106, loss_ctc=115.380, loss_att=141.632, acc=0.412, loss=133.756, backward_time=0.097, grad_norm=64.474, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=4.975e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 07:44:26,261 (trainer:737) INFO: 3epoch:train:13301-13400batch: iter_time=1.091e-04, forward_time=0.105, loss_ctc=91.328, loss_att=121.145, acc=0.469, loss=112.200, backward_time=0.097, grad_norm=51.113, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=5.005e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:45:07,281 (trainer:737) INFO: 3epoch:train:13401-13500batch: iter_time=1.147e-04, forward_time=0.105, loss_ctc=89.287, loss_att=115.721, acc=0.481, loss=107.791, backward_time=0.097, grad_norm=54.581, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=5.035e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 07:45:48,310 (trainer:737) INFO: 3epoch:train:13501-13600batch: iter_time=1.004e-04, forward_time=0.104, loss_ctc=90.652, loss_att=111.297, acc=0.455, loss=105.103, backward_time=0.096, grad_norm=57.436, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.029, optim0_lr0=5.065e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 07:46:29,256 (trainer:737) INFO: 3epoch:train:13601-13700batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=101.676, loss_att=130.564, acc=0.436, loss=121.897, backward_time=0.097, grad_norm=65.100, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.029, optim0_lr0=5.095e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 07:46:52,211 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 07:47:12,128 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 07:47:15,806 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38824df610>)
+[gpuc02:0/16] 2024-01-12 07:47:15,806 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 07:47:15,810 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 07:51:22,718 (trainer:737) INFO: 3epoch:train:13701-13800batch: iter_time=2.251, forward_time=0.104, loss_ctc=92.114, loss_att=109.528, acc=0.459, loss=104.303, backward_time=0.097, grad_norm=60.031, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=5.125e-04, train_time=2.934
+[gpuc02:0/16] 2024-01-12 07:52:03,815 (trainer:737) INFO: 3epoch:train:13801-13900batch: iter_time=1.462e-04, forward_time=0.104, loss_ctc=107.134, loss_att=129.609, acc=0.429, loss=122.867, backward_time=0.098, grad_norm=66.204, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=5.155e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:52:44,788 (trainer:737) INFO: 3epoch:train:13901-14000batch: iter_time=1.651e-04, forward_time=0.105, loss_ctc=92.124, loss_att=127.859, acc=0.460, loss=117.138, backward_time=0.098, grad_norm=52.450, clip=100.000, loss_scale=1.374e+11, optim_step_time=0.030, optim0_lr0=5.185e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 07:53:25,501 (trainer:737) INFO: 3epoch:train:14001-14100batch: iter_time=1.688e-04, forward_time=0.104, loss_ctc=85.437, loss_att=105.096, acc=0.435, loss=99.198, backward_time=0.096, grad_norm=51.020, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.215e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 07:54:06,608 (trainer:737) INFO: 3epoch:train:14101-14200batch: iter_time=1.479e-04, forward_time=0.105, loss_ctc=107.317, loss_att=142.721, acc=0.454, loss=132.100, backward_time=0.098, grad_norm=63.134, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.245e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:54:47,345 (trainer:737) INFO: 3epoch:train:14201-14300batch: iter_time=1.562e-04, forward_time=0.103, loss_ctc=87.943, loss_att=111.043, acc=0.458, loss=104.113, backward_time=0.096, grad_norm=51.088, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.029, optim0_lr0=5.275e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 07:55:28,123 (trainer:737) INFO: 3epoch:train:14301-14400batch: iter_time=1.404e-04, forward_time=0.104, loss_ctc=85.169, loss_att=113.806, acc=0.454, loss=105.215, backward_time=0.096, grad_norm=52.446, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.305e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:56:09,374 (trainer:737) INFO: 3epoch:train:14401-14500batch: iter_time=1.205e-04, forward_time=0.105, loss_ctc=100.044, loss_att=132.685, acc=0.431, loss=122.892, backward_time=0.097, grad_norm=55.807, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.335e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 07:56:50,486 (trainer:737) INFO: 3epoch:train:14501-14600batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=112.228, loss_att=140.006, acc=0.452, loss=131.672, backward_time=0.098, grad_norm=63.643, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.365e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:57:31,336 (trainer:737) INFO: 3epoch:train:14601-14700batch: iter_time=1.070e-04, forward_time=0.104, loss_ctc=78.056, loss_att=97.334, acc=0.508, loss=91.550, backward_time=0.097, grad_norm=45.633, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.029, optim0_lr0=5.395e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 07:58:12,368 (trainer:737) INFO: 3epoch:train:14701-14800batch: iter_time=1.062e-04, forward_time=0.105, loss_ctc=100.927, loss_att=118.398, acc=0.497, loss=113.156, backward_time=0.098, grad_norm=60.267, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.425e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 07:58:53,503 (trainer:737) INFO: 3epoch:train:14801-14900batch: iter_time=1.125e-04, forward_time=0.104, loss_ctc=84.130, loss_att=106.983, acc=0.452, loss=100.127, backward_time=0.096, grad_norm=49.271, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.455e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 07:59:35,037 (trainer:737) INFO: 3epoch:train:14901-15000batch: iter_time=1.040e-04, forward_time=0.105, loss_ctc=101.977, loss_att=120.473, acc=0.478, loss=114.924, backward_time=0.098, grad_norm=64.482, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.485e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 08:19:37,989 (trainer:343) INFO: 3epoch results: [train] iter_time=0.197, forward_time=0.105, loss_ctc=113.807, loss_att=167.481, acc=0.316, loss=151.378, backward_time=0.098, grad_norm=57.719, clip=100.000, loss_scale=5.469e+10, optim_step_time=0.030, optim0_lr0=3.250e-04, train_time=0.625, time=2 hours, 36 minutes and 24.53 seconds, total_count=45000, gpu_max_cached_mem_GB=25.176, [valid] loss_ctc=104.739, cer_ctc=0.508, loss_att=106.919, acc=0.317, cer=0.556, wer=1.000, loss=106.265, time=19 minutes and 53.24 seconds, total_count=14013, gpu_max_cached_mem_GB=25.176
+[gpuc02:0/16] 2024-01-12 08:19:42,244 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 08:19:42,247 (trainer:272) INFO: 4/45epoch started. Estimated time to finish: 5 days, 4 hours and 3 minutes
+[gpuc02:0/16] 2024-01-12 08:19:42,256 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 08:20:02,004 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 08:20:05,632 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38db485c90>)
+[gpuc02:0/16] 2024-01-12 08:20:05,632 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 08:20:05,635 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 08:24:30,717 (trainer:737) INFO: 4epoch:train:1-100batch: iter_time=2.247, forward_time=0.105, loss_ctc=88.816, loss_att=127.793, acc=0.468, loss=116.100, backward_time=0.098, grad_norm=50.065, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.515e-04, train_time=2.884
+[gpuc02:0/16] 2024-01-12 08:25:12,278 (trainer:737) INFO: 4epoch:train:101-200batch: iter_time=9.988e-05, forward_time=0.104, loss_ctc=95.703, loss_att=109.353, acc=0.485, loss=105.258, backward_time=0.098, grad_norm=55.984, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.545e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 08:25:53,018 (trainer:737) INFO: 4epoch:train:201-300batch: iter_time=1.044e-04, forward_time=0.104, loss_ctc=94.204, loss_att=125.592, acc=0.433, loss=116.176, backward_time=0.097, grad_norm=55.926, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.575e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 08:26:34,640 (trainer:737) INFO: 4epoch:train:301-400batch: iter_time=1.147e-04, forward_time=0.104, loss_ctc=92.255, loss_att=111.930, acc=0.490, loss=106.027, backward_time=0.098, grad_norm=60.487, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.605e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 08:27:15,477 (trainer:737) INFO: 4epoch:train:401-500batch: iter_time=1.222e-04, forward_time=0.103, loss_ctc=83.419, loss_att=101.538, acc=0.467, loss=96.102, backward_time=0.098, grad_norm=47.121, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.635e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 08:27:56,597 (trainer:737) INFO: 4epoch:train:501-600batch: iter_time=1.138e-04, forward_time=0.105, loss_ctc=109.418, loss_att=133.993, acc=0.455, loss=126.620, backward_time=0.099, grad_norm=70.259, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.665e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 08:28:37,796 (trainer:737) INFO: 4epoch:train:601-700batch: iter_time=1.085e-04, forward_time=0.104, loss_ctc=94.537, loss_att=107.059, acc=0.462, loss=103.303, backward_time=0.098, grad_norm=50.748, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.695e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 08:29:18,544 (trainer:737) INFO: 4epoch:train:701-800batch: iter_time=1.111e-04, forward_time=0.104, loss_ctc=91.484, loss_att=117.888, acc=0.461, loss=109.967, backward_time=0.098, grad_norm=51.839, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.725e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 08:29:59,894 (trainer:737) INFO: 4epoch:train:801-900batch: iter_time=1.127e-04, forward_time=0.104, loss_ctc=106.429, loss_att=136.538, acc=0.455, loss=127.505, backward_time=0.098, grad_norm=57.441, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.755e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 08:30:41,742 (trainer:737) INFO: 4epoch:train:901-1000batch: iter_time=1.115e-04, forward_time=0.104, loss_ctc=82.145, loss_att=107.769, acc=0.502, loss=100.082, backward_time=0.098, grad_norm=44.517, clip=100.000, loss_scale=2.749e+11, optim_step_time=0.030, optim0_lr0=5.785e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-12 08:31:22,451 (trainer:737) INFO: 4epoch:train:1001-1100batch: iter_time=1.140e-04, forward_time=0.103, loss_ctc=85.017, loss_att=92.300, acc=0.507, loss=90.115, backward_time=0.098, grad_norm=49.494, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.815e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 08:32:05,947 (trainer:737) INFO: 4epoch:train:1101-1200batch: iter_time=1.054e-04, forward_time=0.106, loss_ctc=99.996, loss_att=110.129, acc=0.478, loss=107.089, backward_time=0.098, grad_norm=57.099, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.845e-04, train_time=0.435
+[gpuc02:0/16] 2024-01-12 08:32:28,868 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 08:32:48,962 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 08:32:52,666 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc088ca0>)
+[gpuc02:0/16] 2024-01-12 08:32:52,667 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 08:32:52,670 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 08:39:36,775 (trainer:737) INFO: 4epoch:train:1201-1300batch: iter_time=2.575, forward_time=0.113, loss_ctc=83.232, loss_att=111.019, acc=0.485, loss=102.683, backward_time=0.099, grad_norm=45.257, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.875e-04, train_time=4.508
+[gpuc02:0/16] 2024-01-12 08:40:17,672 (trainer:737) INFO: 4epoch:train:1301-1400batch: iter_time=1.039e-04, forward_time=0.104, loss_ctc=92.487, loss_att=111.904, acc=0.511, loss=106.079, backward_time=0.097, grad_norm=51.382, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.905e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 08:40:58,934 (trainer:737) INFO: 4epoch:train:1401-1500batch: iter_time=1.097e-04, forward_time=0.103, loss_ctc=90.881, loss_att=125.322, acc=0.463, loss=114.990, backward_time=0.096, grad_norm=52.629, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.935e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 08:41:40,062 (trainer:737) INFO: 4epoch:train:1501-1600batch: iter_time=1.049e-04, forward_time=0.103, loss_ctc=83.407, loss_att=104.615, acc=0.475, loss=98.253, backward_time=0.096, grad_norm=49.547, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.965e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 08:42:20,916 (trainer:737) INFO: 4epoch:train:1601-1700batch: iter_time=1.107e-04, forward_time=0.104, loss_ctc=91.852, loss_att=100.601, acc=0.512, loss=97.976, backward_time=0.097, grad_norm=51.695, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=5.995e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 08:43:01,636 (trainer:737) INFO: 4epoch:train:1701-1800batch: iter_time=1.057e-04, forward_time=0.103, loss_ctc=87.592, loss_att=101.321, acc=0.477, loss=97.202, backward_time=0.096, grad_norm=54.870, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.025e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 08:43:43,087 (trainer:737) INFO: 4epoch:train:1801-1900batch: iter_time=1.025e-04, forward_time=0.104, loss_ctc=104.612, loss_att=124.963, acc=0.462, loss=118.857, backward_time=0.098, grad_norm=51.596, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.055e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 08:44:23,799 (trainer:737) INFO: 4epoch:train:1901-2000batch: iter_time=1.023e-04, forward_time=0.103, loss_ctc=95.413, loss_att=105.400, acc=0.487, loss=102.404, backward_time=0.096, grad_norm=52.713, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.085e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 08:45:04,739 (trainer:737) INFO: 4epoch:train:2001-2100batch: iter_time=1.022e-04, forward_time=0.104, loss_ctc=102.257, loss_att=134.636, acc=0.467, loss=124.923, backward_time=0.098, grad_norm=52.630, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.029, optim0_lr0=6.115e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 08:45:45,745 (trainer:737) INFO: 4epoch:train:2101-2200batch: iter_time=1.068e-04, forward_time=0.103, loss_ctc=79.953, loss_att=98.817, acc=0.492, loss=93.158, backward_time=0.096, grad_norm=45.342, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.145e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 08:46:26,571 (trainer:737) INFO: 4epoch:train:2201-2300batch: iter_time=1.033e-04, forward_time=0.103, loss_ctc=79.555, loss_att=101.361, acc=0.509, loss=94.819, backward_time=0.097, grad_norm=47.344, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.175e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 08:47:07,567 (trainer:737) INFO: 4epoch:train:2301-2400batch: iter_time=9.666e-05, forward_time=0.103, loss_ctc=96.917, loss_att=97.292, acc=0.516, loss=97.180, backward_time=0.097, grad_norm=55.470, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.205e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 08:47:49,034 (trainer:737) INFO: 4epoch:train:2401-2500batch: iter_time=1.013e-04, forward_time=0.103, loss_ctc=83.764, loss_att=97.494, acc=0.526, loss=93.375, backward_time=0.097, grad_norm=45.343, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.235e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 08:47:53,383 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 08:48:13,241 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 08:48:16,951 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc0eb7f0>)
+[gpuc02:0/16] 2024-01-12 08:48:16,951 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 08:48:16,954 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 08:52:58,739 (trainer:737) INFO: 4epoch:train:2501-2600batch: iter_time=2.570, forward_time=0.105, loss_ctc=84.833, loss_att=128.861, acc=0.488, loss=115.653, backward_time=0.098, grad_norm=52.860, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.265e-04, train_time=3.097
+[gpuc02:0/16] 2024-01-12 08:53:39,709 (trainer:737) INFO: 4epoch:train:2601-2700batch: iter_time=8.911e-05, forward_time=0.103, loss_ctc=91.847, loss_att=114.663, acc=0.506, loss=107.818, backward_time=0.098, grad_norm=58.372, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.295e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 08:54:20,573 (trainer:737) INFO: 4epoch:train:2701-2800batch: iter_time=9.563e-05, forward_time=0.103, loss_ctc=89.850, loss_att=121.237, acc=0.460, loss=111.821, backward_time=0.098, grad_norm=53.543, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.325e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 08:55:01,544 (trainer:737) INFO: 4epoch:train:2801-2900batch: iter_time=1.005e-04, forward_time=0.104, loss_ctc=87.414, loss_att=107.292, acc=0.524, loss=101.328, backward_time=0.098, grad_norm=49.948, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.355e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 08:55:42,697 (trainer:737) INFO: 4epoch:train:2901-3000batch: iter_time=1.009e-04, forward_time=0.103, loss_ctc=80.048, loss_att=97.575, acc=0.508, loss=92.317, backward_time=0.097, grad_norm=45.530, clip=100.000, loss_scale=5.498e+11, optim_step_time=0.030, optim0_lr0=6.385e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 08:56:24,632 (trainer:737) INFO: 4epoch:train:3001-3100batch: iter_time=1.022e-04, forward_time=0.104, loss_ctc=107.358, loss_att=134.768, acc=0.474, loss=126.545, backward_time=0.098, grad_norm=63.866, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.415e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-12 08:57:05,763 (trainer:737) INFO: 4epoch:train:3101-3200batch: iter_time=1.042e-04, forward_time=0.104, loss_ctc=89.378, loss_att=102.253, acc=0.496, loss=98.391, backward_time=0.097, grad_norm=51.813, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.445e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 08:57:46,689 (trainer:737) INFO: 4epoch:train:3201-3300batch: iter_time=1.094e-04, forward_time=0.105, loss_ctc=86.944, loss_att=117.240, acc=0.486, loss=108.151, backward_time=0.098, grad_norm=51.781, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.475e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 08:58:27,814 (trainer:737) INFO: 4epoch:train:3301-3400batch: iter_time=1.272e-04, forward_time=0.105, loss_ctc=101.911, loss_att=131.182, acc=0.495, loss=122.401, backward_time=0.099, grad_norm=55.441, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.031, optim0_lr0=6.505e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 08:59:09,350 (trainer:737) INFO: 4epoch:train:3401-3500batch: iter_time=1.191e-04, forward_time=0.105, loss_ctc=78.159, loss_att=105.199, acc=0.532, loss=97.087, backward_time=0.098, grad_norm=45.619, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.535e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 08:59:50,229 (trainer:737) INFO: 4epoch:train:3501-3600batch: iter_time=1.182e-04, forward_time=0.103, loss_ctc=81.547, loss_att=85.572, acc=0.535, loss=84.364, backward_time=0.097, grad_norm=49.302, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.565e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:00:31,143 (trainer:737) INFO: 4epoch:train:3601-3700batch: iter_time=1.137e-04, forward_time=0.104, loss_ctc=94.130, loss_att=103.301, acc=0.510, loss=100.550, backward_time=0.098, grad_norm=53.286, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.595e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:00:57,329 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 09:01:17,290 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 09:01:20,950 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fc154bf40>)
+[gpuc02:0/16] 2024-01-12 09:01:20,951 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 09:01:20,954 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 09:05:38,884 (trainer:737) INFO: 4epoch:train:3701-3800batch: iter_time=2.458, forward_time=0.108, loss_ctc=79.696, loss_att=108.948, acc=0.512, loss=100.172, backward_time=0.099, grad_norm=45.753, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.625e-04, train_time=3.077
+[gpuc02:0/16] 2024-01-12 09:06:20,577 (trainer:737) INFO: 4epoch:train:3801-3900batch: iter_time=1.039e-04, forward_time=0.106, loss_ctc=88.954, loss_att=111.400, acc=0.531, loss=104.666, backward_time=0.098, grad_norm=51.537, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.655e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 09:07:01,796 (trainer:737) INFO: 4epoch:train:3901-4000batch: iter_time=1.044e-04, forward_time=0.105, loss_ctc=87.840, loss_att=123.155, acc=0.484, loss=112.560, backward_time=0.098, grad_norm=54.813, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.685e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:07:42,714 (trainer:737) INFO: 4epoch:train:4001-4100batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=79.897, loss_att=98.636, acc=0.506, loss=93.015, backward_time=0.097, grad_norm=47.082, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.715e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:08:23,903 (trainer:737) INFO: 4epoch:train:4101-4200batch: iter_time=1.082e-04, forward_time=0.107, loss_ctc=86.890, loss_att=94.434, acc=0.549, loss=92.171, backward_time=0.097, grad_norm=45.673, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.745e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:09:04,808 (trainer:737) INFO: 4epoch:train:4201-4300batch: iter_time=1.002e-04, forward_time=0.103, loss_ctc=84.021, loss_att=95.776, acc=0.509, loss=92.250, backward_time=0.097, grad_norm=52.551, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.775e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:09:45,938 (trainer:737) INFO: 4epoch:train:4301-4400batch: iter_time=9.812e-05, forward_time=0.105, loss_ctc=101.558, loss_att=125.186, acc=0.479, loss=118.097, backward_time=0.098, grad_norm=55.076, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.805e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:10:26,793 (trainer:737) INFO: 4epoch:train:4401-4500batch: iter_time=1.013e-04, forward_time=0.103, loss_ctc=92.321, loss_att=103.472, acc=0.513, loss=100.126, backward_time=0.096, grad_norm=50.668, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.835e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:11:08,030 (trainer:737) INFO: 4epoch:train:4501-4600batch: iter_time=1.062e-04, forward_time=0.105, loss_ctc=97.957, loss_att=130.207, acc=0.500, loss=120.532, backward_time=0.098, grad_norm=49.637, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.865e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:11:49,181 (trainer:737) INFO: 4epoch:train:4601-4700batch: iter_time=9.721e-05, forward_time=0.103, loss_ctc=78.088, loss_att=95.772, acc=0.523, loss=90.467, backward_time=0.097, grad_norm=46.004, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.895e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:12:30,385 (trainer:737) INFO: 4epoch:train:4701-4800batch: iter_time=1.060e-04, forward_time=0.104, loss_ctc=75.810, loss_att=96.254, acc=0.535, loss=90.121, backward_time=0.097, grad_norm=45.668, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.925e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:13:11,511 (trainer:737) INFO: 4epoch:train:4801-4900batch: iter_time=1.028e-04, forward_time=0.103, loss_ctc=92.700, loss_att=92.496, acc=0.537, loss=92.557, backward_time=0.097, grad_norm=55.404, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.955e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:13:52,350 (trainer:737) INFO: 4epoch:train:4901-5000batch: iter_time=9.323e-05, forward_time=0.103, loss_ctc=80.630, loss_att=91.997, acc=0.553, loss=88.587, backward_time=0.097, grad_norm=43.815, clip=100.000, loss_scale=1.100e+12, optim_step_time=0.030, optim0_lr0=6.985e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:13:55,061 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 09:14:15,512 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 09:14:19,324 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a07e2d40>)
+[gpuc02:0/16] 2024-01-12 09:14:19,324 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 09:14:19,327 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 09:19:40,585 (trainer:737) INFO: 4epoch:train:5001-5100batch: iter_time=2.990, forward_time=0.107, loss_ctc=80.873, loss_att=113.585, acc=0.515, loss=103.771, backward_time=0.098, grad_norm=45.334, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.015e-04, train_time=3.482
+[gpuc02:0/16] 2024-01-12 09:20:21,699 (trainer:737) INFO: 4epoch:train:5101-5200batch: iter_time=1.035e-04, forward_time=0.104, loss_ctc=89.703, loss_att=100.140, acc=0.530, loss=97.009, backward_time=0.098, grad_norm=68.101, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.045e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:21:02,514 (trainer:737) INFO: 4epoch:train:5201-5300batch: iter_time=1.047e-04, forward_time=0.105, loss_ctc=84.776, loss_att=107.470, acc=0.483, loss=100.662, backward_time=0.097, grad_norm=50.977, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.075e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:21:43,424 (trainer:737) INFO: 4epoch:train:5301-5400batch: iter_time=1.054e-04, forward_time=0.105, loss_ctc=83.449, loss_att=98.545, acc=0.542, loss=94.016, backward_time=0.098, grad_norm=45.618, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.105e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:22:24,140 (trainer:737) INFO: 4epoch:train:5401-5500batch: iter_time=1.310e-04, forward_time=0.104, loss_ctc=79.093, loss_att=90.287, acc=0.513, loss=86.929, backward_time=0.097, grad_norm=49.110, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.135e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 09:23:05,204 (trainer:737) INFO: 4epoch:train:5501-5600batch: iter_time=1.152e-04, forward_time=0.105, loss_ctc=105.213, loss_att=122.689, acc=0.492, loss=117.446, backward_time=0.098, grad_norm=58.191, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.165e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 09:23:46,767 (trainer:737) INFO: 4epoch:train:5601-5700batch: iter_time=1.098e-04, forward_time=0.104, loss_ctc=86.996, loss_att=90.959, acc=0.515, loss=89.770, backward_time=0.097, grad_norm=47.055, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.195e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 09:24:27,822 (trainer:737) INFO: 4epoch:train:5701-5800batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=84.787, loss_att=103.784, acc=0.509, loss=98.085, backward_time=0.097, grad_norm=49.995, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.225e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 09:25:08,833 (trainer:737) INFO: 4epoch:train:5801-5900batch: iter_time=1.108e-04, forward_time=0.106, loss_ctc=99.169, loss_att=122.815, acc=0.501, loss=115.721, backward_time=0.099, grad_norm=51.808, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.255e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 09:25:49,745 (trainer:737) INFO: 4epoch:train:5901-6000batch: iter_time=1.130e-04, forward_time=0.105, loss_ctc=75.895, loss_att=96.071, acc=0.543, loss=90.018, backward_time=0.098, grad_norm=44.186, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.285e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:26:30,568 (trainer:737) INFO: 4epoch:train:6001-6100batch: iter_time=1.094e-04, forward_time=0.104, loss_ctc=79.047, loss_att=79.676, acc=0.550, loss=79.487, backward_time=0.097, grad_norm=48.001, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.315e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:27:11,418 (trainer:737) INFO: 4epoch:train:6101-6200batch: iter_time=1.096e-04, forward_time=0.105, loss_ctc=91.630, loss_att=95.277, acc=0.528, loss=94.183, backward_time=0.098, grad_norm=50.002, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.345e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:27:37,132 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 09:27:56,889 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 09:28:00,559 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f388fca7520>)
+[gpuc02:0/16] 2024-01-12 09:28:00,559 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 09:28:00,577 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 09:32:11,277 (trainer:737) INFO: 4epoch:train:6201-6300batch: iter_time=2.299, forward_time=0.108, loss_ctc=77.963, loss_att=98.261, acc=0.528, loss=92.172, backward_time=0.097, grad_norm=44.226, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.375e-04, train_time=2.998
+[gpuc02:0/16] 2024-01-12 09:32:52,368 (trainer:737) INFO: 4epoch:train:6301-6400batch: iter_time=1.131e-04, forward_time=0.106, loss_ctc=85.854, loss_att=98.521, acc=0.552, loss=94.721, backward_time=0.098, grad_norm=47.329, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.405e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:33:33,312 (trainer:737) INFO: 4epoch:train:6401-6500batch: iter_time=1.189e-04, forward_time=0.105, loss_ctc=83.855, loss_att=110.653, acc=0.505, loss=102.614, backward_time=0.098, grad_norm=48.878, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.435e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:34:14,114 (trainer:737) INFO: 4epoch:train:6501-6600batch: iter_time=1.193e-04, forward_time=0.104, loss_ctc=76.911, loss_att=93.122, acc=0.518, loss=88.259, backward_time=0.097, grad_norm=44.355, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.465e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:34:55,021 (trainer:737) INFO: 4epoch:train:6601-6700batch: iter_time=1.155e-04, forward_time=0.105, loss_ctc=85.192, loss_att=87.659, acc=0.558, loss=86.919, backward_time=0.098, grad_norm=48.734, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.495e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:35:36,087 (trainer:737) INFO: 4epoch:train:6701-6800batch: iter_time=1.135e-04, forward_time=0.104, loss_ctc=80.680, loss_att=89.032, acc=0.515, loss=86.527, backward_time=0.097, grad_norm=47.260, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.525e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 09:36:17,686 (trainer:737) INFO: 4epoch:train:6801-6900batch: iter_time=1.086e-04, forward_time=0.106, loss_ctc=98.545, loss_att=114.429, acc=0.501, loss=109.663, backward_time=0.098, grad_norm=52.183, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.555e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 09:36:58,815 (trainer:737) INFO: 4epoch:train:6901-7000batch: iter_time=1.110e-04, forward_time=0.105, loss_ctc=90.341, loss_att=94.062, acc=0.526, loss=92.946, backward_time=0.098, grad_norm=50.330, clip=100.000, loss_scale=2.199e+12, optim_step_time=0.030, optim0_lr0=7.585e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:37:40,183 (trainer:737) INFO: 4epoch:train:7001-7100batch: iter_time=1.116e-04, forward_time=0.106, loss_ctc=94.831, loss_att=121.136, acc=0.507, loss=113.245, backward_time=0.098, grad_norm=46.958, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.615e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 09:38:21,016 (trainer:737) INFO: 4epoch:train:7101-7200batch: iter_time=1.079e-04, forward_time=0.105, loss_ctc=75.211, loss_att=87.572, acc=0.533, loss=83.864, backward_time=0.097, grad_norm=41.907, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.645e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:39:01,966 (trainer:737) INFO: 4epoch:train:7201-7300batch: iter_time=1.043e-04, forward_time=0.105, loss_ctc=73.667, loss_att=90.463, acc=0.549, loss=85.424, backward_time=0.098, grad_norm=42.063, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.675e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:39:42,947 (trainer:737) INFO: 4epoch:train:7301-7400batch: iter_time=1.111e-04, forward_time=0.105, loss_ctc=90.604, loss_att=85.037, acc=0.553, loss=86.707, backward_time=0.098, grad_norm=50.005, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.705e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 09:40:23,851 (trainer:737) INFO: 4epoch:train:7401-7500batch: iter_time=1.060e-04, forward_time=0.105, loss_ctc=78.216, loss_att=84.720, acc=0.565, loss=82.769, backward_time=0.097, grad_norm=41.813, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.735e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:40:26,273 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 09:40:46,169 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 09:40:49,835 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a11d34f0>)
+[gpuc02:0/16] 2024-01-12 09:40:49,835 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 09:40:49,839 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 09:45:18,715 (trainer:737) INFO: 4epoch:train:7501-7600batch: iter_time=2.390, forward_time=0.109, loss_ctc=79.749, loss_att=115.571, acc=0.526, loss=104.824, backward_time=0.099, grad_norm=48.079, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.765e-04, train_time=2.948
+[gpuc02:0/16] 2024-01-12 09:45:59,903 (trainer:737) INFO: 4epoch:train:7601-7700batch: iter_time=1.595e-04, forward_time=0.106, loss_ctc=86.362, loss_att=102.932, acc=0.541, loss=97.961, backward_time=0.098, grad_norm=55.090, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.795e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:46:41,055 (trainer:737) INFO: 4epoch:train:7701-7800batch: iter_time=1.661e-04, forward_time=0.106, loss_ctc=81.738, loss_att=105.141, acc=0.503, loss=98.120, backward_time=0.098, grad_norm=45.586, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.825e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:47:21,933 (trainer:737) INFO: 4epoch:train:7801-7900batch: iter_time=1.664e-04, forward_time=0.106, loss_ctc=80.827, loss_att=96.854, acc=0.559, loss=92.046, backward_time=0.098, grad_norm=45.032, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.855e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:48:02,669 (trainer:737) INFO: 4epoch:train:7901-8000batch: iter_time=1.853e-04, forward_time=0.105, loss_ctc=73.855, loss_att=84.872, acc=0.548, loss=81.567, backward_time=0.098, grad_norm=41.532, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.885e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 09:48:43,765 (trainer:737) INFO: 4epoch:train:8001-8100batch: iter_time=1.745e-04, forward_time=0.106, loss_ctc=102.132, loss_att=122.392, acc=0.508, loss=116.314, backward_time=0.099, grad_norm=59.919, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.915e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:49:25,395 (trainer:737) INFO: 4epoch:train:8101-8200batch: iter_time=1.854e-04, forward_time=0.106, loss_ctc=84.566, loss_att=89.575, acc=0.534, loss=88.072, backward_time=0.098, grad_norm=45.270, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.945e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 09:50:06,294 (trainer:737) INFO: 4epoch:train:8201-8300batch: iter_time=1.885e-04, forward_time=0.106, loss_ctc=82.248, loss_att=104.382, acc=0.524, loss=97.742, backward_time=0.098, grad_norm=46.202, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=7.975e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:50:47,442 (trainer:737) INFO: 4epoch:train:8301-8400batch: iter_time=1.859e-04, forward_time=0.107, loss_ctc=95.343, loss_att=117.784, acc=0.533, loss=111.051, backward_time=0.099, grad_norm=48.787, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.005e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 09:51:28,402 (trainer:737) INFO: 4epoch:train:8401-8500batch: iter_time=1.735e-04, forward_time=0.106, loss_ctc=73.679, loss_att=95.173, acc=0.564, loss=88.725, backward_time=0.099, grad_norm=41.004, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.035e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 09:52:09,249 (trainer:737) INFO: 4epoch:train:8501-8600batch: iter_time=1.704e-04, forward_time=0.105, loss_ctc=75.749, loss_att=75.637, acc=0.568, loss=75.671, backward_time=0.098, grad_norm=44.394, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.065e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:52:50,119 (trainer:737) INFO: 4epoch:train:8601-8700batch: iter_time=1.534e-04, forward_time=0.105, loss_ctc=88.873, loss_att=90.956, acc=0.547, loss=90.331, backward_time=0.098, grad_norm=49.285, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.095e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 09:53:14,771 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 09:53:34,392 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 09:53:38,114 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40d2d8a4a0>)
+[gpuc02:0/16] 2024-01-12 09:53:38,115 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 09:53:38,118 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 09:57:50,729 (trainer:737) INFO: 4epoch:train:8701-8800batch: iter_time=2.294, forward_time=0.105, loss_ctc=75.493, loss_att=97.795, acc=0.547, loss=91.105, backward_time=0.098, grad_norm=40.490, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.125e-04, train_time=3.006
+[gpuc02:0/16] 2024-01-12 09:58:32,174 (trainer:737) INFO: 4epoch:train:8801-8900batch: iter_time=1.104e-04, forward_time=0.105, loss_ctc=83.324, loss_att=99.892, acc=0.567, loss=94.922, backward_time=0.099, grad_norm=47.446, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.155e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 09:59:13,430 (trainer:737) INFO: 4epoch:train:8901-9000batch: iter_time=1.232e-04, forward_time=0.104, loss_ctc=82.184, loss_att=111.630, acc=0.519, loss=102.796, backward_time=0.098, grad_norm=48.727, clip=100.000, loss_scale=4.398e+12, optim_step_time=0.030, optim0_lr0=8.185e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 09:59:54,266 (trainer:737) INFO: 4epoch:train:9001-9100batch: iter_time=1.086e-04, forward_time=0.104, loss_ctc=74.416, loss_att=88.170, acc=0.541, loss=84.044, backward_time=0.098, grad_norm=43.431, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.215e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:00:35,242 (trainer:737) INFO: 4epoch:train:9101-9200batch: iter_time=1.046e-04, forward_time=0.104, loss_ctc=81.765, loss_att=85.117, acc=0.581, loss=84.111, backward_time=0.098, grad_norm=44.572, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.245e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:01:16,112 (trainer:737) INFO: 4epoch:train:9201-9300batch: iter_time=1.098e-04, forward_time=0.104, loss_ctc=78.758, loss_att=85.048, acc=0.545, loss=83.161, backward_time=0.097, grad_norm=44.943, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.275e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:01:57,182 (trainer:737) INFO: 4epoch:train:9301-9400batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=95.262, loss_att=115.642, acc=0.509, loss=109.528, backward_time=0.098, grad_norm=59.720, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.305e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:02:38,061 (trainer:737) INFO: 4epoch:train:9401-9500batch: iter_time=1.151e-04, forward_time=0.104, loss_ctc=88.295, loss_att=94.144, acc=0.542, loss=92.389, backward_time=0.097, grad_norm=44.327, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.335e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:03:19,272 (trainer:737) INFO: 4epoch:train:9501-9600batch: iter_time=1.134e-04, forward_time=0.106, loss_ctc=92.473, loss_att=119.117, acc=0.532, loss=111.124, backward_time=0.099, grad_norm=45.914, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.365e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:04:00,847 (trainer:737) INFO: 4epoch:train:9601-9700batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=72.790, loss_att=86.317, acc=0.555, loss=82.259, backward_time=0.098, grad_norm=41.004, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.395e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 10:04:41,887 (trainer:737) INFO: 4epoch:train:9701-9800batch: iter_time=1.074e-04, forward_time=0.105, loss_ctc=71.457, loss_att=88.676, acc=0.563, loss=83.510, backward_time=0.098, grad_norm=42.889, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.425e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:05:23,128 (trainer:737) INFO: 4epoch:train:9801-9900batch: iter_time=1.089e-04, forward_time=0.107, loss_ctc=88.516, loss_att=82.030, acc=0.570, loss=83.976, backward_time=0.098, grad_norm=50.583, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.455e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:06:04,036 (trainer:737) INFO: 4epoch:train:9901-10000batch: iter_time=9.179e-05, forward_time=0.104, loss_ctc=76.538, loss_att=82.022, acc=0.585, loss=80.377, backward_time=0.098, grad_norm=39.774, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.485e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:06:06,637 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 10:06:27,240 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 10:06:30,987 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f8589fbe0>)
+[gpuc02:0/16] 2024-01-12 10:06:30,987 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 10:06:30,990 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 10:11:08,128 (trainer:737) INFO: 4epoch:train:10001-10100batch: iter_time=2.315, forward_time=0.105, loss_ctc=76.983, loss_att=106.419, acc=0.540, loss=97.588, backward_time=0.098, grad_norm=42.844, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.515e-04, train_time=3.041
+[gpuc02:0/16] 2024-01-12 10:11:49,634 (trainer:737) INFO: 4epoch:train:10101-10200batch: iter_time=1.141e-04, forward_time=0.105, loss_ctc=83.217, loss_att=92.478, acc=0.555, loss=89.700, backward_time=0.097, grad_norm=50.907, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.545e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 10:12:30,780 (trainer:737) INFO: 4epoch:train:10201-10300batch: iter_time=1.386e-04, forward_time=0.105, loss_ctc=80.941, loss_att=98.647, acc=0.512, loss=93.335, backward_time=0.097, grad_norm=43.988, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.575e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:13:11,671 (trainer:737) INFO: 4epoch:train:10301-10400batch: iter_time=1.349e-04, forward_time=0.105, loss_ctc=80.132, loss_att=92.055, acc=0.568, loss=88.478, backward_time=0.097, grad_norm=43.071, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.605e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:13:52,734 (trainer:737) INFO: 4epoch:train:10401-10500batch: iter_time=1.500e-04, forward_time=0.104, loss_ctc=72.950, loss_att=82.710, acc=0.541, loss=79.782, backward_time=0.097, grad_norm=38.513, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.635e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:14:33,869 (trainer:737) INFO: 4epoch:train:10501-10600batch: iter_time=1.397e-04, forward_time=0.107, loss_ctc=101.365, loss_att=114.093, acc=0.521, loss=110.274, backward_time=0.098, grad_norm=54.783, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.665e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:15:14,735 (trainer:737) INFO: 4epoch:train:10601-10700batch: iter_time=1.501e-04, forward_time=0.105, loss_ctc=82.437, loss_att=83.857, acc=0.544, loss=83.431, backward_time=0.097, grad_norm=41.891, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.695e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:15:55,613 (trainer:737) INFO: 4epoch:train:10701-10800batch: iter_time=1.383e-04, forward_time=0.105, loss_ctc=79.589, loss_att=97.318, acc=0.532, loss=91.999, backward_time=0.097, grad_norm=45.077, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.725e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:16:36,978 (trainer:737) INFO: 4epoch:train:10801-10900batch: iter_time=1.097e-04, forward_time=0.105, loss_ctc=94.094, loss_att=113.864, acc=0.528, loss=107.933, backward_time=0.098, grad_norm=47.020, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.755e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 10:17:18,566 (trainer:737) INFO: 4epoch:train:10901-11000batch: iter_time=9.902e-05, forward_time=0.105, loss_ctc=71.778, loss_att=89.104, acc=0.572, loss=83.906, backward_time=0.097, grad_norm=37.765, clip=100.000, loss_scale=8.796e+12, optim_step_time=0.030, optim0_lr0=8.785e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 10:17:59,698 (trainer:737) INFO: 4epoch:train:11001-11100batch: iter_time=1.026e-04, forward_time=0.104, loss_ctc=73.536, loss_att=70.650, acc=0.584, loss=71.516, backward_time=0.097, grad_norm=41.599, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.815e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:18:40,648 (trainer:737) INFO: 4epoch:train:11101-11200batch: iter_time=1.121e-04, forward_time=0.105, loss_ctc=86.923, loss_att=87.573, acc=0.549, loss=87.378, backward_time=0.097, grad_norm=47.965, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.845e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:19:03,397 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 10:19:23,438 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 10:19:27,132 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40c2ea8910>)
+[gpuc02:0/16] 2024-01-12 10:19:27,132 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 10:19:27,135 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 10:23:50,776 (trainer:737) INFO: 4epoch:train:11201-11300batch: iter_time=2.246, forward_time=0.141, loss_ctc=73.348, loss_att=94.492, acc=0.555, loss=88.149, backward_time=0.106, grad_norm=39.815, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.875e-04, train_time=3.101
+[gpuc02:0/16] 2024-01-12 10:24:31,896 (trainer:737) INFO: 4epoch:train:11301-11400batch: iter_time=9.824e-05, forward_time=0.105, loss_ctc=82.820, loss_att=99.140, acc=0.572, loss=94.244, backward_time=0.099, grad_norm=68.371, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.905e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:25:13,103 (trainer:737) INFO: 4epoch:train:11401-11500batch: iter_time=9.009e-05, forward_time=0.108, loss_ctc=79.601, loss_att=109.544, acc=0.529, loss=100.561, backward_time=0.098, grad_norm=46.666, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.935e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:25:53,915 (trainer:737) INFO: 4epoch:train:11501-11600batch: iter_time=9.164e-05, forward_time=0.105, loss_ctc=72.846, loss_att=85.571, acc=0.552, loss=81.753, backward_time=0.098, grad_norm=40.202, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.965e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:26:35,123 (trainer:737) INFO: 4epoch:train:11601-11700batch: iter_time=1.007e-04, forward_time=0.106, loss_ctc=81.498, loss_att=82.855, acc=0.592, loss=82.448, backward_time=0.098, grad_norm=40.938, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=8.995e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:27:16,338 (trainer:737) INFO: 4epoch:train:11701-11800batch: iter_time=1.085e-04, forward_time=0.105, loss_ctc=78.139, loss_att=84.058, acc=0.551, loss=82.282, backward_time=0.098, grad_norm=44.989, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.025e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:27:57,719 (trainer:737) INFO: 4epoch:train:11801-11900batch: iter_time=1.170e-04, forward_time=0.107, loss_ctc=92.138, loss_att=111.211, acc=0.523, loss=105.489, backward_time=0.099, grad_norm=49.392, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.055e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 10:28:38,608 (trainer:737) INFO: 4epoch:train:11901-12000batch: iter_time=1.432e-04, forward_time=0.105, loss_ctc=85.924, loss_att=91.883, acc=0.552, loss=90.096, backward_time=0.098, grad_norm=45.860, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.085e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:29:19,829 (trainer:737) INFO: 4epoch:train:12001-12100batch: iter_time=1.088e-04, forward_time=0.108, loss_ctc=91.552, loss_att=117.567, acc=0.539, loss=109.762, backward_time=0.099, grad_norm=45.014, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.115e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:30:00,714 (trainer:737) INFO: 4epoch:train:12101-12200batch: iter_time=1.066e-04, forward_time=0.106, loss_ctc=71.464, loss_att=82.167, acc=0.572, loss=78.956, backward_time=0.098, grad_norm=39.766, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.145e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:30:41,687 (trainer:737) INFO: 4epoch:train:12201-12300batch: iter_time=1.042e-04, forward_time=0.106, loss_ctc=70.242, loss_att=85.401, acc=0.575, loss=80.854, backward_time=0.098, grad_norm=39.805, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.175e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:31:22,905 (trainer:737) INFO: 4epoch:train:12301-12400batch: iter_time=1.077e-04, forward_time=0.106, loss_ctc=86.671, loss_att=80.069, acc=0.579, loss=82.050, backward_time=0.098, grad_norm=47.135, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.205e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:32:04,391 (trainer:737) INFO: 4epoch:train:12401-12500batch: iter_time=9.962e-05, forward_time=0.105, loss_ctc=74.259, loss_att=78.527, acc=0.595, loss=77.247, backward_time=0.098, grad_norm=37.047, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.235e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 10:32:07,988 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 10:32:27,680 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 10:32:31,338 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c2cb9240>)
+[gpuc02:0/16] 2024-01-12 10:32:31,338 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 10:32:31,341 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 10:37:02,909 (trainer:737) INFO: 4epoch:train:12501-12600batch: iter_time=2.311, forward_time=0.105, loss_ctc=75.584, loss_att=100.883, acc=0.562, loss=93.293, backward_time=0.099, grad_norm=40.398, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.265e-04, train_time=2.985
+[gpuc02:0/16] 2024-01-12 10:37:43,823 (trainer:737) INFO: 4epoch:train:12601-12700batch: iter_time=1.528e-04, forward_time=0.104, loss_ctc=82.268, loss_att=91.316, acc=0.573, loss=88.601, backward_time=0.098, grad_norm=48.156, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.295e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:38:24,707 (trainer:737) INFO: 4epoch:train:12701-12800batch: iter_time=1.454e-04, forward_time=0.104, loss_ctc=78.029, loss_att=95.965, acc=0.528, loss=90.584, backward_time=0.098, grad_norm=42.429, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.325e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:39:05,669 (trainer:737) INFO: 4epoch:train:12801-12900batch: iter_time=1.628e-04, forward_time=0.105, loss_ctc=77.987, loss_att=88.261, acc=0.585, loss=85.179, backward_time=0.098, grad_norm=41.764, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.355e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:39:46,443 (trainer:737) INFO: 4epoch:train:12901-13000batch: iter_time=1.056e-04, forward_time=0.105, loss_ctc=71.414, loss_att=76.982, acc=0.578, loss=75.311, backward_time=0.098, grad_norm=36.516, clip=100.000, loss_scale=1.759e+13, optim_step_time=0.030, optim0_lr0=9.385e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:40:27,574 (trainer:737) INFO: 4epoch:train:13001-13100batch: iter_time=1.113e-04, forward_time=0.106, loss_ctc=96.641, loss_att=114.566, acc=0.530, loss=109.189, backward_time=0.099, grad_norm=53.006, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.415e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:41:08,373 (trainer:737) INFO: 4epoch:train:13101-13200batch: iter_time=1.066e-04, forward_time=0.105, loss_ctc=81.276, loss_att=81.459, acc=0.561, loss=81.404, backward_time=0.097, grad_norm=41.829, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.445e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:41:49,300 (trainer:737) INFO: 4epoch:train:13201-13300batch: iter_time=1.121e-04, forward_time=0.105, loss_ctc=77.642, loss_att=96.172, acc=0.547, loss=90.613, backward_time=0.097, grad_norm=41.312, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.475e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:42:30,448 (trainer:737) INFO: 4epoch:train:13301-13400batch: iter_time=1.118e-04, forward_time=0.106, loss_ctc=91.120, loss_att=108.640, acc=0.560, loss=103.384, backward_time=0.098, grad_norm=42.505, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.505e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:43:11,839 (trainer:737) INFO: 4epoch:train:13401-13500batch: iter_time=1.195e-04, forward_time=0.105, loss_ctc=69.891, loss_att=85.008, acc=0.594, loss=80.473, backward_time=0.098, grad_norm=67.277, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.535e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 10:43:52,968 (trainer:737) INFO: 4epoch:train:13501-13600batch: iter_time=1.173e-04, forward_time=0.105, loss_ctc=73.407, loss_att=70.686, acc=0.593, loss=71.503, backward_time=0.097, grad_norm=40.862, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.565e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:44:34,204 (trainer:737) INFO: 4epoch:train:13601-13700batch: iter_time=1.282e-04, forward_time=0.105, loss_ctc=85.921, loss_att=84.659, acc=0.570, loss=85.038, backward_time=0.097, grad_norm=70.679, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.595e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:44:57,099 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 10:45:16,893 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 10:45:20,851 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fcb62eb30>)
+[gpuc02:0/16] 2024-01-12 10:45:20,852 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 10:45:20,855 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 10:49:38,424 (trainer:737) INFO: 4epoch:train:13701-13800batch: iter_time=2.318, forward_time=0.104, loss_ctc=71.160, loss_att=90.318, acc=0.569, loss=84.571, backward_time=0.097, grad_norm=37.904, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.625e-04, train_time=3.042
+[gpuc02:0/16] 2024-01-12 10:50:20,111 (trainer:737) INFO: 4epoch:train:13801-13900batch: iter_time=9.777e-05, forward_time=0.105, loss_ctc=80.530, loss_att=91.923, acc=0.582, loss=88.505, backward_time=0.097, grad_norm=43.587, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.655e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 10:51:00,958 (trainer:737) INFO: 4epoch:train:13901-14000batch: iter_time=1.118e-04, forward_time=0.104, loss_ctc=78.491, loss_att=102.800, acc=0.537, loss=95.507, backward_time=0.097, grad_norm=46.905, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.685e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:51:42,023 (trainer:737) INFO: 4epoch:train:14001-14100batch: iter_time=1.089e-04, forward_time=0.104, loss_ctc=71.162, loss_att=84.360, acc=0.552, loss=80.401, backward_time=0.096, grad_norm=37.111, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.715e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:52:22,813 (trainer:737) INFO: 4epoch:train:14101-14200batch: iter_time=1.136e-04, forward_time=0.104, loss_ctc=78.408, loss_att=79.675, acc=0.591, loss=79.295, backward_time=0.097, grad_norm=37.535, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.745e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 10:53:03,540 (trainer:737) INFO: 4epoch:train:14201-14300batch: iter_time=1.463e-04, forward_time=0.104, loss_ctc=76.373, loss_att=81.854, acc=0.548, loss=80.210, backward_time=0.096, grad_norm=44.022, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.775e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 10:53:44,525 (trainer:737) INFO: 4epoch:train:14301-14400batch: iter_time=1.706e-04, forward_time=0.105, loss_ctc=90.763, loss_att=105.744, acc=0.529, loss=101.250, backward_time=0.098, grad_norm=45.225, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.805e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 10:54:25,277 (trainer:737) INFO: 4epoch:train:14401-14500batch: iter_time=1.158e-04, forward_time=0.104, loss_ctc=84.572, loss_att=84.049, acc=0.563, loss=84.206, backward_time=0.097, grad_norm=42.052, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.835e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 10:55:06,537 (trainer:737) INFO: 4epoch:train:14501-14600batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=89.678, loss_att=113.119, acc=0.538, loss=106.086, backward_time=0.097, grad_norm=40.678, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.030, optim0_lr0=9.865e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 10:55:47,245 (trainer:737) INFO: 4epoch:train:14601-14700batch: iter_time=9.938e-05, forward_time=0.104, loss_ctc=69.142, loss_att=80.100, acc=0.564, loss=76.813, backward_time=0.096, grad_norm=37.386, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.895e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 10:56:28,122 (trainer:737) INFO: 4epoch:train:14701-14800batch: iter_time=9.695e-05, forward_time=0.104, loss_ctc=67.956, loss_att=82.718, acc=0.579, loss=78.290, backward_time=0.097, grad_norm=37.387, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.925e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 10:57:09,209 (trainer:737) INFO: 4epoch:train:14801-14900batch: iter_time=1.013e-04, forward_time=0.104, loss_ctc=84.745, loss_att=76.025, acc=0.587, loss=78.641, backward_time=0.097, grad_norm=43.654, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.955e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 10:57:50,230 (trainer:737) INFO: 4epoch:train:14901-15000batch: iter_time=9.064e-05, forward_time=0.104, loss_ctc=72.406, loss_att=75.382, acc=0.601, loss=74.489, backward_time=0.097, grad_norm=37.161, clip=100.000, loss_scale=3.518e+13, optim_step_time=0.029, optim0_lr0=9.985e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:17:58,030 (trainer:343) INFO: 4epoch results: [train] iter_time=0.194, forward_time=0.105, loss_ctc=84.633, loss_att=100.002, acc=0.530, loss=95.391, backward_time=0.098, grad_norm=47.853, clip=100.000, loss_scale=9.328e+12, optim_step_time=0.030, optim0_lr0=7.750e-04, train_time=0.632, time=2 hours, 38 minutes and 17.51 seconds, total_count=60000, gpu_max_cached_mem_GB=25.176, [valid] loss_ctc=96.606, cer_ctc=0.469, loss_att=87.819, acc=0.409, cer=0.473, wer=1.000, loss=90.455, time=19 minutes and 57.97 seconds, total_count=18684, gpu_max_cached_mem_GB=25.176
+[gpuc02:0/16] 2024-01-12 11:18:02,505 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 11:18:02,506 (trainer:272) INFO: 5/45epoch started. Estimated time to finish: 5 days, 1 hour and 17 minutes
+[gpuc02:0/16] 2024-01-12 11:18:02,516 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 11:18:21,999 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 11:18:25,547 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38de704100>)
+[gpuc02:0/16] 2024-01-12 11:18:25,547 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 11:18:25,550 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 11:22:47,941 (trainer:737) INFO: 5epoch:train:1-100batch: iter_time=2.250, forward_time=0.105, loss_ctc=97.260, loss_att=106.313, acc=0.540, loss=103.597, backward_time=0.098, grad_norm=52.600, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.996e-04, train_time=2.854
+[gpuc02:0/16] 2024-01-12 11:23:29,215 (trainer:737) INFO: 5epoch:train:101-200batch: iter_time=1.016e-04, forward_time=0.104, loss_ctc=90.211, loss_att=103.673, acc=0.545, loss=99.634, backward_time=0.098, grad_norm=48.283, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.987e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 11:24:09,890 (trainer:737) INFO: 5epoch:train:201-300batch: iter_time=1.059e-04, forward_time=0.103, loss_ctc=76.641, loss_att=77.594, acc=0.573, loss=77.308, backward_time=0.098, grad_norm=44.745, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.979e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 11:24:51,280 (trainer:737) INFO: 5epoch:train:301-400batch: iter_time=1.038e-04, forward_time=0.106, loss_ctc=86.274, loss_att=97.198, acc=0.571, loss=93.921, backward_time=0.099, grad_norm=41.945, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.971e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 11:25:32,560 (trainer:737) INFO: 5epoch:train:401-500batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=78.496, loss_att=84.619, acc=0.579, loss=82.782, backward_time=0.100, grad_norm=37.963, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.963e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 11:26:15,947 (trainer:737) INFO: 5epoch:train:501-600batch: iter_time=1.049e-04, forward_time=0.121, loss_ctc=67.370, loss_att=78.669, acc=0.570, loss=75.280, backward_time=0.099, grad_norm=36.160, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.954e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-12 11:26:58,250 (trainer:737) INFO: 5epoch:train:601-700batch: iter_time=1.067e-04, forward_time=0.110, loss_ctc=81.041, loss_att=100.824, acc=0.564, loss=94.889, backward_time=0.102, grad_norm=39.780, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.946e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-12 11:27:39,113 (trainer:737) INFO: 5epoch:train:701-800batch: iter_time=1.058e-04, forward_time=0.103, loss_ctc=91.768, loss_att=91.593, acc=0.551, loss=91.645, backward_time=0.098, grad_norm=46.389, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.029, optim0_lr0=9.938e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 11:28:21,274 (trainer:737) INFO: 5epoch:train:801-900batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=77.757, loss_att=83.860, acc=0.598, loss=82.029, backward_time=0.099, grad_norm=35.345, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.930e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-12 11:29:02,183 (trainer:737) INFO: 5epoch:train:901-1000batch: iter_time=1.114e-04, forward_time=0.103, loss_ctc=75.408, loss_att=90.377, acc=0.537, loss=85.886, backward_time=0.099, grad_norm=198.387, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.922e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 11:29:43,583 (trainer:737) INFO: 5epoch:train:1001-1100batch: iter_time=1.138e-04, forward_time=0.104, loss_ctc=79.287, loss_att=95.226, acc=0.555, loss=90.445, backward_time=0.098, grad_norm=38.031, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.914e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 11:30:24,492 (trainer:737) INFO: 5epoch:train:1101-1200batch: iter_time=1.099e-04, forward_time=0.104, loss_ctc=86.600, loss_att=102.925, acc=0.566, loss=98.027, backward_time=0.098, grad_norm=47.490, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.905e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 11:30:53,602 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 11:31:12,336 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 11:31:15,960 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f6094eb90>)
+[gpuc02:0/16] 2024-01-12 11:31:15,960 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 11:31:15,964 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 11:36:59,566 (trainer:737) INFO: 5epoch:train:1201-1300batch: iter_time=2.699, forward_time=0.104, loss_ctc=94.848, loss_att=92.497, acc=0.572, loss=93.202, backward_time=0.098, grad_norm=49.980, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.030, optim0_lr0=9.897e-04, train_time=3.951
+[gpuc02:0/16] 2024-01-12 11:37:40,786 (trainer:737) INFO: 5epoch:train:1301-1400batch: iter_time=1.448e-04, forward_time=0.105, loss_ctc=90.313, loss_att=98.066, acc=0.562, loss=95.740, backward_time=0.099, grad_norm=43.245, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.889e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 11:38:22,049 (trainer:737) INFO: 5epoch:train:1401-1500batch: iter_time=1.477e-04, forward_time=0.103, loss_ctc=64.832, loss_att=72.174, acc=0.585, loss=69.971, backward_time=0.097, grad_norm=35.329, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.881e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 11:39:03,774 (trainer:737) INFO: 5epoch:train:1501-1600batch: iter_time=1.524e-04, forward_time=0.104, loss_ctc=87.135, loss_att=90.055, acc=0.577, loss=89.179, backward_time=0.098, grad_norm=43.071, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.873e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 11:39:44,779 (trainer:737) INFO: 5epoch:train:1601-1700batch: iter_time=1.731e-04, forward_time=0.104, loss_ctc=77.181, loss_att=89.106, acc=0.599, loss=85.528, backward_time=0.098, grad_norm=37.464, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.865e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:40:25,509 (trainer:737) INFO: 5epoch:train:1701-1800batch: iter_time=1.982e-04, forward_time=0.103, loss_ctc=66.550, loss_att=77.192, acc=0.574, loss=73.999, backward_time=0.098, grad_norm=32.157, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.857e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 11:41:06,546 (trainer:737) INFO: 5epoch:train:1801-1900batch: iter_time=1.448e-04, forward_time=0.103, loss_ctc=70.672, loss_att=75.460, acc=0.598, loss=74.023, backward_time=0.098, grad_norm=36.239, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.849e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:41:47,649 (trainer:737) INFO: 5epoch:train:1901-2000batch: iter_time=1.673e-04, forward_time=0.104, loss_ctc=93.631, loss_att=106.004, acc=0.541, loss=102.292, backward_time=0.099, grad_norm=111.861, clip=100.000, loss_scale=7.037e+13, optim_step_time=0.031, optim0_lr0=9.841e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 11:42:28,816 (trainer:737) INFO: 5epoch:train:2001-2100batch: iter_time=1.348e-04, forward_time=0.107, loss_ctc=73.261, loss_att=84.613, acc=0.597, loss=81.207, backward_time=0.098, grad_norm=34.046, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.833e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 11:43:10,024 (trainer:737) INFO: 5epoch:train:2101-2200batch: iter_time=1.148e-04, forward_time=0.104, loss_ctc=74.947, loss_att=76.482, acc=0.609, loss=76.022, backward_time=0.098, grad_norm=35.854, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.031, optim0_lr0=9.825e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 11:43:50,780 (trainer:737) INFO: 5epoch:train:2201-2300batch: iter_time=1.141e-04, forward_time=0.103, loss_ctc=68.626, loss_att=85.645, acc=0.535, loss=80.539, backward_time=0.097, grad_norm=37.026, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.817e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 11:44:32,139 (trainer:737) INFO: 5epoch:train:2301-2400batch: iter_time=1.333e-04, forward_time=0.105, loss_ctc=76.754, loss_att=101.504, acc=0.583, loss=94.079, backward_time=0.099, grad_norm=35.086, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.810e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 11:45:13,058 (trainer:737) INFO: 5epoch:train:2401-2500batch: iter_time=1.134e-04, forward_time=0.104, loss_ctc=92.461, loss_att=88.841, acc=0.582, loss=89.927, backward_time=0.098, grad_norm=46.485, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.802e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 11:45:15,487 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 11:45:36,491 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 11:45:40,287 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a1633730>)
+[gpuc02:0/16] 2024-01-12 11:45:40,287 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 11:45:40,290 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 11:50:34,428 (trainer:737) INFO: 5epoch:train:2501-2600batch: iter_time=2.485, forward_time=0.104, loss_ctc=89.112, loss_att=93.926, acc=0.566, loss=92.482, backward_time=0.098, grad_norm=43.517, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.794e-04, train_time=3.213
+[gpuc02:0/16] 2024-01-12 11:51:15,380 (trainer:737) INFO: 5epoch:train:2601-2700batch: iter_time=1.754e-04, forward_time=0.104, loss_ctc=82.372, loss_att=95.304, acc=0.563, loss=91.424, backward_time=0.098, grad_norm=40.973, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.786e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 11:51:56,395 (trainer:737) INFO: 5epoch:train:2701-2800batch: iter_time=1.558e-04, forward_time=0.103, loss_ctc=70.528, loss_att=70.951, acc=0.601, loss=70.824, backward_time=0.097, grad_norm=88.216, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.778e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:52:37,613 (trainer:737) INFO: 5epoch:train:2801-2900batch: iter_time=1.553e-04, forward_time=0.104, loss_ctc=79.216, loss_att=89.130, acc=0.590, loss=86.156, backward_time=0.098, grad_norm=36.450, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.771e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 11:53:18,956 (trainer:737) INFO: 5epoch:train:2901-3000batch: iter_time=1.647e-04, forward_time=0.104, loss_ctc=72.962, loss_att=77.504, acc=0.601, loss=76.142, backward_time=0.098, grad_norm=35.479, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.763e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 11:54:00,321 (trainer:737) INFO: 5epoch:train:3001-3100batch: iter_time=1.776e-04, forward_time=0.103, loss_ctc=63.523, loss_att=75.212, acc=0.580, loss=71.705, backward_time=0.097, grad_norm=33.802, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.755e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 11:54:41,537 (trainer:737) INFO: 5epoch:train:3101-3200batch: iter_time=1.498e-04, forward_time=0.104, loss_ctc=74.930, loss_att=94.477, acc=0.577, loss=88.613, backward_time=0.098, grad_norm=35.504, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.747e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 11:55:22,368 (trainer:737) INFO: 5epoch:train:3201-3300batch: iter_time=1.806e-04, forward_time=0.103, loss_ctc=83.665, loss_att=83.211, acc=0.564, loss=83.347, backward_time=0.097, grad_norm=40.621, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.740e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 11:56:03,302 (trainer:737) INFO: 5epoch:train:3301-3400batch: iter_time=1.555e-04, forward_time=0.104, loss_ctc=73.375, loss_att=79.261, acc=0.610, loss=77.495, backward_time=0.098, grad_norm=32.666, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.732e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 11:56:44,359 (trainer:737) INFO: 5epoch:train:3401-3500batch: iter_time=1.663e-04, forward_time=0.103, loss_ctc=69.636, loss_att=84.747, acc=0.550, loss=80.214, backward_time=0.098, grad_norm=38.036, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.724e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:57:25,167 (trainer:737) INFO: 5epoch:train:3501-3600batch: iter_time=1.744e-04, forward_time=0.103, loss_ctc=73.892, loss_att=91.447, acc=0.564, loss=86.180, backward_time=0.097, grad_norm=35.227, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.031, optim0_lr0=9.717e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 11:58:06,163 (trainer:737) INFO: 5epoch:train:3601-3700batch: iter_time=1.774e-04, forward_time=0.104, loss_ctc=81.322, loss_att=96.945, acc=0.574, loss=92.258, backward_time=0.098, grad_norm=41.170, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.709e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 11:58:31,011 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 11:58:50,344 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 11:58:54,340 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3845b87dc0>)
+[gpuc02:0/16] 2024-01-12 11:58:54,340 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 11:58:54,344 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 12:03:27,056 (trainer:737) INFO: 5epoch:train:3701-3800batch: iter_time=2.606, forward_time=0.121, loss_ctc=88.456, loss_att=83.307, acc=0.587, loss=84.852, backward_time=0.102, grad_norm=43.090, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.701e-04, train_time=3.209
+[gpuc02:0/16] 2024-01-12 12:04:08,073 (trainer:737) INFO: 5epoch:train:3801-3900batch: iter_time=1.682e-04, forward_time=0.104, loss_ctc=86.303, loss_att=92.889, acc=0.571, loss=90.913, backward_time=0.098, grad_norm=40.784, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.694e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:04:49,177 (trainer:737) INFO: 5epoch:train:3901-4000batch: iter_time=1.824e-04, forward_time=0.103, loss_ctc=62.201, loss_att=68.520, acc=0.597, loss=66.624, backward_time=0.097, grad_norm=33.612, clip=100.000, loss_scale=1.407e+14, optim_step_time=0.030, optim0_lr0=9.686e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 12:05:30,026 (trainer:737) INFO: 5epoch:train:4001-4100batch: iter_time=1.892e-04, forward_time=0.104, loss_ctc=82.974, loss_att=84.936, acc=0.595, loss=84.347, backward_time=0.098, grad_norm=40.330, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.679e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 12:06:10,872 (trainer:737) INFO: 5epoch:train:4101-4200batch: iter_time=2.224e-04, forward_time=0.104, loss_ctc=73.241, loss_att=85.484, acc=0.606, loss=81.811, backward_time=0.098, grad_norm=34.322, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.671e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 12:06:51,514 (trainer:737) INFO: 5epoch:train:4201-4300batch: iter_time=2.159e-04, forward_time=0.102, loss_ctc=63.208, loss_att=73.316, acc=0.589, loss=70.283, backward_time=0.097, grad_norm=29.862, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.663e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 12:07:32,108 (trainer:737) INFO: 5epoch:train:4301-4400batch: iter_time=1.808e-04, forward_time=0.103, loss_ctc=67.467, loss_att=72.212, acc=0.605, loss=70.789, backward_time=0.097, grad_norm=34.267, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.656e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 12:08:13,617 (trainer:737) INFO: 5epoch:train:4401-4500batch: iter_time=1.982e-04, forward_time=0.104, loss_ctc=89.375, loss_att=101.259, acc=0.544, loss=97.694, backward_time=0.098, grad_norm=41.304, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.648e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 12:08:54,627 (trainer:737) INFO: 5epoch:train:4501-4600batch: iter_time=1.641e-04, forward_time=0.103, loss_ctc=70.139, loss_att=79.956, acc=0.609, loss=77.011, backward_time=0.097, grad_norm=31.298, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.029, optim0_lr0=9.641e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:09:36,132 (trainer:737) INFO: 5epoch:train:4601-4700batch: iter_time=1.889e-04, forward_time=0.103, loss_ctc=71.876, loss_att=73.064, acc=0.615, loss=72.708, backward_time=0.097, grad_norm=86.056, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.029, optim0_lr0=9.634e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 12:10:16,964 (trainer:737) INFO: 5epoch:train:4701-4800batch: iter_time=1.996e-04, forward_time=0.103, loss_ctc=65.711, loss_att=83.351, acc=0.537, loss=78.059, backward_time=0.096, grad_norm=35.493, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.626e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 12:10:58,149 (trainer:737) INFO: 5epoch:train:4801-4900batch: iter_time=1.723e-04, forward_time=0.105, loss_ctc=73.355, loss_att=96.284, acc=0.594, loss=89.405, backward_time=0.099, grad_norm=32.514, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.029, optim0_lr0=9.619e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:11:38,892 (trainer:737) INFO: 5epoch:train:4901-5000batch: iter_time=1.755e-04, forward_time=0.104, loss_ctc=89.081, loss_att=85.783, acc=0.580, loss=86.773, backward_time=0.097, grad_norm=44.611, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.611e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 12:11:47,676 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 12:12:07,117 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 12:12:11,408 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40d2a1b460>)
+[gpuc02:0/16] 2024-01-12 12:12:11,409 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 12:12:11,412 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 12:17:20,909 (trainer:737) INFO: 5epoch:train:5001-5100batch: iter_time=2.951, forward_time=0.108, loss_ctc=84.714, loss_att=91.866, acc=0.583, loss=89.721, backward_time=0.097, grad_norm=40.069, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.604e-04, train_time=3.420
+[gpuc02:0/16] 2024-01-12 12:18:01,921 (trainer:737) INFO: 5epoch:train:5101-5200batch: iter_time=1.516e-04, forward_time=0.105, loss_ctc=77.757, loss_att=89.960, acc=0.591, loss=86.299, backward_time=0.098, grad_norm=36.866, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.597e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:18:43,189 (trainer:737) INFO: 5epoch:train:5201-5300batch: iter_time=1.719e-04, forward_time=0.106, loss_ctc=66.588, loss_att=67.700, acc=0.616, loss=67.366, backward_time=0.096, grad_norm=55.474, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.589e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:19:24,396 (trainer:737) INFO: 5epoch:train:5301-5400batch: iter_time=1.520e-04, forward_time=0.105, loss_ctc=76.521, loss_att=85.490, acc=0.609, loss=82.799, backward_time=0.098, grad_norm=34.890, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.582e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:20:05,314 (trainer:737) INFO: 5epoch:train:5401-5500batch: iter_time=1.924e-04, forward_time=0.105, loss_ctc=70.527, loss_att=76.238, acc=0.613, loss=74.525, backward_time=0.097, grad_norm=68.696, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.574e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:20:46,370 (trainer:737) INFO: 5epoch:train:5501-5600batch: iter_time=2.038e-04, forward_time=0.105, loss_ctc=60.729, loss_att=71.277, acc=0.603, loss=68.113, backward_time=0.097, grad_norm=32.036, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.567e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:21:27,357 (trainer:737) INFO: 5epoch:train:5601-5700batch: iter_time=1.874e-04, forward_time=0.105, loss_ctc=71.959, loss_att=91.608, acc=0.599, loss=85.713, backward_time=0.098, grad_norm=34.060, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.560e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:22:08,710 (trainer:737) INFO: 5epoch:train:5701-5800batch: iter_time=1.830e-04, forward_time=0.105, loss_ctc=81.228, loss_att=81.350, acc=0.586, loss=81.314, backward_time=0.098, grad_norm=37.005, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.553e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 12:22:49,718 (trainer:737) INFO: 5epoch:train:5801-5900batch: iter_time=1.782e-04, forward_time=0.105, loss_ctc=69.898, loss_att=76.247, acc=0.629, loss=74.342, backward_time=0.098, grad_norm=48.142, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.545e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:23:30,648 (trainer:737) INFO: 5epoch:train:5901-6000batch: iter_time=1.817e-04, forward_time=0.105, loss_ctc=67.050, loss_att=81.713, acc=0.574, loss=77.314, backward_time=0.098, grad_norm=66.483, clip=100.000, loss_scale=2.815e+14, optim_step_time=0.030, optim0_lr0=9.538e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:24:11,545 (trainer:737) INFO: 5epoch:train:6001-6100batch: iter_time=1.776e-04, forward_time=0.104, loss_ctc=71.579, loss_att=88.559, acc=0.583, loss=83.465, backward_time=0.098, grad_norm=32.986, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.531e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:24:52,594 (trainer:737) INFO: 5epoch:train:6101-6200batch: iter_time=1.940e-04, forward_time=0.105, loss_ctc=77.303, loss_att=92.896, acc=0.596, loss=88.218, backward_time=0.098, grad_norm=36.210, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.524e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:25:18,600 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 12:25:38,213 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 12:25:41,916 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3885b34e50>)
+[gpuc02:0/16] 2024-01-12 12:25:41,917 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 12:25:41,920 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 12:30:07,150 (trainer:737) INFO: 5epoch:train:6201-6300batch: iter_time=2.700, forward_time=0.111, loss_ctc=84.175, loss_att=78.952, acc=0.608, loss=80.519, backward_time=0.098, grad_norm=40.242, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.517e-04, train_time=3.145
+[gpuc02:0/16] 2024-01-12 12:30:48,149 (trainer:737) INFO: 5epoch:train:6301-6400batch: iter_time=1.244e-04, forward_time=0.105, loss_ctc=83.441, loss_att=90.677, acc=0.584, loss=88.507, backward_time=0.098, grad_norm=38.887, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.509e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:31:28,771 (trainer:737) INFO: 5epoch:train:6401-6500batch: iter_time=1.239e-04, forward_time=0.102, loss_ctc=60.124, loss_att=66.736, acc=0.611, loss=64.752, backward_time=0.096, grad_norm=32.619, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.502e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 12:32:09,659 (trainer:737) INFO: 5epoch:train:6501-6600batch: iter_time=1.252e-04, forward_time=0.104, loss_ctc=79.399, loss_att=81.428, acc=0.609, loss=80.820, backward_time=0.097, grad_norm=36.881, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.029, optim0_lr0=9.495e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:32:50,856 (trainer:737) INFO: 5epoch:train:6601-6700batch: iter_time=1.291e-04, forward_time=0.103, loss_ctc=72.230, loss_att=83.368, acc=0.619, loss=80.027, backward_time=0.097, grad_norm=33.378, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.488e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:33:31,443 (trainer:737) INFO: 5epoch:train:6701-6800batch: iter_time=1.528e-04, forward_time=0.103, loss_ctc=61.014, loss_att=71.464, acc=0.600, loss=68.329, backward_time=0.096, grad_norm=29.239, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.481e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 12:34:12,138 (trainer:737) INFO: 5epoch:train:6801-6900batch: iter_time=1.523e-04, forward_time=0.103, loss_ctc=64.916, loss_att=69.625, acc=0.619, loss=68.212, backward_time=0.096, grad_norm=31.369, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.474e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 12:34:53,518 (trainer:737) INFO: 5epoch:train:6901-7000batch: iter_time=1.460e-04, forward_time=0.104, loss_ctc=86.224, loss_att=97.752, acc=0.555, loss=94.294, backward_time=0.097, grad_norm=71.899, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.467e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 12:35:34,314 (trainer:737) INFO: 5epoch:train:7001-7100batch: iter_time=1.419e-04, forward_time=0.104, loss_ctc=67.341, loss_att=77.589, acc=0.620, loss=74.515, backward_time=0.097, grad_norm=30.979, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.460e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 12:36:15,419 (trainer:737) INFO: 5epoch:train:7101-7200batch: iter_time=1.486e-04, forward_time=0.103, loss_ctc=68.488, loss_att=69.607, acc=0.627, loss=69.271, backward_time=0.097, grad_norm=34.146, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.453e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 12:36:56,048 (trainer:737) INFO: 5epoch:train:7201-7300batch: iter_time=1.467e-04, forward_time=0.102, loss_ctc=63.131, loss_att=80.717, acc=0.550, loss=75.441, backward_time=0.096, grad_norm=34.193, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.445e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 12:37:37,035 (trainer:737) INFO: 5epoch:train:7301-7400batch: iter_time=1.446e-04, forward_time=0.104, loss_ctc=70.753, loss_att=94.213, acc=0.604, loss=87.175, backward_time=0.097, grad_norm=30.546, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.438e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:38:18,427 (trainer:737) INFO: 5epoch:train:7401-7500batch: iter_time=1.504e-04, forward_time=0.106, loss_ctc=84.280, loss_att=82.699, acc=0.593, loss=83.173, backward_time=0.097, grad_norm=39.764, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.431e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 12:38:27,632 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 12:38:47,174 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 12:38:50,916 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fa34ffe20>)
+[gpuc02:0/16] 2024-01-12 12:38:50,916 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 12:38:50,920 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 12:43:26,074 (trainer:737) INFO: 5epoch:train:7501-7600batch: iter_time=2.553, forward_time=0.105, loss_ctc=82.568, loss_att=83.149, acc=0.602, loss=82.975, backward_time=0.098, grad_norm=39.198, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.424e-04, train_time=3.076
+[gpuc02:0/16] 2024-01-12 12:44:07,516 (trainer:737) INFO: 5epoch:train:7601-7700batch: iter_time=1.402e-04, forward_time=0.104, loss_ctc=75.240, loss_att=84.911, acc=0.596, loss=82.009, backward_time=0.097, grad_norm=35.283, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.418e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 12:44:48,773 (trainer:737) INFO: 5epoch:train:7701-7800batch: iter_time=1.591e-04, forward_time=0.103, loss_ctc=63.865, loss_att=63.798, acc=0.632, loss=63.818, backward_time=0.096, grad_norm=32.692, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.029, optim0_lr0=9.411e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:45:29,769 (trainer:737) INFO: 5epoch:train:7801-7900batch: iter_time=1.575e-04, forward_time=0.105, loss_ctc=73.655, loss_att=81.294, acc=0.615, loss=79.002, backward_time=0.097, grad_norm=34.692, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.029, optim0_lr0=9.404e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:46:10,957 (trainer:737) INFO: 5epoch:train:7901-8000batch: iter_time=1.566e-04, forward_time=0.105, loss_ctc=67.886, loss_att=72.367, acc=0.625, loss=71.023, backward_time=0.097, grad_norm=31.061, clip=100.000, loss_scale=5.629e+14, optim_step_time=0.030, optim0_lr0=9.397e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:46:51,725 (trainer:737) INFO: 5epoch:train:8001-8100batch: iter_time=1.623e-04, forward_time=0.105, loss_ctc=58.680, loss_att=68.654, acc=0.609, loss=65.662, backward_time=0.097, grad_norm=28.993, clip=99.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.390e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 12:47:32,748 (trainer:737) INFO: 5epoch:train:8101-8200batch: iter_time=1.410e-04, forward_time=0.106, loss_ctc=69.821, loss_att=88.293, acc=0.603, loss=82.752, backward_time=0.097, grad_norm=40.088, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.383e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:48:13,866 (trainer:737) INFO: 5epoch:train:8201-8300batch: iter_time=1.650e-04, forward_time=0.105, loss_ctc=78.089, loss_att=76.168, acc=0.588, loss=76.744, backward_time=0.097, grad_norm=37.583, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.376e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 12:48:54,816 (trainer:737) INFO: 5epoch:train:8301-8400batch: iter_time=1.573e-04, forward_time=0.105, loss_ctc=68.157, loss_att=72.453, acc=0.639, loss=71.164, backward_time=0.097, grad_norm=30.597, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.369e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:49:35,715 (trainer:737) INFO: 5epoch:train:8401-8500batch: iter_time=1.690e-04, forward_time=0.105, loss_ctc=65.483, loss_att=78.944, acc=0.572, loss=74.905, backward_time=0.097, grad_norm=36.087, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.362e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:50:16,584 (trainer:737) INFO: 5epoch:train:8501-8600batch: iter_time=1.576e-04, forward_time=0.105, loss_ctc=69.135, loss_att=84.550, acc=0.589, loss=79.925, backward_time=0.097, grad_norm=33.919, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.356e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 12:50:57,854 (trainer:737) INFO: 5epoch:train:8601-8700batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=74.243, loss_att=89.877, acc=0.603, loss=85.187, backward_time=0.098, grad_norm=36.541, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.349e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 12:51:24,506 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 12:51:43,857 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 12:51:47,592 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f387a1174f0>)
+[gpuc02:0/16] 2024-01-12 12:51:47,592 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 12:51:47,595 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 12:56:10,127 (trainer:737) INFO: 5epoch:train:8701-8800batch: iter_time=2.564, forward_time=0.107, loss_ctc=82.951, loss_att=81.177, acc=0.607, loss=81.710, backward_time=0.098, grad_norm=41.302, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.342e-04, train_time=3.123
+[gpuc02:0/16] 2024-01-12 12:56:51,175 (trainer:737) INFO: 5epoch:train:8801-8900batch: iter_time=1.500e-04, forward_time=0.104, loss_ctc=79.810, loss_att=89.428, acc=0.601, loss=86.543, backward_time=0.098, grad_norm=37.315, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.029, optim0_lr0=9.335e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:57:32,306 (trainer:737) INFO: 5epoch:train:8901-9000batch: iter_time=1.577e-04, forward_time=0.102, loss_ctc=57.443, loss_att=65.187, acc=0.627, loss=62.864, backward_time=0.097, grad_norm=30.427, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.328e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 12:58:13,215 (trainer:737) INFO: 5epoch:train:9001-9100batch: iter_time=1.835e-04, forward_time=0.103, loss_ctc=77.276, loss_att=79.415, acc=0.618, loss=78.773, backward_time=0.097, grad_norm=35.414, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.322e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 12:58:54,190 (trainer:737) INFO: 5epoch:train:9101-9200batch: iter_time=1.745e-04, forward_time=0.104, loss_ctc=68.933, loss_att=82.208, acc=0.632, loss=78.225, backward_time=0.098, grad_norm=31.376, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.029, optim0_lr0=9.315e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 12:59:34,956 (trainer:737) INFO: 5epoch:train:9201-9300batch: iter_time=1.524e-04, forward_time=0.103, loss_ctc=59.447, loss_att=70.436, acc=0.609, loss=67.139, backward_time=0.097, grad_norm=28.695, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.308e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 13:00:16,015 (trainer:737) INFO: 5epoch:train:9301-9400batch: iter_time=1.457e-04, forward_time=0.103, loss_ctc=62.985, loss_att=68.311, acc=0.634, loss=66.713, backward_time=0.097, grad_norm=39.351, clip=99.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.301e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:00:57,058 (trainer:737) INFO: 5epoch:train:9401-9500batch: iter_time=1.449e-04, forward_time=0.105, loss_ctc=82.974, loss_att=94.509, acc=0.579, loss=91.048, backward_time=0.099, grad_norm=37.132, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.295e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:01:38,649 (trainer:737) INFO: 5epoch:train:9501-9600batch: iter_time=1.344e-04, forward_time=0.104, loss_ctc=65.441, loss_att=76.717, acc=0.634, loss=73.334, backward_time=0.098, grad_norm=165.488, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.288e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 13:02:19,916 (trainer:737) INFO: 5epoch:train:9601-9700batch: iter_time=1.795e-04, forward_time=0.104, loss_ctc=66.375, loss_att=68.001, acc=0.647, loss=67.513, backward_time=0.098, grad_norm=31.030, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.281e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 13:03:01,218 (trainer:737) INFO: 5epoch:train:9701-9800batch: iter_time=1.636e-04, forward_time=0.103, loss_ctc=60.575, loss_att=77.488, acc=0.571, loss=72.414, backward_time=0.096, grad_norm=31.324, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.275e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 13:03:42,592 (trainer:737) INFO: 5epoch:train:9801-9900batch: iter_time=1.448e-04, forward_time=0.105, loss_ctc=68.934, loss_att=91.791, acc=0.619, loss=84.934, backward_time=0.098, grad_norm=31.261, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.268e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 13:04:23,507 (trainer:737) INFO: 5epoch:train:9901-10000batch: iter_time=1.392e-04, forward_time=0.104, loss_ctc=81.724, loss_att=77.871, acc=0.622, loss=79.027, backward_time=0.097, grad_norm=41.192, clip=100.000, loss_scale=1.126e+15, optim_step_time=0.030, optim0_lr0=9.261e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:04:28,958 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 13:04:48,292 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 13:04:52,022 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38aae9ff70>)
+[gpuc02:0/16] 2024-01-12 13:04:52,022 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 13:04:52,025 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 13:09:35,131 (trainer:737) INFO: 5epoch:train:10001-10100batch: iter_time=2.532, forward_time=0.104, loss_ctc=78.364, loss_att=84.504, acc=0.609, loss=82.662, backward_time=0.097, grad_norm=37.351, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.255e-04, train_time=3.116
+[gpuc02:0/16] 2024-01-12 13:10:16,129 (trainer:737) INFO: 5epoch:train:10101-10200batch: iter_time=1.377e-04, forward_time=0.103, loss_ctc=73.230, loss_att=83.358, acc=0.613, loss=80.319, backward_time=0.097, grad_norm=34.046, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.248e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:10:57,191 (trainer:737) INFO: 5epoch:train:10201-10300batch: iter_time=1.518e-04, forward_time=0.102, loss_ctc=62.330, loss_att=63.268, acc=0.638, loss=62.987, backward_time=0.096, grad_norm=31.194, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.242e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:11:38,135 (trainer:737) INFO: 5epoch:train:10301-10400batch: iter_time=1.343e-04, forward_time=0.103, loss_ctc=71.744, loss_att=79.577, acc=0.630, loss=77.227, backward_time=0.097, grad_norm=45.514, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.235e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:12:18,945 (trainer:737) INFO: 5epoch:train:10401-10500batch: iter_time=1.678e-04, forward_time=0.104, loss_ctc=66.242, loss_att=70.972, acc=0.637, loss=69.553, backward_time=0.097, grad_norm=29.856, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.228e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 13:12:59,912 (trainer:737) INFO: 5epoch:train:10501-10600batch: iter_time=1.505e-04, forward_time=0.103, loss_ctc=56.792, loss_att=66.993, acc=0.621, loss=63.933, backward_time=0.097, grad_norm=28.421, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.222e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:13:40,859 (trainer:737) INFO: 5epoch:train:10601-10700batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=67.286, loss_att=85.396, acc=0.621, loss=79.963, backward_time=0.098, grad_norm=31.574, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.215e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:14:21,653 (trainer:737) INFO: 5epoch:train:10701-10800batch: iter_time=1.510e-04, forward_time=0.103, loss_ctc=76.173, loss_att=74.712, acc=0.608, loss=75.150, backward_time=0.097, grad_norm=34.602, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.209e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 13:15:02,668 (trainer:737) INFO: 5epoch:train:10801-10900batch: iter_time=1.412e-04, forward_time=0.104, loss_ctc=65.759, loss_att=71.597, acc=0.649, loss=69.846, backward_time=0.098, grad_norm=26.629, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.202e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:15:43,564 (trainer:737) INFO: 5epoch:train:10901-11000batch: iter_time=1.553e-04, forward_time=0.103, loss_ctc=63.068, loss_att=77.386, acc=0.590, loss=73.090, backward_time=0.097, grad_norm=33.584, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.196e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:16:24,698 (trainer:737) INFO: 5epoch:train:11001-11100batch: iter_time=1.720e-04, forward_time=0.103, loss_ctc=67.256, loss_att=82.220, acc=0.606, loss=77.731, backward_time=0.097, grad_norm=32.454, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.189e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 13:17:06,558 (trainer:737) INFO: 5epoch:train:11101-11200batch: iter_time=1.801e-04, forward_time=0.107, loss_ctc=71.422, loss_att=85.482, acc=0.624, loss=81.264, backward_time=0.098, grad_norm=33.392, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.183e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-12 13:17:32,275 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 13:17:51,694 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 13:17:55,473 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38888dbcd0>)
+[gpuc02:0/16] 2024-01-12 13:17:55,473 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 13:17:55,476 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 13:22:20,576 (trainer:737) INFO: 5epoch:train:11201-11300batch: iter_time=2.585, forward_time=0.105, loss_ctc=77.727, loss_att=73.593, acc=0.630, loss=74.833, backward_time=0.098, grad_norm=39.810, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.177e-04, train_time=3.140
+[gpuc02:0/16] 2024-01-12 13:23:01,580 (trainer:737) INFO: 5epoch:train:11301-11400batch: iter_time=1.971e-04, forward_time=0.105, loss_ctc=77.576, loss_att=85.385, acc=0.607, loss=83.042, backward_time=0.098, grad_norm=34.247, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.170e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:23:42,226 (trainer:737) INFO: 5epoch:train:11401-11500batch: iter_time=2.119e-04, forward_time=0.103, loss_ctc=55.502, loss_att=63.525, acc=0.630, loss=61.118, backward_time=0.096, grad_norm=27.782, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.164e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 13:24:23,661 (trainer:737) INFO: 5epoch:train:11501-11600batch: iter_time=1.894e-04, forward_time=0.105, loss_ctc=75.237, loss_att=77.231, acc=0.629, loss=76.633, backward_time=0.098, grad_norm=34.953, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.157e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 13:25:04,610 (trainer:737) INFO: 5epoch:train:11601-11700batch: iter_time=2.198e-04, forward_time=0.105, loss_ctc=66.671, loss_att=79.142, acc=0.637, loss=75.401, backward_time=0.097, grad_norm=31.386, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.151e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:25:45,273 (trainer:737) INFO: 5epoch:train:11701-11800batch: iter_time=2.227e-04, forward_time=0.104, loss_ctc=57.374, loss_att=68.217, acc=0.617, loss=64.964, backward_time=0.097, grad_norm=26.620, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.145e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 13:26:26,267 (trainer:737) INFO: 5epoch:train:11801-11900batch: iter_time=1.989e-04, forward_time=0.104, loss_ctc=60.950, loss_att=66.012, acc=0.637, loss=64.493, backward_time=0.097, grad_norm=29.876, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.138e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:27:07,231 (trainer:737) INFO: 5epoch:train:11901-12000batch: iter_time=1.996e-04, forward_time=0.106, loss_ctc=81.013, loss_att=91.652, acc=0.575, loss=88.460, backward_time=0.098, grad_norm=37.105, clip=100.000, loss_scale=2.252e+15, optim_step_time=0.030, optim0_lr0=9.132e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:27:48,076 (trainer:737) INFO: 5epoch:train:12001-12100batch: iter_time=1.846e-04, forward_time=0.104, loss_ctc=63.743, loss_att=73.499, acc=0.638, loss=70.572, backward_time=0.097, grad_norm=29.195, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.125e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 13:28:28,966 (trainer:737) INFO: 5epoch:train:12101-12200batch: iter_time=2.119e-04, forward_time=0.105, loss_ctc=63.932, loss_att=66.273, acc=0.644, loss=65.571, backward_time=0.098, grad_norm=28.945, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.119e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:29:09,671 (trainer:737) INFO: 5epoch:train:12201-12300batch: iter_time=2.068e-04, forward_time=0.104, loss_ctc=59.111, loss_att=75.244, acc=0.571, loss=70.404, backward_time=0.097, grad_norm=30.617, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.113e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 13:29:50,768 (trainer:737) INFO: 5epoch:train:12301-12400batch: iter_time=2.003e-04, forward_time=0.106, loss_ctc=67.064, loss_att=89.987, acc=0.622, loss=83.110, backward_time=0.098, grad_norm=30.779, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.107e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 13:30:32,265 (trainer:737) INFO: 5epoch:train:12401-12500batch: iter_time=1.494e-04, forward_time=0.104, loss_ctc=79.956, loss_att=77.692, acc=0.611, loss=78.371, backward_time=0.097, grad_norm=40.177, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.100e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 13:30:38,867 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 13:30:57,915 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 13:31:01,626 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38aae54f70>)
+[gpuc02:0/16] 2024-01-12 13:31:01,626 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 13:31:01,630 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 13:35:48,238 (trainer:737) INFO: 5epoch:train:12501-12600batch: iter_time=2.562, forward_time=0.105, loss_ctc=76.916, loss_att=82.902, acc=0.617, loss=81.106, backward_time=0.098, grad_norm=36.380, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.094e-04, train_time=3.160
+[gpuc02:0/16] 2024-01-12 13:36:29,218 (trainer:737) INFO: 5epoch:train:12601-12700batch: iter_time=1.889e-04, forward_time=0.105, loss_ctc=71.525, loss_att=81.997, acc=0.623, loss=78.856, backward_time=0.098, grad_norm=33.778, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.088e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:37:10,202 (trainer:737) INFO: 5epoch:train:12701-12800batch: iter_time=1.280e-04, forward_time=0.107, loss_ctc=60.358, loss_att=61.473, acc=0.648, loss=61.139, backward_time=0.097, grad_norm=29.683, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.081e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:37:51,167 (trainer:737) INFO: 5epoch:train:12801-12900batch: iter_time=1.549e-04, forward_time=0.104, loss_ctc=69.206, loss_att=77.229, acc=0.638, loss=74.822, backward_time=0.098, grad_norm=32.344, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.031, optim0_lr0=9.075e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:38:32,188 (trainer:737) INFO: 5epoch:train:12901-13000batch: iter_time=1.526e-04, forward_time=0.104, loss_ctc=65.144, loss_att=69.958, acc=0.645, loss=68.514, backward_time=0.098, grad_norm=29.579, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.031, optim0_lr0=9.069e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:39:12,957 (trainer:737) INFO: 5epoch:train:13001-13100batch: iter_time=1.404e-04, forward_time=0.103, loss_ctc=55.924, loss_att=66.102, acc=0.632, loss=63.049, backward_time=0.097, grad_norm=28.302, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.063e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 13:39:53,974 (trainer:737) INFO: 5epoch:train:13101-13200batch: iter_time=1.160e-04, forward_time=0.104, loss_ctc=65.959, loss_att=83.707, acc=0.628, loss=78.382, backward_time=0.098, grad_norm=30.394, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.031, optim0_lr0=9.057e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:40:35,410 (trainer:737) INFO: 5epoch:train:13201-13300batch: iter_time=1.337e-04, forward_time=0.104, loss_ctc=74.515, loss_att=73.169, acc=0.617, loss=73.573, backward_time=0.097, grad_norm=51.539, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.050e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 13:41:16,745 (trainer:737) INFO: 5epoch:train:13301-13400batch: iter_time=1.548e-04, forward_time=0.105, loss_ctc=64.598, loss_att=69.906, acc=0.658, loss=68.314, backward_time=0.098, grad_norm=30.435, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.044e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 13:41:58,233 (trainer:737) INFO: 5epoch:train:13401-13500batch: iter_time=1.464e-04, forward_time=0.104, loss_ctc=61.372, loss_att=74.215, acc=0.604, loss=70.362, backward_time=0.098, grad_norm=51.309, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.038e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 13:42:39,131 (trainer:737) INFO: 5epoch:train:13501-13600batch: iter_time=1.320e-04, forward_time=0.104, loss_ctc=65.539, loss_att=81.102, acc=0.612, loss=76.433, backward_time=0.098, grad_norm=31.249, clip=99.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.032e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:43:20,759 (trainer:737) INFO: 5epoch:train:13601-13700batch: iter_time=1.567e-04, forward_time=0.105, loss_ctc=70.130, loss_att=86.093, acc=0.624, loss=81.304, backward_time=0.098, grad_norm=33.951, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.026e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 13:43:45,461 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 13:44:05,039 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 13:44:08,721 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f387af9feb0>)
+[gpuc02:0/16] 2024-01-12 13:44:08,721 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 13:44:08,724 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 13:48:27,790 (trainer:737) INFO: 5epoch:train:13701-13800batch: iter_time=2.589, forward_time=0.104, loss_ctc=78.224, loss_att=73.550, acc=0.637, loss=74.952, backward_time=0.098, grad_norm=37.249, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.020e-04, train_time=3.070
+[gpuc02:0/16] 2024-01-12 13:49:08,821 (trainer:737) INFO: 5epoch:train:13801-13900batch: iter_time=1.836e-04, forward_time=0.103, loss_ctc=75.049, loss_att=82.245, acc=0.617, loss=80.086, backward_time=0.098, grad_norm=34.749, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.014e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:49:49,792 (trainer:737) INFO: 5epoch:train:13901-14000batch: iter_time=1.731e-04, forward_time=0.103, loss_ctc=54.141, loss_att=61.191, acc=0.639, loss=59.076, backward_time=0.096, grad_norm=28.518, clip=100.000, loss_scale=4.504e+15, optim_step_time=0.030, optim0_lr0=9.007e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:50:30,769 (trainer:737) INFO: 5epoch:train:14001-14100batch: iter_time=1.874e-04, forward_time=0.104, loss_ctc=73.659, loss_att=75.077, acc=0.636, loss=74.652, backward_time=0.097, grad_norm=33.945, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=9.001e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:51:11,679 (trainer:737) INFO: 5epoch:train:14101-14200batch: iter_time=2.192e-04, forward_time=0.103, loss_ctc=64.893, loss_att=76.683, acc=0.645, loss=73.146, backward_time=0.098, grad_norm=31.547, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.995e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:51:52,370 (trainer:737) INFO: 5epoch:train:14201-14300batch: iter_time=2.124e-04, forward_time=0.102, loss_ctc=56.329, loss_att=67.357, acc=0.623, loss=64.049, backward_time=0.096, grad_norm=28.160, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.989e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 13:52:33,134 (trainer:737) INFO: 5epoch:train:14301-14400batch: iter_time=1.871e-04, forward_time=0.101, loss_ctc=59.849, loss_att=65.238, acc=0.643, loss=63.621, backward_time=0.096, grad_norm=29.644, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.983e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 13:53:14,395 (trainer:737) INFO: 5epoch:train:14401-14500batch: iter_time=1.835e-04, forward_time=0.104, loss_ctc=79.206, loss_att=89.510, acc=0.584, loss=86.419, backward_time=0.097, grad_norm=38.484, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.977e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 13:53:55,256 (trainer:737) INFO: 5epoch:train:14501-14600batch: iter_time=1.882e-04, forward_time=0.103, loss_ctc=62.558, loss_att=71.415, acc=0.647, loss=68.758, backward_time=0.097, grad_norm=28.552, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.971e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 13:54:36,137 (trainer:737) INFO: 5epoch:train:14601-14700batch: iter_time=1.880e-04, forward_time=0.103, loss_ctc=63.743, loss_att=64.617, acc=0.651, loss=64.355, backward_time=0.097, grad_norm=32.454, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.965e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 13:55:16,759 (trainer:737) INFO: 5epoch:train:14701-14800batch: iter_time=2.000e-04, forward_time=0.102, loss_ctc=57.723, loss_att=74.408, acc=0.578, loss=69.402, backward_time=0.096, grad_norm=30.737, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.959e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 13:55:57,771 (trainer:737) INFO: 5epoch:train:14801-14900batch: iter_time=1.979e-04, forward_time=0.103, loss_ctc=65.163, loss_att=87.373, acc=0.630, loss=80.710, backward_time=0.097, grad_norm=28.646, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.953e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 13:56:38,587 (trainer:737) INFO: 5epoch:train:14901-15000batch: iter_time=1.618e-04, forward_time=0.102, loss_ctc=78.018, loss_att=75.299, acc=0.619, loss=76.115, backward_time=0.097, grad_norm=39.584, clip=99.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.947e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 14:16:43,810 (trainer:343) INFO: 5epoch results: [train] iter_time=0.207, forward_time=0.104, loss_ctc=72.504, loss_att=81.030, acc=0.602, loss=78.472, backward_time=0.098, grad_norm=39.604, clip=99.973, loss_scale=1.792e+15, optim_step_time=0.030, optim0_lr0=9.443e-04, train_time=0.634, time=2 hours, 38 minutes and 45.64 seconds, total_count=75000, gpu_max_cached_mem_GB=25.619, [valid] loss_ctc=77.008, cer_ctc=0.396, loss_att=72.614, acc=0.471, cer=0.407, wer=1.000, loss=73.932, time=19 minutes and 55.49 seconds, total_count=23355, gpu_max_cached_mem_GB=25.619
+[gpuc02:0/16] 2024-01-12 14:16:48,519 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 14:16:48,546 (trainer:272) INFO: 6/45epoch started. Estimated time to finish: 4 days, 22 hours and 30 minutes
+[gpuc02:0/16] 2024-01-12 14:16:48,555 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 14:17:07,519 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 14:17:11,098 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38db432b00>)
+[gpuc02:0/16] 2024-01-12 14:17:11,098 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 14:17:11,101 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 14:21:54,806 (trainer:737) INFO: 6epoch:train:1-100batch: iter_time=2.444, forward_time=0.129, loss_ctc=82.485, loss_att=74.006, acc=0.597, loss=76.550, backward_time=0.106, grad_norm=35.089, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.031, optim0_lr0=8.941e-04, train_time=3.062
+[gpuc02:0/16] 2024-01-12 14:22:37,108 (trainer:737) INFO: 6epoch:train:101-200batch: iter_time=1.111e-04, forward_time=0.112, loss_ctc=69.285, loss_att=77.237, acc=0.614, loss=74.851, backward_time=0.099, grad_norm=34.610, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.031, optim0_lr0=8.935e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-12 14:23:18,299 (trainer:737) INFO: 6epoch:train:201-300batch: iter_time=1.199e-04, forward_time=0.104, loss_ctc=73.823, loss_att=83.114, acc=0.624, loss=80.326, backward_time=0.099, grad_norm=34.501, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.929e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 14:24:03,673 (trainer:737) INFO: 6epoch:train:301-400batch: iter_time=1.426e-04, forward_time=0.104, loss_ctc=66.202, loss_att=77.353, acc=0.631, loss=74.007, backward_time=0.098, grad_norm=34.487, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.030, optim0_lr0=8.923e-04, train_time=0.454
+[gpuc02:0/16] 2024-01-12 14:24:46,595 (trainer:737) INFO: 6epoch:train:401-500batch: iter_time=1.448e-04, forward_time=0.104, loss_ctc=63.548, loss_att=79.925, acc=0.597, loss=75.012, backward_time=0.097, grad_norm=32.095, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.917e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-12 14:25:31,500 (trainer:737) INFO: 6epoch:train:501-600batch: iter_time=1.365e-04, forward_time=0.131, loss_ctc=64.546, loss_att=62.600, acc=0.645, loss=63.184, backward_time=0.101, grad_norm=29.843, clip=99.000, loss_scale=9.007e+15, optim_step_time=0.032, optim0_lr0=8.912e-04, train_time=0.449
+[gpuc02:0/16] 2024-01-12 14:26:14,824 (trainer:737) INFO: 6epoch:train:601-700batch: iter_time=1.284e-04, forward_time=0.118, loss_ctc=74.661, loss_att=72.250, acc=0.643, loss=72.973, backward_time=0.099, grad_norm=60.229, clip=99.000, loss_scale=9.007e+15, optim_step_time=0.031, optim0_lr0=8.906e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-12 14:26:55,603 (trainer:737) INFO: 6epoch:train:701-800batch: iter_time=1.240e-04, forward_time=0.104, loss_ctc=76.680, loss_att=90.170, acc=0.594, loss=86.123, backward_time=0.098, grad_norm=35.960, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.900e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 14:27:37,008 (trainer:737) INFO: 6epoch:train:801-900batch: iter_time=1.281e-04, forward_time=0.105, loss_ctc=62.851, loss_att=71.056, acc=0.642, loss=68.595, backward_time=0.098, grad_norm=28.009, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.894e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 14:28:18,304 (trainer:737) INFO: 6epoch:train:901-1000batch: iter_time=1.225e-04, forward_time=0.109, loss_ctc=74.953, loss_att=94.246, acc=0.589, loss=88.458, backward_time=0.099, grad_norm=35.583, clip=100.000, loss_scale=9.007e+15, optim_step_time=0.029, optim0_lr0=8.888e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 14:29:00,912 (trainer:737) INFO: 6epoch:train:1001-1100batch: iter_time=1.373e-04, forward_time=0.106, loss_ctc=61.141, loss_att=68.014, acc=0.630, loss=65.952, backward_time=0.098, grad_norm=31.142, clip=99.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.882e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-12 14:29:43,141 (trainer:737) INFO: 6epoch:train:1101-1200batch: iter_time=1.315e-04, forward_time=0.112, loss_ctc=60.932, loss_att=69.945, acc=0.651, loss=67.241, backward_time=0.101, grad_norm=29.025, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.031, optim0_lr0=8.876e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-12 14:30:25,323 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 14:30:45,242 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 14:30:49,062 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f6631aaa0>)
+[gpuc02:0/16] 2024-01-12 14:30:49,062 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 14:30:49,066 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 14:36:21,879 (trainer:737) INFO: 6epoch:train:1201-1300batch: iter_time=3.574, forward_time=0.104, loss_ctc=81.839, loss_att=76.574, acc=0.612, loss=78.153, backward_time=0.098, grad_norm=36.473, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.871e-04, train_time=3.987
+[gpuc02:0/16] 2024-01-12 14:37:04,100 (trainer:737) INFO: 6epoch:train:1301-1400batch: iter_time=1.618e-04, forward_time=0.104, loss_ctc=67.321, loss_att=74.261, acc=0.636, loss=72.179, backward_time=0.098, grad_norm=31.416, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.865e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-12 14:37:45,453 (trainer:737) INFO: 6epoch:train:1401-1500batch: iter_time=1.944e-04, forward_time=0.104, loss_ctc=65.921, loss_att=83.989, acc=0.629, loss=78.569, backward_time=0.099, grad_norm=30.729, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.859e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 14:38:26,601 (trainer:737) INFO: 6epoch:train:1501-1600batch: iter_time=1.737e-04, forward_time=0.104, loss_ctc=74.884, loss_att=83.448, acc=0.617, loss=80.879, backward_time=0.098, grad_norm=34.369, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.853e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 14:39:08,119 (trainer:737) INFO: 6epoch:train:1601-1700batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=66.515, loss_att=86.889, acc=0.618, loss=80.777, backward_time=0.099, grad_norm=29.944, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.847e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 14:39:48,925 (trainer:737) INFO: 6epoch:train:1701-1800batch: iter_time=1.896e-04, forward_time=0.103, loss_ctc=62.729, loss_att=63.070, acc=0.640, loss=62.968, backward_time=0.098, grad_norm=29.392, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.842e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 14:40:29,971 (trainer:737) INFO: 6epoch:train:1801-1900batch: iter_time=2.183e-04, forward_time=0.104, loss_ctc=57.968, loss_att=71.163, acc=0.645, loss=67.204, backward_time=0.098, grad_norm=26.185, clip=99.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.836e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 14:41:11,497 (trainer:737) INFO: 6epoch:train:1901-2000batch: iter_time=1.834e-04, forward_time=0.104, loss_ctc=73.914, loss_att=86.138, acc=0.611, loss=82.471, backward_time=0.098, grad_norm=33.054, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.830e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 14:41:52,448 (trainer:737) INFO: 6epoch:train:2001-2100batch: iter_time=2.175e-04, forward_time=0.104, loss_ctc=69.335, loss_att=78.117, acc=0.636, loss=75.482, backward_time=0.099, grad_norm=32.327, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.824e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:42:33,630 (trainer:737) INFO: 6epoch:train:2101-2200batch: iter_time=1.802e-04, forward_time=0.104, loss_ctc=69.970, loss_att=71.244, acc=0.647, loss=70.862, backward_time=0.098, grad_norm=31.885, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.819e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 14:43:14,726 (trainer:737) INFO: 6epoch:train:2201-2300batch: iter_time=1.895e-04, forward_time=0.105, loss_ctc=63.796, loss_att=88.356, acc=0.629, loss=80.988, backward_time=0.099, grad_norm=32.271, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.813e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 14:43:55,617 (trainer:737) INFO: 6epoch:train:2301-2400batch: iter_time=1.793e-04, forward_time=0.104, loss_ctc=62.402, loss_att=73.066, acc=0.639, loss=69.867, backward_time=0.098, grad_norm=30.227, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.807e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:44:36,533 (trainer:737) INFO: 6epoch:train:2401-2500batch: iter_time=1.697e-04, forward_time=0.104, loss_ctc=64.277, loss_att=70.234, acc=0.647, loss=68.447, backward_time=0.098, grad_norm=31.398, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.802e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:44:44,088 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 14:45:03,256 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 14:45:07,446 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38705c7ca0>)
+[gpuc02:0/16] 2024-01-12 14:45:07,447 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 14:45:07,450 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 14:49:49,279 (trainer:737) INFO: 6epoch:train:2501-2600batch: iter_time=2.618, forward_time=0.103, loss_ctc=79.194, loss_att=74.503, acc=0.601, loss=75.910, backward_time=0.097, grad_norm=33.169, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.796e-04, train_time=3.127
+[gpuc02:0/16] 2024-01-12 14:50:30,379 (trainer:737) INFO: 6epoch:train:2601-2700batch: iter_time=1.948e-04, forward_time=0.103, loss_ctc=65.980, loss_att=76.494, acc=0.624, loss=73.340, backward_time=0.097, grad_norm=31.603, clip=100.000, loss_scale=1.801e+16, optim_step_time=0.029, optim0_lr0=8.790e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 14:51:11,801 (trainer:737) INFO: 6epoch:train:2701-2800batch: iter_time=1.897e-04, forward_time=0.104, loss_ctc=69.407, loss_att=79.671, acc=0.635, loss=76.592, backward_time=0.097, grad_norm=34.756, clip=99.000, loss_scale=1.801e+16, optim_step_time=0.029, optim0_lr0=8.785e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 14:51:52,667 (trainer:737) INFO: 6epoch:train:2801-2900batch: iter_time=1.731e-04, forward_time=0.103, loss_ctc=62.898, loss_att=75.603, acc=0.641, loss=71.791, backward_time=0.097, grad_norm=30.714, clip=99.000, loss_scale=1.801e+16, optim_step_time=0.029, optim0_lr0=8.779e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 14:52:33,966 (trainer:737) INFO: 6epoch:train:2901-3000batch: iter_time=1.793e-04, forward_time=0.103, loss_ctc=59.966, loss_att=76.707, acc=0.610, loss=71.685, backward_time=0.097, grad_norm=29.860, clip=99.000, loss_scale=1.801e+16, optim_step_time=0.030, optim0_lr0=8.773e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 14:53:14,922 (trainer:737) INFO: 6epoch:train:3001-3100batch: iter_time=1.881e-04, forward_time=0.103, loss_ctc=61.741, loss_att=61.055, acc=0.654, loss=61.261, backward_time=0.096, grad_norm=28.269, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.029, optim0_lr0=8.768e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:53:56,300 (trainer:737) INFO: 6epoch:train:3101-3200batch: iter_time=1.658e-04, forward_time=0.104, loss_ctc=71.687, loss_att=69.692, acc=0.653, loss=70.291, backward_time=0.097, grad_norm=30.705, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.029, optim0_lr0=8.762e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 14:54:37,181 (trainer:737) INFO: 6epoch:train:3201-3300batch: iter_time=1.758e-04, forward_time=0.104, loss_ctc=72.994, loss_att=88.526, acc=0.601, loss=83.866, backward_time=0.097, grad_norm=34.049, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.756e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:55:18,117 (trainer:737) INFO: 6epoch:train:3301-3400batch: iter_time=1.678e-04, forward_time=0.104, loss_ctc=60.423, loss_att=69.016, acc=0.649, loss=66.438, backward_time=0.097, grad_norm=26.676, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.029, optim0_lr0=8.751e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:55:59,605 (trainer:737) INFO: 6epoch:train:3401-3500batch: iter_time=1.772e-04, forward_time=0.104, loss_ctc=70.247, loss_att=90.666, acc=0.601, loss=84.540, backward_time=0.097, grad_norm=33.387, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.745e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 14:56:40,527 (trainer:737) INFO: 6epoch:train:3501-3600batch: iter_time=2.060e-04, forward_time=0.103, loss_ctc=58.001, loss_att=66.028, acc=0.640, loss=63.620, backward_time=0.097, grad_norm=64.464, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.029, optim0_lr0=8.740e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 14:57:21,333 (trainer:737) INFO: 6epoch:train:3601-3700batch: iter_time=1.576e-04, forward_time=0.103, loss_ctc=58.382, loss_att=67.608, acc=0.663, loss=64.840, backward_time=0.097, grad_norm=29.986, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.734e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 14:57:50,123 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 14:58:09,657 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 14:58:13,431 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e536f610>)
+[gpuc02:0/16] 2024-01-12 14:58:13,431 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 14:58:13,434 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 15:03:06,147 (trainer:737) INFO: 6epoch:train:3701-3800batch: iter_time=2.836, forward_time=0.104, loss_ctc=78.614, loss_att=71.631, acc=0.623, loss=73.726, backward_time=0.098, grad_norm=34.713, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.729e-04, train_time=3.448
+[gpuc02:0/16] 2024-01-12 15:03:46,937 (trainer:737) INFO: 6epoch:train:3801-3900batch: iter_time=1.945e-04, forward_time=0.104, loss_ctc=65.853, loss_att=66.458, acc=0.640, loss=66.276, backward_time=0.097, grad_norm=31.558, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.029, optim0_lr0=8.723e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:04:28,189 (trainer:737) INFO: 6epoch:train:3901-4000batch: iter_time=1.951e-04, forward_time=0.107, loss_ctc=63.277, loss_att=76.066, acc=0.633, loss=72.230, backward_time=0.098, grad_norm=29.280, clip=99.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.718e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 15:05:09,175 (trainer:737) INFO: 6epoch:train:4001-4100batch: iter_time=2.182e-04, forward_time=0.104, loss_ctc=71.402, loss_att=80.600, acc=0.614, loss=77.840, backward_time=0.098, grad_norm=33.326, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.712e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:05:50,715 (trainer:737) INFO: 6epoch:train:4101-4200batch: iter_time=1.906e-04, forward_time=0.105, loss_ctc=65.822, loss_att=82.422, acc=0.627, loss=77.442, backward_time=0.098, grad_norm=30.801, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.707e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 15:06:31,793 (trainer:737) INFO: 6epoch:train:4201-4300batch: iter_time=1.815e-04, forward_time=0.104, loss_ctc=61.793, loss_att=61.457, acc=0.646, loss=61.558, backward_time=0.097, grad_norm=30.525, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.701e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:07:12,651 (trainer:737) INFO: 6epoch:train:4301-4400batch: iter_time=1.859e-04, forward_time=0.104, loss_ctc=57.388, loss_att=62.969, acc=0.658, loss=61.295, backward_time=0.098, grad_norm=25.932, clip=97.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.696e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:07:53,778 (trainer:737) INFO: 6epoch:train:4401-4500batch: iter_time=1.749e-04, forward_time=0.105, loss_ctc=72.087, loss_att=82.871, acc=0.611, loss=79.636, backward_time=0.098, grad_norm=32.572, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.690e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:08:34,650 (trainer:737) INFO: 6epoch:train:4501-4600batch: iter_time=2.115e-04, forward_time=0.104, loss_ctc=65.706, loss_att=71.208, acc=0.643, loss=69.557, backward_time=0.098, grad_norm=29.616, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.685e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:09:15,961 (trainer:737) INFO: 6epoch:train:4601-4700batch: iter_time=1.958e-04, forward_time=0.104, loss_ctc=67.745, loss_att=68.377, acc=0.649, loss=68.187, backward_time=0.098, grad_norm=29.814, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.679e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 15:09:56,862 (trainer:737) INFO: 6epoch:train:4701-4800batch: iter_time=1.901e-04, forward_time=0.104, loss_ctc=61.554, loss_att=84.602, acc=0.625, loss=77.688, backward_time=0.098, grad_norm=30.754, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.674e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:10:38,375 (trainer:737) INFO: 6epoch:train:4801-4900batch: iter_time=1.844e-04, forward_time=0.104, loss_ctc=59.938, loss_att=70.506, acc=0.637, loss=67.336, backward_time=0.098, grad_norm=29.598, clip=100.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.668e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 15:11:19,152 (trainer:737) INFO: 6epoch:train:4901-5000batch: iter_time=1.832e-04, forward_time=0.104, loss_ctc=63.081, loss_att=67.216, acc=0.650, loss=65.976, backward_time=0.098, grad_norm=32.865, clip=99.000, loss_scale=3.603e+16, optim_step_time=0.030, optim0_lr0=8.663e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:11:27,364 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 15:11:46,428 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 15:11:50,154 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f383511fc10>)
+[gpuc02:0/16] 2024-01-12 15:11:50,154 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 15:11:50,157 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 15:17:02,716 (trainer:737) INFO: 6epoch:train:5001-5100batch: iter_time=2.770, forward_time=0.127, loss_ctc=76.314, loss_att=75.031, acc=0.613, loss=75.416, backward_time=0.103, grad_norm=31.699, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.657e-04, train_time=3.435
+[gpuc02:0/16] 2024-01-12 15:17:43,959 (trainer:737) INFO: 6epoch:train:5101-5200batch: iter_time=1.697e-04, forward_time=0.104, loss_ctc=64.277, loss_att=79.759, acc=0.634, loss=75.114, backward_time=0.098, grad_norm=30.156, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.652e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 15:18:25,017 (trainer:737) INFO: 6epoch:train:5201-5300batch: iter_time=1.776e-04, forward_time=0.104, loss_ctc=66.358, loss_att=77.960, acc=0.651, loss=74.479, backward_time=0.098, grad_norm=31.364, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.647e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:19:05,974 (trainer:737) INFO: 6epoch:train:5301-5400batch: iter_time=1.980e-04, forward_time=0.104, loss_ctc=61.818, loss_att=75.331, acc=0.652, loss=71.277, backward_time=0.098, grad_norm=30.003, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.641e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:19:46,828 (trainer:737) INFO: 6epoch:train:5401-5500batch: iter_time=1.729e-04, forward_time=0.103, loss_ctc=58.695, loss_att=77.487, acc=0.612, loss=71.850, backward_time=0.098, grad_norm=29.714, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.636e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:20:27,712 (trainer:737) INFO: 6epoch:train:5501-5600batch: iter_time=2.014e-04, forward_time=0.104, loss_ctc=59.670, loss_att=61.891, acc=0.657, loss=61.225, backward_time=0.098, grad_norm=26.937, clip=99.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.631e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:21:08,666 (trainer:737) INFO: 6epoch:train:5601-5700batch: iter_time=1.946e-04, forward_time=0.104, loss_ctc=69.804, loss_att=73.365, acc=0.655, loss=72.296, backward_time=0.097, grad_norm=31.796, clip=98.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.625e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:21:49,664 (trainer:737) INFO: 6epoch:train:5701-5800batch: iter_time=1.746e-04, forward_time=0.104, loss_ctc=71.231, loss_att=88.237, acc=0.612, loss=83.135, backward_time=0.098, grad_norm=34.192, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.620e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:22:30,958 (trainer:737) INFO: 6epoch:train:5801-5900batch: iter_time=1.618e-04, forward_time=0.104, loss_ctc=59.018, loss_att=69.573, acc=0.667, loss=66.407, backward_time=0.098, grad_norm=27.270, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.615e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 15:23:12,480 (trainer:737) INFO: 6epoch:train:5901-6000batch: iter_time=1.754e-04, forward_time=0.107, loss_ctc=68.329, loss_att=90.562, acc=0.617, loss=83.892, backward_time=0.097, grad_norm=32.612, clip=99.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.609e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 15:23:53,555 (trainer:737) INFO: 6epoch:train:6001-6100batch: iter_time=1.836e-04, forward_time=0.103, loss_ctc=58.699, loss_att=66.806, acc=0.651, loss=64.373, backward_time=0.096, grad_norm=31.884, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.604e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:24:34,641 (trainer:737) INFO: 6epoch:train:6101-6200batch: iter_time=1.669e-04, forward_time=0.103, loss_ctc=56.261, loss_att=66.742, acc=0.673, loss=63.598, backward_time=0.097, grad_norm=28.054, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.599e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:25:02,604 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 15:25:21,990 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 15:25:25,754 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3871373970>)
+[gpuc02:0/16] 2024-01-12 15:25:25,754 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 15:25:25,757 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 15:29:50,640 (trainer:737) INFO: 6epoch:train:6201-6300batch: iter_time=2.705, forward_time=0.104, loss_ctc=76.209, loss_att=71.950, acc=0.628, loss=73.228, backward_time=0.097, grad_norm=35.688, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.593e-04, train_time=3.160
+[gpuc02:0/16] 2024-01-12 15:30:31,829 (trainer:737) INFO: 6epoch:train:6301-6400batch: iter_time=1.616e-04, forward_time=0.103, loss_ctc=64.189, loss_att=67.808, acc=0.653, loss=66.722, backward_time=0.097, grad_norm=35.691, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.588e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 15:31:13,211 (trainer:737) INFO: 6epoch:train:6401-6500batch: iter_time=1.619e-04, forward_time=0.104, loss_ctc=61.935, loss_att=78.837, acc=0.646, loss=73.767, backward_time=0.098, grad_norm=27.625, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.583e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 15:31:54,148 (trainer:737) INFO: 6epoch:train:6501-6600batch: iter_time=1.699e-04, forward_time=0.104, loss_ctc=70.096, loss_att=78.760, acc=0.632, loss=76.161, backward_time=0.097, grad_norm=32.629, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.577e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:32:35,201 (trainer:737) INFO: 6epoch:train:6601-6700batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=63.259, loss_att=82.547, acc=0.634, loss=76.760, backward_time=0.098, grad_norm=30.838, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.572e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:33:16,022 (trainer:737) INFO: 6epoch:train:6701-6800batch: iter_time=1.534e-04, forward_time=0.104, loss_ctc=59.771, loss_att=60.524, acc=0.651, loss=60.298, backward_time=0.097, grad_norm=30.065, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.567e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:33:56,889 (trainer:737) INFO: 6epoch:train:6801-6900batch: iter_time=2.030e-04, forward_time=0.104, loss_ctc=56.037, loss_att=68.259, acc=0.657, loss=64.592, backward_time=0.097, grad_norm=26.615, clip=99.000, loss_scale=7.206e+16, optim_step_time=0.029, optim0_lr0=8.562e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:34:38,045 (trainer:737) INFO: 6epoch:train:6901-7000batch: iter_time=2.017e-04, forward_time=0.104, loss_ctc=70.213, loss_att=82.571, acc=0.624, loss=78.863, backward_time=0.098, grad_norm=32.546, clip=100.000, loss_scale=7.206e+16, optim_step_time=0.030, optim0_lr0=8.557e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:35:19,322 (trainer:737) INFO: 6epoch:train:7001-7100batch: iter_time=1.935e-04, forward_time=0.104, loss_ctc=64.912, loss_att=73.098, acc=0.652, loss=70.642, backward_time=0.097, grad_norm=30.631, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.030, optim0_lr0=8.551e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 15:36:00,229 (trainer:737) INFO: 6epoch:train:7101-7200batch: iter_time=1.709e-04, forward_time=0.104, loss_ctc=65.775, loss_att=66.732, acc=0.666, loss=66.445, backward_time=0.097, grad_norm=30.628, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.546e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:36:41,310 (trainer:737) INFO: 6epoch:train:7201-7300batch: iter_time=1.856e-04, forward_time=0.105, loss_ctc=59.848, loss_att=83.065, acc=0.644, loss=76.100, backward_time=0.098, grad_norm=30.582, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.541e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:37:22,213 (trainer:737) INFO: 6epoch:train:7301-7400batch: iter_time=1.680e-04, forward_time=0.104, loss_ctc=58.732, loss_att=70.279, acc=0.650, loss=66.815, backward_time=0.097, grad_norm=29.181, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.536e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:38:03,086 (trainer:737) INFO: 6epoch:train:7401-7500batch: iter_time=1.708e-04, forward_time=0.103, loss_ctc=61.362, loss_att=66.490, acc=0.663, loss=64.951, backward_time=0.097, grad_norm=32.113, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.531e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:38:09,243 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 15:38:29,387 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 15:38:33,061 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3881407d00>)
+[gpuc02:0/16] 2024-01-12 15:38:33,061 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 15:38:33,064 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 15:43:14,723 (trainer:737) INFO: 6epoch:train:7501-7600batch: iter_time=2.616, forward_time=0.104, loss_ctc=76.110, loss_att=71.972, acc=0.613, loss=73.213, backward_time=0.097, grad_norm=34.551, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.525e-04, train_time=3.116
+[gpuc02:0/16] 2024-01-12 15:43:55,610 (trainer:737) INFO: 6epoch:train:7601-7700batch: iter_time=2.010e-04, forward_time=0.104, loss_ctc=62.975, loss_att=74.631, acc=0.633, loss=71.135, backward_time=0.097, grad_norm=30.746, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.520e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:44:36,660 (trainer:737) INFO: 6epoch:train:7701-7800batch: iter_time=2.032e-04, forward_time=0.104, loss_ctc=65.179, loss_att=76.551, acc=0.646, loss=73.139, backward_time=0.097, grad_norm=30.487, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.515e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:45:17,840 (trainer:737) INFO: 6epoch:train:7801-7900batch: iter_time=1.780e-04, forward_time=0.104, loss_ctc=60.687, loss_att=73.946, acc=0.650, loss=69.968, backward_time=0.097, grad_norm=32.172, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.510e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 15:45:58,967 (trainer:737) INFO: 6epoch:train:7901-8000batch: iter_time=1.855e-04, forward_time=0.104, loss_ctc=57.778, loss_att=75.189, acc=0.619, loss=69.966, backward_time=0.097, grad_norm=31.337, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.505e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:46:40,096 (trainer:737) INFO: 6epoch:train:8001-8100batch: iter_time=1.696e-04, forward_time=0.104, loss_ctc=58.400, loss_att=58.576, acc=0.665, loss=58.523, backward_time=0.097, grad_norm=26.948, clip=99.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.500e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:47:21,242 (trainer:737) INFO: 6epoch:train:8101-8200batch: iter_time=1.867e-04, forward_time=0.105, loss_ctc=68.973, loss_att=67.524, acc=0.664, loss=67.958, backward_time=0.097, grad_norm=30.351, clip=99.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.495e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 15:48:02,737 (trainer:737) INFO: 6epoch:train:8201-8300batch: iter_time=1.961e-04, forward_time=0.105, loss_ctc=70.515, loss_att=86.691, acc=0.608, loss=81.838, backward_time=0.097, grad_norm=33.920, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.489e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 15:48:43,991 (trainer:737) INFO: 6epoch:train:8301-8400batch: iter_time=1.890e-04, forward_time=0.105, loss_ctc=57.462, loss_att=67.180, acc=0.659, loss=64.265, backward_time=0.097, grad_norm=26.760, clip=99.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.484e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 15:49:24,975 (trainer:737) INFO: 6epoch:train:8401-8500batch: iter_time=1.905e-04, forward_time=0.105, loss_ctc=67.558, loss_att=88.199, acc=0.612, loss=82.007, backward_time=0.097, grad_norm=31.273, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.479e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:50:05,767 (trainer:737) INFO: 6epoch:train:8501-8600batch: iter_time=2.051e-04, forward_time=0.104, loss_ctc=56.744, loss_att=64.321, acc=0.650, loss=62.048, backward_time=0.096, grad_norm=29.377, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.474e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 15:50:46,751 (trainer:737) INFO: 6epoch:train:8601-8700batch: iter_time=1.737e-04, forward_time=0.104, loss_ctc=55.513, loss_att=65.829, acc=0.672, loss=62.734, backward_time=0.097, grad_norm=27.589, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.469e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:51:13,179 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 15:51:32,450 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 15:51:36,070 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f387680ba90>)
+[gpuc02:0/16] 2024-01-12 15:51:36,070 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 15:51:36,073 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 15:55:59,816 (trainer:737) INFO: 6epoch:train:8701-8800batch: iter_time=2.598, forward_time=0.105, loss_ctc=74.004, loss_att=70.638, acc=0.633, loss=71.648, backward_time=0.098, grad_norm=33.451, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.464e-04, train_time=3.130
+[gpuc02:0/16] 2024-01-12 15:56:40,742 (trainer:737) INFO: 6epoch:train:8801-8900batch: iter_time=2.492e-04, forward_time=0.105, loss_ctc=63.404, loss_att=69.040, acc=0.653, loss=67.349, backward_time=0.098, grad_norm=30.759, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.459e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 15:57:22,096 (trainer:737) INFO: 6epoch:train:8901-9000batch: iter_time=2.511e-04, forward_time=0.106, loss_ctc=60.504, loss_att=77.966, acc=0.650, loss=72.727, backward_time=0.098, grad_norm=28.504, clip=100.000, loss_scale=1.441e+17, optim_step_time=0.029, optim0_lr0=8.454e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 15:58:03,119 (trainer:737) INFO: 6epoch:train:9001-9100batch: iter_time=3.003e-04, forward_time=0.106, loss_ctc=68.850, loss_att=77.861, acc=0.637, loss=75.158, backward_time=0.098, grad_norm=33.630, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.029, optim0_lr0=8.449e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:58:44,168 (trainer:737) INFO: 6epoch:train:9101-9200batch: iter_time=2.844e-04, forward_time=0.106, loss_ctc=62.453, loss_att=82.125, acc=0.637, loss=76.224, backward_time=0.098, grad_norm=30.597, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.029, optim0_lr0=8.444e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 15:59:25,537 (trainer:737) INFO: 6epoch:train:9201-9300batch: iter_time=2.412e-04, forward_time=0.105, loss_ctc=58.464, loss_att=59.912, acc=0.656, loss=59.478, backward_time=0.097, grad_norm=30.441, clip=99.000, loss_scale=2.882e+17, optim_step_time=0.029, optim0_lr0=8.439e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 16:00:06,453 (trainer:737) INFO: 6epoch:train:9301-9400batch: iter_time=2.545e-04, forward_time=0.105, loss_ctc=54.328, loss_att=66.782, acc=0.663, loss=63.046, backward_time=0.097, grad_norm=27.809, clip=99.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.434e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:00:47,507 (trainer:737) INFO: 6epoch:train:9401-9500batch: iter_time=2.538e-04, forward_time=0.106, loss_ctc=69.408, loss_att=82.177, acc=0.628, loss=78.346, backward_time=0.098, grad_norm=30.074, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.429e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:01:29,699 (trainer:737) INFO: 6epoch:train:9501-9600batch: iter_time=2.328e-04, forward_time=0.107, loss_ctc=63.283, loss_att=72.250, acc=0.658, loss=69.560, backward_time=0.098, grad_norm=29.148, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.424e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-12 16:02:10,668 (trainer:737) INFO: 6epoch:train:9601-9700batch: iter_time=2.486e-04, forward_time=0.106, loss_ctc=64.928, loss_att=66.616, acc=0.670, loss=66.109, backward_time=0.098, grad_norm=30.503, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.419e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:02:51,742 (trainer:737) INFO: 6epoch:train:9701-9800batch: iter_time=2.614e-04, forward_time=0.106, loss_ctc=58.910, loss_att=82.949, acc=0.646, loss=75.737, backward_time=0.099, grad_norm=29.875, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.414e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:03:32,899 (trainer:737) INFO: 6epoch:train:9801-9900batch: iter_time=2.416e-04, forward_time=0.105, loss_ctc=57.109, loss_att=69.013, acc=0.656, loss=65.442, backward_time=0.098, grad_norm=29.912, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.409e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:04:14,094 (trainer:737) INFO: 6epoch:train:9901-10000batch: iter_time=2.423e-04, forward_time=0.108, loss_ctc=59.599, loss_att=64.609, acc=0.668, loss=63.106, backward_time=0.098, grad_norm=31.510, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.404e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 16:04:21,791 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 16:04:41,463 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 16:04:45,184 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f7313fc70>)
+[gpuc02:0/16] 2024-01-12 16:04:45,184 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 16:04:45,188 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 16:09:25,650 (trainer:737) INFO: 6epoch:train:10001-10100batch: iter_time=2.651, forward_time=0.105, loss_ctc=74.834, loss_att=68.100, acc=0.631, loss=70.120, backward_time=0.098, grad_norm=32.831, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.399e-04, train_time=3.115
+[gpuc02:0/16] 2024-01-12 16:10:06,684 (trainer:737) INFO: 6epoch:train:10101-10200batch: iter_time=1.765e-04, forward_time=0.105, loss_ctc=61.517, loss_att=72.680, acc=0.652, loss=69.331, backward_time=0.098, grad_norm=29.112, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.394e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:10:47,806 (trainer:737) INFO: 6epoch:train:10201-10300batch: iter_time=2.081e-04, forward_time=0.105, loss_ctc=64.072, loss_att=74.347, acc=0.659, loss=71.264, backward_time=0.098, grad_norm=30.804, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.389e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:11:29,111 (trainer:737) INFO: 6epoch:train:10301-10400batch: iter_time=2.210e-04, forward_time=0.105, loss_ctc=60.032, loss_att=71.947, acc=0.661, loss=68.372, backward_time=0.098, grad_norm=30.073, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.384e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 16:12:09,993 (trainer:737) INFO: 6epoch:train:10401-10500batch: iter_time=2.115e-04, forward_time=0.105, loss_ctc=56.834, loss_att=73.033, acc=0.628, loss=68.173, backward_time=0.098, grad_norm=30.906, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.379e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:13:14,390 (trainer:737) INFO: 6epoch:train:10501-10600batch: iter_time=2.125e-04, forward_time=0.105, loss_ctc=56.925, loss_att=57.473, acc=0.673, loss=57.308, backward_time=0.098, grad_norm=26.143, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.375e-04, train_time=0.644
+[gpuc02:0/16] 2024-01-12 16:13:55,359 (trainer:737) INFO: 6epoch:train:10601-10700batch: iter_time=1.877e-04, forward_time=0.105, loss_ctc=67.532, loss_att=70.337, acc=0.666, loss=69.495, backward_time=0.098, grad_norm=30.684, clip=99.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.370e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:14:36,359 (trainer:737) INFO: 6epoch:train:10701-10800batch: iter_time=1.922e-04, forward_time=0.106, loss_ctc=68.578, loss_att=84.720, acc=0.625, loss=79.877, backward_time=0.098, grad_norm=32.327, clip=99.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.365e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:15:17,403 (trainer:737) INFO: 6epoch:train:10801-10900batch: iter_time=2.065e-04, forward_time=0.106, loss_ctc=56.990, loss_att=67.203, acc=0.676, loss=64.139, backward_time=0.099, grad_norm=26.732, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.360e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:15:58,538 (trainer:737) INFO: 6epoch:train:10901-11000batch: iter_time=2.228e-04, forward_time=0.106, loss_ctc=65.549, loss_att=86.788, acc=0.629, loss=80.417, backward_time=0.099, grad_norm=32.338, clip=100.000, loss_scale=2.882e+17, optim_step_time=0.030, optim0_lr0=8.355e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:16:39,380 (trainer:737) INFO: 6epoch:train:11001-11100batch: iter_time=1.814e-04, forward_time=0.104, loss_ctc=55.398, loss_att=63.668, acc=0.664, loss=61.187, backward_time=0.097, grad_norm=28.088, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.350e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:17:20,534 (trainer:737) INFO: 6epoch:train:11101-11200batch: iter_time=1.862e-04, forward_time=0.103, loss_ctc=54.388, loss_att=64.163, acc=0.681, loss=61.231, backward_time=0.097, grad_norm=26.951, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.345e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:17:46,804 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 16:18:06,702 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 16:18:10,573 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3814a6bd00>)
+[gpuc02:0/16] 2024-01-12 16:18:10,573 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 16:18:10,577 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 16:22:35,655 (trainer:737) INFO: 6epoch:train:11201-11300batch: iter_time=2.620, forward_time=0.106, loss_ctc=73.037, loss_att=69.922, acc=0.638, loss=70.857, backward_time=0.098, grad_norm=34.461, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.341e-04, train_time=3.151
+[gpuc02:0/16] 2024-01-12 16:23:16,749 (trainer:737) INFO: 6epoch:train:11301-11400batch: iter_time=1.514e-04, forward_time=0.104, loss_ctc=61.823, loss_att=68.673, acc=0.647, loss=66.618, backward_time=0.097, grad_norm=32.464, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.336e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:23:57,999 (trainer:737) INFO: 6epoch:train:11401-11500batch: iter_time=1.277e-04, forward_time=0.107, loss_ctc=59.760, loss_att=75.857, acc=0.642, loss=71.028, backward_time=0.098, grad_norm=28.005, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.331e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 16:24:39,500 (trainer:737) INFO: 6epoch:train:11501-11600batch: iter_time=1.292e-04, forward_time=0.104, loss_ctc=67.927, loss_att=78.628, acc=0.626, loss=75.417, backward_time=0.097, grad_norm=34.101, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.326e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 16:25:20,954 (trainer:737) INFO: 6epoch:train:11601-11700batch: iter_time=1.468e-04, forward_time=0.105, loss_ctc=61.555, loss_att=80.157, acc=0.639, loss=74.576, backward_time=0.098, grad_norm=68.316, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.321e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 16:26:01,799 (trainer:737) INFO: 6epoch:train:11701-11800batch: iter_time=1.562e-04, forward_time=0.104, loss_ctc=58.005, loss_att=59.182, acc=0.660, loss=58.829, backward_time=0.097, grad_norm=29.360, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.316e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:26:42,906 (trainer:737) INFO: 6epoch:train:11801-11900batch: iter_time=1.380e-04, forward_time=0.103, loss_ctc=53.537, loss_att=60.540, acc=0.668, loss=58.439, backward_time=0.098, grad_norm=27.245, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.312e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:27:24,073 (trainer:737) INFO: 6epoch:train:11901-12000batch: iter_time=1.789e-04, forward_time=0.104, loss_ctc=68.202, loss_att=80.072, acc=0.623, loss=76.511, backward_time=0.097, grad_norm=31.743, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.307e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:28:04,999 (trainer:737) INFO: 6epoch:train:12001-12100batch: iter_time=1.862e-04, forward_time=0.104, loss_ctc=62.916, loss_att=69.769, acc=0.655, loss=67.713, backward_time=0.097, grad_norm=30.050, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.302e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:28:45,883 (trainer:737) INFO: 6epoch:train:12101-12200batch: iter_time=2.009e-04, forward_time=0.104, loss_ctc=63.404, loss_att=65.794, acc=0.663, loss=65.077, backward_time=0.097, grad_norm=29.790, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.297e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:29:26,871 (trainer:737) INFO: 6epoch:train:12201-12300batch: iter_time=1.863e-04, forward_time=0.105, loss_ctc=58.374, loss_att=82.092, acc=0.638, loss=74.977, backward_time=0.097, grad_norm=31.113, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.293e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:30:07,625 (trainer:737) INFO: 6epoch:train:12301-12400batch: iter_time=1.897e-04, forward_time=0.104, loss_ctc=56.761, loss_att=67.751, acc=0.649, loss=64.454, backward_time=0.096, grad_norm=39.457, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.288e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 16:30:48,414 (trainer:737) INFO: 6epoch:train:12401-12500batch: iter_time=1.699e-04, forward_time=0.104, loss_ctc=59.019, loss_att=64.381, acc=0.665, loss=62.772, backward_time=0.097, grad_norm=30.489, clip=99.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.283e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:30:55,569 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 16:31:15,087 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 16:31:18,766 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38990d7b20>)
+[gpuc02:0/16] 2024-01-12 16:31:18,766 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 16:31:18,769 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 16:36:05,674 (trainer:737) INFO: 6epoch:train:12501-12600batch: iter_time=2.680, forward_time=0.104, loss_ctc=74.033, loss_att=67.795, acc=0.626, loss=69.666, backward_time=0.097, grad_norm=42.011, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.030, optim0_lr0=8.278e-04, train_time=3.172
+[gpuc02:0/16] 2024-01-12 16:36:46,491 (trainer:737) INFO: 6epoch:train:12601-12700batch: iter_time=2.008e-04, forward_time=0.104, loss_ctc=61.271, loss_att=69.545, acc=0.646, loss=67.063, backward_time=0.097, grad_norm=29.077, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.274e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:37:27,386 (trainer:737) INFO: 6epoch:train:12701-12800batch: iter_time=2.007e-04, forward_time=0.104, loss_ctc=62.476, loss_att=72.518, acc=0.658, loss=69.505, backward_time=0.097, grad_norm=29.306, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.269e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:38:08,454 (trainer:737) INFO: 6epoch:train:12801-12900batch: iter_time=2.341e-04, forward_time=0.104, loss_ctc=58.906, loss_att=70.154, acc=0.660, loss=66.779, backward_time=0.097, grad_norm=33.180, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.264e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:38:49,224 (trainer:737) INFO: 6epoch:train:12901-13000batch: iter_time=2.453e-04, forward_time=0.103, loss_ctc=55.880, loss_att=72.999, acc=0.629, loss=67.864, backward_time=0.097, grad_norm=28.425, clip=100.000, loss_scale=5.765e+17, optim_step_time=0.029, optim0_lr0=8.260e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 16:39:30,286 (trainer:737) INFO: 6epoch:train:13001-13100batch: iter_time=2.023e-04, forward_time=0.104, loss_ctc=55.890, loss_att=55.896, acc=0.675, loss=55.894, backward_time=0.097, grad_norm=27.266, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.255e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:40:11,130 (trainer:737) INFO: 6epoch:train:13101-13200batch: iter_time=2.073e-04, forward_time=0.103, loss_ctc=67.112, loss_att=65.954, acc=0.670, loss=66.302, backward_time=0.097, grad_norm=60.127, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.250e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:40:52,270 (trainer:737) INFO: 6epoch:train:13201-13300batch: iter_time=2.104e-04, forward_time=0.104, loss_ctc=67.239, loss_att=82.981, acc=0.619, loss=78.259, backward_time=0.097, grad_norm=32.720, clip=99.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.245e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:41:34,060 (trainer:737) INFO: 6epoch:train:13301-13400batch: iter_time=1.914e-04, forward_time=0.104, loss_ctc=55.758, loss_att=65.123, acc=0.667, loss=62.314, backward_time=0.097, grad_norm=31.033, clip=99.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.241e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-12 16:42:15,044 (trainer:737) INFO: 6epoch:train:13401-13500batch: iter_time=2.047e-04, forward_time=0.104, loss_ctc=65.153, loss_att=84.958, acc=0.621, loss=79.016, backward_time=0.097, grad_norm=34.359, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.236e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:42:55,695 (trainer:737) INFO: 6epoch:train:13501-13600batch: iter_time=2.208e-04, forward_time=0.103, loss_ctc=54.601, loss_att=62.321, acc=0.657, loss=60.005, backward_time=0.096, grad_norm=29.849, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.231e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 16:43:38,210 (trainer:737) INFO: 6epoch:train:13601-13700batch: iter_time=1.866e-04, forward_time=0.103, loss_ctc=53.586, loss_att=63.261, acc=0.682, loss=60.358, backward_time=0.097, grad_norm=26.913, clip=99.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.227e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-12 16:44:06,934 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 16:44:26,531 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 16:44:30,259 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f66f5ffa0>)
+[gpuc02:0/16] 2024-01-12 16:44:30,259 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 16:44:30,262 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 16:49:00,111 (trainer:737) INFO: 6epoch:train:13701-13800batch: iter_time=2.674, forward_time=0.103, loss_ctc=71.496, loss_att=68.961, acc=0.642, loss=69.721, backward_time=0.097, grad_norm=32.754, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.222e-04, train_time=3.219
+[gpuc02:0/16] 2024-01-12 16:49:40,941 (trainer:737) INFO: 6epoch:train:13801-13900batch: iter_time=1.381e-04, forward_time=0.104, loss_ctc=60.862, loss_att=67.491, acc=0.662, loss=65.503, backward_time=0.098, grad_norm=29.059, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.218e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:50:21,920 (trainer:737) INFO: 6epoch:train:13901-14000batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=58.191, loss_att=76.268, acc=0.657, loss=70.845, backward_time=0.098, grad_norm=27.346, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.213e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:51:02,810 (trainer:737) INFO: 6epoch:train:14001-14100batch: iter_time=1.445e-04, forward_time=0.104, loss_ctc=66.232, loss_att=75.866, acc=0.644, loss=72.975, backward_time=0.098, grad_norm=32.004, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.208e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:51:44,107 (trainer:737) INFO: 6epoch:train:14101-14200batch: iter_time=1.332e-04, forward_time=0.104, loss_ctc=60.140, loss_att=79.363, acc=0.647, loss=73.596, backward_time=0.098, grad_norm=29.768, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.204e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 16:52:24,898 (trainer:737) INFO: 6epoch:train:14201-14300batch: iter_time=1.455e-04, forward_time=0.104, loss_ctc=56.920, loss_att=57.743, acc=0.669, loss=57.496, backward_time=0.097, grad_norm=29.123, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.199e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:53:05,749 (trainer:737) INFO: 6epoch:train:14301-14400batch: iter_time=1.488e-04, forward_time=0.104, loss_ctc=52.495, loss_att=65.182, acc=0.670, loss=61.376, backward_time=0.098, grad_norm=26.284, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.195e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 16:53:46,639 (trainer:737) INFO: 6epoch:train:14401-14500batch: iter_time=1.479e-04, forward_time=0.104, loss_ctc=66.875, loss_att=78.722, acc=0.637, loss=75.168, backward_time=0.098, grad_norm=32.174, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.190e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:54:27,704 (trainer:737) INFO: 6epoch:train:14501-14600batch: iter_time=1.464e-04, forward_time=0.105, loss_ctc=62.268, loss_att=71.114, acc=0.662, loss=68.460, backward_time=0.099, grad_norm=29.557, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.185e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 16:55:08,668 (trainer:737) INFO: 6epoch:train:14601-14700batch: iter_time=1.736e-04, forward_time=0.104, loss_ctc=62.713, loss_att=65.467, acc=0.673, loss=64.641, backward_time=0.097, grad_norm=28.872, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.181e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 16:55:49,829 (trainer:737) INFO: 6epoch:train:14701-14800batch: iter_time=1.688e-04, forward_time=0.105, loss_ctc=56.907, loss_att=80.837, acc=0.655, loss=73.658, backward_time=0.098, grad_norm=28.777, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.176e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 16:56:31,026 (trainer:737) INFO: 6epoch:train:14801-14900batch: iter_time=1.565e-04, forward_time=0.104, loss_ctc=55.629, loss_att=66.838, acc=0.662, loss=63.475, backward_time=0.097, grad_norm=28.855, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.172e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 16:57:11,920 (trainer:737) INFO: 6epoch:train:14901-15000batch: iter_time=1.708e-04, forward_time=0.104, loss_ctc=57.893, loss_att=63.969, acc=0.671, loss=62.147, backward_time=0.097, grad_norm=30.948, clip=100.000, loss_scale=1.153e+18, optim_step_time=0.029, optim0_lr0=8.167e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 17:17:17,827 (trainer:343) INFO: 6epoch results: [train] iter_time=0.219, forward_time=0.105, loss_ctc=64.004, loss_att=73.088, acc=0.642, loss=70.363, backward_time=0.098, grad_norm=31.766, clip=99.813, loss_scale=3.056e+17, optim_step_time=0.030, optim0_lr0=8.537e-04, train_time=0.641, time=2 hours, 40 minutes and 34.93 seconds, total_count=90000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=76.714, cer_ctc=0.387, loss_att=70.530, acc=0.492, cer=0.421, wer=1.000, loss=72.385, time=19 minutes and 54.1 seconds, total_count=28026, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-12 17:17:22,812 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 17:17:22,839 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/1epoch.pth
+[gpuc02:0/16] 2024-01-12 17:17:22,839 (trainer:272) INFO: 7/45epoch started. Estimated time to finish: 4 days, 19 hours and 50 minutes
+[gpuc02:0/16] 2024-01-12 17:17:22,849 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 17:17:41,924 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 17:17:45,491 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38918f4d60>)
+[gpuc02:0/16] 2024-01-12 17:17:45,491 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 17:17:45,494 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 17:22:20,836 (trainer:737) INFO: 7epoch:train:1-100batch: iter_time=2.547, forward_time=0.106, loss_ctc=61.274, loss_att=63.083, acc=0.664, loss=62.540, backward_time=0.097, grad_norm=31.191, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.163e-04, train_time=2.980
+[gpuc02:0/16] 2024-01-12 17:23:01,827 (trainer:737) INFO: 7epoch:train:101-200batch: iter_time=1.054e-04, forward_time=0.104, loss_ctc=74.624, loss_att=77.651, acc=0.639, loss=76.743, backward_time=0.098, grad_norm=36.702, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.158e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 17:23:44,586 (trainer:737) INFO: 7epoch:train:201-300batch: iter_time=1.169e-04, forward_time=0.112, loss_ctc=59.040, loss_att=73.748, acc=0.658, loss=69.335, backward_time=0.100, grad_norm=30.425, clip=99.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.154e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-12 17:24:26,937 (trainer:737) INFO: 7epoch:train:301-400batch: iter_time=1.170e-04, forward_time=0.109, loss_ctc=64.732, loss_att=72.244, acc=0.663, loss=69.990, backward_time=0.098, grad_norm=34.160, clip=99.000, loss_scale=2.306e+18, optim_step_time=0.031, optim0_lr0=8.149e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-12 17:25:07,583 (trainer:737) INFO: 7epoch:train:401-500batch: iter_time=1.238e-04, forward_time=0.102, loss_ctc=60.154, loss_att=64.587, acc=0.620, loss=63.257, backward_time=0.096, grad_norm=34.787, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.145e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 17:25:49,173 (trainer:737) INFO: 7epoch:train:501-600batch: iter_time=1.270e-04, forward_time=0.103, loss_ctc=58.231, loss_att=63.478, acc=0.645, loss=61.904, backward_time=0.097, grad_norm=29.845, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.140e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 17:26:32,451 (trainer:737) INFO: 7epoch:train:601-700batch: iter_time=1.186e-04, forward_time=0.103, loss_ctc=54.561, loss_att=55.691, acc=0.674, loss=55.352, backward_time=0.097, grad_norm=27.430, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.136e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-12 17:27:13,790 (trainer:737) INFO: 7epoch:train:701-800batch: iter_time=1.165e-04, forward_time=0.106, loss_ctc=66.676, loss_att=63.606, acc=0.657, loss=64.527, backward_time=0.097, grad_norm=32.335, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.131e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 17:27:57,963 (trainer:737) INFO: 7epoch:train:801-900batch: iter_time=1.100e-04, forward_time=0.122, loss_ctc=49.931, loss_att=52.570, acc=0.629, loss=51.778, backward_time=0.100, grad_norm=27.330, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.031, optim0_lr0=8.127e-04, train_time=0.442
+[gpuc02:0/16] 2024-01-12 17:28:39,956 (trainer:737) INFO: 7epoch:train:901-1000batch: iter_time=1.131e-04, forward_time=0.109, loss_ctc=48.708, loss_att=59.054, acc=0.664, loss=55.950, backward_time=0.096, grad_norm=26.571, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.122e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-12 17:29:27,717 (trainer:737) INFO: 7epoch:train:1001-1100batch: iter_time=1.207e-04, forward_time=0.114, loss_ctc=57.175, loss_att=63.435, acc=0.642, loss=61.557, backward_time=0.100, grad_norm=31.731, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.118e-04, train_time=0.477
+[gpuc02:0/16] 2024-01-12 17:30:09,477 (trainer:737) INFO: 7epoch:train:1101-1200batch: iter_time=1.152e-04, forward_time=0.103, loss_ctc=57.431, loss_att=68.730, acc=0.644, loss=65.340, backward_time=0.100, grad_norm=29.344, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.113e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 17:30:42,787 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 17:31:01,832 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 17:31:05,441 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e413fa30>)
+[gpuc02:0/16] 2024-01-12 17:31:05,441 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 17:31:05,444 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 17:36:39,997 (trainer:737) INFO: 7epoch:train:1201-1300batch: iter_time=3.476, forward_time=0.103, loss_ctc=58.123, loss_att=57.767, acc=0.693, loss=57.874, backward_time=0.098, grad_norm=30.781, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.109e-04, train_time=3.905
+[gpuc02:0/16] 2024-01-12 17:37:21,257 (trainer:737) INFO: 7epoch:train:1301-1400batch: iter_time=1.691e-04, forward_time=0.104, loss_ctc=58.995, loss_att=72.404, acc=0.652, loss=68.381, backward_time=0.098, grad_norm=29.233, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.104e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 17:38:02,470 (trainer:737) INFO: 7epoch:train:1401-1500batch: iter_time=1.900e-04, forward_time=0.104, loss_ctc=68.118, loss_att=73.427, acc=0.675, loss=71.834, backward_time=0.098, grad_norm=33.021, clip=99.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.100e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 17:38:43,550 (trainer:737) INFO: 7epoch:train:1501-1600batch: iter_time=1.695e-04, forward_time=0.104, loss_ctc=67.359, loss_att=73.558, acc=0.666, loss=71.698, backward_time=0.098, grad_norm=31.393, clip=99.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.095e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 17:39:26,190 (trainer:737) INFO: 7epoch:train:1601-1700batch: iter_time=1.762e-04, forward_time=0.113, loss_ctc=59.802, loss_att=69.605, acc=0.650, loss=66.664, backward_time=0.099, grad_norm=34.574, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.091e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-12 17:40:07,838 (trainer:737) INFO: 7epoch:train:1701-1800batch: iter_time=1.795e-04, forward_time=0.109, loss_ctc=52.290, loss_att=61.819, acc=0.646, loss=58.960, backward_time=0.097, grad_norm=28.683, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.087e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 17:40:48,553 (trainer:737) INFO: 7epoch:train:1801-1900batch: iter_time=1.587e-04, forward_time=0.103, loss_ctc=54.593, loss_att=56.573, acc=0.686, loss=55.979, backward_time=0.097, grad_norm=24.918, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.029, optim0_lr0=8.082e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 17:41:29,865 (trainer:737) INFO: 7epoch:train:1901-2000batch: iter_time=1.942e-04, forward_time=0.103, loss_ctc=60.870, loss_att=67.242, acc=0.640, loss=65.331, backward_time=0.097, grad_norm=30.102, clip=100.000, loss_scale=2.306e+18, optim_step_time=0.030, optim0_lr0=8.078e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 17:42:10,433 (trainer:737) INFO: 7epoch:train:2001-2100batch: iter_time=2.115e-04, forward_time=0.102, loss_ctc=52.818, loss_att=48.013, acc=0.666, loss=49.454, backward_time=0.096, grad_norm=28.685, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.073e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 17:42:51,166 (trainer:737) INFO: 7epoch:train:2101-2200batch: iter_time=1.911e-04, forward_time=0.103, loss_ctc=53.465, loss_att=62.222, acc=0.645, loss=59.595, backward_time=0.097, grad_norm=26.754, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.069e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 17:43:32,932 (trainer:737) INFO: 7epoch:train:2201-2300batch: iter_time=1.858e-04, forward_time=0.108, loss_ctc=50.982, loss_att=60.754, acc=0.667, loss=57.822, backward_time=0.098, grad_norm=26.477, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.065e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 17:44:16,250 (trainer:737) INFO: 7epoch:train:2301-2400batch: iter_time=1.823e-04, forward_time=0.112, loss_ctc=55.490, loss_att=62.342, acc=0.660, loss=60.286, backward_time=0.109, grad_norm=31.221, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.031, optim0_lr0=8.060e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-12 17:44:57,253 (trainer:737) INFO: 7epoch:train:2401-2500batch: iter_time=1.824e-04, forward_time=0.104, loss_ctc=51.922, loss_att=60.921, acc=0.675, loss=58.221, backward_time=0.097, grad_norm=27.925, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.056e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 17:45:04,751 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 17:45:24,113 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 17:45:27,762 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38810933a0>)
+[gpuc02:0/16] 2024-01-12 17:45:27,762 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 17:45:27,765 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 17:50:17,053 (trainer:737) INFO: 7epoch:train:2501-2600batch: iter_time=2.729, forward_time=0.112, loss_ctc=58.256, loss_att=61.582, acc=0.670, loss=60.584, backward_time=0.100, grad_norm=28.400, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.052e-04, train_time=3.198
+[gpuc02:0/16] 2024-01-12 17:50:58,085 (trainer:737) INFO: 7epoch:train:2601-2700batch: iter_time=1.704e-04, forward_time=0.105, loss_ctc=72.333, loss_att=77.078, acc=0.646, loss=75.655, backward_time=0.099, grad_norm=34.381, clip=98.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.047e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 17:51:39,185 (trainer:737) INFO: 7epoch:train:2701-2800batch: iter_time=1.529e-04, forward_time=0.105, loss_ctc=56.943, loss_att=71.675, acc=0.666, loss=67.255, backward_time=0.099, grad_norm=28.892, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.043e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 17:52:22,076 (trainer:737) INFO: 7epoch:train:2801-2900batch: iter_time=1.830e-04, forward_time=0.114, loss_ctc=61.532, loss_att=71.109, acc=0.671, loss=68.236, backward_time=0.100, grad_norm=32.083, clip=99.000, loss_scale=4.612e+18, optim_step_time=0.032, optim0_lr0=8.039e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-12 17:53:03,208 (trainer:737) INFO: 7epoch:train:2901-3000batch: iter_time=1.693e-04, forward_time=0.103, loss_ctc=55.662, loss_att=62.126, acc=0.627, loss=60.187, backward_time=0.097, grad_norm=33.827, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.034e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 17:53:44,189 (trainer:737) INFO: 7epoch:train:3001-3100batch: iter_time=2.048e-04, forward_time=0.103, loss_ctc=55.860, loss_att=61.398, acc=0.654, loss=59.737, backward_time=0.097, grad_norm=30.257, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.030e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 17:54:24,983 (trainer:737) INFO: 7epoch:train:3101-3200batch: iter_time=1.713e-04, forward_time=0.104, loss_ctc=52.582, loss_att=53.719, acc=0.679, loss=53.378, backward_time=0.097, grad_norm=25.827, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.026e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 17:55:06,070 (trainer:737) INFO: 7epoch:train:3201-3300batch: iter_time=1.664e-04, forward_time=0.104, loss_ctc=64.364, loss_att=62.127, acc=0.662, loss=62.798, backward_time=0.098, grad_norm=31.762, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.021e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 17:55:47,034 (trainer:737) INFO: 7epoch:train:3301-3400batch: iter_time=1.967e-04, forward_time=0.102, loss_ctc=48.348, loss_att=51.665, acc=0.634, loss=50.670, backward_time=0.096, grad_norm=26.420, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.017e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 17:56:27,680 (trainer:737) INFO: 7epoch:train:3401-3500batch: iter_time=1.617e-04, forward_time=0.103, loss_ctc=46.857, loss_att=58.903, acc=0.668, loss=55.289, backward_time=0.097, grad_norm=25.856, clip=99.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.013e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 17:57:08,896 (trainer:737) INFO: 7epoch:train:3501-3600batch: iter_time=1.816e-04, forward_time=0.106, loss_ctc=54.471, loss_att=61.483, acc=0.650, loss=59.380, backward_time=0.098, grad_norm=30.009, clip=99.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.008e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 17:57:49,822 (trainer:737) INFO: 7epoch:train:3601-3700batch: iter_time=1.621e-04, forward_time=0.104, loss_ctc=55.088, loss_att=67.660, acc=0.650, loss=63.888, backward_time=0.098, grad_norm=28.069, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.004e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 17:58:16,010 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 17:58:35,306 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 17:58:39,072 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fd10aba90>)
+[gpuc02:0/16] 2024-01-12 17:58:39,072 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 17:58:39,075 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 18:04:02,944 (trainer:737) INFO: 7epoch:train:3701-3800batch: iter_time=2.691, forward_time=0.104, loss_ctc=55.788, loss_att=53.368, acc=0.700, loss=54.094, backward_time=0.097, grad_norm=50.538, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=8.000e-04, train_time=3.731
+[gpuc02:0/16] 2024-01-12 18:04:43,904 (trainer:737) INFO: 7epoch:train:3801-3900batch: iter_time=1.619e-04, forward_time=0.105, loss_ctc=57.853, loss_att=66.747, acc=0.649, loss=64.079, backward_time=0.098, grad_norm=28.815, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.029, optim0_lr0=7.996e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:05:24,815 (trainer:737) INFO: 7epoch:train:3901-4000batch: iter_time=1.859e-04, forward_time=0.105, loss_ctc=65.228, loss_att=70.179, acc=0.670, loss=68.694, backward_time=0.097, grad_norm=30.584, clip=100.000, loss_scale=4.612e+18, optim_step_time=0.030, optim0_lr0=7.991e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:06:05,896 (trainer:737) INFO: 7epoch:train:4001-4100batch: iter_time=1.647e-04, forward_time=0.106, loss_ctc=65.961, loss_att=70.775, acc=0.664, loss=69.330, backward_time=0.098, grad_norm=34.275, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.987e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 18:06:46,869 (trainer:737) INFO: 7epoch:train:4101-4200batch: iter_time=2.246e-04, forward_time=0.104, loss_ctc=58.158, loss_att=66.101, acc=0.653, loss=63.718, backward_time=0.097, grad_norm=33.384, clip=99.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.983e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:07:27,816 (trainer:737) INFO: 7epoch:train:4201-4300batch: iter_time=2.135e-04, forward_time=0.104, loss_ctc=51.102, loss_att=60.356, acc=0.643, loss=57.580, backward_time=0.096, grad_norm=69.630, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.979e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:08:08,588 (trainer:737) INFO: 7epoch:train:4301-4400batch: iter_time=1.959e-04, forward_time=0.104, loss_ctc=53.146, loss_att=53.080, acc=0.685, loss=53.100, backward_time=0.096, grad_norm=26.421, clip=99.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.974e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:08:49,394 (trainer:737) INFO: 7epoch:train:4401-4500batch: iter_time=1.874e-04, forward_time=0.105, loss_ctc=59.594, loss_att=65.043, acc=0.646, loss=63.408, backward_time=0.097, grad_norm=31.591, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.970e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:09:29,975 (trainer:737) INFO: 7epoch:train:4501-4600batch: iter_time=2.038e-04, forward_time=0.103, loss_ctc=51.237, loss_att=46.441, acc=0.670, loss=47.880, backward_time=0.095, grad_norm=26.814, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.966e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 18:10:11,443 (trainer:737) INFO: 7epoch:train:4601-4700batch: iter_time=2.094e-04, forward_time=0.104, loss_ctc=51.595, loss_att=60.214, acc=0.645, loss=57.628, backward_time=0.096, grad_norm=26.694, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.029, optim0_lr0=7.962e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 18:10:52,373 (trainer:737) INFO: 7epoch:train:4701-4800batch: iter_time=2.129e-04, forward_time=0.105, loss_ctc=49.579, loss_att=60.876, acc=0.658, loss=57.487, backward_time=0.097, grad_norm=33.306, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.958e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:11:33,437 (trainer:737) INFO: 7epoch:train:4801-4900batch: iter_time=2.081e-04, forward_time=0.104, loss_ctc=54.154, loss_att=60.493, acc=0.661, loss=58.592, backward_time=0.097, grad_norm=29.011, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.953e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:12:14,305 (trainer:737) INFO: 7epoch:train:4901-5000batch: iter_time=1.844e-04, forward_time=0.104, loss_ctc=50.968, loss_att=59.571, acc=0.670, loss=56.990, backward_time=0.097, grad_norm=26.433, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.949e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:12:21,826 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 18:12:41,080 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 18:12:44,615 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38810d8790>)
+[gpuc02:0/16] 2024-01-12 18:12:44,615 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 18:12:44,618 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 18:17:38,356 (trainer:737) INFO: 7epoch:train:5001-5100batch: iter_time=2.659, forward_time=0.104, loss_ctc=57.750, loss_att=57.643, acc=0.679, loss=57.675, backward_time=0.098, grad_norm=31.269, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.945e-04, train_time=3.240
+[gpuc02:0/16] 2024-01-12 18:18:19,387 (trainer:737) INFO: 7epoch:train:5101-5200batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=70.182, loss_att=71.298, acc=0.658, loss=70.963, backward_time=0.098, grad_norm=32.290, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.941e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:19:00,956 (trainer:737) INFO: 7epoch:train:5201-5300batch: iter_time=1.523e-04, forward_time=0.105, loss_ctc=55.539, loss_att=69.382, acc=0.675, loss=65.229, backward_time=0.098, grad_norm=28.138, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.937e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 18:19:41,882 (trainer:737) INFO: 7epoch:train:5301-5400batch: iter_time=1.581e-04, forward_time=0.105, loss_ctc=61.119, loss_att=69.396, acc=0.677, loss=66.913, backward_time=0.098, grad_norm=31.361, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.933e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:20:22,457 (trainer:737) INFO: 7epoch:train:5401-5500batch: iter_time=1.557e-04, forward_time=0.104, loss_ctc=53.686, loss_att=58.484, acc=0.638, loss=57.045, backward_time=0.097, grad_norm=33.032, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.928e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 18:21:03,371 (trainer:737) INFO: 7epoch:train:5501-5600batch: iter_time=1.809e-04, forward_time=0.104, loss_ctc=55.009, loss_att=60.249, acc=0.659, loss=58.677, backward_time=0.097, grad_norm=28.117, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.924e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:21:44,153 (trainer:737) INFO: 7epoch:train:5601-5700batch: iter_time=1.650e-04, forward_time=0.104, loss_ctc=52.040, loss_att=53.362, acc=0.684, loss=52.965, backward_time=0.097, grad_norm=25.651, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.920e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:22:25,027 (trainer:737) INFO: 7epoch:train:5701-5800batch: iter_time=1.689e-04, forward_time=0.105, loss_ctc=63.158, loss_att=60.691, acc=0.666, loss=61.431, backward_time=0.098, grad_norm=31.411, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.916e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:23:05,512 (trainer:737) INFO: 7epoch:train:5801-5900batch: iter_time=1.494e-04, forward_time=0.103, loss_ctc=47.398, loss_att=50.671, acc=0.640, loss=49.689, backward_time=0.096, grad_norm=27.490, clip=100.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.912e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 18:23:46,389 (trainer:737) INFO: 7epoch:train:5901-6000batch: iter_time=1.873e-04, forward_time=0.104, loss_ctc=46.661, loss_att=57.037, acc=0.674, loss=53.924, backward_time=0.097, grad_norm=27.260, clip=99.000, loss_scale=9.223e+18, optim_step_time=0.030, optim0_lr0=7.908e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:24:27,401 (trainer:737) INFO: 7epoch:train:6001-6100batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=53.558, loss_att=60.444, acc=0.654, loss=58.378, backward_time=0.097, grad_norm=28.390, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.904e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:25:08,187 (trainer:737) INFO: 7epoch:train:6101-6200batch: iter_time=1.591e-04, forward_time=0.105, loss_ctc=53.827, loss_att=66.219, acc=0.654, loss=62.501, backward_time=0.097, grad_norm=27.082, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.899e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:25:38,788 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 18:25:58,918 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 18:26:02,531 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3898b5eb30>)
+[gpuc02:0/16] 2024-01-12 18:26:02,531 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 18:26:02,534 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 18:30:23,084 (trainer:737) INFO: 7epoch:train:6201-6300batch: iter_time=2.734, forward_time=0.105, loss_ctc=55.130, loss_att=52.201, acc=0.703, loss=53.079, backward_time=0.097, grad_norm=27.963, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.895e-04, train_time=3.149
+[gpuc02:0/16] 2024-01-12 18:31:04,242 (trainer:737) INFO: 7epoch:train:6301-6400batch: iter_time=1.919e-04, forward_time=0.105, loss_ctc=56.281, loss_att=64.862, acc=0.655, loss=62.288, backward_time=0.097, grad_norm=29.385, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.891e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 18:31:45,470 (trainer:737) INFO: 7epoch:train:6401-6500batch: iter_time=2.079e-04, forward_time=0.105, loss_ctc=65.248, loss_att=68.485, acc=0.675, loss=67.514, backward_time=0.097, grad_norm=33.509, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.887e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 18:32:26,512 (trainer:737) INFO: 7epoch:train:6501-6600batch: iter_time=1.907e-04, forward_time=0.106, loss_ctc=65.303, loss_att=71.402, acc=0.666, loss=69.572, backward_time=0.099, grad_norm=31.381, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.883e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:33:07,187 (trainer:737) INFO: 7epoch:train:6601-6700batch: iter_time=2.240e-04, forward_time=0.103, loss_ctc=57.445, loss_att=65.438, acc=0.657, loss=63.040, backward_time=0.097, grad_norm=33.819, clip=99.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.879e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:33:47,770 (trainer:737) INFO: 7epoch:train:6701-6800batch: iter_time=2.254e-04, forward_time=0.104, loss_ctc=49.912, loss_att=59.550, acc=0.648, loss=56.658, backward_time=0.096, grad_norm=27.904, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.875e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 18:34:28,407 (trainer:737) INFO: 7epoch:train:6801-6900batch: iter_time=2.118e-04, forward_time=0.104, loss_ctc=52.243, loss_att=51.842, acc=0.692, loss=51.962, backward_time=0.096, grad_norm=25.271, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.029, optim0_lr0=7.871e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 18:35:09,132 (trainer:737) INFO: 7epoch:train:6901-7000batch: iter_time=2.103e-04, forward_time=0.104, loss_ctc=58.113, loss_att=63.933, acc=0.651, loss=62.187, backward_time=0.097, grad_norm=29.425, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.867e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:35:50,130 (trainer:737) INFO: 7epoch:train:7001-7100batch: iter_time=2.117e-04, forward_time=0.106, loss_ctc=50.292, loss_att=46.478, acc=0.672, loss=47.622, backward_time=0.096, grad_norm=27.109, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.029, optim0_lr0=7.863e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:36:31,295 (trainer:737) INFO: 7epoch:train:7101-7200batch: iter_time=2.201e-04, forward_time=0.104, loss_ctc=51.627, loss_att=59.311, acc=0.650, loss=57.006, backward_time=0.096, grad_norm=27.860, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.859e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 18:37:12,023 (trainer:737) INFO: 7epoch:train:7201-7300batch: iter_time=2.126e-04, forward_time=0.104, loss_ctc=48.602, loss_att=59.122, acc=0.664, loss=55.966, backward_time=0.096, grad_norm=25.255, clip=99.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.855e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:37:52,719 (trainer:737) INFO: 7epoch:train:7301-7400batch: iter_time=1.848e-04, forward_time=0.104, loss_ctc=52.932, loss_att=58.589, acc=0.667, loss=56.892, backward_time=0.096, grad_norm=29.292, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.851e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:38:33,390 (trainer:737) INFO: 7epoch:train:7401-7500batch: iter_time=1.690e-04, forward_time=0.104, loss_ctc=50.338, loss_att=58.646, acc=0.675, loss=56.153, backward_time=0.096, grad_norm=27.961, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.847e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 18:38:41,249 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 18:39:00,660 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 18:39:04,335 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38aa773fa0>)
+[gpuc02:0/16] 2024-01-12 18:39:04,335 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 18:39:04,339 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 18:43:47,817 (trainer:737) INFO: 7epoch:train:7501-7600batch: iter_time=2.683, forward_time=0.105, loss_ctc=55.875, loss_att=64.427, acc=0.681, loss=61.861, backward_time=0.099, grad_norm=28.921, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.843e-04, train_time=3.144
+[gpuc02:0/16] 2024-01-12 18:44:29,362 (trainer:737) INFO: 7epoch:train:7601-7700batch: iter_time=1.167e-04, forward_time=0.106, loss_ctc=69.397, loss_att=75.769, acc=0.671, loss=73.858, backward_time=0.099, grad_norm=31.825, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.839e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 18:45:10,803 (trainer:737) INFO: 7epoch:train:7701-7800batch: iter_time=1.433e-04, forward_time=0.105, loss_ctc=54.889, loss_att=72.413, acc=0.680, loss=67.156, backward_time=0.099, grad_norm=28.244, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.835e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 18:45:51,825 (trainer:737) INFO: 7epoch:train:7801-7900batch: iter_time=1.500e-04, forward_time=0.104, loss_ctc=60.043, loss_att=71.025, acc=0.682, loss=67.730, backward_time=0.098, grad_norm=30.262, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.831e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:46:32,512 (trainer:737) INFO: 7epoch:train:7901-8000batch: iter_time=1.669e-04, forward_time=0.103, loss_ctc=53.095, loss_att=60.660, acc=0.644, loss=58.390, backward_time=0.097, grad_norm=33.651, clip=100.000, loss_scale=1.845e+19, optim_step_time=0.030, optim0_lr0=7.827e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:47:13,520 (trainer:737) INFO: 7epoch:train:8001-8100batch: iter_time=1.882e-04, forward_time=0.104, loss_ctc=53.961, loss_att=61.019, acc=0.667, loss=58.902, backward_time=0.097, grad_norm=30.347, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.823e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:47:54,363 (trainer:737) INFO: 7epoch:train:8101-8200batch: iter_time=2.197e-04, forward_time=0.104, loss_ctc=51.324, loss_att=56.431, acc=0.685, loss=54.899, backward_time=0.097, grad_norm=25.015, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.819e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:48:35,821 (trainer:737) INFO: 7epoch:train:8201-8300batch: iter_time=2.103e-04, forward_time=0.105, loss_ctc=61.763, loss_att=60.992, acc=0.669, loss=61.223, backward_time=0.097, grad_norm=65.184, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.815e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 18:49:16,852 (trainer:737) INFO: 7epoch:train:8301-8400batch: iter_time=2.299e-04, forward_time=0.103, loss_ctc=46.677, loss_att=51.675, acc=0.641, loss=50.175, backward_time=0.095, grad_norm=26.419, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.811e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:49:58,040 (trainer:737) INFO: 7epoch:train:8401-8500batch: iter_time=1.989e-04, forward_time=0.104, loss_ctc=45.356, loss_att=58.744, acc=0.683, loss=54.728, backward_time=0.097, grad_norm=24.510, clip=99.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.807e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 18:50:38,846 (trainer:737) INFO: 7epoch:train:8501-8600batch: iter_time=1.875e-04, forward_time=0.105, loss_ctc=52.657, loss_att=60.646, acc=0.662, loss=58.249, backward_time=0.097, grad_norm=29.101, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.803e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:51:19,668 (trainer:737) INFO: 7epoch:train:8601-8700batch: iter_time=1.968e-04, forward_time=0.104, loss_ctc=53.191, loss_att=65.326, acc=0.673, loss=61.686, backward_time=0.097, grad_norm=28.003, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.799e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 18:51:45,076 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 18:52:04,407 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 18:52:08,037 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3898b9dcf0>)
+[gpuc02:0/16] 2024-01-12 18:52:08,037 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 18:52:08,041 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 18:56:26,434 (trainer:737) INFO: 7epoch:train:8701-8800batch: iter_time=2.644, forward_time=0.104, loss_ctc=53.680, loss_att=56.642, acc=0.705, loss=55.754, backward_time=0.097, grad_norm=27.877, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.795e-04, train_time=3.067
+[gpuc02:0/16] 2024-01-12 18:57:07,451 (trainer:737) INFO: 7epoch:train:8801-8900batch: iter_time=1.794e-04, forward_time=0.105, loss_ctc=55.523, loss_att=66.623, acc=0.670, loss=63.293, backward_time=0.098, grad_norm=28.236, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.791e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:57:48,408 (trainer:737) INFO: 7epoch:train:8901-9000batch: iter_time=1.774e-04, forward_time=0.105, loss_ctc=63.377, loss_att=68.725, acc=0.690, loss=67.121, backward_time=0.098, grad_norm=30.196, clip=99.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.787e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 18:58:29,435 (trainer:737) INFO: 7epoch:train:9001-9100batch: iter_time=1.771e-04, forward_time=0.105, loss_ctc=64.054, loss_att=70.991, acc=0.679, loss=68.910, backward_time=0.099, grad_norm=31.080, clip=99.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.783e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 18:59:10,202 (trainer:737) INFO: 7epoch:train:9101-9200batch: iter_time=2.100e-04, forward_time=0.104, loss_ctc=55.718, loss_att=66.928, acc=0.663, loss=63.565, backward_time=0.097, grad_norm=33.397, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.779e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 18:59:51,455 (trainer:737) INFO: 7epoch:train:9201-9300batch: iter_time=1.991e-04, forward_time=0.104, loss_ctc=49.402, loss_att=59.909, acc=0.656, loss=56.757, backward_time=0.097, grad_norm=29.664, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.775e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 19:00:32,177 (trainer:737) INFO: 7epoch:train:9301-9400batch: iter_time=1.686e-04, forward_time=0.104, loss_ctc=51.859, loss_att=54.032, acc=0.697, loss=53.380, backward_time=0.097, grad_norm=24.254, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.771e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:01:12,988 (trainer:737) INFO: 7epoch:train:9401-9500batch: iter_time=1.959e-04, forward_time=0.104, loss_ctc=57.929, loss_att=64.480, acc=0.653, loss=62.515, backward_time=0.097, grad_norm=28.376, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.767e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:01:53,537 (trainer:737) INFO: 7epoch:train:9501-9600batch: iter_time=2.330e-04, forward_time=0.103, loss_ctc=49.738, loss_att=46.265, acc=0.679, loss=47.307, backward_time=0.096, grad_norm=27.493, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.763e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 19:02:34,576 (trainer:737) INFO: 7epoch:train:9601-9700batch: iter_time=2.294e-04, forward_time=0.104, loss_ctc=50.137, loss_att=59.688, acc=0.657, loss=56.823, backward_time=0.096, grad_norm=26.181, clip=99.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.759e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:03:15,620 (trainer:737) INFO: 7epoch:train:9701-9800batch: iter_time=2.351e-04, forward_time=0.104, loss_ctc=48.351, loss_att=59.595, acc=0.676, loss=56.221, backward_time=0.097, grad_norm=26.326, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.756e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:03:56,608 (trainer:737) INFO: 7epoch:train:9801-9900batch: iter_time=2.261e-04, forward_time=0.104, loss_ctc=52.471, loss_att=59.898, acc=0.672, loss=57.670, backward_time=0.097, grad_norm=29.002, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.752e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:04:37,341 (trainer:737) INFO: 7epoch:train:9901-10000batch: iter_time=1.803e-04, forward_time=0.104, loss_ctc=49.376, loss_att=59.677, acc=0.685, loss=56.587, backward_time=0.097, grad_norm=27.584, clip=100.000, loss_scale=3.689e+19, optim_step_time=0.029, optim0_lr0=7.748e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:04:43,390 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 19:05:03,035 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 19:05:06,843 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fb85865f0>)
+[gpuc02:0/16] 2024-01-12 19:05:06,844 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 19:05:06,847 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 19:09:48,569 (trainer:737) INFO: 7epoch:train:10001-10100batch: iter_time=2.639, forward_time=0.105, loss_ctc=54.955, loss_att=58.265, acc=0.693, loss=57.272, backward_time=0.097, grad_norm=28.111, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.744e-04, train_time=3.112
+[gpuc02:0/16] 2024-01-12 19:10:29,657 (trainer:737) INFO: 7epoch:train:10101-10200batch: iter_time=1.730e-04, forward_time=0.106, loss_ctc=68.160, loss_att=69.738, acc=0.683, loss=69.265, backward_time=0.098, grad_norm=32.426, clip=99.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.740e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:11:10,785 (trainer:737) INFO: 7epoch:train:10201-10300batch: iter_time=1.708e-04, forward_time=0.106, loss_ctc=54.793, loss_att=69.509, acc=0.687, loss=65.094, backward_time=0.098, grad_norm=27.763, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.736e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:11:51,821 (trainer:737) INFO: 7epoch:train:10301-10400batch: iter_time=1.884e-04, forward_time=0.105, loss_ctc=60.181, loss_att=69.279, acc=0.687, loss=66.549, backward_time=0.098, grad_norm=31.041, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.732e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:12:32,494 (trainer:737) INFO: 7epoch:train:10401-10500batch: iter_time=2.013e-04, forward_time=0.103, loss_ctc=52.113, loss_att=59.051, acc=0.651, loss=56.969, backward_time=0.096, grad_norm=33.807, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.729e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:13:13,869 (trainer:737) INFO: 7epoch:train:10501-10600batch: iter_time=2.010e-04, forward_time=0.104, loss_ctc=53.234, loss_att=59.907, acc=0.671, loss=57.905, backward_time=0.097, grad_norm=27.439, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.725e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 19:13:55,012 (trainer:737) INFO: 7epoch:train:10601-10700batch: iter_time=1.905e-04, forward_time=0.104, loss_ctc=50.909, loss_att=55.056, acc=0.689, loss=53.812, backward_time=0.097, grad_norm=25.601, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.721e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:14:35,917 (trainer:737) INFO: 7epoch:train:10701-10800batch: iter_time=2.014e-04, forward_time=0.105, loss_ctc=61.222, loss_att=59.594, acc=0.674, loss=60.082, backward_time=0.097, grad_norm=32.860, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.717e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:15:16,432 (trainer:737) INFO: 7epoch:train:10801-10900batch: iter_time=2.304e-04, forward_time=0.103, loss_ctc=46.570, loss_att=51.210, acc=0.644, loss=49.818, backward_time=0.096, grad_norm=27.468, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.713e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 19:15:57,171 (trainer:737) INFO: 7epoch:train:10901-11000batch: iter_time=1.586e-04, forward_time=0.103, loss_ctc=44.678, loss_att=56.221, acc=0.691, loss=52.758, backward_time=0.096, grad_norm=24.864, clip=99.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.709e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:16:38,258 (trainer:737) INFO: 7epoch:train:11001-11100batch: iter_time=1.548e-04, forward_time=0.104, loss_ctc=52.013, loss_att=59.761, acc=0.667, loss=57.437, backward_time=0.097, grad_norm=29.308, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.706e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:17:19,285 (trainer:737) INFO: 7epoch:train:11101-11200batch: iter_time=1.793e-04, forward_time=0.107, loss_ctc=53.565, loss_att=64.583, acc=0.676, loss=61.278, backward_time=0.097, grad_norm=29.113, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.702e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:17:47,018 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 19:18:06,851 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 19:18:10,507 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f386d2b7d30>)
+[gpuc02:0/16] 2024-01-12 19:18:10,508 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 19:18:10,511 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 19:22:40,258 (trainer:737) INFO: 7epoch:train:11201-11300batch: iter_time=2.714, forward_time=0.105, loss_ctc=52.759, loss_att=54.999, acc=0.708, loss=54.327, backward_time=0.097, grad_norm=27.421, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.698e-04, train_time=3.209
+[gpuc02:0/16] 2024-01-12 19:23:21,146 (trainer:737) INFO: 7epoch:train:11301-11400batch: iter_time=1.800e-04, forward_time=0.105, loss_ctc=54.935, loss_att=70.698, acc=0.650, loss=65.969, backward_time=0.098, grad_norm=30.708, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.694e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:24:02,062 (trainer:737) INFO: 7epoch:train:11401-11500batch: iter_time=1.726e-04, forward_time=0.106, loss_ctc=61.517, loss_att=69.636, acc=0.676, loss=67.200, backward_time=0.098, grad_norm=32.352, clip=99.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.690e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:24:43,097 (trainer:737) INFO: 7epoch:train:11501-11600batch: iter_time=1.699e-04, forward_time=0.106, loss_ctc=62.996, loss_att=70.224, acc=0.672, loss=68.056, backward_time=0.098, grad_norm=31.538, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.687e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:25:24,331 (trainer:737) INFO: 7epoch:train:11601-11700batch: iter_time=1.747e-04, forward_time=0.104, loss_ctc=55.201, loss_att=65.620, acc=0.661, loss=62.494, backward_time=0.096, grad_norm=39.774, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.030, optim0_lr0=7.683e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 19:26:04,984 (trainer:737) INFO: 7epoch:train:11701-11800batch: iter_time=1.830e-04, forward_time=0.105, loss_ctc=49.111, loss_att=59.412, acc=0.650, loss=56.321, backward_time=0.096, grad_norm=29.186, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.679e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 19:26:45,656 (trainer:737) INFO: 7epoch:train:11801-11900batch: iter_time=1.807e-04, forward_time=0.104, loss_ctc=51.173, loss_att=52.171, acc=0.692, loss=51.871, backward_time=0.096, grad_norm=26.355, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.675e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 19:27:27,308 (trainer:737) INFO: 7epoch:train:11901-12000batch: iter_time=1.906e-04, forward_time=0.105, loss_ctc=56.512, loss_att=63.394, acc=0.655, loss=61.329, backward_time=0.097, grad_norm=30.193, clip=100.000, loss_scale=7.379e+19, optim_step_time=0.029, optim0_lr0=7.671e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 19:28:07,830 (trainer:737) INFO: 7epoch:train:12001-12100batch: iter_time=2.076e-04, forward_time=0.104, loss_ctc=48.830, loss_att=45.094, acc=0.679, loss=46.215, backward_time=0.095, grad_norm=27.383, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.668e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 19:28:48,789 (trainer:737) INFO: 7epoch:train:12101-12200batch: iter_time=1.704e-04, forward_time=0.104, loss_ctc=49.912, loss_att=59.217, acc=0.653, loss=56.425, backward_time=0.096, grad_norm=27.111, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.664e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:29:29,498 (trainer:737) INFO: 7epoch:train:12201-12300batch: iter_time=1.765e-04, forward_time=0.104, loss_ctc=47.512, loss_att=59.518, acc=0.667, loss=55.916, backward_time=0.096, grad_norm=26.632, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.660e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:30:10,228 (trainer:737) INFO: 7epoch:train:12301-12400batch: iter_time=1.798e-04, forward_time=0.105, loss_ctc=51.693, loss_att=59.162, acc=0.670, loss=56.921, backward_time=0.096, grad_norm=28.572, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.656e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:30:50,882 (trainer:737) INFO: 7epoch:train:12401-12500batch: iter_time=1.658e-04, forward_time=0.104, loss_ctc=49.653, loss_att=58.719, acc=0.676, loss=55.999, backward_time=0.096, grad_norm=30.387, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.653e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 19:30:57,740 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 19:31:16,769 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 19:31:20,482 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38cc883790>)
+[gpuc02:0/16] 2024-01-12 19:31:20,482 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 19:31:20,485 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 19:35:56,882 (trainer:737) INFO: 7epoch:train:12501-12600batch: iter_time=2.605, forward_time=0.105, loss_ctc=54.631, loss_att=60.911, acc=0.690, loss=59.027, backward_time=0.098, grad_norm=29.354, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.649e-04, train_time=3.060
+[gpuc02:0/16] 2024-01-12 19:36:38,021 (trainer:737) INFO: 7epoch:train:12601-12700batch: iter_time=1.808e-04, forward_time=0.106, loss_ctc=67.285, loss_att=71.101, acc=0.683, loss=69.957, backward_time=0.099, grad_norm=32.398, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.645e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:37:19,484 (trainer:737) INFO: 7epoch:train:12701-12800batch: iter_time=1.870e-04, forward_time=0.108, loss_ctc=54.080, loss_att=69.977, acc=0.687, loss=65.208, backward_time=0.099, grad_norm=28.989, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.642e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 19:38:00,480 (trainer:737) INFO: 7epoch:train:12801-12900batch: iter_time=2.270e-04, forward_time=0.106, loss_ctc=58.722, loss_att=69.168, acc=0.689, loss=66.034, backward_time=0.098, grad_norm=31.651, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.638e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:38:41,207 (trainer:737) INFO: 7epoch:train:12901-13000batch: iter_time=2.538e-04, forward_time=0.104, loss_ctc=51.788, loss_att=58.441, acc=0.654, loss=56.445, backward_time=0.097, grad_norm=32.459, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.634e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 19:39:22,730 (trainer:737) INFO: 7epoch:train:13001-13100batch: iter_time=2.079e-04, forward_time=0.105, loss_ctc=52.889, loss_att=59.129, acc=0.676, loss=57.257, backward_time=0.098, grad_norm=27.068, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.630e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 19:40:03,563 (trainer:737) INFO: 7epoch:train:13101-13200batch: iter_time=1.941e-04, forward_time=0.105, loss_ctc=50.716, loss_att=55.291, acc=0.691, loss=53.918, backward_time=0.098, grad_norm=26.887, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.627e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:40:44,478 (trainer:737) INFO: 7epoch:train:13201-13300batch: iter_time=1.852e-04, forward_time=0.105, loss_ctc=60.553, loss_att=59.159, acc=0.676, loss=59.577, backward_time=0.098, grad_norm=32.347, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.623e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:41:24,984 (trainer:737) INFO: 7epoch:train:13301-13400batch: iter_time=1.957e-04, forward_time=0.103, loss_ctc=45.090, loss_att=50.020, acc=0.651, loss=48.541, backward_time=0.097, grad_norm=24.422, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.619e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-12 19:42:05,967 (trainer:737) INFO: 7epoch:train:13401-13500batch: iter_time=2.118e-04, forward_time=0.104, loss_ctc=44.423, loss_att=56.617, acc=0.688, loss=52.959, backward_time=0.097, grad_norm=25.703, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.616e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:42:47,073 (trainer:737) INFO: 7epoch:train:13501-13600batch: iter_time=1.953e-04, forward_time=0.105, loss_ctc=51.148, loss_att=59.196, acc=0.670, loss=56.781, backward_time=0.098, grad_norm=28.553, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.612e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:43:28,344 (trainer:737) INFO: 7epoch:train:13601-13700batch: iter_time=2.135e-04, forward_time=0.104, loss_ctc=52.240, loss_att=65.132, acc=0.673, loss=61.264, backward_time=0.097, grad_norm=27.256, clip=99.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.608e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 19:43:54,921 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 19:44:14,299 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 19:44:17,976 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40dc5b79d0>)
+[gpuc02:0/16] 2024-01-12 19:44:17,976 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 19:44:17,979 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 19:48:34,792 (trainer:737) INFO: 7epoch:train:13701-13800batch: iter_time=2.649, forward_time=0.105, loss_ctc=52.565, loss_att=54.092, acc=0.715, loss=53.634, backward_time=0.098, grad_norm=26.733, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.030, optim0_lr0=7.605e-04, train_time=3.064
+[gpuc02:0/16] 2024-01-12 19:49:15,813 (trainer:737) INFO: 7epoch:train:13801-13900batch: iter_time=1.840e-04, forward_time=0.105, loss_ctc=54.291, loss_att=63.880, acc=0.679, loss=61.003, backward_time=0.097, grad_norm=27.054, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.601e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 19:49:56,895 (trainer:737) INFO: 7epoch:train:13901-14000batch: iter_time=1.824e-04, forward_time=0.105, loss_ctc=63.098, loss_att=67.513, acc=0.696, loss=66.189, backward_time=0.098, grad_norm=31.242, clip=100.000, loss_scale=1.476e+20, optim_step_time=0.029, optim0_lr0=7.597e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:50:38,015 (trainer:737) INFO: 7epoch:train:14001-14100batch: iter_time=2.008e-04, forward_time=0.106, loss_ctc=62.821, loss_att=68.891, acc=0.688, loss=67.070, backward_time=0.098, grad_norm=32.130, clip=99.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.594e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:51:18,792 (trainer:737) INFO: 7epoch:train:14101-14200batch: iter_time=1.984e-04, forward_time=0.104, loss_ctc=53.943, loss_att=65.014, acc=0.670, loss=61.693, backward_time=0.096, grad_norm=31.682, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.590e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:51:59,626 (trainer:737) INFO: 7epoch:train:14201-14300batch: iter_time=2.432e-04, forward_time=0.104, loss_ctc=48.589, loss_att=58.804, acc=0.662, loss=55.740, backward_time=0.096, grad_norm=90.691, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.586e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:52:40,431 (trainer:737) INFO: 7epoch:train:14301-14400batch: iter_time=1.976e-04, forward_time=0.104, loss_ctc=50.524, loss_att=53.104, acc=0.703, loss=52.330, backward_time=0.096, grad_norm=25.164, clip=99.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.583e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:53:21,306 (trainer:737) INFO: 7epoch:train:14401-14500batch: iter_time=2.259e-04, forward_time=0.104, loss_ctc=56.326, loss_att=62.810, acc=0.660, loss=60.865, backward_time=0.096, grad_norm=29.739, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.579e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:54:02,277 (trainer:737) INFO: 7epoch:train:14501-14600batch: iter_time=2.281e-04, forward_time=0.103, loss_ctc=48.126, loss_att=45.327, acc=0.684, loss=46.167, backward_time=0.096, grad_norm=27.158, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.575e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 19:54:43,373 (trainer:737) INFO: 7epoch:train:14601-14700batch: iter_time=2.346e-04, forward_time=0.104, loss_ctc=50.123, loss_att=59.320, acc=0.659, loss=56.561, backward_time=0.096, grad_norm=27.756, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.572e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:55:24,493 (trainer:737) INFO: 7epoch:train:14701-14800batch: iter_time=2.325e-04, forward_time=0.104, loss_ctc=47.322, loss_att=57.702, acc=0.682, loss=54.588, backward_time=0.096, grad_norm=25.712, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.568e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 19:56:05,289 (trainer:737) INFO: 7epoch:train:14801-14900batch: iter_time=2.192e-04, forward_time=0.104, loss_ctc=51.455, loss_att=58.214, acc=0.680, loss=56.186, backward_time=0.096, grad_norm=29.812, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.565e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 19:56:46,114 (trainer:737) INFO: 7epoch:train:14901-15000batch: iter_time=1.983e-04, forward_time=0.104, loss_ctc=48.305, loss_att=58.360, acc=0.692, loss=55.344, backward_time=0.096, grad_norm=25.789, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.029, optim0_lr0=7.561e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 20:16:48,408 (trainer:343) INFO: 7epoch results: [train] iter_time=0.219, forward_time=0.105, loss_ctc=55.217, loss_att=61.686, acc=0.668, loss=59.745, backward_time=0.097, grad_norm=30.352, clip=99.840, loss_scale=5.872e+19, optim_step_time=0.030, optim0_lr0=7.850e-04, train_time=0.637, time=2 hours, 39 minutes and 33.83 seconds, total_count=105000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=73.078, cer_ctc=0.358, loss_att=66.020, acc=0.514, cer=0.424, wer=1.000, loss=68.138, time=19 minutes and 51.53 seconds, total_count=32697, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-12 20:16:53,087 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 20:16:53,090 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/2epoch.pth
+[gpuc02:0/16] 2024-01-12 20:16:53,090 (trainer:272) INFO: 8/45epoch started. Estimated time to finish: 4 days, 16 hours and 59 minutes
+[gpuc02:0/16] 2024-01-12 20:16:53,099 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 20:17:11,900 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 20:17:15,414 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38db448ca0>)
+[gpuc02:0/16] 2024-01-12 20:17:15,414 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-12 20:17:15,417 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 20:21:51,462 (trainer:737) INFO: 8epoch:train:1-100batch: iter_time=2.568, forward_time=0.105, loss_ctc=61.791, loss_att=69.377, acc=0.665, loss=67.101, backward_time=0.099, grad_norm=35.994, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.557e-04, train_time=2.983
+[gpuc02:0/16] 2024-01-12 20:22:32,209 (trainer:737) INFO: 8epoch:train:101-200batch: iter_time=1.276e-04, forward_time=0.104, loss_ctc=49.363, loss_att=51.184, acc=0.713, loss=50.637, backward_time=0.099, grad_norm=26.001, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.554e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:23:13,573 (trainer:737) INFO: 8epoch:train:201-300batch: iter_time=1.452e-04, forward_time=0.105, loss_ctc=59.069, loss_att=80.179, acc=0.641, loss=73.846, backward_time=0.100, grad_norm=30.775, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.550e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 20:23:54,271 (trainer:737) INFO: 8epoch:train:301-400batch: iter_time=1.319e-04, forward_time=0.104, loss_ctc=54.135, loss_att=62.385, acc=0.666, loss=59.910, backward_time=0.098, grad_norm=28.109, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.547e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:24:35,690 (trainer:737) INFO: 8epoch:train:401-500batch: iter_time=1.328e-04, forward_time=0.105, loss_ctc=59.608, loss_att=60.594, acc=0.692, loss=60.298, backward_time=0.099, grad_norm=30.458, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.543e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 20:25:17,928 (trainer:737) INFO: 8epoch:train:501-600batch: iter_time=1.267e-04, forward_time=0.104, loss_ctc=51.627, loss_att=58.471, acc=0.691, loss=56.418, backward_time=0.098, grad_norm=29.361, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.540e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-12 20:26:00,037 (trainer:737) INFO: 8epoch:train:601-700batch: iter_time=1.316e-04, forward_time=0.109, loss_ctc=61.677, loss_att=69.373, acc=0.651, loss=67.064, backward_time=0.099, grad_norm=32.077, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.032, optim0_lr0=7.536e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-12 20:26:43,683 (trainer:737) INFO: 8epoch:train:701-800batch: iter_time=1.211e-04, forward_time=0.119, loss_ctc=75.047, loss_att=83.572, acc=0.657, loss=81.015, backward_time=0.101, grad_norm=63.891, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.032, optim0_lr0=7.532e-04, train_time=0.436
+[gpuc02:0/16] 2024-01-12 20:27:24,749 (trainer:737) INFO: 8epoch:train:801-900batch: iter_time=1.213e-04, forward_time=0.106, loss_ctc=62.062, loss_att=78.837, acc=0.682, loss=73.804, backward_time=0.100, grad_norm=31.657, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.529e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 20:28:09,159 (trainer:737) INFO: 8epoch:train:901-1000batch: iter_time=1.288e-04, forward_time=0.115, loss_ctc=66.682, loss_att=70.774, acc=0.656, loss=69.546, backward_time=0.102, grad_norm=36.852, clip=100.000, loss_scale=2.951e+20, optim_step_time=0.030, optim0_lr0=7.525e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-12 20:28:50,495 (trainer:737) INFO: 8epoch:train:1001-1100batch: iter_time=1.295e-04, forward_time=0.108, loss_ctc=55.365, loss_att=57.000, acc=0.690, loss=56.510, backward_time=0.098, grad_norm=26.905, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.029, optim0_lr0=7.522e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 20:29:34,882 (trainer:737) INFO: 8epoch:train:1101-1200batch: iter_time=1.215e-04, forward_time=0.111, loss_ctc=62.534, loss_att=61.931, acc=0.682, loss=62.112, backward_time=0.101, grad_norm=33.315, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.029, optim0_lr0=7.518e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-12 20:30:11,098 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 20:30:30,646 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 20:30:34,340 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f725b36a0>)
+[gpuc02:0/16] 2024-01-12 20:30:34,340 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-12 20:30:34,343 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 20:35:53,342 (trainer:737) INFO: 8epoch:train:1201-1300batch: iter_time=3.211, forward_time=0.113, loss_ctc=58.451, loss_att=79.349, acc=0.638, loss=73.080, backward_time=0.099, grad_norm=35.043, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.515e-04, train_time=3.784
+[gpuc02:0/16] 2024-01-12 20:36:34,254 (trainer:737) INFO: 8epoch:train:1301-1400batch: iter_time=1.904e-04, forward_time=0.104, loss_ctc=53.066, loss_att=55.106, acc=0.691, loss=54.494, backward_time=0.098, grad_norm=29.372, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.511e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 20:37:15,653 (trainer:737) INFO: 8epoch:train:1401-1500batch: iter_time=1.807e-04, forward_time=0.104, loss_ctc=56.884, loss_att=73.370, acc=0.655, loss=68.424, backward_time=0.098, grad_norm=28.403, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.508e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 20:37:56,408 (trainer:737) INFO: 8epoch:train:1501-1600batch: iter_time=1.739e-04, forward_time=0.103, loss_ctc=51.254, loss_att=60.613, acc=0.674, loss=57.805, backward_time=0.097, grad_norm=27.493, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.504e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:38:37,445 (trainer:737) INFO: 8epoch:train:1601-1700batch: iter_time=1.800e-04, forward_time=0.104, loss_ctc=52.974, loss_att=60.563, acc=0.669, loss=58.286, backward_time=0.098, grad_norm=29.698, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.501e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 20:39:18,670 (trainer:737) INFO: 8epoch:train:1701-1800batch: iter_time=1.919e-04, forward_time=0.104, loss_ctc=61.800, loss_att=65.417, acc=0.684, loss=64.332, backward_time=0.099, grad_norm=30.956, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.497e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 20:39:59,398 (trainer:737) INFO: 8epoch:train:1801-1900batch: iter_time=1.723e-04, forward_time=0.103, loss_ctc=48.648, loss_att=56.887, acc=0.667, loss=54.415, backward_time=0.098, grad_norm=28.384, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.494e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:40:40,906 (trainer:737) INFO: 8epoch:train:1901-2000batch: iter_time=1.827e-04, forward_time=0.105, loss_ctc=67.915, loss_att=75.885, acc=0.650, loss=73.494, backward_time=0.099, grad_norm=34.804, clip=99.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.490e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 20:41:23,033 (trainer:737) INFO: 8epoch:train:2001-2100batch: iter_time=1.616e-04, forward_time=0.111, loss_ctc=65.745, loss_att=89.762, acc=0.640, loss=82.557, backward_time=0.101, grad_norm=35.196, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.487e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-12 20:42:06,207 (trainer:737) INFO: 8epoch:train:2101-2200batch: iter_time=1.600e-04, forward_time=0.120, loss_ctc=62.104, loss_att=71.916, acc=0.663, loss=68.973, backward_time=0.102, grad_norm=31.919, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.031, optim0_lr0=7.483e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-12 20:42:47,037 (trainer:737) INFO: 8epoch:train:2201-2300batch: iter_time=1.605e-04, forward_time=0.103, loss_ctc=60.475, loss_att=62.819, acc=0.664, loss=62.116, backward_time=0.098, grad_norm=31.159, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.480e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 20:43:27,808 (trainer:737) INFO: 8epoch:train:2301-2400batch: iter_time=1.790e-04, forward_time=0.103, loss_ctc=58.415, loss_att=55.341, acc=0.673, loss=56.263, backward_time=0.097, grad_norm=31.569, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.476e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:44:08,639 (trainer:737) INFO: 8epoch:train:2401-2500batch: iter_time=1.628e-04, forward_time=0.104, loss_ctc=58.563, loss_att=66.736, acc=0.672, loss=64.284, backward_time=0.098, grad_norm=30.689, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.473e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 20:44:20,504 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 20:44:40,209 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 20:44:43,993 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a25ccf40>)
+[gpuc02:0/16] 2024-01-12 20:44:43,993 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-12 20:44:43,996 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 20:49:41,232 (trainer:737) INFO: 8epoch:train:2501-2600batch: iter_time=2.892, forward_time=0.105, loss_ctc=59.007, loss_att=69.497, acc=0.667, loss=66.350, backward_time=0.098, grad_norm=33.444, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.469e-04, train_time=3.326
+[gpuc02:0/16] 2024-01-12 20:50:22,061 (trainer:737) INFO: 8epoch:train:2601-2700batch: iter_time=1.812e-04, forward_time=0.104, loss_ctc=47.395, loss_att=51.243, acc=0.717, loss=50.089, backward_time=0.097, grad_norm=25.216, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.466e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 20:51:03,013 (trainer:737) INFO: 8epoch:train:2701-2800batch: iter_time=1.918e-04, forward_time=0.104, loss_ctc=56.270, loss_att=77.537, acc=0.649, loss=71.157, backward_time=0.098, grad_norm=29.467, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.462e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 20:51:43,746 (trainer:737) INFO: 8epoch:train:2801-2900batch: iter_time=2.018e-04, forward_time=0.103, loss_ctc=51.984, loss_att=61.834, acc=0.670, loss=58.879, backward_time=0.097, grad_norm=29.545, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.459e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 20:52:24,909 (trainer:737) INFO: 8epoch:train:2901-3000batch: iter_time=1.913e-04, forward_time=0.105, loss_ctc=58.227, loss_att=59.669, acc=0.697, loss=59.237, backward_time=0.098, grad_norm=29.554, clip=100.000, loss_scale=5.903e+20, optim_step_time=0.030, optim0_lr0=7.455e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 20:53:06,026 (trainer:737) INFO: 8epoch:train:3001-3100batch: iter_time=1.885e-04, forward_time=0.103, loss_ctc=50.573, loss_att=57.126, acc=0.697, loss=55.160, backward_time=0.097, grad_norm=27.787, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.452e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 20:53:47,030 (trainer:737) INFO: 8epoch:train:3101-3200batch: iter_time=1.716e-04, forward_time=0.105, loss_ctc=58.413, loss_att=67.982, acc=0.660, loss=65.112, backward_time=0.098, grad_norm=29.939, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.448e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 20:54:28,405 (trainer:737) INFO: 8epoch:train:3201-3300batch: iter_time=1.602e-04, forward_time=0.105, loss_ctc=72.159, loss_att=82.727, acc=0.663, loss=79.557, backward_time=0.099, grad_norm=38.199, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.445e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 20:55:09,834 (trainer:737) INFO: 8epoch:train:3301-3400batch: iter_time=1.521e-04, forward_time=0.105, loss_ctc=59.274, loss_att=77.097, acc=0.691, loss=71.750, backward_time=0.099, grad_norm=29.967, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.031, optim0_lr0=7.441e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 20:55:50,679 (trainer:737) INFO: 8epoch:train:3401-3500batch: iter_time=1.794e-04, forward_time=0.104, loss_ctc=63.117, loss_att=68.718, acc=0.665, loss=67.038, backward_time=0.098, grad_norm=33.827, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.031, optim0_lr0=7.438e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 20:56:32,289 (trainer:737) INFO: 8epoch:train:3501-3600batch: iter_time=1.880e-04, forward_time=0.104, loss_ctc=53.651, loss_att=55.782, acc=0.699, loss=55.143, backward_time=0.097, grad_norm=27.655, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.435e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 20:57:13,443 (trainer:737) INFO: 8epoch:train:3601-3700batch: iter_time=1.557e-04, forward_time=0.104, loss_ctc=59.954, loss_att=59.912, acc=0.689, loss=59.925, backward_time=0.098, grad_norm=31.401, clip=99.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.431e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 20:57:40,358 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 20:57:59,459 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 20:58:03,046 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fd3566fb0>)
+[gpuc02:0/16] 2024-01-12 20:58:03,046 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 20:58:03,049 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 21:02:55,094 (trainer:737) INFO: 8epoch:train:3701-3800batch: iter_time=2.960, forward_time=0.109, loss_ctc=56.587, loss_att=75.553, acc=0.644, loss=69.863, backward_time=0.099, grad_norm=33.670, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.428e-04, train_time=3.416
+[gpuc02:0/16] 2024-01-12 21:03:36,169 (trainer:737) INFO: 8epoch:train:3801-3900batch: iter_time=1.692e-04, forward_time=0.104, loss_ctc=51.052, loss_att=53.006, acc=0.693, loss=52.420, backward_time=0.097, grad_norm=29.854, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.424e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 21:04:17,070 (trainer:737) INFO: 8epoch:train:3901-4000batch: iter_time=1.938e-04, forward_time=0.104, loss_ctc=55.978, loss_att=71.079, acc=0.661, loss=66.549, backward_time=0.098, grad_norm=29.932, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.421e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:04:57,979 (trainer:737) INFO: 8epoch:train:4001-4100batch: iter_time=1.744e-04, forward_time=0.103, loss_ctc=50.433, loss_att=59.604, acc=0.677, loss=56.852, backward_time=0.096, grad_norm=27.319, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.418e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:05:39,481 (trainer:737) INFO: 8epoch:train:4101-4200batch: iter_time=1.630e-04, forward_time=0.106, loss_ctc=52.612, loss_att=59.190, acc=0.671, loss=57.217, backward_time=0.097, grad_norm=29.339, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.414e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 21:06:20,861 (trainer:737) INFO: 8epoch:train:4201-4300batch: iter_time=2.027e-04, forward_time=0.105, loss_ctc=60.759, loss_att=64.595, acc=0.687, loss=63.444, backward_time=0.098, grad_norm=30.736, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.411e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 21:07:01,543 (trainer:737) INFO: 8epoch:train:4301-4400batch: iter_time=1.787e-04, forward_time=0.104, loss_ctc=47.463, loss_att=54.939, acc=0.674, loss=52.696, backward_time=0.096, grad_norm=26.916, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.407e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:07:42,432 (trainer:737) INFO: 8epoch:train:4401-4500batch: iter_time=2.146e-04, forward_time=0.105, loss_ctc=66.517, loss_att=75.093, acc=0.654, loss=72.521, backward_time=0.098, grad_norm=35.070, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.404e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:08:23,494 (trainer:737) INFO: 8epoch:train:4501-4600batch: iter_time=1.868e-04, forward_time=0.105, loss_ctc=64.017, loss_att=87.381, acc=0.646, loss=80.372, backward_time=0.098, grad_norm=33.405, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.401e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:09:04,608 (trainer:737) INFO: 8epoch:train:4601-4700batch: iter_time=1.927e-04, forward_time=0.104, loss_ctc=61.200, loss_att=70.710, acc=0.667, loss=67.857, backward_time=0.098, grad_norm=31.480, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.397e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 21:09:45,667 (trainer:737) INFO: 8epoch:train:4701-4800batch: iter_time=1.835e-04, forward_time=0.104, loss_ctc=58.831, loss_att=62.298, acc=0.665, loss=61.258, backward_time=0.097, grad_norm=31.758, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.394e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:10:26,399 (trainer:737) INFO: 8epoch:train:4801-4900batch: iter_time=2.041e-04, forward_time=0.103, loss_ctc=56.473, loss_att=54.252, acc=0.680, loss=54.918, backward_time=0.097, grad_norm=31.250, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.390e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:11:07,471 (trainer:737) INFO: 8epoch:train:4901-5000batch: iter_time=1.879e-04, forward_time=0.104, loss_ctc=57.184, loss_att=65.808, acc=0.677, loss=63.221, backward_time=0.098, grad_norm=30.420, clip=100.000, loss_scale=1.181e+21, optim_step_time=0.030, optim0_lr0=7.387e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:11:17,192 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-12 21:11:36,367 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 21:11:40,028 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3893db7c70>)
+[gpuc02:0/16] 2024-01-12 21:11:40,028 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 21:11:40,032 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 21:16:55,514 (trainer:737) INFO: 8epoch:train:5001-5100batch: iter_time=3.035, forward_time=0.104, loss_ctc=57.604, loss_att=68.555, acc=0.670, loss=65.270, backward_time=0.100, grad_norm=33.609, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.384e-04, train_time=3.480
+[gpuc02:0/16] 2024-01-12 21:17:36,320 (trainer:737) INFO: 8epoch:train:5101-5200batch: iter_time=1.885e-04, forward_time=0.103, loss_ctc=46.863, loss_att=50.232, acc=0.721, loss=49.221, backward_time=0.098, grad_norm=25.325, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.380e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 21:18:17,296 (trainer:737) INFO: 8epoch:train:5201-5300batch: iter_time=1.757e-04, forward_time=0.104, loss_ctc=55.769, loss_att=78.076, acc=0.651, loss=71.384, backward_time=0.098, grad_norm=29.245, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.377e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:18:58,075 (trainer:737) INFO: 8epoch:train:5301-5400batch: iter_time=2.002e-04, forward_time=0.103, loss_ctc=50.724, loss_att=59.963, acc=0.676, loss=57.191, backward_time=0.097, grad_norm=26.774, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.031, optim0_lr0=7.374e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 21:19:39,074 (trainer:737) INFO: 8epoch:train:5401-5500batch: iter_time=1.906e-04, forward_time=0.104, loss_ctc=56.872, loss_att=58.753, acc=0.702, loss=58.189, backward_time=0.098, grad_norm=28.156, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.370e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:20:20,412 (trainer:737) INFO: 8epoch:train:5501-5600batch: iter_time=2.263e-04, forward_time=0.103, loss_ctc=48.176, loss_att=56.455, acc=0.699, loss=53.971, backward_time=0.097, grad_norm=26.366, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.367e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 21:21:01,338 (trainer:737) INFO: 8epoch:train:5601-5700batch: iter_time=2.051e-04, forward_time=0.104, loss_ctc=58.007, loss_att=66.762, acc=0.663, loss=64.136, backward_time=0.097, grad_norm=31.975, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.364e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:21:42,455 (trainer:737) INFO: 8epoch:train:5701-5800batch: iter_time=1.819e-04, forward_time=0.105, loss_ctc=70.180, loss_att=81.578, acc=0.668, loss=78.159, backward_time=0.098, grad_norm=35.872, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.360e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 21:22:23,870 (trainer:737) INFO: 8epoch:train:5801-5900batch: iter_time=1.797e-04, forward_time=0.106, loss_ctc=58.409, loss_att=76.637, acc=0.691, loss=71.168, backward_time=0.098, grad_norm=30.545, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.357e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 21:23:05,283 (trainer:737) INFO: 8epoch:train:5901-6000batch: iter_time=2.004e-04, forward_time=0.103, loss_ctc=61.971, loss_att=69.013, acc=0.664, loss=66.901, backward_time=0.097, grad_norm=35.179, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.354e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 21:23:46,645 (trainer:737) INFO: 8epoch:train:6001-6100batch: iter_time=1.872e-04, forward_time=0.104, loss_ctc=53.156, loss_att=55.957, acc=0.697, loss=55.117, backward_time=0.097, grad_norm=28.093, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.350e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 21:24:27,854 (trainer:737) INFO: 8epoch:train:6101-6200batch: iter_time=2.089e-04, forward_time=0.107, loss_ctc=58.639, loss_att=59.068, acc=0.693, loss=58.939, backward_time=0.097, grad_norm=29.788, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.347e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 21:24:56,780 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-12 21:25:16,852 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 21:25:20,704 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38986cac20>)
+[gpuc02:0/16] 2024-01-12 21:25:20,704 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 21:25:20,708 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 21:29:41,650 (trainer:737) INFO: 8epoch:train:6201-6300batch: iter_time=2.726, forward_time=0.105, loss_ctc=55.884, loss_att=74.425, acc=0.649, loss=68.862, backward_time=0.098, grad_norm=33.149, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.031, optim0_lr0=7.344e-04, train_time=3.138
+[gpuc02:0/16] 2024-01-12 21:30:22,381 (trainer:737) INFO: 8epoch:train:6301-6400batch: iter_time=2.014e-04, forward_time=0.103, loss_ctc=50.145, loss_att=52.352, acc=0.696, loss=51.690, backward_time=0.097, grad_norm=28.260, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.341e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:31:04,079 (trainer:737) INFO: 8epoch:train:6401-6500batch: iter_time=1.655e-04, forward_time=0.105, loss_ctc=54.797, loss_att=69.718, acc=0.666, loss=65.241, backward_time=0.098, grad_norm=29.029, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.337e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-12 21:31:44,773 (trainer:737) INFO: 8epoch:train:6501-6600batch: iter_time=1.727e-04, forward_time=0.103, loss_ctc=48.965, loss_att=58.238, acc=0.683, loss=55.456, backward_time=0.097, grad_norm=26.634, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.334e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:32:25,829 (trainer:737) INFO: 8epoch:train:6601-6700batch: iter_time=2.078e-04, forward_time=0.104, loss_ctc=51.818, loss_att=58.551, acc=0.675, loss=56.531, backward_time=0.097, grad_norm=29.049, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.331e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:33:06,816 (trainer:737) INFO: 8epoch:train:6701-6800batch: iter_time=1.717e-04, forward_time=0.105, loss_ctc=58.906, loss_att=63.964, acc=0.690, loss=62.447, backward_time=0.098, grad_norm=31.101, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.327e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:33:47,526 (trainer:737) INFO: 8epoch:train:6801-6900batch: iter_time=1.873e-04, forward_time=0.103, loss_ctc=47.129, loss_att=54.492, acc=0.677, loss=52.283, backward_time=0.097, grad_norm=27.527, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.324e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:34:28,528 (trainer:737) INFO: 8epoch:train:6901-7000batch: iter_time=1.881e-04, forward_time=0.104, loss_ctc=65.446, loss_att=73.741, acc=0.658, loss=71.253, backward_time=0.098, grad_norm=34.256, clip=100.000, loss_scale=2.361e+21, optim_step_time=0.030, optim0_lr0=7.321e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:35:09,573 (trainer:737) INFO: 8epoch:train:7001-7100batch: iter_time=1.816e-04, forward_time=0.105, loss_ctc=63.782, loss_att=87.019, acc=0.649, loss=80.048, backward_time=0.098, grad_norm=36.510, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.318e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:35:50,452 (trainer:737) INFO: 8epoch:train:7101-7200batch: iter_time=1.865e-04, forward_time=0.104, loss_ctc=59.864, loss_att=69.821, acc=0.670, loss=66.834, backward_time=0.098, grad_norm=31.256, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.314e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:36:31,300 (trainer:737) INFO: 8epoch:train:7201-7300batch: iter_time=1.892e-04, forward_time=0.104, loss_ctc=58.203, loss_att=61.625, acc=0.668, loss=60.598, backward_time=0.097, grad_norm=29.720, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.311e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 21:37:12,372 (trainer:737) INFO: 8epoch:train:7301-7400batch: iter_time=2.046e-04, forward_time=0.103, loss_ctc=54.849, loss_att=53.188, acc=0.682, loss=53.686, backward_time=0.097, grad_norm=30.471, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.308e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:37:53,552 (trainer:737) INFO: 8epoch:train:7401-7500batch: iter_time=1.655e-04, forward_time=0.104, loss_ctc=56.675, loss_att=65.035, acc=0.678, loss=62.527, backward_time=0.098, grad_norm=30.387, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.305e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 21:38:01,916 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-12 21:38:21,445 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 21:38:25,128 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fb9dc34f0>)
+[gpuc02:0/16] 2024-01-12 21:38:25,128 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-12 21:38:25,131 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 21:43:08,785 (trainer:737) INFO: 8epoch:train:7501-7600batch: iter_time=2.674, forward_time=0.128, loss_ctc=57.412, loss_att=63.407, acc=0.669, loss=61.608, backward_time=0.104, grad_norm=33.404, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.301e-04, train_time=3.152
+[gpuc02:0/16] 2024-01-12 21:43:49,830 (trainer:737) INFO: 8epoch:train:7601-7700batch: iter_time=1.769e-04, forward_time=0.104, loss_ctc=46.332, loss_att=48.261, acc=0.717, loss=47.682, backward_time=0.097, grad_norm=26.421, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.298e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:44:30,703 (trainer:737) INFO: 8epoch:train:7701-7800batch: iter_time=2.134e-04, forward_time=0.105, loss_ctc=55.660, loss_att=75.389, acc=0.647, loss=69.470, backward_time=0.097, grad_norm=30.350, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.295e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:45:11,334 (trainer:737) INFO: 8epoch:train:7801-7900batch: iter_time=2.162e-04, forward_time=0.103, loss_ctc=50.284, loss_att=57.191, acc=0.676, loss=55.119, backward_time=0.097, grad_norm=28.031, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.292e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 21:45:52,184 (trainer:737) INFO: 8epoch:train:7901-8000batch: iter_time=2.108e-04, forward_time=0.105, loss_ctc=56.435, loss_att=57.187, acc=0.696, loss=56.961, backward_time=0.098, grad_norm=29.751, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.288e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 21:46:33,155 (trainer:737) INFO: 8epoch:train:8001-8100batch: iter_time=2.071e-04, forward_time=0.104, loss_ctc=49.919, loss_att=56.640, acc=0.687, loss=54.624, backward_time=0.097, grad_norm=29.342, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.285e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:47:14,270 (trainer:737) INFO: 8epoch:train:8101-8200batch: iter_time=1.891e-04, forward_time=0.104, loss_ctc=57.410, loss_att=66.234, acc=0.663, loss=63.587, backward_time=0.097, grad_norm=30.263, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.282e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 21:47:55,202 (trainer:737) INFO: 8epoch:train:8201-8300batch: iter_time=2.189e-04, forward_time=0.105, loss_ctc=69.806, loss_att=79.810, acc=0.658, loss=76.809, backward_time=0.098, grad_norm=37.116, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.279e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 21:48:37,220 (trainer:737) INFO: 8epoch:train:8301-8400batch: iter_time=1.881e-04, forward_time=0.106, loss_ctc=58.193, loss_att=75.839, acc=0.678, loss=70.545, backward_time=0.098, grad_norm=31.456, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.275e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-12 21:49:17,963 (trainer:737) INFO: 8epoch:train:8401-8500batch: iter_time=2.181e-04, forward_time=0.105, loss_ctc=61.662, loss_att=67.407, acc=0.658, loss=65.683, backward_time=0.097, grad_norm=34.116, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.029, optim0_lr0=7.272e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:49:58,664 (trainer:737) INFO: 8epoch:train:8501-8600batch: iter_time=1.993e-04, forward_time=0.104, loss_ctc=52.235, loss_att=54.287, acc=0.696, loss=53.672, backward_time=0.097, grad_norm=27.894, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.269e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 21:50:39,734 (trainer:737) INFO: 8epoch:train:8601-8700batch: iter_time=2.242e-04, forward_time=0.105, loss_ctc=58.223, loss_att=59.085, acc=0.686, loss=58.827, backward_time=0.097, grad_norm=30.464, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.266e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:51:07,217 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-12 21:51:26,505 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 21:51:30,203 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38986e3430>)
+[gpuc02:0/16] 2024-01-12 21:51:30,204 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 21:51:30,207 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 21:56:16,246 (trainer:737) INFO: 8epoch:train:8701-8800batch: iter_time=2.630, forward_time=0.105, loss_ctc=55.193, loss_att=74.435, acc=0.654, loss=68.663, backward_time=0.098, grad_norm=34.067, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.263e-04, train_time=3.365
+[gpuc02:0/16] 2024-01-12 21:56:57,111 (trainer:737) INFO: 8epoch:train:8801-8900batch: iter_time=1.966e-04, forward_time=0.104, loss_ctc=49.597, loss_att=52.487, acc=0.710, loss=51.620, backward_time=0.097, grad_norm=29.435, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.259e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 21:57:38,111 (trainer:737) INFO: 8epoch:train:8901-9000batch: iter_time=1.835e-04, forward_time=0.104, loss_ctc=54.160, loss_att=70.814, acc=0.672, loss=65.818, backward_time=0.098, grad_norm=28.584, clip=100.000, loss_scale=4.722e+21, optim_step_time=0.030, optim0_lr0=7.256e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:58:19,089 (trainer:737) INFO: 8epoch:train:9001-9100batch: iter_time=2.048e-04, forward_time=0.102, loss_ctc=48.732, loss_att=58.869, acc=0.687, loss=55.828, backward_time=0.097, grad_norm=26.362, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.253e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 21:59:00,220 (trainer:737) INFO: 8epoch:train:9101-9200batch: iter_time=2.005e-04, forward_time=0.104, loss_ctc=50.553, loss_att=60.767, acc=0.681, loss=57.703, backward_time=0.097, grad_norm=28.373, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.250e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 21:59:41,820 (trainer:737) INFO: 8epoch:train:9201-9300batch: iter_time=1.618e-04, forward_time=0.105, loss_ctc=57.865, loss_att=63.305, acc=0.704, loss=61.673, backward_time=0.098, grad_norm=30.427, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.247e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 22:00:22,976 (trainer:737) INFO: 8epoch:train:9301-9400batch: iter_time=1.834e-04, forward_time=0.104, loss_ctc=45.996, loss_att=53.975, acc=0.686, loss=51.581, backward_time=0.097, grad_norm=25.494, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.244e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:01:04,159 (trainer:737) INFO: 8epoch:train:9401-9500batch: iter_time=1.804e-04, forward_time=0.105, loss_ctc=63.796, loss_att=73.953, acc=0.670, loss=70.906, backward_time=0.099, grad_norm=33.110, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.240e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:01:45,685 (trainer:737) INFO: 8epoch:train:9501-9600batch: iter_time=1.704e-04, forward_time=0.106, loss_ctc=62.281, loss_att=85.246, acc=0.674, loss=78.357, backward_time=0.099, grad_norm=34.490, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.237e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-12 22:02:26,762 (trainer:737) INFO: 8epoch:train:9601-9700batch: iter_time=1.908e-04, forward_time=0.105, loss_ctc=59.400, loss_att=70.316, acc=0.679, loss=67.042, backward_time=0.098, grad_norm=32.879, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.234e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:03:08,016 (trainer:737) INFO: 8epoch:train:9701-9800batch: iter_time=1.754e-04, forward_time=0.104, loss_ctc=59.530, loss_att=61.577, acc=0.680, loss=60.963, backward_time=0.097, grad_norm=37.124, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.231e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:03:48,943 (trainer:737) INFO: 8epoch:train:9801-9900batch: iter_time=1.927e-04, forward_time=0.104, loss_ctc=54.620, loss_att=53.695, acc=0.688, loss=53.973, backward_time=0.098, grad_norm=29.643, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.228e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:04:30,215 (trainer:737) INFO: 8epoch:train:9901-10000batch: iter_time=1.755e-04, forward_time=0.107, loss_ctc=56.137, loss_att=64.689, acc=0.693, loss=62.123, backward_time=0.098, grad_norm=95.614, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.225e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:04:36,287 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-12 22:04:56,151 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 22:04:59,997 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38cfd93b80>)
+[gpuc02:0/16] 2024-01-12 22:04:59,997 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-12 22:05:00,000 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 22:09:59,335 (trainer:737) INFO: 8epoch:train:10001-10100batch: iter_time=2.623, forward_time=0.106, loss_ctc=56.859, loss_att=64.430, acc=0.682, loss=62.159, backward_time=0.099, grad_norm=33.005, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.222e-04, train_time=3.291
+[gpuc02:0/16] 2024-01-12 22:10:40,208 (trainer:737) INFO: 8epoch:train:10101-10200batch: iter_time=1.768e-04, forward_time=0.105, loss_ctc=46.010, loss_att=48.751, acc=0.725, loss=47.929, backward_time=0.098, grad_norm=26.469, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.218e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:11:21,174 (trainer:737) INFO: 8epoch:train:10201-10300batch: iter_time=1.750e-04, forward_time=0.105, loss_ctc=55.024, loss_att=75.988, acc=0.657, loss=69.699, backward_time=0.098, grad_norm=30.906, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.215e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:12:01,849 (trainer:737) INFO: 8epoch:train:10301-10400batch: iter_time=2.037e-04, forward_time=0.104, loss_ctc=50.146, loss_att=59.863, acc=0.678, loss=56.948, backward_time=0.097, grad_norm=27.565, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.212e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:12:42,781 (trainer:737) INFO: 8epoch:train:10401-10500batch: iter_time=1.872e-04, forward_time=0.105, loss_ctc=55.319, loss_att=57.172, acc=0.706, loss=56.616, backward_time=0.098, grad_norm=28.418, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.209e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:13:23,635 (trainer:737) INFO: 8epoch:train:10501-10600batch: iter_time=2.234e-04, forward_time=0.104, loss_ctc=48.159, loss_att=55.927, acc=0.705, loss=53.597, backward_time=0.097, grad_norm=28.518, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.206e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:14:04,648 (trainer:737) INFO: 8epoch:train:10601-10700batch: iter_time=2.071e-04, forward_time=0.105, loss_ctc=56.482, loss_att=66.365, acc=0.668, loss=63.400, backward_time=0.097, grad_norm=30.833, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.203e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:14:45,792 (trainer:737) INFO: 8epoch:train:10701-10800batch: iter_time=2.175e-04, forward_time=0.106, loss_ctc=68.666, loss_att=79.755, acc=0.673, loss=76.429, backward_time=0.098, grad_norm=35.100, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.200e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:15:27,196 (trainer:737) INFO: 8epoch:train:10801-10900batch: iter_time=2.160e-04, forward_time=0.106, loss_ctc=56.919, loss_att=74.894, acc=0.699, loss=69.501, backward_time=0.098, grad_norm=30.070, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.197e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 22:16:08,356 (trainer:737) INFO: 8epoch:train:10901-11000batch: iter_time=1.961e-04, forward_time=0.104, loss_ctc=61.508, loss_att=67.861, acc=0.671, loss=65.955, backward_time=0.097, grad_norm=34.519, clip=100.000, loss_scale=9.445e+21, optim_step_time=0.030, optim0_lr0=7.193e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:16:49,226 (trainer:737) INFO: 8epoch:train:11001-11100batch: iter_time=2.211e-04, forward_time=0.104, loss_ctc=52.309, loss_att=54.772, acc=0.704, loss=54.033, backward_time=0.097, grad_norm=27.899, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.190e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:17:30,408 (trainer:737) INFO: 8epoch:train:11101-11200batch: iter_time=2.250e-04, forward_time=0.105, loss_ctc=57.011, loss_att=58.211, acc=0.696, loss=57.851, backward_time=0.098, grad_norm=29.959, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.187e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:17:57,737 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-12 22:18:17,243 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 22:18:20,862 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3896a3ba00>)
+[gpuc02:0/16] 2024-01-12 22:18:20,862 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-12 22:18:20,865 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 22:22:43,360 (trainer:737) INFO: 8epoch:train:11201-11300batch: iter_time=2.719, forward_time=0.105, loss_ctc=54.252, loss_att=73.493, acc=0.655, loss=67.721, backward_time=0.098, grad_norm=33.194, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.184e-04, train_time=3.129
+[gpuc02:0/16] 2024-01-12 22:23:24,146 (trainer:737) INFO: 8epoch:train:11301-11400batch: iter_time=1.791e-04, forward_time=0.104, loss_ctc=48.951, loss_att=51.341, acc=0.704, loss=50.624, backward_time=0.097, grad_norm=29.152, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.181e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:24:05,320 (trainer:737) INFO: 8epoch:train:11401-11500batch: iter_time=1.714e-04, forward_time=0.107, loss_ctc=53.465, loss_att=69.313, acc=0.669, loss=64.558, backward_time=0.097, grad_norm=30.014, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.178e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:24:46,043 (trainer:737) INFO: 8epoch:train:11501-11600batch: iter_time=1.933e-04, forward_time=0.104, loss_ctc=48.446, loss_att=58.065, acc=0.685, loss=55.179, backward_time=0.096, grad_norm=36.040, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.175e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:25:27,347 (trainer:737) INFO: 8epoch:train:11601-11700batch: iter_time=1.821e-04, forward_time=0.104, loss_ctc=50.540, loss_att=57.489, acc=0.679, loss=55.405, backward_time=0.097, grad_norm=29.162, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.172e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 22:26:08,268 (trainer:737) INFO: 8epoch:train:11701-11800batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=60.107, loss_att=62.428, acc=0.696, loss=61.731, backward_time=0.098, grad_norm=31.288, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.169e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:26:48,949 (trainer:737) INFO: 8epoch:train:11801-11900batch: iter_time=1.749e-04, forward_time=0.104, loss_ctc=46.147, loss_att=54.466, acc=0.678, loss=51.970, backward_time=0.096, grad_norm=27.655, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.166e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:27:30,553 (trainer:737) INFO: 8epoch:train:11901-12000batch: iter_time=1.690e-04, forward_time=0.105, loss_ctc=63.710, loss_att=72.564, acc=0.663, loss=69.908, backward_time=0.098, grad_norm=33.613, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.163e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-12 22:28:11,936 (trainer:737) INFO: 8epoch:train:12001-12100batch: iter_time=1.792e-04, forward_time=0.106, loss_ctc=62.119, loss_att=85.187, acc=0.654, loss=78.267, backward_time=0.098, grad_norm=34.169, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.160e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 22:28:52,826 (trainer:737) INFO: 8epoch:train:12101-12200batch: iter_time=1.935e-04, forward_time=0.104, loss_ctc=59.341, loss_att=68.641, acc=0.676, loss=65.851, backward_time=0.097, grad_norm=32.004, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.157e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:29:33,571 (trainer:737) INFO: 8epoch:train:12201-12300batch: iter_time=1.674e-04, forward_time=0.104, loss_ctc=57.217, loss_att=59.976, acc=0.673, loss=59.148, backward_time=0.097, grad_norm=31.448, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.153e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:30:14,575 (trainer:737) INFO: 8epoch:train:12301-12400batch: iter_time=1.787e-04, forward_time=0.104, loss_ctc=54.906, loss_att=53.027, acc=0.687, loss=53.590, backward_time=0.097, grad_norm=32.205, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.150e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:30:55,441 (trainer:737) INFO: 8epoch:train:12401-12500batch: iter_time=1.596e-04, forward_time=0.104, loss_ctc=55.667, loss_att=64.079, acc=0.683, loss=61.555, backward_time=0.097, grad_norm=32.512, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.147e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:31:01,713 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-12 22:31:21,190 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 22:31:24,970 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c67d0040>)
+[gpuc02:0/16] 2024-01-12 22:31:24,971 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-12 22:31:24,974 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 22:36:16,694 (trainer:737) INFO: 8epoch:train:12501-12600batch: iter_time=2.799, forward_time=0.106, loss_ctc=55.577, loss_att=62.962, acc=0.669, loss=60.747, backward_time=0.099, grad_norm=32.787, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.144e-04, train_time=3.212
+[gpuc02:0/16] 2024-01-12 22:36:57,778 (trainer:737) INFO: 8epoch:train:12601-12700batch: iter_time=2.035e-04, forward_time=0.104, loss_ctc=45.438, loss_att=47.504, acc=0.720, loss=46.884, backward_time=0.097, grad_norm=25.577, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.141e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:37:38,715 (trainer:737) INFO: 8epoch:train:12701-12800batch: iter_time=2.329e-04, forward_time=0.104, loss_ctc=54.682, loss_att=74.906, acc=0.650, loss=68.839, backward_time=0.098, grad_norm=29.958, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.138e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:38:19,664 (trainer:737) INFO: 8epoch:train:12801-12900batch: iter_time=2.698e-04, forward_time=0.103, loss_ctc=49.944, loss_att=56.786, acc=0.680, loss=54.733, backward_time=0.097, grad_norm=27.696, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.135e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:39:00,848 (trainer:737) INFO: 8epoch:train:12901-13000batch: iter_time=2.475e-04, forward_time=0.104, loss_ctc=55.470, loss_att=56.140, acc=0.701, loss=55.939, backward_time=0.098, grad_norm=28.501, clip=100.000, loss_scale=1.889e+22, optim_step_time=0.030, optim0_lr0=7.132e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:39:41,524 (trainer:737) INFO: 8epoch:train:13001-13100batch: iter_time=2.169e-04, forward_time=0.103, loss_ctc=47.571, loss_att=55.676, acc=0.692, loss=53.244, backward_time=0.097, grad_norm=27.447, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.129e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:40:22,689 (trainer:737) INFO: 8epoch:train:13101-13200batch: iter_time=2.408e-04, forward_time=0.104, loss_ctc=55.673, loss_att=65.216, acc=0.667, loss=62.353, backward_time=0.098, grad_norm=30.303, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.126e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:41:03,683 (trainer:737) INFO: 8epoch:train:13201-13300batch: iter_time=2.373e-04, forward_time=0.105, loss_ctc=67.924, loss_att=79.171, acc=0.662, loss=75.797, backward_time=0.098, grad_norm=37.065, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.123e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:41:44,931 (trainer:737) INFO: 8epoch:train:13301-13400batch: iter_time=2.199e-04, forward_time=0.104, loss_ctc=56.395, loss_att=73.963, acc=0.684, loss=68.692, backward_time=0.098, grad_norm=30.182, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.120e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:42:25,696 (trainer:737) INFO: 8epoch:train:13401-13500batch: iter_time=1.862e-04, forward_time=0.103, loss_ctc=61.927, loss_att=66.689, acc=0.661, loss=65.260, backward_time=0.097, grad_norm=38.179, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.117e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:43:06,821 (trainer:737) INFO: 8epoch:train:13501-13600batch: iter_time=2.421e-04, forward_time=0.104, loss_ctc=51.136, loss_att=53.986, acc=0.699, loss=53.131, backward_time=0.097, grad_norm=27.839, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.114e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:43:47,624 (trainer:737) INFO: 8epoch:train:13601-13700batch: iter_time=2.434e-04, forward_time=0.105, loss_ctc=56.910, loss_att=58.352, acc=0.690, loss=57.919, backward_time=0.098, grad_norm=31.404, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.111e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:44:15,135 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-12 22:44:34,621 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 22:44:38,296 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c847fa30>)
+[gpuc02:0/16] 2024-01-12 22:44:38,296 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-12 22:44:38,300 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 22:49:06,151 (trainer:737) INFO: 8epoch:train:13701-13800batch: iter_time=2.651, forward_time=0.105, loss_ctc=54.189, loss_att=73.486, acc=0.658, loss=67.697, backward_time=0.098, grad_norm=32.799, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.108e-04, train_time=3.185
+[gpuc02:0/16] 2024-01-12 22:49:47,083 (trainer:737) INFO: 8epoch:train:13801-13900batch: iter_time=1.857e-04, forward_time=0.104, loss_ctc=48.374, loss_att=51.373, acc=0.712, loss=50.473, backward_time=0.097, grad_norm=28.554, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.105e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 22:50:28,176 (trainer:737) INFO: 8epoch:train:13901-14000batch: iter_time=1.867e-04, forward_time=0.104, loss_ctc=53.233, loss_att=69.344, acc=0.677, loss=64.511, backward_time=0.098, grad_norm=29.533, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.102e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:51:08,964 (trainer:737) INFO: 8epoch:train:14001-14100batch: iter_time=2.164e-04, forward_time=0.104, loss_ctc=47.205, loss_att=57.378, acc=0.694, loss=54.326, backward_time=0.096, grad_norm=26.902, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.099e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:51:49,994 (trainer:737) INFO: 8epoch:train:14101-14200batch: iter_time=2.057e-04, forward_time=0.104, loss_ctc=50.037, loss_att=60.389, acc=0.680, loss=57.283, backward_time=0.097, grad_norm=28.506, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.096e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:52:30,978 (trainer:737) INFO: 8epoch:train:14201-14300batch: iter_time=1.951e-04, forward_time=0.104, loss_ctc=57.898, loss_att=63.170, acc=0.707, loss=61.589, backward_time=0.097, grad_norm=29.834, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.093e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:53:11,723 (trainer:737) INFO: 8epoch:train:14301-14400batch: iter_time=1.948e-04, forward_time=0.103, loss_ctc=45.529, loss_att=53.909, acc=0.691, loss=51.395, backward_time=0.096, grad_norm=26.540, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.090e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 22:53:52,777 (trainer:737) INFO: 8epoch:train:14401-14500batch: iter_time=1.994e-04, forward_time=0.105, loss_ctc=63.213, loss_att=73.242, acc=0.674, loss=70.233, backward_time=0.097, grad_norm=34.757, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.087e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:54:33,974 (trainer:737) INFO: 8epoch:train:14501-14600batch: iter_time=1.972e-04, forward_time=0.106, loss_ctc=62.079, loss_att=85.603, acc=0.677, loss=78.545, backward_time=0.099, grad_norm=39.923, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.084e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 22:55:14,950 (trainer:737) INFO: 8epoch:train:14601-14700batch: iter_time=1.901e-04, forward_time=0.104, loss_ctc=57.919, loss_att=68.489, acc=0.688, loss=65.318, backward_time=0.098, grad_norm=30.470, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.081e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 22:55:55,759 (trainer:737) INFO: 8epoch:train:14701-14800batch: iter_time=2.063e-04, forward_time=0.103, loss_ctc=56.866, loss_att=59.925, acc=0.686, loss=59.007, backward_time=0.096, grad_norm=34.224, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.078e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 22:56:36,834 (trainer:737) INFO: 8epoch:train:14801-14900batch: iter_time=2.016e-04, forward_time=0.103, loss_ctc=53.531, loss_att=52.895, acc=0.694, loss=53.086, backward_time=0.096, grad_norm=29.037, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.075e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 22:57:18,050 (trainer:737) INFO: 8epoch:train:14901-15000batch: iter_time=1.835e-04, forward_time=0.104, loss_ctc=54.828, loss_att=63.560, acc=0.697, loss=60.940, backward_time=0.097, grad_norm=29.323, clip=100.000, loss_scale=3.778e+22, optim_step_time=0.030, optim0_lr0=7.072e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 23:17:26,401 (trainer:343) INFO: 8epoch results: [train] iter_time=0.223, forward_time=0.105, loss_ctc=56.276, loss_att=64.842, acc=0.678, loss=62.272, backward_time=0.098, grad_norm=31.418, clip=99.987, loss_scale=1.002e+22, optim_step_time=0.030, optim0_lr0=7.307e-04, train_time=0.641, time=2 hours, 40 minutes and 35.52 seconds, total_count=120000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=68.041, cer_ctc=0.347, loss_att=64.001, acc=0.523, cer=0.410, wer=1.000, loss=65.213, time=19 minutes and 57.62 seconds, total_count=37368, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-12 23:17:31,149 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-12 23:17:31,155 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/3epoch.pth
+[gpuc02:0/16] 2024-01-12 23:17:31,155 (trainer:272) INFO: 9/45epoch started. Estimated time to finish: 4 days, 14 hours and 11 minutes
+[gpuc02:0/16] 2024-01-12 23:17:31,166 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-12 23:17:50,157 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 23:17:53,686 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f439d51b670>)
+[gpuc02:0/16] 2024-01-12 23:17:53,686 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-12 23:17:53,689 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 23:22:26,791 (trainer:737) INFO: 9epoch:train:1-100batch: iter_time=2.533, forward_time=0.116, loss_ctc=53.825, loss_att=57.739, acc=0.679, loss=56.565, backward_time=0.099, grad_norm=32.796, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.031, optim0_lr0=7.070e-04, train_time=2.956
+[gpuc02:0/16] 2024-01-12 23:23:07,648 (trainer:737) INFO: 9epoch:train:101-200batch: iter_time=1.320e-04, forward_time=0.103, loss_ctc=53.887, loss_att=56.934, acc=0.668, loss=56.020, backward_time=0.097, grad_norm=33.153, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.067e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 23:23:48,421 (trainer:737) INFO: 9epoch:train:201-300batch: iter_time=1.243e-04, forward_time=0.102, loss_ctc=50.487, loss_att=56.667, acc=0.654, loss=54.813, backward_time=0.096, grad_norm=32.581, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.064e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 23:24:29,206 (trainer:737) INFO: 9epoch:train:301-400batch: iter_time=1.278e-04, forward_time=0.104, loss_ctc=57.676, loss_att=65.512, acc=0.670, loss=63.161, backward_time=0.097, grad_norm=34.227, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.061e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 23:25:10,084 (trainer:737) INFO: 9epoch:train:401-500batch: iter_time=1.368e-04, forward_time=0.103, loss_ctc=50.520, loss_att=51.054, acc=0.704, loss=50.894, backward_time=0.097, grad_norm=31.583, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.058e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:25:51,286 (trainer:737) INFO: 9epoch:train:501-600batch: iter_time=1.479e-04, forward_time=0.104, loss_ctc=58.901, loss_att=62.637, acc=0.671, loss=61.516, backward_time=0.097, grad_norm=32.668, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.055e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 23:26:33,354 (trainer:737) INFO: 9epoch:train:601-700batch: iter_time=1.359e-04, forward_time=0.103, loss_ctc=53.718, loss_att=60.116, acc=0.651, loss=58.196, backward_time=0.097, grad_norm=33.406, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.052e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-12 23:27:14,093 (trainer:737) INFO: 9epoch:train:701-800batch: iter_time=1.244e-04, forward_time=0.104, loss_ctc=65.433, loss_att=59.089, acc=0.673, loss=60.992, backward_time=0.097, grad_norm=36.950, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.049e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 23:27:54,712 (trainer:737) INFO: 9epoch:train:801-900batch: iter_time=1.444e-04, forward_time=0.103, loss_ctc=52.131, loss_att=56.575, acc=0.675, loss=55.242, backward_time=0.097, grad_norm=32.747, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.046e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 23:28:35,346 (trainer:737) INFO: 9epoch:train:901-1000batch: iter_time=1.202e-04, forward_time=0.103, loss_ctc=57.279, loss_att=62.892, acc=0.659, loss=61.208, backward_time=0.097, grad_norm=35.204, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.043e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 23:29:15,992 (trainer:737) INFO: 9epoch:train:1001-1100batch: iter_time=1.318e-04, forward_time=0.104, loss_ctc=60.537, loss_att=55.067, acc=0.671, loss=56.708, backward_time=0.097, grad_norm=36.324, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.040e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 23:29:57,117 (trainer:737) INFO: 9epoch:train:1101-1200batch: iter_time=1.444e-04, forward_time=0.104, loss_ctc=61.209, loss_att=71.148, acc=0.660, loss=68.166, backward_time=0.099, grad_norm=34.513, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.037e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 23:30:24,977 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-12 23:30:43,772 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 23:30:47,484 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c40b3ee0>)
+[gpuc02:0/16] 2024-01-12 23:30:47,484 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-12 23:30:47,487 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 23:35:23,551 (trainer:737) INFO: 9epoch:train:1201-1300batch: iter_time=2.853, forward_time=0.105, loss_ctc=48.239, loss_att=63.188, acc=0.656, loss=58.703, backward_time=0.097, grad_norm=29.270, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.034e-04, train_time=3.264
+[gpuc02:0/16] 2024-01-12 23:36:04,476 (trainer:737) INFO: 9epoch:train:1301-1400batch: iter_time=2.142e-04, forward_time=0.105, loss_ctc=59.305, loss_att=61.629, acc=0.667, loss=60.932, backward_time=0.098, grad_norm=34.775, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.032e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:36:45,256 (trainer:737) INFO: 9epoch:train:1401-1500batch: iter_time=2.150e-04, forward_time=0.102, loss_ctc=45.564, loss_att=48.374, acc=0.678, loss=47.531, backward_time=0.096, grad_norm=27.615, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.029e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 23:37:26,371 (trainer:737) INFO: 9epoch:train:1501-1600batch: iter_time=1.627e-04, forward_time=0.104, loss_ctc=57.049, loss_att=62.315, acc=0.671, loss=60.735, backward_time=0.099, grad_norm=32.981, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.031, optim0_lr0=7.026e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 23:38:07,091 (trainer:737) INFO: 9epoch:train:1601-1700batch: iter_time=1.966e-04, forward_time=0.103, loss_ctc=51.459, loss_att=50.969, acc=0.703, loss=51.116, backward_time=0.096, grad_norm=31.202, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.023e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-12 23:38:48,117 (trainer:737) INFO: 9epoch:train:1701-1800batch: iter_time=2.050e-04, forward_time=0.103, loss_ctc=57.906, loss_att=59.498, acc=0.685, loss=59.020, backward_time=0.097, grad_norm=32.331, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.030, optim0_lr0=7.020e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 23:39:29,209 (trainer:737) INFO: 9epoch:train:1801-1900batch: iter_time=1.669e-04, forward_time=0.106, loss_ctc=51.206, loss_att=62.294, acc=0.663, loss=58.967, backward_time=0.097, grad_norm=30.293, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.017e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 23:40:10,415 (trainer:737) INFO: 9epoch:train:1901-2000batch: iter_time=2.003e-04, forward_time=0.103, loss_ctc=58.777, loss_att=60.924, acc=0.653, loss=60.280, backward_time=0.096, grad_norm=34.657, clip=100.000, loss_scale=7.556e+22, optim_step_time=0.029, optim0_lr0=7.014e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 23:40:51,306 (trainer:737) INFO: 9epoch:train:2001-2100batch: iter_time=1.881e-04, forward_time=0.103, loss_ctc=58.402, loss_att=48.233, acc=0.700, loss=51.284, backward_time=0.097, grad_norm=31.653, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.029, optim0_lr0=7.011e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:41:31,948 (trainer:737) INFO: 9epoch:train:2101-2200batch: iter_time=2.080e-04, forward_time=0.102, loss_ctc=55.356, loss_att=61.455, acc=0.668, loss=59.625, backward_time=0.096, grad_norm=32.551, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.029, optim0_lr0=7.009e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-12 23:42:13,019 (trainer:737) INFO: 9epoch:train:2201-2300batch: iter_time=2.182e-04, forward_time=0.103, loss_ctc=57.844, loss_att=63.446, acc=0.641, loss=61.765, backward_time=0.097, grad_norm=34.988, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=7.006e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 23:42:53,925 (trainer:737) INFO: 9epoch:train:2301-2400batch: iter_time=1.764e-04, forward_time=0.104, loss_ctc=52.307, loss_att=54.905, acc=0.705, loss=54.126, backward_time=0.097, grad_norm=29.474, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.029, optim0_lr0=7.003e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:43:34,803 (trainer:737) INFO: 9epoch:train:2401-2500batch: iter_time=1.741e-04, forward_time=0.104, loss_ctc=60.606, loss_att=75.665, acc=0.635, loss=71.147, backward_time=0.098, grad_norm=33.549, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=7.000e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:43:42,536 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-12 23:44:02,177 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 23:44:05,982 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e5343820>)
+[gpuc02:0/16] 2024-01-12 23:44:05,982 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-12 23:44:05,985 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-12 23:48:54,738 (trainer:737) INFO: 9epoch:train:2501-2600batch: iter_time=2.633, forward_time=0.106, loss_ctc=52.144, loss_att=57.079, acc=0.698, loss=55.599, backward_time=0.098, grad_norm=30.655, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.997e-04, train_time=3.199
+[gpuc02:0/16] 2024-01-12 23:49:36,850 (trainer:737) INFO: 9epoch:train:2601-2700batch: iter_time=0.001, forward_time=0.111, loss_ctc=50.867, loss_att=56.311, acc=0.672, loss=54.678, backward_time=0.099, grad_norm=30.705, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.031, optim0_lr0=6.994e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-12 23:50:19,075 (trainer:737) INFO: 9epoch:train:2701-2800batch: iter_time=1.638e-04, forward_time=0.115, loss_ctc=48.765, loss_att=57.706, acc=0.662, loss=55.024, backward_time=0.100, grad_norm=29.264, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.032, optim0_lr0=6.991e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-12 23:51:00,185 (trainer:737) INFO: 9epoch:train:2801-2900batch: iter_time=1.965e-04, forward_time=0.104, loss_ctc=55.530, loss_att=64.576, acc=0.680, loss=61.862, backward_time=0.098, grad_norm=32.406, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.989e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-12 23:51:41,369 (trainer:737) INFO: 9epoch:train:2901-3000batch: iter_time=1.716e-04, forward_time=0.103, loss_ctc=49.008, loss_att=53.503, acc=0.706, loss=52.155, backward_time=0.098, grad_norm=28.327, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.986e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-12 23:52:22,721 (trainer:737) INFO: 9epoch:train:3001-3100batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=56.851, loss_att=63.763, acc=0.678, loss=61.689, backward_time=0.098, grad_norm=31.533, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.983e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-12 23:53:03,734 (trainer:737) INFO: 9epoch:train:3101-3200batch: iter_time=1.588e-04, forward_time=0.104, loss_ctc=51.579, loss_att=61.752, acc=0.664, loss=58.700, backward_time=0.098, grad_norm=30.481, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.980e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 23:53:44,689 (trainer:737) INFO: 9epoch:train:3201-3300batch: iter_time=1.681e-04, forward_time=0.105, loss_ctc=63.077, loss_att=58.867, acc=0.691, loss=60.130, backward_time=0.098, grad_norm=34.218, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.977e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-12 23:54:25,715 (trainer:737) INFO: 9epoch:train:3301-3400batch: iter_time=1.415e-04, forward_time=0.105, loss_ctc=50.228, loss_att=55.275, acc=0.688, loss=53.761, backward_time=0.099, grad_norm=31.666, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.031, optim0_lr0=6.974e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 23:55:06,568 (trainer:737) INFO: 9epoch:train:3401-3500batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=54.660, loss_att=60.225, acc=0.677, loss=58.556, backward_time=0.099, grad_norm=32.043, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.031, optim0_lr0=6.972e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-12 23:55:47,572 (trainer:737) INFO: 9epoch:train:3501-3600batch: iter_time=1.788e-04, forward_time=0.103, loss_ctc=57.270, loss_att=54.330, acc=0.685, loss=55.212, backward_time=0.096, grad_norm=32.313, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.969e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-12 23:56:29,048 (trainer:737) INFO: 9epoch:train:3601-3700batch: iter_time=1.596e-04, forward_time=0.105, loss_ctc=58.940, loss_att=69.777, acc=0.677, loss=66.526, backward_time=0.098, grad_norm=33.841, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.966e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-12 23:56:58,012 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-12 23:57:16,896 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-12 23:57:20,546 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fd4182260>)
+[gpuc02:0/16] 2024-01-12 23:57:20,546 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-12 23:57:20,549 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 00:02:30,217 (trainer:737) INFO: 9epoch:train:3701-3800batch: iter_time=3.159, forward_time=0.134, loss_ctc=47.035, loss_att=64.241, acc=0.669, loss=59.079, backward_time=0.101, grad_norm=27.629, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.031, optim0_lr0=6.963e-04, train_time=3.612
+[gpuc02:0/16] 2024-01-13 00:03:11,244 (trainer:737) INFO: 9epoch:train:3801-3900batch: iter_time=1.939e-04, forward_time=0.104, loss_ctc=57.984, loss_att=61.877, acc=0.681, loss=60.709, backward_time=0.097, grad_norm=35.567, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.960e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:03:51,912 (trainer:737) INFO: 9epoch:train:3901-4000batch: iter_time=1.802e-04, forward_time=0.103, loss_ctc=44.719, loss_att=47.510, acc=0.692, loss=46.673, backward_time=0.096, grad_norm=26.746, clip=100.000, loss_scale=1.511e+23, optim_step_time=0.030, optim0_lr0=6.957e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 00:04:32,820 (trainer:737) INFO: 9epoch:train:4001-4100batch: iter_time=1.695e-04, forward_time=0.104, loss_ctc=55.908, loss_att=64.007, acc=0.675, loss=61.577, backward_time=0.097, grad_norm=34.271, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.955e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:05:14,103 (trainer:737) INFO: 9epoch:train:4101-4200batch: iter_time=2.160e-04, forward_time=0.103, loss_ctc=49.954, loss_att=51.376, acc=0.704, loss=50.950, backward_time=0.096, grad_norm=29.969, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.952e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 00:05:55,502 (trainer:737) INFO: 9epoch:train:4201-4300batch: iter_time=2.020e-04, forward_time=0.104, loss_ctc=56.582, loss_att=62.496, acc=0.685, loss=60.722, backward_time=0.097, grad_norm=31.174, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.949e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 00:06:36,588 (trainer:737) INFO: 9epoch:train:4301-4400batch: iter_time=1.937e-04, forward_time=0.104, loss_ctc=49.945, loss_att=62.618, acc=0.679, loss=58.816, backward_time=0.097, grad_norm=30.170, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.946e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:07:17,386 (trainer:737) INFO: 9epoch:train:4401-4500batch: iter_time=2.013e-04, forward_time=0.104, loss_ctc=57.113, loss_att=62.449, acc=0.668, loss=60.848, backward_time=0.097, grad_norm=32.003, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.943e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 00:07:58,441 (trainer:737) INFO: 9epoch:train:4501-4600batch: iter_time=2.040e-04, forward_time=0.104, loss_ctc=56.899, loss_att=50.175, acc=0.705, loss=52.192, backward_time=0.097, grad_norm=32.093, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.941e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:08:39,642 (trainer:737) INFO: 9epoch:train:4601-4700batch: iter_time=1.914e-04, forward_time=0.103, loss_ctc=53.728, loss_att=59.735, acc=0.682, loss=57.933, backward_time=0.096, grad_norm=33.388, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.938e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 00:09:20,683 (trainer:737) INFO: 9epoch:train:4701-4800batch: iter_time=1.675e-04, forward_time=0.104, loss_ctc=55.810, loss_att=62.845, acc=0.655, loss=60.734, backward_time=0.097, grad_norm=33.286, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.935e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:10:01,922 (trainer:737) INFO: 9epoch:train:4801-4900batch: iter_time=1.884e-04, forward_time=0.104, loss_ctc=51.050, loss_att=53.875, acc=0.722, loss=53.027, backward_time=0.097, grad_norm=29.071, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.932e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 00:10:42,852 (trainer:737) INFO: 9epoch:train:4901-5000batch: iter_time=1.786e-04, forward_time=0.104, loss_ctc=60.162, loss_att=78.238, acc=0.647, loss=72.815, backward_time=0.098, grad_norm=36.061, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.029, optim0_lr0=6.930e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:10:52,725 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 00:11:12,067 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 00:11:15,714 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3896fa6680>)
+[gpuc02:0/16] 2024-01-13 00:11:15,714 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 00:11:15,717 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 00:16:12,526 (trainer:737) INFO: 9epoch:train:5001-5100batch: iter_time=2.831, forward_time=0.121, loss_ctc=50.502, loss_att=55.927, acc=0.688, loss=54.300, backward_time=0.101, grad_norm=29.699, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.031, optim0_lr0=6.927e-04, train_time=3.296
+[gpuc02:0/16] 2024-01-13 00:16:53,293 (trainer:737) INFO: 9epoch:train:5101-5200batch: iter_time=1.950e-04, forward_time=0.103, loss_ctc=50.177, loss_att=54.369, acc=0.675, loss=53.112, backward_time=0.096, grad_norm=30.366, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.924e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:17:33,954 (trainer:737) INFO: 9epoch:train:5201-5300batch: iter_time=1.832e-04, forward_time=0.103, loss_ctc=47.532, loss_att=55.391, acc=0.658, loss=53.033, backward_time=0.096, grad_norm=30.189, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.921e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 00:18:14,721 (trainer:737) INFO: 9epoch:train:5301-5400batch: iter_time=1.725e-04, forward_time=0.104, loss_ctc=54.119, loss_att=62.638, acc=0.683, loss=60.082, backward_time=0.097, grad_norm=30.521, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.918e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:18:55,657 (trainer:737) INFO: 9epoch:train:5401-5500batch: iter_time=1.992e-04, forward_time=0.104, loss_ctc=48.356, loss_att=49.758, acc=0.715, loss=49.338, backward_time=0.097, grad_norm=28.663, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.916e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:19:36,482 (trainer:737) INFO: 9epoch:train:5501-5600batch: iter_time=2.037e-04, forward_time=0.104, loss_ctc=55.496, loss_att=60.203, acc=0.683, loss=58.791, backward_time=0.097, grad_norm=29.744, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.913e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 00:20:17,112 (trainer:737) INFO: 9epoch:train:5601-5700batch: iter_time=2.208e-04, forward_time=0.103, loss_ctc=51.201, loss_att=58.752, acc=0.664, loss=56.487, backward_time=0.096, grad_norm=30.386, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.030, optim0_lr0=6.910e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 00:20:58,027 (trainer:737) INFO: 9epoch:train:5701-5800batch: iter_time=2.136e-04, forward_time=0.105, loss_ctc=62.112, loss_att=56.376, acc=0.681, loss=58.097, backward_time=0.098, grad_norm=33.556, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.031, optim0_lr0=6.907e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:21:38,792 (trainer:737) INFO: 9epoch:train:5801-5900batch: iter_time=1.985e-04, forward_time=0.105, loss_ctc=48.738, loss_att=53.800, acc=0.686, loss=52.281, backward_time=0.098, grad_norm=30.482, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.031, optim0_lr0=6.905e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:22:19,924 (trainer:737) INFO: 9epoch:train:5901-6000batch: iter_time=1.940e-04, forward_time=0.106, loss_ctc=54.714, loss_att=61.124, acc=0.670, loss=59.201, backward_time=0.099, grad_norm=33.868, clip=100.000, loss_scale=3.022e+23, optim_step_time=0.031, optim0_lr0=6.902e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:23:01,220 (trainer:737) INFO: 9epoch:train:6001-6100batch: iter_time=1.908e-04, forward_time=0.106, loss_ctc=56.460, loss_att=53.052, acc=0.678, loss=54.074, backward_time=0.098, grad_norm=33.920, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.031, optim0_lr0=6.899e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 00:23:42,409 (trainer:737) INFO: 9epoch:train:6101-6200batch: iter_time=1.745e-04, forward_time=0.105, loss_ctc=57.989, loss_att=68.088, acc=0.670, loss=65.058, backward_time=0.099, grad_norm=34.665, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.031, optim0_lr0=6.897e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 00:24:10,546 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 00:24:30,286 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 00:24:33,882 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f389188b4c0>)
+[gpuc02:0/16] 2024-01-13 00:24:33,882 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 00:24:33,886 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 00:29:09,937 (trainer:737) INFO: 9epoch:train:6201-6300batch: iter_time=2.606, forward_time=0.109, loss_ctc=47.418, loss_att=64.221, acc=0.663, loss=59.180, backward_time=0.099, grad_norm=29.345, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.894e-04, train_time=3.275
+[gpuc02:0/16] 2024-01-13 00:29:51,103 (trainer:737) INFO: 9epoch:train:6301-6400batch: iter_time=2.295e-04, forward_time=0.105, loss_ctc=56.379, loss_att=61.437, acc=0.681, loss=59.919, backward_time=0.098, grad_norm=33.471, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.891e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:30:31,808 (trainer:737) INFO: 9epoch:train:6401-6500batch: iter_time=2.485e-04, forward_time=0.103, loss_ctc=44.249, loss_att=47.694, acc=0.693, loss=46.661, backward_time=0.097, grad_norm=27.736, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.888e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:31:13,184 (trainer:737) INFO: 9epoch:train:6501-6600batch: iter_time=1.846e-04, forward_time=0.105, loss_ctc=54.633, loss_att=63.927, acc=0.679, loss=61.139, backward_time=0.098, grad_norm=32.900, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.886e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 00:31:53,909 (trainer:737) INFO: 9epoch:train:6601-6700batch: iter_time=2.356e-04, forward_time=0.103, loss_ctc=49.405, loss_att=50.421, acc=0.709, loss=50.116, backward_time=0.097, grad_norm=27.471, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.883e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:32:34,657 (trainer:737) INFO: 9epoch:train:6701-6800batch: iter_time=1.930e-04, forward_time=0.104, loss_ctc=55.778, loss_att=61.062, acc=0.692, loss=59.477, backward_time=0.097, grad_norm=29.839, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.880e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:33:15,466 (trainer:737) INFO: 9epoch:train:6801-6900batch: iter_time=1.769e-04, forward_time=0.104, loss_ctc=49.913, loss_att=63.975, acc=0.675, loss=59.756, backward_time=0.097, grad_norm=30.449, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.877e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 00:33:56,193 (trainer:737) INFO: 9epoch:train:6901-7000batch: iter_time=1.853e-04, forward_time=0.105, loss_ctc=56.555, loss_att=61.835, acc=0.670, loss=60.251, backward_time=0.097, grad_norm=32.353, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.875e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:34:36,905 (trainer:737) INFO: 9epoch:train:7001-7100batch: iter_time=1.834e-04, forward_time=0.104, loss_ctc=55.015, loss_att=49.903, acc=0.705, loss=51.437, backward_time=0.097, grad_norm=32.930, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.872e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:35:17,849 (trainer:737) INFO: 9epoch:train:7101-7200batch: iter_time=2.172e-04, forward_time=0.107, loss_ctc=53.619, loss_att=60.663, acc=0.680, loss=58.550, backward_time=0.097, grad_norm=30.303, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.869e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:35:58,621 (trainer:737) INFO: 9epoch:train:7201-7300batch: iter_time=1.984e-04, forward_time=0.104, loss_ctc=55.550, loss_att=63.152, acc=0.653, loss=60.872, backward_time=0.096, grad_norm=34.790, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.867e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:36:39,791 (trainer:737) INFO: 9epoch:train:7301-7400batch: iter_time=2.053e-04, forward_time=0.105, loss_ctc=50.639, loss_att=52.586, acc=0.728, loss=52.002, backward_time=0.097, grad_norm=27.955, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.864e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:37:20,767 (trainer:737) INFO: 9epoch:train:7401-7500batch: iter_time=1.627e-04, forward_time=0.105, loss_ctc=59.252, loss_att=78.497, acc=0.647, loss=72.723, backward_time=0.098, grad_norm=34.134, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.029, optim0_lr0=6.861e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:37:25,707 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 00:37:45,358 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 00:37:49,136 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3898ab3490>)
+[gpuc02:0/16] 2024-01-13 00:37:49,136 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 00:37:49,139 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 00:42:26,602 (trainer:737) INFO: 9epoch:train:7501-7600batch: iter_time=2.616, forward_time=0.105, loss_ctc=51.200, loss_att=52.552, acc=0.708, loss=52.146, backward_time=0.097, grad_norm=30.668, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.859e-04, train_time=3.058
+[gpuc02:0/16] 2024-01-13 00:43:07,721 (trainer:737) INFO: 9epoch:train:7601-7700batch: iter_time=2.156e-04, forward_time=0.104, loss_ctc=49.857, loss_att=54.660, acc=0.679, loss=53.219, backward_time=0.097, grad_norm=29.897, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.856e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:43:48,749 (trainer:737) INFO: 9epoch:train:7701-7800batch: iter_time=1.958e-04, forward_time=0.105, loss_ctc=47.010, loss_att=55.753, acc=0.671, loss=53.131, backward_time=0.097, grad_norm=29.046, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.853e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:44:30,157 (trainer:737) INFO: 9epoch:train:7801-7900batch: iter_time=2.174e-04, forward_time=0.105, loss_ctc=53.755, loss_att=62.340, acc=0.689, loss=59.765, backward_time=0.098, grad_norm=31.908, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.851e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 00:45:10,906 (trainer:737) INFO: 9epoch:train:7901-8000batch: iter_time=2.378e-04, forward_time=0.105, loss_ctc=48.108, loss_att=52.051, acc=0.711, loss=50.868, backward_time=0.098, grad_norm=59.086, clip=100.000, loss_scale=6.045e+23, optim_step_time=0.030, optim0_lr0=6.848e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:45:51,805 (trainer:737) INFO: 9epoch:train:8001-8100batch: iter_time=2.086e-04, forward_time=0.105, loss_ctc=54.693, loss_att=62.672, acc=0.684, loss=60.278, backward_time=0.098, grad_norm=29.749, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.845e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:46:32,589 (trainer:737) INFO: 9epoch:train:8101-8200batch: iter_time=2.232e-04, forward_time=0.105, loss_ctc=50.818, loss_att=60.259, acc=0.671, loss=57.427, backward_time=0.097, grad_norm=30.832, clip=99.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.842e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 00:47:14,014 (trainer:737) INFO: 9epoch:train:8201-8300batch: iter_time=1.924e-04, forward_time=0.106, loss_ctc=61.406, loss_att=56.508, acc=0.698, loss=57.977, backward_time=0.098, grad_norm=33.039, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.840e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 00:47:54,735 (trainer:737) INFO: 9epoch:train:8301-8400batch: iter_time=1.958e-04, forward_time=0.105, loss_ctc=48.669, loss_att=55.151, acc=0.690, loss=53.206, backward_time=0.097, grad_norm=79.995, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.837e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:48:35,825 (trainer:737) INFO: 9epoch:train:8401-8500batch: iter_time=1.828e-04, forward_time=0.105, loss_ctc=53.196, loss_att=60.422, acc=0.677, loss=58.254, backward_time=0.098, grad_norm=30.990, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.834e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 00:49:16,831 (trainer:737) INFO: 9epoch:train:8501-8600batch: iter_time=1.988e-04, forward_time=0.104, loss_ctc=54.647, loss_att=52.690, acc=0.693, loss=53.277, backward_time=0.097, grad_norm=30.331, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.832e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:49:57,836 (trainer:737) INFO: 9epoch:train:8601-8700batch: iter_time=1.781e-04, forward_time=0.106, loss_ctc=57.396, loss_att=67.621, acc=0.687, loss=64.553, backward_time=0.098, grad_norm=32.388, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.829e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 00:50:24,318 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 00:50:43,896 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 00:50:47,600 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38925aa230>)
+[gpuc02:0/16] 2024-01-13 00:50:47,600 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 00:50:47,604 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 00:55:07,002 (trainer:737) INFO: 9epoch:train:8701-8800batch: iter_time=2.659, forward_time=0.106, loss_ctc=46.135, loss_att=65.795, acc=0.663, loss=59.897, backward_time=0.097, grad_norm=27.886, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.827e-04, train_time=3.091
+[gpuc02:0/16] 2024-01-13 00:55:47,943 (trainer:737) INFO: 9epoch:train:8801-8900batch: iter_time=2.208e-04, forward_time=0.104, loss_ctc=55.498, loss_att=61.933, acc=0.672, loss=60.002, backward_time=0.098, grad_norm=33.371, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.824e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:56:28,559 (trainer:737) INFO: 9epoch:train:8901-9000batch: iter_time=2.290e-04, forward_time=0.102, loss_ctc=43.920, loss_att=48.224, acc=0.687, loss=46.932, backward_time=0.096, grad_norm=27.949, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.821e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 00:57:09,322 (trainer:737) INFO: 9epoch:train:9001-9100batch: iter_time=2.133e-04, forward_time=0.103, loss_ctc=53.907, loss_att=61.638, acc=0.678, loss=59.319, backward_time=0.097, grad_norm=32.191, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.819e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:57:50,225 (trainer:737) INFO: 9epoch:train:9101-9200batch: iter_time=2.346e-04, forward_time=0.102, loss_ctc=49.305, loss_att=50.477, acc=0.711, loss=50.125, backward_time=0.097, grad_norm=29.530, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.816e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 00:58:30,977 (trainer:737) INFO: 9epoch:train:9201-9300batch: iter_time=2.163e-04, forward_time=0.103, loss_ctc=54.692, loss_att=58.738, acc=0.693, loss=57.524, backward_time=0.097, grad_norm=30.209, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.030, optim0_lr0=6.813e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 00:59:12,346 (trainer:737) INFO: 9epoch:train:9301-9400batch: iter_time=2.087e-04, forward_time=0.103, loss_ctc=49.280, loss_att=62.119, acc=0.670, loss=58.267, backward_time=0.097, grad_norm=29.610, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.811e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 00:59:53,294 (trainer:737) INFO: 9epoch:train:9401-9500batch: iter_time=2.203e-04, forward_time=0.103, loss_ctc=55.510, loss_att=59.655, acc=0.661, loss=58.412, backward_time=0.097, grad_norm=31.741, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.808e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 01:00:34,314 (trainer:737) INFO: 9epoch:train:9501-9600batch: iter_time=2.022e-04, forward_time=0.103, loss_ctc=54.729, loss_att=47.081, acc=0.707, loss=49.376, backward_time=0.097, grad_norm=57.337, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.805e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:01:15,350 (trainer:737) INFO: 9epoch:train:9601-9700batch: iter_time=2.207e-04, forward_time=0.103, loss_ctc=53.370, loss_att=60.840, acc=0.675, loss=58.599, backward_time=0.097, grad_norm=87.861, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.803e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:01:56,479 (trainer:737) INFO: 9epoch:train:9701-9800batch: iter_time=2.233e-04, forward_time=0.103, loss_ctc=55.465, loss_att=61.997, acc=0.650, loss=60.038, backward_time=0.097, grad_norm=35.669, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.800e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:02:37,641 (trainer:737) INFO: 9epoch:train:9801-9900batch: iter_time=2.103e-04, forward_time=0.103, loss_ctc=50.029, loss_att=53.994, acc=0.712, loss=52.805, backward_time=0.098, grad_norm=27.537, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.798e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:03:19,048 (trainer:737) INFO: 9epoch:train:9901-10000batch: iter_time=1.795e-04, forward_time=0.104, loss_ctc=58.598, loss_att=75.821, acc=0.640, loss=70.654, backward_time=0.098, grad_norm=33.228, clip=100.000, loss_scale=1.209e+24, optim_step_time=0.029, optim0_lr0=6.795e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 01:03:25,393 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 01:03:44,684 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 01:03:48,462 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38cc8be5c0>)
+[gpuc02:0/16] 2024-01-13 01:03:48,462 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 01:03:48,466 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 01:08:28,650 (trainer:737) INFO: 9epoch:train:10001-10100batch: iter_time=2.650, forward_time=0.105, loss_ctc=50.644, loss_att=53.980, acc=0.708, loss=52.979, backward_time=0.097, grad_norm=31.318, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.029, optim0_lr0=6.792e-04, train_time=3.096
+[gpuc02:0/16] 2024-01-13 01:09:09,404 (trainer:737) INFO: 9epoch:train:10101-10200batch: iter_time=1.890e-04, forward_time=0.103, loss_ctc=49.091, loss_att=54.289, acc=0.681, loss=52.730, backward_time=0.097, grad_norm=30.782, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.029, optim0_lr0=6.790e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:09:50,165 (trainer:737) INFO: 9epoch:train:10201-10300batch: iter_time=1.797e-04, forward_time=0.102, loss_ctc=46.868, loss_att=56.046, acc=0.671, loss=53.293, backward_time=0.096, grad_norm=29.693, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.029, optim0_lr0=6.787e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:10:31,019 (trainer:737) INFO: 9epoch:train:10301-10400batch: iter_time=1.879e-04, forward_time=0.104, loss_ctc=53.357, loss_att=63.041, acc=0.686, loss=60.135, backward_time=0.097, grad_norm=31.280, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.029, optim0_lr0=6.784e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:11:11,814 (trainer:737) INFO: 9epoch:train:10401-10500batch: iter_time=1.978e-04, forward_time=0.105, loss_ctc=47.899, loss_att=52.265, acc=0.714, loss=50.955, backward_time=0.097, grad_norm=27.658, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.782e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:11:52,888 (trainer:737) INFO: 9epoch:train:10501-10600batch: iter_time=2.005e-04, forward_time=0.105, loss_ctc=54.707, loss_att=61.491, acc=0.688, loss=59.456, backward_time=0.098, grad_norm=30.487, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.779e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:12:33,763 (trainer:737) INFO: 9epoch:train:10601-10700batch: iter_time=2.150e-04, forward_time=0.105, loss_ctc=49.459, loss_att=59.258, acc=0.675, loss=56.318, backward_time=0.097, grad_norm=30.174, clip=99.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.777e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 01:13:14,730 (trainer:737) INFO: 9epoch:train:10701-10800batch: iter_time=1.868e-04, forward_time=0.106, loss_ctc=59.272, loss_att=56.563, acc=0.700, loss=57.376, backward_time=0.098, grad_norm=32.689, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.774e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 01:13:55,594 (trainer:737) INFO: 9epoch:train:10801-10900batch: iter_time=1.832e-04, forward_time=0.104, loss_ctc=47.438, loss_att=53.803, acc=0.695, loss=51.893, backward_time=0.097, grad_norm=31.714, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.772e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:14:36,774 (trainer:737) INFO: 9epoch:train:10901-11000batch: iter_time=1.916e-04, forward_time=0.105, loss_ctc=53.037, loss_att=58.992, acc=0.685, loss=57.205, backward_time=0.098, grad_norm=39.581, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.769e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 01:15:17,666 (trainer:737) INFO: 9epoch:train:11001-11100batch: iter_time=1.958e-04, forward_time=0.105, loss_ctc=55.060, loss_att=52.723, acc=0.692, loss=53.424, backward_time=0.097, grad_norm=32.651, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.766e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 01:15:58,739 (trainer:737) INFO: 9epoch:train:11101-11200batch: iter_time=1.871e-04, forward_time=0.106, loss_ctc=56.851, loss_att=67.605, acc=0.688, loss=64.379, backward_time=0.098, grad_norm=31.156, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.764e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:16:25,422 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 01:16:44,772 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 01:16:48,802 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40e2b7aaa0>)
+[gpuc02:0/16] 2024-01-13 01:16:48,802 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 01:16:48,805 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 01:21:24,067 (trainer:737) INFO: 9epoch:train:11201-11300batch: iter_time=2.631, forward_time=0.106, loss_ctc=45.988, loss_att=63.583, acc=0.673, loss=58.304, backward_time=0.099, grad_norm=28.042, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.761e-04, train_time=3.253
+[gpuc02:0/16] 2024-01-13 01:22:05,116 (trainer:737) INFO: 9epoch:train:11301-11400batch: iter_time=1.877e-04, forward_time=0.104, loss_ctc=55.590, loss_att=59.034, acc=0.689, loss=58.001, backward_time=0.099, grad_norm=34.278, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.759e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:22:45,813 (trainer:737) INFO: 9epoch:train:11401-11500batch: iter_time=1.877e-04, forward_time=0.103, loss_ctc=42.957, loss_att=46.155, acc=0.699, loss=45.196, backward_time=0.097, grad_norm=26.732, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.756e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:23:26,812 (trainer:737) INFO: 9epoch:train:11501-11600batch: iter_time=1.809e-04, forward_time=0.104, loss_ctc=54.346, loss_att=61.862, acc=0.686, loss=59.607, backward_time=0.098, grad_norm=31.719, clip=99.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.753e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:24:07,604 (trainer:737) INFO: 9epoch:train:11601-11700batch: iter_time=2.045e-04, forward_time=0.104, loss_ctc=48.536, loss_att=50.139, acc=0.712, loss=49.658, backward_time=0.098, grad_norm=28.353, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.751e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:24:48,784 (trainer:737) INFO: 9epoch:train:11701-11800batch: iter_time=2.199e-04, forward_time=0.104, loss_ctc=54.398, loss_att=60.680, acc=0.693, loss=58.795, backward_time=0.098, grad_norm=30.997, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.748e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 01:25:29,942 (trainer:737) INFO: 9epoch:train:11801-11900batch: iter_time=2.266e-04, forward_time=0.105, loss_ctc=48.679, loss_att=62.001, acc=0.682, loss=58.005, backward_time=0.098, grad_norm=28.020, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.746e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:26:10,813 (trainer:737) INFO: 9epoch:train:11901-12000batch: iter_time=2.183e-04, forward_time=0.104, loss_ctc=55.887, loss_att=61.008, acc=0.675, loss=59.472, backward_time=0.098, grad_norm=32.092, clip=100.000, loss_scale=2.418e+24, optim_step_time=0.030, optim0_lr0=6.743e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:26:51,660 (trainer:737) INFO: 9epoch:train:12001-12100batch: iter_time=2.023e-04, forward_time=0.104, loss_ctc=54.495, loss_att=49.378, acc=0.709, loss=50.913, backward_time=0.097, grad_norm=33.610, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.741e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:27:32,482 (trainer:737) INFO: 9epoch:train:12101-12200batch: iter_time=2.072e-04, forward_time=0.104, loss_ctc=51.964, loss_att=58.610, acc=0.687, loss=56.616, backward_time=0.097, grad_norm=30.253, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.738e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:28:13,326 (trainer:737) INFO: 9epoch:train:12201-12300batch: iter_time=2.406e-04, forward_time=0.103, loss_ctc=53.411, loss_att=62.203, acc=0.658, loss=59.566, backward_time=0.097, grad_norm=30.904, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.736e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:28:55,088 (trainer:737) INFO: 9epoch:train:12301-12400batch: iter_time=2.226e-04, forward_time=0.105, loss_ctc=49.302, loss_att=51.306, acc=0.732, loss=50.705, backward_time=0.098, grad_norm=26.881, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.733e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 01:29:36,109 (trainer:737) INFO: 9epoch:train:12401-12500batch: iter_time=1.917e-04, forward_time=0.105, loss_ctc=57.830, loss_att=76.395, acc=0.654, loss=70.826, backward_time=0.098, grad_norm=33.682, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.730e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:29:44,437 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 01:30:03,524 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 01:30:07,195 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38cf015e10>)
+[gpuc02:0/16] 2024-01-13 01:30:07,195 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 01:30:07,198 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 01:35:02,654 (trainer:737) INFO: 9epoch:train:12501-12600batch: iter_time=2.735, forward_time=0.104, loss_ctc=49.465, loss_att=55.901, acc=0.692, loss=53.970, backward_time=0.097, grad_norm=32.031, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.728e-04, train_time=3.265
+[gpuc02:0/16] 2024-01-13 01:35:43,430 (trainer:737) INFO: 9epoch:train:12601-12700batch: iter_time=2.072e-04, forward_time=0.104, loss_ctc=48.633, loss_att=53.637, acc=0.681, loss=52.136, backward_time=0.097, grad_norm=29.793, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.725e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:36:24,090 (trainer:737) INFO: 9epoch:train:12701-12800batch: iter_time=2.212e-04, forward_time=0.102, loss_ctc=46.051, loss_att=54.412, acc=0.664, loss=51.903, backward_time=0.097, grad_norm=29.284, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.723e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 01:37:05,135 (trainer:737) INFO: 9epoch:train:12801-12900batch: iter_time=2.096e-04, forward_time=0.103, loss_ctc=52.703, loss_att=62.320, acc=0.687, loss=59.435, backward_time=0.097, grad_norm=32.083, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.720e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:37:45,861 (trainer:737) INFO: 9epoch:train:12901-13000batch: iter_time=2.207e-04, forward_time=0.103, loss_ctc=47.490, loss_att=48.930, acc=0.720, loss=48.498, backward_time=0.097, grad_norm=28.899, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.718e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:38:27,046 (trainer:737) INFO: 9epoch:train:13001-13100batch: iter_time=1.973e-04, forward_time=0.106, loss_ctc=53.919, loss_att=59.317, acc=0.689, loss=57.698, backward_time=0.098, grad_norm=31.922, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.715e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 01:39:08,302 (trainer:737) INFO: 9epoch:train:13101-13200batch: iter_time=2.331e-04, forward_time=0.103, loss_ctc=49.136, loss_att=56.894, acc=0.671, loss=54.567, backward_time=0.097, grad_norm=31.677, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.713e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 01:39:49,137 (trainer:737) INFO: 9epoch:train:13201-13300batch: iter_time=2.157e-04, forward_time=0.105, loss_ctc=58.691, loss_att=54.871, acc=0.687, loss=56.017, backward_time=0.097, grad_norm=33.476, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.710e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:40:30,303 (trainer:737) INFO: 9epoch:train:13301-13400batch: iter_time=2.071e-04, forward_time=0.103, loss_ctc=47.173, loss_att=53.308, acc=0.692, loss=51.468, backward_time=0.097, grad_norm=29.860, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.708e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 01:41:11,373 (trainer:737) INFO: 9epoch:train:13401-13500batch: iter_time=2.218e-04, forward_time=0.104, loss_ctc=53.826, loss_att=60.243, acc=0.675, loss=58.318, backward_time=0.097, grad_norm=34.295, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.705e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 01:41:52,663 (trainer:737) INFO: 9epoch:train:13501-13600batch: iter_time=2.255e-04, forward_time=0.103, loss_ctc=53.501, loss_att=51.490, acc=0.686, loss=52.094, backward_time=0.097, grad_norm=31.173, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.703e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 01:42:34,496 (trainer:737) INFO: 9epoch:train:13601-13700batch: iter_time=2.299e-04, forward_time=0.105, loss_ctc=56.153, loss_att=67.147, acc=0.677, loss=63.849, backward_time=0.098, grad_norm=32.228, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.700e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-13 01:43:02,816 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 01:43:22,375 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 01:43:26,179 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3894ead5d0>)
+[gpuc02:0/16] 2024-01-13 01:43:26,179 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 01:43:26,183 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 01:47:52,292 (trainer:737) INFO: 9epoch:train:13701-13800batch: iter_time=2.728, forward_time=0.104, loss_ctc=46.536, loss_att=61.458, acc=0.667, loss=56.981, backward_time=0.097, grad_norm=29.034, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.030, optim0_lr0=6.698e-04, train_time=3.178
+[gpuc02:0/16] 2024-01-13 01:48:33,735 (trainer:737) INFO: 9epoch:train:13801-13900batch: iter_time=2.533e-04, forward_time=0.105, loss_ctc=54.873, loss_att=59.616, acc=0.679, loss=58.193, backward_time=0.097, grad_norm=34.430, clip=100.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.695e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 01:49:14,384 (trainer:737) INFO: 9epoch:train:13901-14000batch: iter_time=2.582e-04, forward_time=0.103, loss_ctc=42.921, loss_att=46.973, acc=0.691, loss=45.757, backward_time=0.096, grad_norm=26.611, clip=99.000, loss_scale=4.836e+24, optim_step_time=0.029, optim0_lr0=6.693e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 01:49:55,241 (trainer:737) INFO: 9epoch:train:14001-14100batch: iter_time=2.580e-04, forward_time=0.105, loss_ctc=52.982, loss_att=60.223, acc=0.682, loss=58.050, backward_time=0.097, grad_norm=30.904, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.690e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:50:35,940 (trainer:737) INFO: 9epoch:train:14101-14200batch: iter_time=2.290e-04, forward_time=0.104, loss_ctc=48.061, loss_att=47.941, acc=0.717, loss=47.977, backward_time=0.096, grad_norm=28.093, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.688e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:51:16,783 (trainer:737) INFO: 9epoch:train:14201-14300batch: iter_time=2.154e-04, forward_time=0.104, loss_ctc=53.596, loss_att=58.011, acc=0.698, loss=56.686, backward_time=0.097, grad_norm=29.426, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.685e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:51:57,597 (trainer:737) INFO: 9epoch:train:14301-14400batch: iter_time=2.246e-04, forward_time=0.104, loss_ctc=48.539, loss_att=60.375, acc=0.675, loss=56.824, backward_time=0.097, grad_norm=29.956, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.683e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 01:52:38,270 (trainer:737) INFO: 9epoch:train:14401-14500batch: iter_time=2.311e-04, forward_time=0.104, loss_ctc=54.724, loss_att=58.622, acc=0.664, loss=57.453, backward_time=0.096, grad_norm=33.722, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.680e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:53:19,001 (trainer:737) INFO: 9epoch:train:14501-14600batch: iter_time=1.960e-04, forward_time=0.104, loss_ctc=53.050, loss_att=45.901, acc=0.714, loss=48.046, backward_time=0.096, grad_norm=31.226, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.678e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:53:59,755 (trainer:737) INFO: 9epoch:train:14601-14700batch: iter_time=2.097e-04, forward_time=0.104, loss_ctc=52.124, loss_att=58.631, acc=0.681, loss=56.679, backward_time=0.096, grad_norm=32.216, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.675e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:54:40,522 (trainer:737) INFO: 9epoch:train:14701-14800batch: iter_time=1.865e-04, forward_time=0.104, loss_ctc=53.699, loss_att=59.557, acc=0.658, loss=57.800, backward_time=0.096, grad_norm=35.496, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.673e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 01:55:21,404 (trainer:737) INFO: 9epoch:train:14801-14900batch: iter_time=1.868e-04, forward_time=0.104, loss_ctc=49.177, loss_att=53.568, acc=0.715, loss=52.251, backward_time=0.097, grad_norm=29.891, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.670e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 01:56:05,880 (trainer:737) INFO: 9epoch:train:14901-15000batch: iter_time=1.487e-04, forward_time=0.129, loss_ctc=57.552, loss_att=73.445, acc=0.646, loss=68.677, backward_time=0.106, grad_norm=34.066, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.668e-04, train_time=0.445
+[gpuc02:0/16] 2024-01-13 02:17:14,302 (trainer:343) INFO: 9epoch results: [train] iter_time=0.218, forward_time=0.105, loss_ctc=53.027, loss_att=58.587, acc=0.682, loss=56.919, backward_time=0.097, grad_norm=32.525, clip=99.973, loss_scale=1.924e+24, optim_step_time=0.030, optim0_lr0=6.863e-04, train_time=0.634, time=2 hours, 38 minutes and 54.9 seconds, total_count=135000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=65.176, cer_ctc=0.325, loss_att=61.382, acc=0.533, cer=0.449, wer=1.000, loss=62.520, time=20 minutes and 48.01 seconds, total_count=42039, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 02:17:19,542 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-13 02:17:19,557 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/4epoch.pth
+[gpuc02:0/16] 2024-01-13 02:17:19,557 (trainer:272) INFO: 10/45epoch started. Estimated time to finish: 4 days, 11 hours and 17 minutes
+[gpuc02:0/16] 2024-01-13 02:17:19,568 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 02:17:38,334 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 02:17:41,875 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc0466b0>)
+[gpuc02:0/16] 2024-01-13 02:17:41,876 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 02:17:41,879 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 02:22:15,849 (trainer:737) INFO: 10epoch:train:1-100batch: iter_time=2.550, forward_time=0.105, loss_ctc=57.487, loss_att=64.028, acc=0.692, loss=62.066, backward_time=0.098, grad_norm=34.325, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.665e-04, train_time=2.963
+[gpuc02:0/16] 2024-01-13 02:22:56,928 (trainer:737) INFO: 10epoch:train:101-200batch: iter_time=1.255e-04, forward_time=0.104, loss_ctc=53.906, loss_att=59.247, acc=0.706, loss=57.645, backward_time=0.098, grad_norm=31.879, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.663e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 02:23:37,899 (trainer:737) INFO: 10epoch:train:201-300batch: iter_time=1.191e-04, forward_time=0.103, loss_ctc=40.797, loss_att=44.916, acc=0.702, loss=43.680, backward_time=0.097, grad_norm=25.595, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.660e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 02:24:19,109 (trainer:737) INFO: 10epoch:train:301-400batch: iter_time=1.365e-04, forward_time=0.105, loss_ctc=59.377, loss_att=73.027, acc=0.682, loss=68.932, backward_time=0.099, grad_norm=31.664, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.029, optim0_lr0=6.658e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 02:25:00,119 (trainer:737) INFO: 10epoch:train:401-500batch: iter_time=1.341e-04, forward_time=0.105, loss_ctc=48.780, loss_att=59.942, acc=0.685, loss=56.593, backward_time=0.099, grad_norm=32.123, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.656e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 02:25:40,984 (trainer:737) INFO: 10epoch:train:501-600batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=52.095, loss_att=56.760, acc=0.724, loss=55.361, backward_time=0.099, grad_norm=29.613, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.653e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 02:26:22,951 (trainer:737) INFO: 10epoch:train:601-700batch: iter_time=1.237e-04, forward_time=0.110, loss_ctc=55.624, loss_att=59.671, acc=0.691, loss=58.457, backward_time=0.102, grad_norm=38.357, clip=99.000, loss_scale=9.671e+24, optim_step_time=0.031, optim0_lr0=6.651e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 02:27:06,005 (trainer:737) INFO: 10epoch:train:701-800batch: iter_time=1.240e-04, forward_time=0.105, loss_ctc=48.560, loss_att=59.262, acc=0.685, loss=56.052, backward_time=0.098, grad_norm=28.712, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.648e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-13 02:27:48,934 (trainer:737) INFO: 10epoch:train:801-900batch: iter_time=1.250e-04, forward_time=0.104, loss_ctc=65.482, loss_att=68.937, acc=0.677, loss=67.900, backward_time=0.099, grad_norm=39.734, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.646e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-13 02:28:32,648 (trainer:737) INFO: 10epoch:train:901-1000batch: iter_time=1.273e-04, forward_time=0.105, loss_ctc=69.457, loss_att=76.286, acc=0.670, loss=74.237, backward_time=0.100, grad_norm=39.951, clip=100.000, loss_scale=9.671e+24, optim_step_time=0.030, optim0_lr0=6.643e-04, train_time=0.437
+[gpuc02:0/16] 2024-01-13 02:29:15,152 (trainer:737) INFO: 10epoch:train:1001-1100batch: iter_time=1.321e-04, forward_time=0.103, loss_ctc=48.896, loss_att=42.149, acc=0.731, loss=44.173, backward_time=0.098, grad_norm=27.096, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.641e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-13 02:29:57,902 (trainer:737) INFO: 10epoch:train:1101-1200batch: iter_time=1.257e-04, forward_time=0.117, loss_ctc=59.559, loss_att=72.238, acc=0.675, loss=68.434, backward_time=0.105, grad_norm=33.415, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.031, optim0_lr0=6.638e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-13 02:30:33,974 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 02:30:53,040 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 02:30:56,789 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc047220>)
+[gpuc02:0/16] 2024-01-13 02:30:56,789 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 02:30:56,793 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 02:37:03,098 (trainer:737) INFO: 10epoch:train:1201-1300batch: iter_time=3.041, forward_time=0.106, loss_ctc=52.845, loss_att=56.467, acc=0.710, loss=55.381, backward_time=0.099, grad_norm=30.645, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.636e-04, train_time=4.252
+[gpuc02:0/16] 2024-01-13 02:37:44,057 (trainer:737) INFO: 10epoch:train:1301-1400batch: iter_time=1.630e-04, forward_time=0.102, loss_ctc=52.894, loss_att=57.611, acc=0.685, loss=56.196, backward_time=0.097, grad_norm=31.725, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.029, optim0_lr0=6.634e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 02:38:25,822 (trainer:737) INFO: 10epoch:train:1401-1500batch: iter_time=1.440e-04, forward_time=0.108, loss_ctc=45.574, loss_att=52.100, acc=0.700, loss=50.142, backward_time=0.098, grad_norm=28.179, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.631e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 02:39:06,557 (trainer:737) INFO: 10epoch:train:1501-1600batch: iter_time=1.543e-04, forward_time=0.103, loss_ctc=45.232, loss_att=53.867, acc=0.680, loss=51.276, backward_time=0.098, grad_norm=27.173, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.629e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 02:39:49,604 (trainer:737) INFO: 10epoch:train:1601-1700batch: iter_time=1.463e-04, forward_time=0.111, loss_ctc=52.648, loss_att=67.097, acc=0.682, loss=62.762, backward_time=0.100, grad_norm=30.608, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.626e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-13 02:40:30,614 (trainer:737) INFO: 10epoch:train:1701-1800batch: iter_time=1.715e-04, forward_time=0.104, loss_ctc=54.250, loss_att=62.375, acc=0.690, loss=59.937, backward_time=0.099, grad_norm=33.767, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.624e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 02:41:15,031 (trainer:737) INFO: 10epoch:train:1801-1900batch: iter_time=1.543e-04, forward_time=0.103, loss_ctc=53.721, loss_att=53.884, acc=0.694, loss=53.835, backward_time=0.098, grad_norm=34.513, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.621e-04, train_time=0.444
+[gpuc02:0/16] 2024-01-13 02:41:55,745 (trainer:737) INFO: 10epoch:train:1901-2000batch: iter_time=1.645e-04, forward_time=0.103, loss_ctc=45.967, loss_att=52.192, acc=0.705, loss=50.325, backward_time=0.098, grad_norm=27.981, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.619e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 02:42:36,548 (trainer:737) INFO: 10epoch:train:2001-2100batch: iter_time=1.805e-04, forward_time=0.103, loss_ctc=54.793, loss_att=65.062, acc=0.669, loss=61.981, backward_time=0.098, grad_norm=32.459, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.617e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 02:43:17,369 (trainer:737) INFO: 10epoch:train:2101-2200batch: iter_time=1.569e-04, forward_time=0.104, loss_ctc=69.123, loss_att=66.545, acc=0.677, loss=67.318, backward_time=0.098, grad_norm=44.381, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.614e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 02:43:58,201 (trainer:737) INFO: 10epoch:train:2201-2300batch: iter_time=1.535e-04, forward_time=0.103, loss_ctc=53.945, loss_att=63.124, acc=0.691, loss=60.370, backward_time=0.098, grad_norm=29.530, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.612e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 02:44:38,916 (trainer:737) INFO: 10epoch:train:2301-2400batch: iter_time=1.508e-04, forward_time=0.103, loss_ctc=55.647, loss_att=54.303, acc=0.697, loss=54.706, backward_time=0.098, grad_norm=32.851, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.609e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 02:45:19,656 (trainer:737) INFO: 10epoch:train:2401-2500batch: iter_time=1.378e-04, forward_time=0.103, loss_ctc=50.359, loss_att=57.477, acc=0.691, loss=55.342, backward_time=0.098, grad_norm=31.057, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.607e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 02:45:30,826 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 02:45:50,331 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 02:45:53,999 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fe21b5f00>)
+[gpuc02:0/16] 2024-01-13 02:45:53,999 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 02:45:54,003 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 02:51:12,872 (trainer:737) INFO: 10epoch:train:2501-2600batch: iter_time=3.122, forward_time=0.104, loss_ctc=55.729, loss_att=60.695, acc=0.689, loss=59.205, backward_time=0.098, grad_norm=30.815, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.605e-04, train_time=3.532
+[gpuc02:0/16] 2024-01-13 02:51:53,768 (trainer:737) INFO: 10epoch:train:2601-2700batch: iter_time=1.617e-04, forward_time=0.104, loss_ctc=51.178, loss_att=56.398, acc=0.704, loss=54.832, backward_time=0.098, grad_norm=30.707, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.602e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 02:52:34,255 (trainer:737) INFO: 10epoch:train:2701-2800batch: iter_time=2.414e-04, forward_time=0.103, loss_ctc=39.261, loss_att=43.556, acc=0.695, loss=42.268, backward_time=0.097, grad_norm=24.240, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.030, optim0_lr0=6.600e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-13 02:53:15,472 (trainer:737) INFO: 10epoch:train:2801-2900batch: iter_time=2.074e-04, forward_time=0.104, loss_ctc=57.113, loss_att=71.068, acc=0.675, loss=66.882, backward_time=0.098, grad_norm=31.318, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.029, optim0_lr0=6.597e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 02:53:56,642 (trainer:737) INFO: 10epoch:train:2901-3000batch: iter_time=2.127e-04, forward_time=0.103, loss_ctc=47.827, loss_att=55.625, acc=0.689, loss=53.286, backward_time=0.097, grad_norm=31.153, clip=100.000, loss_scale=1.934e+25, optim_step_time=0.029, optim0_lr0=6.595e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 02:54:37,906 (trainer:737) INFO: 10epoch:train:3001-3100batch: iter_time=2.104e-04, forward_time=0.104, loss_ctc=48.912, loss_att=54.071, acc=0.723, loss=52.523, backward_time=0.097, grad_norm=27.967, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.593e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 02:55:18,863 (trainer:737) INFO: 10epoch:train:3101-3200batch: iter_time=1.906e-04, forward_time=0.104, loss_ctc=53.774, loss_att=57.695, acc=0.689, loss=56.519, backward_time=0.097, grad_norm=33.375, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.590e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 02:56:00,021 (trainer:737) INFO: 10epoch:train:3201-3300batch: iter_time=1.935e-04, forward_time=0.103, loss_ctc=47.518, loss_att=56.875, acc=0.684, loss=54.068, backward_time=0.097, grad_norm=27.014, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.588e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 02:56:40,780 (trainer:737) INFO: 10epoch:train:3301-3400batch: iter_time=2.054e-04, forward_time=0.103, loss_ctc=62.450, loss_att=67.041, acc=0.671, loss=65.663, backward_time=0.097, grad_norm=40.608, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.585e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 02:57:22,102 (trainer:737) INFO: 10epoch:train:3401-3500batch: iter_time=2.020e-04, forward_time=0.104, loss_ctc=65.090, loss_att=74.551, acc=0.660, loss=71.713, backward_time=0.097, grad_norm=37.492, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.583e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 02:58:02,722 (trainer:737) INFO: 10epoch:train:3501-3600batch: iter_time=1.915e-04, forward_time=0.102, loss_ctc=48.091, loss_att=41.404, acc=0.734, loss=43.410, backward_time=0.096, grad_norm=25.470, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.581e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 02:58:43,567 (trainer:737) INFO: 10epoch:train:3601-3700batch: iter_time=2.121e-04, forward_time=0.104, loss_ctc=57.300, loss_att=68.559, acc=0.675, loss=65.182, backward_time=0.097, grad_norm=34.694, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.578e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 02:59:10,459 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 02:59:29,639 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 02:59:33,362 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fe3c53d90>)
+[gpuc02:0/16] 2024-01-13 02:59:33,362 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 02:59:33,366 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 03:03:58,747 (trainer:737) INFO: 10epoch:train:3701-3800batch: iter_time=2.695, forward_time=0.104, loss_ctc=51.893, loss_att=56.633, acc=0.713, loss=55.211, backward_time=0.097, grad_norm=30.629, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.576e-04, train_time=3.152
+[gpuc02:0/16] 2024-01-13 03:04:39,678 (trainer:737) INFO: 10epoch:train:3801-3900batch: iter_time=2.083e-04, forward_time=0.104, loss_ctc=52.485, loss_att=57.098, acc=0.700, loss=55.714, backward_time=0.098, grad_norm=31.655, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.029, optim0_lr0=6.574e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:05:20,893 (trainer:737) INFO: 10epoch:train:3901-4000batch: iter_time=2.412e-04, forward_time=0.105, loss_ctc=44.422, loss_att=51.439, acc=0.712, loss=49.334, backward_time=0.098, grad_norm=27.845, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.571e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 03:06:02,265 (trainer:737) INFO: 10epoch:train:4001-4100batch: iter_time=2.098e-04, forward_time=0.107, loss_ctc=44.518, loss_att=53.654, acc=0.696, loss=50.913, backward_time=0.098, grad_norm=27.111, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.569e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 03:06:43,264 (trainer:737) INFO: 10epoch:train:4101-4200batch: iter_time=2.019e-04, forward_time=0.105, loss_ctc=51.225, loss_att=68.960, acc=0.691, loss=63.639, backward_time=0.098, grad_norm=30.665, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.566e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:07:24,871 (trainer:737) INFO: 10epoch:train:4201-4300batch: iter_time=2.296e-04, forward_time=0.106, loss_ctc=53.048, loss_att=62.651, acc=0.699, loss=59.770, backward_time=0.099, grad_norm=31.287, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.564e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 03:08:05,770 (trainer:737) INFO: 10epoch:train:4301-4400batch: iter_time=2.236e-04, forward_time=0.104, loss_ctc=52.274, loss_att=53.842, acc=0.707, loss=53.372, backward_time=0.098, grad_norm=31.336, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.562e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:08:46,571 (trainer:737) INFO: 10epoch:train:4401-4500batch: iter_time=2.324e-04, forward_time=0.105, loss_ctc=45.666, loss_att=53.673, acc=0.707, loss=51.271, backward_time=0.098, grad_norm=27.973, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.559e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:09:27,778 (trainer:737) INFO: 10epoch:train:4501-4600batch: iter_time=2.422e-04, forward_time=0.105, loss_ctc=53.844, loss_att=64.528, acc=0.684, loss=61.323, backward_time=0.098, grad_norm=31.331, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.557e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 03:10:09,258 (trainer:737) INFO: 10epoch:train:4601-4700batch: iter_time=2.396e-04, forward_time=0.105, loss_ctc=66.209, loss_att=65.667, acc=0.690, loss=65.830, backward_time=0.098, grad_norm=42.770, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.555e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 03:10:50,657 (trainer:737) INFO: 10epoch:train:4701-4800batch: iter_time=2.503e-04, forward_time=0.105, loss_ctc=52.773, loss_att=63.386, acc=0.702, loss=60.202, backward_time=0.098, grad_norm=29.608, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.552e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 03:11:31,638 (trainer:737) INFO: 10epoch:train:4801-4900batch: iter_time=2.393e-04, forward_time=0.104, loss_ctc=55.380, loss_att=56.604, acc=0.701, loss=56.237, backward_time=0.098, grad_norm=32.374, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.550e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:12:12,648 (trainer:737) INFO: 10epoch:train:4901-5000batch: iter_time=1.911e-04, forward_time=0.104, loss_ctc=49.420, loss_att=56.690, acc=0.701, loss=54.509, backward_time=0.098, grad_norm=30.345, clip=100.000, loss_scale=3.869e+25, optim_step_time=0.030, optim0_lr0=6.548e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:12:20,137 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 03:12:39,266 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 03:12:42,913 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fb9cf9900>)
+[gpuc02:0/16] 2024-01-13 03:12:42,913 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 03:12:42,916 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 03:17:38,997 (trainer:737) INFO: 10epoch:train:5001-5100batch: iter_time=2.661, forward_time=0.125, loss_ctc=55.117, loss_att=62.306, acc=0.688, loss=60.150, backward_time=0.104, grad_norm=31.513, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.031, optim0_lr0=6.545e-04, train_time=3.263
+[gpuc02:0/16] 2024-01-13 03:18:19,974 (trainer:737) INFO: 10epoch:train:5101-5200batch: iter_time=2.057e-04, forward_time=0.105, loss_ctc=50.616, loss_att=57.306, acc=0.704, loss=55.299, backward_time=0.098, grad_norm=31.266, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.543e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:19:00,591 (trainer:737) INFO: 10epoch:train:5201-5300batch: iter_time=2.370e-04, forward_time=0.103, loss_ctc=38.930, loss_att=43.999, acc=0.695, loss=42.478, backward_time=0.097, grad_norm=25.184, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.541e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 03:19:41,654 (trainer:737) INFO: 10epoch:train:5301-5400batch: iter_time=2.077e-04, forward_time=0.105, loss_ctc=56.024, loss_att=70.748, acc=0.678, loss=66.331, backward_time=0.098, grad_norm=33.485, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.538e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:20:22,454 (trainer:737) INFO: 10epoch:train:5401-5500batch: iter_time=2.704e-04, forward_time=0.105, loss_ctc=47.754, loss_att=55.190, acc=0.693, loss=52.959, backward_time=0.097, grad_norm=30.844, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.536e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:21:03,356 (trainer:737) INFO: 10epoch:train:5501-5600batch: iter_time=2.197e-04, forward_time=0.105, loss_ctc=48.840, loss_att=54.190, acc=0.726, loss=52.585, backward_time=0.098, grad_norm=28.887, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.534e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:21:44,176 (trainer:737) INFO: 10epoch:train:5601-5700batch: iter_time=2.245e-04, forward_time=0.105, loss_ctc=51.939, loss_att=56.557, acc=0.695, loss=55.172, backward_time=0.097, grad_norm=30.523, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.531e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:22:25,933 (trainer:737) INFO: 10epoch:train:5701-5800batch: iter_time=2.454e-04, forward_time=0.104, loss_ctc=47.252, loss_att=57.032, acc=0.685, loss=54.098, backward_time=0.097, grad_norm=26.921, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.529e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 03:23:07,100 (trainer:737) INFO: 10epoch:train:5801-5900batch: iter_time=2.626e-04, forward_time=0.105, loss_ctc=60.822, loss_att=67.210, acc=0.671, loss=65.294, backward_time=0.097, grad_norm=38.960, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.527e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 03:23:48,201 (trainer:737) INFO: 10epoch:train:5901-6000batch: iter_time=2.576e-04, forward_time=0.105, loss_ctc=63.214, loss_att=74.021, acc=0.663, loss=70.779, backward_time=0.097, grad_norm=37.680, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.524e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 03:24:29,724 (trainer:737) INFO: 10epoch:train:6001-6100batch: iter_time=2.443e-04, forward_time=0.105, loss_ctc=47.513, loss_att=40.987, acc=0.735, loss=42.945, backward_time=0.097, grad_norm=25.966, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.522e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 03:25:11,174 (trainer:737) INFO: 10epoch:train:6101-6200batch: iter_time=2.438e-04, forward_time=0.107, loss_ctc=57.114, loss_att=67.365, acc=0.681, loss=64.290, backward_time=0.097, grad_norm=34.758, clip=99.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.520e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 03:25:38,926 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 03:25:57,921 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 03:26:01,608 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f388c4cf190>)
+[gpuc02:0/16] 2024-01-13 03:26:01,608 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 03:26:01,612 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 03:30:54,446 (trainer:737) INFO: 10epoch:train:6201-6300batch: iter_time=2.780, forward_time=0.104, loss_ctc=51.720, loss_att=56.473, acc=0.712, loss=55.047, backward_time=0.097, grad_norm=30.531, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.517e-04, train_time=3.432
+[gpuc02:0/16] 2024-01-13 03:31:35,161 (trainer:737) INFO: 10epoch:train:6301-6400batch: iter_time=2.391e-04, forward_time=0.103, loss_ctc=51.282, loss_att=57.169, acc=0.698, loss=55.403, backward_time=0.097, grad_norm=30.209, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.515e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 03:32:16,002 (trainer:737) INFO: 10epoch:train:6401-6500batch: iter_time=2.343e-04, forward_time=0.104, loss_ctc=43.495, loss_att=50.465, acc=0.715, loss=48.374, backward_time=0.098, grad_norm=28.050, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.513e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:32:56,822 (trainer:737) INFO: 10epoch:train:6501-6600batch: iter_time=2.353e-04, forward_time=0.104, loss_ctc=43.841, loss_att=52.674, acc=0.699, loss=50.024, backward_time=0.098, grad_norm=25.559, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.511e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:33:37,751 (trainer:737) INFO: 10epoch:train:6601-6700batch: iter_time=2.274e-04, forward_time=0.104, loss_ctc=50.884, loss_att=67.765, acc=0.693, loss=62.701, backward_time=0.097, grad_norm=31.561, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.508e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:34:18,717 (trainer:737) INFO: 10epoch:train:6701-6800batch: iter_time=2.322e-04, forward_time=0.105, loss_ctc=52.042, loss_att=61.468, acc=0.702, loss=58.640, backward_time=0.098, grad_norm=31.404, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.506e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:34:59,509 (trainer:737) INFO: 10epoch:train:6801-6900batch: iter_time=2.206e-04, forward_time=0.104, loss_ctc=51.462, loss_att=54.036, acc=0.708, loss=53.264, backward_time=0.097, grad_norm=31.398, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.029, optim0_lr0=6.504e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:35:40,266 (trainer:737) INFO: 10epoch:train:6901-7000batch: iter_time=2.568e-04, forward_time=0.104, loss_ctc=44.655, loss_att=52.959, acc=0.707, loss=50.468, backward_time=0.097, grad_norm=28.385, clip=100.000, loss_scale=7.737e+25, optim_step_time=0.030, optim0_lr0=6.501e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 03:36:21,172 (trainer:737) INFO: 10epoch:train:7001-7100batch: iter_time=2.357e-04, forward_time=0.105, loss_ctc=52.830, loss_att=63.656, acc=0.686, loss=60.408, backward_time=0.097, grad_norm=31.231, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.499e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:37:02,081 (trainer:737) INFO: 10epoch:train:7101-7200batch: iter_time=2.189e-04, forward_time=0.105, loss_ctc=64.946, loss_att=65.030, acc=0.694, loss=65.005, backward_time=0.097, grad_norm=42.205, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.497e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:37:42,950 (trainer:737) INFO: 10epoch:train:7201-7300batch: iter_time=2.166e-04, forward_time=0.105, loss_ctc=51.995, loss_att=63.636, acc=0.705, loss=60.144, backward_time=0.098, grad_norm=30.143, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.495e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:38:23,732 (trainer:737) INFO: 10epoch:train:7301-7400batch: iter_time=2.263e-04, forward_time=0.104, loss_ctc=54.171, loss_att=55.532, acc=0.703, loss=55.123, backward_time=0.097, grad_norm=31.297, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.492e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:39:04,492 (trainer:737) INFO: 10epoch:train:7401-7500batch: iter_time=2.459e-04, forward_time=0.103, loss_ctc=48.587, loss_att=55.934, acc=0.705, loss=53.730, backward_time=0.097, grad_norm=31.296, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.490e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 03:39:11,618 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 03:39:31,019 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 03:39:34,701 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fa2593d90>)
+[gpuc02:0/16] 2024-01-13 03:39:34,701 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 03:39:34,704 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 03:44:26,755 (trainer:737) INFO: 10epoch:train:7501-7600batch: iter_time=2.807, forward_time=0.107, loss_ctc=54.492, loss_att=61.514, acc=0.692, loss=59.408, backward_time=0.098, grad_norm=30.162, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.488e-04, train_time=3.222
+[gpuc02:0/16] 2024-01-13 03:45:08,011 (trainer:737) INFO: 10epoch:train:7601-7700batch: iter_time=1.891e-04, forward_time=0.105, loss_ctc=50.203, loss_att=57.475, acc=0.705, loss=55.293, backward_time=0.098, grad_norm=31.786, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.485e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 03:45:48,614 (trainer:737) INFO: 10epoch:train:7701-7800batch: iter_time=2.111e-04, forward_time=0.103, loss_ctc=38.369, loss_att=43.727, acc=0.697, loss=42.120, backward_time=0.096, grad_norm=23.451, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.483e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 03:46:30,128 (trainer:737) INFO: 10epoch:train:7801-7900batch: iter_time=1.964e-04, forward_time=0.106, loss_ctc=55.473, loss_att=69.321, acc=0.681, loss=65.167, backward_time=0.098, grad_norm=31.292, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.481e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 03:47:11,715 (trainer:737) INFO: 10epoch:train:7901-8000batch: iter_time=2.030e-04, forward_time=0.104, loss_ctc=47.156, loss_att=54.516, acc=0.697, loss=52.308, backward_time=0.097, grad_norm=29.571, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.479e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 03:47:52,639 (trainer:737) INFO: 10epoch:train:8001-8100batch: iter_time=2.363e-04, forward_time=0.105, loss_ctc=48.635, loss_att=53.634, acc=0.728, loss=52.134, backward_time=0.098, grad_norm=29.719, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.476e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:48:33,703 (trainer:737) INFO: 10epoch:train:8101-8200batch: iter_time=2.046e-04, forward_time=0.105, loss_ctc=51.655, loss_att=55.765, acc=0.698, loss=54.532, backward_time=0.098, grad_norm=31.767, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.474e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 03:49:14,885 (trainer:737) INFO: 10epoch:train:8201-8300batch: iter_time=2.344e-04, forward_time=0.105, loss_ctc=45.826, loss_att=55.959, acc=0.688, loss=52.919, backward_time=0.098, grad_norm=27.161, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.472e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 03:49:55,656 (trainer:737) INFO: 10epoch:train:8301-8400batch: iter_time=2.046e-04, forward_time=0.105, loss_ctc=60.121, loss_att=67.454, acc=0.672, loss=65.254, backward_time=0.098, grad_norm=40.813, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.470e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 03:50:36,500 (trainer:737) INFO: 10epoch:train:8401-8500batch: iter_time=2.001e-04, forward_time=0.105, loss_ctc=62.620, loss_att=73.112, acc=0.665, loss=69.965, backward_time=0.098, grad_norm=37.763, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.467e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:51:17,244 (trainer:737) INFO: 10epoch:train:8501-8600batch: iter_time=2.311e-04, forward_time=0.104, loss_ctc=46.996, loss_att=40.222, acc=0.739, loss=42.254, backward_time=0.097, grad_norm=26.644, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.465e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 03:51:58,114 (trainer:737) INFO: 10epoch:train:8601-8700batch: iter_time=2.289e-04, forward_time=0.105, loss_ctc=55.902, loss_att=66.700, acc=0.683, loss=63.460, backward_time=0.098, grad_norm=34.817, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.029, optim0_lr0=6.463e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 03:52:23,842 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 03:52:43,542 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 03:52:47,241 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f388cc0f430>)
+[gpuc02:0/16] 2024-01-13 03:52:47,241 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 03:52:47,244 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 03:57:18,318 (trainer:737) INFO: 10epoch:train:8701-8800batch: iter_time=2.732, forward_time=0.105, loss_ctc=51.817, loss_att=55.925, acc=0.714, loss=54.692, backward_time=0.098, grad_norm=31.573, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.461e-04, train_time=3.202
+[gpuc02:0/16] 2024-01-13 03:57:59,412 (trainer:737) INFO: 10epoch:train:8801-8900batch: iter_time=2.433e-04, forward_time=0.104, loss_ctc=50.916, loss_att=55.197, acc=0.704, loss=53.913, backward_time=0.098, grad_norm=29.981, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.458e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 03:58:40,298 (trainer:737) INFO: 10epoch:train:8901-9000batch: iter_time=2.322e-04, forward_time=0.105, loss_ctc=43.085, loss_att=50.179, acc=0.716, loss=48.051, backward_time=0.098, grad_norm=27.690, clip=100.000, loss_scale=1.547e+26, optim_step_time=0.030, optim0_lr0=6.456e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 03:59:21,362 (trainer:737) INFO: 10epoch:train:9001-9100batch: iter_time=2.440e-04, forward_time=0.104, loss_ctc=43.734, loss_att=52.523, acc=0.701, loss=49.886, backward_time=0.098, grad_norm=26.574, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.454e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:00:02,403 (trainer:737) INFO: 10epoch:train:9101-9200batch: iter_time=2.346e-04, forward_time=0.105, loss_ctc=49.998, loss_att=67.399, acc=0.695, loss=62.179, backward_time=0.098, grad_norm=30.006, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.452e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:00:43,468 (trainer:737) INFO: 10epoch:train:9201-9300batch: iter_time=1.998e-04, forward_time=0.105, loss_ctc=51.903, loss_att=61.896, acc=0.703, loss=58.898, backward_time=0.098, grad_norm=32.770, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.449e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:01:24,908 (trainer:737) INFO: 10epoch:train:9301-9400batch: iter_time=2.067e-04, forward_time=0.105, loss_ctc=50.812, loss_att=52.831, acc=0.711, loss=52.225, backward_time=0.098, grad_norm=31.565, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.447e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 04:02:05,848 (trainer:737) INFO: 10epoch:train:9401-9500batch: iter_time=2.449e-04, forward_time=0.104, loss_ctc=44.378, loss_att=52.625, acc=0.711, loss=50.151, backward_time=0.098, grad_norm=28.877, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.445e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:02:46,848 (trainer:737) INFO: 10epoch:train:9501-9600batch: iter_time=2.539e-04, forward_time=0.105, loss_ctc=52.390, loss_att=63.582, acc=0.689, loss=60.224, backward_time=0.098, grad_norm=32.115, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.443e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:03:29,155 (trainer:737) INFO: 10epoch:train:9601-9700batch: iter_time=2.446e-04, forward_time=0.105, loss_ctc=64.102, loss_att=64.639, acc=0.694, loss=64.478, backward_time=0.098, grad_norm=42.233, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.440e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-13 04:04:10,160 (trainer:737) INFO: 10epoch:train:9701-9800batch: iter_time=2.385e-04, forward_time=0.105, loss_ctc=51.632, loss_att=62.883, acc=0.707, loss=59.508, backward_time=0.098, grad_norm=29.569, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.438e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:04:51,096 (trainer:737) INFO: 10epoch:train:9801-9900batch: iter_time=2.182e-04, forward_time=0.104, loss_ctc=53.501, loss_att=54.679, acc=0.707, loss=54.325, backward_time=0.098, grad_norm=31.585, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.436e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:05:31,978 (trainer:737) INFO: 10epoch:train:9901-10000batch: iter_time=2.109e-04, forward_time=0.104, loss_ctc=48.990, loss_att=55.350, acc=0.706, loss=53.442, backward_time=0.098, grad_norm=31.667, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.434e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:05:36,943 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 04:05:56,819 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 04:06:00,958 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c7b1f370>)
+[gpuc02:0/16] 2024-01-13 04:06:00,958 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 04:06:00,961 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 04:10:50,771 (trainer:737) INFO: 10epoch:train:10001-10100batch: iter_time=2.589, forward_time=0.104, loss_ctc=54.511, loss_att=60.305, acc=0.704, loss=58.567, backward_time=0.097, grad_norm=30.504, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.432e-04, train_time=3.188
+[gpuc02:0/16] 2024-01-13 04:11:31,724 (trainer:737) INFO: 10epoch:train:10101-10200batch: iter_time=1.779e-04, forward_time=0.104, loss_ctc=49.531, loss_att=55.786, acc=0.718, loss=53.909, backward_time=0.097, grad_norm=31.203, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.429e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:12:12,358 (trainer:737) INFO: 10epoch:train:10201-10300batch: iter_time=2.094e-04, forward_time=0.103, loss_ctc=38.173, loss_att=42.493, acc=0.713, loss=41.197, backward_time=0.096, grad_norm=22.695, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.427e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 04:12:53,505 (trainer:737) INFO: 10epoch:train:10301-10400batch: iter_time=1.776e-04, forward_time=0.106, loss_ctc=54.884, loss_att=69.507, acc=0.692, loss=65.120, backward_time=0.098, grad_norm=31.676, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.425e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 04:13:34,482 (trainer:737) INFO: 10epoch:train:10401-10500batch: iter_time=1.734e-04, forward_time=0.105, loss_ctc=46.402, loss_att=57.536, acc=0.695, loss=54.196, backward_time=0.097, grad_norm=30.524, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.423e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:14:15,431 (trainer:737) INFO: 10epoch:train:10501-10600batch: iter_time=2.189e-04, forward_time=0.106, loss_ctc=47.819, loss_att=53.723, acc=0.735, loss=51.952, backward_time=0.098, grad_norm=27.775, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.420e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:14:56,264 (trainer:737) INFO: 10epoch:train:10601-10700batch: iter_time=2.103e-04, forward_time=0.105, loss_ctc=50.912, loss_att=57.157, acc=0.700, loss=55.283, backward_time=0.097, grad_norm=32.433, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.418e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:15:37,059 (trainer:737) INFO: 10epoch:train:10701-10800batch: iter_time=2.258e-04, forward_time=0.104, loss_ctc=45.768, loss_att=57.442, acc=0.693, loss=53.940, backward_time=0.097, grad_norm=26.770, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.416e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:16:18,018 (trainer:737) INFO: 10epoch:train:10801-10900batch: iter_time=1.988e-04, forward_time=0.105, loss_ctc=58.973, loss_att=65.294, acc=0.688, loss=63.398, backward_time=0.097, grad_norm=37.830, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.029, optim0_lr0=6.414e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:16:59,349 (trainer:737) INFO: 10epoch:train:10901-11000batch: iter_time=1.885e-04, forward_time=0.106, loss_ctc=60.932, loss_att=71.867, acc=0.683, loss=68.586, backward_time=0.098, grad_norm=39.026, clip=100.000, loss_scale=3.095e+26, optim_step_time=0.030, optim0_lr0=6.412e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 04:17:40,330 (trainer:737) INFO: 10epoch:train:11001-11100batch: iter_time=2.083e-04, forward_time=0.104, loss_ctc=46.664, loss_att=40.886, acc=0.738, loss=42.620, backward_time=0.096, grad_norm=26.294, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.409e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:18:21,278 (trainer:737) INFO: 10epoch:train:11101-11200batch: iter_time=2.050e-04, forward_time=0.105, loss_ctc=55.181, loss_att=69.260, acc=0.687, loss=65.036, backward_time=0.098, grad_norm=33.668, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.407e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:18:45,638 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 04:19:05,036 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 04:19:08,608 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f388d8a0280>)
+[gpuc02:0/16] 2024-01-13 04:19:08,609 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 04:19:08,612 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 04:23:38,251 (trainer:737) INFO: 10epoch:train:11201-11300batch: iter_time=2.639, forward_time=0.106, loss_ctc=51.184, loss_att=54.302, acc=0.724, loss=53.366, backward_time=0.098, grad_norm=31.553, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.405e-04, train_time=3.170
+[gpuc02:0/16] 2024-01-13 04:24:19,511 (trainer:737) INFO: 10epoch:train:11301-11400batch: iter_time=1.546e-04, forward_time=0.105, loss_ctc=50.694, loss_att=53.936, acc=0.706, loss=52.963, backward_time=0.098, grad_norm=30.093, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.029, optim0_lr0=6.403e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 04:25:00,843 (trainer:737) INFO: 10epoch:train:11401-11500batch: iter_time=1.917e-04, forward_time=0.104, loss_ctc=42.982, loss_att=48.831, acc=0.719, loss=47.076, backward_time=0.097, grad_norm=27.140, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.401e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 04:25:41,588 (trainer:737) INFO: 10epoch:train:11501-11600batch: iter_time=1.622e-04, forward_time=0.104, loss_ctc=43.653, loss_att=51.078, acc=0.705, loss=48.850, backward_time=0.097, grad_norm=26.691, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.399e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:26:22,744 (trainer:737) INFO: 10epoch:train:11601-11700batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=50.203, loss_att=66.924, acc=0.697, loss=61.908, backward_time=0.098, grad_norm=31.028, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.396e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 04:27:04,079 (trainer:737) INFO: 10epoch:train:11701-11800batch: iter_time=1.863e-04, forward_time=0.105, loss_ctc=52.174, loss_att=60.475, acc=0.706, loss=57.985, backward_time=0.098, grad_norm=32.242, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.394e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 04:27:45,578 (trainer:737) INFO: 10epoch:train:11801-11900batch: iter_time=1.842e-04, forward_time=0.108, loss_ctc=50.526, loss_att=52.418, acc=0.712, loss=51.851, backward_time=0.098, grad_norm=29.828, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.392e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 04:28:26,425 (trainer:737) INFO: 10epoch:train:11901-12000batch: iter_time=1.849e-04, forward_time=0.105, loss_ctc=44.794, loss_att=52.789, acc=0.709, loss=50.390, backward_time=0.098, grad_norm=30.047, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.390e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:29:07,360 (trainer:737) INFO: 10epoch:train:12001-12100batch: iter_time=1.894e-04, forward_time=0.105, loss_ctc=52.067, loss_att=62.431, acc=0.692, loss=59.322, backward_time=0.098, grad_norm=31.475, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.388e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:29:48,281 (trainer:737) INFO: 10epoch:train:12101-12200batch: iter_time=1.942e-04, forward_time=0.105, loss_ctc=62.437, loss_att=63.838, acc=0.698, loss=63.418, backward_time=0.098, grad_norm=41.081, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.385e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:30:29,132 (trainer:737) INFO: 10epoch:train:12201-12300batch: iter_time=1.711e-04, forward_time=0.105, loss_ctc=51.178, loss_att=61.743, acc=0.709, loss=58.573, backward_time=0.098, grad_norm=29.163, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.383e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:31:09,900 (trainer:737) INFO: 10epoch:train:12301-12400batch: iter_time=1.956e-04, forward_time=0.104, loss_ctc=53.607, loss_att=55.296, acc=0.707, loss=54.789, backward_time=0.097, grad_norm=31.512, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.381e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:31:50,631 (trainer:737) INFO: 10epoch:train:12401-12500batch: iter_time=2.118e-04, forward_time=0.104, loss_ctc=48.447, loss_att=55.974, acc=0.709, loss=53.716, backward_time=0.098, grad_norm=31.581, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.379e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:31:59,553 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 04:32:18,996 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 04:32:22,730 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38be55add0>)
+[gpuc02:0/16] 2024-01-13 04:32:22,730 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 04:32:22,733 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 04:37:08,787 (trainer:737) INFO: 10epoch:train:12501-12600batch: iter_time=2.715, forward_time=0.106, loss_ctc=53.957, loss_att=62.294, acc=0.692, loss=59.793, backward_time=0.099, grad_norm=31.472, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.377e-04, train_time=3.181
+[gpuc02:0/16] 2024-01-13 04:37:49,666 (trainer:737) INFO: 10epoch:train:12601-12700batch: iter_time=2.047e-04, forward_time=0.104, loss_ctc=48.541, loss_att=57.461, acc=0.707, loss=54.785, backward_time=0.098, grad_norm=30.500, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.029, optim0_lr0=6.375e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:38:30,156 (trainer:737) INFO: 10epoch:train:12701-12800batch: iter_time=2.311e-04, forward_time=0.103, loss_ctc=37.602, loss_att=43.869, acc=0.700, loss=41.989, backward_time=0.096, grad_norm=25.669, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.372e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-13 04:39:11,415 (trainer:737) INFO: 10epoch:train:12801-12900batch: iter_time=2.442e-04, forward_time=0.106, loss_ctc=54.293, loss_att=70.482, acc=0.682, loss=65.625, backward_time=0.099, grad_norm=31.842, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.370e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 04:39:52,175 (trainer:737) INFO: 10epoch:train:12901-13000batch: iter_time=2.559e-04, forward_time=0.104, loss_ctc=46.000, loss_att=54.832, acc=0.699, loss=52.183, backward_time=0.097, grad_norm=37.651, clip=100.000, loss_scale=6.190e+26, optim_step_time=0.030, optim0_lr0=6.368e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:40:33,377 (trainer:737) INFO: 10epoch:train:13001-13100batch: iter_time=2.309e-04, forward_time=0.105, loss_ctc=47.000, loss_att=52.940, acc=0.731, loss=51.158, backward_time=0.098, grad_norm=28.669, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.366e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 04:41:14,416 (trainer:737) INFO: 10epoch:train:13101-13200batch: iter_time=2.251e-04, forward_time=0.104, loss_ctc=50.458, loss_att=56.043, acc=0.698, loss=54.367, backward_time=0.097, grad_norm=32.097, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.364e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:41:55,273 (trainer:737) INFO: 10epoch:train:13201-13300batch: iter_time=2.134e-04, forward_time=0.104, loss_ctc=45.559, loss_att=56.451, acc=0.691, loss=53.183, backward_time=0.097, grad_norm=27.851, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.362e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:42:36,497 (trainer:737) INFO: 10epoch:train:13301-13400batch: iter_time=2.193e-04, forward_time=0.105, loss_ctc=59.071, loss_att=66.783, acc=0.674, loss=64.470, backward_time=0.098, grad_norm=40.413, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.360e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 04:43:18,181 (trainer:737) INFO: 10epoch:train:13401-13500batch: iter_time=2.151e-04, forward_time=0.105, loss_ctc=60.228, loss_att=72.850, acc=0.666, loss=69.063, backward_time=0.098, grad_norm=37.375, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.357e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 04:43:58,912 (trainer:737) INFO: 10epoch:train:13501-13600batch: iter_time=2.072e-04, forward_time=0.104, loss_ctc=46.152, loss_att=40.011, acc=0.740, loss=41.853, backward_time=0.097, grad_norm=25.372, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.355e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:44:40,357 (trainer:737) INFO: 10epoch:train:13601-13700batch: iter_time=2.432e-04, forward_time=0.107, loss_ctc=55.435, loss_att=66.749, acc=0.684, loss=63.355, backward_time=0.098, grad_norm=34.055, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.353e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 04:45:07,442 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 04:45:26,751 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 04:45:30,571 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40c0efeb00>)
+[gpuc02:0/16] 2024-01-13 04:45:30,572 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 04:45:30,575 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 04:49:56,103 (trainer:737) INFO: 10epoch:train:13701-13800batch: iter_time=2.617, forward_time=0.107, loss_ctc=50.799, loss_att=54.272, acc=0.714, loss=53.230, backward_time=0.098, grad_norm=31.084, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.351e-04, train_time=3.157
+[gpuc02:0/16] 2024-01-13 04:50:36,865 (trainer:737) INFO: 10epoch:train:13801-13900batch: iter_time=2.012e-04, forward_time=0.104, loss_ctc=50.215, loss_att=54.210, acc=0.697, loss=53.011, backward_time=0.097, grad_norm=29.370, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.349e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:51:17,614 (trainer:737) INFO: 10epoch:train:13901-14000batch: iter_time=1.923e-04, forward_time=0.104, loss_ctc=42.335, loss_att=48.917, acc=0.711, loss=46.943, backward_time=0.098, grad_norm=27.886, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.347e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:51:58,395 (trainer:737) INFO: 10epoch:train:14001-14100batch: iter_time=2.111e-04, forward_time=0.104, loss_ctc=42.864, loss_att=51.482, acc=0.692, loss=48.897, backward_time=0.097, grad_norm=27.078, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.345e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:52:39,403 (trainer:737) INFO: 10epoch:train:14101-14200batch: iter_time=2.081e-04, forward_time=0.104, loss_ctc=49.046, loss_att=64.008, acc=0.695, loss=59.519, backward_time=0.098, grad_norm=31.231, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.030, optim0_lr0=6.343e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:53:20,445 (trainer:737) INFO: 10epoch:train:14201-14300batch: iter_time=2.333e-04, forward_time=0.105, loss_ctc=50.598, loss_att=59.899, acc=0.700, loss=57.109, backward_time=0.098, grad_norm=31.579, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.340e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 04:54:01,212 (trainer:737) INFO: 10epoch:train:14301-14400batch: iter_time=2.403e-04, forward_time=0.103, loss_ctc=50.956, loss_att=51.239, acc=0.706, loss=51.154, backward_time=0.097, grad_norm=31.469, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.338e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:54:41,896 (trainer:737) INFO: 10epoch:train:14401-14500batch: iter_time=2.234e-04, forward_time=0.104, loss_ctc=44.233, loss_att=50.524, acc=0.713, loss=48.637, backward_time=0.097, grad_norm=27.836, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.336e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:55:22,854 (trainer:737) INFO: 10epoch:train:14501-14600batch: iter_time=2.333e-04, forward_time=0.104, loss_ctc=51.907, loss_att=62.314, acc=0.679, loss=59.192, backward_time=0.098, grad_norm=31.793, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.334e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 04:56:03,630 (trainer:737) INFO: 10epoch:train:14601-14700batch: iter_time=2.273e-04, forward_time=0.104, loss_ctc=61.728, loss_att=63.581, acc=0.688, loss=63.025, backward_time=0.098, grad_norm=40.027, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.332e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:56:44,451 (trainer:737) INFO: 10epoch:train:14701-14800batch: iter_time=2.385e-04, forward_time=0.104, loss_ctc=50.176, loss_att=60.840, acc=0.702, loss=57.641, backward_time=0.098, grad_norm=29.643, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.330e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 04:57:25,200 (trainer:737) INFO: 10epoch:train:14801-14900batch: iter_time=2.081e-04, forward_time=0.104, loss_ctc=52.530, loss_att=52.067, acc=0.708, loss=52.206, backward_time=0.097, grad_norm=30.980, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.328e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 04:58:06,154 (trainer:737) INFO: 10epoch:train:14901-15000batch: iter_time=2.006e-04, forward_time=0.103, loss_ctc=47.840, loss_att=55.050, acc=0.700, loss=52.887, backward_time=0.097, grad_norm=31.430, clip=100.000, loss_scale=1.238e+27, optim_step_time=0.029, optim0_lr0=6.326e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 05:18:15,797 (trainer:343) INFO: 10epoch results: [train] iter_time=0.220, forward_time=0.105, loss_ctc=51.529, loss_att=58.361, acc=0.698, loss=56.312, backward_time=0.098, grad_norm=31.372, clip=99.987, loss_scale=3.282e+26, optim_step_time=0.030, optim0_lr0=6.491e-04, train_time=0.643, time=2 hours, 40 minutes and 59.27 seconds, total_count=150000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=65.265, cer_ctc=0.328, loss_att=62.695, acc=0.540, cer=0.439, wer=0.999, loss=63.466, time=19 minutes and 56.8 seconds, total_count=46710, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 05:18:20,594 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-13 05:18:20,603 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/5epoch.pth
+[gpuc02:0/16] 2024-01-13 05:18:20,603 (trainer:272) INFO: 11/45epoch started. Estimated time to finish: 4 days, 8 hours and 26 minutes
+[gpuc02:0/16] 2024-01-13 05:18:20,615 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 05:18:39,425 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 05:18:42,944 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e413fbb0>)
+[gpuc02:0/16] 2024-01-13 05:18:42,944 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 05:18:42,947 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 05:23:28,733 (trainer:737) INFO: 11epoch:train:1-100batch: iter_time=2.562, forward_time=0.157, loss_ctc=59.368, loss_att=56.940, acc=0.698, loss=57.668, backward_time=0.110, grad_norm=34.920, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.031, optim0_lr0=6.323e-04, train_time=3.081
+[gpuc02:0/16] 2024-01-13 05:24:11,634 (trainer:737) INFO: 11epoch:train:101-200batch: iter_time=1.294e-04, forward_time=0.116, loss_ctc=67.261, loss_att=80.685, acc=0.636, loss=76.658, backward_time=0.099, grad_norm=52.244, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.321e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-13 05:24:52,925 (trainer:737) INFO: 11epoch:train:201-300batch: iter_time=1.306e-04, forward_time=0.110, loss_ctc=55.509, loss_att=55.709, acc=0.682, loss=55.649, backward_time=0.098, grad_norm=36.983, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.319e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:25:34,268 (trainer:737) INFO: 11epoch:train:301-400batch: iter_time=1.266e-04, forward_time=0.104, loss_ctc=54.618, loss_att=60.447, acc=0.692, loss=58.698, backward_time=0.099, grad_norm=33.910, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.317e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:26:15,282 (trainer:737) INFO: 11epoch:train:401-500batch: iter_time=1.410e-04, forward_time=0.103, loss_ctc=47.090, loss_att=54.559, acc=0.688, loss=52.318, backward_time=0.099, grad_norm=29.971, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.315e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:26:56,890 (trainer:737) INFO: 11epoch:train:501-600batch: iter_time=1.325e-04, forward_time=0.104, loss_ctc=54.355, loss_att=56.793, acc=0.708, loss=56.062, backward_time=0.099, grad_norm=34.651, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.313e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 05:27:37,884 (trainer:737) INFO: 11epoch:train:601-700batch: iter_time=1.305e-04, forward_time=0.104, loss_ctc=50.925, loss_att=58.338, acc=0.690, loss=56.114, backward_time=0.099, grad_norm=32.045, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.311e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:28:20,028 (trainer:737) INFO: 11epoch:train:701-800batch: iter_time=1.253e-04, forward_time=0.112, loss_ctc=50.993, loss_att=54.562, acc=0.677, loss=53.491, backward_time=0.099, grad_norm=32.424, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.032, optim0_lr0=6.309e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-13 05:29:01,495 (trainer:737) INFO: 11epoch:train:801-900batch: iter_time=1.265e-04, forward_time=0.105, loss_ctc=58.203, loss_att=65.791, acc=0.695, loss=63.515, backward_time=0.100, grad_norm=36.337, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.307e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 05:29:42,849 (trainer:737) INFO: 11epoch:train:901-1000batch: iter_time=1.309e-04, forward_time=0.105, loss_ctc=63.160, loss_att=63.119, acc=0.704, loss=63.131, backward_time=0.099, grad_norm=37.047, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.305e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:30:25,644 (trainer:737) INFO: 11epoch:train:1001-1100batch: iter_time=1.323e-04, forward_time=0.111, loss_ctc=57.907, loss_att=62.784, acc=0.678, loss=61.321, backward_time=0.103, grad_norm=36.584, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.032, optim0_lr0=6.303e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-13 05:31:11,631 (trainer:737) INFO: 11epoch:train:1101-1200batch: iter_time=1.354e-04, forward_time=0.105, loss_ctc=55.741, loss_att=57.922, acc=0.687, loss=57.268, backward_time=0.100, grad_norm=35.541, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.300e-04, train_time=0.459
+[gpuc02:0/16] 2024-01-13 05:31:50,444 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 05:32:09,501 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 05:32:13,143 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3f885f2a40>)
+[gpuc02:0/16] 2024-01-13 05:32:13,143 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 05:32:13,146 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 05:37:56,420 (trainer:737) INFO: 11epoch:train:1201-1300batch: iter_time=3.494, forward_time=0.105, loss_ctc=52.982, loss_att=56.123, acc=0.678, loss=55.181, backward_time=0.098, grad_norm=33.707, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.298e-04, train_time=4.048
+[gpuc02:0/16] 2024-01-13 05:38:37,683 (trainer:737) INFO: 11epoch:train:1301-1400batch: iter_time=2.000e-04, forward_time=0.104, loss_ctc=57.765, loss_att=66.100, acc=0.664, loss=63.599, backward_time=0.098, grad_norm=35.225, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.296e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:39:18,860 (trainer:737) INFO: 11epoch:train:1401-1500batch: iter_time=1.801e-04, forward_time=0.104, loss_ctc=58.268, loss_att=66.020, acc=0.670, loss=63.695, backward_time=0.097, grad_norm=48.180, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.294e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:39:59,692 (trainer:737) INFO: 11epoch:train:1501-1600batch: iter_time=1.858e-04, forward_time=0.104, loss_ctc=59.675, loss_att=59.315, acc=0.676, loss=59.423, backward_time=0.097, grad_norm=35.802, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.292e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:40:40,488 (trainer:737) INFO: 11epoch:train:1601-1700batch: iter_time=1.867e-04, forward_time=0.103, loss_ctc=51.000, loss_att=56.141, acc=0.695, loss=54.599, backward_time=0.097, grad_norm=34.148, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.290e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:41:21,732 (trainer:737) INFO: 11epoch:train:1701-1800batch: iter_time=1.955e-04, forward_time=0.104, loss_ctc=51.430, loss_att=58.036, acc=0.703, loss=56.054, backward_time=0.098, grad_norm=32.386, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.288e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:42:02,392 (trainer:737) INFO: 11epoch:train:1801-1900batch: iter_time=2.094e-04, forward_time=0.103, loss_ctc=49.724, loss_att=55.634, acc=0.697, loss=53.861, backward_time=0.097, grad_norm=31.838, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.286e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 05:42:43,567 (trainer:737) INFO: 11epoch:train:1901-2000batch: iter_time=2.062e-04, forward_time=0.103, loss_ctc=46.294, loss_att=53.311, acc=0.690, loss=51.206, backward_time=0.097, grad_norm=32.549, clip=100.000, loss_scale=2.476e+27, optim_step_time=0.030, optim0_lr0=6.284e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:43:25,036 (trainer:737) INFO: 11epoch:train:2001-2100batch: iter_time=1.683e-04, forward_time=0.108, loss_ctc=58.067, loss_att=67.434, acc=0.689, loss=64.624, backward_time=0.098, grad_norm=33.598, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.282e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 05:44:05,887 (trainer:737) INFO: 11epoch:train:2101-2200batch: iter_time=1.819e-04, forward_time=0.104, loss_ctc=58.014, loss_att=54.846, acc=0.715, loss=55.796, backward_time=0.098, grad_norm=36.832, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.280e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:44:47,121 (trainer:737) INFO: 11epoch:train:2201-2300batch: iter_time=1.841e-04, forward_time=0.103, loss_ctc=59.037, loss_att=62.439, acc=0.684, loss=61.418, backward_time=0.097, grad_norm=35.950, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.278e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:45:28,152 (trainer:737) INFO: 11epoch:train:2301-2400batch: iter_time=1.792e-04, forward_time=0.104, loss_ctc=56.202, loss_att=58.520, acc=0.694, loss=57.825, backward_time=0.098, grad_norm=34.603, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.276e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:46:08,960 (trainer:737) INFO: 11epoch:train:2401-2500batch: iter_time=1.732e-04, forward_time=0.103, loss_ctc=51.309, loss_att=59.365, acc=0.656, loss=56.948, backward_time=0.097, grad_norm=35.444, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.273e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:46:15,336 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 05:46:34,984 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 05:46:38,647 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e53ddfc0>)
+[gpuc02:0/16] 2024-01-13 05:46:38,647 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 05:46:38,650 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 05:51:44,733 (trainer:737) INFO: 11epoch:train:2501-2600batch: iter_time=2.926, forward_time=0.118, loss_ctc=57.201, loss_att=55.652, acc=0.703, loss=56.117, backward_time=0.101, grad_norm=31.463, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.271e-04, train_time=3.357
+[gpuc02:0/16] 2024-01-13 05:52:25,650 (trainer:737) INFO: 11epoch:train:2601-2700batch: iter_time=2.244e-04, forward_time=0.104, loss_ctc=62.161, loss_att=78.342, acc=0.642, loss=73.487, backward_time=0.098, grad_norm=46.824, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.269e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 05:53:06,954 (trainer:737) INFO: 11epoch:train:2701-2800batch: iter_time=4.029e-04, forward_time=0.105, loss_ctc=53.404, loss_att=53.857, acc=0.690, loss=53.721, backward_time=0.100, grad_norm=34.167, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.031, optim0_lr0=6.267e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:53:47,951 (trainer:737) INFO: 11epoch:train:2801-2900batch: iter_time=1.561e-04, forward_time=0.104, loss_ctc=52.165, loss_att=58.539, acc=0.699, loss=56.627, backward_time=0.099, grad_norm=32.583, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.031, optim0_lr0=6.265e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:54:29,292 (trainer:737) INFO: 11epoch:train:2901-3000batch: iter_time=1.495e-04, forward_time=0.104, loss_ctc=46.183, loss_att=54.143, acc=0.692, loss=51.755, backward_time=0.099, grad_norm=29.914, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.031, optim0_lr0=6.263e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:55:10,366 (trainer:737) INFO: 11epoch:train:3001-3100batch: iter_time=2.067e-04, forward_time=0.103, loss_ctc=52.296, loss_att=54.758, acc=0.714, loss=54.020, backward_time=0.097, grad_norm=32.651, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.261e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 05:55:51,159 (trainer:737) INFO: 11epoch:train:3101-3200batch: iter_time=1.909e-04, forward_time=0.104, loss_ctc=48.776, loss_att=57.457, acc=0.693, loss=54.853, backward_time=0.098, grad_norm=30.574, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.259e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:56:31,940 (trainer:737) INFO: 11epoch:train:3201-3300batch: iter_time=2.124e-04, forward_time=0.104, loss_ctc=48.716, loss_att=51.683, acc=0.687, loss=50.793, backward_time=0.096, grad_norm=31.639, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.257e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 05:57:13,216 (trainer:737) INFO: 11epoch:train:3301-3400batch: iter_time=1.909e-04, forward_time=0.105, loss_ctc=55.707, loss_att=63.932, acc=0.705, loss=61.465, backward_time=0.098, grad_norm=34.727, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.029, optim0_lr0=6.255e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 05:57:54,477 (trainer:737) INFO: 11epoch:train:3401-3500batch: iter_time=2.333e-04, forward_time=0.105, loss_ctc=59.719, loss_att=60.954, acc=0.710, loss=60.583, backward_time=0.098, grad_norm=35.022, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.253e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 05:58:35,501 (trainer:737) INFO: 11epoch:train:3501-3600batch: iter_time=1.912e-04, forward_time=0.104, loss_ctc=55.703, loss_att=61.004, acc=0.683, loss=59.414, backward_time=0.097, grad_norm=34.200, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.251e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:59:16,500 (trainer:737) INFO: 11epoch:train:3601-3700batch: iter_time=1.913e-04, forward_time=0.104, loss_ctc=53.698, loss_att=55.411, acc=0.694, loss=54.897, backward_time=0.097, grad_norm=34.510, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.249e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 05:59:45,705 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 06:00:05,302 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 06:00:09,260 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3859f57550>)
+[gpuc02:0/16] 2024-01-13 06:00:09,260 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 06:00:09,264 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 06:04:43,937 (trainer:737) INFO: 11epoch:train:3701-3800batch: iter_time=2.853, forward_time=0.107, loss_ctc=52.055, loss_att=55.793, acc=0.680, loss=54.671, backward_time=0.097, grad_norm=33.075, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.030, optim0_lr0=6.247e-04, train_time=3.274
+[gpuc02:0/16] 2024-01-13 06:05:24,862 (trainer:737) INFO: 11epoch:train:3801-3900batch: iter_time=2.265e-04, forward_time=0.104, loss_ctc=56.493, loss_att=64.959, acc=0.667, loss=62.419, backward_time=0.098, grad_norm=34.574, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.029, optim0_lr0=6.245e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:06:05,731 (trainer:737) INFO: 11epoch:train:3901-4000batch: iter_time=2.314e-04, forward_time=0.104, loss_ctc=57.403, loss_att=65.745, acc=0.672, loss=63.242, backward_time=0.097, grad_norm=48.383, clip=100.000, loss_scale=4.952e+27, optim_step_time=0.029, optim0_lr0=6.243e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:06:46,786 (trainer:737) INFO: 11epoch:train:4001-4100batch: iter_time=1.775e-04, forward_time=0.104, loss_ctc=58.039, loss_att=58.374, acc=0.678, loss=58.274, backward_time=0.097, grad_norm=33.327, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.029, optim0_lr0=6.241e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:07:27,610 (trainer:737) INFO: 11epoch:train:4101-4200batch: iter_time=2.068e-04, forward_time=0.103, loss_ctc=50.367, loss_att=55.960, acc=0.697, loss=54.282, backward_time=0.097, grad_norm=33.963, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.029, optim0_lr0=6.239e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:08:08,651 (trainer:737) INFO: 11epoch:train:4201-4300batch: iter_time=1.966e-04, forward_time=0.103, loss_ctc=49.774, loss_att=55.933, acc=0.708, loss=54.085, backward_time=0.097, grad_norm=30.601, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.029, optim0_lr0=6.237e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:08:49,444 (trainer:737) INFO: 11epoch:train:4301-4400batch: iter_time=1.977e-04, forward_time=0.102, loss_ctc=48.622, loss_att=55.396, acc=0.702, loss=53.364, backward_time=0.097, grad_norm=29.331, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.029, optim0_lr0=6.235e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:09:30,295 (trainer:737) INFO: 11epoch:train:4401-4500batch: iter_time=2.092e-04, forward_time=0.105, loss_ctc=45.408, loss_att=52.519, acc=0.693, loss=50.386, backward_time=0.097, grad_norm=31.415, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.233e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:10:11,422 (trainer:737) INFO: 11epoch:train:4501-4600batch: iter_time=1.822e-04, forward_time=0.106, loss_ctc=57.278, loss_att=67.244, acc=0.692, loss=64.254, backward_time=0.099, grad_norm=32.845, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.231e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:10:52,890 (trainer:737) INFO: 11epoch:train:4601-4700batch: iter_time=1.644e-04, forward_time=0.104, loss_ctc=56.511, loss_att=53.745, acc=0.718, loss=54.575, backward_time=0.098, grad_norm=37.486, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.031, optim0_lr0=6.229e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 06:11:34,163 (trainer:737) INFO: 11epoch:train:4701-4800batch: iter_time=1.486e-04, forward_time=0.104, loss_ctc=56.953, loss_att=61.483, acc=0.687, loss=60.124, backward_time=0.099, grad_norm=34.344, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.031, optim0_lr0=6.227e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 06:12:15,124 (trainer:737) INFO: 11epoch:train:4801-4900batch: iter_time=1.789e-04, forward_time=0.105, loss_ctc=55.348, loss_att=57.311, acc=0.697, loss=56.722, backward_time=0.098, grad_norm=34.404, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.225e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:12:55,846 (trainer:737) INFO: 11epoch:train:4901-5000batch: iter_time=1.599e-04, forward_time=0.103, loss_ctc=49.956, loss_att=58.418, acc=0.660, loss=55.879, backward_time=0.096, grad_norm=33.531, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.223e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 06:13:03,034 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 06:13:22,184 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 06:13:25,842 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38accb73d0>)
+[gpuc02:0/16] 2024-01-13 06:13:25,843 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 06:13:25,846 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 06:19:00,581 (trainer:737) INFO: 11epoch:train:5001-5100batch: iter_time=2.531, forward_time=0.105, loss_ctc=56.956, loss_att=57.666, acc=0.714, loss=57.453, backward_time=0.098, grad_norm=33.918, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.221e-04, train_time=3.647
+[gpuc02:0/16] 2024-01-13 06:19:42,245 (trainer:737) INFO: 11epoch:train:5101-5200batch: iter_time=1.078e-04, forward_time=0.108, loss_ctc=62.252, loss_att=82.374, acc=0.643, loss=76.337, backward_time=0.099, grad_norm=51.124, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.219e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 06:20:23,089 (trainer:737) INFO: 11epoch:train:5201-5300batch: iter_time=1.048e-04, forward_time=0.104, loss_ctc=51.967, loss_att=56.710, acc=0.693, loss=55.287, backward_time=0.097, grad_norm=32.844, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.217e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:21:04,263 (trainer:737) INFO: 11epoch:train:5301-5400batch: iter_time=1.208e-04, forward_time=0.105, loss_ctc=50.929, loss_att=61.194, acc=0.701, loss=58.114, backward_time=0.098, grad_norm=32.832, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.215e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:21:45,235 (trainer:737) INFO: 11epoch:train:5401-5500batch: iter_time=1.315e-04, forward_time=0.106, loss_ctc=45.303, loss_att=56.459, acc=0.702, loss=53.113, backward_time=0.098, grad_norm=27.566, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.029, optim0_lr0=6.213e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:22:26,534 (trainer:737) INFO: 11epoch:train:5501-5600batch: iter_time=1.378e-04, forward_time=0.106, loss_ctc=51.488, loss_att=59.538, acc=0.718, loss=57.123, backward_time=0.098, grad_norm=30.763, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.211e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 06:23:07,460 (trainer:737) INFO: 11epoch:train:5601-5700batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=48.135, loss_att=57.084, acc=0.697, loss=54.399, backward_time=0.097, grad_norm=32.618, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.209e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:23:48,661 (trainer:737) INFO: 11epoch:train:5701-5800batch: iter_time=1.389e-04, forward_time=0.104, loss_ctc=47.600, loss_att=52.126, acc=0.693, loss=50.768, backward_time=0.097, grad_norm=33.438, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.207e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:24:30,092 (trainer:737) INFO: 11epoch:train:5801-5900batch: iter_time=1.478e-04, forward_time=0.106, loss_ctc=54.706, loss_att=65.317, acc=0.706, loss=62.134, backward_time=0.098, grad_norm=35.134, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.205e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 06:25:11,227 (trainer:737) INFO: 11epoch:train:5901-6000batch: iter_time=1.458e-04, forward_time=0.106, loss_ctc=58.425, loss_att=61.419, acc=0.716, loss=60.520, backward_time=0.098, grad_norm=36.166, clip=100.000, loss_scale=9.904e+27, optim_step_time=0.030, optim0_lr0=6.203e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:25:52,494 (trainer:737) INFO: 11epoch:train:6001-6100batch: iter_time=1.530e-04, forward_time=0.105, loss_ctc=55.002, loss_att=63.359, acc=0.686, loss=60.852, backward_time=0.097, grad_norm=33.916, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.201e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:26:33,485 (trainer:737) INFO: 11epoch:train:6101-6200batch: iter_time=1.485e-04, forward_time=0.104, loss_ctc=52.766, loss_att=55.932, acc=0.708, loss=54.982, backward_time=0.097, grad_norm=33.281, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.199e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:26:59,436 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 06:27:19,136 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 06:27:22,961 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38a817b640>)
+[gpuc02:0/16] 2024-01-13 06:27:22,961 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 06:27:22,965 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 06:31:45,392 (trainer:737) INFO: 11epoch:train:6201-6300batch: iter_time=2.416, forward_time=0.104, loss_ctc=50.475, loss_att=55.405, acc=0.698, loss=53.926, backward_time=0.097, grad_norm=31.395, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.197e-04, train_time=3.119
+[gpuc02:0/16] 2024-01-13 06:32:26,671 (trainer:737) INFO: 11epoch:train:6301-6400batch: iter_time=1.615e-04, forward_time=0.105, loss_ctc=56.367, loss_att=66.618, acc=0.675, loss=63.542, backward_time=0.098, grad_norm=33.321, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.195e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 06:33:07,696 (trainer:737) INFO: 11epoch:train:6401-6500batch: iter_time=1.279e-04, forward_time=0.105, loss_ctc=54.989, loss_att=64.503, acc=0.681, loss=61.649, backward_time=0.098, grad_norm=42.341, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.193e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:33:48,977 (trainer:737) INFO: 11epoch:train:6501-6600batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=57.799, loss_att=61.436, acc=0.686, loss=60.345, backward_time=0.098, grad_norm=34.646, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.191e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 06:34:30,025 (trainer:737) INFO: 11epoch:train:6601-6700batch: iter_time=1.408e-04, forward_time=0.106, loss_ctc=49.779, loss_att=56.641, acc=0.704, loss=54.582, backward_time=0.098, grad_norm=30.386, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.189e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:35:11,279 (trainer:737) INFO: 11epoch:train:6701-6800batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=49.287, loss_att=56.817, acc=0.721, loss=54.558, backward_time=0.098, grad_norm=30.605, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.187e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:35:52,237 (trainer:737) INFO: 11epoch:train:6801-6900batch: iter_time=1.242e-04, forward_time=0.105, loss_ctc=47.879, loss_att=58.567, acc=0.704, loss=55.360, backward_time=0.098, grad_norm=30.029, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.185e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:36:33,363 (trainer:737) INFO: 11epoch:train:6901-7000batch: iter_time=1.147e-04, forward_time=0.104, loss_ctc=45.001, loss_att=52.808, acc=0.698, loss=50.466, backward_time=0.097, grad_norm=32.688, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.183e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:37:15,126 (trainer:737) INFO: 11epoch:train:7001-7100batch: iter_time=1.112e-04, forward_time=0.106, loss_ctc=56.406, loss_att=66.491, acc=0.703, loss=63.465, backward_time=0.098, grad_norm=31.264, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.029, optim0_lr0=6.181e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 06:37:56,182 (trainer:737) INFO: 11epoch:train:7101-7200batch: iter_time=1.501e-04, forward_time=0.105, loss_ctc=55.833, loss_att=55.802, acc=0.720, loss=55.811, backward_time=0.098, grad_norm=36.760, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.179e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:38:37,193 (trainer:737) INFO: 11epoch:train:7201-7300batch: iter_time=1.416e-04, forward_time=0.105, loss_ctc=55.712, loss_att=62.070, acc=0.693, loss=60.163, backward_time=0.098, grad_norm=33.668, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.177e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:39:18,818 (trainer:737) INFO: 11epoch:train:7301-7400batch: iter_time=1.531e-04, forward_time=0.110, loss_ctc=54.741, loss_att=59.257, acc=0.706, loss=57.902, backward_time=0.099, grad_norm=34.685, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.175e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 06:39:59,684 (trainer:737) INFO: 11epoch:train:7401-7500batch: iter_time=1.413e-04, forward_time=0.104, loss_ctc=49.061, loss_att=59.137, acc=0.673, loss=56.114, backward_time=0.097, grad_norm=31.581, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.173e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:40:03,668 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 06:40:22,869 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 06:40:26,618 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38680ffd00>)
+[gpuc02:0/16] 2024-01-13 06:40:26,618 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 06:40:26,621 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 06:45:13,177 (trainer:737) INFO: 11epoch:train:7501-7600batch: iter_time=2.468, forward_time=0.107, loss_ctc=56.365, loss_att=54.692, acc=0.717, loss=55.194, backward_time=0.098, grad_norm=31.822, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.171e-04, train_time=3.135
+[gpuc02:0/16] 2024-01-13 06:45:54,330 (trainer:737) INFO: 11epoch:train:7601-7700batch: iter_time=1.633e-04, forward_time=0.105, loss_ctc=60.463, loss_att=80.577, acc=0.649, loss=74.543, backward_time=0.098, grad_norm=51.615, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.169e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:46:35,262 (trainer:737) INFO: 11epoch:train:7701-7800batch: iter_time=1.657e-04, forward_time=0.105, loss_ctc=51.863, loss_att=54.485, acc=0.698, loss=53.699, backward_time=0.097, grad_norm=33.233, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.167e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:47:16,357 (trainer:737) INFO: 11epoch:train:7801-7900batch: iter_time=1.761e-04, forward_time=0.106, loss_ctc=50.540, loss_att=58.217, acc=0.711, loss=55.914, backward_time=0.098, grad_norm=31.791, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.165e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:47:57,352 (trainer:737) INFO: 11epoch:train:7901-8000batch: iter_time=1.742e-04, forward_time=0.106, loss_ctc=45.396, loss_att=54.032, acc=0.710, loss=51.441, backward_time=0.098, grad_norm=29.584, clip=100.000, loss_scale=1.981e+28, optim_step_time=0.030, optim0_lr0=6.163e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:48:38,245 (trainer:737) INFO: 11epoch:train:8001-8100batch: iter_time=1.803e-04, forward_time=0.104, loss_ctc=51.267, loss_att=58.741, acc=0.719, loss=56.499, backward_time=0.097, grad_norm=32.146, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.161e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 06:49:19,382 (trainer:737) INFO: 11epoch:train:8101-8200batch: iter_time=1.882e-04, forward_time=0.104, loss_ctc=47.821, loss_att=56.827, acc=0.698, loss=54.126, backward_time=0.097, grad_norm=30.365, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.159e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 06:50:00,572 (trainer:737) INFO: 11epoch:train:8201-8300batch: iter_time=1.810e-04, forward_time=0.105, loss_ctc=47.701, loss_att=51.260, acc=0.698, loss=50.192, backward_time=0.097, grad_norm=32.245, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.157e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:50:41,969 (trainer:737) INFO: 11epoch:train:8301-8400batch: iter_time=1.520e-04, forward_time=0.106, loss_ctc=54.685, loss_att=65.077, acc=0.709, loss=61.959, backward_time=0.098, grad_norm=36.744, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.156e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 06:51:23,013 (trainer:737) INFO: 11epoch:train:8401-8500batch: iter_time=1.446e-04, forward_time=0.106, loss_ctc=57.920, loss_att=60.054, acc=0.719, loss=59.414, backward_time=0.098, grad_norm=34.176, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.154e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 06:52:04,376 (trainer:737) INFO: 11epoch:train:8501-8600batch: iter_time=1.837e-04, forward_time=0.104, loss_ctc=53.928, loss_att=62.484, acc=0.688, loss=59.917, backward_time=0.097, grad_norm=33.643, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.152e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 06:52:45,232 (trainer:737) INFO: 11epoch:train:8601-8700batch: iter_time=1.871e-04, forward_time=0.105, loss_ctc=52.223, loss_att=55.053, acc=0.710, loss=54.204, backward_time=0.097, grad_norm=33.303, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.150e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 06:53:10,544 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 06:53:30,479 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 06:53:34,260 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38bbbfb700>)
+[gpuc02:0/16] 2024-01-13 06:53:34,261 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 06:53:34,264 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 06:57:57,745 (trainer:737) INFO: 11epoch:train:8701-8800batch: iter_time=2.521, forward_time=0.105, loss_ctc=50.587, loss_att=55.449, acc=0.698, loss=53.991, backward_time=0.098, grad_norm=31.947, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.148e-04, train_time=3.125
+[gpuc02:0/16] 2024-01-13 06:58:38,947 (trainer:737) INFO: 11epoch:train:8801-8900batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=54.773, loss_att=64.914, acc=0.677, loss=61.872, backward_time=0.098, grad_norm=32.482, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.146e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 06:59:19,802 (trainer:737) INFO: 11epoch:train:8901-9000batch: iter_time=1.552e-04, forward_time=0.105, loss_ctc=53.627, loss_att=63.573, acc=0.685, loss=60.589, backward_time=0.098, grad_norm=43.689, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.144e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:00:01,024 (trainer:737) INFO: 11epoch:train:9001-9100batch: iter_time=1.569e-04, forward_time=0.106, loss_ctc=56.991, loss_att=60.114, acc=0.690, loss=59.177, backward_time=0.098, grad_norm=35.257, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.142e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:00:41,944 (trainer:737) INFO: 11epoch:train:9101-9200batch: iter_time=1.600e-04, forward_time=0.106, loss_ctc=49.613, loss_att=56.078, acc=0.706, loss=54.139, backward_time=0.098, grad_norm=32.968, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.140e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:01:23,137 (trainer:737) INFO: 11epoch:train:9201-9300batch: iter_time=1.466e-04, forward_time=0.106, loss_ctc=48.971, loss_att=56.104, acc=0.723, loss=53.964, backward_time=0.098, grad_norm=29.151, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.138e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:02:03,993 (trainer:737) INFO: 11epoch:train:9301-9400batch: iter_time=1.597e-04, forward_time=0.106, loss_ctc=47.518, loss_att=58.657, acc=0.706, loss=55.316, backward_time=0.098, grad_norm=29.823, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.136e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:02:44,742 (trainer:737) INFO: 11epoch:train:9401-9500batch: iter_time=1.691e-04, forward_time=0.106, loss_ctc=44.550, loss_att=51.931, acc=0.701, loss=49.716, backward_time=0.098, grad_norm=30.772, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.134e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 07:03:26,425 (trainer:737) INFO: 11epoch:train:9501-9600batch: iter_time=1.544e-04, forward_time=0.107, loss_ctc=55.886, loss_att=65.799, acc=0.706, loss=62.825, backward_time=0.099, grad_norm=33.187, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.132e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 07:04:07,648 (trainer:737) INFO: 11epoch:train:9601-9700batch: iter_time=1.658e-04, forward_time=0.106, loss_ctc=54.765, loss_att=54.650, acc=0.722, loss=54.684, backward_time=0.098, grad_norm=35.458, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.130e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:04:48,653 (trainer:737) INFO: 11epoch:train:9701-9800batch: iter_time=1.493e-04, forward_time=0.106, loss_ctc=55.506, loss_att=61.175, acc=0.695, loss=59.474, backward_time=0.098, grad_norm=34.229, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.128e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:05:29,815 (trainer:737) INFO: 11epoch:train:9801-9900batch: iter_time=1.567e-04, forward_time=0.107, loss_ctc=54.285, loss_att=58.902, acc=0.709, loss=57.517, backward_time=0.098, grad_norm=32.609, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.127e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 07:06:10,672 (trainer:737) INFO: 11epoch:train:9901-10000batch: iter_time=1.287e-04, forward_time=0.105, loss_ctc=49.800, loss_att=58.711, acc=0.675, loss=56.037, backward_time=0.098, grad_norm=33.401, clip=100.000, loss_scale=3.961e+28, optim_step_time=0.030, optim0_lr0=6.125e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:06:14,884 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 07:06:33,958 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 07:06:37,662 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38d2eab9a0>)
+[gpuc02:0/16] 2024-01-13 07:06:37,662 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 07:06:37,665 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 07:11:44,080 (trainer:737) INFO: 11epoch:train:10001-10100batch: iter_time=2.431, forward_time=0.106, loss_ctc=56.581, loss_att=54.057, acc=0.720, loss=54.814, backward_time=0.098, grad_norm=32.762, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.123e-04, train_time=3.334
+[gpuc02:0/16] 2024-01-13 07:12:25,092 (trainer:737) INFO: 11epoch:train:10101-10200batch: iter_time=1.426e-04, forward_time=0.105, loss_ctc=58.673, loss_att=78.421, acc=0.653, loss=72.497, backward_time=0.099, grad_norm=48.165, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.121e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:13:05,850 (trainer:737) INFO: 11epoch:train:10201-10300batch: iter_time=1.332e-04, forward_time=0.104, loss_ctc=51.288, loss_att=54.683, acc=0.699, loss=53.665, backward_time=0.097, grad_norm=33.004, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.119e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 07:13:47,640 (trainer:737) INFO: 11epoch:train:10301-10400batch: iter_time=1.466e-04, forward_time=0.105, loss_ctc=50.573, loss_att=58.616, acc=0.709, loss=56.203, backward_time=0.099, grad_norm=33.280, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.117e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-13 07:14:28,424 (trainer:737) INFO: 11epoch:train:10401-10500batch: iter_time=1.581e-04, forward_time=0.105, loss_ctc=45.194, loss_att=53.888, acc=0.708, loss=51.280, backward_time=0.098, grad_norm=28.797, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.115e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:15:09,226 (trainer:737) INFO: 11epoch:train:10501-10600batch: iter_time=1.551e-04, forward_time=0.104, loss_ctc=50.917, loss_att=58.060, acc=0.722, loss=55.917, backward_time=0.098, grad_norm=29.748, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.113e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:15:50,059 (trainer:737) INFO: 11epoch:train:10601-10700batch: iter_time=1.812e-04, forward_time=0.104, loss_ctc=47.627, loss_att=56.486, acc=0.701, loss=53.828, backward_time=0.097, grad_norm=29.996, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.111e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:16:30,868 (trainer:737) INFO: 11epoch:train:10701-10800batch: iter_time=1.632e-04, forward_time=0.105, loss_ctc=47.126, loss_att=50.891, acc=0.698, loss=49.761, backward_time=0.097, grad_norm=31.336, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.109e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:17:12,014 (trainer:737) INFO: 11epoch:train:10801-10900batch: iter_time=1.755e-04, forward_time=0.106, loss_ctc=53.889, loss_att=64.554, acc=0.711, loss=61.355, backward_time=0.099, grad_norm=34.041, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.107e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 07:17:53,694 (trainer:737) INFO: 11epoch:train:10901-11000batch: iter_time=1.716e-04, forward_time=0.108, loss_ctc=57.336, loss_att=59.858, acc=0.720, loss=59.102, backward_time=0.099, grad_norm=34.640, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.106e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 07:18:34,869 (trainer:737) INFO: 11epoch:train:11001-11100batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=53.243, loss_att=61.313, acc=0.692, loss=58.892, backward_time=0.098, grad_norm=32.216, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.104e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:19:17,709 (trainer:737) INFO: 11epoch:train:11101-11200batch: iter_time=1.509e-04, forward_time=0.121, loss_ctc=51.363, loss_att=53.977, acc=0.714, loss=53.193, backward_time=0.102, grad_norm=33.357, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.033, optim0_lr0=6.102e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-13 07:19:42,889 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 07:20:01,979 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 07:20:05,743 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38bbea6110>)
+[gpuc02:0/16] 2024-01-13 07:20:05,743 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 07:20:05,747 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 07:24:42,300 (trainer:737) INFO: 11epoch:train:11201-11300batch: iter_time=2.466, forward_time=0.105, loss_ctc=50.423, loss_att=53.807, acc=0.706, loss=52.792, backward_time=0.099, grad_norm=33.043, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.031, optim0_lr0=6.100e-04, train_time=3.246
+[gpuc02:0/16] 2024-01-13 07:25:23,315 (trainer:737) INFO: 11epoch:train:11301-11400batch: iter_time=1.207e-04, forward_time=0.104, loss_ctc=55.106, loss_att=65.240, acc=0.679, loss=62.200, backward_time=0.098, grad_norm=33.261, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.098e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:26:04,138 (trainer:737) INFO: 11epoch:train:11401-11500batch: iter_time=1.788e-04, forward_time=0.103, loss_ctc=54.611, loss_att=63.622, acc=0.687, loss=60.918, backward_time=0.097, grad_norm=46.629, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.030, optim0_lr0=6.096e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:26:45,107 (trainer:737) INFO: 11epoch:train:11501-11600batch: iter_time=1.288e-04, forward_time=0.104, loss_ctc=56.636, loss_att=60.852, acc=0.686, loss=59.587, backward_time=0.097, grad_norm=33.744, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.094e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:27:26,018 (trainer:737) INFO: 11epoch:train:11601-11700batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=49.003, loss_att=55.803, acc=0.707, loss=53.763, backward_time=0.097, grad_norm=30.569, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.092e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:28:07,272 (trainer:737) INFO: 11epoch:train:11701-11800batch: iter_time=1.282e-04, forward_time=0.104, loss_ctc=48.693, loss_att=55.490, acc=0.725, loss=53.451, backward_time=0.097, grad_norm=28.804, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.090e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:28:48,204 (trainer:737) INFO: 11epoch:train:11801-11900batch: iter_time=1.081e-04, forward_time=0.104, loss_ctc=48.022, loss_att=57.624, acc=0.709, loss=54.743, backward_time=0.097, grad_norm=30.549, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.089e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:29:29,613 (trainer:737) INFO: 11epoch:train:11901-12000batch: iter_time=1.164e-04, forward_time=0.104, loss_ctc=44.138, loss_att=52.211, acc=0.698, loss=49.789, backward_time=0.097, grad_norm=33.276, clip=100.000, loss_scale=7.923e+28, optim_step_time=0.029, optim0_lr0=6.087e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 07:30:10,793 (trainer:737) INFO: 11epoch:train:12001-12100batch: iter_time=1.092e-04, forward_time=0.105, loss_ctc=55.520, loss_att=65.049, acc=0.707, loss=62.190, backward_time=0.098, grad_norm=31.569, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.029, optim0_lr0=6.085e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 07:30:52,367 (trainer:737) INFO: 11epoch:train:12101-12200batch: iter_time=1.183e-04, forward_time=0.105, loss_ctc=54.281, loss_att=53.361, acc=0.727, loss=53.637, backward_time=0.097, grad_norm=35.636, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.083e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 07:31:33,363 (trainer:737) INFO: 11epoch:train:12201-12300batch: iter_time=1.131e-04, forward_time=0.104, loss_ctc=54.198, loss_att=60.582, acc=0.695, loss=58.666, backward_time=0.097, grad_norm=33.357, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.081e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:32:14,357 (trainer:737) INFO: 11epoch:train:12301-12400batch: iter_time=1.146e-04, forward_time=0.104, loss_ctc=53.453, loss_att=58.423, acc=0.712, loss=56.932, backward_time=0.097, grad_norm=32.191, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.079e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:32:55,151 (trainer:737) INFO: 11epoch:train:12401-12500batch: iter_time=1.431e-04, forward_time=0.103, loss_ctc=48.825, loss_att=57.658, acc=0.682, loss=55.008, backward_time=0.097, grad_norm=32.715, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.077e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:33:00,138 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 07:33:19,303 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 07:33:23,111 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38d2a7ca30>)
+[gpuc02:0/16] 2024-01-13 07:33:23,111 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 07:33:23,114 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 07:38:26,720 (trainer:737) INFO: 11epoch:train:12501-12600batch: iter_time=2.509, forward_time=0.106, loss_ctc=55.616, loss_att=57.301, acc=0.703, loss=56.796, backward_time=0.099, grad_norm=32.709, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.075e-04, train_time=3.315
+[gpuc02:0/16] 2024-01-13 07:39:08,022 (trainer:737) INFO: 11epoch:train:12601-12700batch: iter_time=1.578e-04, forward_time=0.106, loss_ctc=56.300, loss_att=80.869, acc=0.640, loss=73.498, backward_time=0.099, grad_norm=41.400, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.074e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 07:39:49,070 (trainer:737) INFO: 11epoch:train:12701-12800batch: iter_time=1.706e-04, forward_time=0.107, loss_ctc=50.840, loss_att=53.846, acc=0.694, loss=52.944, backward_time=0.097, grad_norm=32.921, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.072e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:40:30,082 (trainer:737) INFO: 11epoch:train:12801-12900batch: iter_time=1.564e-04, forward_time=0.106, loss_ctc=50.267, loss_att=58.537, acc=0.705, loss=56.056, backward_time=0.098, grad_norm=33.819, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.070e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:41:11,100 (trainer:737) INFO: 11epoch:train:12901-13000batch: iter_time=1.844e-04, forward_time=0.104, loss_ctc=44.743, loss_att=55.169, acc=0.692, loss=52.041, backward_time=0.097, grad_norm=28.902, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.029, optim0_lr0=6.068e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:41:51,958 (trainer:737) INFO: 11epoch:train:13001-13100batch: iter_time=1.956e-04, forward_time=0.105, loss_ctc=50.495, loss_att=54.621, acc=0.718, loss=53.383, backward_time=0.097, grad_norm=31.025, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.066e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:42:32,722 (trainer:737) INFO: 11epoch:train:13101-13200batch: iter_time=2.026e-04, forward_time=0.104, loss_ctc=47.098, loss_att=56.875, acc=0.699, loss=53.942, backward_time=0.098, grad_norm=31.350, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.064e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 07:43:14,095 (trainer:737) INFO: 11epoch:train:13201-13300batch: iter_time=1.682e-04, forward_time=0.104, loss_ctc=46.392, loss_att=51.176, acc=0.691, loss=49.741, backward_time=0.097, grad_norm=30.133, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.062e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 07:43:55,095 (trainer:737) INFO: 11epoch:train:13301-13400batch: iter_time=1.593e-04, forward_time=0.105, loss_ctc=53.296, loss_att=62.709, acc=0.711, loss=59.885, backward_time=0.099, grad_norm=35.689, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.061e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:44:36,079 (trainer:737) INFO: 11epoch:train:13401-13500batch: iter_time=1.717e-04, forward_time=0.105, loss_ctc=56.722, loss_att=60.088, acc=0.714, loss=59.078, backward_time=0.098, grad_norm=34.098, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.059e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:45:17,185 (trainer:737) INFO: 11epoch:train:13501-13600batch: iter_time=1.975e-04, forward_time=0.104, loss_ctc=53.379, loss_att=59.710, acc=0.689, loss=57.811, backward_time=0.098, grad_norm=33.318, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.057e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 07:45:57,939 (trainer:737) INFO: 11epoch:train:13601-13700batch: iter_time=1.936e-04, forward_time=0.104, loss_ctc=51.947, loss_att=55.438, acc=0.698, loss=54.391, backward_time=0.097, grad_norm=33.443, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.055e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 07:46:22,745 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 07:46:42,337 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 07:46:46,061 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a6afa76d0>)
+[gpuc02:0/16] 2024-01-13 07:46:46,061 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 07:46:46,064 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 07:51:24,259 (trainer:737) INFO: 11epoch:train:13701-13800batch: iter_time=2.483, forward_time=0.147, loss_ctc=50.230, loss_att=55.515, acc=0.685, loss=53.930, backward_time=0.107, grad_norm=31.525, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.031, optim0_lr0=6.053e-04, train_time=3.263
+[gpuc02:0/16] 2024-01-13 07:52:05,252 (trainer:737) INFO: 11epoch:train:13801-13900batch: iter_time=1.243e-04, forward_time=0.105, loss_ctc=54.803, loss_att=65.332, acc=0.670, loss=62.173, backward_time=0.098, grad_norm=34.987, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.051e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:52:46,212 (trainer:737) INFO: 11epoch:train:13901-14000batch: iter_time=1.526e-04, forward_time=0.104, loss_ctc=51.975, loss_att=63.231, acc=0.681, loss=59.855, backward_time=0.098, grad_norm=42.469, clip=100.000, loss_scale=1.585e+29, optim_step_time=0.030, optim0_lr0=6.049e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:53:27,131 (trainer:737) INFO: 11epoch:train:14001-14100batch: iter_time=1.655e-04, forward_time=0.105, loss_ctc=56.202, loss_att=58.196, acc=0.683, loss=57.598, backward_time=0.098, grad_norm=34.916, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.048e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:54:08,906 (trainer:737) INFO: 11epoch:train:14101-14200batch: iter_time=1.612e-04, forward_time=0.105, loss_ctc=48.670, loss_att=54.888, acc=0.703, loss=53.023, backward_time=0.098, grad_norm=31.966, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.046e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-13 07:54:49,814 (trainer:737) INFO: 11epoch:train:14201-14300batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=48.261, loss_att=56.359, acc=0.708, loss=53.930, backward_time=0.097, grad_norm=28.714, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.044e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:55:30,582 (trainer:737) INFO: 11epoch:train:14301-14400batch: iter_time=1.474e-04, forward_time=0.105, loss_ctc=47.659, loss_att=54.646, acc=0.705, loss=52.550, backward_time=0.097, grad_norm=31.528, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.042e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 07:56:11,402 (trainer:737) INFO: 11epoch:train:14401-14500batch: iter_time=1.811e-04, forward_time=0.105, loss_ctc=43.850, loss_att=51.218, acc=0.701, loss=49.008, backward_time=0.097, grad_norm=30.321, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.040e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:56:52,406 (trainer:737) INFO: 11epoch:train:14501-14600batch: iter_time=1.461e-04, forward_time=0.106, loss_ctc=54.739, loss_att=65.396, acc=0.700, loss=62.199, backward_time=0.098, grad_norm=33.972, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.038e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 07:57:33,375 (trainer:737) INFO: 11epoch:train:14601-14700batch: iter_time=1.229e-04, forward_time=0.105, loss_ctc=54.330, loss_att=52.075, acc=0.727, loss=52.751, backward_time=0.098, grad_norm=34.576, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.037e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:58:14,189 (trainer:737) INFO: 11epoch:train:14701-14800batch: iter_time=1.125e-04, forward_time=0.105, loss_ctc=54.326, loss_att=60.394, acc=0.692, loss=58.574, backward_time=0.098, grad_norm=35.427, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.035e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 07:58:55,138 (trainer:737) INFO: 11epoch:train:14801-14900batch: iter_time=1.215e-04, forward_time=0.106, loss_ctc=53.120, loss_att=56.462, acc=0.703, loss=55.459, backward_time=0.098, grad_norm=32.824, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.033e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 07:59:36,465 (trainer:737) INFO: 11epoch:train:14901-15000batch: iter_time=1.236e-04, forward_time=0.103, loss_ctc=48.706, loss_att=58.037, acc=0.664, loss=55.238, backward_time=0.096, grad_norm=34.009, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.031e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 08:19:41,872 (trainer:343) INFO: 11epoch results: [train] iter_time=0.211, forward_time=0.106, loss_ctc=52.876, loss_att=59.163, acc=0.695, loss=57.277, backward_time=0.098, grad_norm=34.092, clip=100.000, loss_scale=6.305e+28, optim_step_time=0.030, optim0_lr0=6.174e-04, train_time=0.645, time=2 hours, 41 minutes and 25.66 seconds, total_count=165000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=61.414, cer_ctc=0.314, loss_att=60.853, acc=0.549, cer=0.422, wer=1.000, loss=61.021, time=19 minutes and 55.41 seconds, total_count=51381, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 08:19:46,639 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-13 08:19:46,643 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/6epoch.pth
+[gpuc02:0/16] 2024-01-13 08:19:46,643 (trainer:272) INFO: 12/45epoch started. Estimated time to finish: 4 days, 5 hours and 34 minutes
+[gpuc02:0/16] 2024-01-13 08:19:46,652 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 08:20:05,389 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 08:20:08,814 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38f5b7d0c0>)
+[gpuc02:0/16] 2024-01-13 08:20:08,814 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 08:20:08,817 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 08:24:56,777 (trainer:737) INFO: 12epoch:train:1-100batch: iter_time=2.364, forward_time=0.105, loss_ctc=47.498, loss_att=66.106, acc=0.697, loss=60.523, backward_time=0.099, grad_norm=29.623, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.029e-04, train_time=3.101
+[gpuc02:0/16] 2024-01-13 08:25:37,876 (trainer:737) INFO: 12epoch:train:101-200batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=50.101, loss_att=53.208, acc=0.717, loss=52.276, backward_time=0.098, grad_norm=33.174, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.027e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 08:26:18,817 (trainer:737) INFO: 12epoch:train:201-300batch: iter_time=1.082e-04, forward_time=0.104, loss_ctc=48.782, loss_att=50.715, acc=0.706, loss=50.135, backward_time=0.098, grad_norm=32.427, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.026e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 08:26:59,790 (trainer:737) INFO: 12epoch:train:301-400batch: iter_time=1.086e-04, forward_time=0.103, loss_ctc=62.000, loss_att=52.873, acc=0.726, loss=55.611, backward_time=0.098, grad_norm=40.558, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.024e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:27:42,091 (trainer:737) INFO: 12epoch:train:401-500batch: iter_time=1.206e-04, forward_time=0.116, loss_ctc=51.238, loss_att=58.514, acc=0.673, loss=56.331, backward_time=0.099, grad_norm=36.793, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.022e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-13 08:28:22,925 (trainer:737) INFO: 12epoch:train:501-600batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=55.203, loss_att=60.558, acc=0.692, loss=58.952, backward_time=0.098, grad_norm=35.698, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.020e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 08:29:03,740 (trainer:737) INFO: 12epoch:train:601-700batch: iter_time=1.117e-04, forward_time=0.104, loss_ctc=58.601, loss_att=61.437, acc=0.701, loss=60.586, backward_time=0.098, grad_norm=36.608, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.018e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 08:29:45,844 (trainer:737) INFO: 12epoch:train:701-800batch: iter_time=1.185e-04, forward_time=0.113, loss_ctc=53.513, loss_att=58.605, acc=0.704, loss=57.078, backward_time=0.099, grad_norm=33.543, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.032, optim0_lr0=6.017e-04, train_time=0.421
+[gpuc02:0/16] 2024-01-13 08:30:28,089 (trainer:737) INFO: 12epoch:train:801-900batch: iter_time=1.040e-04, forward_time=0.108, loss_ctc=53.655, loss_att=57.439, acc=0.713, loss=56.304, backward_time=0.098, grad_norm=34.868, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.033, optim0_lr0=6.015e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 08:31:13,437 (trainer:737) INFO: 12epoch:train:901-1000batch: iter_time=1.048e-04, forward_time=0.112, loss_ctc=62.646, loss_att=66.463, acc=0.691, loss=65.318, backward_time=0.124, grad_norm=41.374, clip=100.000, loss_scale=3.169e+29, optim_step_time=0.030, optim0_lr0=6.013e-04, train_time=0.453
+[gpuc02:0/16] 2024-01-13 08:31:54,422 (trainer:737) INFO: 12epoch:train:1001-1100batch: iter_time=1.200e-04, forward_time=0.104, loss_ctc=46.016, loss_att=55.844, acc=0.710, loss=52.896, backward_time=0.098, grad_norm=33.248, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.011e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:32:36,736 (trainer:737) INFO: 12epoch:train:1101-1200batch: iter_time=1.284e-04, forward_time=0.109, loss_ctc=65.442, loss_att=58.318, acc=0.732, loss=60.455, backward_time=0.099, grad_norm=41.108, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.031, optim0_lr0=6.009e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 08:33:25,706 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 08:33:45,097 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 08:33:48,868 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3868962860>)
+[gpuc02:0/16] 2024-01-13 08:33:48,868 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 08:33:48,871 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 08:41:04,405 (trainer:737) INFO: 12epoch:train:1201-1300batch: iter_time=4.444, forward_time=0.109, loss_ctc=47.442, loss_att=56.922, acc=0.709, loss=54.078, backward_time=0.098, grad_norm=32.302, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.007e-04, train_time=5.077
+[gpuc02:0/16] 2024-01-13 08:41:45,395 (trainer:737) INFO: 12epoch:train:1301-1400batch: iter_time=1.265e-04, forward_time=0.103, loss_ctc=47.807, loss_att=54.766, acc=0.714, loss=52.678, backward_time=0.097, grad_norm=32.157, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.006e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:42:26,355 (trainer:737) INFO: 12epoch:train:1401-1500batch: iter_time=1.466e-04, forward_time=0.104, loss_ctc=49.489, loss_att=53.185, acc=0.700, loss=52.076, backward_time=0.097, grad_norm=31.844, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.004e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 08:43:07,376 (trainer:737) INFO: 12epoch:train:1501-1600batch: iter_time=1.644e-04, forward_time=0.104, loss_ctc=55.975, loss_att=58.886, acc=0.704, loss=58.013, backward_time=0.098, grad_norm=35.531, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.002e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:43:48,057 (trainer:737) INFO: 12epoch:train:1601-1700batch: iter_time=1.742e-04, forward_time=0.103, loss_ctc=59.096, loss_att=52.589, acc=0.688, loss=54.541, backward_time=0.097, grad_norm=42.707, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=6.000e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 08:44:28,918 (trainer:737) INFO: 12epoch:train:1701-1800batch: iter_time=1.550e-04, forward_time=0.103, loss_ctc=43.690, loss_att=54.958, acc=0.680, loss=51.577, backward_time=0.097, grad_norm=31.339, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.998e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 08:45:10,672 (trainer:737) INFO: 12epoch:train:1801-1900batch: iter_time=1.649e-04, forward_time=0.109, loss_ctc=55.268, loss_att=59.703, acc=0.705, loss=58.373, backward_time=0.100, grad_norm=32.925, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.997e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 08:45:51,573 (trainer:737) INFO: 12epoch:train:1901-2000batch: iter_time=1.421e-04, forward_time=0.105, loss_ctc=62.393, loss_att=61.538, acc=0.692, loss=61.795, backward_time=0.099, grad_norm=37.206, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.995e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 08:46:32,382 (trainer:737) INFO: 12epoch:train:2001-2100batch: iter_time=1.634e-04, forward_time=0.104, loss_ctc=48.524, loss_att=57.808, acc=0.699, loss=55.023, backward_time=0.098, grad_norm=31.851, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.993e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 08:47:13,113 (trainer:737) INFO: 12epoch:train:2101-2200batch: iter_time=1.511e-04, forward_time=0.104, loss_ctc=66.119, loss_att=61.105, acc=0.701, loss=62.609, backward_time=0.097, grad_norm=42.726, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.991e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 08:47:53,841 (trainer:737) INFO: 12epoch:train:2201-2300batch: iter_time=1.460e-04, forward_time=0.103, loss_ctc=46.560, loss_att=60.330, acc=0.685, loss=56.199, backward_time=0.097, grad_norm=34.197, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.990e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 08:48:35,609 (trainer:737) INFO: 12epoch:train:2301-2400batch: iter_time=1.606e-04, forward_time=0.111, loss_ctc=50.567, loss_att=45.567, acc=0.727, loss=47.067, backward_time=0.099, grad_norm=32.135, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.031, optim0_lr0=5.988e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 08:49:18,053 (trainer:737) INFO: 12epoch:train:2401-2500batch: iter_time=1.370e-04, forward_time=0.112, loss_ctc=59.317, loss_att=57.334, acc=0.720, loss=57.929, backward_time=0.102, grad_norm=40.191, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.986e-04, train_time=0.424
+[gpuc02:0/16] 2024-01-13 08:49:26,310 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 08:49:45,358 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 08:49:49,207 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e537e5c0>)
+[gpuc02:0/16] 2024-01-13 08:49:49,207 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 08:49:49,210 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 08:54:44,489 (trainer:737) INFO: 12epoch:train:2501-2600batch: iter_time=2.682, forward_time=0.119, loss_ctc=46.594, loss_att=65.368, acc=0.702, loss=59.736, backward_time=0.101, grad_norm=31.169, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.031, optim0_lr0=5.984e-04, train_time=3.264
+[gpuc02:0/16] 2024-01-13 08:55:25,372 (trainer:737) INFO: 12epoch:train:2601-2700batch: iter_time=1.506e-04, forward_time=0.104, loss_ctc=48.638, loss_att=52.675, acc=0.720, loss=51.464, backward_time=0.098, grad_norm=31.372, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.982e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 08:56:06,156 (trainer:737) INFO: 12epoch:train:2701-2800batch: iter_time=1.390e-04, forward_time=0.104, loss_ctc=47.683, loss_att=50.041, acc=0.712, loss=49.334, backward_time=0.098, grad_norm=29.116, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.981e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 08:56:47,534 (trainer:737) INFO: 12epoch:train:2801-2900batch: iter_time=1.582e-04, forward_time=0.104, loss_ctc=60.170, loss_att=52.956, acc=0.727, loss=55.120, backward_time=0.098, grad_norm=37.248, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.979e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 08:57:28,534 (trainer:737) INFO: 12epoch:train:2901-3000batch: iter_time=1.252e-04, forward_time=0.104, loss_ctc=49.920, loss_att=57.928, acc=0.676, loss=55.526, backward_time=0.098, grad_norm=35.474, clip=100.000, loss_scale=6.338e+29, optim_step_time=0.030, optim0_lr0=5.977e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:58:09,583 (trainer:737) INFO: 12epoch:train:3001-3100batch: iter_time=1.739e-04, forward_time=0.104, loss_ctc=53.667, loss_att=58.453, acc=0.700, loss=57.017, backward_time=0.098, grad_norm=32.320, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.975e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 08:58:50,497 (trainer:737) INFO: 12epoch:train:3101-3200batch: iter_time=1.322e-04, forward_time=0.105, loss_ctc=55.515, loss_att=59.649, acc=0.710, loss=58.409, backward_time=0.098, grad_norm=36.249, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.973e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 08:59:31,693 (trainer:737) INFO: 12epoch:train:3201-3300batch: iter_time=1.148e-04, forward_time=0.105, loss_ctc=52.312, loss_att=57.762, acc=0.706, loss=56.127, backward_time=0.098, grad_norm=31.452, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.972e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 09:00:12,833 (trainer:737) INFO: 12epoch:train:3301-3400batch: iter_time=1.181e-04, forward_time=0.105, loss_ctc=51.563, loss_att=56.525, acc=0.717, loss=55.036, backward_time=0.098, grad_norm=33.100, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.970e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 09:00:53,846 (trainer:737) INFO: 12epoch:train:3401-3500batch: iter_time=1.135e-04, forward_time=0.104, loss_ctc=61.063, loss_att=65.812, acc=0.699, loss=64.387, backward_time=0.097, grad_norm=41.198, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.968e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:01:34,574 (trainer:737) INFO: 12epoch:train:3501-3600batch: iter_time=1.460e-04, forward_time=0.104, loss_ctc=45.331, loss_att=55.195, acc=0.714, loss=52.236, backward_time=0.098, grad_norm=32.999, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.966e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 09:02:15,434 (trainer:737) INFO: 12epoch:train:3601-3700batch: iter_time=1.521e-04, forward_time=0.105, loss_ctc=62.989, loss_att=56.284, acc=0.737, loss=58.296, backward_time=0.098, grad_norm=39.360, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.965e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:02:41,019 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 09:02:59,995 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 09:03:03,618 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fa0f0ada0>)
+[gpuc02:0/16] 2024-01-13 09:03:03,618 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 09:03:03,621 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 09:07:41,475 (trainer:737) INFO: 12epoch:train:3701-3800batch: iter_time=2.539, forward_time=0.105, loss_ctc=46.626, loss_att=54.137, acc=0.721, loss=51.884, backward_time=0.098, grad_norm=30.898, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.963e-04, train_time=3.260
+[gpuc02:0/16] 2024-01-13 09:08:22,411 (trainer:737) INFO: 12epoch:train:3801-3900batch: iter_time=1.561e-04, forward_time=0.104, loss_ctc=46.985, loss_att=56.757, acc=0.714, loss=53.826, backward_time=0.098, grad_norm=32.855, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.031, optim0_lr0=5.961e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:09:03,830 (trainer:737) INFO: 12epoch:train:3901-4000batch: iter_time=1.449e-04, forward_time=0.104, loss_ctc=48.029, loss_att=51.527, acc=0.712, loss=50.477, backward_time=0.098, grad_norm=34.540, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.959e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 09:09:45,136 (trainer:737) INFO: 12epoch:train:4001-4100batch: iter_time=1.790e-04, forward_time=0.104, loss_ctc=56.713, loss_att=57.496, acc=0.718, loss=57.261, backward_time=0.098, grad_norm=34.936, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.958e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 09:10:26,136 (trainer:737) INFO: 12epoch:train:4101-4200batch: iter_time=1.698e-04, forward_time=0.103, loss_ctc=58.684, loss_att=50.322, acc=0.708, loss=52.831, backward_time=0.098, grad_norm=38.734, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.956e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:11:07,397 (trainer:737) INFO: 12epoch:train:4201-4300batch: iter_time=1.499e-04, forward_time=0.104, loss_ctc=42.921, loss_att=54.053, acc=0.687, loss=50.713, backward_time=0.098, grad_norm=30.002, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.954e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 09:11:48,432 (trainer:737) INFO: 12epoch:train:4301-4400batch: iter_time=1.425e-04, forward_time=0.105, loss_ctc=54.493, loss_att=58.503, acc=0.716, loss=57.300, backward_time=0.098, grad_norm=32.596, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.952e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:12:29,789 (trainer:737) INFO: 12epoch:train:4401-4500batch: iter_time=1.501e-04, forward_time=0.106, loss_ctc=61.270, loss_att=59.425, acc=0.709, loss=59.978, backward_time=0.099, grad_norm=36.197, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.950e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 09:13:11,037 (trainer:737) INFO: 12epoch:train:4501-4600batch: iter_time=1.496e-04, forward_time=0.104, loss_ctc=48.059, loss_att=58.026, acc=0.712, loss=55.036, backward_time=0.098, grad_norm=31.775, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.949e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 09:13:52,102 (trainer:737) INFO: 12epoch:train:4601-4700batch: iter_time=1.592e-04, forward_time=0.104, loss_ctc=63.081, loss_att=60.261, acc=0.706, loss=61.107, backward_time=0.098, grad_norm=42.362, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.947e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:14:33,023 (trainer:737) INFO: 12epoch:train:4701-4800batch: iter_time=1.634e-04, forward_time=0.104, loss_ctc=45.683, loss_att=60.884, acc=0.699, loss=56.324, backward_time=0.098, grad_norm=32.382, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.945e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:15:14,131 (trainer:737) INFO: 12epoch:train:4801-4900batch: iter_time=1.448e-04, forward_time=0.104, loss_ctc=49.811, loss_att=45.453, acc=0.736, loss=46.761, backward_time=0.098, grad_norm=31.195, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.943e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 09:15:55,242 (trainer:737) INFO: 12epoch:train:4901-5000batch: iter_time=1.397e-04, forward_time=0.104, loss_ctc=56.975, loss_att=55.369, acc=0.735, loss=55.851, backward_time=0.098, grad_norm=40.145, clip=100.000, loss_scale=1.268e+30, optim_step_time=0.030, optim0_lr0=5.942e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 09:15:59,302 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 09:16:18,483 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 09:16:22,071 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3886d11300>)
+[gpuc02:0/16] 2024-01-13 09:16:22,071 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 09:16:22,075 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 09:21:45,047 (trainer:737) INFO: 12epoch:train:5001-5100batch: iter_time=2.540, forward_time=0.105, loss_ctc=45.822, loss_att=65.517, acc=0.701, loss=59.608, backward_time=0.099, grad_norm=30.769, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.940e-04, train_time=3.498
+[gpuc02:0/16] 2024-01-13 09:22:26,111 (trainer:737) INFO: 12epoch:train:5101-5200batch: iter_time=1.534e-04, forward_time=0.103, loss_ctc=47.922, loss_att=50.695, acc=0.720, loss=49.863, backward_time=0.097, grad_norm=32.628, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.938e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:23:06,789 (trainer:737) INFO: 12epoch:train:5201-5300batch: iter_time=1.914e-04, forward_time=0.103, loss_ctc=47.672, loss_att=50.415, acc=0.706, loss=49.592, backward_time=0.097, grad_norm=31.749, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.936e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 09:23:47,561 (trainer:737) INFO: 12epoch:train:5301-5400batch: iter_time=2.058e-04, forward_time=0.105, loss_ctc=58.987, loss_att=52.750, acc=0.722, loss=54.621, backward_time=0.098, grad_norm=39.076, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.935e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:24:28,538 (trainer:737) INFO: 12epoch:train:5401-5500batch: iter_time=1.933e-04, forward_time=0.103, loss_ctc=49.623, loss_att=57.563, acc=0.666, loss=55.181, backward_time=0.097, grad_norm=34.974, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.933e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:25:09,336 (trainer:737) INFO: 12epoch:train:5501-5600batch: iter_time=1.837e-04, forward_time=0.104, loss_ctc=52.735, loss_att=59.149, acc=0.691, loss=57.225, backward_time=0.097, grad_norm=35.176, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.931e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:25:50,192 (trainer:737) INFO: 12epoch:train:5601-5700batch: iter_time=3.176e-04, forward_time=0.104, loss_ctc=54.451, loss_att=58.769, acc=0.706, loss=57.474, backward_time=0.097, grad_norm=32.988, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.930e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:26:31,883 (trainer:737) INFO: 12epoch:train:5701-5800batch: iter_time=1.661e-04, forward_time=0.107, loss_ctc=51.516, loss_att=56.950, acc=0.696, loss=55.320, backward_time=0.097, grad_norm=33.527, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.029, optim0_lr0=5.928e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 09:27:12,933 (trainer:737) INFO: 12epoch:train:5801-5900batch: iter_time=1.581e-04, forward_time=0.105, loss_ctc=51.174, loss_att=56.410, acc=0.709, loss=54.839, backward_time=0.098, grad_norm=36.715, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.926e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:27:54,384 (trainer:737) INFO: 12epoch:train:5901-6000batch: iter_time=1.327e-04, forward_time=0.104, loss_ctc=57.673, loss_att=61.472, acc=0.692, loss=60.332, backward_time=0.099, grad_norm=41.797, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.924e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 09:28:35,459 (trainer:737) INFO: 12epoch:train:6001-6100batch: iter_time=1.852e-04, forward_time=0.104, loss_ctc=44.338, loss_att=53.998, acc=0.707, loss=51.100, backward_time=0.097, grad_norm=31.722, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.029, optim0_lr0=5.923e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 09:29:16,331 (trainer:737) INFO: 12epoch:train:6101-6200batch: iter_time=1.791e-04, forward_time=0.104, loss_ctc=62.460, loss_att=57.352, acc=0.731, loss=58.884, backward_time=0.097, grad_norm=39.595, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.921e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:29:42,511 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 09:30:02,108 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 09:30:05,727 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc6b2dd0>)
+[gpuc02:0/16] 2024-01-13 09:30:05,727 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 09:30:05,731 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 09:34:36,926 (trainer:737) INFO: 12epoch:train:6201-6300batch: iter_time=2.645, forward_time=0.105, loss_ctc=46.561, loss_att=55.886, acc=0.709, loss=53.089, backward_time=0.099, grad_norm=30.452, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.031, optim0_lr0=5.919e-04, train_time=3.206
+[gpuc02:0/16] 2024-01-13 09:35:17,844 (trainer:737) INFO: 12epoch:train:6301-6400batch: iter_time=1.222e-04, forward_time=0.104, loss_ctc=46.703, loss_att=56.925, acc=0.713, loss=53.858, backward_time=0.097, grad_norm=32.428, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.917e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:35:58,727 (trainer:737) INFO: 12epoch:train:6401-6500batch: iter_time=1.372e-04, forward_time=0.104, loss_ctc=48.072, loss_att=52.477, acc=0.713, loss=51.156, backward_time=0.098, grad_norm=30.195, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.916e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:36:39,637 (trainer:737) INFO: 12epoch:train:6501-6600batch: iter_time=1.709e-04, forward_time=0.104, loss_ctc=55.558, loss_att=58.109, acc=0.717, loss=57.344, backward_time=0.097, grad_norm=33.575, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.914e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:37:20,373 (trainer:737) INFO: 12epoch:train:6601-6700batch: iter_time=1.636e-04, forward_time=0.104, loss_ctc=57.481, loss_att=50.821, acc=0.706, loss=52.819, backward_time=0.097, grad_norm=39.166, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.912e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 09:38:01,705 (trainer:737) INFO: 12epoch:train:6701-6800batch: iter_time=1.532e-04, forward_time=0.104, loss_ctc=42.031, loss_att=54.566, acc=0.688, loss=50.806, backward_time=0.097, grad_norm=29.038, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.911e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 09:38:42,738 (trainer:737) INFO: 12epoch:train:6801-6900batch: iter_time=1.674e-04, forward_time=0.105, loss_ctc=54.157, loss_att=59.305, acc=0.717, loss=57.761, backward_time=0.099, grad_norm=31.719, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.909e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:39:23,791 (trainer:737) INFO: 12epoch:train:6901-7000batch: iter_time=1.583e-04, forward_time=0.106, loss_ctc=60.378, loss_att=59.596, acc=0.711, loss=59.831, backward_time=0.098, grad_norm=36.220, clip=100.000, loss_scale=2.535e+30, optim_step_time=0.030, optim0_lr0=5.907e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:40:04,988 (trainer:737) INFO: 12epoch:train:7001-7100batch: iter_time=1.760e-04, forward_time=0.105, loss_ctc=47.315, loss_att=58.152, acc=0.713, loss=54.901, backward_time=0.097, grad_norm=30.714, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.905e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 09:40:45,810 (trainer:737) INFO: 12epoch:train:7101-7200batch: iter_time=1.571e-04, forward_time=0.104, loss_ctc=62.170, loss_att=59.213, acc=0.714, loss=60.100, backward_time=0.097, grad_norm=40.910, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.904e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:41:26,700 (trainer:737) INFO: 12epoch:train:7201-7300batch: iter_time=1.637e-04, forward_time=0.105, loss_ctc=44.787, loss_att=60.440, acc=0.701, loss=55.745, backward_time=0.098, grad_norm=30.968, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.902e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:42:08,227 (trainer:737) INFO: 12epoch:train:7301-7400batch: iter_time=1.731e-04, forward_time=0.105, loss_ctc=49.485, loss_att=45.237, acc=0.737, loss=46.512, backward_time=0.097, grad_norm=32.158, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.900e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 09:42:49,006 (trainer:737) INFO: 12epoch:train:7401-7500batch: iter_time=1.475e-04, forward_time=0.104, loss_ctc=56.865, loss_att=55.417, acc=0.736, loss=55.851, backward_time=0.097, grad_norm=38.702, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.899e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:42:53,733 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 09:43:13,025 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 09:43:16,659 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38ddb278b0>)
+[gpuc02:0/16] 2024-01-13 09:43:16,659 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 09:43:16,662 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 09:49:23,431 (trainer:737) INFO: 12epoch:train:7501-7600batch: iter_time=2.558, forward_time=0.105, loss_ctc=45.525, loss_att=64.671, acc=0.698, loss=58.927, backward_time=0.098, grad_norm=31.328, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.897e-04, train_time=3.944
+[gpuc02:0/16] 2024-01-13 09:50:04,273 (trainer:737) INFO: 12epoch:train:7601-7700batch: iter_time=1.236e-04, forward_time=0.104, loss_ctc=47.186, loss_att=50.457, acc=0.719, loss=49.475, backward_time=0.098, grad_norm=31.821, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.895e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:50:45,077 (trainer:737) INFO: 12epoch:train:7701-7800batch: iter_time=1.381e-04, forward_time=0.104, loss_ctc=46.461, loss_att=49.615, acc=0.709, loss=48.669, backward_time=0.098, grad_norm=30.352, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.893e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:51:26,072 (trainer:737) INFO: 12epoch:train:7801-7900batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=58.801, loss_att=51.334, acc=0.727, loss=53.574, backward_time=0.098, grad_norm=38.560, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.892e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:52:07,085 (trainer:737) INFO: 12epoch:train:7901-8000batch: iter_time=1.280e-04, forward_time=0.104, loss_ctc=49.275, loss_att=57.373, acc=0.668, loss=54.943, backward_time=0.098, grad_norm=36.014, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.890e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 09:52:47,931 (trainer:737) INFO: 12epoch:train:8001-8100batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=52.722, loss_att=58.966, acc=0.692, loss=57.093, backward_time=0.098, grad_norm=34.840, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.888e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:53:28,863 (trainer:737) INFO: 12epoch:train:8101-8200batch: iter_time=1.333e-04, forward_time=0.104, loss_ctc=54.857, loss_att=59.205, acc=0.704, loss=57.901, backward_time=0.098, grad_norm=37.615, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.887e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:54:10,372 (trainer:737) INFO: 12epoch:train:8201-8300batch: iter_time=1.377e-04, forward_time=0.104, loss_ctc=51.104, loss_att=56.276, acc=0.698, loss=54.724, backward_time=0.098, grad_norm=33.551, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.885e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 09:54:51,230 (trainer:737) INFO: 12epoch:train:8301-8400batch: iter_time=1.426e-04, forward_time=0.104, loss_ctc=50.478, loss_att=55.796, acc=0.711, loss=54.201, backward_time=0.098, grad_norm=32.703, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.883e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:55:32,020 (trainer:737) INFO: 12epoch:train:8401-8500batch: iter_time=1.665e-04, forward_time=0.105, loss_ctc=56.895, loss_att=62.931, acc=0.692, loss=61.120, backward_time=0.097, grad_norm=41.472, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.881e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 09:56:12,778 (trainer:737) INFO: 12epoch:train:8501-8600batch: iter_time=1.608e-04, forward_time=0.104, loss_ctc=44.312, loss_att=53.509, acc=0.707, loss=50.750, backward_time=0.097, grad_norm=33.313, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.880e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 09:56:53,673 (trainer:737) INFO: 12epoch:train:8601-8700batch: iter_time=1.539e-04, forward_time=0.105, loss_ctc=60.469, loss_att=55.716, acc=0.732, loss=57.142, backward_time=0.098, grad_norm=39.098, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.878e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 09:57:18,232 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 09:57:37,316 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 09:57:40,892 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38dc1f96c0>)
+[gpuc02:0/16] 2024-01-13 09:57:40,892 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 09:57:40,895 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 10:02:12,230 (trainer:737) INFO: 12epoch:train:8701-8800batch: iter_time=2.503, forward_time=0.104, loss_ctc=45.542, loss_att=55.423, acc=0.710, loss=52.459, backward_time=0.098, grad_norm=30.803, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.876e-04, train_time=3.185
+[gpuc02:0/16] 2024-01-13 10:02:53,219 (trainer:737) INFO: 12epoch:train:8801-8900batch: iter_time=1.748e-04, forward_time=0.104, loss_ctc=45.736, loss_att=56.689, acc=0.716, loss=53.403, backward_time=0.097, grad_norm=31.040, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.875e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:03:34,231 (trainer:737) INFO: 12epoch:train:8901-9000batch: iter_time=1.622e-04, forward_time=0.104, loss_ctc=47.916, loss_att=52.403, acc=0.714, loss=51.057, backward_time=0.097, grad_norm=32.340, clip=100.000, loss_scale=5.071e+30, optim_step_time=0.030, optim0_lr0=5.873e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:04:15,548 (trainer:737) INFO: 12epoch:train:9001-9100batch: iter_time=1.958e-04, forward_time=0.105, loss_ctc=54.406, loss_att=56.952, acc=0.720, loss=56.188, backward_time=0.098, grad_norm=34.445, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.871e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 10:04:56,218 (trainer:737) INFO: 12epoch:train:9101-9200batch: iter_time=1.910e-04, forward_time=0.104, loss_ctc=57.203, loss_att=50.503, acc=0.708, loss=52.513, backward_time=0.097, grad_norm=40.193, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.870e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 10:05:36,951 (trainer:737) INFO: 12epoch:train:9201-9300batch: iter_time=1.680e-04, forward_time=0.104, loss_ctc=42.109, loss_att=54.408, acc=0.690, loss=50.718, backward_time=0.098, grad_norm=30.037, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.868e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:06:18,125 (trainer:737) INFO: 12epoch:train:9301-9400batch: iter_time=1.630e-04, forward_time=0.105, loss_ctc=53.862, loss_att=58.724, acc=0.718, loss=57.265, backward_time=0.098, grad_norm=33.243, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.031, optim0_lr0=5.866e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 10:06:59,182 (trainer:737) INFO: 12epoch:train:9401-9500batch: iter_time=1.566e-04, forward_time=0.105, loss_ctc=59.576, loss_att=58.604, acc=0.713, loss=58.896, backward_time=0.099, grad_norm=37.176, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.865e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:07:40,316 (trainer:737) INFO: 12epoch:train:9501-9600batch: iter_time=1.568e-04, forward_time=0.107, loss_ctc=46.725, loss_att=57.635, acc=0.714, loss=54.362, backward_time=0.098, grad_norm=30.836, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.863e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:08:21,629 (trainer:737) INFO: 12epoch:train:9601-9700batch: iter_time=1.510e-04, forward_time=0.105, loss_ctc=62.396, loss_att=61.208, acc=0.713, loss=61.565, backward_time=0.098, grad_norm=45.813, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.031, optim0_lr0=5.861e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 10:09:02,710 (trainer:737) INFO: 12epoch:train:9701-9800batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=44.280, loss_att=60.332, acc=0.700, loss=55.516, backward_time=0.098, grad_norm=31.004, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.031, optim0_lr0=5.860e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:09:43,480 (trainer:737) INFO: 12epoch:train:9801-9900batch: iter_time=1.395e-04, forward_time=0.105, loss_ctc=49.214, loss_att=45.239, acc=0.737, loss=46.431, backward_time=0.098, grad_norm=31.234, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.858e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:10:24,451 (trainer:737) INFO: 12epoch:train:9901-10000batch: iter_time=1.456e-04, forward_time=0.104, loss_ctc=56.433, loss_att=55.160, acc=0.739, loss=55.542, backward_time=0.098, grad_norm=38.993, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.856e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:10:31,513 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 10:10:50,914 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 10:10:54,627 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3b0a7e5660>)
+[gpuc02:0/16] 2024-01-13 10:10:54,627 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 10:10:54,630 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 10:15:41,974 (trainer:737) INFO: 12epoch:train:10001-10100batch: iter_time=2.559, forward_time=0.105, loss_ctc=45.360, loss_att=64.655, acc=0.699, loss=58.866, backward_time=0.098, grad_norm=32.053, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.855e-04, train_time=3.175
+[gpuc02:0/16] 2024-01-13 10:16:22,842 (trainer:737) INFO: 12epoch:train:10101-10200batch: iter_time=1.892e-04, forward_time=0.104, loss_ctc=47.086, loss_att=49.973, acc=0.720, loss=49.107, backward_time=0.098, grad_norm=32.629, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.853e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:17:03,566 (trainer:737) INFO: 12epoch:train:10201-10300batch: iter_time=1.657e-04, forward_time=0.103, loss_ctc=46.289, loss_att=49.196, acc=0.710, loss=48.324, backward_time=0.097, grad_norm=32.371, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.851e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:17:44,405 (trainer:737) INFO: 12epoch:train:10301-10400batch: iter_time=1.711e-04, forward_time=0.104, loss_ctc=58.089, loss_att=51.440, acc=0.727, loss=53.435, backward_time=0.097, grad_norm=39.554, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.850e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:18:25,484 (trainer:737) INFO: 12epoch:train:10401-10500batch: iter_time=1.806e-04, forward_time=0.104, loss_ctc=49.046, loss_att=56.671, acc=0.671, loss=54.383, backward_time=0.097, grad_norm=35.699, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.848e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:19:06,395 (trainer:737) INFO: 12epoch:train:10501-10600batch: iter_time=1.763e-04, forward_time=0.106, loss_ctc=52.580, loss_att=58.313, acc=0.696, loss=56.593, backward_time=0.098, grad_norm=35.676, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.846e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:19:47,327 (trainer:737) INFO: 12epoch:train:10601-10700batch: iter_time=1.487e-04, forward_time=0.105, loss_ctc=54.542, loss_att=58.305, acc=0.706, loss=57.176, backward_time=0.098, grad_norm=34.621, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.845e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:20:28,486 (trainer:737) INFO: 12epoch:train:10701-10800batch: iter_time=1.426e-04, forward_time=0.105, loss_ctc=50.727, loss_att=56.285, acc=0.698, loss=54.617, backward_time=0.097, grad_norm=32.620, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.843e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:21:09,599 (trainer:737) INFO: 12epoch:train:10801-10900batch: iter_time=1.295e-04, forward_time=0.105, loss_ctc=49.909, loss_att=55.767, acc=0.712, loss=54.010, backward_time=0.097, grad_norm=33.504, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.841e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:21:50,719 (trainer:737) INFO: 12epoch:train:10901-11000batch: iter_time=1.366e-04, forward_time=0.104, loss_ctc=56.645, loss_att=62.136, acc=0.693, loss=60.488, backward_time=0.097, grad_norm=42.098, clip=100.000, loss_scale=1.014e+31, optim_step_time=0.030, optim0_lr0=5.840e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:22:31,740 (trainer:737) INFO: 12epoch:train:11001-11100batch: iter_time=1.509e-04, forward_time=0.104, loss_ctc=43.654, loss_att=53.110, acc=0.708, loss=50.273, backward_time=0.097, grad_norm=31.809, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.838e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:23:12,892 (trainer:737) INFO: 12epoch:train:11101-11200batch: iter_time=1.628e-04, forward_time=0.106, loss_ctc=61.799, loss_att=56.827, acc=0.733, loss=58.319, backward_time=0.097, grad_norm=40.294, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.836e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:23:37,490 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 10:23:56,824 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 10:24:00,387 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fc0bae5f0>)
+[gpuc02:0/16] 2024-01-13 10:24:00,387 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 10:24:00,390 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 10:28:37,184 (trainer:737) INFO: 12epoch:train:11201-11300batch: iter_time=2.479, forward_time=0.104, loss_ctc=45.716, loss_att=52.836, acc=0.711, loss=50.700, backward_time=0.097, grad_norm=31.285, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.835e-04, train_time=3.243
+[gpuc02:0/16] 2024-01-13 10:29:17,864 (trainer:737) INFO: 12epoch:train:11301-11400batch: iter_time=1.300e-04, forward_time=0.104, loss_ctc=46.175, loss_att=53.258, acc=0.720, loss=51.133, backward_time=0.098, grad_norm=32.155, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.833e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:29:58,579 (trainer:737) INFO: 12epoch:train:11401-11500batch: iter_time=1.551e-04, forward_time=0.104, loss_ctc=47.555, loss_att=51.318, acc=0.709, loss=50.189, backward_time=0.097, grad_norm=31.514, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.831e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:30:39,345 (trainer:737) INFO: 12epoch:train:11501-11600batch: iter_time=1.496e-04, forward_time=0.104, loss_ctc=54.700, loss_att=57.422, acc=0.711, loss=56.605, backward_time=0.098, grad_norm=34.403, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.830e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:31:20,026 (trainer:737) INFO: 12epoch:train:11601-11700batch: iter_time=1.564e-04, forward_time=0.103, loss_ctc=56.275, loss_att=50.524, acc=0.697, loss=52.249, backward_time=0.097, grad_norm=40.204, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.828e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:32:00,952 (trainer:737) INFO: 12epoch:train:11701-11800batch: iter_time=1.768e-04, forward_time=0.104, loss_ctc=41.705, loss_att=52.690, acc=0.689, loss=49.394, backward_time=0.097, grad_norm=30.693, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.826e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:32:42,137 (trainer:737) INFO: 12epoch:train:11801-11900batch: iter_time=1.925e-04, forward_time=0.105, loss_ctc=53.912, loss_att=57.985, acc=0.714, loss=56.763, backward_time=0.098, grad_norm=31.941, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.825e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 10:33:23,125 (trainer:737) INFO: 12epoch:train:11901-12000batch: iter_time=1.882e-04, forward_time=0.106, loss_ctc=59.989, loss_att=59.606, acc=0.700, loss=59.721, backward_time=0.098, grad_norm=37.158, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.823e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:34:03,920 (trainer:737) INFO: 12epoch:train:12001-12100batch: iter_time=1.684e-04, forward_time=0.105, loss_ctc=46.405, loss_att=56.470, acc=0.707, loss=53.451, backward_time=0.098, grad_norm=31.989, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.821e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:34:44,689 (trainer:737) INFO: 12epoch:train:12101-12200batch: iter_time=1.550e-04, forward_time=0.105, loss_ctc=61.953, loss_att=59.340, acc=0.708, loss=60.124, backward_time=0.097, grad_norm=43.305, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.820e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:35:25,420 (trainer:737) INFO: 12epoch:train:12201-12300batch: iter_time=1.798e-04, forward_time=0.106, loss_ctc=44.856, loss_att=59.351, acc=0.691, loss=55.003, backward_time=0.098, grad_norm=36.755, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.818e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:36:06,152 (trainer:737) INFO: 12epoch:train:12301-12400batch: iter_time=1.685e-04, forward_time=0.105, loss_ctc=48.608, loss_att=44.217, acc=0.733, loss=45.534, backward_time=0.097, grad_norm=33.003, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.816e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:36:46,909 (trainer:737) INFO: 12epoch:train:12401-12500batch: iter_time=1.588e-04, forward_time=0.104, loss_ctc=56.054, loss_att=55.465, acc=0.726, loss=55.642, backward_time=0.098, grad_norm=38.619, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.815e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:36:53,961 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 10:37:13,171 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 10:37:16,870 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38fcdf1510>)
+[gpuc02:0/16] 2024-01-13 10:37:16,871 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 10:37:16,874 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 10:42:06,599 (trainer:737) INFO: 12epoch:train:12501-12600batch: iter_time=2.543, forward_time=0.105, loss_ctc=45.190, loss_att=65.536, acc=0.706, loss=59.432, backward_time=0.098, grad_norm=30.980, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.813e-04, train_time=3.197
+[gpuc02:0/16] 2024-01-13 10:42:47,402 (trainer:737) INFO: 12epoch:train:12601-12700batch: iter_time=1.699e-04, forward_time=0.104, loss_ctc=47.318, loss_att=51.991, acc=0.726, loss=50.589, backward_time=0.097, grad_norm=31.684, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.812e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:43:28,076 (trainer:737) INFO: 12epoch:train:12701-12800batch: iter_time=1.670e-04, forward_time=0.104, loss_ctc=46.409, loss_att=49.297, acc=0.718, loss=48.431, backward_time=0.097, grad_norm=31.959, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.810e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:44:09,065 (trainer:737) INFO: 12epoch:train:12801-12900batch: iter_time=1.725e-04, forward_time=0.105, loss_ctc=58.348, loss_att=51.359, acc=0.732, loss=53.456, backward_time=0.098, grad_norm=37.583, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.029, optim0_lr0=5.808e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:44:49,844 (trainer:737) INFO: 12epoch:train:12901-13000batch: iter_time=1.862e-04, forward_time=0.104, loss_ctc=49.012, loss_att=56.989, acc=0.684, loss=54.596, backward_time=0.098, grad_norm=35.801, clip=100.000, loss_scale=2.028e+31, optim_step_time=0.030, optim0_lr0=5.807e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:45:30,736 (trainer:737) INFO: 12epoch:train:13001-13100batch: iter_time=1.676e-04, forward_time=0.105, loss_ctc=52.244, loss_att=58.611, acc=0.704, loss=56.701, backward_time=0.098, grad_norm=34.090, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.805e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:46:12,013 (trainer:737) INFO: 12epoch:train:13101-13200batch: iter_time=1.637e-04, forward_time=0.104, loss_ctc=53.285, loss_att=58.513, acc=0.713, loss=56.945, backward_time=0.098, grad_norm=34.624, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.803e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 10:46:52,961 (trainer:737) INFO: 12epoch:train:13201-13300batch: iter_time=1.692e-04, forward_time=0.105, loss_ctc=50.479, loss_att=56.402, acc=0.713, loss=54.625, backward_time=0.098, grad_norm=33.652, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.802e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:47:33,809 (trainer:737) INFO: 12epoch:train:13301-13400batch: iter_time=1.432e-04, forward_time=0.105, loss_ctc=49.955, loss_att=55.405, acc=0.725, loss=53.770, backward_time=0.098, grad_norm=32.664, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.800e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:48:14,900 (trainer:737) INFO: 12epoch:train:13401-13500batch: iter_time=1.591e-04, forward_time=0.104, loss_ctc=55.847, loss_att=63.053, acc=0.702, loss=60.891, backward_time=0.097, grad_norm=40.831, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.798e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 10:48:56,100 (trainer:737) INFO: 12epoch:train:13501-13600batch: iter_time=1.645e-04, forward_time=0.105, loss_ctc=43.510, loss_att=53.815, acc=0.720, loss=50.724, backward_time=0.097, grad_norm=30.928, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.797e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 10:49:37,511 (trainer:737) INFO: 12epoch:train:13601-13700batch: iter_time=1.732e-04, forward_time=0.104, loss_ctc=59.897, loss_att=54.922, acc=0.741, loss=56.414, backward_time=0.098, grad_norm=41.351, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.795e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 10:50:08,181 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 10:50:27,342 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 10:50:31,278 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38deb4e860>)
+[gpuc02:0/16] 2024-01-13 10:50:31,279 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 10:50:31,282 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 10:55:00,445 (trainer:737) INFO: 12epoch:train:13701-13800batch: iter_time=2.806, forward_time=0.106, loss_ctc=45.410, loss_att=54.541, acc=0.716, loss=51.801, backward_time=0.098, grad_norm=31.029, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.794e-04, train_time=3.229
+[gpuc02:0/16] 2024-01-13 10:55:41,343 (trainer:737) INFO: 12epoch:train:13801-13900batch: iter_time=1.807e-04, forward_time=0.105, loss_ctc=45.410, loss_att=53.695, acc=0.719, loss=51.210, backward_time=0.097, grad_norm=32.726, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.792e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:56:22,197 (trainer:737) INFO: 12epoch:train:13901-14000batch: iter_time=1.967e-04, forward_time=0.105, loss_ctc=47.444, loss_att=52.474, acc=0.707, loss=50.965, backward_time=0.098, grad_norm=32.890, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.790e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 10:57:03,111 (trainer:737) INFO: 12epoch:train:14001-14100batch: iter_time=2.094e-04, forward_time=0.104, loss_ctc=53.681, loss_att=56.432, acc=0.716, loss=55.607, backward_time=0.097, grad_norm=33.529, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.789e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:57:43,830 (trainer:737) INFO: 12epoch:train:14101-14200batch: iter_time=2.118e-04, forward_time=0.104, loss_ctc=55.119, loss_att=49.686, acc=0.699, loss=51.316, backward_time=0.097, grad_norm=39.946, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.787e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 10:58:24,855 (trainer:737) INFO: 12epoch:train:14201-14300batch: iter_time=1.449e-04, forward_time=0.104, loss_ctc=41.651, loss_att=53.502, acc=0.690, loss=49.947, backward_time=0.097, grad_norm=30.287, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.786e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 10:59:05,798 (trainer:737) INFO: 12epoch:train:14301-14400batch: iter_time=1.249e-04, forward_time=0.105, loss_ctc=52.996, loss_att=58.702, acc=0.712, loss=56.990, backward_time=0.098, grad_norm=33.955, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.784e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 10:59:46,821 (trainer:737) INFO: 12epoch:train:14401-14500batch: iter_time=1.280e-04, forward_time=0.106, loss_ctc=59.148, loss_att=59.143, acc=0.701, loss=59.144, backward_time=0.099, grad_norm=36.885, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.782e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 11:00:27,702 (trainer:737) INFO: 12epoch:train:14501-14600batch: iter_time=1.469e-04, forward_time=0.105, loss_ctc=46.552, loss_att=56.755, acc=0.706, loss=53.694, backward_time=0.098, grad_norm=30.713, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.781e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 11:01:08,559 (trainer:737) INFO: 12epoch:train:14601-14700batch: iter_time=1.690e-04, forward_time=0.104, loss_ctc=61.284, loss_att=58.217, acc=0.707, loss=59.137, backward_time=0.097, grad_norm=44.844, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.779e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 11:01:49,715 (trainer:737) INFO: 12epoch:train:14701-14800batch: iter_time=1.916e-04, forward_time=0.105, loss_ctc=44.163, loss_att=59.030, acc=0.690, loss=54.570, backward_time=0.097, grad_norm=33.675, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.777e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 11:02:30,835 (trainer:737) INFO: 12epoch:train:14801-14900batch: iter_time=1.652e-04, forward_time=0.105, loss_ctc=48.403, loss_att=44.176, acc=0.733, loss=45.444, backward_time=0.097, grad_norm=33.031, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.029, optim0_lr0=5.776e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 11:03:12,065 (trainer:737) INFO: 12epoch:train:14901-15000batch: iter_time=1.753e-04, forward_time=0.104, loss_ctc=55.472, loss_att=54.560, acc=0.727, loss=54.834, backward_time=0.097, grad_norm=39.299, clip=100.000, loss_scale=4.056e+31, optim_step_time=0.030, optim0_lr0=5.774e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 11:23:14,190 (trainer:343) INFO: 12epoch results: [train] iter_time=0.218, forward_time=0.105, loss_ctc=51.927, loss_att=56.146, acc=0.709, loss=54.880, backward_time=0.098, grad_norm=34.770, clip=100.000, loss_scale=1.075e+31, optim_step_time=0.030, optim0_lr0=5.899e-04, train_time=0.653, time=2 hours, 43 minutes and 39.74 seconds, total_count=180000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=62.625, cer_ctc=0.310, loss_att=62.345, acc=0.549, cer=0.409, wer=1.000, loss=62.429, time=19 minutes and 47.62 seconds, total_count=56052, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 11:23:19,101 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-13 11:23:19,105 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/7epoch.pth
+[gpuc02:0/16] 2024-01-13 11:23:19,105 (trainer:272) INFO: 13/45epoch started. Estimated time to finish: 4 days, 2 hours and 47 minutes
+[gpuc02:0/16] 2024-01-13 11:23:19,117 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 11:23:38,003 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 11:23:41,947 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38f5b7d5d0>)
+[gpuc02:0/16] 2024-01-13 11:23:41,947 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 11:23:41,950 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 11:28:15,569 (trainer:737) INFO: 13epoch:train:1-100batch: iter_time=2.392, forward_time=0.105, loss_ctc=62.114, loss_att=67.230, acc=0.678, loss=65.695, backward_time=0.098, grad_norm=41.994, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.773e-04, train_time=2.964
+[gpuc02:0/16] 2024-01-13 11:28:57,255 (trainer:737) INFO: 13epoch:train:101-200batch: iter_time=1.069e-04, forward_time=0.104, loss_ctc=46.830, loss_att=58.153, acc=0.692, loss=54.757, backward_time=0.098, grad_norm=33.462, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.771e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 11:29:38,634 (trainer:737) INFO: 13epoch:train:201-300batch: iter_time=1.040e-04, forward_time=0.104, loss_ctc=66.872, loss_att=65.716, acc=0.694, loss=66.063, backward_time=0.099, grad_norm=43.395, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.769e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 11:30:21,169 (trainer:737) INFO: 13epoch:train:301-400batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=62.969, loss_att=65.054, acc=0.719, loss=64.429, backward_time=0.100, grad_norm=42.023, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.768e-04, train_time=0.425
+[gpuc02:0/16] 2024-01-13 11:31:02,977 (trainer:737) INFO: 13epoch:train:401-500batch: iter_time=1.109e-04, forward_time=0.104, loss_ctc=65.672, loss_att=62.721, acc=0.676, loss=63.606, backward_time=0.099, grad_norm=46.295, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.766e-04, train_time=0.418
+[gpuc02:0/16] 2024-01-13 11:31:54,227 (trainer:737) INFO: 13epoch:train:501-600batch: iter_time=1.188e-04, forward_time=0.111, loss_ctc=41.274, loss_att=42.468, acc=0.720, loss=42.109, backward_time=0.103, grad_norm=29.664, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.031, optim0_lr0=5.765e-04, train_time=0.512
+[gpuc02:0/16] 2024-01-13 11:32:36,413 (trainer:737) INFO: 13epoch:train:601-700batch: iter_time=1.124e-04, forward_time=0.116, loss_ctc=56.846, loss_att=60.658, acc=0.711, loss=59.514, backward_time=0.101, grad_norm=36.281, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.031, optim0_lr0=5.763e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 11:33:17,533 (trainer:737) INFO: 13epoch:train:701-800batch: iter_time=1.124e-04, forward_time=0.104, loss_ctc=45.764, loss_att=51.715, acc=0.745, loss=49.930, backward_time=0.102, grad_norm=30.605, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.761e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 11:34:00,513 (trainer:737) INFO: 13epoch:train:801-900batch: iter_time=1.227e-04, forward_time=0.113, loss_ctc=56.630, loss_att=59.504, acc=0.729, loss=58.642, backward_time=0.099, grad_norm=36.070, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.034, optim0_lr0=5.760e-04, train_time=0.430
+[gpuc02:0/16] 2024-01-13 11:34:41,741 (trainer:737) INFO: 13epoch:train:901-1000batch: iter_time=1.294e-04, forward_time=0.105, loss_ctc=56.698, loss_att=53.796, acc=0.705, loss=54.666, backward_time=0.098, grad_norm=38.458, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.033, optim0_lr0=5.758e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 11:35:24,832 (trainer:737) INFO: 13epoch:train:1001-1100batch: iter_time=1.313e-04, forward_time=0.114, loss_ctc=55.290, loss_att=51.639, acc=0.722, loss=52.734, backward_time=0.109, grad_norm=37.981, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.757e-04, train_time=0.431
+[gpuc02:0/16] 2024-01-13 11:36:09,751 (trainer:737) INFO: 13epoch:train:1101-1200batch: iter_time=1.330e-04, forward_time=0.115, loss_ctc=56.659, loss_att=59.383, acc=0.729, loss=58.566, backward_time=0.101, grad_norm=34.206, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.755e-04, train_time=0.449
+[gpuc02:0/16] 2024-01-13 11:36:46,142 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 11:37:05,140 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 11:37:08,692 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40bf72b9a0>)
+[gpuc02:0/16] 2024-01-13 11:37:08,693 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 11:37:08,696 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 11:43:36,631 (trainer:737) INFO: 13epoch:train:1201-1300batch: iter_time=4.047, forward_time=0.105, loss_ctc=63.629, loss_att=66.436, acc=0.680, loss=65.594, backward_time=0.099, grad_norm=45.235, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.754e-04, train_time=4.468
+[gpuc02:0/16] 2024-01-13 11:44:17,468 (trainer:737) INFO: 13epoch:train:1301-1400batch: iter_time=1.174e-04, forward_time=0.104, loss_ctc=48.999, loss_att=61.744, acc=0.682, loss=57.920, backward_time=0.098, grad_norm=34.231, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.752e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 11:44:58,669 (trainer:737) INFO: 13epoch:train:1401-1500batch: iter_time=1.278e-04, forward_time=0.104, loss_ctc=47.037, loss_att=54.705, acc=0.703, loss=52.404, backward_time=0.098, grad_norm=32.479, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.750e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 11:45:40,058 (trainer:737) INFO: 13epoch:train:1501-1600batch: iter_time=1.260e-04, forward_time=0.106, loss_ctc=73.343, loss_att=70.746, acc=0.715, loss=71.525, backward_time=0.099, grad_norm=49.916, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.749e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 11:46:21,558 (trainer:737) INFO: 13epoch:train:1601-1700batch: iter_time=1.267e-04, forward_time=0.106, loss_ctc=60.313, loss_att=65.645, acc=0.681, loss=64.045, backward_time=0.099, grad_norm=39.477, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.747e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 11:47:02,324 (trainer:737) INFO: 13epoch:train:1701-1800batch: iter_time=1.611e-04, forward_time=0.104, loss_ctc=52.079, loss_att=47.845, acc=0.722, loss=49.115, backward_time=0.097, grad_norm=37.876, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.746e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 11:47:43,405 (trainer:737) INFO: 13epoch:train:1801-1900batch: iter_time=1.546e-04, forward_time=0.103, loss_ctc=45.014, loss_att=42.653, acc=0.728, loss=43.361, backward_time=0.097, grad_norm=30.175, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.744e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 11:48:24,740 (trainer:737) INFO: 13epoch:train:1901-2000batch: iter_time=1.204e-04, forward_time=0.106, loss_ctc=54.897, loss_att=64.975, acc=0.727, loss=61.952, backward_time=0.099, grad_norm=34.969, clip=100.000, loss_scale=8.113e+31, optim_step_time=0.030, optim0_lr0=5.742e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 11:49:05,469 (trainer:737) INFO: 13epoch:train:2001-2100batch: iter_time=1.172e-04, forward_time=0.104, loss_ctc=40.819, loss_att=53.041, acc=0.731, loss=49.375, backward_time=0.097, grad_norm=27.645, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.029, optim0_lr0=5.741e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 11:49:46,237 (trainer:737) INFO: 13epoch:train:2101-2200batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=63.564, loss_att=53.325, acc=0.731, loss=56.397, backward_time=0.098, grad_norm=41.217, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.739e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 11:50:27,043 (trainer:737) INFO: 13epoch:train:2201-2300batch: iter_time=1.099e-04, forward_time=0.104, loss_ctc=52.671, loss_att=53.748, acc=0.700, loss=53.425, backward_time=0.097, grad_norm=36.594, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.738e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 11:51:09,269 (trainer:737) INFO: 13epoch:train:2301-2400batch: iter_time=1.238e-04, forward_time=0.105, loss_ctc=50.650, loss_att=48.098, acc=0.741, loss=48.863, backward_time=0.098, grad_norm=31.761, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.736e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 11:51:50,955 (trainer:737) INFO: 13epoch:train:2401-2500batch: iter_time=1.425e-04, forward_time=0.105, loss_ctc=62.063, loss_att=68.964, acc=0.702, loss=66.894, backward_time=0.098, grad_norm=41.647, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.735e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 11:51:58,235 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 11:52:17,437 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 11:52:21,048 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f42d06a78b0>)
+[gpuc02:0/16] 2024-01-13 11:52:21,048 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 11:52:21,051 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 11:57:12,763 (trainer:737) INFO: 13epoch:train:2501-2600batch: iter_time=2.543, forward_time=0.107, loss_ctc=58.841, loss_att=63.395, acc=0.683, loss=62.029, backward_time=0.098, grad_norm=38.622, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.733e-04, train_time=3.218
+[gpuc02:0/16] 2024-01-13 11:57:53,534 (trainer:737) INFO: 13epoch:train:2601-2700batch: iter_time=1.390e-04, forward_time=0.104, loss_ctc=44.827, loss_att=57.119, acc=0.690, loss=53.431, backward_time=0.098, grad_norm=35.610, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.731e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 11:58:34,709 (trainer:737) INFO: 13epoch:train:2701-2800batch: iter_time=1.374e-04, forward_time=0.105, loss_ctc=64.074, loss_att=62.230, acc=0.702, loss=62.783, backward_time=0.098, grad_norm=45.513, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.029, optim0_lr0=5.730e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 11:59:16,119 (trainer:737) INFO: 13epoch:train:2801-2900batch: iter_time=1.307e-04, forward_time=0.105, loss_ctc=58.577, loss_att=60.538, acc=0.730, loss=59.950, backward_time=0.099, grad_norm=37.166, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.728e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 11:59:57,556 (trainer:737) INFO: 13epoch:train:2901-3000batch: iter_time=1.344e-04, forward_time=0.107, loss_ctc=62.069, loss_att=60.040, acc=0.684, loss=60.649, backward_time=0.098, grad_norm=42.103, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.727e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:00:38,709 (trainer:737) INFO: 13epoch:train:3001-3100batch: iter_time=1.241e-04, forward_time=0.103, loss_ctc=40.653, loss_att=41.781, acc=0.725, loss=41.442, backward_time=0.096, grad_norm=28.578, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.725e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:01:20,093 (trainer:737) INFO: 13epoch:train:3101-3200batch: iter_time=1.170e-04, forward_time=0.105, loss_ctc=55.839, loss_att=59.361, acc=0.717, loss=58.304, backward_time=0.098, grad_norm=36.669, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.724e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:02:00,914 (trainer:737) INFO: 13epoch:train:3201-3300batch: iter_time=1.358e-04, forward_time=0.104, loss_ctc=45.212, loss_att=50.311, acc=0.749, loss=48.781, backward_time=0.097, grad_norm=30.133, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.722e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:02:41,846 (trainer:737) INFO: 13epoch:train:3301-3400batch: iter_time=1.376e-04, forward_time=0.105, loss_ctc=54.740, loss_att=59.396, acc=0.728, loss=57.999, backward_time=0.098, grad_norm=35.765, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.720e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:03:23,121 (trainer:737) INFO: 13epoch:train:3401-3500batch: iter_time=1.384e-04, forward_time=0.104, loss_ctc=54.624, loss_att=52.490, acc=0.712, loss=53.130, backward_time=0.097, grad_norm=37.867, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.719e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 12:04:04,213 (trainer:737) INFO: 13epoch:train:3501-3600batch: iter_time=1.048e-04, forward_time=0.104, loss_ctc=53.166, loss_att=50.980, acc=0.723, loss=51.636, backward_time=0.098, grad_norm=34.141, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.029, optim0_lr0=5.717e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:04:45,171 (trainer:737) INFO: 13epoch:train:3601-3700batch: iter_time=1.163e-04, forward_time=0.105, loss_ctc=54.892, loss_att=58.093, acc=0.732, loss=57.133, backward_time=0.098, grad_norm=33.094, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.716e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:05:12,602 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 12:05:31,998 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 12:05:35,713 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fa2709750>)
+[gpuc02:0/16] 2024-01-13 12:05:35,714 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 12:05:35,717 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 12:10:07,069 (trainer:737) INFO: 13epoch:train:3701-3800batch: iter_time=2.714, forward_time=0.129, loss_ctc=62.207, loss_att=65.149, acc=0.686, loss=64.267, backward_time=0.101, grad_norm=43.596, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.031, optim0_lr0=5.714e-04, train_time=3.219
+[gpuc02:0/16] 2024-01-13 12:10:48,538 (trainer:737) INFO: 13epoch:train:3801-3900batch: iter_time=1.841e-04, forward_time=0.106, loss_ctc=47.781, loss_att=59.724, acc=0.689, loss=56.141, backward_time=0.098, grad_norm=34.596, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.713e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:11:29,402 (trainer:737) INFO: 13epoch:train:3901-4000batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=46.624, loss_att=54.194, acc=0.705, loss=51.923, backward_time=0.097, grad_norm=33.837, clip=100.000, loss_scale=1.623e+32, optim_step_time=0.030, optim0_lr0=5.711e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:12:10,693 (trainer:737) INFO: 13epoch:train:4001-4100batch: iter_time=1.518e-04, forward_time=0.107, loss_ctc=73.188, loss_att=69.353, acc=0.718, loss=70.503, backward_time=0.099, grad_norm=47.007, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.710e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 12:12:52,068 (trainer:737) INFO: 13epoch:train:4101-4200batch: iter_time=1.712e-04, forward_time=0.106, loss_ctc=59.709, loss_att=65.628, acc=0.678, loss=63.852, backward_time=0.098, grad_norm=40.422, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.708e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:13:32,961 (trainer:737) INFO: 13epoch:train:4201-4300batch: iter_time=1.815e-04, forward_time=0.105, loss_ctc=50.600, loss_att=47.078, acc=0.723, loss=48.135, backward_time=0.097, grad_norm=35.459, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.707e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:14:13,627 (trainer:737) INFO: 13epoch:train:4301-4400batch: iter_time=1.620e-04, forward_time=0.104, loss_ctc=44.544, loss_att=41.763, acc=0.730, loss=42.598, backward_time=0.096, grad_norm=29.809, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.705e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 12:14:55,004 (trainer:737) INFO: 13epoch:train:4401-4500batch: iter_time=1.620e-04, forward_time=0.107, loss_ctc=54.061, loss_att=63.886, acc=0.727, loss=60.938, backward_time=0.098, grad_norm=35.120, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.029, optim0_lr0=5.703e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:15:36,114 (trainer:737) INFO: 13epoch:train:4501-4600batch: iter_time=1.427e-04, forward_time=0.105, loss_ctc=39.602, loss_att=52.077, acc=0.733, loss=48.334, backward_time=0.097, grad_norm=26.804, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.702e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:16:17,388 (trainer:737) INFO: 13epoch:train:4601-4700batch: iter_time=1.742e-04, forward_time=0.105, loss_ctc=61.678, loss_att=52.207, acc=0.736, loss=55.049, backward_time=0.097, grad_norm=40.126, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.700e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 12:16:58,739 (trainer:737) INFO: 13epoch:train:4701-4800batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=51.449, loss_att=54.046, acc=0.699, loss=53.267, backward_time=0.097, grad_norm=34.985, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.699e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 12:17:40,633 (trainer:737) INFO: 13epoch:train:4801-4900batch: iter_time=1.631e-04, forward_time=0.109, loss_ctc=49.640, loss_att=46.900, acc=0.747, loss=47.722, backward_time=0.099, grad_norm=31.279, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.697e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 12:18:21,775 (trainer:737) INFO: 13epoch:train:4901-5000batch: iter_time=1.321e-04, forward_time=0.106, loss_ctc=60.845, loss_att=68.197, acc=0.706, loss=65.991, backward_time=0.098, grad_norm=41.799, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.696e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:18:31,252 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 12:18:51,393 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 12:18:55,138 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39e2daaad0>)
+[gpuc02:0/16] 2024-01-13 12:18:55,138 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 12:18:55,141 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 12:23:51,472 (trainer:737) INFO: 13epoch:train:5001-5100batch: iter_time=2.615, forward_time=0.105, loss_ctc=57.702, loss_att=64.991, acc=0.676, loss=62.804, backward_time=0.098, grad_norm=38.848, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.694e-04, train_time=3.297
+[gpuc02:0/16] 2024-01-13 12:24:32,305 (trainer:737) INFO: 13epoch:train:5101-5200batch: iter_time=1.184e-04, forward_time=0.104, loss_ctc=44.028, loss_att=59.122, acc=0.683, loss=54.594, backward_time=0.098, grad_norm=33.016, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.693e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:25:13,293 (trainer:737) INFO: 13epoch:train:5201-5300batch: iter_time=1.158e-04, forward_time=0.104, loss_ctc=62.827, loss_att=62.066, acc=0.698, loss=62.294, backward_time=0.098, grad_norm=41.497, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.691e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 12:25:54,373 (trainer:737) INFO: 13epoch:train:5301-5400batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=58.101, loss_att=60.894, acc=0.715, loss=60.056, backward_time=0.099, grad_norm=39.024, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.690e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:26:35,152 (trainer:737) INFO: 13epoch:train:5401-5500batch: iter_time=1.624e-04, forward_time=0.103, loss_ctc=61.281, loss_att=60.392, acc=0.678, loss=60.659, backward_time=0.098, grad_norm=41.460, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.688e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:27:16,010 (trainer:737) INFO: 13epoch:train:5501-5600batch: iter_time=1.840e-04, forward_time=0.103, loss_ctc=40.243, loss_att=42.066, acc=0.717, loss=41.519, backward_time=0.096, grad_norm=29.002, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.686e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:27:57,337 (trainer:737) INFO: 13epoch:train:5601-5700batch: iter_time=1.600e-04, forward_time=0.104, loss_ctc=54.421, loss_att=60.449, acc=0.698, loss=58.640, backward_time=0.098, grad_norm=36.437, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.685e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 12:28:38,038 (trainer:737) INFO: 13epoch:train:5701-5800batch: iter_time=1.527e-04, forward_time=0.104, loss_ctc=44.540, loss_att=51.198, acc=0.742, loss=49.201, backward_time=0.099, grad_norm=30.754, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.031, optim0_lr0=5.683e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 12:29:19,428 (trainer:737) INFO: 13epoch:train:5801-5900batch: iter_time=1.958e-04, forward_time=0.104, loss_ctc=53.878, loss_att=59.727, acc=0.718, loss=57.972, backward_time=0.098, grad_norm=36.540, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.682e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:30:00,050 (trainer:737) INFO: 13epoch:train:5901-6000batch: iter_time=2.005e-04, forward_time=0.104, loss_ctc=54.169, loss_att=52.807, acc=0.701, loss=53.216, backward_time=0.096, grad_norm=38.890, clip=100.000, loss_scale=3.245e+32, optim_step_time=0.030, optim0_lr0=5.680e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 12:30:40,847 (trainer:737) INFO: 13epoch:train:6001-6100batch: iter_time=1.826e-04, forward_time=0.105, loss_ctc=53.266, loss_att=49.207, acc=0.725, loss=50.424, backward_time=0.097, grad_norm=36.339, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.679e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:31:21,619 (trainer:737) INFO: 13epoch:train:6101-6200batch: iter_time=1.752e-04, forward_time=0.105, loss_ctc=53.711, loss_att=57.646, acc=0.727, loss=56.466, backward_time=0.097, grad_norm=32.622, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.029, optim0_lr0=5.677e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:31:49,869 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 12:32:09,670 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 12:32:13,472 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39b7e8a890>)
+[gpuc02:0/16] 2024-01-13 12:32:13,473 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 12:32:13,476 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 12:36:42,578 (trainer:737) INFO: 13epoch:train:6201-6300batch: iter_time=2.634, forward_time=0.104, loss_ctc=61.630, loss_att=64.080, acc=0.681, loss=63.345, backward_time=0.097, grad_norm=42.167, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.676e-04, train_time=3.209
+[gpuc02:0/16] 2024-01-13 12:37:23,700 (trainer:737) INFO: 13epoch:train:6301-6400batch: iter_time=1.494e-04, forward_time=0.103, loss_ctc=46.575, loss_att=61.221, acc=0.676, loss=56.827, backward_time=0.097, grad_norm=34.597, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.674e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:38:04,572 (trainer:737) INFO: 13epoch:train:6401-6500batch: iter_time=1.357e-04, forward_time=0.103, loss_ctc=46.039, loss_att=53.055, acc=0.707, loss=50.950, backward_time=0.097, grad_norm=32.806, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.673e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:38:45,840 (trainer:737) INFO: 13epoch:train:6501-6600batch: iter_time=1.292e-04, forward_time=0.106, loss_ctc=71.427, loss_att=69.096, acc=0.712, loss=69.795, backward_time=0.100, grad_norm=48.691, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.671e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 12:39:26,963 (trainer:737) INFO: 13epoch:train:6601-6700batch: iter_time=1.505e-04, forward_time=0.105, loss_ctc=58.538, loss_att=62.971, acc=0.674, loss=61.641, backward_time=0.099, grad_norm=39.682, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.670e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:40:07,724 (trainer:737) INFO: 13epoch:train:6701-6800batch: iter_time=1.377e-04, forward_time=0.103, loss_ctc=50.751, loss_att=46.508, acc=0.720, loss=47.781, backward_time=0.098, grad_norm=37.445, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.668e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 12:40:48,460 (trainer:737) INFO: 13epoch:train:6801-6900batch: iter_time=1.253e-04, forward_time=0.103, loss_ctc=44.110, loss_att=42.342, acc=0.723, loss=42.872, backward_time=0.097, grad_norm=30.842, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.667e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 12:41:29,492 (trainer:737) INFO: 13epoch:train:6901-7000batch: iter_time=1.386e-04, forward_time=0.105, loss_ctc=53.266, loss_att=64.423, acc=0.711, loss=61.076, backward_time=0.098, grad_norm=36.522, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.665e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 12:42:10,257 (trainer:737) INFO: 13epoch:train:7001-7100batch: iter_time=1.420e-04, forward_time=0.104, loss_ctc=39.375, loss_att=52.251, acc=0.724, loss=48.388, backward_time=0.096, grad_norm=27.352, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.664e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 12:42:51,262 (trainer:737) INFO: 13epoch:train:7101-7200batch: iter_time=1.417e-04, forward_time=0.104, loss_ctc=62.188, loss_att=52.467, acc=0.727, loss=55.383, backward_time=0.097, grad_norm=44.168, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.662e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 12:43:32,271 (trainer:737) INFO: 13epoch:train:7201-7300batch: iter_time=1.391e-04, forward_time=0.104, loss_ctc=51.023, loss_att=52.441, acc=0.695, loss=52.015, backward_time=0.096, grad_norm=35.529, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.661e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 12:44:13,385 (trainer:737) INFO: 13epoch:train:7301-7400batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=49.271, loss_att=47.325, acc=0.743, loss=47.909, backward_time=0.097, grad_norm=31.758, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.659e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 12:44:54,808 (trainer:737) INFO: 13epoch:train:7401-7500batch: iter_time=1.266e-04, forward_time=0.105, loss_ctc=60.217, loss_att=67.507, acc=0.698, loss=65.320, backward_time=0.098, grad_norm=42.300, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.658e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 12:44:58,517 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 12:45:18,370 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 12:45:22,011 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fd8126920>)
+[gpuc02:0/16] 2024-01-13 12:45:22,011 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 12:45:22,015 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 12:50:13,311 (trainer:737) INFO: 13epoch:train:7501-7600batch: iter_time=2.772, forward_time=0.104, loss_ctc=56.952, loss_att=61.099, acc=0.683, loss=59.855, backward_time=0.097, grad_norm=38.173, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.656e-04, train_time=3.185
+[gpuc02:0/16] 2024-01-13 12:50:54,139 (trainer:737) INFO: 13epoch:train:7601-7700batch: iter_time=1.748e-04, forward_time=0.104, loss_ctc=43.545, loss_att=56.883, acc=0.688, loss=52.882, backward_time=0.098, grad_norm=32.648, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.655e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:51:35,618 (trainer:737) INFO: 13epoch:train:7701-7800batch: iter_time=1.708e-04, forward_time=0.104, loss_ctc=62.570, loss_att=60.851, acc=0.702, loss=61.367, backward_time=0.098, grad_norm=42.751, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.653e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 12:52:17,171 (trainer:737) INFO: 13epoch:train:7801-7900batch: iter_time=1.661e-04, forward_time=0.105, loss_ctc=57.813, loss_att=59.223, acc=0.719, loss=58.800, backward_time=0.098, grad_norm=39.053, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.652e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 12:52:58,223 (trainer:737) INFO: 13epoch:train:7901-8000batch: iter_time=1.880e-04, forward_time=0.103, loss_ctc=60.400, loss_att=59.033, acc=0.681, loss=59.443, backward_time=0.097, grad_norm=41.237, clip=100.000, loss_scale=6.490e+32, optim_step_time=0.030, optim0_lr0=5.650e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 12:53:38,807 (trainer:737) INFO: 13epoch:train:8001-8100batch: iter_time=2.093e-04, forward_time=0.104, loss_ctc=39.990, loss_att=40.556, acc=0.721, loss=40.386, backward_time=0.097, grad_norm=29.495, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.649e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 12:54:19,653 (trainer:737) INFO: 13epoch:train:8101-8200batch: iter_time=2.146e-04, forward_time=0.105, loss_ctc=54.291, loss_att=59.582, acc=0.703, loss=57.994, backward_time=0.098, grad_norm=34.962, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.647e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 12:55:01,185 (trainer:737) INFO: 13epoch:train:8201-8300batch: iter_time=1.842e-04, forward_time=0.104, loss_ctc=44.671, loss_att=49.941, acc=0.744, loss=48.360, backward_time=0.097, grad_norm=29.620, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.646e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 12:55:42,067 (trainer:737) INFO: 13epoch:train:8301-8400batch: iter_time=1.913e-04, forward_time=0.105, loss_ctc=53.673, loss_att=58.860, acc=0.721, loss=57.304, backward_time=0.098, grad_norm=36.546, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.644e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:56:22,816 (trainer:737) INFO: 13epoch:train:8401-8500batch: iter_time=1.948e-04, forward_time=0.104, loss_ctc=54.266, loss_att=52.075, acc=0.702, loss=52.733, backward_time=0.097, grad_norm=38.870, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.643e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 12:57:03,689 (trainer:737) INFO: 13epoch:train:8501-8600batch: iter_time=1.585e-04, forward_time=0.105, loss_ctc=53.518, loss_att=48.641, acc=0.727, loss=50.104, backward_time=0.098, grad_norm=36.137, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.641e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 12:57:44,931 (trainer:737) INFO: 13epoch:train:8601-8700batch: iter_time=1.793e-04, forward_time=0.108, loss_ctc=54.151, loss_att=57.775, acc=0.729, loss=56.688, backward_time=0.098, grad_norm=34.250, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.640e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 12:58:11,739 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 12:58:32,486 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 12:58:36,369 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a130af6d0>)
+[gpuc02:0/16] 2024-01-13 12:58:36,369 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 12:58:36,372 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 13:03:05,203 (trainer:737) INFO: 13epoch:train:8701-8800batch: iter_time=2.652, forward_time=0.105, loss_ctc=60.816, loss_att=63.106, acc=0.683, loss=62.419, backward_time=0.098, grad_norm=42.087, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.638e-04, train_time=3.202
+[gpuc02:0/16] 2024-01-13 13:03:46,056 (trainer:737) INFO: 13epoch:train:8801-8900batch: iter_time=1.924e-04, forward_time=0.104, loss_ctc=46.122, loss_att=59.921, acc=0.680, loss=55.781, backward_time=0.097, grad_norm=33.984, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.637e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:04:27,137 (trainer:737) INFO: 13epoch:train:8901-9000batch: iter_time=2.033e-04, forward_time=0.104, loss_ctc=45.516, loss_att=52.665, acc=0.709, loss=50.520, backward_time=0.097, grad_norm=30.733, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.635e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:05:08,303 (trainer:737) INFO: 13epoch:train:9001-9100batch: iter_time=2.134e-04, forward_time=0.107, loss_ctc=70.844, loss_att=68.562, acc=0.713, loss=69.247, backward_time=0.098, grad_norm=45.742, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.029, optim0_lr0=5.634e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:05:49,598 (trainer:737) INFO: 13epoch:train:9101-9200batch: iter_time=1.974e-04, forward_time=0.105, loss_ctc=58.141, loss_att=62.587, acc=0.676, loss=61.253, backward_time=0.097, grad_norm=38.982, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.632e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 13:06:30,370 (trainer:737) INFO: 13epoch:train:9201-9300batch: iter_time=2.269e-04, forward_time=0.105, loss_ctc=50.217, loss_att=45.750, acc=0.722, loss=47.090, backward_time=0.097, grad_norm=38.011, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.029, optim0_lr0=5.631e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:07:10,981 (trainer:737) INFO: 13epoch:train:9301-9400batch: iter_time=2.104e-04, forward_time=0.104, loss_ctc=44.527, loss_att=41.965, acc=0.725, loss=42.734, backward_time=0.096, grad_norm=30.307, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.629e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 13:07:51,929 (trainer:737) INFO: 13epoch:train:9401-9500batch: iter_time=2.014e-04, forward_time=0.105, loss_ctc=53.413, loss_att=63.732, acc=0.714, loss=60.636, backward_time=0.097, grad_norm=36.712, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.628e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 13:08:32,972 (trainer:737) INFO: 13epoch:train:9501-9600batch: iter_time=2.142e-04, forward_time=0.105, loss_ctc=39.008, loss_att=51.842, acc=0.726, loss=47.992, backward_time=0.096, grad_norm=28.063, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.626e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:09:14,080 (trainer:737) INFO: 13epoch:train:9601-9700batch: iter_time=2.258e-04, forward_time=0.105, loss_ctc=61.435, loss_att=52.307, acc=0.729, loss=55.045, backward_time=0.097, grad_norm=42.793, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.625e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:09:55,125 (trainer:737) INFO: 13epoch:train:9701-9800batch: iter_time=1.940e-04, forward_time=0.105, loss_ctc=50.955, loss_att=52.302, acc=0.696, loss=51.898, backward_time=0.097, grad_norm=36.967, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.623e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:10:36,707 (trainer:737) INFO: 13epoch:train:9801-9900batch: iter_time=2.124e-04, forward_time=0.106, loss_ctc=49.090, loss_att=47.092, acc=0.745, loss=47.691, backward_time=0.098, grad_norm=31.168, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.622e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 13:11:18,044 (trainer:737) INFO: 13epoch:train:9901-10000batch: iter_time=2.015e-04, forward_time=0.105, loss_ctc=60.225, loss_att=67.166, acc=0.700, loss=65.084, backward_time=0.098, grad_norm=39.333, clip=100.000, loss_scale=1.298e+33, optim_step_time=0.030, optim0_lr0=5.620e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 13:11:25,045 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 13:11:44,529 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 13:11:48,228 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fb03574f0>)
+[gpuc02:0/16] 2024-01-13 13:11:48,229 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 13:11:48,232 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 13:16:45,380 (trainer:737) INFO: 13epoch:train:10001-10100batch: iter_time=2.521, forward_time=0.107, loss_ctc=56.731, loss_att=60.703, acc=0.685, loss=59.511, backward_time=0.097, grad_norm=38.898, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.619e-04, train_time=3.273
+[gpuc02:0/16] 2024-01-13 13:17:26,381 (trainer:737) INFO: 13epoch:train:10101-10200batch: iter_time=1.346e-04, forward_time=0.105, loss_ctc=43.477, loss_att=56.247, acc=0.689, loss=52.416, backward_time=0.096, grad_norm=33.177, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.617e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:18:07,295 (trainer:737) INFO: 13epoch:train:10201-10300batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=62.101, loss_att=59.961, acc=0.704, loss=60.603, backward_time=0.097, grad_norm=40.652, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.029, optim0_lr0=5.616e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 13:18:48,319 (trainer:737) INFO: 13epoch:train:10301-10400batch: iter_time=1.510e-04, forward_time=0.106, loss_ctc=56.641, loss_att=58.878, acc=0.718, loss=58.207, backward_time=0.098, grad_norm=39.632, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.614e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:19:29,120 (trainer:737) INFO: 13epoch:train:10401-10500batch: iter_time=1.564e-04, forward_time=0.104, loss_ctc=59.843, loss_att=58.334, acc=0.683, loss=58.787, backward_time=0.097, grad_norm=42.686, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.613e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:20:09,668 (trainer:737) INFO: 13epoch:train:10501-10600batch: iter_time=1.488e-04, forward_time=0.104, loss_ctc=39.474, loss_att=40.203, acc=0.722, loss=39.984, backward_time=0.097, grad_norm=30.388, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.611e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-13 13:20:50,699 (trainer:737) INFO: 13epoch:train:10601-10700batch: iter_time=1.524e-04, forward_time=0.105, loss_ctc=54.024, loss_att=58.939, acc=0.703, loss=57.465, backward_time=0.098, grad_norm=36.216, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.610e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:21:31,685 (trainer:737) INFO: 13epoch:train:10701-10800batch: iter_time=1.626e-04, forward_time=0.104, loss_ctc=43.991, loss_att=49.967, acc=0.746, loss=48.174, backward_time=0.097, grad_norm=30.676, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.608e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:22:12,964 (trainer:737) INFO: 13epoch:train:10801-10900batch: iter_time=1.548e-04, forward_time=0.105, loss_ctc=53.401, loss_att=58.703, acc=0.722, loss=57.113, backward_time=0.098, grad_norm=35.941, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.607e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 13:22:54,347 (trainer:737) INFO: 13epoch:train:10901-11000batch: iter_time=1.467e-04, forward_time=0.104, loss_ctc=53.254, loss_att=51.362, acc=0.705, loss=51.930, backward_time=0.097, grad_norm=42.202, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.605e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 13:23:35,328 (trainer:737) INFO: 13epoch:train:11001-11100batch: iter_time=1.514e-04, forward_time=0.105, loss_ctc=52.009, loss_att=47.838, acc=0.728, loss=49.090, backward_time=0.098, grad_norm=34.979, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.604e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:24:16,657 (trainer:737) INFO: 13epoch:train:11101-11200batch: iter_time=1.535e-04, forward_time=0.105, loss_ctc=53.374, loss_att=57.212, acc=0.731, loss=56.060, backward_time=0.098, grad_norm=32.525, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.603e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 13:24:43,884 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 13:25:02,794 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 13:25:06,478 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a13064970>)
+[gpuc02:0/16] 2024-01-13 13:25:06,478 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 13:25:06,482 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 13:29:44,120 (trainer:737) INFO: 13epoch:train:11201-11300batch: iter_time=2.655, forward_time=0.105, loss_ctc=60.576, loss_att=65.539, acc=0.681, loss=64.050, backward_time=0.098, grad_norm=44.739, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.601e-04, train_time=3.274
+[gpuc02:0/16] 2024-01-13 13:30:25,123 (trainer:737) INFO: 13epoch:train:11301-11400batch: iter_time=1.572e-04, forward_time=0.104, loss_ctc=46.176, loss_att=61.369, acc=0.685, loss=56.811, backward_time=0.098, grad_norm=33.190, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.600e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:31:06,613 (trainer:737) INFO: 13epoch:train:11401-11500batch: iter_time=1.583e-04, forward_time=0.104, loss_ctc=45.494, loss_att=55.275, acc=0.706, loss=52.341, backward_time=0.098, grad_norm=32.292, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.598e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 13:31:47,859 (trainer:737) INFO: 13epoch:train:11501-11600batch: iter_time=1.618e-04, forward_time=0.106, loss_ctc=69.789, loss_att=69.396, acc=0.721, loss=69.514, backward_time=0.099, grad_norm=48.403, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.597e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 13:32:28,987 (trainer:737) INFO: 13epoch:train:11601-11700batch: iter_time=1.580e-04, forward_time=0.106, loss_ctc=57.836, loss_att=65.345, acc=0.685, loss=63.092, backward_time=0.098, grad_norm=38.265, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.595e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:33:09,823 (trainer:737) INFO: 13epoch:train:11701-11800batch: iter_time=1.701e-04, forward_time=0.103, loss_ctc=48.493, loss_att=46.878, acc=0.728, loss=47.363, backward_time=0.097, grad_norm=35.639, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.594e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:33:50,995 (trainer:737) INFO: 13epoch:train:11801-11900batch: iter_time=2.089e-04, forward_time=0.104, loss_ctc=43.926, loss_att=42.079, acc=0.732, loss=42.634, backward_time=0.096, grad_norm=31.201, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.029, optim0_lr0=5.592e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 13:34:32,542 (trainer:737) INFO: 13epoch:train:11901-12000batch: iter_time=1.536e-04, forward_time=0.105, loss_ctc=52.851, loss_att=64.302, acc=0.731, loss=60.867, backward_time=0.098, grad_norm=35.906, clip=100.000, loss_scale=2.596e+33, optim_step_time=0.030, optim0_lr0=5.591e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 13:35:13,320 (trainer:737) INFO: 13epoch:train:12001-12100batch: iter_time=1.814e-04, forward_time=0.104, loss_ctc=39.328, loss_att=52.785, acc=0.734, loss=48.748, backward_time=0.097, grad_norm=28.523, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.589e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:35:54,186 (trainer:737) INFO: 13epoch:train:12101-12200batch: iter_time=2.053e-04, forward_time=0.104, loss_ctc=60.692, loss_att=52.511, acc=0.736, loss=54.965, backward_time=0.097, grad_norm=41.375, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.588e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:36:35,010 (trainer:737) INFO: 13epoch:train:12201-12300batch: iter_time=1.721e-04, forward_time=0.104, loss_ctc=50.607, loss_att=53.183, acc=0.705, loss=52.410, backward_time=0.097, grad_norm=36.854, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.587e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:37:15,977 (trainer:737) INFO: 13epoch:train:12301-12400batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=49.061, loss_att=47.797, acc=0.746, loss=48.176, backward_time=0.098, grad_norm=31.978, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.585e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 13:37:57,102 (trainer:737) INFO: 13epoch:train:12401-12500batch: iter_time=1.424e-04, forward_time=0.105, loss_ctc=59.681, loss_att=68.305, acc=0.706, loss=65.718, backward_time=0.098, grad_norm=40.598, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.584e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:38:03,795 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 13:38:23,416 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 13:38:27,482 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39ae4df790>)
+[gpuc02:0/16] 2024-01-13 13:38:27,482 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 13:38:27,485 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 13:43:27,486 (trainer:737) INFO: 13epoch:train:12501-12600batch: iter_time=2.673, forward_time=0.104, loss_ctc=56.375, loss_att=63.030, acc=0.681, loss=61.033, backward_time=0.097, grad_norm=37.773, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.582e-04, train_time=3.304
+[gpuc02:0/16] 2024-01-13 13:44:08,349 (trainer:737) INFO: 13epoch:train:12601-12700batch: iter_time=1.826e-04, forward_time=0.104, loss_ctc=42.796, loss_att=56.138, acc=0.694, loss=52.135, backward_time=0.096, grad_norm=33.627, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.581e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 13:44:49,388 (trainer:737) INFO: 13epoch:train:12701-12800batch: iter_time=1.406e-04, forward_time=0.104, loss_ctc=61.325, loss_att=60.845, acc=0.703, loss=60.989, backward_time=0.097, grad_norm=44.349, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.579e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:45:30,562 (trainer:737) INFO: 13epoch:train:12801-12900batch: iter_time=1.441e-04, forward_time=0.105, loss_ctc=56.424, loss_att=58.765, acc=0.719, loss=58.063, backward_time=0.098, grad_norm=37.826, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.578e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 13:46:11,777 (trainer:737) INFO: 13epoch:train:12901-13000batch: iter_time=1.555e-04, forward_time=0.104, loss_ctc=58.882, loss_att=58.897, acc=0.681, loss=58.893, backward_time=0.097, grad_norm=42.017, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.576e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 13:46:52,468 (trainer:737) INFO: 13epoch:train:13001-13100batch: iter_time=1.637e-04, forward_time=0.104, loss_ctc=39.239, loss_att=40.503, acc=0.722, loss=40.124, backward_time=0.096, grad_norm=30.571, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.575e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 13:47:33,504 (trainer:737) INFO: 13epoch:train:13101-13200batch: iter_time=1.696e-04, forward_time=0.104, loss_ctc=54.269, loss_att=59.214, acc=0.703, loss=57.731, backward_time=0.097, grad_norm=36.592, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.573e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:48:14,666 (trainer:737) INFO: 13epoch:train:13201-13300batch: iter_time=1.700e-04, forward_time=0.103, loss_ctc=44.080, loss_att=50.152, acc=0.745, loss=48.330, backward_time=0.096, grad_norm=31.404, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.029, optim0_lr0=5.572e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:48:55,698 (trainer:737) INFO: 13epoch:train:13301-13400batch: iter_time=1.679e-04, forward_time=0.105, loss_ctc=52.233, loss_att=58.610, acc=0.722, loss=56.697, backward_time=0.097, grad_norm=36.678, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.029, optim0_lr0=5.571e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 13:49:36,850 (trainer:737) INFO: 13epoch:train:13401-13500batch: iter_time=1.704e-04, forward_time=0.104, loss_ctc=53.629, loss_att=52.475, acc=0.702, loss=52.821, backward_time=0.096, grad_norm=38.109, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.029, optim0_lr0=5.569e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:50:17,812 (trainer:737) INFO: 13epoch:train:13501-13600batch: iter_time=1.800e-04, forward_time=0.105, loss_ctc=51.667, loss_att=47.927, acc=0.728, loss=49.049, backward_time=0.097, grad_norm=35.516, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.029, optim0_lr0=5.568e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 13:50:59,394 (trainer:737) INFO: 13epoch:train:13601-13700batch: iter_time=1.889e-04, forward_time=0.105, loss_ctc=52.664, loss_att=56.918, acc=0.732, loss=55.642, backward_time=0.098, grad_norm=33.104, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.029, optim0_lr0=5.566e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 13:51:26,958 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 13:51:46,016 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 13:51:49,647 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a0fc423e0>)
+[gpuc02:0/16] 2024-01-13 13:51:49,648 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 13:51:49,651 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 13:56:33,493 (trainer:737) INFO: 13epoch:train:13701-13800batch: iter_time=2.556, forward_time=0.105, loss_ctc=59.646, loss_att=64.401, acc=0.683, loss=62.975, backward_time=0.097, grad_norm=42.348, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.565e-04, train_time=3.341
+[gpuc02:0/16] 2024-01-13 13:57:14,650 (trainer:737) INFO: 13epoch:train:13801-13900batch: iter_time=1.833e-04, forward_time=0.104, loss_ctc=45.846, loss_att=59.776, acc=0.690, loss=55.597, backward_time=0.097, grad_norm=33.997, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.563e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 13:57:55,847 (trainer:737) INFO: 13epoch:train:13901-14000batch: iter_time=1.720e-04, forward_time=0.105, loss_ctc=45.270, loss_att=54.481, acc=0.707, loss=51.718, backward_time=0.098, grad_norm=33.070, clip=100.000, loss_scale=5.192e+33, optim_step_time=0.030, optim0_lr0=5.562e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 13:58:37,338 (trainer:737) INFO: 13epoch:train:14001-14100batch: iter_time=2.059e-04, forward_time=0.106, loss_ctc=69.805, loss_att=68.824, acc=0.722, loss=69.118, backward_time=0.099, grad_norm=49.673, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.561e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 13:59:18,698 (trainer:737) INFO: 13epoch:train:14101-14200batch: iter_time=1.827e-04, forward_time=0.106, loss_ctc=57.284, loss_att=65.826, acc=0.682, loss=63.264, backward_time=0.098, grad_norm=40.564, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.559e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 13:59:59,719 (trainer:737) INFO: 13epoch:train:14201-14300batch: iter_time=1.791e-04, forward_time=0.105, loss_ctc=47.950, loss_att=46.637, acc=0.728, loss=47.031, backward_time=0.097, grad_norm=35.701, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.558e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:00:40,682 (trainer:737) INFO: 13epoch:train:14301-14400batch: iter_time=1.925e-04, forward_time=0.104, loss_ctc=43.654, loss_att=41.727, acc=0.735, loss=42.305, backward_time=0.097, grad_norm=29.739, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.556e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 14:01:22,209 (trainer:737) INFO: 13epoch:train:14401-14500batch: iter_time=1.735e-04, forward_time=0.106, loss_ctc=53.436, loss_att=64.943, acc=0.730, loss=61.491, backward_time=0.099, grad_norm=36.011, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.555e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 14:02:03,282 (trainer:737) INFO: 13epoch:train:14501-14600batch: iter_time=1.673e-04, forward_time=0.104, loss_ctc=38.871, loss_att=52.584, acc=0.735, loss=48.470, backward_time=0.097, grad_norm=27.646, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.553e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 14:02:44,660 (trainer:737) INFO: 13epoch:train:14601-14700batch: iter_time=1.597e-04, forward_time=0.108, loss_ctc=60.501, loss_att=51.416, acc=0.740, loss=54.142, backward_time=0.097, grad_norm=67.087, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.552e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 14:03:25,679 (trainer:737) INFO: 13epoch:train:14701-14800batch: iter_time=1.601e-04, forward_time=0.105, loss_ctc=49.935, loss_att=53.299, acc=0.703, loss=52.290, backward_time=0.097, grad_norm=36.803, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.551e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:04:06,727 (trainer:737) INFO: 13epoch:train:14801-14900batch: iter_time=1.608e-04, forward_time=0.105, loss_ctc=48.551, loss_att=46.478, acc=0.751, loss=47.100, backward_time=0.098, grad_norm=30.779, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.029, optim0_lr0=5.549e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:04:48,704 (trainer:737) INFO: 13epoch:train:14901-15000batch: iter_time=1.265e-04, forward_time=0.106, loss_ctc=58.978, loss_att=67.648, acc=0.708, loss=65.047, backward_time=0.099, grad_norm=39.710, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.548e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 14:24:56,667 (trainer:343) INFO: 13epoch results: [train] iter_time=0.219, forward_time=0.105, loss_ctc=53.420, loss_att=56.535, acc=0.711, loss=55.601, backward_time=0.098, grad_norm=36.855, clip=100.000, loss_scale=2.066e+33, optim_step_time=0.030, optim0_lr0=5.658e-04, train_time=0.646, time=2 hours, 41 minutes and 42.03 seconds, total_count=195000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=58.037, cer_ctc=0.308, loss_att=56.017, acc=0.570, cer=0.374, wer=0.999, loss=56.623, time=19 minutes and 55.33 seconds, total_count=60723, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 14:25:01,745 (trainer:391) INFO: The best model has been updated: valid.acc, valid.total_count
+[gpuc02:0/16] 2024-01-13 14:25:01,749 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/8epoch.pth
+[gpuc02:0/16] 2024-01-13 14:25:01,749 (trainer:272) INFO: 14/45epoch started. Estimated time to finish: 3 days, 23 hours and 52 minutes
+[gpuc02:0/16] 2024-01-13 14:25:01,758 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 14:25:20,716 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 14:25:24,261 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38f5b7d0c0>)
+[gpuc02:0/16] 2024-01-13 14:25:24,262 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 14:25:24,265 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 14:30:08,091 (trainer:737) INFO: 14epoch:train:1-100batch: iter_time=2.536, forward_time=0.105, loss_ctc=55.522, loss_att=73.256, acc=0.689, loss=67.936, backward_time=0.099, grad_norm=38.771, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.546e-04, train_time=3.063
+[gpuc02:0/16] 2024-01-13 14:30:48,943 (trainer:737) INFO: 14epoch:train:101-200batch: iter_time=1.116e-04, forward_time=0.104, loss_ctc=59.108, loss_att=59.913, acc=0.689, loss=59.671, backward_time=0.098, grad_norm=37.896, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.545e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 14:31:29,777 (trainer:737) INFO: 14epoch:train:201-300batch: iter_time=1.165e-04, forward_time=0.104, loss_ctc=48.269, loss_att=60.860, acc=0.692, loss=57.083, backward_time=0.098, grad_norm=35.084, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.543e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 14:32:10,996 (trainer:737) INFO: 14epoch:train:301-400batch: iter_time=1.177e-04, forward_time=0.103, loss_ctc=47.267, loss_att=48.623, acc=0.720, loss=48.216, backward_time=0.097, grad_norm=35.178, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.542e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 14:32:53,047 (trainer:737) INFO: 14epoch:train:401-500batch: iter_time=1.206e-04, forward_time=0.103, loss_ctc=53.853, loss_att=46.282, acc=0.715, loss=48.554, backward_time=0.097, grad_norm=38.060, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.541e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 14:33:35,063 (trainer:737) INFO: 14epoch:train:501-600batch: iter_time=1.208e-04, forward_time=0.106, loss_ctc=51.111, loss_att=54.198, acc=0.702, loss=53.272, backward_time=0.102, grad_norm=37.685, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.539e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 14:34:17,647 (trainer:737) INFO: 14epoch:train:601-700batch: iter_time=1.129e-04, forward_time=0.108, loss_ctc=49.358, loss_att=58.953, acc=0.690, loss=56.074, backward_time=0.097, grad_norm=34.689, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.032, optim0_lr0=5.538e-04, train_time=0.426
+[gpuc02:0/16] 2024-01-13 14:34:58,692 (trainer:737) INFO: 14epoch:train:701-800batch: iter_time=1.305e-04, forward_time=0.104, loss_ctc=50.339, loss_att=50.159, acc=0.716, loss=50.213, backward_time=0.097, grad_norm=37.535, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.536e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:35:41,518 (trainer:737) INFO: 14epoch:train:801-900batch: iter_time=1.285e-04, forward_time=0.111, loss_ctc=47.232, loss_att=48.565, acc=0.729, loss=48.165, backward_time=0.098, grad_norm=32.697, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.535e-04, train_time=0.428
+[gpuc02:0/16] 2024-01-13 14:36:22,230 (trainer:737) INFO: 14epoch:train:901-1000batch: iter_time=1.185e-04, forward_time=0.104, loss_ctc=56.763, loss_att=49.931, acc=0.743, loss=51.981, backward_time=0.098, grad_norm=43.700, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.534e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 14:37:16,879 (trainer:737) INFO: 14epoch:train:1001-1100batch: iter_time=1.278e-04, forward_time=0.123, loss_ctc=49.174, loss_att=49.408, acc=0.719, loss=49.338, backward_time=0.120, grad_norm=33.085, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.032, optim0_lr0=5.532e-04, train_time=0.546
+[gpuc02:0/16] 2024-01-13 14:38:00,163 (trainer:737) INFO: 14epoch:train:1101-1200batch: iter_time=1.200e-04, forward_time=0.120, loss_ctc=51.912, loss_att=56.418, acc=0.700, loss=55.067, backward_time=0.100, grad_norm=37.696, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.033, optim0_lr0=5.531e-04, train_time=0.433
+[gpuc02:0/16] 2024-01-13 14:38:39,329 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 14:38:59,245 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 14:39:03,296 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a0d1c97e0>)
+[gpuc02:0/16] 2024-01-13 14:39:03,296 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 14:39:03,299 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 14:46:44,362 (trainer:737) INFO: 14epoch:train:1201-1300batch: iter_time=3.695, forward_time=0.141, loss_ctc=46.749, loss_att=61.084, acc=0.703, loss=56.783, backward_time=0.104, grad_norm=32.837, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.033, optim0_lr0=5.529e-04, train_time=5.242
+[gpuc02:0/16] 2024-01-13 14:47:25,544 (trainer:737) INFO: 14epoch:train:1301-1400batch: iter_time=1.324e-04, forward_time=0.104, loss_ctc=58.140, loss_att=65.277, acc=0.694, loss=63.136, backward_time=0.098, grad_norm=40.887, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.528e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 14:48:06,566 (trainer:737) INFO: 14epoch:train:1401-1500batch: iter_time=1.415e-04, forward_time=0.103, loss_ctc=53.006, loss_att=60.207, acc=0.682, loss=58.047, backward_time=0.097, grad_norm=35.472, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.526e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:48:47,578 (trainer:737) INFO: 14epoch:train:1501-1600batch: iter_time=1.409e-04, forward_time=0.105, loss_ctc=46.821, loss_att=54.652, acc=0.725, loss=52.303, backward_time=0.098, grad_norm=34.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.525e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:49:28,250 (trainer:737) INFO: 14epoch:train:1601-1700batch: iter_time=1.395e-04, forward_time=0.103, loss_ctc=48.163, loss_att=42.608, acc=0.733, loss=44.275, backward_time=0.096, grad_norm=32.625, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.524e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 14:50:09,264 (trainer:737) INFO: 14epoch:train:1701-1800batch: iter_time=1.521e-04, forward_time=0.104, loss_ctc=54.604, loss_att=50.629, acc=0.693, loss=51.821, backward_time=0.097, grad_norm=38.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.522e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:50:50,473 (trainer:737) INFO: 14epoch:train:1801-1900batch: iter_time=1.345e-04, forward_time=0.104, loss_ctc=52.496, loss_att=64.609, acc=0.690, loss=60.975, backward_time=0.097, grad_norm=37.970, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.521e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 14:51:31,611 (trainer:737) INFO: 14epoch:train:1901-2000batch: iter_time=1.382e-04, forward_time=0.103, loss_ctc=47.960, loss_att=51.312, acc=0.700, loss=50.306, backward_time=0.097, grad_norm=32.620, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.519e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 14:52:12,787 (trainer:737) INFO: 14epoch:train:2001-2100batch: iter_time=1.484e-04, forward_time=0.103, loss_ctc=46.213, loss_att=42.933, acc=0.746, loss=43.917, backward_time=0.097, grad_norm=31.047, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.518e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 14:52:53,861 (trainer:737) INFO: 14epoch:train:2101-2200batch: iter_time=1.262e-04, forward_time=0.104, loss_ctc=46.452, loss_att=48.656, acc=0.741, loss=47.995, backward_time=0.098, grad_norm=32.951, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.517e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 14:53:34,539 (trainer:737) INFO: 14epoch:train:2201-2300batch: iter_time=1.397e-04, forward_time=0.103, loss_ctc=49.572, loss_att=48.240, acc=0.721, loss=48.640, backward_time=0.097, grad_norm=41.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.515e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 14:54:15,773 (trainer:737) INFO: 14epoch:train:2301-2400batch: iter_time=1.403e-04, forward_time=0.103, loss_ctc=54.390, loss_att=49.068, acc=0.717, loss=50.665, backward_time=0.097, grad_norm=35.020, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.514e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 14:54:56,553 (trainer:737) INFO: 14epoch:train:2401-2500batch: iter_time=1.352e-04, forward_time=0.103, loss_ctc=46.305, loss_att=57.429, acc=0.707, loss=54.092, backward_time=0.097, grad_norm=33.937, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.512e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 14:55:14,704 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 14:55:33,739 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 14:55:37,412 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38e9282860>)
+[gpuc02:0/16] 2024-01-13 14:55:37,412 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 14:55:37,416 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 15:01:18,069 (trainer:737) INFO: 14epoch:train:2501-2600batch: iter_time=3.389, forward_time=0.108, loss_ctc=53.200, loss_att=72.913, acc=0.707, loss=66.999, backward_time=0.099, grad_norm=35.439, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.511e-04, train_time=3.815
+[gpuc02:0/16] 2024-01-13 15:01:59,036 (trainer:737) INFO: 14epoch:train:2601-2700batch: iter_time=1.489e-04, forward_time=0.104, loss_ctc=57.521, loss_att=61.892, acc=0.693, loss=60.581, backward_time=0.098, grad_norm=38.702, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.510e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:02:39,929 (trainer:737) INFO: 14epoch:train:2701-2800batch: iter_time=1.479e-04, forward_time=0.104, loss_ctc=46.814, loss_att=61.979, acc=0.695, loss=57.429, backward_time=0.098, grad_norm=34.442, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.508e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:03:20,699 (trainer:737) INFO: 14epoch:train:2801-2900batch: iter_time=1.738e-04, forward_time=0.104, loss_ctc=45.931, loss_att=48.958, acc=0.725, loss=48.050, backward_time=0.098, grad_norm=31.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.507e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:04:01,695 (trainer:737) INFO: 14epoch:train:2901-3000batch: iter_time=1.588e-04, forward_time=0.103, loss_ctc=50.780, loss_att=46.592, acc=0.723, loss=47.848, backward_time=0.098, grad_norm=35.504, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.505e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:04:42,792 (trainer:737) INFO: 14epoch:train:3001-3100batch: iter_time=1.578e-04, forward_time=0.104, loss_ctc=50.023, loss_att=52.612, acc=0.718, loss=51.835, backward_time=0.098, grad_norm=36.264, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.504e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 15:05:23,602 (trainer:737) INFO: 14epoch:train:3101-3200batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=48.495, loss_att=60.030, acc=0.700, loss=56.569, backward_time=0.098, grad_norm=32.776, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.503e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:06:04,361 (trainer:737) INFO: 14epoch:train:3201-3300batch: iter_time=1.634e-04, forward_time=0.103, loss_ctc=48.901, loss_att=49.663, acc=0.726, loss=49.434, backward_time=0.098, grad_norm=33.701, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.501e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:06:45,413 (trainer:737) INFO: 14epoch:train:3301-3400batch: iter_time=1.605e-04, forward_time=0.104, loss_ctc=45.612, loss_att=46.555, acc=0.745, loss=46.272, backward_time=0.098, grad_norm=31.193, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.500e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:07:26,578 (trainer:737) INFO: 14epoch:train:3401-3500batch: iter_time=1.523e-04, forward_time=0.104, loss_ctc=55.041, loss_att=50.971, acc=0.757, loss=52.192, backward_time=0.099, grad_norm=42.461, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.499e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 15:08:07,609 (trainer:737) INFO: 14epoch:train:3501-3600batch: iter_time=1.364e-04, forward_time=0.104, loss_ctc=48.115, loss_att=49.691, acc=0.723, loss=49.218, backward_time=0.098, grad_norm=32.390, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.497e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:08:29,514 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 15:08:49,215 (trainer:737) INFO: 14epoch:train:3601-3700batch: iter_time=1.294e-04, forward_time=0.104, loss_ctc=50.389, loss_att=55.577, acc=0.708, loss=54.021, backward_time=0.098, grad_norm=35.016, clip=100.000, loss_scale=3.168e+34, optim_step_time=0.031, optim0_lr0=5.496e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 15:09:15,353 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 15:09:34,778 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 15:09:38,526 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3996479990>)
+[gpuc02:0/16] 2024-01-13 15:09:38,526 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 15:09:38,529 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 15:14:10,523 (trainer:737) INFO: 14epoch:train:3701-3800batch: iter_time=2.719, forward_time=0.105, loss_ctc=45.352, loss_att=59.993, acc=0.720, loss=55.601, backward_time=0.099, grad_norm=30.764, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.494e-04, train_time=3.213
+[gpuc02:0/16] 2024-01-13 15:14:52,068 (trainer:737) INFO: 14epoch:train:3801-3900batch: iter_time=1.704e-04, forward_time=0.106, loss_ctc=57.097, loss_att=68.368, acc=0.696, loss=64.987, backward_time=0.098, grad_norm=40.525, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.493e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 15:15:33,206 (trainer:737) INFO: 14epoch:train:3901-4000batch: iter_time=1.729e-04, forward_time=0.104, loss_ctc=51.366, loss_att=59.619, acc=0.693, loss=57.143, backward_time=0.097, grad_norm=35.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.492e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 15:16:14,457 (trainer:737) INFO: 14epoch:train:4001-4100batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=46.026, loss_att=55.463, acc=0.729, loss=52.632, backward_time=0.098, grad_norm=34.131, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.490e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 15:16:55,398 (trainer:737) INFO: 14epoch:train:4101-4200batch: iter_time=2.106e-04, forward_time=0.104, loss_ctc=47.367, loss_att=42.301, acc=0.738, loss=43.821, backward_time=0.097, grad_norm=33.244, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.489e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:17:36,241 (trainer:737) INFO: 14epoch:train:4201-4300batch: iter_time=1.956e-04, forward_time=0.106, loss_ctc=53.314, loss_att=53.044, acc=0.698, loss=53.125, backward_time=0.098, grad_norm=37.188, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.488e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:18:17,152 (trainer:737) INFO: 14epoch:train:4301-4400batch: iter_time=1.633e-04, forward_time=0.105, loss_ctc=51.366, loss_att=64.183, acc=0.702, loss=60.338, backward_time=0.097, grad_norm=37.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.486e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:18:58,210 (trainer:737) INFO: 14epoch:train:4401-4500batch: iter_time=1.955e-04, forward_time=0.107, loss_ctc=47.263, loss_att=51.082, acc=0.715, loss=49.936, backward_time=0.097, grad_norm=33.696, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.485e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:19:39,051 (trainer:737) INFO: 14epoch:train:4501-4600batch: iter_time=2.074e-04, forward_time=0.104, loss_ctc=45.594, loss_att=41.896, acc=0.755, loss=43.006, backward_time=0.097, grad_norm=31.369, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.483e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:20:20,522 (trainer:737) INFO: 14epoch:train:4601-4700batch: iter_time=1.730e-04, forward_time=0.105, loss_ctc=45.333, loss_att=49.973, acc=0.755, loss=48.581, backward_time=0.098, grad_norm=40.809, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.482e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 15:21:01,286 (trainer:737) INFO: 14epoch:train:4701-4800batch: iter_time=1.828e-04, forward_time=0.104, loss_ctc=48.194, loss_att=47.675, acc=0.734, loss=47.831, backward_time=0.097, grad_norm=40.946, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.481e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:21:42,176 (trainer:737) INFO: 14epoch:train:4801-4900batch: iter_time=1.968e-04, forward_time=0.106, loss_ctc=53.294, loss_att=49.777, acc=0.721, loss=50.832, backward_time=0.098, grad_norm=33.222, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.479e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:22:22,984 (trainer:737) INFO: 14epoch:train:4901-5000batch: iter_time=1.873e-04, forward_time=0.105, loss_ctc=45.323, loss_att=56.371, acc=0.716, loss=53.056, backward_time=0.097, grad_norm=33.036, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.478e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:22:29,626 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 15:22:48,925 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 15:22:52,595 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f384dd81720>)
+[gpuc02:0/16] 2024-01-13 15:22:52,595 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 15:22:52,598 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 15:27:56,646 (trainer:737) INFO: 14epoch:train:5001-5100batch: iter_time=2.921, forward_time=0.106, loss_ctc=52.213, loss_att=72.597, acc=0.696, loss=66.482, backward_time=0.098, grad_norm=36.677, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.477e-04, train_time=3.336
+[gpuc02:0/16] 2024-01-13 15:28:37,618 (trainer:737) INFO: 14epoch:train:5101-5200batch: iter_time=1.519e-04, forward_time=0.105, loss_ctc=57.816, loss_att=59.577, acc=0.693, loss=59.048, backward_time=0.097, grad_norm=41.482, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.475e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:29:18,565 (trainer:737) INFO: 14epoch:train:5201-5300batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=46.498, loss_att=60.472, acc=0.696, loss=56.280, backward_time=0.097, grad_norm=35.300, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.474e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:29:59,334 (trainer:737) INFO: 14epoch:train:5301-5400batch: iter_time=1.375e-04, forward_time=0.104, loss_ctc=45.255, loss_att=47.520, acc=0.725, loss=46.840, backward_time=0.096, grad_norm=32.394, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.472e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:30:40,103 (trainer:737) INFO: 14epoch:train:5401-5500batch: iter_time=1.806e-04, forward_time=0.104, loss_ctc=51.056, loss_att=44.794, acc=0.721, loss=46.673, backward_time=0.097, grad_norm=35.349, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.471e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:31:21,082 (trainer:737) INFO: 14epoch:train:5501-5600batch: iter_time=1.253e-04, forward_time=0.105, loss_ctc=49.143, loss_att=53.425, acc=0.707, loss=52.140, backward_time=0.097, grad_norm=35.931, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.470e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:32:02,175 (trainer:737) INFO: 14epoch:train:5601-5700batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=47.871, loss_att=59.454, acc=0.693, loss=55.979, backward_time=0.096, grad_norm=34.541, clip=100.000, loss_scale=3.053e+34, optim_step_time=0.030, optim0_lr0=5.468e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 15:32:41,820 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 15:32:43,456 (trainer:737) INFO: 14epoch:train:5701-5800batch: iter_time=1.267e-04, forward_time=0.104, loss_ctc=48.357, loss_att=49.088, acc=0.722, loss=48.869, backward_time=0.096, grad_norm=34.879, clip=100.000, loss_scale=4.070e+34, optim_step_time=0.030, optim0_lr0=5.467e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 15:33:24,406 (trainer:737) INFO: 14epoch:train:5801-5900batch: iter_time=1.123e-04, forward_time=0.105, loss_ctc=45.083, loss_att=47.522, acc=0.734, loss=46.790, backward_time=0.097, grad_norm=31.666, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.466e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:34:05,662 (trainer:737) INFO: 14epoch:train:5901-6000batch: iter_time=1.178e-04, forward_time=0.105, loss_ctc=53.387, loss_att=48.992, acc=0.748, loss=50.310, backward_time=0.097, grad_norm=45.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.464e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 15:34:46,518 (trainer:737) INFO: 14epoch:train:6001-6100batch: iter_time=1.429e-04, forward_time=0.104, loss_ctc=47.040, loss_att=48.169, acc=0.725, loss=47.830, backward_time=0.096, grad_norm=32.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.463e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:35:27,359 (trainer:737) INFO: 14epoch:train:6101-6200batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=49.728, loss_att=55.113, acc=0.705, loss=53.498, backward_time=0.097, grad_norm=34.889, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.462e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:35:52,435 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 15:36:12,224 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 15:36:16,289 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39d477bb80>)
+[gpuc02:0/16] 2024-01-13 15:36:16,290 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 15:36:16,293 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 15:40:48,105 (trainer:737) INFO: 14epoch:train:6201-6300batch: iter_time=2.582, forward_time=0.105, loss_ctc=45.042, loss_att=59.908, acc=0.707, loss=55.448, backward_time=0.098, grad_norm=30.175, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.460e-04, train_time=3.207
+[gpuc02:0/16] 2024-01-13 15:41:29,098 (trainer:737) INFO: 14epoch:train:6301-6400batch: iter_time=1.632e-04, forward_time=0.105, loss_ctc=56.511, loss_att=64.408, acc=0.698, loss=62.039, backward_time=0.098, grad_norm=40.458, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.459e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:42:10,326 (trainer:737) INFO: 14epoch:train:6401-6500batch: iter_time=1.673e-04, forward_time=0.103, loss_ctc=51.589, loss_att=59.175, acc=0.687, loss=56.899, backward_time=0.097, grad_norm=36.797, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.457e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 15:42:51,754 (trainer:737) INFO: 14epoch:train:6501-6600batch: iter_time=1.473e-04, forward_time=0.105, loss_ctc=45.996, loss_att=54.311, acc=0.728, loss=51.816, backward_time=0.098, grad_norm=33.892, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.456e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 15:43:32,336 (trainer:737) INFO: 14epoch:train:6601-6700batch: iter_time=1.733e-04, forward_time=0.103, loss_ctc=46.795, loss_att=41.572, acc=0.737, loss=43.139, backward_time=0.096, grad_norm=31.183, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.455e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 15:44:13,361 (trainer:737) INFO: 14epoch:train:6701-6800batch: iter_time=1.475e-04, forward_time=0.104, loss_ctc=53.529, loss_att=50.365, acc=0.698, loss=51.314, backward_time=0.097, grad_norm=36.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.453e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:44:54,722 (trainer:737) INFO: 14epoch:train:6801-6900batch: iter_time=1.478e-04, forward_time=0.104, loss_ctc=50.885, loss_att=63.850, acc=0.692, loss=59.960, backward_time=0.097, grad_norm=37.785, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.452e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 15:45:35,404 (trainer:737) INFO: 14epoch:train:6901-7000batch: iter_time=1.748e-04, forward_time=0.104, loss_ctc=46.389, loss_att=51.077, acc=0.705, loss=49.671, backward_time=0.097, grad_norm=32.853, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.451e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:46:16,099 (trainer:737) INFO: 14epoch:train:7001-7100batch: iter_time=1.628e-04, forward_time=0.104, loss_ctc=45.334, loss_att=42.397, acc=0.749, loss=43.278, backward_time=0.097, grad_norm=31.541, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.449e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 15:46:56,953 (trainer:737) INFO: 14epoch:train:7101-7200batch: iter_time=1.626e-04, forward_time=0.105, loss_ctc=45.382, loss_att=48.465, acc=0.744, loss=47.540, backward_time=0.098, grad_norm=30.699, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.448e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:47:37,510 (trainer:737) INFO: 14epoch:train:7201-7300batch: iter_time=1.598e-04, forward_time=0.104, loss_ctc=48.548, loss_att=47.847, acc=0.724, loss=48.057, backward_time=0.097, grad_norm=42.020, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.447e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-13 15:48:18,305 (trainer:737) INFO: 14epoch:train:7301-7400batch: iter_time=1.567e-04, forward_time=0.105, loss_ctc=53.330, loss_att=48.726, acc=0.721, loss=50.108, backward_time=0.097, grad_norm=34.973, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.445e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:48:59,145 (trainer:737) INFO: 14epoch:train:7401-7500batch: iter_time=1.242e-04, forward_time=0.104, loss_ctc=44.824, loss_att=56.605, acc=0.708, loss=53.071, backward_time=0.099, grad_norm=32.675, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.444e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:49:04,035 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 15:49:23,262 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 15:49:26,929 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39cc7274f0>)
+[gpuc02:0/16] 2024-01-13 15:49:26,929 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 15:49:26,933 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 15:54:30,251 (trainer:737) INFO: 14epoch:train:7501-7600batch: iter_time=2.711, forward_time=0.106, loss_ctc=52.206, loss_att=71.499, acc=0.713, loss=65.711, backward_time=0.099, grad_norm=36.861, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.443e-04, train_time=3.311
+[gpuc02:0/16] 2024-01-13 15:55:11,758 (trainer:737) INFO: 14epoch:train:7601-7700batch: iter_time=1.750e-04, forward_time=0.105, loss_ctc=56.117, loss_att=60.367, acc=0.697, loss=59.092, backward_time=0.098, grad_norm=38.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.441e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 15:55:52,691 (trainer:737) INFO: 14epoch:train:7701-7800batch: iter_time=1.780e-04, forward_time=0.105, loss_ctc=45.646, loss_att=60.777, acc=0.700, loss=56.237, backward_time=0.099, grad_norm=34.209, clip=100.000, loss_scale=2.160e+34, optim_step_time=0.030, optim0_lr0=5.440e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 15:56:02,909 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 15:56:33,481 (trainer:737) INFO: 14epoch:train:7801-7900batch: iter_time=1.846e-04, forward_time=0.105, loss_ctc=44.398, loss_att=47.980, acc=0.731, loss=46.905, backward_time=0.098, grad_norm=33.544, clip=100.000, loss_scale=2.580e+34, optim_step_time=0.031, optim0_lr0=5.439e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:57:14,552 (trainer:737) INFO: 14epoch:train:7901-8000batch: iter_time=1.942e-04, forward_time=0.108, loss_ctc=50.591, loss_att=45.997, acc=0.729, loss=47.375, backward_time=0.098, grad_norm=36.060, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.437e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 15:57:55,407 (trainer:737) INFO: 14epoch:train:8001-8100batch: iter_time=1.775e-04, forward_time=0.105, loss_ctc=49.186, loss_att=52.279, acc=0.721, loss=51.351, backward_time=0.098, grad_norm=35.384, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.436e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 15:58:36,575 (trainer:737) INFO: 14epoch:train:8101-8200batch: iter_time=1.944e-04, forward_time=0.105, loss_ctc=47.308, loss_att=58.950, acc=0.706, loss=55.457, backward_time=0.098, grad_norm=32.980, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.435e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 15:59:17,806 (trainer:737) INFO: 14epoch:train:8201-8300batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=47.925, loss_att=48.345, acc=0.733, loss=48.219, backward_time=0.098, grad_norm=31.372, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.433e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 15:59:58,610 (trainer:737) INFO: 14epoch:train:8301-8400batch: iter_time=1.911e-04, forward_time=0.105, loss_ctc=45.146, loss_att=46.657, acc=0.747, loss=46.203, backward_time=0.098, grad_norm=31.625, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.432e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:00:41,815 (trainer:737) INFO: 14epoch:train:8401-8500batch: iter_time=1.514e-04, forward_time=0.105, loss_ctc=52.459, loss_att=49.933, acc=0.763, loss=50.690, backward_time=0.099, grad_norm=43.530, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.431e-04, train_time=0.432
+[gpuc02:0/16] 2024-01-13 16:01:22,854 (trainer:737) INFO: 14epoch:train:8501-8600batch: iter_time=1.696e-04, forward_time=0.105, loss_ctc=46.992, loss_att=49.449, acc=0.725, loss=48.711, backward_time=0.098, grad_norm=32.028, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.429e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:02:03,683 (trainer:737) INFO: 14epoch:train:8601-8700batch: iter_time=1.670e-04, forward_time=0.105, loss_ctc=49.921, loss_att=54.870, acc=0.714, loss=53.385, backward_time=0.098, grad_norm=35.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.428e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:02:29,347 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 16:02:49,869 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 16:02:53,831 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3b9a19e680>)
+[gpuc02:0/16] 2024-01-13 16:02:53,832 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 16:02:53,835 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 16:07:21,926 (trainer:737) INFO: 14epoch:train:8701-8800batch: iter_time=2.660, forward_time=0.105, loss_ctc=44.299, loss_att=61.696, acc=0.710, loss=56.477, backward_time=0.098, grad_norm=30.543, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.427e-04, train_time=3.182
+[gpuc02:0/16] 2024-01-13 16:08:03,531 (trainer:737) INFO: 14epoch:train:8801-8900batch: iter_time=1.333e-04, forward_time=0.105, loss_ctc=55.495, loss_att=65.375, acc=0.698, loss=62.411, backward_time=0.098, grad_norm=41.183, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.425e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 16:08:24,201 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 16:08:44,528 (trainer:737) INFO: 14epoch:train:8901-9000batch: iter_time=1.539e-04, forward_time=0.104, loss_ctc=51.133, loss_att=60.306, acc=0.686, loss=57.554, backward_time=0.097, grad_norm=36.082, clip=100.000, loss_scale=1.552e+34, optim_step_time=0.030, optim0_lr0=5.424e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:09:25,423 (trainer:737) INFO: 14epoch:train:9001-9100batch: iter_time=1.547e-04, forward_time=0.104, loss_ctc=45.658, loss_att=54.755, acc=0.727, loss=52.026, backward_time=0.098, grad_norm=32.340, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.423e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:10:05,944 (trainer:737) INFO: 14epoch:train:9101-9200batch: iter_time=1.724e-04, forward_time=0.102, loss_ctc=46.767, loss_att=42.383, acc=0.738, loss=43.699, backward_time=0.097, grad_norm=31.987, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.421e-04, train_time=0.405
+[gpuc02:0/16] 2024-01-13 16:10:46,928 (trainer:737) INFO: 14epoch:train:9201-9300batch: iter_time=1.613e-04, forward_time=0.104, loss_ctc=52.726, loss_att=50.133, acc=0.699, loss=50.911, backward_time=0.098, grad_norm=38.707, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.420e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:11:27,913 (trainer:737) INFO: 14epoch:train:9301-9400batch: iter_time=1.577e-04, forward_time=0.104, loss_ctc=50.225, loss_att=63.976, acc=0.693, loss=59.851, backward_time=0.098, grad_norm=36.465, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.419e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:12:08,747 (trainer:737) INFO: 14epoch:train:9401-9500batch: iter_time=1.603e-04, forward_time=0.104, loss_ctc=46.355, loss_att=50.564, acc=0.710, loss=49.301, backward_time=0.098, grad_norm=31.026, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.417e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:12:49,652 (trainer:737) INFO: 14epoch:train:9501-9600batch: iter_time=1.589e-04, forward_time=0.104, loss_ctc=45.472, loss_att=41.951, acc=0.752, loss=43.007, backward_time=0.098, grad_norm=30.678, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.416e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:13:31,204 (trainer:737) INFO: 14epoch:train:9601-9700batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=45.148, loss_att=48.770, acc=0.742, loss=47.684, backward_time=0.098, grad_norm=33.841, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.415e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 16:14:11,893 (trainer:737) INFO: 14epoch:train:9701-9800batch: iter_time=1.619e-04, forward_time=0.103, loss_ctc=48.198, loss_att=48.267, acc=0.727, loss=48.246, backward_time=0.097, grad_norm=42.751, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.413e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 16:14:52,830 (trainer:737) INFO: 14epoch:train:9801-9900batch: iter_time=1.624e-04, forward_time=0.104, loss_ctc=53.163, loss_att=48.632, acc=0.721, loss=49.991, backward_time=0.097, grad_norm=33.677, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.412e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:15:33,712 (trainer:737) INFO: 14epoch:train:9901-10000batch: iter_time=1.558e-04, forward_time=0.105, loss_ctc=44.530, loss_att=56.946, acc=0.708, loss=53.221, backward_time=0.097, grad_norm=33.275, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.411e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:15:41,362 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 16:16:01,465 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 16:16:05,512 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39f428b2b0>)
+[gpuc02:0/16] 2024-01-13 16:16:05,512 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 16:16:05,515 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 16:20:51,229 (trainer:737) INFO: 14epoch:train:10001-10100batch: iter_time=2.663, forward_time=0.106, loss_ctc=51.503, loss_att=71.022, acc=0.713, loss=65.166, backward_time=0.100, grad_norm=35.752, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.409e-04, train_time=3.175
+[gpuc02:0/16] 2024-01-13 16:21:32,426 (trainer:737) INFO: 14epoch:train:10101-10200batch: iter_time=1.454e-04, forward_time=0.105, loss_ctc=56.488, loss_att=60.260, acc=0.700, loss=59.128, backward_time=0.098, grad_norm=37.505, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.408e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 16:22:13,340 (trainer:737) INFO: 14epoch:train:10201-10300batch: iter_time=1.621e-04, forward_time=0.104, loss_ctc=44.668, loss_att=60.481, acc=0.701, loss=55.737, backward_time=0.098, grad_norm=33.699, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.407e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:22:54,154 (trainer:737) INFO: 14epoch:train:10301-10400batch: iter_time=1.906e-04, forward_time=0.104, loss_ctc=44.127, loss_att=48.028, acc=0.731, loss=46.858, backward_time=0.098, grad_norm=32.202, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.405e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:23:35,002 (trainer:737) INFO: 14epoch:train:10401-10500batch: iter_time=1.702e-04, forward_time=0.104, loss_ctc=50.451, loss_att=46.626, acc=0.728, loss=47.773, backward_time=0.098, grad_norm=35.127, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.404e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:24:15,936 (trainer:737) INFO: 14epoch:train:10501-10600batch: iter_time=1.696e-04, forward_time=0.104, loss_ctc=48.243, loss_att=51.332, acc=0.726, loss=50.405, backward_time=0.098, grad_norm=38.031, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.403e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:24:57,033 (trainer:737) INFO: 14epoch:train:10601-10700batch: iter_time=1.763e-04, forward_time=0.104, loss_ctc=46.739, loss_att=59.573, acc=0.703, loss=55.723, backward_time=0.097, grad_norm=34.042, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.401e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 16:25:37,830 (trainer:737) INFO: 14epoch:train:10701-10800batch: iter_time=1.712e-04, forward_time=0.104, loss_ctc=48.047, loss_att=48.901, acc=0.731, loss=48.645, backward_time=0.097, grad_norm=34.980, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.400e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:26:18,942 (trainer:737) INFO: 14epoch:train:10801-10900batch: iter_time=1.564e-04, forward_time=0.104, loss_ctc=44.964, loss_att=46.745, acc=0.748, loss=46.211, backward_time=0.097, grad_norm=32.939, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.399e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 16:27:00,153 (trainer:737) INFO: 14epoch:train:10901-11000batch: iter_time=1.642e-04, forward_time=0.104, loss_ctc=52.743, loss_att=50.568, acc=0.762, loss=51.220, backward_time=0.097, grad_norm=41.673, clip=100.000, loss_scale=1.558e+34, optim_step_time=0.030, optim0_lr0=5.398e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 16:27:41,199 (trainer:737) INFO: 14epoch:train:11001-11100batch: iter_time=1.888e-04, forward_time=0.104, loss_ctc=46.633, loss_att=48.493, acc=0.728, loss=47.935, backward_time=0.098, grad_norm=32.801, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.396e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:28:22,056 (trainer:737) INFO: 14epoch:train:11101-11200batch: iter_time=1.988e-04, forward_time=0.104, loss_ctc=49.490, loss_att=54.884, acc=0.715, loss=53.266, backward_time=0.098, grad_norm=34.711, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.395e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:28:47,198 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 16:29:07,421 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 16:29:11,234 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39a95a65c0>)
+[gpuc02:0/16] 2024-01-13 16:29:11,235 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 16:29:11,238 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 16:33:38,036 (trainer:737) INFO: 14epoch:train:11201-11300batch: iter_time=2.607, forward_time=0.104, loss_ctc=43.932, loss_att=58.462, acc=0.724, loss=54.103, backward_time=0.098, grad_norm=30.933, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.394e-04, train_time=3.160
+[gpuc02:0/16] 2024-01-13 16:34:19,080 (trainer:737) INFO: 14epoch:train:11301-11400batch: iter_time=1.262e-04, forward_time=0.105, loss_ctc=55.698, loss_att=66.300, acc=0.703, loss=63.119, backward_time=0.098, grad_norm=40.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.392e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:34:59,944 (trainer:737) INFO: 14epoch:train:11401-11500batch: iter_time=1.343e-04, forward_time=0.104, loss_ctc=51.311, loss_att=59.592, acc=0.697, loss=57.107, backward_time=0.097, grad_norm=37.096, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.391e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:35:40,949 (trainer:737) INFO: 14epoch:train:11501-11600batch: iter_time=1.340e-04, forward_time=0.105, loss_ctc=45.290, loss_att=54.279, acc=0.735, loss=51.582, backward_time=0.098, grad_norm=33.243, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.390e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:36:21,925 (trainer:737) INFO: 14epoch:train:11601-11700batch: iter_time=1.690e-04, forward_time=0.103, loss_ctc=45.587, loss_att=41.836, acc=0.741, loss=42.961, backward_time=0.096, grad_norm=31.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.388e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:37:03,014 (trainer:737) INFO: 14epoch:train:11701-11800batch: iter_time=1.628e-04, forward_time=0.104, loss_ctc=53.067, loss_att=52.237, acc=0.705, loss=52.486, backward_time=0.097, grad_norm=38.214, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.387e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 16:37:44,284 (trainer:737) INFO: 14epoch:train:11801-11900batch: iter_time=1.619e-04, forward_time=0.105, loss_ctc=49.925, loss_att=63.465, acc=0.705, loss=59.403, backward_time=0.098, grad_norm=36.056, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.386e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 16:38:25,062 (trainer:737) INFO: 14epoch:train:11901-12000batch: iter_time=1.806e-04, forward_time=0.104, loss_ctc=45.728, loss_att=49.709, acc=0.723, loss=48.515, backward_time=0.097, grad_norm=32.978, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.385e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:39:06,063 (trainer:737) INFO: 14epoch:train:12001-12100batch: iter_time=1.741e-04, forward_time=0.107, loss_ctc=45.350, loss_att=41.579, acc=0.758, loss=42.710, backward_time=0.097, grad_norm=30.866, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.383e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:39:47,289 (trainer:737) INFO: 14epoch:train:12101-12200batch: iter_time=1.352e-04, forward_time=0.105, loss_ctc=44.831, loss_att=49.427, acc=0.758, loss=48.049, backward_time=0.098, grad_norm=32.195, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.382e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 16:40:28,475 (trainer:737) INFO: 14epoch:train:12201-12300batch: iter_time=1.538e-04, forward_time=0.104, loss_ctc=46.949, loss_att=47.277, acc=0.735, loss=47.179, backward_time=0.097, grad_norm=40.537, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.381e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 16:41:09,899 (trainer:737) INFO: 14epoch:train:12301-12400batch: iter_time=1.555e-04, forward_time=0.104, loss_ctc=52.642, loss_att=49.236, acc=0.726, loss=50.258, backward_time=0.098, grad_norm=34.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.379e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 16:41:50,992 (trainer:737) INFO: 14epoch:train:12401-12500batch: iter_time=1.392e-04, forward_time=0.104, loss_ctc=44.112, loss_att=55.997, acc=0.717, loss=52.432, backward_time=0.097, grad_norm=32.973, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.378e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 16:41:57,259 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 16:42:16,679 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 16:42:20,411 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f398847b130>)
+[gpuc02:0/16] 2024-01-13 16:42:20,411 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 16:42:20,414 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 16:47:13,580 (trainer:737) INFO: 14epoch:train:12501-12600batch: iter_time=2.598, forward_time=0.107, loss_ctc=51.782, loss_att=66.830, acc=0.721, loss=62.316, backward_time=0.099, grad_norm=35.555, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.377e-04, train_time=3.226
+[gpuc02:0/16] 2024-01-13 16:47:54,527 (trainer:737) INFO: 14epoch:train:12601-12700batch: iter_time=1.919e-04, forward_time=0.105, loss_ctc=55.946, loss_att=60.368, acc=0.699, loss=59.041, backward_time=0.098, grad_norm=38.826, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.375e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:48:35,504 (trainer:737) INFO: 14epoch:train:12701-12800batch: iter_time=1.824e-04, forward_time=0.105, loss_ctc=44.837, loss_att=59.018, acc=0.705, loss=54.764, backward_time=0.098, grad_norm=33.733, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.374e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 16:49:16,661 (trainer:737) INFO: 14epoch:train:12801-12900batch: iter_time=1.633e-04, forward_time=0.105, loss_ctc=43.855, loss_att=46.944, acc=0.734, loss=46.017, backward_time=0.098, grad_norm=30.600, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.373e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 16:49:57,503 (trainer:737) INFO: 14epoch:train:12901-13000batch: iter_time=1.896e-04, forward_time=0.104, loss_ctc=50.030, loss_att=45.430, acc=0.731, loss=46.810, backward_time=0.097, grad_norm=35.612, clip=100.000, loss_scale=3.115e+34, optim_step_time=0.030, optim0_lr0=5.372e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:50:00,332 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 16:50:38,453 (trainer:737) INFO: 14epoch:train:13001-13100batch: iter_time=1.810e-04, forward_time=0.105, loss_ctc=47.947, loss_att=51.120, acc=0.724, loss=50.168, backward_time=0.098, grad_norm=35.540, clip=100.000, loss_scale=2.203e+34, optim_step_time=0.030, optim0_lr0=5.370e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:51:19,369 (trainer:737) INFO: 14epoch:train:13101-13200batch: iter_time=1.555e-04, forward_time=0.105, loss_ctc=46.933, loss_att=58.854, acc=0.710, loss=55.278, backward_time=0.098, grad_norm=34.686, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.369e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:52:00,175 (trainer:737) INFO: 14epoch:train:13201-13300batch: iter_time=1.702e-04, forward_time=0.104, loss_ctc=47.592, loss_att=48.060, acc=0.738, loss=47.920, backward_time=0.098, grad_norm=31.938, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.368e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 16:52:41,052 (trainer:737) INFO: 14epoch:train:13301-13400batch: iter_time=1.596e-04, forward_time=0.105, loss_ctc=44.730, loss_att=45.788, acc=0.749, loss=45.470, backward_time=0.098, grad_norm=31.542, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.366e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:53:21,967 (trainer:737) INFO: 14epoch:train:13401-13500batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=51.625, loss_att=49.151, acc=0.765, loss=49.893, backward_time=0.098, grad_norm=41.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.365e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:54:03,419 (trainer:737) INFO: 14epoch:train:13501-13600batch: iter_time=1.622e-04, forward_time=0.105, loss_ctc=46.695, loss_att=48.881, acc=0.728, loss=48.225, backward_time=0.098, grad_norm=32.378, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.364e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 16:54:44,335 (trainer:737) INFO: 14epoch:train:13601-13700batch: iter_time=1.767e-04, forward_time=0.104, loss_ctc=49.108, loss_att=54.432, acc=0.716, loss=52.834, backward_time=0.098, grad_norm=34.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.363e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 16:55:12,637 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 16:55:32,660 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 16:55:36,485 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3997ccf3a0>)
+[gpuc02:0/16] 2024-01-13 16:55:36,485 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 16:55:36,488 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 17:00:09,191 (trainer:737) INFO: 14epoch:train:13701-13800batch: iter_time=2.768, forward_time=0.105, loss_ctc=43.663, loss_att=61.709, acc=0.711, loss=56.295, backward_time=0.099, grad_norm=33.369, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.361e-04, train_time=3.248
+[gpuc02:0/16] 2024-01-13 17:00:51,105 (trainer:737) INFO: 14epoch:train:13801-13900batch: iter_time=1.869e-04, forward_time=0.105, loss_ctc=55.245, loss_att=66.392, acc=0.698, loss=63.048, backward_time=0.098, grad_norm=39.653, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.360e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 17:01:31,907 (trainer:737) INFO: 14epoch:train:13901-14000batch: iter_time=1.695e-04, forward_time=0.104, loss_ctc=50.047, loss_att=60.609, acc=0.686, loss=57.441, backward_time=0.097, grad_norm=35.474, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.359e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 17:02:12,858 (trainer:737) INFO: 14epoch:train:14001-14100batch: iter_time=1.491e-04, forward_time=0.104, loss_ctc=44.672, loss_att=54.868, acc=0.729, loss=51.809, backward_time=0.098, grad_norm=34.493, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.357e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:02:53,517 (trainer:737) INFO: 14epoch:train:14101-14200batch: iter_time=1.811e-04, forward_time=0.103, loss_ctc=45.727, loss_att=41.617, acc=0.740, loss=42.850, backward_time=0.096, grad_norm=33.898, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.356e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 17:03:34,357 (trainer:737) INFO: 14epoch:train:14201-14300batch: iter_time=1.949e-04, forward_time=0.104, loss_ctc=52.031, loss_att=50.345, acc=0.700, loss=50.851, backward_time=0.097, grad_norm=37.417, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.355e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 17:04:15,296 (trainer:737) INFO: 14epoch:train:14301-14400batch: iter_time=1.974e-04, forward_time=0.105, loss_ctc=49.377, loss_att=63.764, acc=0.694, loss=59.448, backward_time=0.097, grad_norm=38.155, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.354e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:04:56,058 (trainer:737) INFO: 14epoch:train:14401-14500batch: iter_time=2.010e-04, forward_time=0.103, loss_ctc=45.772, loss_att=51.189, acc=0.706, loss=49.564, backward_time=0.096, grad_norm=32.819, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.352e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 17:05:36,822 (trainer:737) INFO: 14epoch:train:14501-14600batch: iter_time=1.767e-04, forward_time=0.103, loss_ctc=44.824, loss_att=42.462, acc=0.750, loss=43.170, backward_time=0.097, grad_norm=31.581, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.351e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 17:06:17,709 (trainer:737) INFO: 14epoch:train:14601-14700batch: iter_time=1.630e-04, forward_time=0.104, loss_ctc=44.072, loss_att=48.365, acc=0.747, loss=47.077, backward_time=0.097, grad_norm=31.345, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.350e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:06:58,374 (trainer:737) INFO: 14epoch:train:14701-14800batch: iter_time=1.794e-04, forward_time=0.102, loss_ctc=47.399, loss_att=48.724, acc=0.726, loss=48.326, backward_time=0.096, grad_norm=42.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.348e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 17:07:39,256 (trainer:737) INFO: 14epoch:train:14801-14900batch: iter_time=1.493e-04, forward_time=0.105, loss_ctc=52.181, loss_att=48.102, acc=0.724, loss=49.326, backward_time=0.097, grad_norm=34.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.347e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:08:20,629 (trainer:737) INFO: 14epoch:train:14901-15000batch: iter_time=1.442e-04, forward_time=0.103, loss_ctc=43.917, loss_att=56.036, acc=0.713, loss=52.401, backward_time=0.097, grad_norm=32.957, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.346e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 17:28:20,301 (trainer:343) INFO: 14epoch results: [train] iter_time=0.226, forward_time=0.105, loss_ctc=49.078, loss_att=53.681, acc=0.718, loss=52.300, backward_time=0.098, grad_norm=35.270, clip=100.000, loss_scale=1.991e+34, optim_step_time=0.030, optim0_lr0=5.444e-04, train_time=0.653, time=2 hours, 43 minutes and 29.56 seconds, total_count=210000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=57.965, cer_ctc=0.297, loss_att=57.033, acc=0.564, cer=0.368, wer=0.999, loss=57.313, time=19 minutes and 48.66 seconds, total_count=65394, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 17:28:25,385 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-13 17:28:25,438 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/9epoch.pth
+[gpuc02:0/16] 2024-01-13 17:28:25,438 (trainer:272) INFO: 15/45epoch started. Estimated time to finish: 3 days, 21 hours and 55.64 seconds
+[gpuc02:0/16] 2024-01-13 17:28:25,447 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 17:28:44,389 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 17:28:47,962 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38de704100>)
+[gpuc02:0/16] 2024-01-13 17:28:47,962 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 17:28:47,965 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 17:33:27,144 (trainer:737) INFO: 15epoch:train:1-100batch: iter_time=2.530, forward_time=0.106, loss_ctc=47.549, loss_att=57.641, acc=0.699, loss=54.613, backward_time=0.099, grad_norm=34.879, clip=100.000, loss_scale=4.008e+34, optim_step_time=0.030, optim0_lr0=5.345e-04, train_time=3.017
+[gpuc02:0/16] 2024-01-13 17:34:07,934 (trainer:737) INFO: 15epoch:train:101-200batch: iter_time=1.247e-04, forward_time=0.102, loss_ctc=49.748, loss_att=45.930, acc=0.718, loss=47.075, backward_time=0.098, grad_norm=39.830, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.343e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 17:34:48,808 (trainer:737) INFO: 15epoch:train:201-300batch: iter_time=1.241e-04, forward_time=0.104, loss_ctc=49.974, loss_att=58.842, acc=0.704, loss=56.181, backward_time=0.099, grad_norm=38.346, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.342e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:35:30,000 (trainer:737) INFO: 15epoch:train:301-400batch: iter_time=1.282e-04, forward_time=0.103, loss_ctc=54.832, loss_att=60.027, acc=0.686, loss=58.469, backward_time=0.098, grad_norm=41.395, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.341e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 17:36:11,040 (trainer:737) INFO: 15epoch:train:401-500batch: iter_time=1.250e-04, forward_time=0.104, loss_ctc=57.504, loss_att=63.895, acc=0.701, loss=61.978, backward_time=0.099, grad_norm=38.305, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.340e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 17:36:54,461 (trainer:737) INFO: 15epoch:train:501-600batch: iter_time=1.273e-04, forward_time=0.112, loss_ctc=49.393, loss_att=51.285, acc=0.726, loss=50.718, backward_time=0.100, grad_norm=35.736, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.338e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-13 17:37:35,535 (trainer:737) INFO: 15epoch:train:601-700batch: iter_time=1.236e-04, forward_time=0.105, loss_ctc=57.821, loss_att=69.241, acc=0.690, loss=65.815, backward_time=0.099, grad_norm=40.698, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.337e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 17:38:16,220 (trainer:737) INFO: 15epoch:train:701-800batch: iter_time=1.327e-04, forward_time=0.104, loss_ctc=53.709, loss_att=53.939, acc=0.709, loss=53.870, backward_time=0.098, grad_norm=40.455, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.336e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 17:38:57,542 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 17:39:01,032 (trainer:737) INFO: 15epoch:train:801-900batch: iter_time=1.338e-04, forward_time=0.104, loss_ctc=50.106, loss_att=47.251, acc=0.762, loss=48.108, backward_time=0.099, grad_norm=34.179, clip=100.000, loss_scale=4.007e+34, optim_step_time=0.030, optim0_lr0=5.334e-04, train_time=0.448
+[gpuc02:0/16] 2024-01-13 17:39:43,263 (trainer:737) INFO: 15epoch:train:901-1000batch: iter_time=1.241e-04, forward_time=0.103, loss_ctc=52.605, loss_att=48.406, acc=0.733, loss=49.666, backward_time=0.098, grad_norm=35.720, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.333e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 17:40:24,965 (trainer:737) INFO: 15epoch:train:1001-1100batch: iter_time=1.327e-04, forward_time=0.107, loss_ctc=48.744, loss_att=58.368, acc=0.703, loss=55.481, backward_time=0.105, grad_norm=34.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.332e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 17:41:14,036 (trainer:737) INFO: 15epoch:train:1101-1200batch: iter_time=1.257e-04, forward_time=0.106, loss_ctc=51.655, loss_att=62.653, acc=0.704, loss=59.354, backward_time=0.103, grad_norm=34.837, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.331e-04, train_time=0.490
+[gpuc02:0/16] 2024-01-13 17:41:46,410 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 17:42:05,616 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 17:42:09,366 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39897aff10>)
+[gpuc02:0/16] 2024-01-13 17:42:09,366 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 17:42:09,369 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 17:47:43,781 (trainer:737) INFO: 15epoch:train:1201-1300batch: iter_time=2.830, forward_time=0.123, loss_ctc=53.304, loss_att=63.957, acc=0.700, loss=60.761, backward_time=0.099, grad_norm=39.083, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.329e-04, train_time=3.897
+[gpuc02:0/16] 2024-01-13 17:48:24,560 (trainer:737) INFO: 15epoch:train:1301-1400batch: iter_time=1.429e-04, forward_time=0.103, loss_ctc=48.903, loss_att=48.896, acc=0.698, loss=48.898, backward_time=0.097, grad_norm=36.449, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.328e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 17:49:05,656 (trainer:737) INFO: 15epoch:train:1401-1500batch: iter_time=1.273e-04, forward_time=0.103, loss_ctc=48.842, loss_att=58.498, acc=0.694, loss=55.601, backward_time=0.098, grad_norm=35.237, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.327e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 17:49:46,325 (trainer:737) INFO: 15epoch:train:1501-1600batch: iter_time=1.305e-04, forward_time=0.103, loss_ctc=51.661, loss_att=55.720, acc=0.692, loss=54.502, backward_time=0.097, grad_norm=43.045, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.326e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 17:50:27,057 (trainer:737) INFO: 15epoch:train:1601-1700batch: iter_time=1.347e-04, forward_time=0.103, loss_ctc=50.300, loss_att=57.860, acc=0.694, loss=55.592, backward_time=0.097, grad_norm=36.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.324e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 17:51:08,118 (trainer:737) INFO: 15epoch:train:1701-1800batch: iter_time=1.341e-04, forward_time=0.106, loss_ctc=55.171, loss_att=58.250, acc=0.712, loss=57.326, backward_time=0.098, grad_norm=39.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.323e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 17:51:52,129 (trainer:737) INFO: 15epoch:train:1801-1900batch: iter_time=1.090e-04, forward_time=0.114, loss_ctc=53.107, loss_att=56.507, acc=0.707, loss=55.487, backward_time=0.112, grad_norm=36.313, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.322e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-13 17:52:33,046 (trainer:737) INFO: 15epoch:train:1901-2000batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=51.928, loss_att=61.339, acc=0.694, loss=58.516, backward_time=0.098, grad_norm=35.808, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.321e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:53:14,174 (trainer:737) INFO: 15epoch:train:2001-2100batch: iter_time=1.191e-04, forward_time=0.104, loss_ctc=52.324, loss_att=51.931, acc=0.727, loss=52.049, backward_time=0.098, grad_norm=40.768, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.319e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 17:53:54,968 (trainer:737) INFO: 15epoch:train:2101-2200batch: iter_time=1.193e-04, forward_time=0.104, loss_ctc=45.283, loss_att=38.279, acc=0.760, loss=40.380, backward_time=0.098, grad_norm=29.935, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.318e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 17:54:35,844 (trainer:737) INFO: 15epoch:train:2201-2300batch: iter_time=1.450e-04, forward_time=0.104, loss_ctc=51.414, loss_att=55.346, acc=0.707, loss=54.166, backward_time=0.098, grad_norm=36.762, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.317e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 17:55:19,818 (trainer:737) INFO: 15epoch:train:2301-2400batch: iter_time=1.349e-04, forward_time=0.115, loss_ctc=49.620, loss_att=62.938, acc=0.702, loss=58.943, backward_time=0.101, grad_norm=33.660, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.316e-04, train_time=0.440
+[gpuc02:0/16] 2024-01-13 17:56:04,874 (trainer:737) INFO: 15epoch:train:2401-2500batch: iter_time=1.241e-04, forward_time=0.130, loss_ctc=58.660, loss_att=65.257, acc=0.692, loss=63.278, backward_time=0.101, grad_norm=40.167, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.034, optim0_lr0=5.314e-04, train_time=0.450
+[gpuc02:0/16] 2024-01-13 17:56:37,684 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 17:56:57,003 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 17:57:00,788 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38ded61210>)
+[gpuc02:0/16] 2024-01-13 17:57:00,788 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 17:57:00,792 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 18:04:05,085 (trainer:737) INFO: 15epoch:train:2501-2600batch: iter_time=4.287, forward_time=0.104, loss_ctc=45.676, loss_att=55.914, acc=0.705, loss=52.842, backward_time=0.098, grad_norm=32.787, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.313e-04, train_time=4.802
+[gpuc02:0/16] 2024-01-13 18:04:45,801 (trainer:737) INFO: 15epoch:train:2601-2700batch: iter_time=1.270e-04, forward_time=0.103, loss_ctc=46.446, loss_att=44.065, acc=0.725, loss=44.779, backward_time=0.097, grad_norm=35.156, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.312e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 18:05:26,693 (trainer:737) INFO: 15epoch:train:2701-2800batch: iter_time=1.379e-04, forward_time=0.103, loss_ctc=48.117, loss_att=58.167, acc=0.709, loss=55.152, backward_time=0.099, grad_norm=37.305, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.311e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:06:07,450 (trainer:737) INFO: 15epoch:train:2801-2900batch: iter_time=1.372e-04, forward_time=0.103, loss_ctc=51.420, loss_att=59.329, acc=0.690, loss=56.956, backward_time=0.098, grad_norm=43.174, clip=100.000, loss_scale=2.222e+34, optim_step_time=0.030, optim0_lr0=5.309e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 18:06:48,442 (trainer:737) INFO: 15epoch:train:2901-3000batch: iter_time=1.338e-04, forward_time=0.105, loss_ctc=57.173, loss_att=62.990, acc=0.706, loss=61.245, backward_time=0.099, grad_norm=40.618, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.308e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 18:07:29,516 (trainer:737) INFO: 15epoch:train:3001-3100batch: iter_time=1.667e-04, forward_time=0.104, loss_ctc=48.013, loss_att=50.463, acc=0.730, loss=49.728, backward_time=0.098, grad_norm=33.704, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.307e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 18:08:10,632 (trainer:737) INFO: 15epoch:train:3101-3200batch: iter_time=1.648e-04, forward_time=0.105, loss_ctc=56.014, loss_att=68.424, acc=0.693, loss=64.701, backward_time=0.099, grad_norm=38.390, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.306e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 18:08:51,447 (trainer:737) INFO: 15epoch:train:3201-3300batch: iter_time=1.802e-04, forward_time=0.104, loss_ctc=52.752, loss_att=53.415, acc=0.713, loss=53.216, backward_time=0.098, grad_norm=38.428, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.304e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:09:32,921 (trainer:737) INFO: 15epoch:train:3301-3400batch: iter_time=1.585e-04, forward_time=0.105, loss_ctc=48.702, loss_att=47.493, acc=0.764, loss=47.856, backward_time=0.098, grad_norm=33.035, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.303e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 18:10:13,742 (trainer:737) INFO: 15epoch:train:3401-3500batch: iter_time=1.396e-04, forward_time=0.104, loss_ctc=50.831, loss_att=48.061, acc=0.736, loss=48.892, backward_time=0.098, grad_norm=35.134, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.302e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:10:17,388 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 18:10:54,568 (trainer:737) INFO: 15epoch:train:3501-3600batch: iter_time=1.640e-04, forward_time=0.104, loss_ctc=47.001, loss_att=56.574, acc=0.711, loss=53.702, backward_time=0.098, grad_norm=33.135, clip=100.000, loss_scale=2.245e+34, optim_step_time=0.031, optim0_lr0=5.301e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:11:36,048 (trainer:737) INFO: 15epoch:train:3601-3700batch: iter_time=1.475e-04, forward_time=0.107, loss_ctc=50.136, loss_att=61.587, acc=0.707, loss=58.152, backward_time=0.099, grad_norm=34.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.299e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 18:12:04,244 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 18:12:23,690 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 18:12:27,421 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39ff687a60>)
+[gpuc02:0/16] 2024-01-13 18:12:27,421 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 18:12:27,424 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 18:17:17,957 (trainer:737) INFO: 15epoch:train:3701-3800batch: iter_time=2.708, forward_time=0.104, loss_ctc=50.375, loss_att=62.304, acc=0.703, loss=58.725, backward_time=0.098, grad_norm=39.191, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.298e-04, train_time=3.419
+[gpuc02:0/16] 2024-01-13 18:17:58,734 (trainer:737) INFO: 15epoch:train:3801-3900batch: iter_time=1.590e-04, forward_time=0.104, loss_ctc=48.024, loss_att=48.428, acc=0.699, loss=48.307, backward_time=0.097, grad_norm=37.923, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.297e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:18:39,999 (trainer:737) INFO: 15epoch:train:3901-4000batch: iter_time=1.800e-04, forward_time=0.104, loss_ctc=48.053, loss_att=57.747, acc=0.695, loss=54.839, backward_time=0.097, grad_norm=34.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.296e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 18:19:20,874 (trainer:737) INFO: 15epoch:train:4001-4100batch: iter_time=1.973e-04, forward_time=0.103, loss_ctc=51.067, loss_att=55.593, acc=0.695, loss=54.235, backward_time=0.096, grad_norm=41.235, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.294e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:20:02,827 (trainer:737) INFO: 15epoch:train:4101-4200batch: iter_time=2.046e-04, forward_time=0.104, loss_ctc=49.879, loss_att=57.489, acc=0.695, loss=55.206, backward_time=0.097, grad_norm=37.400, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.293e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 18:20:43,744 (trainer:737) INFO: 15epoch:train:4201-4300batch: iter_time=1.998e-04, forward_time=0.104, loss_ctc=54.515, loss_att=58.028, acc=0.714, loss=56.974, backward_time=0.097, grad_norm=34.891, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.292e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:21:24,653 (trainer:737) INFO: 15epoch:train:4301-4400batch: iter_time=1.926e-04, forward_time=0.105, loss_ctc=52.381, loss_att=55.833, acc=0.710, loss=54.797, backward_time=0.097, grad_norm=35.980, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.291e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:22:05,602 (trainer:737) INFO: 15epoch:train:4401-4500batch: iter_time=1.990e-04, forward_time=0.104, loss_ctc=50.834, loss_att=60.437, acc=0.696, loss=57.556, backward_time=0.097, grad_norm=34.607, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.290e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:22:46,641 (trainer:737) INFO: 15epoch:train:4501-4600batch: iter_time=1.974e-04, forward_time=0.105, loss_ctc=51.570, loss_att=51.175, acc=0.731, loss=51.293, backward_time=0.097, grad_norm=39.094, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.288e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 18:23:27,751 (trainer:737) INFO: 15epoch:train:4601-4700batch: iter_time=1.994e-04, forward_time=0.105, loss_ctc=44.859, loss_att=37.839, acc=0.762, loss=39.945, backward_time=0.097, grad_norm=30.098, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.287e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 18:24:08,680 (trainer:737) INFO: 15epoch:train:4701-4800batch: iter_time=1.828e-04, forward_time=0.105, loss_ctc=49.296, loss_att=54.334, acc=0.712, loss=52.823, backward_time=0.098, grad_norm=34.032, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.286e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:24:49,598 (trainer:737) INFO: 15epoch:train:4801-4900batch: iter_time=1.833e-04, forward_time=0.105, loss_ctc=48.448, loss_att=61.855, acc=0.705, loss=57.833, backward_time=0.098, grad_norm=33.453, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.285e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:25:30,447 (trainer:737) INFO: 15epoch:train:4901-5000batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=56.999, loss_att=64.137, acc=0.695, loss=61.996, backward_time=0.098, grad_norm=39.540, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.283e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:25:36,525 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 18:25:56,178 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 18:25:59,903 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a31d6bfa0>)
+[gpuc02:0/16] 2024-01-13 18:25:59,903 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 18:25:59,907 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 18:30:53,717 (trainer:737) INFO: 15epoch:train:5001-5100batch: iter_time=2.640, forward_time=0.114, loss_ctc=45.148, loss_att=53.159, acc=0.690, loss=50.756, backward_time=0.099, grad_norm=35.693, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.282e-04, train_time=3.232
+[gpuc02:0/16] 2024-01-13 18:31:34,407 (trainer:737) INFO: 15epoch:train:5101-5200batch: iter_time=1.302e-04, forward_time=0.103, loss_ctc=45.926, loss_att=42.872, acc=0.716, loss=43.788, backward_time=0.098, grad_norm=36.683, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.281e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 18:32:15,192 (trainer:737) INFO: 15epoch:train:5201-5300batch: iter_time=1.440e-04, forward_time=0.103, loss_ctc=48.098, loss_att=57.458, acc=0.698, loss=54.650, backward_time=0.097, grad_norm=35.514, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.280e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:32:55,803 (trainer:737) INFO: 15epoch:train:5301-5400batch: iter_time=1.822e-04, forward_time=0.104, loss_ctc=50.323, loss_att=58.797, acc=0.689, loss=56.255, backward_time=0.097, grad_norm=48.967, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.278e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 18:33:36,666 (trainer:737) INFO: 15epoch:train:5401-5500batch: iter_time=1.482e-04, forward_time=0.105, loss_ctc=56.366, loss_att=62.061, acc=0.695, loss=60.352, backward_time=0.098, grad_norm=38.392, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.277e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:34:17,693 (trainer:737) INFO: 15epoch:train:5501-5600batch: iter_time=1.272e-04, forward_time=0.104, loss_ctc=47.406, loss_att=48.973, acc=0.732, loss=48.503, backward_time=0.097, grad_norm=36.834, clip=100.000, loss_scale=3.967e+34, optim_step_time=0.030, optim0_lr0=5.276e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 18:34:58,854 (trainer:737) INFO: 15epoch:train:5601-5700batch: iter_time=1.451e-04, forward_time=0.104, loss_ctc=54.494, loss_att=64.683, acc=0.690, loss=61.626, backward_time=0.097, grad_norm=38.807, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.275e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 18:35:40,222 (trainer:737) INFO: 15epoch:train:5701-5800batch: iter_time=1.247e-04, forward_time=0.104, loss_ctc=52.265, loss_att=51.875, acc=0.707, loss=51.992, backward_time=0.097, grad_norm=39.392, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.274e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 18:36:21,597 (trainer:737) INFO: 15epoch:train:5801-5900batch: iter_time=1.301e-04, forward_time=0.104, loss_ctc=48.083, loss_att=44.398, acc=0.767, loss=45.504, backward_time=0.097, grad_norm=33.303, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.272e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 18:36:32,536 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 18:37:02,787 (trainer:737) INFO: 15epoch:train:5901-6000batch: iter_time=1.262e-04, forward_time=0.103, loss_ctc=49.846, loss_att=48.150, acc=0.727, loss=48.659, backward_time=0.097, grad_norm=35.722, clip=100.000, loss_scale=2.622e+34, optim_step_time=0.030, optim0_lr0=5.271e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 18:37:43,711 (trainer:737) INFO: 15epoch:train:6001-6100batch: iter_time=1.312e-04, forward_time=0.104, loss_ctc=46.344, loss_att=56.537, acc=0.706, loss=53.479, backward_time=0.097, grad_norm=32.053, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.270e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:38:24,607 (trainer:737) INFO: 15epoch:train:6101-6200batch: iter_time=1.379e-04, forward_time=0.104, loss_ctc=49.747, loss_att=60.705, acc=0.702, loss=57.418, backward_time=0.097, grad_norm=33.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.269e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:38:52,837 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 18:39:12,009 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 18:39:15,714 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39ff94cdf0>)
+[gpuc02:0/16] 2024-01-13 18:39:15,714 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 18:39:15,717 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 18:43:43,176 (trainer:737) INFO: 15epoch:train:6201-6300batch: iter_time=2.764, forward_time=0.104, loss_ctc=49.082, loss_att=59.333, acc=0.707, loss=56.258, backward_time=0.097, grad_norm=36.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.267e-04, train_time=3.185
+[gpuc02:0/16] 2024-01-13 18:44:24,117 (trainer:737) INFO: 15epoch:train:6301-6400batch: iter_time=1.341e-04, forward_time=0.103, loss_ctc=46.925, loss_att=46.690, acc=0.703, loss=46.761, backward_time=0.097, grad_norm=35.010, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.266e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:45:05,009 (trainer:737) INFO: 15epoch:train:6401-6500batch: iter_time=1.265e-04, forward_time=0.104, loss_ctc=47.103, loss_att=55.672, acc=0.698, loss=53.102, backward_time=0.098, grad_norm=35.478, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.265e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:45:35,926 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 18:45:45,675 (trainer:737) INFO: 15epoch:train:6501-6600batch: iter_time=1.455e-04, forward_time=0.104, loss_ctc=50.177, loss_att=54.393, acc=0.694, loss=53.128, backward_time=0.097, grad_norm=37.059, clip=100.000, loss_scale=1.825e+34, optim_step_time=0.030, optim0_lr0=5.264e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 18:46:26,430 (trainer:737) INFO: 15epoch:train:6601-6700batch: iter_time=1.553e-04, forward_time=0.104, loss_ctc=48.976, loss_att=56.658, acc=0.695, loss=54.354, backward_time=0.098, grad_norm=36.665, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.263e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 18:47:07,313 (trainer:737) INFO: 15epoch:train:6701-6800batch: iter_time=1.408e-04, forward_time=0.104, loss_ctc=54.195, loss_att=57.714, acc=0.713, loss=56.658, backward_time=0.098, grad_norm=37.865, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.261e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:47:48,177 (trainer:737) INFO: 15epoch:train:6801-6900batch: iter_time=1.758e-04, forward_time=0.105, loss_ctc=51.606, loss_att=54.921, acc=0.712, loss=53.927, backward_time=0.098, grad_norm=35.258, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.260e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:48:29,337 (trainer:737) INFO: 15epoch:train:6901-7000batch: iter_time=1.512e-04, forward_time=0.106, loss_ctc=50.353, loss_att=59.146, acc=0.700, loss=56.508, backward_time=0.098, grad_norm=35.470, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.259e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 18:49:10,169 (trainer:737) INFO: 15epoch:train:7001-7100batch: iter_time=1.590e-04, forward_time=0.105, loss_ctc=51.619, loss_att=50.576, acc=0.733, loss=50.889, backward_time=0.098, grad_norm=38.211, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.258e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:49:50,946 (trainer:737) INFO: 15epoch:train:7101-7200batch: iter_time=1.548e-04, forward_time=0.105, loss_ctc=44.259, loss_att=37.193, acc=0.765, loss=39.313, backward_time=0.097, grad_norm=29.989, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.257e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:50:32,126 (trainer:737) INFO: 15epoch:train:7201-7300batch: iter_time=1.448e-04, forward_time=0.108, loss_ctc=48.580, loss_att=54.821, acc=0.710, loss=52.949, backward_time=0.098, grad_norm=33.859, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.255e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 18:51:13,072 (trainer:737) INFO: 15epoch:train:7301-7400batch: iter_time=1.658e-04, forward_time=0.105, loss_ctc=48.789, loss_att=60.740, acc=0.709, loss=57.155, backward_time=0.098, grad_norm=33.058, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.254e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 18:51:54,254 (trainer:737) INFO: 15epoch:train:7401-7500batch: iter_time=1.463e-04, forward_time=0.105, loss_ctc=55.581, loss_att=63.495, acc=0.697, loss=61.121, backward_time=0.098, grad_norm=39.060, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.253e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 18:52:01,058 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 18:52:20,160 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 18:52:23,907 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3c39a9fca0>)
+[gpuc02:0/16] 2024-01-13 18:52:23,907 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 18:52:23,910 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 18:57:13,091 (trainer:737) INFO: 15epoch:train:7501-7600batch: iter_time=2.581, forward_time=0.105, loss_ctc=44.560, loss_att=56.886, acc=0.706, loss=53.188, backward_time=0.098, grad_norm=35.041, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.252e-04, train_time=3.188
+[gpuc02:0/16] 2024-01-13 18:57:53,950 (trainer:737) INFO: 15epoch:train:7601-7700batch: iter_time=1.521e-04, forward_time=0.103, loss_ctc=45.841, loss_att=44.314, acc=0.725, loss=44.772, backward_time=0.097, grad_norm=34.030, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.251e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:58:35,205 (trainer:737) INFO: 15epoch:train:7701-7800batch: iter_time=1.313e-04, forward_time=0.104, loss_ctc=46.959, loss_att=58.268, acc=0.711, loss=54.875, backward_time=0.099, grad_norm=35.859, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.249e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 18:59:16,039 (trainer:737) INFO: 15epoch:train:7801-7900batch: iter_time=1.389e-04, forward_time=0.103, loss_ctc=49.370, loss_att=58.253, acc=0.694, loss=55.588, backward_time=0.098, grad_norm=40.903, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.248e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 18:59:57,313 (trainer:737) INFO: 15epoch:train:7901-8000batch: iter_time=1.495e-04, forward_time=0.104, loss_ctc=55.651, loss_att=62.619, acc=0.708, loss=60.529, backward_time=0.098, grad_norm=37.407, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.247e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:00:38,220 (trainer:737) INFO: 15epoch:train:8001-8100batch: iter_time=1.509e-04, forward_time=0.104, loss_ctc=46.692, loss_att=49.893, acc=0.732, loss=48.933, backward_time=0.098, grad_norm=33.990, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.030, optim0_lr0=5.246e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:01:19,574 (trainer:737) INFO: 15epoch:train:8101-8200batch: iter_time=1.324e-04, forward_time=0.105, loss_ctc=54.294, loss_att=68.174, acc=0.696, loss=64.010, backward_time=0.099, grad_norm=37.626, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.245e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:02:00,932 (trainer:737) INFO: 15epoch:train:8201-8300batch: iter_time=1.712e-04, forward_time=0.103, loss_ctc=51.623, loss_att=53.397, acc=0.715, loss=52.865, backward_time=0.098, grad_norm=37.757, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.243e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:02:41,947 (trainer:737) INFO: 15epoch:train:8301-8400batch: iter_time=1.482e-04, forward_time=0.105, loss_ctc=47.492, loss_att=46.305, acc=0.767, loss=46.661, backward_time=0.099, grad_norm=32.350, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.242e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:03:22,806 (trainer:737) INFO: 15epoch:train:8401-8500batch: iter_time=1.596e-04, forward_time=0.104, loss_ctc=49.175, loss_att=47.771, acc=0.738, loss=48.192, backward_time=0.098, grad_norm=34.620, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.031, optim0_lr0=5.241e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:04:03,715 (trainer:737) INFO: 15epoch:train:8501-8600batch: iter_time=1.697e-04, forward_time=0.103, loss_ctc=46.350, loss_att=56.681, acc=0.712, loss=53.582, backward_time=0.098, grad_norm=34.064, clip=100.000, loss_scale=1.288e+34, optim_step_time=0.030, optim0_lr0=5.240e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:04:44,697 (trainer:737) INFO: 15epoch:train:8601-8700batch: iter_time=1.948e-04, forward_time=0.104, loss_ctc=48.890, loss_att=61.419, acc=0.711, loss=57.661, backward_time=0.098, grad_norm=34.337, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.239e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:05:11,156 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 19:05:30,356 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 19:05:34,084 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a2d4a5cc0>)
+[gpuc02:0/16] 2024-01-13 19:05:34,084 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 19:05:34,088 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 19:10:02,226 (trainer:737) INFO: 15epoch:train:8701-8800batch: iter_time=2.687, forward_time=0.106, loss_ctc=48.901, loss_att=58.928, acc=0.724, loss=55.920, backward_time=0.098, grad_norm=37.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.237e-04, train_time=3.175
+[gpuc02:0/16] 2024-01-13 19:10:43,528 (trainer:737) INFO: 15epoch:train:8801-8900batch: iter_time=1.863e-04, forward_time=0.103, loss_ctc=46.755, loss_att=46.871, acc=0.710, loss=46.836, backward_time=0.097, grad_norm=35.130, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.236e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:11:24,702 (trainer:737) INFO: 15epoch:train:8901-9000batch: iter_time=1.888e-04, forward_time=0.104, loss_ctc=47.221, loss_att=56.491, acc=0.713, loss=53.710, backward_time=0.098, grad_norm=33.921, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.235e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 19:12:06,697 (trainer:737) INFO: 15epoch:train:9001-9100batch: iter_time=1.206e-04, forward_time=0.103, loss_ctc=50.215, loss_att=55.204, acc=0.701, loss=53.707, backward_time=0.098, grad_norm=40.479, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.234e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 19:12:48,713 (trainer:737) INFO: 15epoch:train:9101-9200batch: iter_time=1.057e-04, forward_time=0.104, loss_ctc=49.966, loss_att=57.832, acc=0.704, loss=55.473, backward_time=0.098, grad_norm=38.057, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.233e-04, train_time=0.420
+[gpuc02:0/16] 2024-01-13 19:13:29,756 (trainer:737) INFO: 15epoch:train:9201-9300batch: iter_time=1.380e-04, forward_time=0.104, loss_ctc=53.581, loss_att=58.054, acc=0.721, loss=56.712, backward_time=0.099, grad_norm=38.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.231e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:14:10,735 (trainer:737) INFO: 15epoch:train:9301-9400batch: iter_time=1.843e-04, forward_time=0.104, loss_ctc=51.748, loss_att=58.311, acc=0.711, loss=56.342, backward_time=0.098, grad_norm=35.672, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.230e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:14:51,760 (trainer:737) INFO: 15epoch:train:9401-9500batch: iter_time=1.364e-04, forward_time=0.104, loss_ctc=50.163, loss_att=59.350, acc=0.712, loss=56.594, backward_time=0.099, grad_norm=32.561, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.229e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:15:32,720 (trainer:737) INFO: 15epoch:train:9501-9600batch: iter_time=2.032e-04, forward_time=0.104, loss_ctc=50.371, loss_att=51.346, acc=0.740, loss=51.054, backward_time=0.097, grad_norm=38.549, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.228e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:16:13,640 (trainer:737) INFO: 15epoch:train:9601-9700batch: iter_time=1.720e-04, forward_time=0.104, loss_ctc=44.407, loss_att=38.446, acc=0.768, loss=40.234, backward_time=0.098, grad_norm=31.166, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.227e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:16:54,564 (trainer:737) INFO: 15epoch:train:9701-9800batch: iter_time=1.841e-04, forward_time=0.104, loss_ctc=48.469, loss_att=54.006, acc=0.720, loss=52.345, backward_time=0.098, grad_norm=34.944, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.225e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:17:36,785 (trainer:737) INFO: 15epoch:train:9801-9900batch: iter_time=1.735e-04, forward_time=0.105, loss_ctc=48.431, loss_att=61.980, acc=0.715, loss=57.915, backward_time=0.098, grad_norm=32.847, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.224e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 19:18:17,773 (trainer:737) INFO: 15epoch:train:9901-10000batch: iter_time=1.766e-04, forward_time=0.104, loss_ctc=55.637, loss_att=64.454, acc=0.704, loss=61.809, backward_time=0.098, grad_norm=39.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.223e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:18:23,874 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 19:18:43,016 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 19:18:46,791 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fb04ce710>)
+[gpuc02:0/16] 2024-01-13 19:18:46,791 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 19:18:46,795 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 19:23:41,825 (trainer:737) INFO: 15epoch:train:10001-10100batch: iter_time=2.821, forward_time=0.104, loss_ctc=44.967, loss_att=51.719, acc=0.712, loss=49.693, backward_time=0.097, grad_norm=34.372, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.222e-04, train_time=3.240
+[gpuc02:0/16] 2024-01-13 19:24:22,483 (trainer:737) INFO: 15epoch:train:10101-10200batch: iter_time=1.392e-04, forward_time=0.104, loss_ctc=45.356, loss_att=42.662, acc=0.730, loss=43.471, backward_time=0.097, grad_norm=36.269, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.221e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 19:25:03,600 (trainer:737) INFO: 15epoch:train:10201-10300batch: iter_time=1.664e-04, forward_time=0.103, loss_ctc=47.167, loss_att=56.414, acc=0.713, loss=53.640, backward_time=0.098, grad_norm=35.785, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.219e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 19:25:44,326 (trainer:737) INFO: 15epoch:train:10301-10400batch: iter_time=1.808e-04, forward_time=0.103, loss_ctc=48.729, loss_att=58.091, acc=0.695, loss=55.283, backward_time=0.097, grad_norm=39.402, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.218e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 19:26:25,639 (trainer:737) INFO: 15epoch:train:10401-10500batch: iter_time=1.402e-04, forward_time=0.104, loss_ctc=56.256, loss_att=61.455, acc=0.711, loss=59.895, backward_time=0.099, grad_norm=40.071, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.217e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:27:06,439 (trainer:737) INFO: 15epoch:train:10501-10600batch: iter_time=1.216e-04, forward_time=0.104, loss_ctc=46.437, loss_att=48.524, acc=0.735, loss=47.898, backward_time=0.098, grad_norm=34.980, clip=100.000, loss_scale=2.575e+34, optim_step_time=0.030, optim0_lr0=5.216e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:27:47,504 (trainer:737) INFO: 15epoch:train:10601-10700batch: iter_time=1.140e-04, forward_time=0.105, loss_ctc=53.975, loss_att=67.258, acc=0.697, loss=63.273, backward_time=0.099, grad_norm=39.083, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.215e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:28:11,572 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 19:28:28,290 (trainer:737) INFO: 15epoch:train:10701-10800batch: iter_time=1.185e-04, forward_time=0.104, loss_ctc=51.273, loss_att=52.794, acc=0.717, loss=52.338, backward_time=0.098, grad_norm=36.713, clip=100.000, loss_scale=3.294e+34, optim_step_time=0.030, optim0_lr0=5.214e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:29:09,580 (trainer:737) INFO: 15epoch:train:10801-10900batch: iter_time=1.313e-04, forward_time=0.104, loss_ctc=47.106, loss_att=46.014, acc=0.768, loss=46.342, backward_time=0.098, grad_norm=31.992, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.212e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 19:29:51,339 (trainer:737) INFO: 15epoch:train:10901-11000batch: iter_time=1.206e-04, forward_time=0.104, loss_ctc=48.559, loss_att=47.190, acc=0.740, loss=47.600, backward_time=0.098, grad_norm=34.262, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.211e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 19:30:32,818 (trainer:737) INFO: 15epoch:train:11001-11100batch: iter_time=1.207e-04, forward_time=0.104, loss_ctc=45.779, loss_att=55.920, acc=0.715, loss=52.878, backward_time=0.099, grad_norm=33.416, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.210e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 19:31:13,870 (trainer:737) INFO: 15epoch:train:11101-11200batch: iter_time=1.261e-04, forward_time=0.105, loss_ctc=48.917, loss_att=60.435, acc=0.712, loss=56.979, backward_time=0.098, grad_norm=34.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.209e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:31:43,895 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 19:32:03,971 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 19:32:08,148 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3fa772fe80>)
+[gpuc02:0/16] 2024-01-13 19:32:08,148 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 19:32:08,151 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 19:36:42,250 (trainer:737) INFO: 15epoch:train:11201-11300batch: iter_time=2.873, forward_time=0.105, loss_ctc=48.934, loss_att=63.220, acc=0.703, loss=58.934, backward_time=0.098, grad_norm=36.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.208e-04, train_time=3.284
+[gpuc02:0/16] 2024-01-13 19:37:22,996 (trainer:737) INFO: 15epoch:train:11301-11400batch: iter_time=2.046e-04, forward_time=0.104, loss_ctc=46.784, loss_att=48.275, acc=0.701, loss=47.828, backward_time=0.097, grad_norm=37.735, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.206e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 19:38:03,811 (trainer:737) INFO: 15epoch:train:11401-11500batch: iter_time=2.027e-04, forward_time=0.103, loss_ctc=46.484, loss_att=58.116, acc=0.696, loss=54.627, backward_time=0.098, grad_norm=35.947, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.205e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:38:44,489 (trainer:737) INFO: 15epoch:train:11501-11600batch: iter_time=2.183e-04, forward_time=0.102, loss_ctc=49.145, loss_att=54.683, acc=0.697, loss=53.021, backward_time=0.096, grad_norm=41.440, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.204e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 19:39:25,865 (trainer:737) INFO: 15epoch:train:11601-11700batch: iter_time=1.852e-04, forward_time=0.103, loss_ctc=49.152, loss_att=57.096, acc=0.699, loss=54.713, backward_time=0.097, grad_norm=38.086, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.203e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 19:40:06,862 (trainer:737) INFO: 15epoch:train:11701-11800batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=53.933, loss_att=57.453, acc=0.716, loss=56.397, backward_time=0.098, grad_norm=36.865, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.202e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:40:48,073 (trainer:737) INFO: 15epoch:train:11801-11900batch: iter_time=1.379e-04, forward_time=0.104, loss_ctc=50.989, loss_att=55.327, acc=0.713, loss=54.026, backward_time=0.098, grad_norm=36.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.201e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 19:41:28,976 (trainer:737) INFO: 15epoch:train:11901-12000batch: iter_time=1.987e-04, forward_time=0.104, loss_ctc=49.471, loss_att=58.899, acc=0.702, loss=56.071, backward_time=0.098, grad_norm=33.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.199e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:42:09,938 (trainer:737) INFO: 15epoch:train:12001-12100batch: iter_time=1.378e-04, forward_time=0.104, loss_ctc=50.258, loss_att=50.598, acc=0.733, loss=50.496, backward_time=0.098, grad_norm=37.666, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.198e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:42:50,770 (trainer:737) INFO: 15epoch:train:12101-12200batch: iter_time=1.365e-04, forward_time=0.104, loss_ctc=43.346, loss_att=36.855, acc=0.766, loss=38.802, backward_time=0.097, grad_norm=29.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.197e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:43:31,673 (trainer:737) INFO: 15epoch:train:12201-12300batch: iter_time=1.471e-04, forward_time=0.104, loss_ctc=48.068, loss_att=53.774, acc=0.715, loss=52.062, backward_time=0.098, grad_norm=36.255, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.196e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:44:12,600 (trainer:737) INFO: 15epoch:train:12301-12400batch: iter_time=1.171e-04, forward_time=0.104, loss_ctc=48.027, loss_att=61.372, acc=0.708, loss=57.369, backward_time=0.098, grad_norm=35.259, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.195e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:44:53,484 (trainer:737) INFO: 15epoch:train:12401-12500batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=55.401, loss_att=63.755, acc=0.697, loss=61.249, backward_time=0.097, grad_norm=42.865, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.194e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:44:59,671 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 19:45:19,467 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 19:45:23,143 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a508dbb50>)
+[gpuc02:0/16] 2024-01-13 19:45:23,143 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 19:45:23,147 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 19:50:16,140 (trainer:737) INFO: 15epoch:train:12501-12600batch: iter_time=2.684, forward_time=0.114, loss_ctc=44.225, loss_att=52.539, acc=0.693, loss=50.045, backward_time=0.100, grad_norm=33.058, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.192e-04, train_time=3.226
+[gpuc02:0/16] 2024-01-13 19:50:57,404 (trainer:737) INFO: 15epoch:train:12601-12700batch: iter_time=1.981e-04, forward_time=0.104, loss_ctc=44.650, loss_att=42.322, acc=0.720, loss=43.021, backward_time=0.097, grad_norm=34.902, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.191e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 19:51:38,189 (trainer:737) INFO: 15epoch:train:12701-12800batch: iter_time=1.742e-04, forward_time=0.104, loss_ctc=47.123, loss_att=57.370, acc=0.700, loss=54.296, backward_time=0.097, grad_norm=36.686, clip=100.000, loss_scale=2.928e+34, optim_step_time=0.030, optim0_lr0=5.190e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:52:19,131 (trainer:737) INFO: 15epoch:train:12801-12900batch: iter_time=2.005e-04, forward_time=0.103, loss_ctc=48.590, loss_att=58.077, acc=0.694, loss=55.231, backward_time=0.096, grad_norm=39.586, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.189e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:53:00,634 (trainer:737) INFO: 15epoch:train:12901-13000batch: iter_time=1.560e-04, forward_time=0.107, loss_ctc=55.668, loss_att=61.830, acc=0.698, loss=59.981, backward_time=0.098, grad_norm=39.612, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.188e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 19:53:41,477 (trainer:737) INFO: 15epoch:train:13001-13100batch: iter_time=1.489e-04, forward_time=0.104, loss_ctc=46.029, loss_att=48.255, acc=0.736, loss=47.587, backward_time=0.098, grad_norm=34.378, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.187e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:54:22,503 (trainer:737) INFO: 15epoch:train:13101-13200batch: iter_time=1.333e-04, forward_time=0.105, loss_ctc=54.110, loss_att=64.027, acc=0.692, loss=61.052, backward_time=0.099, grad_norm=38.955, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.185e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:55:03,485 (trainer:737) INFO: 15epoch:train:13201-13300batch: iter_time=1.370e-04, forward_time=0.104, loss_ctc=51.043, loss_att=50.814, acc=0.711, loss=50.883, backward_time=0.099, grad_norm=38.214, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.184e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 19:55:44,608 (trainer:737) INFO: 15epoch:train:13301-13400batch: iter_time=1.387e-04, forward_time=0.106, loss_ctc=47.144, loss_att=44.133, acc=0.770, loss=45.037, backward_time=0.099, grad_norm=32.596, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.183e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 19:56:25,532 (trainer:737) INFO: 15epoch:train:13401-13500batch: iter_time=1.379e-04, forward_time=0.104, loss_ctc=48.364, loss_att=47.762, acc=0.730, loss=47.943, backward_time=0.098, grad_norm=34.468, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.182e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 19:57:06,376 (trainer:737) INFO: 15epoch:train:13501-13600batch: iter_time=1.411e-04, forward_time=0.104, loss_ctc=45.701, loss_att=55.909, acc=0.707, loss=52.847, backward_time=0.098, grad_norm=32.732, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.181e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 19:57:47,572 (trainer:737) INFO: 15epoch:train:13601-13700batch: iter_time=1.489e-04, forward_time=0.104, loss_ctc=48.443, loss_att=59.773, acc=0.704, loss=56.374, backward_time=0.098, grad_norm=33.395, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.180e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 19:58:13,352 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 19:58:32,703 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 19:58:36,577 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a20967ca0>)
+[gpuc02:0/16] 2024-01-13 19:58:36,577 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 19:58:36,580 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 20:03:08,785 (trainer:737) INFO: 15epoch:train:13701-13800batch: iter_time=2.733, forward_time=0.104, loss_ctc=48.828, loss_att=61.917, acc=0.717, loss=57.991, backward_time=0.098, grad_norm=38.523, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.178e-04, train_time=3.212
+[gpuc02:0/16] 2024-01-13 20:03:49,689 (trainer:737) INFO: 15epoch:train:13801-13900batch: iter_time=1.793e-04, forward_time=0.104, loss_ctc=46.033, loss_att=47.661, acc=0.711, loss=47.173, backward_time=0.097, grad_norm=37.400, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.177e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 20:04:30,722 (trainer:737) INFO: 15epoch:train:13901-14000batch: iter_time=1.703e-04, forward_time=0.104, loss_ctc=47.014, loss_att=57.682, acc=0.712, loss=54.482, backward_time=0.098, grad_norm=35.992, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.176e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:05:11,440 (trainer:737) INFO: 15epoch:train:14001-14100batch: iter_time=1.852e-04, forward_time=0.103, loss_ctc=48.268, loss_att=53.718, acc=0.703, loss=52.083, backward_time=0.096, grad_norm=42.203, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.175e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 20:05:52,293 (trainer:737) INFO: 15epoch:train:14101-14200batch: iter_time=1.765e-04, forward_time=0.103, loss_ctc=48.483, loss_att=57.220, acc=0.707, loss=54.599, backward_time=0.097, grad_norm=38.278, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.174e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 20:06:33,485 (trainer:737) INFO: 15epoch:train:14201-14300batch: iter_time=1.658e-04, forward_time=0.104, loss_ctc=52.881, loss_att=58.449, acc=0.721, loss=56.779, backward_time=0.098, grad_norm=37.344, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.173e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 20:07:14,480 (trainer:737) INFO: 15epoch:train:14301-14400batch: iter_time=1.483e-04, forward_time=0.104, loss_ctc=51.020, loss_att=58.067, acc=0.712, loss=55.953, backward_time=0.098, grad_norm=35.830, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.172e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:07:55,488 (trainer:737) INFO: 15epoch:train:14401-14500batch: iter_time=1.738e-04, forward_time=0.104, loss_ctc=49.447, loss_att=58.736, acc=0.714, loss=55.949, backward_time=0.099, grad_norm=35.395, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.170e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:08:36,433 (trainer:737) INFO: 15epoch:train:14501-14600batch: iter_time=1.565e-04, forward_time=0.104, loss_ctc=49.317, loss_att=50.900, acc=0.742, loss=50.425, backward_time=0.098, grad_norm=37.578, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.169e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 20:09:17,350 (trainer:737) INFO: 15epoch:train:14601-14700batch: iter_time=1.607e-04, forward_time=0.104, loss_ctc=43.836, loss_att=38.278, acc=0.770, loss=39.946, backward_time=0.098, grad_norm=29.830, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.168e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 20:09:58,536 (trainer:737) INFO: 15epoch:train:14701-14800batch: iter_time=1.644e-04, forward_time=0.104, loss_ctc=47.272, loss_att=53.616, acc=0.724, loss=51.713, backward_time=0.098, grad_norm=33.981, clip=100.000, loss_scale=5.857e+34, optim_step_time=0.030, optim0_lr0=5.167e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 20:10:37,485 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 20:10:39,552 (trainer:737) INFO: 15epoch:train:14801-14900batch: iter_time=1.494e-04, forward_time=0.104, loss_ctc=47.764, loss_att=62.153, acc=0.715, loss=57.836, backward_time=0.099, grad_norm=32.509, clip=100.000, loss_scale=8.098e+34, optim_step_time=0.030, optim0_lr0=5.166e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:11:20,536 (trainer:737) INFO: 15epoch:train:14901-15000batch: iter_time=1.607e-04, forward_time=0.104, loss_ctc=54.885, loss_att=63.480, acc=0.705, loss=60.902, backward_time=0.098, grad_norm=38.927, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.165e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:31:19,551 (trainer:343) INFO: 15epoch results: [train] iter_time=0.228, forward_time=0.105, loss_ctc=49.783, loss_att=55.219, acc=0.713, loss=53.588, backward_time=0.098, grad_norm=36.374, clip=100.000, loss_scale=2.577e+34, optim_step_time=0.030, optim0_lr0=5.253e-04, train_time=0.651, time=2 hours, 43 minutes and 6.59 seconds, total_count=225000, gpu_max_cached_mem_GB=26.045, [valid] loss_ctc=54.366, cer_ctc=0.290, loss_att=56.824, acc=0.569, cer=0.349, wer=1.000, loss=56.087, time=19 minutes and 47.31 seconds, total_count=70065, gpu_max_cached_mem_GB=26.045
+[gpuc02:0/16] 2024-01-13 20:31:24,647 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc02:0/16] 2024-01-13 20:31:24,652 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/10epoch.pth
+[gpuc02:0/16] 2024-01-13 20:31:24,652 (trainer:272) INFO: 16/45epoch started. Estimated time to finish: 3 days, 18 hours and 6 minutes
+[gpuc02:0/16] 2024-01-13 20:31:24,662 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc02:0/16] 2024-01-13 20:31:43,557 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 20:31:47,112 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38c55ee410>)
+[gpuc02:0/16] 2024-01-13 20:31:47,112 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc02:0/16] 2024-01-13 20:31:47,115 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 20:36:45,779 (trainer:737) INFO: 16epoch:train:1-100batch: iter_time=2.721, forward_time=0.148, loss_ctc=45.553, loss_att=50.026, acc=0.718, loss=48.684, backward_time=0.110, grad_norm=33.183, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.164e-04, train_time=3.211
+[gpuc02:0/16] 2024-01-13 20:37:26,552 (trainer:737) INFO: 16epoch:train:101-200batch: iter_time=1.218e-04, forward_time=0.103, loss_ctc=54.074, loss_att=60.226, acc=0.700, loss=58.380, backward_time=0.098, grad_norm=49.584, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.162e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 20:38:07,712 (trainer:737) INFO: 16epoch:train:201-300batch: iter_time=1.190e-04, forward_time=0.104, loss_ctc=55.750, loss_att=59.329, acc=0.699, loss=58.256, backward_time=0.098, grad_norm=46.234, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.161e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 20:38:48,414 (trainer:737) INFO: 16epoch:train:301-400batch: iter_time=1.326e-04, forward_time=0.103, loss_ctc=44.825, loss_att=56.523, acc=0.713, loss=53.014, backward_time=0.099, grad_norm=33.769, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.031, optim0_lr0=5.160e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 20:39:33,980 (trainer:737) INFO: 16epoch:train:401-500batch: iter_time=1.277e-04, forward_time=0.123, loss_ctc=52.113, loss_att=59.987, acc=0.725, loss=57.625, backward_time=0.103, grad_norm=37.675, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.033, optim0_lr0=5.159e-04, train_time=0.455
+[gpuc02:0/16] 2024-01-13 20:40:15,536 (trainer:737) INFO: 16epoch:train:501-600batch: iter_time=1.286e-04, forward_time=0.112, loss_ctc=43.051, loss_att=51.486, acc=0.728, loss=48.956, backward_time=0.098, grad_norm=33.658, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.158e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 20:40:56,627 (trainer:737) INFO: 16epoch:train:601-700batch: iter_time=1.288e-04, forward_time=0.104, loss_ctc=50.923, loss_att=57.415, acc=0.730, loss=55.468, backward_time=0.100, grad_norm=36.884, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.157e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 20:41:38,347 (trainer:737) INFO: 16epoch:train:701-800batch: iter_time=1.393e-04, forward_time=0.103, loss_ctc=47.222, loss_att=49.292, acc=0.717, loss=48.671, backward_time=0.098, grad_norm=36.409, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.156e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 20:42:21,752 (trainer:737) INFO: 16epoch:train:801-900batch: iter_time=1.353e-04, forward_time=0.104, loss_ctc=43.738, loss_att=49.039, acc=0.712, loss=47.448, backward_time=0.101, grad_norm=33.896, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.154e-04, train_time=0.434
+[gpuc02:0/16] 2024-01-13 20:43:04,660 (trainer:737) INFO: 16epoch:train:901-1000batch: iter_time=1.236e-04, forward_time=0.102, loss_ctc=42.860, loss_att=48.893, acc=0.710, loss=47.083, backward_time=0.098, grad_norm=35.601, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.153e-04, train_time=0.429
+[gpuc02:0/16] 2024-01-13 20:43:47,006 (trainer:737) INFO: 16epoch:train:1001-1100batch: iter_time=1.136e-04, forward_time=0.107, loss_ctc=46.051, loss_att=48.255, acc=0.732, loss=47.594, backward_time=0.099, grad_norm=34.919, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.152e-04, train_time=0.423
+[gpuc02:0/16] 2024-01-13 20:44:29,253 (trainer:737) INFO: 16epoch:train:1101-1200batch: iter_time=1.013e-04, forward_time=0.104, loss_ctc=51.244, loss_att=52.682, acc=0.722, loss=52.251, backward_time=0.098, grad_norm=35.988, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.151e-04, train_time=0.422
+[gpuc02:0/16] 2024-01-13 20:45:19,973 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc02:0/16] 2024-01-13 20:45:39,287 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 20:45:43,042 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f38b3643e80>)
+[gpuc02:0/16] 2024-01-13 20:45:43,042 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc02:0/16] 2024-01-13 20:45:43,046 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 20:53:11,348 (trainer:737) INFO: 16epoch:train:1201-1300batch: iter_time=4.685, forward_time=0.132, loss_ctc=54.939, loss_att=64.533, acc=0.709, loss=61.654, backward_time=0.100, grad_norm=43.156, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.150e-04, train_time=5.221
+[gpuc02:0/16] 2024-01-13 20:53:52,312 (trainer:737) INFO: 16epoch:train:1301-1400batch: iter_time=1.838e-04, forward_time=0.104, loss_ctc=53.679, loss_att=56.247, acc=0.711, loss=55.477, backward_time=0.098, grad_norm=44.992, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.149e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:54:33,107 (trainer:737) INFO: 16epoch:train:1401-1500batch: iter_time=1.594e-04, forward_time=0.104, loss_ctc=46.413, loss_att=55.485, acc=0.698, loss=52.764, backward_time=0.098, grad_norm=40.405, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.148e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 20:55:14,059 (trainer:737) INFO: 16epoch:train:1501-1600batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=50.334, loss_att=60.356, acc=0.724, loss=57.350, backward_time=0.099, grad_norm=40.375, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.146e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 20:55:54,972 (trainer:737) INFO: 16epoch:train:1601-1700batch: iter_time=1.504e-04, forward_time=0.105, loss_ctc=47.663, loss_att=57.050, acc=0.723, loss=54.234, backward_time=0.098, grad_norm=33.194, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.145e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 20:56:36,273 (trainer:737) INFO: 16epoch:train:1701-1800batch: iter_time=1.592e-04, forward_time=0.105, loss_ctc=50.137, loss_att=62.584, acc=0.730, loss=58.850, backward_time=0.099, grad_norm=35.327, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.144e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 20:57:17,272 (trainer:737) INFO: 16epoch:train:1801-1900batch: iter_time=1.664e-04, forward_time=0.104, loss_ctc=46.715, loss_att=56.032, acc=0.732, loss=53.237, backward_time=0.099, grad_norm=35.006, clip=100.000, loss_scale=4.362e+34, optim_step_time=0.029, optim0_lr0=5.143e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 20:57:38,979 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 20:57:58,437 (trainer:737) INFO: 16epoch:train:1901-2000batch: iter_time=1.515e-04, forward_time=0.105, loss_ctc=46.193, loss_att=52.814, acc=0.738, loss=50.828, backward_time=0.098, grad_norm=35.604, clip=100.000, loss_scale=6.336e+34, optim_step_time=0.030, optim0_lr0=5.142e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 20:58:39,156 (trainer:737) INFO: 16epoch:train:2001-2100batch: iter_time=1.377e-04, forward_time=0.104, loss_ctc=42.643, loss_att=45.676, acc=0.728, loss=44.766, backward_time=0.098, grad_norm=34.437, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.141e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 20:59:19,908 (trainer:737) INFO: 16epoch:train:2101-2200batch: iter_time=1.440e-04, forward_time=0.104, loss_ctc=44.530, loss_att=54.784, acc=0.706, loss=51.708, backward_time=0.098, grad_norm=34.121, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.140e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 21:00:00,879 (trainer:737) INFO: 16epoch:train:2201-2300batch: iter_time=1.658e-04, forward_time=0.104, loss_ctc=45.722, loss_att=47.846, acc=0.744, loss=47.209, backward_time=0.097, grad_norm=34.308, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.138e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:00:43,587 (trainer:737) INFO: 16epoch:train:2301-2400batch: iter_time=1.250e-04, forward_time=0.105, loss_ctc=47.842, loss_att=52.812, acc=0.733, loss=51.321, backward_time=0.098, grad_norm=34.857, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.137e-04, train_time=0.427
+[gpuc02:0/16] 2024-01-13 21:01:24,374 (trainer:737) INFO: 16epoch:train:2401-2500batch: iter_time=1.271e-04, forward_time=0.104, loss_ctc=55.787, loss_att=56.183, acc=0.723, loss=56.064, backward_time=0.097, grad_norm=42.571, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.136e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:01:28,935 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc02:0/16] 2024-01-13 21:01:48,395 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 21:01:52,301 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a34587790>)
+[gpuc02:0/16] 2024-01-13 21:01:52,301 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc02:0/16] 2024-01-13 21:01:52,305 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 21:08:35,001 (trainer:737) INFO: 16epoch:train:2501-2600batch: iter_time=2.627, forward_time=0.104, loss_ctc=44.490, loss_att=48.351, acc=0.730, loss=47.193, backward_time=0.098, grad_norm=32.152, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.135e-04, train_time=4.306
+[gpuc02:0/16] 2024-01-13 21:08:48,102 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 21:09:15,950 (trainer:737) INFO: 16epoch:train:2601-2700batch: iter_time=1.387e-04, forward_time=0.103, loss_ctc=52.292, loss_att=61.585, acc=0.703, loss=58.797, backward_time=0.098, grad_norm=44.848, clip=100.000, loss_scale=2.727e+34, optim_step_time=0.030, optim0_lr0=5.134e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:09:56,969 (trainer:737) INFO: 16epoch:train:2701-2800batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=52.756, loss_att=59.580, acc=0.700, loss=57.533, backward_time=0.098, grad_norm=45.121, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.133e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:10:37,792 (trainer:737) INFO: 16epoch:train:2801-2900batch: iter_time=1.486e-04, forward_time=0.104, loss_ctc=44.261, loss_att=56.997, acc=0.722, loss=53.176, backward_time=0.098, grad_norm=32.609, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.132e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:11:19,441 (trainer:737) INFO: 16epoch:train:2901-3000batch: iter_time=1.559e-04, forward_time=0.105, loss_ctc=51.287, loss_att=59.430, acc=0.739, loss=56.987, backward_time=0.099, grad_norm=37.046, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.131e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 21:12:00,295 (trainer:737) INFO: 16epoch:train:3001-3100batch: iter_time=1.561e-04, forward_time=0.103, loss_ctc=42.249, loss_att=54.160, acc=0.732, loss=50.587, backward_time=0.098, grad_norm=32.758, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.129e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:12:41,363 (trainer:737) INFO: 16epoch:train:3101-3200batch: iter_time=1.401e-04, forward_time=0.105, loss_ctc=49.437, loss_att=57.114, acc=0.738, loss=54.811, backward_time=0.098, grad_norm=34.037, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.128e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:13:22,518 (trainer:737) INFO: 16epoch:train:3201-3300batch: iter_time=1.707e-04, forward_time=0.103, loss_ctc=46.233, loss_att=49.075, acc=0.727, loss=48.222, backward_time=0.098, grad_norm=35.432, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.127e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:14:03,405 (trainer:737) INFO: 16epoch:train:3301-3400batch: iter_time=1.742e-04, forward_time=0.104, loss_ctc=43.563, loss_att=50.760, acc=0.724, loss=48.601, backward_time=0.098, grad_norm=34.007, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.126e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:14:44,179 (trainer:737) INFO: 16epoch:train:3401-3500batch: iter_time=1.676e-04, forward_time=0.103, loss_ctc=41.887, loss_att=47.393, acc=0.730, loss=45.741, backward_time=0.098, grad_norm=33.586, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.125e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:15:25,146 (trainer:737) INFO: 16epoch:train:3501-3600batch: iter_time=1.417e-04, forward_time=0.104, loss_ctc=44.303, loss_att=46.083, acc=0.756, loss=45.549, backward_time=0.099, grad_norm=33.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.124e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:16:06,151 (trainer:737) INFO: 16epoch:train:3601-3700batch: iter_time=1.573e-04, forward_time=0.104, loss_ctc=50.036, loss_att=52.013, acc=0.727, loss=51.420, backward_time=0.098, grad_norm=34.789, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.123e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:16:33,369 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc02:0/16] 2024-01-13 21:16:52,605 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 21:16:56,329 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39b282bf40>)
+[gpuc02:0/16] 2024-01-13 21:16:56,329 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc02:0/16] 2024-01-13 21:16:56,333 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 21:21:36,010 (trainer:737) INFO: 16epoch:train:3701-3800batch: iter_time=2.650, forward_time=0.103, loss_ctc=52.985, loss_att=61.377, acc=0.716, loss=58.859, backward_time=0.098, grad_norm=43.278, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.122e-04, train_time=3.298
+[gpuc02:0/16] 2024-01-13 21:22:17,098 (trainer:737) INFO: 16epoch:train:3801-3900batch: iter_time=1.674e-04, forward_time=0.106, loss_ctc=52.783, loss_att=54.897, acc=0.714, loss=54.263, backward_time=0.098, grad_norm=43.099, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.120e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:22:58,135 (trainer:737) INFO: 16epoch:train:3901-4000batch: iter_time=1.470e-04, forward_time=0.104, loss_ctc=45.186, loss_att=53.261, acc=0.702, loss=50.838, backward_time=0.097, grad_norm=39.392, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.119e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:23:39,882 (trainer:737) INFO: 16epoch:train:4001-4100batch: iter_time=1.540e-04, forward_time=0.105, loss_ctc=49.397, loss_att=58.763, acc=0.727, loss=55.953, backward_time=0.098, grad_norm=52.029, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.118e-04, train_time=0.417
+[gpuc02:0/16] 2024-01-13 21:24:21,265 (trainer:737) INFO: 16epoch:train:4101-4200batch: iter_time=1.636e-04, forward_time=0.104, loss_ctc=47.022, loss_att=55.926, acc=0.728, loss=53.255, backward_time=0.098, grad_norm=33.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.117e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 21:25:02,531 (trainer:737) INFO: 16epoch:train:4201-4300batch: iter_time=1.458e-04, forward_time=0.105, loss_ctc=49.169, loss_att=61.321, acc=0.734, loss=57.675, backward_time=0.099, grad_norm=33.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.116e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 21:25:43,649 (trainer:737) INFO: 16epoch:train:4301-4400batch: iter_time=1.449e-04, forward_time=0.104, loss_ctc=46.447, loss_att=55.090, acc=0.735, loss=52.497, backward_time=0.099, grad_norm=34.175, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.115e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:26:24,650 (trainer:737) INFO: 16epoch:train:4401-4500batch: iter_time=1.420e-04, forward_time=0.105, loss_ctc=45.299, loss_att=52.068, acc=0.740, loss=50.037, backward_time=0.098, grad_norm=34.935, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.114e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:27:05,432 (trainer:737) INFO: 16epoch:train:4501-4600batch: iter_time=1.353e-04, forward_time=0.103, loss_ctc=43.325, loss_att=45.859, acc=0.729, loss=45.099, backward_time=0.097, grad_norm=34.684, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.113e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:27:46,806 (trainer:737) INFO: 16epoch:train:4601-4700batch: iter_time=1.455e-04, forward_time=0.104, loss_ctc=43.723, loss_att=53.245, acc=0.711, loss=50.388, backward_time=0.097, grad_norm=34.759, clip=100.000, loss_scale=3.489e+34, optim_step_time=0.029, optim0_lr0=5.112e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 21:28:27,657 (trainer:737) INFO: 16epoch:train:4701-4800batch: iter_time=1.657e-04, forward_time=0.104, loss_ctc=44.886, loss_att=47.109, acc=0.746, loss=46.442, backward_time=0.097, grad_norm=32.996, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.110e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:29:00,986 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 21:29:08,723 (trainer:737) INFO: 16epoch:train:4801-4900batch: iter_time=1.397e-04, forward_time=0.104, loss_ctc=46.942, loss_att=52.720, acc=0.735, loss=50.986, backward_time=0.098, grad_norm=33.689, clip=100.000, loss_scale=3.755e+34, optim_step_time=0.030, optim0_lr0=5.109e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:29:49,845 (trainer:737) INFO: 16epoch:train:4901-5000batch: iter_time=1.351e-04, forward_time=0.104, loss_ctc=54.245, loss_att=56.557, acc=0.724, loss=55.863, backward_time=0.098, grad_norm=42.622, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.108e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:29:57,657 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc02:0/16] 2024-01-13 21:30:16,705 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 21:30:20,267 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a34524a00>)
+[gpuc02:0/16] 2024-01-13 21:30:20,267 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc02:0/16] 2024-01-13 21:30:20,270 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 21:35:16,461 (trainer:737) INFO: 16epoch:train:5001-5100batch: iter_time=2.567, forward_time=0.124, loss_ctc=44.022, loss_att=48.099, acc=0.732, loss=46.876, backward_time=0.101, grad_norm=32.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.107e-04, train_time=3.266
+[gpuc02:0/16] 2024-01-13 21:35:57,394 (trainer:737) INFO: 16epoch:train:5101-5200batch: iter_time=1.675e-04, forward_time=0.104, loss_ctc=50.366, loss_att=59.486, acc=0.707, loss=56.750, backward_time=0.098, grad_norm=46.407, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.106e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:36:38,595 (trainer:737) INFO: 16epoch:train:5201-5300batch: iter_time=1.505e-04, forward_time=0.104, loss_ctc=52.474, loss_att=58.051, acc=0.707, loss=56.378, backward_time=0.098, grad_norm=43.497, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.105e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 21:37:19,414 (trainer:737) INFO: 16epoch:train:5301-5400batch: iter_time=1.886e-04, forward_time=0.104, loss_ctc=43.986, loss_att=56.553, acc=0.724, loss=52.783, backward_time=0.098, grad_norm=33.324, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.104e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:38:00,457 (trainer:737) INFO: 16epoch:train:5401-5500batch: iter_time=1.770e-04, forward_time=0.105, loss_ctc=50.339, loss_att=59.215, acc=0.742, loss=56.552, backward_time=0.099, grad_norm=35.115, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.103e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:38:41,520 (trainer:737) INFO: 16epoch:train:5501-5600batch: iter_time=2.217e-04, forward_time=0.104, loss_ctc=41.420, loss_att=52.952, acc=0.735, loss=49.492, backward_time=0.098, grad_norm=32.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.102e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:39:22,664 (trainer:737) INFO: 16epoch:train:5601-5700batch: iter_time=1.881e-04, forward_time=0.105, loss_ctc=49.145, loss_att=56.607, acc=0.741, loss=54.368, backward_time=0.099, grad_norm=34.903, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.100e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:40:04,013 (trainer:737) INFO: 16epoch:train:5701-5800batch: iter_time=1.948e-04, forward_time=0.104, loss_ctc=46.097, loss_att=48.251, acc=0.730, loss=47.605, backward_time=0.097, grad_norm=35.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.099e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 21:40:45,907 (trainer:737) INFO: 16epoch:train:5801-5900batch: iter_time=1.636e-04, forward_time=0.106, loss_ctc=42.850, loss_att=49.369, acc=0.728, loss=47.413, backward_time=0.098, grad_norm=33.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.098e-04, train_time=0.419
+[gpuc02:0/16] 2024-01-13 21:41:27,237 (trainer:737) INFO: 16epoch:train:5901-6000batch: iter_time=1.789e-04, forward_time=0.103, loss_ctc=41.137, loss_att=46.721, acc=0.733, loss=45.046, backward_time=0.098, grad_norm=32.412, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.097e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 21:42:08,418 (trainer:737) INFO: 16epoch:train:6001-6100batch: iter_time=1.947e-04, forward_time=0.104, loss_ctc=44.258, loss_att=46.472, acc=0.755, loss=45.808, backward_time=0.098, grad_norm=32.187, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.096e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 21:42:49,371 (trainer:737) INFO: 16epoch:train:6101-6200batch: iter_time=1.542e-04, forward_time=0.104, loss_ctc=49.984, loss_att=52.837, acc=0.724, loss=51.981, backward_time=0.098, grad_norm=36.181, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.095e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:43:16,636 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc02:0/16] 2024-01-13 21:43:37,146 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 21:43:41,022 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39b28655a0>)
+[gpuc02:0/16] 2024-01-13 21:43:41,022 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc02:0/16] 2024-01-13 21:43:41,026 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 21:48:20,923 (trainer:737) INFO: 16epoch:train:6201-6300batch: iter_time=2.751, forward_time=0.142, loss_ctc=52.257, loss_att=63.382, acc=0.710, loss=60.044, backward_time=0.104, grad_norm=43.163, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.032, optim0_lr0=5.094e-04, train_time=3.315
+[gpuc02:0/16] 2024-01-13 21:49:01,838 (trainer:737) INFO: 16epoch:train:6301-6400batch: iter_time=1.133e-04, forward_time=0.104, loss_ctc=52.845, loss_att=56.511, acc=0.712, loss=55.411, backward_time=0.098, grad_norm=42.789, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.093e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:49:42,644 (trainer:737) INFO: 16epoch:train:6401-6500batch: iter_time=1.136e-04, forward_time=0.103, loss_ctc=45.111, loss_att=54.934, acc=0.698, loss=51.987, backward_time=0.098, grad_norm=38.507, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.092e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:50:23,609 (trainer:737) INFO: 16epoch:train:6501-6600batch: iter_time=1.265e-04, forward_time=0.104, loss_ctc=48.951, loss_att=58.820, acc=0.728, loss=55.859, backward_time=0.098, grad_norm=40.525, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.091e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:51:04,460 (trainer:737) INFO: 16epoch:train:6601-6700batch: iter_time=1.253e-04, forward_time=0.104, loss_ctc=46.855, loss_att=57.091, acc=0.717, loss=54.020, backward_time=0.098, grad_norm=35.629, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.089e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:51:45,504 (trainer:737) INFO: 16epoch:train:6701-6800batch: iter_time=1.232e-04, forward_time=0.105, loss_ctc=48.794, loss_att=61.555, acc=0.728, loss=57.727, backward_time=0.098, grad_norm=33.296, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.088e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 21:52:26,645 (trainer:737) INFO: 16epoch:train:6801-6900batch: iter_time=1.556e-04, forward_time=0.104, loss_ctc=45.785, loss_att=52.898, acc=0.730, loss=50.764, backward_time=0.098, grad_norm=34.675, clip=100.000, loss_scale=2.472e+34, optim_step_time=0.030, optim0_lr0=5.087e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:53:07,470 (trainer:737) INFO: 16epoch:train:6901-7000batch: iter_time=1.891e-04, forward_time=0.104, loss_ctc=45.085, loss_att=51.346, acc=0.736, loss=49.468, backward_time=0.097, grad_norm=35.267, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.086e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:53:48,401 (trainer:737) INFO: 16epoch:train:7001-7100batch: iter_time=1.978e-04, forward_time=0.103, loss_ctc=42.626, loss_att=43.479, acc=0.728, loss=43.223, backward_time=0.097, grad_norm=34.456, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.085e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:54:29,045 (trainer:737) INFO: 16epoch:train:7101-7200batch: iter_time=1.565e-04, forward_time=0.103, loss_ctc=43.517, loss_att=55.528, acc=0.688, loss=51.925, backward_time=0.097, grad_norm=35.776, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.084e-04, train_time=0.406
+[gpuc02:0/16] 2024-01-13 21:55:09,944 (trainer:737) INFO: 16epoch:train:7201-7300batch: iter_time=1.630e-04, forward_time=0.104, loss_ctc=44.527, loss_att=47.800, acc=0.733, loss=46.818, backward_time=0.097, grad_norm=32.924, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.083e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 21:55:51,102 (trainer:737) INFO: 16epoch:train:7301-7400batch: iter_time=1.847e-04, forward_time=0.104, loss_ctc=46.783, loss_att=52.943, acc=0.728, loss=51.095, backward_time=0.097, grad_norm=35.761, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.082e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 21:56:31,947 (trainer:737) INFO: 16epoch:train:7401-7500batch: iter_time=1.584e-04, forward_time=0.104, loss_ctc=53.776, loss_att=55.991, acc=0.725, loss=55.326, backward_time=0.097, grad_norm=42.521, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.081e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 21:56:39,005 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc02:0/16] 2024-01-13 21:56:58,237 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 21:57:01,909 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39e2685f00>)
+[gpuc02:0/16] 2024-01-13 21:57:01,909 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc02:0/16] 2024-01-13 21:57:01,912 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 22:01:56,740 (trainer:737) INFO: 16epoch:train:7501-7600batch: iter_time=2.528, forward_time=0.104, loss_ctc=43.965, loss_att=49.905, acc=0.731, loss=48.123, backward_time=0.098, grad_norm=32.982, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.080e-04, train_time=3.248
+[gpuc02:0/16] 2024-01-13 22:02:37,876 (trainer:737) INFO: 16epoch:train:7601-7700batch: iter_time=1.732e-04, forward_time=0.103, loss_ctc=49.719, loss_att=60.010, acc=0.707, loss=56.923, backward_time=0.099, grad_norm=43.923, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.079e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:03:18,854 (trainer:737) INFO: 16epoch:train:7701-7800batch: iter_time=1.931e-04, forward_time=0.104, loss_ctc=51.522, loss_att=58.222, acc=0.708, loss=56.212, backward_time=0.099, grad_norm=44.710, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.077e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:03:59,737 (trainer:737) INFO: 16epoch:train:7801-7900batch: iter_time=1.769e-04, forward_time=0.104, loss_ctc=43.554, loss_att=56.676, acc=0.723, loss=52.740, backward_time=0.098, grad_norm=32.265, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.076e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:04:40,795 (trainer:737) INFO: 16epoch:train:7901-8000batch: iter_time=1.689e-04, forward_time=0.105, loss_ctc=50.447, loss_att=59.213, acc=0.741, loss=56.583, backward_time=0.100, grad_norm=36.521, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.075e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:05:21,655 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 22:05:21,662 (trainer:737) INFO: 16epoch:train:8001-8100batch: iter_time=1.765e-04, forward_time=0.104, loss_ctc=41.451, loss_att=53.452, acc=0.736, loss=49.852, backward_time=0.099, grad_norm=32.508, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.074e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:06:02,975 (trainer:737) INFO: 16epoch:train:8101-8200batch: iter_time=1.455e-04, forward_time=0.105, loss_ctc=49.003, loss_att=56.506, acc=0.742, loss=54.255, backward_time=0.099, grad_norm=34.551, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.073e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 22:06:43,790 (trainer:737) INFO: 16epoch:train:8201-8300batch: iter_time=1.809e-04, forward_time=0.103, loss_ctc=44.917, loss_att=48.122, acc=0.731, loss=47.161, backward_time=0.098, grad_norm=34.552, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.072e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:07:24,645 (trainer:737) INFO: 16epoch:train:8301-8400batch: iter_time=2.060e-04, forward_time=0.103, loss_ctc=42.402, loss_att=49.395, acc=0.730, loss=47.297, backward_time=0.098, grad_norm=35.489, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.071e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:08:05,463 (trainer:737) INFO: 16epoch:train:8401-8500batch: iter_time=2.217e-04, forward_time=0.103, loss_ctc=41.344, loss_att=47.239, acc=0.732, loss=45.471, backward_time=0.098, grad_norm=32.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.070e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:08:46,465 (trainer:737) INFO: 16epoch:train:8501-8600batch: iter_time=1.780e-04, forward_time=0.105, loss_ctc=43.811, loss_att=46.590, acc=0.755, loss=45.756, backward_time=0.098, grad_norm=31.729, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.069e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:09:27,414 (trainer:737) INFO: 16epoch:train:8601-8700batch: iter_time=1.719e-04, forward_time=0.104, loss_ctc=49.596, loss_att=52.803, acc=0.725, loss=51.841, backward_time=0.098, grad_norm=36.717, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.068e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:09:54,882 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc02:0/16] 2024-01-13 22:10:14,931 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 22:10:18,763 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39df83a1d0>)
+[gpuc02:0/16] 2024-01-13 22:10:18,764 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc02:0/16] 2024-01-13 22:10:18,767 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 22:15:05,422 (trainer:737) INFO: 16epoch:train:8701-8800batch: iter_time=2.617, forward_time=0.104, loss_ctc=51.411, loss_att=62.037, acc=0.710, loss=58.849, backward_time=0.098, grad_norm=42.150, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.067e-04, train_time=3.380
+[gpuc02:0/16] 2024-01-13 22:15:46,572 (trainer:737) INFO: 16epoch:train:8801-8900batch: iter_time=1.643e-04, forward_time=0.103, loss_ctc=50.282, loss_att=53.975, acc=0.716, loss=52.867, backward_time=0.097, grad_norm=41.267, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.065e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:16:27,669 (trainer:737) INFO: 16epoch:train:8901-9000batch: iter_time=1.609e-04, forward_time=0.104, loss_ctc=44.964, loss_att=54.149, acc=0.700, loss=51.394, backward_time=0.098, grad_norm=37.599, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.064e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:17:09,121 (trainer:737) INFO: 16epoch:train:9001-9100batch: iter_time=2.155e-04, forward_time=0.104, loss_ctc=47.817, loss_att=57.849, acc=0.730, loss=54.839, backward_time=0.098, grad_norm=38.650, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.063e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 22:17:50,650 (trainer:737) INFO: 16epoch:train:9101-9200batch: iter_time=1.941e-04, forward_time=0.104, loss_ctc=46.604, loss_att=56.506, acc=0.717, loss=53.535, backward_time=0.098, grad_norm=34.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.062e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 22:18:32,261 (trainer:737) INFO: 16epoch:train:9201-9300batch: iter_time=1.536e-04, forward_time=0.105, loss_ctc=49.125, loss_att=61.679, acc=0.730, loss=57.912, backward_time=0.099, grad_norm=35.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.061e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 22:19:13,199 (trainer:737) INFO: 16epoch:train:9301-9400batch: iter_time=1.579e-04, forward_time=0.105, loss_ctc=45.581, loss_att=53.189, acc=0.729, loss=50.907, backward_time=0.098, grad_norm=34.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.060e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:19:54,125 (trainer:737) INFO: 16epoch:train:9401-9500batch: iter_time=1.720e-04, forward_time=0.105, loss_ctc=44.484, loss_att=51.013, acc=0.737, loss=49.055, backward_time=0.098, grad_norm=35.507, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.059e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:20:35,090 (trainer:737) INFO: 16epoch:train:9501-9600batch: iter_time=1.859e-04, forward_time=0.107, loss_ctc=42.681, loss_att=43.457, acc=0.726, loss=43.224, backward_time=0.097, grad_norm=33.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.058e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:21:16,358 (trainer:737) INFO: 16epoch:train:9601-9700batch: iter_time=1.825e-04, forward_time=0.103, loss_ctc=43.136, loss_att=54.207, acc=0.691, loss=50.886, backward_time=0.098, grad_norm=37.765, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.057e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 22:21:57,135 (trainer:737) INFO: 16epoch:train:9701-9800batch: iter_time=1.789e-04, forward_time=0.105, loss_ctc=44.125, loss_att=47.146, acc=0.736, loss=46.240, backward_time=0.098, grad_norm=32.743, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.056e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:22:38,088 (trainer:737) INFO: 16epoch:train:9801-9900batch: iter_time=1.593e-04, forward_time=0.106, loss_ctc=46.444, loss_att=52.507, acc=0.726, loss=50.688, backward_time=0.098, grad_norm=35.293, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.055e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:23:18,856 (trainer:737) INFO: 16epoch:train:9901-10000batch: iter_time=1.353e-04, forward_time=0.104, loss_ctc=53.287, loss_att=57.260, acc=0.722, loss=56.068, backward_time=0.098, grad_norm=41.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.054e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 22:23:23,094 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc02:0/16] 2024-01-13 22:23:42,793 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 22:23:46,555 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a0c94fe20>)
+[gpuc02:0/16] 2024-01-13 22:23:46,555 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc02:0/16] 2024-01-13 22:23:46,559 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 22:28:49,700 (trainer:737) INFO: 16epoch:train:10001-10100batch: iter_time=2.659, forward_time=0.104, loss_ctc=43.457, loss_att=46.991, acc=0.728, loss=45.931, backward_time=0.097, grad_norm=30.992, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.053e-04, train_time=3.308
+[gpuc02:0/16] 2024-01-13 22:29:21,497 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 22:29:30,969 (trainer:737) INFO: 16epoch:train:10101-10200batch: iter_time=1.697e-04, forward_time=0.104, loss_ctc=49.316, loss_att=58.101, acc=0.707, loss=55.465, backward_time=0.099, grad_norm=45.209, clip=100.000, loss_scale=3.671e+34, optim_step_time=0.031, optim0_lr0=5.051e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 22:30:14,867 (trainer:737) INFO: 16epoch:train:10201-10300batch: iter_time=1.648e-04, forward_time=0.104, loss_ctc=51.562, loss_att=57.721, acc=0.706, loss=55.874, backward_time=0.098, grad_norm=44.766, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.050e-04, train_time=0.439
+[gpuc02:0/16] 2024-01-13 22:30:55,925 (trainer:737) INFO: 16epoch:train:10301-10400batch: iter_time=2.086e-04, forward_time=0.104, loss_ctc=43.210, loss_att=55.453, acc=0.719, loss=51.780, backward_time=0.097, grad_norm=33.021, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.049e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:31:36,902 (trainer:737) INFO: 16epoch:train:10401-10500batch: iter_time=1.939e-04, forward_time=0.105, loss_ctc=49.907, loss_att=59.066, acc=0.730, loss=56.318, backward_time=0.098, grad_norm=33.636, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.048e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:32:17,707 (trainer:737) INFO: 16epoch:train:10501-10600batch: iter_time=1.963e-04, forward_time=0.104, loss_ctc=40.987, loss_att=50.306, acc=0.733, loss=47.510, backward_time=0.097, grad_norm=31.981, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.047e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:32:58,860 (trainer:737) INFO: 16epoch:train:10601-10700batch: iter_time=1.269e-04, forward_time=0.106, loss_ctc=48.445, loss_att=56.067, acc=0.738, loss=53.781, backward_time=0.100, grad_norm=38.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.046e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:33:40,047 (trainer:737) INFO: 16epoch:train:10701-10800batch: iter_time=1.425e-04, forward_time=0.104, loss_ctc=45.243, loss_att=47.553, acc=0.726, loss=46.860, backward_time=0.099, grad_norm=34.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.032, optim0_lr0=5.045e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 22:34:20,820 (trainer:737) INFO: 16epoch:train:10801-10900batch: iter_time=1.353e-04, forward_time=0.105, loss_ctc=42.581, loss_att=47.971, acc=0.717, loss=46.354, backward_time=0.099, grad_norm=34.109, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.044e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 22:35:01,938 (trainer:737) INFO: 16epoch:train:10901-11000batch: iter_time=1.438e-04, forward_time=0.104, loss_ctc=41.010, loss_att=47.407, acc=0.717, loss=45.488, backward_time=0.099, grad_norm=33.625, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.043e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:35:43,457 (trainer:737) INFO: 16epoch:train:11001-11100batch: iter_time=1.248e-04, forward_time=0.104, loss_ctc=43.731, loss_att=46.979, acc=0.740, loss=46.005, backward_time=0.099, grad_norm=33.540, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.042e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 22:36:24,872 (trainer:737) INFO: 16epoch:train:11101-11200batch: iter_time=1.391e-04, forward_time=0.105, loss_ctc=49.915, loss_att=52.208, acc=0.728, loss=51.520, backward_time=0.100, grad_norm=36.950, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.041e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 22:36:53,073 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc02:0/16] 2024-01-13 22:37:12,714 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 22:37:16,450 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f40f44fbac0>)
+[gpuc02:0/16] 2024-01-13 22:37:16,450 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc02:0/16] 2024-01-13 22:37:16,453 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 22:41:55,233 (trainer:737) INFO: 16epoch:train:11201-11300batch: iter_time=2.714, forward_time=0.104, loss_ctc=50.672, loss_att=60.568, acc=0.714, loss=57.599, backward_time=0.099, grad_norm=41.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.031, optim0_lr0=5.040e-04, train_time=3.303
+[gpuc02:0/16] 2024-01-13 22:42:36,116 (trainer:737) INFO: 16epoch:train:11301-11400batch: iter_time=1.501e-04, forward_time=0.104, loss_ctc=51.227, loss_att=52.948, acc=0.721, loss=52.432, backward_time=0.097, grad_norm=44.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.039e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:43:17,053 (trainer:737) INFO: 16epoch:train:11401-11500batch: iter_time=1.573e-04, forward_time=0.103, loss_ctc=44.333, loss_att=52.134, acc=0.703, loss=49.793, backward_time=0.097, grad_norm=38.506, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.038e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:43:58,033 (trainer:737) INFO: 16epoch:train:11501-11600batch: iter_time=1.499e-04, forward_time=0.104, loss_ctc=47.368, loss_att=56.331, acc=0.732, loss=53.642, backward_time=0.097, grad_norm=38.326, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.036e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:44:38,899 (trainer:737) INFO: 16epoch:train:11601-11700batch: iter_time=1.648e-04, forward_time=0.104, loss_ctc=46.659, loss_att=55.532, acc=0.719, loss=52.870, backward_time=0.097, grad_norm=33.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.035e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:45:20,251 (trainer:737) INFO: 16epoch:train:11701-11800batch: iter_time=1.865e-04, forward_time=0.104, loss_ctc=48.821, loss_att=61.038, acc=0.731, loss=57.373, backward_time=0.098, grad_norm=35.065, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.034e-04, train_time=0.413
+[gpuc02:0/16] 2024-01-13 22:46:01,192 (trainer:737) INFO: 16epoch:train:11801-11900batch: iter_time=1.652e-04, forward_time=0.104, loss_ctc=44.972, loss_att=51.735, acc=0.732, loss=49.706, backward_time=0.097, grad_norm=34.914, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.033e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:46:42,066 (trainer:737) INFO: 16epoch:train:11901-12000batch: iter_time=1.638e-04, forward_time=0.103, loss_ctc=44.949, loss_att=51.316, acc=0.738, loss=49.406, backward_time=0.097, grad_norm=34.310, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.032e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 22:47:22,795 (trainer:737) INFO: 16epoch:train:12001-12100batch: iter_time=1.941e-04, forward_time=0.103, loss_ctc=42.239, loss_att=43.003, acc=0.728, loss=42.774, backward_time=0.096, grad_norm=33.553, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.031e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 22:48:03,536 (trainer:737) INFO: 16epoch:train:12101-12200batch: iter_time=1.938e-04, forward_time=0.103, loss_ctc=43.090, loss_att=53.423, acc=0.696, loss=50.323, backward_time=0.096, grad_norm=36.126, clip=100.000, loss_scale=2.555e+34, optim_step_time=0.030, optim0_lr0=5.030e-04, train_time=0.407
+[gpuc02:0/16] 2024-01-13 22:48:44,380 (trainer:737) INFO: 16epoch:train:12201-12300batch: iter_time=1.927e-04, forward_time=0.105, loss_ctc=44.412, loss_att=47.097, acc=0.737, loss=46.292, backward_time=0.097, grad_norm=32.184, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.029e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:48:47,201 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc02:0/16] 2024-01-13 22:49:25,228 (trainer:737) INFO: 16epoch:train:12301-12400batch: iter_time=1.776e-04, forward_time=0.105, loss_ctc=46.489, loss_att=52.066, acc=0.729, loss=50.393, backward_time=0.097, grad_norm=35.569, clip=100.000, loss_scale=2.203e+34, optim_step_time=0.030, optim0_lr0=5.028e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 22:50:06,309 (trainer:737) INFO: 16epoch:train:12401-12500batch: iter_time=1.654e-04, forward_time=0.103, loss_ctc=53.076, loss_att=56.580, acc=0.723, loss=55.529, backward_time=0.097, grad_norm=42.934, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.027e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:50:10,109 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc02:0/16] 2024-01-13 22:50:29,230 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 22:50:32,970 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f3a0d0f3c70>)
+[gpuc02:0/16] 2024-01-13 22:50:32,970 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc02:0/16] 2024-01-13 22:50:32,974 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 22:55:43,484 (trainer:737) INFO: 16epoch:train:12501-12600batch: iter_time=2.747, forward_time=0.105, loss_ctc=43.350, loss_att=51.437, acc=0.729, loss=49.011, backward_time=0.098, grad_norm=32.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.026e-04, train_time=3.372
+[gpuc02:0/16] 2024-01-13 22:56:24,476 (trainer:737) INFO: 16epoch:train:12601-12700batch: iter_time=1.760e-04, forward_time=0.104, loss_ctc=49.608, loss_att=60.073, acc=0.709, loss=56.933, backward_time=0.098, grad_norm=44.345, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.025e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 22:57:06,051 (trainer:737) INFO: 16epoch:train:12701-12800batch: iter_time=1.652e-04, forward_time=0.104, loss_ctc=50.949, loss_att=57.989, acc=0.709, loss=55.877, backward_time=0.098, grad_norm=45.341, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.024e-04, train_time=0.416
+[gpuc02:0/16] 2024-01-13 22:57:47,222 (trainer:737) INFO: 16epoch:train:12801-12900batch: iter_time=1.910e-04, forward_time=0.103, loss_ctc=43.382, loss_att=57.482, acc=0.723, loss=53.252, backward_time=0.098, grad_norm=33.201, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.023e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:58:28,608 (trainer:737) INFO: 16epoch:train:12901-13000batch: iter_time=1.912e-04, forward_time=0.106, loss_ctc=49.826, loss_att=59.306, acc=0.743, loss=56.462, backward_time=0.099, grad_norm=35.295, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.022e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 22:59:09,752 (trainer:737) INFO: 16epoch:train:13001-13100batch: iter_time=1.747e-04, forward_time=0.104, loss_ctc=40.907, loss_att=53.024, acc=0.737, loss=49.389, backward_time=0.098, grad_norm=31.559, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.021e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 22:59:50,723 (trainer:737) INFO: 16epoch:train:13101-13200batch: iter_time=1.724e-04, forward_time=0.105, loss_ctc=48.168, loss_att=56.500, acc=0.744, loss=54.000, backward_time=0.098, grad_norm=34.084, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.020e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:00:31,504 (trainer:737) INFO: 16epoch:train:13201-13300batch: iter_time=2.068e-04, forward_time=0.104, loss_ctc=45.002, loss_att=48.535, acc=0.729, loss=47.475, backward_time=0.097, grad_norm=35.203, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.018e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 23:01:12,339 (trainer:737) INFO: 16epoch:train:13301-13400batch: iter_time=1.960e-04, forward_time=0.104, loss_ctc=43.203, loss_att=50.544, acc=0.729, loss=48.342, backward_time=0.097, grad_norm=33.985, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.017e-04, train_time=0.408
+[gpuc02:0/16] 2024-01-13 23:01:53,720 (trainer:737) INFO: 16epoch:train:13401-13500batch: iter_time=1.879e-04, forward_time=0.104, loss_ctc=40.927, loss_att=47.321, acc=0.733, loss=45.403, backward_time=0.097, grad_norm=31.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.016e-04, train_time=0.414
+[gpuc02:0/16] 2024-01-13 23:02:34,739 (trainer:737) INFO: 16epoch:train:13501-13600batch: iter_time=1.999e-04, forward_time=0.105, loss_ctc=43.606, loss_att=46.653, acc=0.756, loss=45.739, backward_time=0.097, grad_norm=31.894, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.015e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 23:03:15,654 (trainer:737) INFO: 16epoch:train:13601-13700batch: iter_time=1.953e-04, forward_time=0.104, loss_ctc=49.494, loss_att=52.297, acc=0.727, loss=51.456, backward_time=0.097, grad_norm=35.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.014e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:03:42,837 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc02:0/16] 2024-01-13 23:04:02,115 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc02:0/16] 2024-01-13 23:04:06,044 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7f39b68b7f40>)
+[gpuc02:0/16] 2024-01-13 23:04:06,044 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc02:0/16] 2024-01-13 23:04:06,047 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc02:0/16] 2024-01-13 23:09:27,411 (trainer:737) INFO: 16epoch:train:13701-13800batch: iter_time=2.584, forward_time=0.104, loss_ctc=50.435, loss_att=62.446, acc=0.713, loss=58.843, backward_time=0.097, grad_norm=42.297, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.013e-04, train_time=3.717
+[gpuc02:0/16] 2024-01-13 23:10:08,319 (trainer:737) INFO: 16epoch:train:13801-13900batch: iter_time=1.587e-04, forward_time=0.104, loss_ctc=50.781, loss_att=53.739, acc=0.720, loss=52.851, backward_time=0.097, grad_norm=44.728, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.012e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:10:49,499 (trainer:737) INFO: 16epoch:train:13901-14000batch: iter_time=1.391e-04, forward_time=0.104, loss_ctc=43.943, loss_att=53.173, acc=0.704, loss=50.404, backward_time=0.097, grad_norm=36.564, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.011e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 23:11:30,633 (trainer:737) INFO: 16epoch:train:14001-14100batch: iter_time=1.645e-04, forward_time=0.103, loss_ctc=47.714, loss_att=57.435, acc=0.731, loss=54.519, backward_time=0.097, grad_norm=39.249, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.010e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 23:12:11,624 (trainer:737) INFO: 16epoch:train:14101-14200batch: iter_time=1.811e-04, forward_time=0.105, loss_ctc=46.440, loss_att=56.037, acc=0.720, loss=53.158, backward_time=0.097, grad_norm=34.604, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.030, optim0_lr0=5.009e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 23:12:53,111 (trainer:737) INFO: 16epoch:train:14201-14300batch: iter_time=1.647e-04, forward_time=0.104, loss_ctc=48.248, loss_att=60.734, acc=0.732, loss=56.988, backward_time=0.098, grad_norm=34.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.029, optim0_lr0=5.008e-04, train_time=0.415
+[gpuc02:0/16] 2024-01-13 23:13:34,192 (trainer:737) INFO: 16epoch:train:14301-14400batch: iter_time=1.709e-04, forward_time=0.104, loss_ctc=44.834, loss_att=52.410, acc=0.731, loss=50.137, backward_time=0.097, grad_norm=35.167, clip=100.000, loss_scale=4.008e+34, optim_step_time=0.029, optim0_lr0=5.007e-04, train_time=0.411
+[gpuc02:0/16] 2024-01-13 23:14:15,157 (trainer:737) INFO: 16epoch:train:14401-14500batch: iter_time=1.637e-04, forward_time=0.104, loss_ctc=44.224, loss_att=50.929, acc=0.739, loss=48.917, backward_time=0.097, grad_norm=36.990, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.006e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:14:56,099 (trainer:737) INFO: 16epoch:train:14501-14600batch: iter_time=1.548e-04, forward_time=0.104, loss_ctc=42.897, loss_att=43.690, acc=0.729, loss=43.452, backward_time=0.096, grad_norm=34.588, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.005e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:15:37,314 (trainer:737) INFO: 16epoch:train:14601-14700batch: iter_time=1.447e-04, forward_time=0.103, loss_ctc=42.928, loss_att=53.683, acc=0.694, loss=50.457, backward_time=0.097, grad_norm=35.296, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.004e-04, train_time=0.412
+[gpuc02:0/16] 2024-01-13 23:16:18,271 (trainer:737) INFO: 16epoch:train:14701-14800batch: iter_time=1.486e-04, forward_time=0.104, loss_ctc=43.751, loss_att=46.789, acc=0.738, loss=45.877, backward_time=0.097, grad_norm=33.797, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.029, optim0_lr0=5.003e-04, train_time=0.409
+[gpuc02:0/16] 2024-01-13 23:16:59,257 (trainer:737) INFO: 16epoch:train:14801-14900batch: iter_time=1.515e-04, forward_time=0.105, loss_ctc=46.166, loss_att=51.230, acc=0.730, loss=49.711, backward_time=0.098, grad_norm=36.086, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.002e-04, train_time=0.410
+[gpuc02:0/16] 2024-01-13 23:17:40,481 (trainer:737) INFO: 16epoch:train:14901-15000batch: iter_time=1.391e-04, forward_time=0.104, loss_ctc=53.157, loss_att=57.391, acc=0.722, loss=56.121, backward_time=0.097, grad_norm=45.375, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.030, optim0_lr0=5.001e-04, train_time=0.412
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.
+slurmstepd: error: *** STEP 2855139.0 ON gpuc02 CANCELLED AT 2024-01-13T23:24:35 DUE TO TIME LIMIT ***
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..8b778daf6962a273c23980f1ad8d8337f0776ebe
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
@@ -0,0 +1,4388 @@
+# Running on gpuc04.delta.ncsa.illinois.edu
+# Started at Tue Jan 16 12:19:43 CST 2024
+# SLURMD_NODENAME=gpuc04
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=128
+# SLURM_CPUS_PER_TASK=128
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GPUS_ON_NODE=8
+# SLURM_GTIDS=0
+# SLURM_JOBID=2863201
+# SLURM_JOB_ACCOUNT=bbjs-delta-gpu
+# SLURM_JOB_CPUS_PER_NODE='128(x2)'
+# SLURM_JOB_END_TIME=1705601961
+# SLURM_JOB_GID=202
+# SLURM_JOB_GPUS=0,1,2,3,4,5,6,7
+# SLURM_JOB_ID=2863201
+# SLURM_JOB_NAME=exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/train.log
+# SLURM_JOB_NODELIST='gpuc[04,06]'
+# SLURM_JOB_NUM_NODES=2
+# SLURM_JOB_PARTITION=gpuA100x8
+# SLURM_JOB_QOS=bbjs-delta-gpu
+# SLURM_JOB_START_TIME=1705429161
+# SLURM_JOB_UID=68077
+# SLURM_JOB_USER=peng6
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_NODE=2000000
+# SLURM_NNODES=2
+# SLURM_NODEID=0
+# SLURM_NODELIST='gpuc[04,06]'
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1
+# SLURM_SUBMIT_HOST=dt-login01.delta.ncsa.illinois.edu
+# SLURM_TASKS_PER_NODE='1(x2)'
+# SLURM_TASK_PID=3119733
+# SLURM_TOPOLOGY_ADDR=ss00.ss05.gpuc04
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9984:109
+# srun --export=ALL python3 -m espnet2.bin.s2t_train --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_2e5d3388-5a62-4d9b-abf3-231f106e2517 
+/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_2e5d3388-5a62-4d9b-abf3-231f106e2517
+[gpuc04:0/16] 2024-01-16 12:21:43,253 (distributed_c10d:319) INFO: Added key: store_based_barrier_key:1 to store for rank: 0
+[gpuc04:0/16] 2024-01-16 12:21:53,265 (distributed_c10d:337) INFO: Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=16, worker_count=8, timeout=0:30:00)
+[gpuc04:0/16] 2024-01-16 12:22:03,272 (distributed_c10d:337) INFO: Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=16, worker_count=8, timeout=0:30:00)
+[gpuc04:0/16] 2024-01-16 12:22:13,275 (distributed_c10d:337) INFO: Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=16, worker_count=8, timeout=0:30:00)
+/scratch/bbjs/peng6/espnet-whisper-public/tools/miniconda/envs/espnet/bin/python3 /scratch/bbjs/peng6/espnet-whisper-public/espnet2/bin/s2t_train.py --use_preprocessor true --bpemodel data/token_list/bpe_unigram50000/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram50000/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_v3/wav.scp,speech,kaldi_ark --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/speech_shape --resume true --fold_length 80000 --output_dir exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000 --config conf/train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/wav.scp,speech,kaldi_ark --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/speech_shape --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.prev,text_prev,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_prev_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text.ctc,text_ctc,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_ctc_shape.bpe --fold_length 150 --train_data_path_and_name_and_type exp/s2t_stats_raw_bpe50000/splits12/text,text,text --train_shape_file exp/s2t_stats_raw_bpe50000/splits12/text_shape.bpe --multiple_iterator true --valid_data_path_and_name_and_type dump/raw/dev_v3/text.prev,text_prev,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_prev_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text.ctc,text_ctc,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_ctc_shape.bpe --valid_data_path_and_name_and_type dump/raw/dev_v3/text,text,text --valid_shape_file exp/s2t_stats_raw_bpe50000/valid/text_shape.bpe --ngpu 8 --multiprocessing_distributed true --dist_launcher slurm --dist_init_method file:///scratch/bbjs/peng6/espnet-whisper-public/egs2/owsm_v3.1/s2t1/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/.dist_init_2e5d3388-5a62-4d9b-abf3-231f106e2517
+[gpuc04:0/16] 2024-01-16 12:22:23,285 (distributed_c10d:337) INFO: Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=16, worker_count=8, timeout=0:30:00)
+[gpuc04:0/16] 2024-01-16 12:22:27,832 (distributed_c10d:353) INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
+[gpuc04:0/16] 2024-01-16 12:22:27,910 (s2t:464) INFO: Vocabulary size: 50002
+[gpuc04:0/16] 2024-01-16 12:22:35,227 (abs_task:1231) INFO: pytorch.version=1.13.1, cuda.available=True, cudnn.version=8500, cudnn.benchmark=False, cudnn.deterministic=True
+[gpuc04:0/16] 2024-01-16 12:22:35,232 (abs_task:1232) INFO: Model structure:
+ESPnetS2TModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=512, win_length=400, hop_length=160, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=512, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=1, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=1, axis=time)
+  )
+  (normalize): GlobalMVN(stats_file=exp/s2t_stats_raw_bpe50000/train/feats_stats.npz, norm_means=True, norm_vars=True)
+  (encoder): EBranchformerEncoder(
+    (embed): Conv2dSubsampling(
+      (conv): Sequential(
+        (0): Conv2d(1, 384, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=7296, out_features=384, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.05, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (1): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (2): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (3): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (4): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+      (5): EBranchformerEncoderLayer(
+        (attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (cgmlp): ConvolutionalGatingMLP(
+          (channel_proj1): Sequential(
+            (0): Linear(in_features=384, out_features=1536, bias=True)
+            (1): GELU(approximate='none')
+          )
+          (csgu): ConvolutionalSpatialGatingUnit(
+            (norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (conv): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+            (act): Identity()
+            (dropout): Dropout(p=0.05, inplace=False)
+          )
+          (channel_proj2): Linear(in_features=768, out_features=384, bias=True)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (feed_forward_macaron): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): Swish()
+        )
+        (norm_ff): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_ff_macaron): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mha): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_mlp): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm_final): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+        (depthwise_conv_fusion): Conv1d(768, 768, kernel_size=(31,), stride=(1,), padding=(15,), groups=768)
+        (merge_proj): Linear(in_features=768, out_features=384, bias=True)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+  )
+  (decoder): TransformerDecoder(
+    (embed): Sequential(
+      (0): Embedding(50002, 384)
+      (1): PositionalEncoding(
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+    (output_layer): Linear(in_features=384, out_features=50002, bias=True)
+    (decoders): MultiSequential(
+      (0): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (1): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (2): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (3): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (4): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+      (5): DecoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (src_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=384, out_features=384, bias=True)
+          (linear_k): Linear(in_features=384, out_features=384, bias=True)
+          (linear_v): Linear(in_features=384, out_features=384, bias=True)
+          (linear_out): Linear(in_features=384, out_features=384, bias=True)
+          (dropout): Identity()
+          (q_norm): Identity()
+          (k_norm): Identity()
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=384, out_features=1536, bias=True)
+          (w_2): Linear(in_features=1536, out_features=384, bias=True)
+          (dropout): Dropout(p=0.05, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (norm3): LayerNorm((384,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.05, inplace=False)
+      )
+    )
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=384, out_features=50002, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetS2TModel
+    Total Number of model parameters: 101.18 M
+    Number of trainable parameters: 101.18 M (100.0%)
+    Size: 404.73 MB
+    Type: torch.float32
+[gpuc04:0/16] 2024-01-16 12:22:35,232 (abs_task:1235) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.9, 0.98]
+    capturable: False
+    eps: 1e-06
+    foreach: None
+    initial_lr: 0.001
+    lr: 3.3333333333333334e-09
+    maximize: False
+    weight_decay: 0.0
+)
+[gpuc04:0/16] 2024-01-16 12:22:35,232 (abs_task:1236) INFO: Scheduler: PiecewiseLinearWarmupLR(warmup_steps_list=[0, 30000, 60000], warmup_lr_list=[0.0, 0.0001, 0.001])
+[gpuc04:0/16] 2024-01-16 12:22:35,256 (abs_task:1245) INFO: Saving the configuration in exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml
+[gpuc04:0/16] 2024-01-16 12:22:41,155 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 12:22:42,154 (abs_task:1616) INFO: [valid] dataset:
+ESPnetDataset(
+  speech: {"path": "dump/raw/dev_v3/wav.scp", "type": "kaldi_ark"}
+  text_prev: {"path": "dump/raw/dev_v3/text.prev", "type": "text"}
+  text_ctc: {"path": "dump/raw/dev_v3/text.ctc", "type": "text"}
+  text: {"path": "dump/raw/dev_v3/text", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe74e9cb9a0>)
+[gpuc04:0/16] 2024-01-16 12:22:42,154 (abs_task:1617) INFO: [valid] Batch sampler: UnsortedBatchSampler(N-batch=4671, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/valid/speech_shape, 
+[gpuc04:0/16] 2024-01-16 12:22:42,155 (abs_task:1618) INFO: [valid] mini-batch sizes summary: N-batch=4671, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 12:22:47,903 (trainer:159) INFO: The training was resumed using exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/checkpoint.pth
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+[gpuc04:0/16] 2024-01-16 12:22:56,194 (trainer:284) INFO: 34/45epoch started
+[gpuc04:0/16] 2024-01-16 12:22:56,243 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-16 12:23:15,381 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 12:23:18,958 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe68bfcd390>)
+[gpuc04:0/16] 2024-01-16 12:23:18,958 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-16 12:23:18,962 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
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+[gpuc04:0/16] 2024-01-16 12:32:45,705 (trainer:737) INFO: 34epoch:train:701-800batch: iter_time=1.088e-04, forward_time=0.101, loss_ctc=34.050, loss_att=38.402, acc=0.773, loss=37.096, backward_time=0.096, grad_norm=35.335, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.479e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 12:33:27,156 (trainer:737) INFO: 34epoch:train:801-900batch: iter_time=1.036e-04, forward_time=0.101, loss_ctc=44.480, loss_att=46.207, acc=0.736, loss=45.689, backward_time=0.096, grad_norm=46.932, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.479e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 12:34:08,761 (trainer:737) INFO: 34epoch:train:901-1000batch: iter_time=1.090e-04, forward_time=0.102, loss_ctc=50.127, loss_att=56.351, acc=0.729, loss=54.484, backward_time=0.097, grad_norm=52.637, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.479e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 12:34:50,536 (trainer:737) INFO: 34epoch:train:1001-1100batch: iter_time=1.022e-04, forward_time=0.104, loss_ctc=43.226, loss_att=43.405, acc=0.749, loss=43.352, backward_time=0.096, grad_norm=48.175, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.478e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 12:35:32,002 (trainer:737) INFO: 34epoch:train:1101-1200batch: iter_time=9.983e-05, forward_time=0.102, loss_ctc=43.968, loss_att=44.742, acc=0.729, loss=44.510, backward_time=0.096, grad_norm=43.065, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.478e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 12:36:04,832 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-16 12:36:24,818 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 12:36:28,409 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe68bfcdf30>)
+[gpuc04:0/16] 2024-01-16 12:36:28,409 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-16 12:36:28,412 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 12:40:28,927 (trainer:737) INFO: 34epoch:train:1201-1300batch: iter_time=2.408, forward_time=0.103, loss_ctc=45.373, loss_att=54.476, acc=0.727, loss=51.745, backward_time=0.096, grad_norm=43.973, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.478e-04, train_time=2.969
+[gpuc04:0/16] 2024-01-16 12:41:10,916 (trainer:737) INFO: 34epoch:train:1301-1400batch: iter_time=9.258e-05, forward_time=0.103, loss_ctc=51.696, loss_att=56.843, acc=0.734, loss=55.299, backward_time=0.096, grad_norm=51.253, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.477e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 12:41:52,671 (trainer:737) INFO: 34epoch:train:1401-1500batch: iter_time=9.773e-05, forward_time=0.102, loss_ctc=41.554, loss_att=43.289, acc=0.738, loss=42.769, backward_time=0.095, grad_norm=38.798, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.477e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 12:42:37,084 (trainer:737) INFO: 34epoch:train:1501-1600batch: iter_time=9.513e-05, forward_time=0.101, loss_ctc=39.511, loss_att=42.134, acc=0.750, loss=41.347, backward_time=0.096, grad_norm=39.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.477e-04, train_time=0.444
+[gpuc04:0/16] 2024-01-16 12:43:19,588 (trainer:737) INFO: 34epoch:train:1601-1700batch: iter_time=1.007e-04, forward_time=0.103, loss_ctc=45.982, loss_att=57.980, acc=0.729, loss=54.381, backward_time=0.097, grad_norm=43.116, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.476e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 12:44:01,431 (trainer:737) INFO: 34epoch:train:1701-1800batch: iter_time=1.021e-04, forward_time=0.102, loss_ctc=44.968, loss_att=55.485, acc=0.714, loss=52.330, backward_time=0.096, grad_norm=43.293, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.476e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 12:44:43,372 (trainer:737) INFO: 34epoch:train:1801-1900batch: iter_time=9.788e-05, forward_time=0.103, loss_ctc=38.162, loss_att=46.586, acc=0.777, loss=44.059, backward_time=0.097, grad_norm=38.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.475e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 12:45:25,175 (trainer:737) INFO: 34epoch:train:1901-2000batch: iter_time=1.005e-04, forward_time=0.101, loss_ctc=37.443, loss_att=41.276, acc=0.778, loss=40.126, backward_time=0.096, grad_norm=36.289, clip=100.000, loss_scale=2.160e+34, optim_step_time=0.038, optim0_lr0=3.475e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 12:45:51,871 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 12:46:06,859 (trainer:737) INFO: 34epoch:train:2001-2100batch: iter_time=9.998e-05, forward_time=0.101, loss_ctc=39.907, loss_att=45.404, acc=0.747, loss=43.755, backward_time=0.095, grad_norm=42.217, clip=100.000, loss_scale=3.399e+34, optim_step_time=0.038, optim0_lr0=3.475e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 12:46:48,800 (trainer:737) INFO: 34epoch:train:2101-2200batch: iter_time=9.738e-05, forward_time=0.103, loss_ctc=45.229, loss_att=55.175, acc=0.731, loss=52.191, backward_time=0.096, grad_norm=44.477, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.474e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 12:47:30,758 (trainer:737) INFO: 34epoch:train:2201-2300batch: iter_time=9.855e-05, forward_time=0.104, loss_ctc=47.147, loss_att=51.940, acc=0.740, loss=50.502, backward_time=0.096, grad_norm=51.274, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.474e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 12:48:12,595 (trainer:737) INFO: 34epoch:train:2301-2400batch: iter_time=9.693e-05, forward_time=0.102, loss_ctc=39.274, loss_att=40.219, acc=0.760, loss=39.936, backward_time=0.096, grad_norm=40.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.474e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 12:48:54,278 (trainer:737) INFO: 34epoch:train:2401-2500batch: iter_time=8.664e-05, forward_time=0.102, loss_ctc=42.143, loss_att=45.280, acc=0.748, loss=44.339, backward_time=0.096, grad_norm=41.632, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.473e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 12:48:58,680 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-16 12:49:17,863 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 12:49:21,807 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe745f3e350>)
+[gpuc04:0/16] 2024-01-16 12:49:21,807 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-16 12:49:21,810 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 12:53:42,457 (trainer:737) INFO: 34epoch:train:2501-2600batch: iter_time=2.387, forward_time=0.103, loss_ctc=55.830, loss_att=63.261, acc=0.711, loss=61.032, backward_time=0.096, grad_norm=53.162, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.473e-04, train_time=2.882
+[gpuc04:0/16] 2024-01-16 12:54:24,432 (trainer:737) INFO: 34epoch:train:2601-2700batch: iter_time=1.068e-04, forward_time=0.102, loss_ctc=48.433, loss_att=50.384, acc=0.737, loss=49.799, backward_time=0.096, grad_norm=43.495, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.473e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 12:55:06,171 (trainer:737) INFO: 34epoch:train:2701-2800batch: iter_time=9.123e-05, forward_time=0.101, loss_ctc=35.911, loss_att=38.303, acc=0.753, loss=37.585, backward_time=0.095, grad_norm=36.023, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.472e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 12:55:19,565 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 12:55:48,187 (trainer:737) INFO: 34epoch:train:2801-2900batch: iter_time=9.593e-05, forward_time=0.103, loss_ctc=43.361, loss_att=56.225, acc=0.730, loss=52.366, backward_time=0.096, grad_norm=42.435, clip=100.000, loss_scale=1.364e+34, optim_step_time=0.038, optim0_lr0=3.472e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 12:56:29,992 (trainer:737) INFO: 34epoch:train:2901-3000batch: iter_time=1.009e-04, forward_time=0.102, loss_ctc=43.250, loss_att=47.075, acc=0.737, loss=45.928, backward_time=0.096, grad_norm=42.652, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.472e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 12:57:11,875 (trainer:737) INFO: 34epoch:train:3001-3100batch: iter_time=9.430e-05, forward_time=0.102, loss_ctc=43.257, loss_att=54.516, acc=0.738, loss=51.139, backward_time=0.096, grad_norm=47.525, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.471e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 12:57:56,103 (trainer:737) INFO: 34epoch:train:3101-3200batch: iter_time=9.688e-05, forward_time=0.103, loss_ctc=39.747, loss_att=49.840, acc=0.771, loss=46.812, backward_time=0.097, grad_norm=38.191, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.471e-04, train_time=0.442
+[gpuc04:0/16] 2024-01-16 12:58:38,254 (trainer:737) INFO: 34epoch:train:3201-3300batch: iter_time=1.004e-04, forward_time=0.101, loss_ctc=33.522, loss_att=36.957, acc=0.783, loss=35.927, backward_time=0.095, grad_norm=33.404, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.471e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 12:59:20,059 (trainer:737) INFO: 34epoch:train:3301-3400batch: iter_time=1.033e-04, forward_time=0.103, loss_ctc=43.356, loss_att=44.366, acc=0.748, loss=44.063, backward_time=0.096, grad_norm=42.705, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.470e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:00:02,392 (trainer:737) INFO: 34epoch:train:3401-3500batch: iter_time=1.050e-04, forward_time=0.104, loss_ctc=48.267, loss_att=60.350, acc=0.723, loss=56.725, backward_time=0.097, grad_norm=48.376, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.470e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 13:00:44,229 (trainer:737) INFO: 34epoch:train:3501-3600batch: iter_time=1.040e-04, forward_time=0.102, loss_ctc=41.908, loss_att=43.396, acc=0.755, loss=42.950, backward_time=0.096, grad_norm=45.877, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.470e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:01:26,071 (trainer:737) INFO: 34epoch:train:3601-3700batch: iter_time=1.028e-04, forward_time=0.103, loss_ctc=42.893, loss_att=43.216, acc=0.746, loss=43.119, backward_time=0.096, grad_norm=42.222, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.469e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:01:49,940 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-16 13:02:09,741 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 13:02:13,379 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe3d833a200>)
+[gpuc04:0/16] 2024-01-16 13:02:13,379 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-16 13:02:13,382 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 13:06:12,285 (trainer:737) INFO: 34epoch:train:3701-3800batch: iter_time=2.284, forward_time=0.103, loss_ctc=44.738, loss_att=54.283, acc=0.730, loss=51.419, backward_time=0.096, grad_norm=43.605, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.469e-04, train_time=2.862
+[gpuc04:0/16] 2024-01-16 13:06:54,091 (trainer:737) INFO: 34epoch:train:3801-3900batch: iter_time=9.132e-05, forward_time=0.102, loss_ctc=50.251, loss_att=54.246, acc=0.733, loss=53.048, backward_time=0.095, grad_norm=47.074, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.468e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:07:35,799 (trainer:737) INFO: 34epoch:train:3901-4000batch: iter_time=1.115e-04, forward_time=0.101, loss_ctc=40.983, loss_att=42.380, acc=0.737, loss=41.961, backward_time=0.095, grad_norm=38.381, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.468e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:08:17,544 (trainer:737) INFO: 34epoch:train:4001-4100batch: iter_time=1.026e-04, forward_time=0.101, loss_ctc=39.039, loss_att=41.788, acc=0.742, loss=40.963, backward_time=0.094, grad_norm=40.348, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.468e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:08:59,471 (trainer:737) INFO: 34epoch:train:4101-4200batch: iter_time=1.034e-04, forward_time=0.102, loss_ctc=45.615, loss_att=57.015, acc=0.727, loss=53.595, backward_time=0.096, grad_norm=43.538, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.467e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:09:41,191 (trainer:737) INFO: 34epoch:train:4201-4300batch: iter_time=1.014e-04, forward_time=0.102, loss_ctc=44.163, loss_att=53.779, acc=0.707, loss=50.894, backward_time=0.095, grad_norm=44.254, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.467e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:10:23,031 (trainer:737) INFO: 34epoch:train:4301-4400batch: iter_time=1.018e-04, forward_time=0.102, loss_ctc=37.576, loss_att=46.232, acc=0.771, loss=43.635, backward_time=0.095, grad_norm=40.416, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.467e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:11:04,718 (trainer:737) INFO: 34epoch:train:4401-4500batch: iter_time=1.100e-04, forward_time=0.102, loss_ctc=36.708, loss_att=42.645, acc=0.760, loss=40.864, backward_time=0.095, grad_norm=37.704, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.466e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:11:46,459 (trainer:737) INFO: 34epoch:train:4501-4600batch: iter_time=1.065e-04, forward_time=0.101, loss_ctc=38.956, loss_att=45.341, acc=0.744, loss=43.425, backward_time=0.095, grad_norm=41.195, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.466e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:12:28,402 (trainer:737) INFO: 34epoch:train:4601-4700batch: iter_time=1.035e-04, forward_time=0.102, loss_ctc=44.967, loss_att=52.683, acc=0.731, loss=50.369, backward_time=0.095, grad_norm=43.857, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.466e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:13:10,331 (trainer:737) INFO: 34epoch:train:4701-4800batch: iter_time=9.893e-05, forward_time=0.102, loss_ctc=45.566, loss_att=48.036, acc=0.745, loss=47.295, backward_time=0.096, grad_norm=48.644, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.465e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:13:53,485 (trainer:737) INFO: 34epoch:train:4801-4900batch: iter_time=9.778e-05, forward_time=0.102, loss_ctc=38.616, loss_att=39.399, acc=0.753, loss=39.165, backward_time=0.095, grad_norm=40.145, clip=100.000, loss_scale=1.745e+34, optim_step_time=0.037, optim0_lr0=3.465e-04, train_time=0.431
+[gpuc04:0/16] 2024-01-16 13:14:37,280 (trainer:737) INFO: 34epoch:train:4901-5000batch: iter_time=1.001e-04, forward_time=0.102, loss_ctc=40.990, loss_att=44.873, acc=0.737, loss=43.708, backward_time=0.096, grad_norm=42.436, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.465e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-16 13:14:40,021 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-16 13:14:59,288 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 13:15:02,805 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb82e9e890>)
+[gpuc04:0/16] 2024-01-16 13:15:02,805 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-16 13:15:02,808 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 13:19:22,521 (trainer:737) INFO: 34epoch:train:5001-5100batch: iter_time=2.274, forward_time=0.103, loss_ctc=54.943, loss_att=63.768, acc=0.710, loss=61.121, backward_time=0.096, grad_norm=52.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.464e-04, train_time=2.852
+[gpuc04:0/16] 2024-01-16 13:20:04,494 (trainer:737) INFO: 34epoch:train:5101-5200batch: iter_time=1.012e-04, forward_time=0.102, loss_ctc=47.242, loss_att=51.104, acc=0.736, loss=49.945, backward_time=0.096, grad_norm=42.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.464e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 13:20:46,159 (trainer:737) INFO: 34epoch:train:5201-5300batch: iter_time=1.014e-04, forward_time=0.101, loss_ctc=35.908, loss_att=38.873, acc=0.755, loss=37.983, backward_time=0.095, grad_norm=36.301, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.464e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 13:21:28,280 (trainer:737) INFO: 34epoch:train:5301-5400batch: iter_time=9.608e-05, forward_time=0.102, loss_ctc=42.947, loss_att=55.979, acc=0.731, loss=52.069, backward_time=0.096, grad_norm=42.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.463e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 13:22:10,432 (trainer:737) INFO: 34epoch:train:5401-5500batch: iter_time=8.420e-05, forward_time=0.104, loss_ctc=42.271, loss_att=47.527, acc=0.736, loss=45.950, backward_time=0.096, grad_norm=41.884, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.463e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 13:22:52,403 (trainer:737) INFO: 34epoch:train:5501-5600batch: iter_time=9.270e-05, forward_time=0.102, loss_ctc=42.232, loss_att=53.257, acc=0.741, loss=49.949, backward_time=0.096, grad_norm=44.754, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.463e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:23:34,433 (trainer:737) INFO: 34epoch:train:5601-5700batch: iter_time=9.066e-05, forward_time=0.102, loss_ctc=38.960, loss_att=49.681, acc=0.771, loss=46.465, backward_time=0.096, grad_norm=39.017, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.462e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 13:24:16,188 (trainer:737) INFO: 34epoch:train:5701-5800batch: iter_time=9.883e-05, forward_time=0.101, loss_ctc=33.671, loss_att=36.107, acc=0.788, loss=35.376, backward_time=0.095, grad_norm=33.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.462e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:24:58,036 (trainer:737) INFO: 34epoch:train:5801-5900batch: iter_time=8.859e-05, forward_time=0.102, loss_ctc=42.142, loss_att=45.318, acc=0.744, loss=44.365, backward_time=0.095, grad_norm=43.368, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.462e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:25:40,095 (trainer:737) INFO: 34epoch:train:5901-6000batch: iter_time=9.581e-05, forward_time=0.103, loss_ctc=47.548, loss_att=59.719, acc=0.725, loss=56.068, backward_time=0.097, grad_norm=48.359, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.461e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 13:26:18,503 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 13:26:21,901 (trainer:737) INFO: 34epoch:train:6001-6100batch: iter_time=9.646e-05, forward_time=0.102, loss_ctc=40.944, loss_att=43.570, acc=0.754, loss=42.782, backward_time=0.095, grad_norm=42.461, clip=100.000, loss_scale=1.993e+34, optim_step_time=0.038, optim0_lr0=3.461e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:27:03,786 (trainer:737) INFO: 34epoch:train:6101-6200batch: iter_time=8.939e-05, forward_time=0.102, loss_ctc=42.586, loss_att=42.848, acc=0.747, loss=42.769, backward_time=0.095, grad_norm=40.674, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.461e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:27:27,425 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-16 13:27:46,647 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 13:27:50,290 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe41d835360>)
+[gpuc04:0/16] 2024-01-16 13:27:50,290 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-16 13:27:50,293 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 13:31:52,817 (trainer:737) INFO: 34epoch:train:6201-6300batch: iter_time=2.303, forward_time=0.103, loss_ctc=44.703, loss_att=53.947, acc=0.729, loss=51.174, backward_time=0.096, grad_norm=43.249, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.460e-04, train_time=2.890
+[gpuc04:0/16] 2024-01-16 13:32:34,748 (trainer:737) INFO: 34epoch:train:6301-6400batch: iter_time=9.928e-05, forward_time=0.102, loss_ctc=50.455, loss_att=53.943, acc=0.735, loss=52.896, backward_time=0.096, grad_norm=48.900, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.460e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:33:16,487 (trainer:737) INFO: 34epoch:train:6401-6500batch: iter_time=9.868e-05, forward_time=0.102, loss_ctc=41.093, loss_att=42.498, acc=0.737, loss=42.076, backward_time=0.095, grad_norm=37.646, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.459e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:33:58,249 (trainer:737) INFO: 34epoch:train:6501-6600batch: iter_time=9.762e-05, forward_time=0.103, loss_ctc=38.929, loss_att=41.486, acc=0.745, loss=40.719, backward_time=0.095, grad_norm=38.592, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.459e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:34:40,212 (trainer:737) INFO: 34epoch:train:6601-6700batch: iter_time=1.072e-04, forward_time=0.103, loss_ctc=45.173, loss_att=56.776, acc=0.725, loss=53.295, backward_time=0.096, grad_norm=43.345, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.459e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:35:21,983 (trainer:737) INFO: 34epoch:train:6701-6800batch: iter_time=9.694e-05, forward_time=0.102, loss_ctc=43.904, loss_att=52.913, acc=0.713, loss=50.210, backward_time=0.095, grad_norm=44.628, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.458e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:36:03,874 (trainer:737) INFO: 34epoch:train:6801-6900batch: iter_time=9.444e-05, forward_time=0.102, loss_ctc=37.580, loss_att=45.912, acc=0.774, loss=43.412, backward_time=0.096, grad_norm=59.653, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.458e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:36:45,587 (trainer:737) INFO: 34epoch:train:6901-7000batch: iter_time=9.435e-05, forward_time=0.102, loss_ctc=36.355, loss_att=42.002, acc=0.763, loss=40.308, backward_time=0.095, grad_norm=37.069, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.458e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:37:27,245 (trainer:737) INFO: 34epoch:train:7001-7100batch: iter_time=9.160e-05, forward_time=0.102, loss_ctc=38.978, loss_att=45.543, acc=0.746, loss=43.574, backward_time=0.095, grad_norm=43.108, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.457e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 13:38:09,129 (trainer:737) INFO: 34epoch:train:7101-7200batch: iter_time=9.219e-05, forward_time=0.103, loss_ctc=44.759, loss_att=52.578, acc=0.731, loss=50.232, backward_time=0.096, grad_norm=42.344, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.457e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:38:50,972 (trainer:737) INFO: 34epoch:train:7201-7300batch: iter_time=9.550e-05, forward_time=0.103, loss_ctc=45.356, loss_att=46.675, acc=0.750, loss=46.279, backward_time=0.096, grad_norm=49.733, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.457e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:39:33,001 (trainer:737) INFO: 34epoch:train:7301-7400batch: iter_time=9.800e-05, forward_time=0.105, loss_ctc=38.280, loss_att=38.986, acc=0.757, loss=38.774, backward_time=0.095, grad_norm=38.924, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.456e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 13:40:14,614 (trainer:737) INFO: 34epoch:train:7401-7500batch: iter_time=9.142e-05, forward_time=0.102, loss_ctc=41.021, loss_att=44.608, acc=0.739, loss=43.532, backward_time=0.095, grad_norm=42.266, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.456e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 13:40:17,377 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-16 13:40:37,087 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 13:40:40,670 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdbb85bfac0>)
+[gpuc04:0/16] 2024-01-16 13:40:40,670 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-16 13:40:40,673 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 13:45:03,679 (trainer:737) INFO: 34epoch:train:7501-7600batch: iter_time=2.309, forward_time=0.108, loss_ctc=53.802, loss_att=62.565, acc=0.709, loss=59.936, backward_time=0.097, grad_norm=49.083, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.456e-04, train_time=2.890
+[gpuc04:0/16] 2024-01-16 13:45:45,508 (trainer:737) INFO: 34epoch:train:7601-7700batch: iter_time=9.551e-05, forward_time=0.102, loss_ctc=47.850, loss_att=47.368, acc=0.736, loss=47.512, backward_time=0.095, grad_norm=42.040, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.455e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:46:27,151 (trainer:737) INFO: 34epoch:train:7701-7800batch: iter_time=9.324e-05, forward_time=0.101, loss_ctc=35.330, loss_att=37.635, acc=0.750, loss=36.944, backward_time=0.094, grad_norm=36.043, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.455e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 13:47:09,370 (trainer:737) INFO: 34epoch:train:7801-7900batch: iter_time=1.714e-04, forward_time=0.103, loss_ctc=42.458, loss_att=54.504, acc=0.727, loss=50.890, backward_time=0.096, grad_norm=43.261, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.455e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 13:47:51,626 (trainer:737) INFO: 34epoch:train:7901-8000batch: iter_time=9.231e-05, forward_time=0.102, loss_ctc=42.528, loss_att=46.534, acc=0.730, loss=45.332, backward_time=0.095, grad_norm=41.454, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.454e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 13:48:35,130 (trainer:737) INFO: 34epoch:train:8001-8100batch: iter_time=1.151e-04, forward_time=0.102, loss_ctc=42.688, loss_att=51.573, acc=0.738, loss=48.908, backward_time=0.095, grad_norm=47.160, clip=100.000, loss_scale=1.122e+34, optim_step_time=0.038, optim0_lr0=3.454e-04, train_time=0.435
+[gpuc04:0/16] 2024-01-16 13:49:17,311 (trainer:737) INFO: 34epoch:train:8101-8200batch: iter_time=8.948e-05, forward_time=0.103, loss_ctc=38.681, loss_att=50.451, acc=0.753, loss=46.920, backward_time=0.096, grad_norm=38.033, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.454e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 13:49:58,860 (trainer:737) INFO: 34epoch:train:8201-8300batch: iter_time=9.542e-05, forward_time=0.102, loss_ctc=32.922, loss_att=36.954, acc=0.781, loss=35.745, backward_time=0.094, grad_norm=33.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.453e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 13:50:40,579 (trainer:737) INFO: 34epoch:train:8301-8400batch: iter_time=9.926e-05, forward_time=0.103, loss_ctc=42.029, loss_att=44.720, acc=0.740, loss=43.912, backward_time=0.095, grad_norm=42.363, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.453e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:51:22,535 (trainer:737) INFO: 34epoch:train:8401-8500batch: iter_time=9.190e-05, forward_time=0.103, loss_ctc=47.657, loss_att=54.228, acc=0.736, loss=52.257, backward_time=0.096, grad_norm=47.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.453e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 13:52:04,317 (trainer:737) INFO: 34epoch:train:8501-8600batch: iter_time=9.246e-05, forward_time=0.103, loss_ctc=40.703, loss_att=43.178, acc=0.751, loss=42.435, backward_time=0.095, grad_norm=43.298, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.452e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:52:46,038 (trainer:737) INFO: 34epoch:train:8601-8700batch: iter_time=9.407e-05, forward_time=0.103, loss_ctc=42.511, loss_att=43.075, acc=0.735, loss=42.906, backward_time=0.095, grad_norm=43.812, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.452e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 13:53:11,306 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-16 13:53:30,378 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 13:53:33,911 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb56f4fa30>)
+[gpuc04:0/16] 2024-01-16 13:53:33,911 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-16 13:53:33,914 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 13:57:48,390 (trainer:737) INFO: 34epoch:train:8701-8800batch: iter_time=2.486, forward_time=0.102, loss_ctc=43.932, loss_att=53.004, acc=0.733, loss=50.282, backward_time=0.096, grad_norm=41.810, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.452e-04, train_time=3.023
+[gpuc04:0/16] 2024-01-16 13:58:30,402 (trainer:737) INFO: 34epoch:train:8801-8900batch: iter_time=8.076e-05, forward_time=0.102, loss_ctc=49.634, loss_att=55.945, acc=0.738, loss=54.051, backward_time=0.096, grad_norm=48.060, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.451e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 13:59:12,250 (trainer:737) INFO: 34epoch:train:8901-9000batch: iter_time=8.322e-05, forward_time=0.101, loss_ctc=41.095, loss_att=43.495, acc=0.740, loss=42.775, backward_time=0.096, grad_norm=41.473, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.451e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 13:59:54,142 (trainer:737) INFO: 34epoch:train:9001-9100batch: iter_time=7.881e-05, forward_time=0.102, loss_ctc=38.713, loss_att=41.503, acc=0.754, loss=40.666, backward_time=0.096, grad_norm=40.569, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.451e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:00:36,440 (trainer:737) INFO: 34epoch:train:9101-9200batch: iter_time=8.965e-05, forward_time=0.103, loss_ctc=45.046, loss_att=57.599, acc=0.733, loss=53.833, backward_time=0.096, grad_norm=43.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.450e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 14:01:18,277 (trainer:737) INFO: 34epoch:train:9201-9300batch: iter_time=9.353e-05, forward_time=0.101, loss_ctc=43.388, loss_att=55.637, acc=0.714, loss=51.962, backward_time=0.095, grad_norm=42.620, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.450e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:02:00,274 (trainer:737) INFO: 34epoch:train:9301-9400batch: iter_time=9.237e-05, forward_time=0.103, loss_ctc=37.490, loss_att=46.526, acc=0.779, loss=43.815, backward_time=0.097, grad_norm=48.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.450e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:02:42,121 (trainer:737) INFO: 34epoch:train:9401-9500batch: iter_time=9.419e-05, forward_time=0.102, loss_ctc=36.632, loss_att=40.786, acc=0.782, loss=39.540, backward_time=0.096, grad_norm=36.232, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.449e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:03:23,848 (trainer:737) INFO: 34epoch:train:9501-9600batch: iter_time=9.302e-05, forward_time=0.102, loss_ctc=38.042, loss_att=44.389, acc=0.754, loss=42.485, backward_time=0.096, grad_norm=41.078, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.449e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:04:05,885 (trainer:737) INFO: 34epoch:train:9601-9700batch: iter_time=9.822e-05, forward_time=0.103, loss_ctc=45.119, loss_att=55.390, acc=0.732, loss=52.308, backward_time=0.097, grad_norm=45.462, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.448e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:04:47,885 (trainer:737) INFO: 34epoch:train:9701-9800batch: iter_time=1.061e-04, forward_time=0.103, loss_ctc=44.773, loss_att=50.980, acc=0.741, loss=49.118, backward_time=0.097, grad_norm=49.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.448e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:05:29,811 (trainer:737) INFO: 34epoch:train:9801-9900batch: iter_time=9.956e-05, forward_time=0.102, loss_ctc=37.834, loss_att=39.214, acc=0.764, loss=38.800, backward_time=0.096, grad_norm=39.995, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.448e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:06:11,689 (trainer:737) INFO: 34epoch:train:9901-10000batch: iter_time=9.898e-05, forward_time=0.102, loss_ctc=40.632, loss_att=44.404, acc=0.749, loss=43.273, backward_time=0.096, grad_norm=41.007, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.447e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:06:14,530 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-16 14:06:33,910 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 14:06:37,541 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdbb85724a0>)
+[gpuc04:0/16] 2024-01-16 14:06:37,541 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-16 14:06:37,545 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 14:11:03,320 (trainer:737) INFO: 34epoch:train:10001-10100batch: iter_time=2.266, forward_time=0.103, loss_ctc=53.350, loss_att=62.058, acc=0.714, loss=59.445, backward_time=0.096, grad_norm=52.083, clip=100.000, loss_scale=2.243e+34, optim_step_time=0.038, optim0_lr0=3.447e-04, train_time=2.916
+[gpuc04:0/16] 2024-01-16 14:11:34,729 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 14:11:45,196 (trainer:737) INFO: 34epoch:train:10101-10200batch: iter_time=9.080e-05, forward_time=0.103, loss_ctc=47.457, loss_att=49.826, acc=0.739, loss=49.116, backward_time=0.096, grad_norm=46.294, clip=100.000, loss_scale=3.629e+34, optim_step_time=0.038, optim0_lr0=3.447e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:12:26,894 (trainer:737) INFO: 34epoch:train:10201-10300batch: iter_time=1.084e-04, forward_time=0.101, loss_ctc=35.284, loss_att=37.923, acc=0.757, loss=37.131, backward_time=0.095, grad_norm=35.027, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.446e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:13:08,947 (trainer:737) INFO: 34epoch:train:10301-10400batch: iter_time=1.087e-04, forward_time=0.103, loss_ctc=42.415, loss_att=55.101, acc=0.732, loss=51.295, backward_time=0.097, grad_norm=42.497, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.446e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:13:15,201 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 14:13:50,714 (trainer:737) INFO: 34epoch:train:10401-10500batch: iter_time=9.509e-05, forward_time=0.102, loss_ctc=42.543, loss_att=46.686, acc=0.739, loss=45.443, backward_time=0.096, grad_norm=42.826, clip=100.000, loss_scale=1.185e+34, optim_step_time=0.038, optim0_lr0=3.446e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:14:32,502 (trainer:737) INFO: 34epoch:train:10501-10600batch: iter_time=9.640e-05, forward_time=0.103, loss_ctc=42.344, loss_att=53.455, acc=0.741, loss=50.122, backward_time=0.096, grad_norm=45.576, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.445e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:15:14,473 (trainer:737) INFO: 34epoch:train:10601-10700batch: iter_time=9.780e-05, forward_time=0.104, loss_ctc=38.778, loss_att=49.288, acc=0.774, loss=46.135, backward_time=0.097, grad_norm=38.020, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.445e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:15:56,119 (trainer:737) INFO: 34epoch:train:10701-10800batch: iter_time=9.335e-05, forward_time=0.102, loss_ctc=32.967, loss_att=36.730, acc=0.786, loss=35.601, backward_time=0.095, grad_norm=32.303, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.445e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:16:37,953 (trainer:737) INFO: 34epoch:train:10801-10900batch: iter_time=9.684e-05, forward_time=0.102, loss_ctc=41.371, loss_att=43.879, acc=0.751, loss=43.126, backward_time=0.095, grad_norm=42.097, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.444e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:17:19,975 (trainer:737) INFO: 34epoch:train:10901-11000batch: iter_time=9.205e-05, forward_time=0.103, loss_ctc=47.449, loss_att=59.330, acc=0.726, loss=55.766, backward_time=0.096, grad_norm=46.458, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.444e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:18:02,059 (trainer:737) INFO: 34epoch:train:11001-11100batch: iter_time=9.809e-05, forward_time=0.105, loss_ctc=40.873, loss_att=42.692, acc=0.758, loss=42.146, backward_time=0.096, grad_norm=42.799, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.444e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 14:18:43,843 (trainer:737) INFO: 34epoch:train:11101-11200batch: iter_time=9.579e-05, forward_time=0.102, loss_ctc=41.888, loss_att=42.563, acc=0.748, loss=42.361, backward_time=0.095, grad_norm=39.385, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.443e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:19:09,844 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-16 14:19:28,911 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 14:19:32,485 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb56ef8c70>)
+[gpuc04:0/16] 2024-01-16 14:19:32,485 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-16 14:19:32,488 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 14:27:15,569 (trainer:737) INFO: 34epoch:train:11201-11300batch: iter_time=4.534, forward_time=0.220, loss_ctc=43.694, loss_att=51.793, acc=0.738, loss=49.363, backward_time=0.124, grad_norm=41.890, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.443e-04, train_time=5.117
+[gpuc04:0/16] 2024-01-16 14:27:58,035 (trainer:737) INFO: 34epoch:train:11301-11400batch: iter_time=1.127e-04, forward_time=0.103, loss_ctc=49.139, loss_att=53.895, acc=0.742, loss=52.468, backward_time=0.096, grad_norm=48.100, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.443e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 14:28:39,995 (trainer:737) INFO: 34epoch:train:11401-11500batch: iter_time=1.148e-04, forward_time=0.102, loss_ctc=40.851, loss_att=43.152, acc=0.740, loss=42.462, backward_time=0.095, grad_norm=41.020, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.442e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:29:22,225 (trainer:737) INFO: 34epoch:train:11501-11600batch: iter_time=1.213e-04, forward_time=0.103, loss_ctc=38.809, loss_att=40.468, acc=0.756, loss=39.970, backward_time=0.096, grad_norm=40.331, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.442e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 14:30:06,085 (trainer:737) INFO: 34epoch:train:11601-11700batch: iter_time=1.083e-04, forward_time=0.104, loss_ctc=45.050, loss_att=56.697, acc=0.735, loss=53.203, backward_time=0.096, grad_norm=42.119, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.442e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-16 14:30:48,114 (trainer:737) INFO: 34epoch:train:11701-11800batch: iter_time=1.115e-04, forward_time=0.103, loss_ctc=43.329, loss_att=55.042, acc=0.715, loss=51.528, backward_time=0.095, grad_norm=44.639, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.037, optim0_lr0=3.441e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 14:31:30,293 (trainer:737) INFO: 34epoch:train:11801-11900batch: iter_time=1.125e-04, forward_time=0.104, loss_ctc=37.187, loss_att=45.882, acc=0.780, loss=43.274, backward_time=0.096, grad_norm=40.837, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.441e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 14:32:11,978 (trainer:737) INFO: 34epoch:train:11901-12000batch: iter_time=1.157e-04, forward_time=0.104, loss_ctc=36.358, loss_att=40.194, acc=0.785, loss=39.043, backward_time=0.095, grad_norm=35.657, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.441e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:32:53,623 (trainer:737) INFO: 34epoch:train:12001-12100batch: iter_time=1.186e-04, forward_time=0.102, loss_ctc=37.723, loss_att=44.179, acc=0.756, loss=42.242, backward_time=0.095, grad_norm=41.508, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.440e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:33:35,519 (trainer:737) INFO: 34epoch:train:12101-12200batch: iter_time=1.198e-04, forward_time=0.105, loss_ctc=44.644, loss_att=54.675, acc=0.733, loss=51.666, backward_time=0.097, grad_norm=42.322, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.440e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 14:34:17,608 (trainer:737) INFO: 34epoch:train:12201-12300batch: iter_time=1.980e-04, forward_time=0.106, loss_ctc=44.885, loss_att=50.161, acc=0.742, loss=48.578, backward_time=0.098, grad_norm=49.337, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.440e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 14:34:59,429 (trainer:737) INFO: 34epoch:train:12301-12400batch: iter_time=1.286e-04, forward_time=0.104, loss_ctc=37.614, loss_att=39.294, acc=0.764, loss=38.790, backward_time=0.096, grad_norm=36.462, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.439e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:35:41,046 (trainer:737) INFO: 34epoch:train:12401-12500batch: iter_time=1.030e-04, forward_time=0.104, loss_ctc=40.814, loss_att=44.025, acc=0.751, loss=43.062, backward_time=0.095, grad_norm=39.267, clip=100.000, loss_scale=1.921e+34, optim_step_time=0.038, optim0_lr0=3.439e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:35:49,843 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-16 14:36:11,035 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 14:36:14,800 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb8a1e08e0>)
+[gpuc04:0/16] 2024-01-16 14:36:14,800 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-16 14:36:14,803 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 14:40:43,540 (trainer:737) INFO: 34epoch:train:12501-12600batch: iter_time=2.605, forward_time=0.104, loss_ctc=53.255, loss_att=64.237, acc=0.706, loss=60.942, backward_time=0.096, grad_norm=51.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.439e-04, train_time=3.025
+[gpuc04:0/16] 2024-01-16 14:41:25,307 (trainer:737) INFO: 34epoch:train:12601-12700batch: iter_time=1.689e-04, forward_time=0.104, loss_ctc=47.029, loss_att=49.680, acc=0.732, loss=48.884, backward_time=0.096, grad_norm=43.015, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.438e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:42:06,769 (trainer:737) INFO: 34epoch:train:12701-12800batch: iter_time=1.274e-04, forward_time=0.102, loss_ctc=35.344, loss_att=38.310, acc=0.751, loss=37.420, backward_time=0.094, grad_norm=36.345, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.438e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 14:42:48,584 (trainer:737) INFO: 34epoch:train:12801-12900batch: iter_time=1.275e-04, forward_time=0.104, loss_ctc=42.200, loss_att=55.177, acc=0.727, loss=51.284, backward_time=0.096, grad_norm=42.918, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.438e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:43:30,180 (trainer:737) INFO: 34epoch:train:12901-13000batch: iter_time=1.275e-04, forward_time=0.103, loss_ctc=42.044, loss_att=47.112, acc=0.731, loss=45.591, backward_time=0.095, grad_norm=41.616, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.437e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:44:11,911 (trainer:737) INFO: 34epoch:train:13001-13100batch: iter_time=1.397e-04, forward_time=0.106, loss_ctc=42.369, loss_att=52.070, acc=0.739, loss=49.160, backward_time=0.095, grad_norm=44.537, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.437e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:44:53,624 (trainer:737) INFO: 34epoch:train:13101-13200batch: iter_time=1.414e-04, forward_time=0.106, loss_ctc=38.683, loss_att=50.264, acc=0.755, loss=46.790, backward_time=0.096, grad_norm=38.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.437e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:45:35,209 (trainer:737) INFO: 34epoch:train:13201-13300batch: iter_time=1.297e-04, forward_time=0.104, loss_ctc=33.696, loss_att=37.756, acc=0.777, loss=36.538, backward_time=0.095, grad_norm=33.017, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.436e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:46:16,774 (trainer:737) INFO: 34epoch:train:13301-13400batch: iter_time=1.402e-04, forward_time=0.103, loss_ctc=41.460, loss_att=44.564, acc=0.743, loss=43.633, backward_time=0.094, grad_norm=43.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.436e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 14:46:58,563 (trainer:737) INFO: 34epoch:train:13401-13500batch: iter_time=1.373e-04, forward_time=0.104, loss_ctc=46.978, loss_att=55.055, acc=0.733, loss=52.632, backward_time=0.096, grad_norm=44.518, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.436e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:47:40,168 (trainer:737) INFO: 34epoch:train:13501-13600batch: iter_time=1.268e-04, forward_time=0.103, loss_ctc=40.064, loss_att=42.060, acc=0.755, loss=41.461, backward_time=0.095, grad_norm=41.763, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.435e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:48:21,759 (trainer:737) INFO: 34epoch:train:13601-13700batch: iter_time=1.581e-04, forward_time=0.104, loss_ctc=41.835, loss_att=42.946, acc=0.735, loss=42.613, backward_time=0.095, grad_norm=40.821, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.435e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:48:46,252 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-16 14:49:05,777 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 14:49:09,370 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb4a50fee0>)
+[gpuc04:0/16] 2024-01-16 14:49:09,370 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-16 14:49:09,373 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 14:53:19,789 (trainer:737) INFO: 34epoch:train:13701-13800batch: iter_time=2.559, forward_time=0.104, loss_ctc=43.632, loss_att=52.733, acc=0.731, loss=50.003, backward_time=0.096, grad_norm=43.244, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.435e-04, train_time=2.980
+[gpuc04:0/16] 2024-01-16 14:54:01,591 (trainer:737) INFO: 34epoch:train:13801-13900batch: iter_time=1.427e-04, forward_time=0.105, loss_ctc=49.062, loss_att=51.616, acc=0.738, loss=50.850, backward_time=0.096, grad_norm=46.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.434e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:54:43,221 (trainer:737) INFO: 34epoch:train:13901-14000batch: iter_time=1.293e-04, forward_time=0.103, loss_ctc=41.051, loss_att=41.794, acc=0.739, loss=41.571, backward_time=0.096, grad_norm=40.250, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.434e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:55:24,880 (trainer:737) INFO: 34epoch:train:14001-14100batch: iter_time=1.300e-04, forward_time=0.103, loss_ctc=38.205, loss_att=40.294, acc=0.748, loss=39.667, backward_time=0.096, grad_norm=38.828, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.434e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:56:06,748 (trainer:737) INFO: 34epoch:train:14101-14200batch: iter_time=1.446e-04, forward_time=0.104, loss_ctc=44.853, loss_att=55.198, acc=0.731, loss=52.094, backward_time=0.097, grad_norm=44.441, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.433e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:56:48,482 (trainer:737) INFO: 34epoch:train:14201-14300batch: iter_time=1.250e-04, forward_time=0.103, loss_ctc=43.569, loss_att=53.749, acc=0.711, loss=50.695, backward_time=0.096, grad_norm=44.570, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.433e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:57:30,305 (trainer:737) INFO: 34epoch:train:14301-14400batch: iter_time=1.092e-04, forward_time=0.102, loss_ctc=37.199, loss_att=45.915, acc=0.774, loss=43.300, backward_time=0.095, grad_norm=40.859, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.037, optim0_lr0=3.433e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 14:58:12,028 (trainer:737) INFO: 34epoch:train:14401-14500batch: iter_time=1.095e-04, forward_time=0.102, loss_ctc=36.579, loss_att=41.704, acc=0.765, loss=40.166, backward_time=0.095, grad_norm=36.535, clip=100.000, loss_scale=3.842e+34, optim_step_time=0.037, optim0_lr0=3.432e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 14:58:53,689 (trainer:737) INFO: 34epoch:train:14501-14600batch: iter_time=1.185e-04, forward_time=0.102, loss_ctc=37.330, loss_att=44.797, acc=0.746, loss=42.557, backward_time=0.095, grad_norm=42.464, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.037, optim0_lr0=3.432e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 14:59:35,486 (trainer:737) INFO: 34epoch:train:14601-14700batch: iter_time=1.065e-04, forward_time=0.102, loss_ctc=44.567, loss_att=51.944, acc=0.733, loss=49.731, backward_time=0.095, grad_norm=43.545, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.037, optim0_lr0=3.432e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 15:00:17,407 (trainer:737) INFO: 34epoch:train:14701-14800batch: iter_time=1.161e-04, forward_time=0.103, loss_ctc=44.874, loss_att=47.341, acc=0.748, loss=46.601, backward_time=0.095, grad_norm=50.615, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.037, optim0_lr0=3.431e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 15:00:59,072 (trainer:737) INFO: 34epoch:train:14801-14900batch: iter_time=1.082e-04, forward_time=0.102, loss_ctc=37.764, loss_att=39.072, acc=0.757, loss=38.680, backward_time=0.094, grad_norm=39.097, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.037, optim0_lr0=3.431e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 15:01:40,705 (trainer:737) INFO: 34epoch:train:14901-15000batch: iter_time=1.125e-04, forward_time=0.102, loss_ctc=40.661, loss_att=44.490, acc=0.739, loss=43.341, backward_time=0.095, grad_norm=42.005, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.037, optim0_lr0=3.431e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 15:21:35,585 (trainer:343) INFO: 34epoch results: [train] iter_time=0.205, forward_time=0.104, loss_ctc=42.404, loss_att=48.231, acc=0.743, loss=46.483, backward_time=0.096, grad_norm=42.737, clip=100.000, loss_scale=1.763e+34, optim_step_time=0.038, optim0_lr0=3.456e-04, train_time=0.635, time=2 hours, 38 minutes and 56.39 seconds, total_count=510000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=49.820, cer_ctc=0.257, loss_att=52.352, acc=0.596, cer=0.373, wer=0.998, loss=51.592, time=19 minutes and 42.71 seconds, total_count=158814, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-16 15:21:45,350 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-16 15:21:45,370 (trainer:272) INFO: 35/45epoch started. Estimated time to finish: 1 day, 8 hours and 47 minutes
+[gpuc04:0/16] 2024-01-16 15:21:45,380 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-16 15:22:03,692 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 15:22:07,315 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb8a1e0d30>)
+[gpuc04:0/16] 2024-01-16 15:22:07,315 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-16 15:22:07,318 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 15:26:20,583 (trainer:737) INFO: 35epoch:train:1-100batch: iter_time=2.315, forward_time=0.106, loss_ctc=48.898, loss_att=61.071, acc=0.727, loss=57.419, backward_time=0.098, grad_norm=46.303, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.430e-04, train_time=2.752
+[gpuc04:0/16] 2024-01-16 15:27:02,326 (trainer:737) INFO: 35epoch:train:101-200batch: iter_time=1.182e-04, forward_time=0.104, loss_ctc=43.900, loss_att=45.204, acc=0.761, loss=44.813, backward_time=0.097, grad_norm=44.583, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.430e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 15:27:44,155 (trainer:737) INFO: 35epoch:train:201-300batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=43.133, loss_att=56.253, acc=0.722, loss=52.317, backward_time=0.098, grad_norm=44.293, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.430e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 15:28:43,155 (trainer:737) INFO: 35epoch:train:301-400batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=39.868, loss_att=46.432, acc=0.751, loss=44.463, backward_time=0.097, grad_norm=43.839, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.429e-04, train_time=0.590
+[gpuc04:0/16] 2024-01-16 15:29:25,499 (trainer:737) INFO: 35epoch:train:401-500batch: iter_time=1.090e-04, forward_time=0.106, loss_ctc=52.378, loss_att=62.016, acc=0.738, loss=59.124, backward_time=0.098, grad_norm=48.951, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.429e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 15:30:07,360 (trainer:737) INFO: 35epoch:train:501-600batch: iter_time=1.129e-04, forward_time=0.105, loss_ctc=42.770, loss_att=53.549, acc=0.739, loss=50.315, backward_time=0.098, grad_norm=46.426, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.429e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 15:30:24,892 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 15:30:52,300 (trainer:737) INFO: 35epoch:train:601-700batch: iter_time=1.155e-04, forward_time=0.124, loss_ctc=40.525, loss_att=45.252, acc=0.767, loss=43.834, backward_time=0.100, grad_norm=39.134, clip=100.000, loss_scale=2.937e+34, optim_step_time=0.042, optim0_lr0=3.428e-04, train_time=0.449
+[gpuc04:0/16] 2024-01-16 15:31:36,533 (trainer:737) INFO: 35epoch:train:701-800batch: iter_time=1.140e-04, forward_time=0.105, loss_ctc=44.195, loss_att=56.195, acc=0.723, loss=52.595, backward_time=0.097, grad_norm=46.569, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.428e-04, train_time=0.442
+[gpuc04:0/16] 2024-01-16 15:32:19,519 (trainer:737) INFO: 35epoch:train:801-900batch: iter_time=1.183e-04, forward_time=0.104, loss_ctc=37.393, loss_att=47.335, acc=0.710, loss=44.353, backward_time=0.097, grad_norm=41.816, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.428e-04, train_time=0.430
+[gpuc04:0/16] 2024-01-16 15:33:01,590 (trainer:737) INFO: 35epoch:train:901-1000batch: iter_time=1.131e-04, forward_time=0.105, loss_ctc=43.670, loss_att=50.036, acc=0.747, loss=48.126, backward_time=0.098, grad_norm=41.245, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.427e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 15:33:43,569 (trainer:737) INFO: 35epoch:train:1001-1100batch: iter_time=1.244e-04, forward_time=0.104, loss_ctc=42.077, loss_att=45.674, acc=0.737, loss=44.595, backward_time=0.097, grad_norm=45.765, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.427e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 15:34:25,935 (trainer:737) INFO: 35epoch:train:1101-1200batch: iter_time=1.155e-04, forward_time=0.105, loss_ctc=46.694, loss_att=51.014, acc=0.742, loss=49.718, backward_time=0.097, grad_norm=49.268, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.427e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 15:35:01,311 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-16 15:35:20,255 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 15:35:23,889 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb966e0d90>)
+[gpuc04:0/16] 2024-01-16 15:35:23,889 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-16 15:35:23,892 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 15:41:23,482 (trainer:737) INFO: 35epoch:train:1201-1300batch: iter_time=2.906, forward_time=0.106, loss_ctc=45.071, loss_att=58.145, acc=0.740, loss=54.223, backward_time=0.098, grad_norm=43.292, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.426e-04, train_time=4.175
+[gpuc04:0/16] 2024-01-16 15:42:05,746 (trainer:737) INFO: 35epoch:train:1301-1400batch: iter_time=1.178e-04, forward_time=0.107, loss_ctc=46.550, loss_att=54.405, acc=0.745, loss=52.049, backward_time=0.098, grad_norm=43.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.426e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 15:42:47,767 (trainer:737) INFO: 35epoch:train:1401-1500batch: iter_time=1.153e-04, forward_time=0.105, loss_ctc=43.670, loss_att=40.082, acc=0.753, loss=41.158, backward_time=0.096, grad_norm=42.641, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.425e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 15:43:30,319 (trainer:737) INFO: 35epoch:train:1501-1600batch: iter_time=1.336e-04, forward_time=0.110, loss_ctc=40.983, loss_att=56.581, acc=0.736, loss=51.902, backward_time=0.099, grad_norm=40.706, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.425e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 15:44:16,561 (trainer:737) INFO: 35epoch:train:1601-1700batch: iter_time=1.266e-04, forward_time=0.120, loss_ctc=46.120, loss_att=58.242, acc=0.726, loss=54.605, backward_time=0.114, grad_norm=50.745, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=3.425e-04, train_time=0.462
+[gpuc04:0/16] 2024-01-16 15:45:06,704 (trainer:737) INFO: 35epoch:train:1701-1800batch: iter_time=1.388e-04, forward_time=0.125, loss_ctc=44.730, loss_att=54.979, acc=0.765, loss=51.904, backward_time=0.109, grad_norm=41.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.049, optim0_lr0=3.424e-04, train_time=0.501
+[gpuc04:0/16] 2024-01-16 15:45:49,677 (trainer:737) INFO: 35epoch:train:1801-1900batch: iter_time=1.507e-04, forward_time=0.113, loss_ctc=39.877, loss_att=46.676, acc=0.757, loss=44.636, backward_time=0.099, grad_norm=43.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.424e-04, train_time=0.429
+[gpuc04:0/16] 2024-01-16 15:46:33,441 (trainer:737) INFO: 35epoch:train:1901-2000batch: iter_time=1.376e-04, forward_time=0.105, loss_ctc=44.094, loss_att=45.784, acc=0.749, loss=45.277, backward_time=0.097, grad_norm=42.730, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.424e-04, train_time=0.437
+[gpuc04:0/16] 2024-01-16 15:47:15,349 (trainer:737) INFO: 35epoch:train:2001-2100batch: iter_time=1.534e-04, forward_time=0.106, loss_ctc=40.229, loss_att=58.231, acc=0.705, loss=52.830, backward_time=0.097, grad_norm=41.038, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.423e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 15:47:57,326 (trainer:737) INFO: 35epoch:train:2101-2200batch: iter_time=1.527e-04, forward_time=0.105, loss_ctc=37.923, loss_att=47.328, acc=0.729, loss=44.507, backward_time=0.096, grad_norm=41.105, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.423e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 15:48:40,037 (trainer:737) INFO: 35epoch:train:2201-2300batch: iter_time=1.350e-04, forward_time=0.106, loss_ctc=41.849, loss_att=41.897, acc=0.749, loss=41.883, backward_time=0.096, grad_norm=40.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.423e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-16 15:49:23,341 (trainer:737) INFO: 35epoch:train:2301-2400batch: iter_time=1.245e-04, forward_time=0.106, loss_ctc=40.791, loss_att=44.951, acc=0.760, loss=43.703, backward_time=0.097, grad_norm=43.618, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.422e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-16 15:50:05,829 (trainer:737) INFO: 35epoch:train:2401-2500batch: iter_time=1.291e-04, forward_time=0.106, loss_ctc=50.535, loss_att=61.652, acc=0.721, loss=58.317, backward_time=0.097, grad_norm=51.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.422e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 15:50:30,686 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-16 15:50:49,851 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 15:50:53,930 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb61236260>)
+[gpuc04:0/16] 2024-01-16 15:50:53,930 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-16 15:50:53,933 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 15:58:08,694 (trainer:737) INFO: 35epoch:train:2501-2600batch: iter_time=4.218, forward_time=0.107, loss_ctc=46.656, loss_att=56.877, acc=0.737, loss=53.811, backward_time=0.099, grad_norm=44.421, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.422e-04, train_time=4.828
+[gpuc04:0/16] 2024-01-16 15:58:50,481 (trainer:737) INFO: 35epoch:train:2601-2700batch: iter_time=1.363e-04, forward_time=0.104, loss_ctc=42.863, loss_att=44.431, acc=0.764, loss=43.960, backward_time=0.096, grad_norm=43.164, clip=100.000, loss_scale=3.282e+34, optim_step_time=0.039, optim0_lr0=3.421e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 15:59:25,619 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 15:59:32,325 (trainer:737) INFO: 35epoch:train:2701-2800batch: iter_time=1.225e-04, forward_time=0.104, loss_ctc=42.040, loss_att=53.975, acc=0.729, loss=50.395, backward_time=0.096, grad_norm=43.376, clip=100.000, loss_scale=3.818e+34, optim_step_time=0.039, optim0_lr0=3.421e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 16:00:14,140 (trainer:737) INFO: 35epoch:train:2801-2900batch: iter_time=1.264e-04, forward_time=0.103, loss_ctc=39.497, loss_att=46.235, acc=0.753, loss=44.213, backward_time=0.096, grad_norm=41.439, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.421e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 16:00:56,147 (trainer:737) INFO: 35epoch:train:2901-3000batch: iter_time=1.447e-04, forward_time=0.104, loss_ctc=50.176, loss_att=61.596, acc=0.741, loss=58.170, backward_time=0.097, grad_norm=45.873, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.420e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 16:01:38,029 (trainer:737) INFO: 35epoch:train:3001-3100batch: iter_time=1.321e-04, forward_time=0.104, loss_ctc=41.688, loss_att=52.283, acc=0.743, loss=49.105, backward_time=0.097, grad_norm=42.660, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.420e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 16:02:19,782 (trainer:737) INFO: 35epoch:train:3101-3200batch: iter_time=1.475e-04, forward_time=0.104, loss_ctc=40.660, loss_att=44.778, acc=0.770, loss=43.543, backward_time=0.096, grad_norm=38.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.420e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:03:01,555 (trainer:737) INFO: 35epoch:train:3201-3300batch: iter_time=1.647e-04, forward_time=0.104, loss_ctc=43.493, loss_att=56.015, acc=0.723, loss=52.259, backward_time=0.096, grad_norm=47.348, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.419e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 16:03:43,036 (trainer:737) INFO: 35epoch:train:3301-3400batch: iter_time=1.569e-04, forward_time=0.103, loss_ctc=36.491, loss_att=46.005, acc=0.715, loss=43.151, backward_time=0.095, grad_norm=40.393, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.419e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:04:25,002 (trainer:737) INFO: 35epoch:train:3401-3500batch: iter_time=1.478e-04, forward_time=0.104, loss_ctc=42.932, loss_att=49.373, acc=0.749, loss=47.441, backward_time=0.096, grad_norm=39.996, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.419e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 16:05:06,528 (trainer:737) INFO: 35epoch:train:3501-3600batch: iter_time=1.403e-04, forward_time=0.103, loss_ctc=40.048, loss_att=44.433, acc=0.738, loss=43.117, backward_time=0.095, grad_norm=44.713, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.418e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:05:48,271 (trainer:737) INFO: 35epoch:train:3601-3700batch: iter_time=1.426e-04, forward_time=0.104, loss_ctc=45.149, loss_att=50.003, acc=0.745, loss=48.547, backward_time=0.096, grad_norm=45.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.418e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:06:15,461 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-16 16:06:34,980 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 16:06:38,528 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc5152ecb0>)
+[gpuc04:0/16] 2024-01-16 16:06:38,529 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-16 16:06:38,532 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 16:10:43,496 (trainer:737) INFO: 35epoch:train:3701-3800batch: iter_time=2.533, forward_time=0.104, loss_ctc=44.185, loss_att=59.870, acc=0.732, loss=55.165, backward_time=0.097, grad_norm=44.119, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.418e-04, train_time=2.952
+[gpuc04:0/16] 2024-01-16 16:11:25,803 (trainer:737) INFO: 35epoch:train:3801-3900batch: iter_time=1.597e-04, forward_time=0.110, loss_ctc=46.172, loss_att=57.348, acc=0.729, loss=53.995, backward_time=0.097, grad_norm=43.965, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.417e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 16:12:07,416 (trainer:737) INFO: 35epoch:train:3901-4000batch: iter_time=1.875e-04, forward_time=0.104, loss_ctc=42.897, loss_att=40.145, acc=0.751, loss=40.971, backward_time=0.096, grad_norm=41.264, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.417e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:12:49,184 (trainer:737) INFO: 35epoch:train:4001-4100batch: iter_time=1.699e-04, forward_time=0.105, loss_ctc=40.295, loss_att=54.416, acc=0.726, loss=50.180, backward_time=0.097, grad_norm=41.174, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.417e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:13:30,940 (trainer:737) INFO: 35epoch:train:4101-4200batch: iter_time=1.442e-04, forward_time=0.104, loss_ctc=45.030, loss_att=56.742, acc=0.718, loss=53.229, backward_time=0.096, grad_norm=47.998, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.416e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:14:12,705 (trainer:737) INFO: 35epoch:train:4201-4300batch: iter_time=1.330e-04, forward_time=0.104, loss_ctc=44.960, loss_att=55.943, acc=0.755, loss=52.648, backward_time=0.096, grad_norm=42.662, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.416e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:14:54,371 (trainer:737) INFO: 35epoch:train:4301-4400batch: iter_time=1.240e-04, forward_time=0.103, loss_ctc=39.423, loss_att=46.743, acc=0.748, loss=44.547, backward_time=0.096, grad_norm=42.011, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.416e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:15:35,948 (trainer:737) INFO: 35epoch:train:4401-4500batch: iter_time=1.252e-04, forward_time=0.104, loss_ctc=43.139, loss_att=44.617, acc=0.742, loss=44.173, backward_time=0.096, grad_norm=47.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.415e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:16:17,507 (trainer:737) INFO: 35epoch:train:4501-4600batch: iter_time=1.171e-04, forward_time=0.104, loss_ctc=39.832, loss_att=56.246, acc=0.702, loss=51.322, backward_time=0.095, grad_norm=40.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.415e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:16:58,956 (trainer:737) INFO: 35epoch:train:4601-4700batch: iter_time=1.316e-04, forward_time=0.103, loss_ctc=37.814, loss_att=46.869, acc=0.714, loss=44.152, backward_time=0.095, grad_norm=41.655, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.415e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 16:17:40,471 (trainer:737) INFO: 35epoch:train:4701-4800batch: iter_time=1.196e-04, forward_time=0.103, loss_ctc=41.103, loss_att=42.398, acc=0.742, loss=42.009, backward_time=0.095, grad_norm=38.641, clip=100.000, loss_scale=2.409e+34, optim_step_time=0.038, optim0_lr0=3.415e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:18:22,029 (trainer:737) INFO: 35epoch:train:4801-4900batch: iter_time=1.187e-04, forward_time=0.103, loss_ctc=40.125, loss_att=43.252, acc=0.757, loss=42.314, backward_time=0.095, grad_norm=42.677, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.414e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:18:40,354 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 16:19:03,693 (trainer:737) INFO: 35epoch:train:4901-5000batch: iter_time=1.340e-04, forward_time=0.104, loss_ctc=49.664, loss_att=61.493, acc=0.718, loss=57.944, backward_time=0.097, grad_norm=49.689, clip=100.000, loss_scale=2.979e+34, optim_step_time=0.038, optim0_lr0=3.414e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:19:10,890 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-16 16:19:30,216 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 16:19:33,765 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc86892950>)
+[gpuc04:0/16] 2024-01-16 16:19:33,765 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-16 16:19:33,769 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 16:23:59,100 (trainer:737) INFO: 35epoch:train:5001-5100batch: iter_time=2.441, forward_time=0.106, loss_ctc=46.621, loss_att=57.066, acc=0.727, loss=53.933, backward_time=0.097, grad_norm=44.631, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.414e-04, train_time=2.954
+[gpuc04:0/16] 2024-01-16 16:24:40,807 (trainer:737) INFO: 35epoch:train:5101-5200batch: iter_time=1.803e-04, forward_time=0.104, loss_ctc=42.299, loss_att=44.235, acc=0.757, loss=43.654, backward_time=0.096, grad_norm=43.290, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.413e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:25:22,795 (trainer:737) INFO: 35epoch:train:5201-5300batch: iter_time=1.617e-04, forward_time=0.105, loss_ctc=41.632, loss_att=54.746, acc=0.717, loss=50.812, backward_time=0.097, grad_norm=42.996, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.413e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 16:26:04,551 (trainer:737) INFO: 35epoch:train:5301-5400batch: iter_time=1.483e-04, forward_time=0.104, loss_ctc=39.501, loss_att=45.008, acc=0.743, loss=43.356, backward_time=0.096, grad_norm=41.086, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.413e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:26:46,692 (trainer:737) INFO: 35epoch:train:5401-5500batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=49.240, loss_att=60.611, acc=0.733, loss=57.200, backward_time=0.097, grad_norm=48.904, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.412e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 16:27:28,669 (trainer:737) INFO: 35epoch:train:5501-5600batch: iter_time=1.314e-04, forward_time=0.104, loss_ctc=41.151, loss_att=51.832, acc=0.734, loss=48.628, backward_time=0.097, grad_norm=48.454, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.412e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 16:28:10,265 (trainer:737) INFO: 35epoch:train:5601-5700batch: iter_time=1.365e-04, forward_time=0.104, loss_ctc=40.445, loss_att=42.887, acc=0.762, loss=42.154, backward_time=0.096, grad_norm=39.601, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.412e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:28:51,915 (trainer:737) INFO: 35epoch:train:5701-5800batch: iter_time=1.357e-04, forward_time=0.104, loss_ctc=42.189, loss_att=55.265, acc=0.716, loss=51.342, backward_time=0.096, grad_norm=44.875, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.411e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:29:33,579 (trainer:737) INFO: 35epoch:train:5801-5900batch: iter_time=1.141e-04, forward_time=0.105, loss_ctc=35.893, loss_att=45.317, acc=0.705, loss=42.490, backward_time=0.095, grad_norm=39.838, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.411e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:30:15,256 (trainer:737) INFO: 35epoch:train:5901-6000batch: iter_time=1.069e-04, forward_time=0.103, loss_ctc=42.764, loss_att=47.767, acc=0.741, loss=46.266, backward_time=0.096, grad_norm=39.573, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.411e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:30:56,718 (trainer:737) INFO: 35epoch:train:6001-6100batch: iter_time=1.221e-04, forward_time=0.103, loss_ctc=39.296, loss_att=43.160, acc=0.729, loss=42.001, backward_time=0.095, grad_norm=43.605, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.410e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 16:31:38,391 (trainer:737) INFO: 35epoch:train:6101-6200batch: iter_time=1.333e-04, forward_time=0.103, loss_ctc=44.392, loss_att=49.523, acc=0.739, loss=47.983, backward_time=0.096, grad_norm=45.879, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.410e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:32:02,834 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-16 16:32:22,391 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 16:32:25,995 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc7fa96c50>)
+[gpuc04:0/16] 2024-01-16 16:32:25,995 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-16 16:32:25,998 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 16:36:34,775 (trainer:737) INFO: 35epoch:train:6201-6300batch: iter_time=2.419, forward_time=0.104, loss_ctc=44.228, loss_att=56.588, acc=0.740, loss=52.880, backward_time=0.097, grad_norm=43.326, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.410e-04, train_time=2.964
+[gpuc04:0/16] 2024-01-16 16:37:16,544 (trainer:737) INFO: 35epoch:train:6301-6400batch: iter_time=1.111e-04, forward_time=0.104, loss_ctc=45.514, loss_att=55.570, acc=0.734, loss=52.553, backward_time=0.096, grad_norm=44.977, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.409e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:37:58,176 (trainer:737) INFO: 35epoch:train:6401-6500batch: iter_time=1.316e-04, forward_time=0.103, loss_ctc=42.767, loss_att=40.079, acc=0.751, loss=40.886, backward_time=0.095, grad_norm=43.299, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.409e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:38:40,092 (trainer:737) INFO: 35epoch:train:6501-6600batch: iter_time=1.233e-04, forward_time=0.104, loss_ctc=40.462, loss_att=53.735, acc=0.726, loss=49.753, backward_time=0.096, grad_norm=40.498, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.409e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 16:39:21,886 (trainer:737) INFO: 35epoch:train:6601-6700batch: iter_time=1.144e-04, forward_time=0.104, loss_ctc=44.638, loss_att=56.157, acc=0.717, loss=52.701, backward_time=0.096, grad_norm=47.658, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.408e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 16:40:03,979 (trainer:737) INFO: 35epoch:train:6701-6800batch: iter_time=1.157e-04, forward_time=0.103, loss_ctc=44.187, loss_att=54.880, acc=0.756, loss=51.672, backward_time=0.096, grad_norm=42.582, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.408e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 16:40:47,064 (trainer:737) INFO: 35epoch:train:6801-6900batch: iter_time=1.247e-04, forward_time=0.103, loss_ctc=39.480, loss_att=46.231, acc=0.751, loss=44.206, backward_time=0.096, grad_norm=41.434, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.408e-04, train_time=0.431
+[gpuc04:0/16] 2024-01-16 16:41:20,774 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 16:41:29,548 (trainer:737) INFO: 35epoch:train:6901-7000batch: iter_time=1.182e-04, forward_time=0.104, loss_ctc=42.841, loss_att=44.286, acc=0.743, loss=43.853, backward_time=0.096, grad_norm=45.303, clip=100.000, loss_scale=2.790e+34, optim_step_time=0.038, optim0_lr0=3.407e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 16:42:11,784 (trainer:737) INFO: 35epoch:train:7001-7100batch: iter_time=1.139e-04, forward_time=0.104, loss_ctc=39.375, loss_att=56.132, acc=0.700, loss=51.105, backward_time=0.096, grad_norm=41.299, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.407e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 16:42:53,312 (trainer:737) INFO: 35epoch:train:7101-7200batch: iter_time=1.143e-04, forward_time=0.102, loss_ctc=37.451, loss_att=45.627, acc=0.718, loss=43.174, backward_time=0.095, grad_norm=40.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.407e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:43:34,907 (trainer:737) INFO: 35epoch:train:7201-7300batch: iter_time=1.083e-04, forward_time=0.103, loss_ctc=40.366, loss_att=42.896, acc=0.739, loss=42.137, backward_time=0.095, grad_norm=39.068, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.406e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:44:16,831 (trainer:737) INFO: 35epoch:train:7301-7400batch: iter_time=1.211e-04, forward_time=0.103, loss_ctc=39.715, loss_att=42.545, acc=0.758, loss=41.696, backward_time=0.096, grad_norm=44.015, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.406e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 16:44:59,011 (trainer:737) INFO: 35epoch:train:7401-7500batch: iter_time=1.076e-04, forward_time=0.104, loss_ctc=49.405, loss_att=61.081, acc=0.719, loss=57.578, backward_time=0.096, grad_norm=51.226, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.406e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 16:45:03,881 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-16 16:45:23,080 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 16:45:26,643 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc931ca8c0>)
+[gpuc04:0/16] 2024-01-16 16:45:26,644 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-16 16:45:26,647 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 16:49:56,452 (trainer:737) INFO: 35epoch:train:7501-7600batch: iter_time=2.456, forward_time=0.106, loss_ctc=46.205, loss_att=56.346, acc=0.728, loss=53.304, backward_time=0.097, grad_norm=43.945, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.405e-04, train_time=2.974
+[gpuc04:0/16] 2024-01-16 16:50:38,119 (trainer:737) INFO: 35epoch:train:7601-7700batch: iter_time=1.271e-04, forward_time=0.104, loss_ctc=42.969, loss_att=44.342, acc=0.758, loss=43.930, backward_time=0.096, grad_norm=44.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.405e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:51:19,856 (trainer:737) INFO: 35epoch:train:7701-7800batch: iter_time=1.220e-04, forward_time=0.103, loss_ctc=41.874, loss_att=54.094, acc=0.722, loss=50.428, backward_time=0.096, grad_norm=42.287, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.405e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:52:01,569 (trainer:737) INFO: 35epoch:train:7801-7900batch: iter_time=1.190e-04, forward_time=0.104, loss_ctc=39.220, loss_att=42.812, acc=0.751, loss=41.735, backward_time=0.096, grad_norm=40.952, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.404e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:52:43,344 (trainer:737) INFO: 35epoch:train:7901-8000batch: iter_time=1.317e-04, forward_time=0.104, loss_ctc=49.001, loss_att=60.851, acc=0.735, loss=57.296, backward_time=0.097, grad_norm=48.695, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.404e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 16:53:25,089 (trainer:737) INFO: 35epoch:train:8001-8100batch: iter_time=1.186e-04, forward_time=0.104, loss_ctc=41.404, loss_att=51.204, acc=0.738, loss=48.264, backward_time=0.096, grad_norm=44.807, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.404e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:54:06,750 (trainer:737) INFO: 35epoch:train:8101-8200batch: iter_time=1.194e-04, forward_time=0.103, loss_ctc=40.050, loss_att=42.436, acc=0.764, loss=41.720, backward_time=0.096, grad_norm=38.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.403e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 16:54:48,718 (trainer:737) INFO: 35epoch:train:8201-8300batch: iter_time=1.302e-04, forward_time=0.104, loss_ctc=42.357, loss_att=54.671, acc=0.721, loss=50.977, backward_time=0.096, grad_norm=44.648, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.403e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 16:55:30,139 (trainer:737) INFO: 35epoch:train:8301-8400batch: iter_time=1.305e-04, forward_time=0.103, loss_ctc=35.811, loss_att=44.642, acc=0.707, loss=41.993, backward_time=0.095, grad_norm=39.015, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.403e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 16:56:11,852 (trainer:737) INFO: 35epoch:train:8401-8500batch: iter_time=1.308e-04, forward_time=0.104, loss_ctc=42.662, loss_att=48.029, acc=0.740, loss=46.419, backward_time=0.096, grad_norm=40.150, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.402e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:56:53,356 (trainer:737) INFO: 35epoch:train:8501-8600batch: iter_time=1.175e-04, forward_time=0.104, loss_ctc=38.722, loss_att=42.364, acc=0.734, loss=41.272, backward_time=0.094, grad_norm=44.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.402e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 16:57:35,052 (trainer:737) INFO: 35epoch:train:8601-8700batch: iter_time=1.194e-04, forward_time=0.104, loss_ctc=44.499, loss_att=48.666, acc=0.744, loss=47.416, backward_time=0.095, grad_norm=46.665, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.402e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 16:58:00,852 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-16 16:58:19,840 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 16:58:23,438 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc8316f340>)
+[gpuc04:0/16] 2024-01-16 16:58:23,438 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-16 16:58:23,441 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 17:02:32,408 (trainer:737) INFO: 35epoch:train:8701-8800batch: iter_time=2.415, forward_time=0.104, loss_ctc=43.544, loss_att=60.164, acc=0.741, loss=55.178, backward_time=0.097, grad_norm=44.597, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.401e-04, train_time=2.973
+[gpuc04:0/16] 2024-01-16 17:03:14,179 (trainer:737) INFO: 35epoch:train:8801-8900batch: iter_time=1.220e-04, forward_time=0.104, loss_ctc=45.348, loss_att=55.601, acc=0.744, loss=52.525, backward_time=0.097, grad_norm=42.489, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.401e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:03:55,772 (trainer:737) INFO: 35epoch:train:8901-9000batch: iter_time=1.114e-04, forward_time=0.103, loss_ctc=41.989, loss_att=38.911, acc=0.758, loss=39.834, backward_time=0.096, grad_norm=41.087, clip=100.000, loss_scale=2.513e+34, optim_step_time=0.038, optim0_lr0=3.401e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:04:37,733 (trainer:737) INFO: 35epoch:train:9001-9100batch: iter_time=1.129e-04, forward_time=0.105, loss_ctc=40.023, loss_att=56.531, acc=0.737, loss=51.579, backward_time=0.097, grad_norm=40.696, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.400e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:05:04,132 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 17:05:19,654 (trainer:737) INFO: 35epoch:train:9101-9200batch: iter_time=1.062e-04, forward_time=0.104, loss_ctc=44.392, loss_att=58.119, acc=0.727, loss=54.001, backward_time=0.097, grad_norm=47.492, clip=100.000, loss_scale=3.378e+34, optim_step_time=0.038, optim0_lr0=3.400e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:06:01,546 (trainer:737) INFO: 35epoch:train:9201-9300batch: iter_time=1.205e-04, forward_time=0.104, loss_ctc=44.451, loss_att=55.705, acc=0.765, loss=52.329, backward_time=0.097, grad_norm=40.862, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.400e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:06:43,309 (trainer:737) INFO: 35epoch:train:9301-9400batch: iter_time=1.131e-04, forward_time=0.104, loss_ctc=39.152, loss_att=46.746, acc=0.759, loss=44.468, backward_time=0.097, grad_norm=41.575, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.399e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:07:25,009 (trainer:737) INFO: 35epoch:train:9401-9500batch: iter_time=1.258e-04, forward_time=0.103, loss_ctc=43.475, loss_att=46.030, acc=0.750, loss=45.263, backward_time=0.096, grad_norm=42.518, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.399e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:08:06,718 (trainer:737) INFO: 35epoch:train:9501-9600batch: iter_time=1.485e-04, forward_time=0.104, loss_ctc=39.452, loss_att=58.308, acc=0.706, loss=52.651, backward_time=0.096, grad_norm=39.002, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.399e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:08:48,298 (trainer:737) INFO: 35epoch:train:9601-9700batch: iter_time=1.540e-04, forward_time=0.103, loss_ctc=37.053, loss_att=47.209, acc=0.732, loss=44.162, backward_time=0.096, grad_norm=38.480, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.398e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:09:30,162 (trainer:737) INFO: 35epoch:train:9701-9800batch: iter_time=1.458e-04, forward_time=0.106, loss_ctc=39.951, loss_att=41.227, acc=0.753, loss=40.844, backward_time=0.096, grad_norm=37.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.398e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:10:11,837 (trainer:737) INFO: 35epoch:train:9801-9900batch: iter_time=1.474e-04, forward_time=0.103, loss_ctc=39.328, loss_att=44.531, acc=0.763, loss=42.970, backward_time=0.096, grad_norm=41.564, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.398e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:10:53,681 (trainer:737) INFO: 35epoch:train:9901-10000batch: iter_time=1.231e-04, forward_time=0.104, loss_ctc=48.858, loss_att=61.511, acc=0.724, loss=57.716, backward_time=0.097, grad_norm=48.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.397e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:10:58,055 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-16 17:11:17,294 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 17:11:20,881 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc429a6080>)
+[gpuc04:0/16] 2024-01-16 17:11:20,881 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-16 17:11:20,884 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 17:15:47,199 (trainer:737) INFO: 35epoch:train:10001-10100batch: iter_time=2.496, forward_time=0.104, loss_ctc=46.004, loss_att=58.541, acc=0.724, loss=54.780, backward_time=0.096, grad_norm=42.493, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.397e-04, train_time=2.935
+[gpuc04:0/16] 2024-01-16 17:16:29,735 (trainer:737) INFO: 35epoch:train:10101-10200batch: iter_time=9.113e-05, forward_time=0.104, loss_ctc=42.659, loss_att=44.778, acc=0.758, loss=44.142, backward_time=0.095, grad_norm=43.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.397e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 17:17:11,473 (trainer:737) INFO: 35epoch:train:10201-10300batch: iter_time=1.098e-04, forward_time=0.104, loss_ctc=41.453, loss_att=54.399, acc=0.719, loss=50.515, backward_time=0.096, grad_norm=41.448, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.396e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:17:53,702 (trainer:737) INFO: 35epoch:train:10301-10400batch: iter_time=1.104e-04, forward_time=0.104, loss_ctc=38.960, loss_att=44.550, acc=0.745, loss=42.873, backward_time=0.095, grad_norm=41.742, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.396e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 17:18:35,781 (trainer:737) INFO: 35epoch:train:10401-10500batch: iter_time=1.267e-04, forward_time=0.105, loss_ctc=48.403, loss_att=60.197, acc=0.735, loss=56.659, backward_time=0.096, grad_norm=46.916, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.396e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 17:19:18,321 (trainer:737) INFO: 35epoch:train:10501-10600batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=41.084, loss_att=51.705, acc=0.737, loss=48.519, backward_time=0.096, grad_norm=45.423, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.395e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 17:19:34,511 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 17:19:59,891 (trainer:737) INFO: 35epoch:train:10601-10700batch: iter_time=1.047e-04, forward_time=0.104, loss_ctc=40.069, loss_att=42.716, acc=0.763, loss=41.922, backward_time=0.095, grad_norm=40.667, clip=100.000, loss_scale=1.437e+34, optim_step_time=0.038, optim0_lr0=3.395e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 17:20:41,805 (trainer:737) INFO: 35epoch:train:10701-10800batch: iter_time=9.513e-05, forward_time=0.104, loss_ctc=41.989, loss_att=55.434, acc=0.717, loss=51.401, backward_time=0.095, grad_norm=48.212, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.395e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:21:23,154 (trainer:737) INFO: 35epoch:train:10801-10900batch: iter_time=1.074e-04, forward_time=0.103, loss_ctc=35.509, loss_att=45.197, acc=0.706, loss=42.291, backward_time=0.094, grad_norm=37.252, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.394e-04, train_time=0.413
+[gpuc04:0/16] 2024-01-16 17:22:05,068 (trainer:737) INFO: 35epoch:train:10901-11000batch: iter_time=1.059e-04, forward_time=0.105, loss_ctc=42.211, loss_att=47.593, acc=0.742, loss=45.978, backward_time=0.095, grad_norm=37.543, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.394e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:22:46,790 (trainer:737) INFO: 35epoch:train:11001-11100batch: iter_time=9.831e-05, forward_time=0.104, loss_ctc=38.474, loss_att=42.709, acc=0.732, loss=41.439, backward_time=0.094, grad_norm=42.824, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.394e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:23:28,407 (trainer:737) INFO: 35epoch:train:11101-11200batch: iter_time=9.648e-05, forward_time=0.104, loss_ctc=44.218, loss_att=49.304, acc=0.741, loss=47.778, backward_time=0.095, grad_norm=43.027, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.393e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:23:53,876 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-16 17:24:13,131 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 17:24:16,795 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc83124ac0>)
+[gpuc04:0/16] 2024-01-16 17:24:16,795 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-16 17:24:16,799 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 17:28:30,151 (trainer:737) INFO: 35epoch:train:11201-11300batch: iter_time=2.418, forward_time=0.108, loss_ctc=43.276, loss_att=58.436, acc=0.744, loss=53.888, backward_time=0.097, grad_norm=42.756, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.393e-04, train_time=3.017
+[gpuc04:0/16] 2024-01-16 17:29:11,957 (trainer:737) INFO: 35epoch:train:11301-11400batch: iter_time=1.052e-04, forward_time=0.104, loss_ctc=44.889, loss_att=55.652, acc=0.742, loss=52.423, backward_time=0.096, grad_norm=43.652, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.393e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:29:53,542 (trainer:737) INFO: 35epoch:train:11401-11500batch: iter_time=1.115e-04, forward_time=0.104, loss_ctc=42.258, loss_att=39.456, acc=0.757, loss=40.297, backward_time=0.095, grad_norm=41.848, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.393e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:30:35,371 (trainer:737) INFO: 35epoch:train:11501-11600batch: iter_time=1.080e-04, forward_time=0.105, loss_ctc=39.754, loss_att=56.829, acc=0.737, loss=51.706, backward_time=0.096, grad_norm=40.946, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.392e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:31:17,187 (trainer:737) INFO: 35epoch:train:11601-11700batch: iter_time=1.210e-04, forward_time=0.105, loss_ctc=43.654, loss_att=56.196, acc=0.730, loss=52.433, backward_time=0.096, grad_norm=47.350, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.392e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:31:59,034 (trainer:737) INFO: 35epoch:train:11701-11800batch: iter_time=1.211e-04, forward_time=0.105, loss_ctc=43.963, loss_att=54.792, acc=0.767, loss=51.544, backward_time=0.096, grad_norm=40.636, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.392e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:32:40,772 (trainer:737) INFO: 35epoch:train:11801-11900batch: iter_time=1.343e-04, forward_time=0.105, loss_ctc=38.723, loss_att=46.672, acc=0.759, loss=44.288, backward_time=0.096, grad_norm=42.257, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.391e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:33:22,698 (trainer:737) INFO: 35epoch:train:11901-12000batch: iter_time=1.398e-04, forward_time=0.104, loss_ctc=42.724, loss_att=46.342, acc=0.747, loss=45.256, backward_time=0.095, grad_norm=47.903, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.391e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 17:34:04,399 (trainer:737) INFO: 35epoch:train:12001-12100batch: iter_time=1.215e-04, forward_time=0.104, loss_ctc=38.895, loss_att=58.172, acc=0.704, loss=52.389, backward_time=0.096, grad_norm=40.386, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.391e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:34:45,948 (trainer:737) INFO: 35epoch:train:12101-12200batch: iter_time=1.362e-04, forward_time=0.103, loss_ctc=36.805, loss_att=46.205, acc=0.734, loss=43.385, backward_time=0.095, grad_norm=40.130, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.390e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 17:35:27,558 (trainer:737) INFO: 35epoch:train:12201-12300batch: iter_time=1.303e-04, forward_time=0.103, loss_ctc=39.676, loss_att=41.509, acc=0.755, loss=40.959, backward_time=0.095, grad_norm=37.922, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.390e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:36:10,210 (trainer:737) INFO: 35epoch:train:12301-12400batch: iter_time=1.362e-04, forward_time=0.104, loss_ctc=39.283, loss_att=44.766, acc=0.763, loss=43.121, backward_time=0.096, grad_norm=41.822, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.390e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-16 17:36:52,649 (trainer:737) INFO: 35epoch:train:12401-12500batch: iter_time=1.380e-04, forward_time=0.104, loss_ctc=48.456, loss_att=61.909, acc=0.721, loss=57.873, backward_time=0.096, grad_norm=48.896, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.389e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 17:36:58,276 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-16 17:37:17,263 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 17:37:20,877 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc5a6e0ee0>)
+[gpuc04:0/16] 2024-01-16 17:37:20,878 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-16 17:37:20,881 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 17:41:45,104 (trainer:737) INFO: 35epoch:train:12501-12600batch: iter_time=2.416, forward_time=0.106, loss_ctc=46.163, loss_att=56.418, acc=0.740, loss=53.342, backward_time=0.097, grad_norm=43.267, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.389e-04, train_time=2.924
+[gpuc04:0/16] 2024-01-16 17:42:26,733 (trainer:737) INFO: 35epoch:train:12601-12700batch: iter_time=1.094e-04, forward_time=0.105, loss_ctc=42.611, loss_att=43.636, acc=0.768, loss=43.329, backward_time=0.096, grad_norm=43.415, clip=100.000, loss_scale=1.672e+34, optim_step_time=0.038, optim0_lr0=3.389e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:43:08,472 (trainer:737) INFO: 35epoch:train:12701-12800batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=41.026, loss_att=53.502, acc=0.733, loss=49.759, backward_time=0.097, grad_norm=42.498, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.388e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:43:50,202 (trainer:737) INFO: 35epoch:train:12801-12900batch: iter_time=1.202e-04, forward_time=0.104, loss_ctc=38.669, loss_att=45.916, acc=0.755, loss=43.742, backward_time=0.097, grad_norm=41.679, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.388e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:44:32,040 (trainer:737) INFO: 35epoch:train:12901-13000batch: iter_time=9.897e-05, forward_time=0.105, loss_ctc=48.376, loss_att=60.833, acc=0.739, loss=57.096, backward_time=0.097, grad_norm=48.237, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.388e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:45:13,814 (trainer:737) INFO: 35epoch:train:13001-13100batch: iter_time=1.147e-04, forward_time=0.104, loss_ctc=40.896, loss_att=51.538, acc=0.748, loss=48.345, backward_time=0.097, grad_norm=43.807, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.387e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:45:55,443 (trainer:737) INFO: 35epoch:train:13101-13200batch: iter_time=1.155e-04, forward_time=0.104, loss_ctc=40.145, loss_att=44.146, acc=0.773, loss=42.946, backward_time=0.096, grad_norm=38.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.387e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:46:37,181 (trainer:737) INFO: 35epoch:train:13201-13300batch: iter_time=1.156e-04, forward_time=0.104, loss_ctc=41.481, loss_att=54.405, acc=0.732, loss=50.528, backward_time=0.097, grad_norm=46.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.387e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:47:18,644 (trainer:737) INFO: 35epoch:train:13301-13400batch: iter_time=1.082e-04, forward_time=0.104, loss_ctc=35.402, loss_att=46.237, acc=0.715, loss=42.986, backward_time=0.096, grad_norm=37.928, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.386e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 17:48:00,366 (trainer:737) INFO: 35epoch:train:13401-13500batch: iter_time=1.099e-04, forward_time=0.105, loss_ctc=42.375, loss_att=49.979, acc=0.748, loss=47.698, backward_time=0.096, grad_norm=39.259, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.386e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:48:41,849 (trainer:737) INFO: 35epoch:train:13501-13600batch: iter_time=1.107e-04, forward_time=0.103, loss_ctc=38.074, loss_att=44.035, acc=0.740, loss=42.247, backward_time=0.095, grad_norm=43.218, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.386e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 17:49:23,544 (trainer:737) INFO: 35epoch:train:13601-13700batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=43.880, loss_att=49.336, acc=0.749, loss=47.699, backward_time=0.096, grad_norm=45.185, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.385e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:49:49,289 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-16 17:50:08,488 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 17:50:12,489 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc775c2a10>)
+[gpuc04:0/16] 2024-01-16 17:50:12,489 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-16 17:50:12,492 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 17:54:20,771 (trainer:737) INFO: 35epoch:train:13701-13800batch: iter_time=2.472, forward_time=0.106, loss_ctc=43.365, loss_att=59.242, acc=0.734, loss=54.479, backward_time=0.097, grad_norm=43.908, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.385e-04, train_time=2.972
+[gpuc04:0/16] 2024-01-16 17:55:03,453 (trainer:737) INFO: 35epoch:train:13801-13900batch: iter_time=9.515e-05, forward_time=0.104, loss_ctc=45.038, loss_att=56.648, acc=0.732, loss=53.165, backward_time=0.096, grad_norm=43.664, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.385e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-16 17:55:45,861 (trainer:737) INFO: 35epoch:train:13901-14000batch: iter_time=1.025e-04, forward_time=0.104, loss_ctc=42.297, loss_att=39.629, acc=0.754, loss=40.429, backward_time=0.095, grad_norm=42.602, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.384e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 17:56:27,582 (trainer:737) INFO: 35epoch:train:14001-14100batch: iter_time=1.040e-04, forward_time=0.105, loss_ctc=40.235, loss_att=54.645, acc=0.728, loss=50.322, backward_time=0.096, grad_norm=41.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.384e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 17:57:09,575 (trainer:737) INFO: 35epoch:train:14101-14200batch: iter_time=1.035e-04, forward_time=0.105, loss_ctc=43.953, loss_att=56.773, acc=0.721, loss=52.927, backward_time=0.096, grad_norm=49.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.384e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 17:57:52,024 (trainer:737) INFO: 35epoch:train:14201-14300batch: iter_time=1.156e-04, forward_time=0.104, loss_ctc=44.073, loss_att=54.987, acc=0.757, loss=51.713, backward_time=0.096, grad_norm=41.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.383e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 17:58:33,647 (trainer:737) INFO: 35epoch:train:14301-14400batch: iter_time=1.069e-04, forward_time=0.104, loss_ctc=38.564, loss_att=45.712, acc=0.752, loss=43.568, backward_time=0.096, grad_norm=42.114, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.383e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 17:59:15,513 (trainer:737) INFO: 35epoch:train:14401-14500batch: iter_time=1.074e-04, forward_time=0.104, loss_ctc=42.614, loss_att=43.812, acc=0.743, loss=43.453, backward_time=0.095, grad_norm=46.774, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.383e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 17:59:57,677 (trainer:737) INFO: 35epoch:train:14501-14600batch: iter_time=1.200e-04, forward_time=0.104, loss_ctc=38.958, loss_att=55.328, acc=0.704, loss=50.417, backward_time=0.095, grad_norm=40.932, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.382e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 18:00:39,111 (trainer:737) INFO: 35epoch:train:14601-14700batch: iter_time=1.086e-04, forward_time=0.103, loss_ctc=36.686, loss_att=45.985, acc=0.717, loss=43.196, backward_time=0.095, grad_norm=40.028, clip=100.000, loss_scale=3.344e+34, optim_step_time=0.038, optim0_lr0=3.382e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 18:01:01,954 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 18:01:20,480 (trainer:737) INFO: 35epoch:train:14701-14800batch: iter_time=1.031e-04, forward_time=0.103, loss_ctc=39.410, loss_att=41.986, acc=0.744, loss=41.213, backward_time=0.095, grad_norm=39.265, clip=100.000, loss_scale=3.210e+34, optim_step_time=0.038, optim0_lr0=3.382e-04, train_time=0.413
+[gpuc04:0/16] 2024-01-16 18:02:02,055 (trainer:737) INFO: 35epoch:train:14801-14900batch: iter_time=1.022e-04, forward_time=0.104, loss_ctc=39.487, loss_att=42.949, acc=0.759, loss=41.910, backward_time=0.095, grad_norm=42.129, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.381e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:02:43,729 (trainer:737) INFO: 35epoch:train:14901-15000batch: iter_time=9.961e-05, forward_time=0.104, loss_ctc=48.982, loss_att=61.039, acc=0.720, loss=57.422, backward_time=0.096, grad_norm=51.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.381e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 18:22:43,373 (trainer:343) INFO: 35epoch results: [train] iter_time=0.210, forward_time=0.105, loss_ctc=42.320, loss_att=50.654, acc=0.739, loss=48.154, backward_time=0.096, grad_norm=43.219, clip=100.000, loss_scale=2.115e+34, optim_step_time=0.038, optim0_lr0=3.406e-04, train_time=0.644, time=2 hours, 41 minutes and 7.39 seconds, total_count=525000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=50.705, cer_ctc=0.256, loss_att=52.693, acc=0.602, cer=0.363, wer=0.999, loss=52.096, time=19 minutes and 50.46 seconds, total_count=163485, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-16 18:22:48,282 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-16 18:22:48,339 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/25epoch.pth, exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/30epoch.pth
+[gpuc04:0/16] 2024-01-16 18:22:48,339 (trainer:272) INFO: 36/45epoch started. Estimated time to finish: 1 day, 5 hours and 59 minutes
+[gpuc04:0/16] 2024-01-16 18:22:48,348 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-16 18:23:07,162 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 18:23:10,753 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb545efb80>)
+[gpuc04:0/16] 2024-01-16 18:23:10,753 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-16 18:23:10,756 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 18:27:28,524 (trainer:737) INFO: 36epoch:train:1-100batch: iter_time=2.383, forward_time=0.104, loss_ctc=55.215, loss_att=61.154, acc=0.706, loss=59.372, backward_time=0.097, grad_norm=62.519, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.381e-04, train_time=2.801
+[gpuc04:0/16] 2024-01-16 18:28:10,132 (trainer:737) INFO: 36epoch:train:101-200batch: iter_time=1.015e-04, forward_time=0.103, loss_ctc=47.460, loss_att=52.279, acc=0.719, loss=50.833, backward_time=0.097, grad_norm=48.675, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.381e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:28:51,645 (trainer:737) INFO: 36epoch:train:201-300batch: iter_time=1.008e-04, forward_time=0.103, loss_ctc=54.556, loss_att=45.518, acc=0.729, loss=48.229, backward_time=0.096, grad_norm=50.056, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.380e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 18:29:33,360 (trainer:737) INFO: 36epoch:train:301-400batch: iter_time=9.875e-05, forward_time=0.104, loss_ctc=45.377, loss_att=58.747, acc=0.718, loss=54.736, backward_time=0.098, grad_norm=49.077, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.380e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 18:30:17,300 (trainer:737) INFO: 36epoch:train:401-500batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=45.085, loss_att=56.824, acc=0.723, loss=53.302, backward_time=0.098, grad_norm=45.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.380e-04, train_time=0.439
+[gpuc04:0/16] 2024-01-16 18:31:00,645 (trainer:737) INFO: 36epoch:train:501-600batch: iter_time=1.100e-04, forward_time=0.112, loss_ctc=46.440, loss_att=58.282, acc=0.733, loss=54.729, backward_time=0.101, grad_norm=48.045, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.379e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-16 18:31:42,431 (trainer:737) INFO: 36epoch:train:601-700batch: iter_time=1.007e-04, forward_time=0.104, loss_ctc=53.496, loss_att=55.476, acc=0.731, loss=54.882, backward_time=0.098, grad_norm=53.476, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.379e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 18:32:23,852 (trainer:737) INFO: 36epoch:train:701-800batch: iter_time=1.086e-04, forward_time=0.104, loss_ctc=43.566, loss_att=47.415, acc=0.733, loss=46.260, backward_time=0.097, grad_norm=46.832, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.379e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 18:33:19,939 (trainer:737) INFO: 36epoch:train:801-900batch: iter_time=1.114e-04, forward_time=0.123, loss_ctc=52.152, loss_att=59.095, acc=0.732, loss=57.012, backward_time=0.102, grad_norm=51.356, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.378e-04, train_time=0.561
+[gpuc04:0/16] 2024-01-16 18:34:10,648 (trainer:737) INFO: 36epoch:train:901-1000batch: iter_time=1.105e-04, forward_time=0.160, loss_ctc=51.569, loss_att=53.415, acc=0.722, loss=52.861, backward_time=0.104, grad_norm=58.047, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.045, optim0_lr0=3.378e-04, train_time=0.506
+[gpuc04:0/16] 2024-01-16 18:34:52,228 (trainer:737) INFO: 36epoch:train:1001-1100batch: iter_time=1.098e-04, forward_time=0.104, loss_ctc=48.907, loss_att=51.253, acc=0.719, loss=50.549, backward_time=0.096, grad_norm=48.600, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.378e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:35:35,611 (trainer:737) INFO: 36epoch:train:1101-1200batch: iter_time=1.107e-04, forward_time=0.104, loss_ctc=44.179, loss_att=50.361, acc=0.717, loss=48.507, backward_time=0.097, grad_norm=47.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.377e-04, train_time=0.434
+[gpuc04:0/16] 2024-01-16 18:36:16,083 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-16 18:36:35,097 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 18:36:39,026 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe3f476b1f0>)
+[gpuc04:0/16] 2024-01-16 18:36:39,026 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-16 18:36:39,030 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 18:42:51,590 (trainer:737) INFO: 36epoch:train:1201-1300batch: iter_time=3.867, forward_time=0.103, loss_ctc=45.752, loss_att=59.699, acc=0.693, loss=55.515, backward_time=0.097, grad_norm=52.368, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.377e-04, train_time=4.360
+[gpuc04:0/16] 2024-01-16 18:43:33,771 (trainer:737) INFO: 36epoch:train:1301-1400batch: iter_time=1.538e-04, forward_time=0.106, loss_ctc=54.903, loss_att=58.012, acc=0.719, loss=57.079, backward_time=0.098, grad_norm=69.167, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.377e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 18:44:15,434 (trainer:737) INFO: 36epoch:train:1401-1500batch: iter_time=1.605e-04, forward_time=0.106, loss_ctc=54.780, loss_att=51.982, acc=0.722, loss=52.822, backward_time=0.098, grad_norm=48.882, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.376e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:44:57,061 (trainer:737) INFO: 36epoch:train:1501-1600batch: iter_time=2.016e-04, forward_time=0.105, loss_ctc=41.498, loss_att=44.418, acc=0.729, loss=43.542, backward_time=0.096, grad_norm=41.669, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.376e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:45:38,914 (trainer:737) INFO: 36epoch:train:1601-1700batch: iter_time=1.685e-04, forward_time=0.106, loss_ctc=48.448, loss_att=66.978, acc=0.709, loss=61.419, backward_time=0.098, grad_norm=49.833, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.376e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 18:46:20,594 (trainer:737) INFO: 36epoch:train:1701-1800batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=42.230, loss_att=52.733, acc=0.747, loss=49.582, backward_time=0.097, grad_norm=47.894, clip=100.000, loss_scale=3.012e+34, optim_step_time=0.038, optim0_lr0=3.375e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 18:46:43,107 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 18:47:02,287 (trainer:737) INFO: 36epoch:train:1801-1900batch: iter_time=1.556e-04, forward_time=0.104, loss_ctc=52.294, loss_att=60.256, acc=0.718, loss=57.867, backward_time=0.097, grad_norm=51.690, clip=100.000, loss_scale=3.189e+34, optim_step_time=0.039, optim0_lr0=3.375e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 18:47:43,827 (trainer:737) INFO: 36epoch:train:1901-2000batch: iter_time=1.641e-04, forward_time=0.103, loss_ctc=43.916, loss_att=47.411, acc=0.740, loss=46.362, backward_time=0.096, grad_norm=44.664, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.375e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 18:48:25,595 (trainer:737) INFO: 36epoch:train:2001-2100batch: iter_time=1.436e-04, forward_time=0.104, loss_ctc=45.816, loss_att=53.732, acc=0.728, loss=51.357, backward_time=0.096, grad_norm=47.926, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.374e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 18:49:07,488 (trainer:737) INFO: 36epoch:train:2101-2200batch: iter_time=1.467e-04, forward_time=0.104, loss_ctc=52.213, loss_att=55.488, acc=0.739, loss=54.506, backward_time=0.096, grad_norm=48.901, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.374e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 18:49:48,918 (trainer:737) INFO: 36epoch:train:2201-2300batch: iter_time=1.416e-04, forward_time=0.103, loss_ctc=47.737, loss_att=51.472, acc=0.729, loss=50.351, backward_time=0.095, grad_norm=54.052, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.374e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 18:50:30,383 (trainer:737) INFO: 36epoch:train:2301-2400batch: iter_time=1.607e-04, forward_time=0.103, loss_ctc=44.649, loss_att=49.094, acc=0.710, loss=47.760, backward_time=0.096, grad_norm=47.116, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.373e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 18:51:11,958 (trainer:737) INFO: 36epoch:train:2401-2500batch: iter_time=1.478e-04, forward_time=0.103, loss_ctc=46.071, loss_att=57.815, acc=0.708, loss=54.292, backward_time=0.096, grad_norm=48.236, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.373e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:51:16,589 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-16 18:51:35,556 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 18:51:39,125 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc869c3b80>)
+[gpuc04:0/16] 2024-01-16 18:51:39,125 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-16 18:51:39,128 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 18:56:07,197 (trainer:737) INFO: 36epoch:train:2501-2600batch: iter_time=2.528, forward_time=0.103, loss_ctc=53.811, loss_att=64.962, acc=0.710, loss=61.617, backward_time=0.096, grad_norm=61.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.373e-04, train_time=2.952
+[gpuc04:0/16] 2024-01-16 18:56:48,791 (trainer:737) INFO: 36epoch:train:2601-2700batch: iter_time=1.153e-04, forward_time=0.103, loss_ctc=45.938, loss_att=52.770, acc=0.736, loss=50.720, backward_time=0.096, grad_norm=46.066, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.373e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 18:57:30,631 (trainer:737) INFO: 36epoch:train:2701-2800batch: iter_time=1.126e-04, forward_time=0.105, loss_ctc=52.058, loss_att=47.253, acc=0.739, loss=48.695, backward_time=0.096, grad_norm=47.505, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.372e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 18:58:13,441 (trainer:737) INFO: 36epoch:train:2801-2900batch: iter_time=1.343e-04, forward_time=0.103, loss_ctc=44.136, loss_att=58.086, acc=0.733, loss=53.901, backward_time=0.096, grad_norm=44.980, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.372e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-16 18:58:55,517 (trainer:737) INFO: 36epoch:train:2901-3000batch: iter_time=1.225e-04, forward_time=0.104, loss_ctc=44.007, loss_att=57.224, acc=0.734, loss=53.259, backward_time=0.096, grad_norm=43.502, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.372e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 18:59:37,349 (trainer:737) INFO: 36epoch:train:3001-3100batch: iter_time=1.230e-04, forward_time=0.104, loss_ctc=45.587, loss_att=58.210, acc=0.737, loss=54.423, backward_time=0.096, grad_norm=46.848, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.371e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:00:19,184 (trainer:737) INFO: 36epoch:train:3101-3200batch: iter_time=1.254e-04, forward_time=0.104, loss_ctc=50.233, loss_att=53.972, acc=0.741, loss=52.851, backward_time=0.097, grad_norm=48.750, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.371e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:01:00,743 (trainer:737) INFO: 36epoch:train:3201-3300batch: iter_time=1.487e-04, forward_time=0.103, loss_ctc=42.348, loss_att=47.663, acc=0.739, loss=46.068, backward_time=0.095, grad_norm=46.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.371e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:01:42,549 (trainer:737) INFO: 36epoch:train:3301-3400batch: iter_time=1.237e-04, forward_time=0.104, loss_ctc=50.339, loss_att=58.094, acc=0.739, loss=55.768, backward_time=0.097, grad_norm=49.492, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.370e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:02:25,343 (trainer:737) INFO: 36epoch:train:3401-3500batch: iter_time=1.154e-04, forward_time=0.107, loss_ctc=49.221, loss_att=53.882, acc=0.733, loss=52.484, backward_time=0.100, grad_norm=54.890, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.370e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-16 19:03:10,943 (trainer:737) INFO: 36epoch:train:3501-3600batch: iter_time=1.135e-04, forward_time=0.125, loss_ctc=47.500, loss_att=50.629, acc=0.733, loss=49.690, backward_time=0.098, grad_norm=47.255, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.370e-04, train_time=0.456
+[gpuc04:0/16] 2024-01-16 19:03:54,157 (trainer:737) INFO: 36epoch:train:3601-3700batch: iter_time=1.302e-04, forward_time=0.111, loss_ctc=42.434, loss_att=48.216, acc=0.742, loss=46.481, backward_time=0.099, grad_norm=46.737, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.369e-04, train_time=0.432
+[gpuc04:0/16] 2024-01-16 19:04:26,850 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-16 19:04:46,255 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 19:04:49,946 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4307a38e0>)
+[gpuc04:0/16] 2024-01-16 19:04:49,946 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-16 19:04:49,950 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 19:10:46,318 (trainer:737) INFO: 36epoch:train:3701-3800batch: iter_time=3.008, forward_time=0.104, loss_ctc=44.611, loss_att=60.118, acc=0.702, loss=55.466, backward_time=0.096, grad_norm=50.848, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.369e-04, train_time=4.121
+[gpuc04:0/16] 2024-01-16 19:11:27,987 (trainer:737) INFO: 36epoch:train:3801-3900batch: iter_time=1.286e-04, forward_time=0.104, loss_ctc=54.112, loss_att=59.112, acc=0.719, loss=57.612, backward_time=0.096, grad_norm=65.843, clip=100.000, loss_scale=3.032e+34, optim_step_time=0.038, optim0_lr0=3.369e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:11:35,638 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 19:12:10,028 (trainer:737) INFO: 36epoch:train:3901-4000batch: iter_time=1.386e-04, forward_time=0.104, loss_ctc=52.592, loss_att=51.705, acc=0.725, loss=51.971, backward_time=0.096, grad_norm=45.677, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.038, optim0_lr0=3.368e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 19:12:51,975 (trainer:737) INFO: 36epoch:train:4001-4100batch: iter_time=1.533e-04, forward_time=0.104, loss_ctc=41.421, loss_att=44.551, acc=0.731, loss=43.612, backward_time=0.096, grad_norm=42.180, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.368e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:13:34,538 (trainer:737) INFO: 36epoch:train:4101-4200batch: iter_time=1.653e-04, forward_time=0.105, loss_ctc=48.097, loss_att=67.731, acc=0.709, loss=61.841, backward_time=0.097, grad_norm=50.400, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.368e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 19:14:16,406 (trainer:737) INFO: 36epoch:train:4201-4300batch: iter_time=1.693e-04, forward_time=0.105, loss_ctc=41.620, loss_att=53.534, acc=0.747, loss=49.960, backward_time=0.096, grad_norm=42.815, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.367e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:14:58,079 (trainer:737) INFO: 36epoch:train:4301-4400batch: iter_time=1.601e-04, forward_time=0.104, loss_ctc=51.100, loss_att=58.583, acc=0.724, loss=56.338, backward_time=0.096, grad_norm=50.268, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.367e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:15:40,457 (trainer:737) INFO: 36epoch:train:4401-4500batch: iter_time=1.501e-04, forward_time=0.104, loss_ctc=42.765, loss_att=47.166, acc=0.740, loss=45.846, backward_time=0.096, grad_norm=42.601, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.367e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 19:16:22,355 (trainer:737) INFO: 36epoch:train:4501-4600batch: iter_time=1.658e-04, forward_time=0.105, loss_ctc=45.066, loss_att=52.864, acc=0.731, loss=50.525, backward_time=0.096, grad_norm=49.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.366e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:17:04,568 (trainer:737) INFO: 36epoch:train:4601-4700batch: iter_time=1.561e-04, forward_time=0.107, loss_ctc=51.690, loss_att=55.212, acc=0.740, loss=54.155, backward_time=0.096, grad_norm=49.427, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.366e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 19:17:46,066 (trainer:737) INFO: 36epoch:train:4701-4800batch: iter_time=1.519e-04, forward_time=0.104, loss_ctc=46.990, loss_att=51.240, acc=0.729, loss=49.965, backward_time=0.096, grad_norm=53.376, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.366e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:18:27,618 (trainer:737) INFO: 36epoch:train:4801-4900batch: iter_time=1.475e-04, forward_time=0.104, loss_ctc=44.641, loss_att=49.678, acc=0.708, loss=48.167, backward_time=0.096, grad_norm=46.199, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.366e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:19:09,273 (trainer:737) INFO: 36epoch:train:4901-5000batch: iter_time=1.283e-04, forward_time=0.104, loss_ctc=45.446, loss_att=57.257, acc=0.709, loss=53.714, backward_time=0.097, grad_norm=47.412, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.365e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:19:13,878 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-16 19:19:33,016 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 19:19:36,571 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4539c34f0>)
+[gpuc04:0/16] 2024-01-16 19:19:36,571 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-16 19:19:36,575 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 19:24:06,393 (trainer:737) INFO: 36epoch:train:5001-5100batch: iter_time=2.527, forward_time=0.105, loss_ctc=52.788, loss_att=62.996, acc=0.715, loss=59.933, backward_time=0.097, grad_norm=58.022, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.365e-04, train_time=2.971
+[gpuc04:0/16] 2024-01-16 19:24:48,024 (trainer:737) INFO: 36epoch:train:5101-5200batch: iter_time=1.380e-04, forward_time=0.105, loss_ctc=45.545, loss_att=52.392, acc=0.735, loss=50.338, backward_time=0.096, grad_norm=45.910, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.365e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:25:14,601 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 19:25:29,573 (trainer:737) INFO: 36epoch:train:5201-5300batch: iter_time=1.358e-04, forward_time=0.105, loss_ctc=51.249, loss_att=45.837, acc=0.748, loss=47.461, backward_time=0.095, grad_norm=46.541, clip=100.000, loss_scale=1.699e+34, optim_step_time=0.038, optim0_lr0=3.364e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:26:11,301 (trainer:737) INFO: 36epoch:train:5301-5400batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=43.892, loss_att=58.592, acc=0.730, loss=54.182, backward_time=0.096, grad_norm=44.578, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.364e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:26:53,349 (trainer:737) INFO: 36epoch:train:5401-5500batch: iter_time=1.352e-04, forward_time=0.106, loss_ctc=43.388, loss_att=56.761, acc=0.735, loss=52.750, backward_time=0.096, grad_norm=44.718, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.364e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 19:27:35,277 (trainer:737) INFO: 36epoch:train:5501-5600batch: iter_time=1.546e-04, forward_time=0.105, loss_ctc=44.502, loss_att=57.502, acc=0.739, loss=53.602, backward_time=0.096, grad_norm=44.307, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.363e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:28:17,261 (trainer:737) INFO: 36epoch:train:5601-5700batch: iter_time=1.349e-04, forward_time=0.106, loss_ctc=50.386, loss_att=53.728, acc=0.741, loss=52.726, backward_time=0.096, grad_norm=58.778, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.363e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 19:28:59,213 (trainer:737) INFO: 36epoch:train:5701-5800batch: iter_time=1.474e-04, forward_time=0.105, loss_ctc=41.856, loss_att=47.180, acc=0.743, loss=45.583, backward_time=0.095, grad_norm=44.456, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.363e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:29:41,300 (trainer:737) INFO: 36epoch:train:5801-5900batch: iter_time=1.420e-04, forward_time=0.105, loss_ctc=50.055, loss_att=58.474, acc=0.739, loss=55.948, backward_time=0.096, grad_norm=48.397, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.362e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 19:30:23,431 (trainer:737) INFO: 36epoch:train:5901-6000batch: iter_time=1.280e-04, forward_time=0.104, loss_ctc=48.129, loss_att=53.641, acc=0.733, loss=51.988, backward_time=0.096, grad_norm=52.281, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.362e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 19:31:05,579 (trainer:737) INFO: 36epoch:train:6001-6100batch: iter_time=1.297e-04, forward_time=0.104, loss_ctc=47.333, loss_att=50.984, acc=0.734, loss=49.889, backward_time=0.096, grad_norm=46.892, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.362e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 19:31:47,180 (trainer:737) INFO: 36epoch:train:6101-6200batch: iter_time=1.349e-04, forward_time=0.105, loss_ctc=42.349, loss_att=48.866, acc=0.740, loss=46.910, backward_time=0.096, grad_norm=45.984, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.361e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:32:15,121 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-16 19:32:34,148 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 19:32:37,835 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc9b3dbe80>)
+[gpuc04:0/16] 2024-01-16 19:32:37,835 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-16 19:32:37,838 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 19:36:53,349 (trainer:737) INFO: 36epoch:train:6201-6300batch: iter_time=2.641, forward_time=0.105, loss_ctc=43.663, loss_att=58.302, acc=0.710, loss=53.910, backward_time=0.096, grad_norm=48.991, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.361e-04, train_time=3.061
+[gpuc04:0/16] 2024-01-16 19:37:35,205 (trainer:737) INFO: 36epoch:train:6301-6400batch: iter_time=1.844e-04, forward_time=0.105, loss_ctc=54.611, loss_att=59.697, acc=0.731, loss=58.171, backward_time=0.097, grad_norm=67.666, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.361e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:38:16,883 (trainer:737) INFO: 36epoch:train:6401-6500batch: iter_time=1.581e-04, forward_time=0.104, loss_ctc=52.530, loss_att=53.152, acc=0.739, loss=52.966, backward_time=0.096, grad_norm=44.753, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.360e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:38:58,392 (trainer:737) INFO: 36epoch:train:6501-6600batch: iter_time=1.530e-04, forward_time=0.103, loss_ctc=41.035, loss_att=43.234, acc=0.746, loss=42.574, backward_time=0.095, grad_norm=38.998, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.360e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:39:40,560 (trainer:737) INFO: 36epoch:train:6601-6700batch: iter_time=1.586e-04, forward_time=0.105, loss_ctc=47.715, loss_att=67.268, acc=0.718, loss=61.402, backward_time=0.097, grad_norm=48.836, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.360e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 19:40:22,256 (trainer:737) INFO: 36epoch:train:6701-6800batch: iter_time=1.696e-04, forward_time=0.104, loss_ctc=41.477, loss_att=52.210, acc=0.760, loss=48.990, backward_time=0.096, grad_norm=45.345, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.359e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:41:03,911 (trainer:737) INFO: 36epoch:train:6801-6900batch: iter_time=1.583e-04, forward_time=0.104, loss_ctc=50.237, loss_att=59.706, acc=0.718, loss=56.865, backward_time=0.096, grad_norm=50.473, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.359e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:41:45,518 (trainer:737) INFO: 36epoch:train:6901-7000batch: iter_time=1.451e-04, forward_time=0.104, loss_ctc=43.038, loss_att=47.142, acc=0.746, loss=45.911, backward_time=0.096, grad_norm=42.876, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.359e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:42:27,147 (trainer:737) INFO: 36epoch:train:7001-7100batch: iter_time=1.725e-04, forward_time=0.104, loss_ctc=45.171, loss_att=52.982, acc=0.737, loss=50.639, backward_time=0.096, grad_norm=47.133, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.359e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:43:08,899 (trainer:737) INFO: 36epoch:train:7101-7200batch: iter_time=1.558e-04, forward_time=0.104, loss_ctc=51.162, loss_att=55.346, acc=0.750, loss=54.091, backward_time=0.096, grad_norm=47.986, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.358e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:43:50,386 (trainer:737) INFO: 36epoch:train:7201-7300batch: iter_time=1.541e-04, forward_time=0.104, loss_ctc=46.210, loss_att=50.772, acc=0.738, loss=49.403, backward_time=0.096, grad_norm=53.958, clip=100.000, loss_scale=1.412e+34, optim_step_time=0.039, optim0_lr0=3.358e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:44:32,195 (trainer:737) INFO: 36epoch:train:7301-7400batch: iter_time=1.490e-04, forward_time=0.104, loss_ctc=44.106, loss_att=48.689, acc=0.729, loss=47.314, backward_time=0.096, grad_norm=45.153, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.358e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:44:41,347 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 19:45:14,149 (trainer:737) INFO: 36epoch:train:7401-7500batch: iter_time=1.347e-04, forward_time=0.104, loss_ctc=45.275, loss_att=58.139, acc=0.725, loss=54.280, backward_time=0.097, grad_norm=47.954, clip=100.000, loss_scale=1.259e+34, optim_step_time=0.038, optim0_lr0=3.357e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:45:18,485 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-16 19:45:37,877 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 19:45:41,614 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc9f572980>)
+[gpuc04:0/16] 2024-01-16 19:45:41,614 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-16 19:45:41,617 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 19:50:13,713 (trainer:737) INFO: 36epoch:train:7501-7600batch: iter_time=2.516, forward_time=0.114, loss_ctc=51.188, loss_att=60.716, acc=0.716, loss=57.858, backward_time=0.100, grad_norm=57.987, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.357e-04, train_time=2.995
+[gpuc04:0/16] 2024-01-16 19:50:55,816 (trainer:737) INFO: 36epoch:train:7601-7700batch: iter_time=1.253e-04, forward_time=0.106, loss_ctc=44.824, loss_att=49.890, acc=0.742, loss=48.370, backward_time=0.097, grad_norm=44.366, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.357e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 19:51:37,540 (trainer:737) INFO: 36epoch:train:7701-7800batch: iter_time=1.260e-04, forward_time=0.105, loss_ctc=50.479, loss_att=46.409, acc=0.742, loss=47.630, backward_time=0.097, grad_norm=45.673, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.356e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:52:19,331 (trainer:737) INFO: 36epoch:train:7801-7900batch: iter_time=1.702e-04, forward_time=0.106, loss_ctc=43.630, loss_att=56.972, acc=0.736, loss=52.970, backward_time=0.097, grad_norm=43.784, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.356e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:53:01,079 (trainer:737) INFO: 36epoch:train:7901-8000batch: iter_time=1.324e-04, forward_time=0.105, loss_ctc=43.231, loss_att=55.562, acc=0.738, loss=51.863, backward_time=0.097, grad_norm=42.260, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.356e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:53:42,775 (trainer:737) INFO: 36epoch:train:8001-8100batch: iter_time=1.198e-04, forward_time=0.106, loss_ctc=44.578, loss_att=57.583, acc=0.738, loss=53.681, backward_time=0.097, grad_norm=47.234, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.355e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 19:54:24,586 (trainer:737) INFO: 36epoch:train:8101-8200batch: iter_time=1.327e-04, forward_time=0.106, loss_ctc=50.078, loss_att=53.058, acc=0.744, loss=52.164, backward_time=0.097, grad_norm=49.580, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.355e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 19:55:06,636 (trainer:737) INFO: 36epoch:train:8201-8300batch: iter_time=1.139e-04, forward_time=0.110, loss_ctc=41.336, loss_att=47.165, acc=0.742, loss=45.416, backward_time=0.096, grad_norm=44.755, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.355e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 19:55:48,563 (trainer:737) INFO: 36epoch:train:8301-8400batch: iter_time=1.129e-04, forward_time=0.106, loss_ctc=49.441, loss_att=58.231, acc=0.739, loss=55.594, backward_time=0.096, grad_norm=50.730, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.354e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 19:56:30,216 (trainer:737) INFO: 36epoch:train:8401-8500batch: iter_time=1.537e-04, forward_time=0.106, loss_ctc=47.511, loss_att=52.590, acc=0.736, loss=51.066, backward_time=0.096, grad_norm=52.410, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.354e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:57:11,771 (trainer:737) INFO: 36epoch:train:8501-8600batch: iter_time=1.406e-04, forward_time=0.105, loss_ctc=47.006, loss_att=50.243, acc=0.736, loss=49.271, backward_time=0.096, grad_norm=46.190, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.354e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 19:57:53,434 (trainer:737) INFO: 36epoch:train:8601-8700batch: iter_time=1.293e-04, forward_time=0.105, loss_ctc=41.919, loss_att=47.199, acc=0.744, loss=45.615, backward_time=0.096, grad_norm=45.523, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.354e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 19:58:20,127 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-16 19:58:39,726 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 19:58:43,343 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc6057eb30>)
+[gpuc04:0/16] 2024-01-16 19:58:43,343 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-16 19:58:43,347 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 20:02:55,070 (trainer:737) INFO: 36epoch:train:8701-8800batch: iter_time=2.592, forward_time=0.105, loss_ctc=42.991, loss_att=59.946, acc=0.702, loss=54.860, backward_time=0.096, grad_norm=51.408, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.353e-04, train_time=3.016
+[gpuc04:0/16] 2024-01-16 20:03:36,859 (trainer:737) INFO: 36epoch:train:8801-8900batch: iter_time=1.448e-04, forward_time=0.105, loss_ctc=53.591, loss_att=60.223, acc=0.716, loss=58.233, backward_time=0.097, grad_norm=58.855, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.353e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:04:19,063 (trainer:737) INFO: 36epoch:train:8901-9000batch: iter_time=1.562e-04, forward_time=0.105, loss_ctc=51.385, loss_att=52.514, acc=0.720, loss=52.175, backward_time=0.097, grad_norm=44.790, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.353e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 20:05:00,899 (trainer:737) INFO: 36epoch:train:9001-9100batch: iter_time=1.687e-04, forward_time=0.104, loss_ctc=40.867, loss_att=45.157, acc=0.729, loss=43.870, backward_time=0.096, grad_norm=39.765, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.352e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:05:43,010 (trainer:737) INFO: 36epoch:train:9101-9200batch: iter_time=1.392e-04, forward_time=0.106, loss_ctc=47.039, loss_att=67.487, acc=0.710, loss=61.352, backward_time=0.097, grad_norm=46.466, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.352e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 20:06:24,769 (trainer:737) INFO: 36epoch:train:9201-9300batch: iter_time=1.477e-04, forward_time=0.105, loss_ctc=41.547, loss_att=53.584, acc=0.748, loss=49.973, backward_time=0.096, grad_norm=44.775, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.352e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:07:06,993 (trainer:737) INFO: 36epoch:train:9301-9400batch: iter_time=1.469e-04, forward_time=0.104, loss_ctc=50.782, loss_att=58.414, acc=0.723, loss=56.124, backward_time=0.096, grad_norm=49.917, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.351e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 20:07:48,926 (trainer:737) INFO: 36epoch:train:9401-9500batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=42.359, loss_att=47.527, acc=0.742, loss=45.977, backward_time=0.096, grad_norm=42.759, clip=100.000, loss_scale=1.848e+34, optim_step_time=0.039, optim0_lr0=3.351e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:08:31,190 (trainer:737) INFO: 36epoch:train:9501-9600batch: iter_time=1.465e-04, forward_time=0.106, loss_ctc=44.351, loss_att=53.349, acc=0.730, loss=50.650, backward_time=0.096, grad_norm=47.881, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.351e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 20:09:13,204 (trainer:737) INFO: 36epoch:train:9601-9700batch: iter_time=1.390e-04, forward_time=0.106, loss_ctc=50.824, loss_att=55.340, acc=0.740, loss=53.985, backward_time=0.096, grad_norm=62.621, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.350e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 20:09:55,051 (trainer:737) INFO: 36epoch:train:9701-9800batch: iter_time=1.465e-04, forward_time=0.105, loss_ctc=45.505, loss_att=50.693, acc=0.732, loss=49.137, backward_time=0.095, grad_norm=50.724, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.350e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:10:36,635 (trainer:737) INFO: 36epoch:train:9801-9900batch: iter_time=1.503e-04, forward_time=0.104, loss_ctc=43.927, loss_att=49.730, acc=0.710, loss=47.989, backward_time=0.095, grad_norm=47.307, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.350e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:11:18,324 (trainer:737) INFO: 36epoch:train:9901-10000batch: iter_time=1.330e-04, forward_time=0.104, loss_ctc=44.688, loss_att=57.315, acc=0.711, loss=53.527, backward_time=0.096, grad_norm=47.846, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.349e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:11:22,917 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-16 20:11:42,790 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 20:11:46,403 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4715be230>)
+[gpuc04:0/16] 2024-01-16 20:11:46,403 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-16 20:11:46,406 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 20:16:11,309 (trainer:737) INFO: 36epoch:train:10001-10100batch: iter_time=2.498, forward_time=0.105, loss_ctc=51.656, loss_att=61.756, acc=0.717, loss=58.726, backward_time=0.097, grad_norm=65.333, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.349e-04, train_time=2.930
+[gpuc04:0/16] 2024-01-16 20:16:53,104 (trainer:737) INFO: 36epoch:train:10101-10200batch: iter_time=1.335e-04, forward_time=0.104, loss_ctc=44.973, loss_att=52.266, acc=0.737, loss=50.078, backward_time=0.096, grad_norm=45.822, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.349e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:17:34,830 (trainer:737) INFO: 36epoch:train:10201-10300batch: iter_time=1.460e-04, forward_time=0.105, loss_ctc=50.352, loss_att=45.400, acc=0.750, loss=46.886, backward_time=0.096, grad_norm=43.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.348e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:18:16,733 (trainer:737) INFO: 36epoch:train:10301-10400batch: iter_time=1.742e-04, forward_time=0.107, loss_ctc=43.460, loss_att=58.039, acc=0.733, loss=53.665, backward_time=0.097, grad_norm=43.687, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.348e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:18:58,627 (trainer:737) INFO: 36epoch:train:10401-10500batch: iter_time=1.807e-04, forward_time=0.105, loss_ctc=43.190, loss_att=56.102, acc=0.736, loss=52.228, backward_time=0.097, grad_norm=43.296, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.348e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:19:40,438 (trainer:737) INFO: 36epoch:train:10501-10600batch: iter_time=1.778e-04, forward_time=0.105, loss_ctc=44.446, loss_att=56.842, acc=0.740, loss=53.123, backward_time=0.097, grad_norm=46.302, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.348e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:20:22,390 (trainer:737) INFO: 36epoch:train:10601-10700batch: iter_time=1.708e-04, forward_time=0.106, loss_ctc=50.254, loss_att=53.403, acc=0.744, loss=52.458, backward_time=0.097, grad_norm=47.817, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.347e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:21:04,079 (trainer:737) INFO: 36epoch:train:10701-10800batch: iter_time=1.804e-04, forward_time=0.106, loss_ctc=40.953, loss_att=46.700, acc=0.744, loss=44.976, backward_time=0.096, grad_norm=44.804, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.347e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:21:46,812 (trainer:737) INFO: 36epoch:train:10801-10900batch: iter_time=1.627e-04, forward_time=0.105, loss_ctc=49.695, loss_att=58.426, acc=0.740, loss=55.806, backward_time=0.097, grad_norm=50.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.347e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-16 20:22:28,848 (trainer:737) INFO: 36epoch:train:10901-11000batch: iter_time=1.791e-04, forward_time=0.106, loss_ctc=48.112, loss_att=52.410, acc=0.735, loss=51.121, backward_time=0.097, grad_norm=50.432, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.346e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 20:23:10,567 (trainer:737) INFO: 36epoch:train:11001-11100batch: iter_time=1.978e-04, forward_time=0.106, loss_ctc=46.980, loss_att=50.844, acc=0.735, loss=49.685, backward_time=0.097, grad_norm=46.865, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.346e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:23:52,941 (trainer:737) INFO: 36epoch:train:11101-11200batch: iter_time=1.814e-04, forward_time=0.106, loss_ctc=41.912, loss_att=48.297, acc=0.741, loss=46.382, backward_time=0.098, grad_norm=46.361, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.346e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 20:24:17,886 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-16 20:24:38,132 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 20:24:41,715 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc66d7fa60>)
+[gpuc04:0/16] 2024-01-16 20:24:41,715 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-16 20:24:41,719 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 20:28:44,241 (trainer:737) INFO: 36epoch:train:11201-11300batch: iter_time=2.464, forward_time=0.105, loss_ctc=43.037, loss_att=58.995, acc=0.705, loss=54.208, backward_time=0.097, grad_norm=48.632, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.345e-04, train_time=2.913
+[gpuc04:0/16] 2024-01-16 20:29:26,003 (trainer:737) INFO: 36epoch:train:11301-11400batch: iter_time=1.355e-04, forward_time=0.106, loss_ctc=53.444, loss_att=58.948, acc=0.720, loss=57.297, backward_time=0.096, grad_norm=68.623, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.345e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:30:07,657 (trainer:737) INFO: 36epoch:train:11401-11500batch: iter_time=1.208e-04, forward_time=0.105, loss_ctc=51.552, loss_att=51.764, acc=0.726, loss=51.700, backward_time=0.096, grad_norm=46.056, clip=100.000, loss_scale=3.697e+34, optim_step_time=0.039, optim0_lr0=3.345e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:30:49,270 (trainer:737) INFO: 36epoch:train:11501-11600batch: iter_time=1.419e-04, forward_time=0.105, loss_ctc=40.605, loss_att=44.258, acc=0.733, loss=43.162, backward_time=0.096, grad_norm=40.418, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.344e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:30:53,026 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 20:31:31,125 (trainer:737) INFO: 36epoch:train:11601-11700batch: iter_time=1.358e-04, forward_time=0.107, loss_ctc=46.734, loss_att=67.624, acc=0.710, loss=61.357, backward_time=0.097, grad_norm=46.162, clip=100.000, loss_scale=2.245e+34, optim_step_time=0.039, optim0_lr0=3.344e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:32:12,915 (trainer:737) INFO: 36epoch:train:11701-11800batch: iter_time=1.315e-04, forward_time=0.106, loss_ctc=40.839, loss_att=52.538, acc=0.750, loss=49.028, backward_time=0.097, grad_norm=42.236, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.344e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:32:54,727 (trainer:737) INFO: 36epoch:train:11801-11900batch: iter_time=1.189e-04, forward_time=0.106, loss_ctc=49.824, loss_att=57.645, acc=0.726, loss=55.298, backward_time=0.097, grad_norm=48.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.343e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:33:36,487 (trainer:737) INFO: 36epoch:train:11901-12000batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=42.531, loss_att=47.667, acc=0.741, loss=46.126, backward_time=0.097, grad_norm=42.472, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.343e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:34:18,203 (trainer:737) INFO: 36epoch:train:12001-12100batch: iter_time=1.320e-04, forward_time=0.106, loss_ctc=44.665, loss_att=52.900, acc=0.732, loss=50.430, backward_time=0.097, grad_norm=47.191, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.343e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:35:00,034 (trainer:737) INFO: 36epoch:train:12101-12200batch: iter_time=1.218e-04, forward_time=0.106, loss_ctc=50.768, loss_att=54.983, acc=0.741, loss=53.719, backward_time=0.096, grad_norm=49.624, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.343e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 20:35:41,670 (trainer:737) INFO: 36epoch:train:12201-12300batch: iter_time=1.315e-04, forward_time=0.104, loss_ctc=45.912, loss_att=50.481, acc=0.732, loss=49.110, backward_time=0.095, grad_norm=51.681, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.342e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:36:23,390 (trainer:737) INFO: 36epoch:train:12301-12400batch: iter_time=1.323e-04, forward_time=0.105, loss_ctc=44.085, loss_att=49.709, acc=0.709, loss=48.022, backward_time=0.096, grad_norm=47.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.342e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:37:05,421 (trainer:737) INFO: 36epoch:train:12401-12500batch: iter_time=1.103e-04, forward_time=0.108, loss_ctc=44.270, loss_att=57.669, acc=0.710, loss=53.650, backward_time=0.096, grad_norm=47.187, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.342e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 20:37:10,932 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-16 20:37:29,866 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 20:37:33,476 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdcc0886980>)
+[gpuc04:0/16] 2024-01-16 20:37:33,476 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-16 20:37:33,479 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 20:42:02,777 (trainer:737) INFO: 36epoch:train:12501-12600batch: iter_time=2.467, forward_time=0.107, loss_ctc=51.408, loss_att=63.154, acc=0.713, loss=59.630, backward_time=0.097, grad_norm=63.597, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.341e-04, train_time=2.973
+[gpuc04:0/16] 2024-01-16 20:42:44,882 (trainer:737) INFO: 36epoch:train:12601-12700batch: iter_time=1.463e-04, forward_time=0.106, loss_ctc=43.882, loss_att=51.017, acc=0.739, loss=48.876, backward_time=0.096, grad_norm=45.149, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.341e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 20:43:26,588 (trainer:737) INFO: 36epoch:train:12701-12800batch: iter_time=1.633e-04, forward_time=0.106, loss_ctc=49.800, loss_att=46.698, acc=0.742, loss=47.629, backward_time=0.097, grad_norm=46.063, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.341e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:44:08,507 (trainer:737) INFO: 36epoch:train:12801-12900batch: iter_time=1.760e-04, forward_time=0.107, loss_ctc=43.418, loss_att=57.503, acc=0.736, loss=53.278, backward_time=0.097, grad_norm=42.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.340e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:44:50,967 (trainer:737) INFO: 36epoch:train:12901-13000batch: iter_time=1.384e-04, forward_time=0.106, loss_ctc=42.671, loss_att=56.251, acc=0.738, loss=52.177, backward_time=0.097, grad_norm=44.628, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.340e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 20:45:33,373 (trainer:737) INFO: 36epoch:train:13001-13100batch: iter_time=1.885e-04, forward_time=0.106, loss_ctc=44.237, loss_att=57.029, acc=0.740, loss=53.191, backward_time=0.097, grad_norm=44.725, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.340e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 20:46:15,259 (trainer:737) INFO: 36epoch:train:13101-13200batch: iter_time=1.812e-04, forward_time=0.106, loss_ctc=50.266, loss_att=53.111, acc=0.746, loss=52.258, backward_time=0.097, grad_norm=50.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.339e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 20:46:48,557 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 20:46:56,915 (trainer:737) INFO: 36epoch:train:13201-13300batch: iter_time=1.875e-04, forward_time=0.106, loss_ctc=40.908, loss_att=47.623, acc=0.742, loss=45.609, backward_time=0.097, grad_norm=43.934, clip=100.000, loss_scale=1.867e+34, optim_step_time=0.039, optim0_lr0=3.339e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:47:39,482 (trainer:737) INFO: 36epoch:train:13301-13400batch: iter_time=1.637e-04, forward_time=0.107, loss_ctc=48.861, loss_att=57.872, acc=0.739, loss=55.169, backward_time=0.097, grad_norm=48.656, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.339e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 20:48:21,229 (trainer:737) INFO: 36epoch:train:13401-13500batch: iter_time=1.995e-04, forward_time=0.106, loss_ctc=47.626, loss_att=52.539, acc=0.736, loss=51.065, backward_time=0.096, grad_norm=50.358, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.339e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:49:02,955 (trainer:737) INFO: 36epoch:train:13501-13600batch: iter_time=1.760e-04, forward_time=0.105, loss_ctc=46.688, loss_att=50.585, acc=0.736, loss=49.416, backward_time=0.096, grad_norm=46.314, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.338e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:49:45,554 (trainer:737) INFO: 36epoch:train:13601-13700batch: iter_time=1.701e-04, forward_time=0.107, loss_ctc=41.795, loss_att=48.089, acc=0.746, loss=46.201, backward_time=0.098, grad_norm=46.866, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.338e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-16 20:50:11,636 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-16 20:50:30,985 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 20:50:34,660 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc625f7e20>)
+[gpuc04:0/16] 2024-01-16 20:50:34,660 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-16 20:50:34,664 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 20:54:40,159 (trainer:737) INFO: 36epoch:train:13701-13800batch: iter_time=2.485, forward_time=0.108, loss_ctc=43.027, loss_att=59.566, acc=0.703, loss=54.604, backward_time=0.097, grad_norm=48.440, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.338e-04, train_time=2.946
+[gpuc04:0/16] 2024-01-16 20:55:21,913 (trainer:737) INFO: 36epoch:train:13801-13900batch: iter_time=1.356e-04, forward_time=0.106, loss_ctc=53.119, loss_att=58.787, acc=0.717, loss=57.087, backward_time=0.096, grad_norm=63.645, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.337e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:56:03,565 (trainer:737) INFO: 36epoch:train:13901-14000batch: iter_time=1.384e-04, forward_time=0.104, loss_ctc=50.558, loss_att=52.344, acc=0.723, loss=51.808, backward_time=0.096, grad_norm=43.511, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.337e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 20:56:45,076 (trainer:737) INFO: 36epoch:train:14001-14100batch: iter_time=1.409e-04, forward_time=0.105, loss_ctc=40.254, loss_att=44.299, acc=0.732, loss=43.085, backward_time=0.095, grad_norm=40.629, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.337e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 20:57:27,190 (trainer:737) INFO: 36epoch:train:14101-14200batch: iter_time=1.537e-04, forward_time=0.110, loss_ctc=46.694, loss_att=67.029, acc=0.713, loss=60.929, backward_time=0.097, grad_norm=45.664, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.336e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 20:58:08,959 (trainer:737) INFO: 36epoch:train:14201-14300batch: iter_time=1.307e-04, forward_time=0.106, loss_ctc=40.926, loss_att=52.208, acc=0.749, loss=48.824, backward_time=0.096, grad_norm=44.370, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.336e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:58:50,694 (trainer:737) INFO: 36epoch:train:14301-14400batch: iter_time=1.327e-04, forward_time=0.105, loss_ctc=49.884, loss_att=58.009, acc=0.725, loss=55.572, backward_time=0.096, grad_norm=49.360, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.336e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 20:59:32,354 (trainer:737) INFO: 36epoch:train:14401-14500batch: iter_time=1.373e-04, forward_time=0.106, loss_ctc=42.163, loss_att=47.177, acc=0.743, loss=45.673, backward_time=0.096, grad_norm=44.743, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.335e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 21:00:14,090 (trainer:737) INFO: 36epoch:train:14501-14600batch: iter_time=1.497e-04, forward_time=0.106, loss_ctc=44.150, loss_att=53.100, acc=0.731, loss=50.415, backward_time=0.096, grad_norm=47.766, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.335e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:00:55,841 (trainer:737) INFO: 36epoch:train:14601-14700batch: iter_time=1.298e-04, forward_time=0.106, loss_ctc=50.234, loss_att=54.997, acc=0.742, loss=53.568, backward_time=0.096, grad_norm=49.645, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.335e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:01:37,717 (trainer:737) INFO: 36epoch:train:14701-14800batch: iter_time=1.365e-04, forward_time=0.105, loss_ctc=44.800, loss_att=49.819, acc=0.733, loss=48.314, backward_time=0.095, grad_norm=50.169, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.335e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 21:02:19,394 (trainer:737) INFO: 36epoch:train:14801-14900batch: iter_time=1.569e-04, forward_time=0.106, loss_ctc=43.702, loss_att=49.619, acc=0.711, loss=47.844, backward_time=0.096, grad_norm=46.139, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.334e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:03:01,681 (trainer:737) INFO: 36epoch:train:14901-15000batch: iter_time=1.241e-04, forward_time=0.105, loss_ctc=44.260, loss_att=57.031, acc=0.713, loss=53.199, backward_time=0.096, grad_norm=46.913, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.334e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 21:23:03,497 (trainer:343) INFO: 36epoch results: [train] iter_time=0.213, forward_time=0.106, loss_ctc=46.723, loss_att=54.068, acc=0.730, loss=51.865, backward_time=0.097, grad_norm=48.632, clip=100.000, loss_scale=1.729e+34, optim_step_time=0.039, optim0_lr0=3.357e-04, train_time=0.641, time=2 hours, 40 minutes and 23.45 seconds, total_count=540000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=50.071, cer_ctc=0.261, loss_att=52.086, acc=0.595, cer=0.409, wer=0.999, loss=51.481, time=19 minutes and 51.51 seconds, total_count=168156, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-16 21:23:08,638 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-16 21:23:08,677 (trainer:272) INFO: 37/45epoch started. Estimated time to finish: 1 day, 3 hours and 37.45 seconds
+[gpuc04:0/16] 2024-01-16 21:23:08,688 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-16 21:23:27,752 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 21:23:31,319 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb80bd7df0>)
+[gpuc04:0/16] 2024-01-16 21:23:31,320 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-16 21:23:31,323 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 21:27:48,705 (trainer:737) INFO: 37epoch:train:1-100batch: iter_time=2.281, forward_time=0.106, loss_ctc=45.833, loss_att=49.373, acc=0.729, loss=48.311, backward_time=0.097, grad_norm=47.416, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.334e-04, train_time=2.800
+[gpuc04:0/16] 2024-01-16 21:28:30,731 (trainer:737) INFO: 37epoch:train:101-200batch: iter_time=1.076e-04, forward_time=0.104, loss_ctc=46.739, loss_att=45.259, acc=0.743, loss=45.703, backward_time=0.097, grad_norm=44.009, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.333e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 21:29:12,940 (trainer:737) INFO: 37epoch:train:201-300batch: iter_time=1.162e-04, forward_time=0.105, loss_ctc=45.447, loss_att=55.730, acc=0.731, loss=52.645, backward_time=0.097, grad_norm=46.977, clip=100.000, loss_scale=1.246e+34, optim_step_time=0.039, optim0_lr0=3.333e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 21:29:54,679 (trainer:737) INFO: 37epoch:train:301-400batch: iter_time=1.163e-04, forward_time=0.107, loss_ctc=51.893, loss_att=50.916, acc=0.736, loss=51.209, backward_time=0.097, grad_norm=55.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.333e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:30:38,382 (trainer:737) INFO: 37epoch:train:401-500batch: iter_time=1.125e-04, forward_time=0.119, loss_ctc=50.783, loss_att=59.721, acc=0.714, loss=57.040, backward_time=0.099, grad_norm=56.433, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.332e-04, train_time=0.436
+[gpuc04:0/16] 2024-01-16 21:31:20,020 (trainer:737) INFO: 37epoch:train:501-600batch: iter_time=1.138e-04, forward_time=0.105, loss_ctc=44.775, loss_att=56.820, acc=0.716, loss=53.206, backward_time=0.097, grad_norm=45.342, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.332e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:32:02,418 (trainer:737) INFO: 37epoch:train:601-700batch: iter_time=1.070e-04, forward_time=0.107, loss_ctc=47.200, loss_att=54.073, acc=0.723, loss=52.011, backward_time=0.096, grad_norm=48.459, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.332e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 21:32:43,810 (trainer:737) INFO: 37epoch:train:701-800batch: iter_time=1.195e-04, forward_time=0.104, loss_ctc=39.940, loss_att=37.850, acc=0.765, loss=38.477, backward_time=0.096, grad_norm=39.400, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.331e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 21:33:25,305 (trainer:737) INFO: 37epoch:train:801-900batch: iter_time=1.105e-04, forward_time=0.104, loss_ctc=43.175, loss_att=45.560, acc=0.725, loss=44.844, backward_time=0.096, grad_norm=44.261, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.331e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 21:34:16,252 (trainer:737) INFO: 37epoch:train:901-1000batch: iter_time=1.071e-04, forward_time=0.157, loss_ctc=44.127, loss_att=55.118, acc=0.728, loss=51.821, backward_time=0.125, grad_norm=44.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=3.331e-04, train_time=0.509
+[gpuc04:0/16] 2024-01-16 21:35:00,836 (trainer:737) INFO: 37epoch:train:1001-1100batch: iter_time=1.091e-04, forward_time=0.113, loss_ctc=44.911, loss_att=48.270, acc=0.745, loss=47.262, backward_time=0.098, grad_norm=44.195, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.330e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-16 21:35:42,468 (trainer:737) INFO: 37epoch:train:1101-1200batch: iter_time=1.100e-04, forward_time=0.106, loss_ctc=41.064, loss_att=43.284, acc=0.736, loss=42.618, backward_time=0.096, grad_norm=40.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.330e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 21:36:33,940 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-16 21:36:52,586 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 21:36:55,997 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc98b2e470>)
+[gpuc04:0/16] 2024-01-16 21:36:55,997 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-16 21:36:56,000 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 21:43:47,444 (trainer:737) INFO: 37epoch:train:1201-1300batch: iter_time=4.150, forward_time=0.104, loss_ctc=42.801, loss_att=48.946, acc=0.739, loss=47.102, backward_time=0.096, grad_norm=48.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.330e-04, train_time=4.850
+[gpuc04:0/16] 2024-01-16 21:44:29,779 (trainer:737) INFO: 37epoch:train:1301-1400batch: iter_time=1.071e-04, forward_time=0.106, loss_ctc=45.236, loss_att=47.231, acc=0.736, loss=46.632, backward_time=0.097, grad_norm=45.570, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.330e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 21:45:11,916 (trainer:737) INFO: 37epoch:train:1401-1500batch: iter_time=1.212e-04, forward_time=0.105, loss_ctc=45.365, loss_att=57.734, acc=0.740, loss=54.023, backward_time=0.097, grad_norm=46.505, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.329e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 21:45:53,855 (trainer:737) INFO: 37epoch:train:1501-1600batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=46.356, loss_att=49.009, acc=0.758, loss=48.213, backward_time=0.096, grad_norm=49.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.329e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 21:46:36,344 (trainer:737) INFO: 37epoch:train:1601-1700batch: iter_time=1.122e-04, forward_time=0.105, loss_ctc=52.938, loss_att=57.517, acc=0.736, loss=56.143, backward_time=0.097, grad_norm=55.182, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.329e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-16 21:47:18,395 (trainer:737) INFO: 37epoch:train:1701-1800batch: iter_time=1.128e-04, forward_time=0.106, loss_ctc=47.254, loss_att=57.346, acc=0.731, loss=54.318, backward_time=0.097, grad_norm=48.414, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.328e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 21:48:00,004 (trainer:737) INFO: 37epoch:train:1801-1900batch: iter_time=1.014e-04, forward_time=0.105, loss_ctc=43.989, loss_att=53.732, acc=0.721, loss=50.809, backward_time=0.097, grad_norm=51.178, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.328e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 21:48:41,733 (trainer:737) INFO: 37epoch:train:1901-2000batch: iter_time=1.161e-04, forward_time=0.105, loss_ctc=43.821, loss_att=49.585, acc=0.757, loss=47.856, backward_time=0.097, grad_norm=44.009, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.328e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 21:49:23,355 (trainer:737) INFO: 37epoch:train:2001-2100batch: iter_time=1.138e-04, forward_time=0.105, loss_ctc=45.845, loss_att=45.516, acc=0.751, loss=45.615, backward_time=0.097, grad_norm=44.906, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.327e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 21:50:05,191 (trainer:737) INFO: 37epoch:train:2101-2200batch: iter_time=1.156e-04, forward_time=0.107, loss_ctc=37.161, loss_att=41.554, acc=0.752, loss=40.236, backward_time=0.097, grad_norm=39.723, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.327e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 21:50:47,069 (trainer:737) INFO: 37epoch:train:2201-2300batch: iter_time=9.643e-05, forward_time=0.105, loss_ctc=47.157, loss_att=57.393, acc=0.745, loss=54.322, backward_time=0.098, grad_norm=48.143, clip=100.000, loss_scale=2.492e+34, optim_step_time=0.039, optim0_lr0=3.327e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 21:51:28,599 (trainer:737) INFO: 37epoch:train:2301-2400batch: iter_time=1.040e-04, forward_time=0.104, loss_ctc=35.220, loss_att=38.906, acc=0.765, loss=37.800, backward_time=0.097, grad_norm=35.788, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.327e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 21:52:10,157 (trainer:737) INFO: 37epoch:train:2401-2500batch: iter_time=1.035e-04, forward_time=0.104, loss_ctc=46.064, loss_att=49.307, acc=0.729, loss=48.334, backward_time=0.097, grad_norm=46.247, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.326e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 21:52:12,487 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-16 21:52:32,660 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 21:52:36,368 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe7460a7850>)
+[gpuc04:0/16] 2024-01-16 21:52:36,368 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-16 21:52:36,372 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 21:57:42,257 (trainer:737) INFO: 37epoch:train:2501-2600batch: iter_time=2.310, forward_time=0.105, loss_ctc=44.519, loss_att=46.963, acc=0.752, loss=46.230, backward_time=0.097, grad_norm=43.842, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.326e-04, train_time=3.321
+[gpuc04:0/16] 2024-01-16 21:58:24,080 (trainer:737) INFO: 37epoch:train:2601-2700batch: iter_time=1.124e-04, forward_time=0.104, loss_ctc=45.955, loss_att=45.324, acc=0.750, loss=45.513, backward_time=0.096, grad_norm=43.188, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.326e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 21:58:53,148 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 21:59:06,358 (trainer:737) INFO: 37epoch:train:2701-2800batch: iter_time=1.017e-04, forward_time=0.105, loss_ctc=44.595, loss_att=56.703, acc=0.745, loss=53.070, backward_time=0.097, grad_norm=43.439, clip=100.000, loss_scale=3.503e+34, optim_step_time=0.038, optim0_lr0=3.325e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 21:59:47,989 (trainer:737) INFO: 37epoch:train:2801-2900batch: iter_time=1.085e-04, forward_time=0.104, loss_ctc=50.208, loss_att=49.569, acc=0.753, loss=49.761, backward_time=0.096, grad_norm=51.268, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.325e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:00:30,263 (trainer:737) INFO: 37epoch:train:2901-3000batch: iter_time=1.182e-04, forward_time=0.105, loss_ctc=48.545, loss_att=59.929, acc=0.722, loss=56.514, backward_time=0.097, grad_norm=51.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.325e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-16 22:01:12,532 (trainer:737) INFO: 37epoch:train:3001-3100batch: iter_time=1.302e-04, forward_time=0.105, loss_ctc=44.452, loss_att=55.404, acc=0.744, loss=52.119, backward_time=0.097, grad_norm=45.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.324e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 22:01:54,211 (trainer:737) INFO: 37epoch:train:3101-3200batch: iter_time=1.223e-04, forward_time=0.105, loss_ctc=46.264, loss_att=55.296, acc=0.727, loss=52.586, backward_time=0.097, grad_norm=48.599, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.324e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:02:36,443 (trainer:737) INFO: 37epoch:train:3201-3300batch: iter_time=1.236e-04, forward_time=0.105, loss_ctc=39.475, loss_att=37.705, acc=0.775, loss=38.236, backward_time=0.096, grad_norm=38.780, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.324e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 22:03:18,248 (trainer:737) INFO: 37epoch:train:3301-3400batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=42.658, loss_att=44.384, acc=0.741, loss=43.866, backward_time=0.096, grad_norm=44.447, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.323e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 22:03:59,894 (trainer:737) INFO: 37epoch:train:3401-3500batch: iter_time=9.641e-05, forward_time=0.106, loss_ctc=42.336, loss_att=53.989, acc=0.737, loss=50.493, backward_time=0.097, grad_norm=43.335, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.323e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:04:41,565 (trainer:737) INFO: 37epoch:train:3501-3600batch: iter_time=1.020e-04, forward_time=0.106, loss_ctc=44.437, loss_att=49.501, acc=0.753, loss=47.982, backward_time=0.097, grad_norm=44.823, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.323e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:05:23,066 (trainer:737) INFO: 37epoch:train:3601-3700batch: iter_time=1.014e-04, forward_time=0.105, loss_ctc=39.138, loss_att=41.464, acc=0.750, loss=40.766, backward_time=0.095, grad_norm=38.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.323e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 22:05:46,283 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-16 22:06:06,628 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 22:06:10,602 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe3f58034f0>)
+[gpuc04:0/16] 2024-01-16 22:06:10,602 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-16 22:06:10,605 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 22:10:20,236 (trainer:737) INFO: 37epoch:train:3701-3800batch: iter_time=2.305, forward_time=0.103, loss_ctc=42.775, loss_att=49.694, acc=0.736, loss=47.618, backward_time=0.096, grad_norm=46.717, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.322e-04, train_time=2.971
+[gpuc04:0/16] 2024-01-16 22:11:01,993 (trainer:737) INFO: 37epoch:train:3801-3900batch: iter_time=9.791e-05, forward_time=0.104, loss_ctc=44.693, loss_att=46.988, acc=0.730, loss=46.300, backward_time=0.095, grad_norm=44.343, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.322e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:11:43,629 (trainer:737) INFO: 37epoch:train:3901-4000batch: iter_time=1.098e-04, forward_time=0.105, loss_ctc=44.899, loss_att=57.671, acc=0.730, loss=53.840, backward_time=0.096, grad_norm=44.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.322e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:12:25,742 (trainer:737) INFO: 37epoch:train:4001-4100batch: iter_time=9.666e-05, forward_time=0.105, loss_ctc=46.739, loss_att=50.136, acc=0.749, loss=49.117, backward_time=0.096, grad_norm=49.116, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.321e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 22:13:07,341 (trainer:737) INFO: 37epoch:train:4101-4200batch: iter_time=1.046e-04, forward_time=0.105, loss_ctc=50.901, loss_att=56.667, acc=0.731, loss=54.938, backward_time=0.096, grad_norm=51.642, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.321e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:13:49,567 (trainer:737) INFO: 37epoch:train:4201-4300batch: iter_time=9.719e-05, forward_time=0.105, loss_ctc=46.632, loss_att=56.638, acc=0.723, loss=53.636, backward_time=0.096, grad_norm=45.927, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.321e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 22:14:31,412 (trainer:737) INFO: 37epoch:train:4301-4400batch: iter_time=1.038e-04, forward_time=0.104, loss_ctc=43.453, loss_att=54.325, acc=0.704, loss=51.063, backward_time=0.095, grad_norm=52.024, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.320e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 22:15:13,376 (trainer:737) INFO: 37epoch:train:4401-4500batch: iter_time=1.031e-04, forward_time=0.104, loss_ctc=43.403, loss_att=48.525, acc=0.754, loss=46.988, backward_time=0.096, grad_norm=42.621, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.320e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:15:55,205 (trainer:737) INFO: 37epoch:train:4501-4600batch: iter_time=9.998e-05, forward_time=0.104, loss_ctc=45.502, loss_att=45.693, acc=0.740, loss=45.636, backward_time=0.096, grad_norm=45.321, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.320e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 22:16:36,962 (trainer:737) INFO: 37epoch:train:4601-4700batch: iter_time=9.146e-05, forward_time=0.104, loss_ctc=36.559, loss_att=41.787, acc=0.750, loss=40.219, backward_time=0.095, grad_norm=39.593, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.319e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:17:19,200 (trainer:737) INFO: 37epoch:train:4701-4800batch: iter_time=1.019e-04, forward_time=0.105, loss_ctc=46.873, loss_att=56.877, acc=0.738, loss=53.876, backward_time=0.097, grad_norm=50.032, clip=100.000, loss_scale=2.721e+34, optim_step_time=0.039, optim0_lr0=3.319e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 22:17:48,182 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 22:18:00,622 (trainer:737) INFO: 37epoch:train:4801-4900batch: iter_time=1.086e-04, forward_time=0.104, loss_ctc=35.121, loss_att=38.548, acc=0.760, loss=37.520, backward_time=0.095, grad_norm=37.110, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.039, optim0_lr0=3.319e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 22:18:42,551 (trainer:737) INFO: 37epoch:train:4901-5000batch: iter_time=8.846e-05, forward_time=0.104, loss_ctc=44.837, loss_att=49.778, acc=0.715, loss=48.295, backward_time=0.096, grad_norm=45.812, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.319e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:18:45,030 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-16 22:19:05,305 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 22:19:08,889 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdca0647a60>)
+[gpuc04:0/16] 2024-01-16 22:19:08,889 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-16 22:19:08,892 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 22:24:10,281 (trainer:737) INFO: 37epoch:train:5001-5100batch: iter_time=2.459, forward_time=0.105, loss_ctc=44.134, loss_att=47.298, acc=0.738, loss=46.349, backward_time=0.097, grad_norm=45.443, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.318e-04, train_time=3.277
+[gpuc04:0/16] 2024-01-16 22:24:52,025 (trainer:737) INFO: 37epoch:train:5101-5200batch: iter_time=1.135e-04, forward_time=0.104, loss_ctc=45.457, loss_att=44.873, acc=0.745, loss=45.048, backward_time=0.096, grad_norm=43.649, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.318e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:25:34,251 (trainer:737) INFO: 37epoch:train:5201-5300batch: iter_time=9.797e-05, forward_time=0.108, loss_ctc=44.157, loss_att=55.520, acc=0.737, loss=52.111, backward_time=0.096, grad_norm=44.183, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.318e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 22:26:16,041 (trainer:737) INFO: 37epoch:train:5301-5400batch: iter_time=1.086e-04, forward_time=0.104, loss_ctc=48.377, loss_att=50.642, acc=0.738, loss=49.963, backward_time=0.096, grad_norm=51.226, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.317e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 22:26:57,697 (trainer:737) INFO: 37epoch:train:5401-5500batch: iter_time=1.083e-04, forward_time=0.105, loss_ctc=48.185, loss_att=57.668, acc=0.724, loss=54.823, backward_time=0.097, grad_norm=50.398, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.317e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:27:39,866 (trainer:737) INFO: 37epoch:train:5501-5600batch: iter_time=1.129e-04, forward_time=0.106, loss_ctc=44.031, loss_att=56.485, acc=0.722, loss=52.749, backward_time=0.097, grad_norm=45.446, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.317e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 22:28:21,606 (trainer:737) INFO: 37epoch:train:5601-5700batch: iter_time=1.113e-04, forward_time=0.105, loss_ctc=44.929, loss_att=53.071, acc=0.729, loss=50.628, backward_time=0.096, grad_norm=46.020, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.316e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:29:03,304 (trainer:737) INFO: 37epoch:train:5701-5800batch: iter_time=1.134e-04, forward_time=0.104, loss_ctc=39.305, loss_att=37.696, acc=0.770, loss=38.179, backward_time=0.095, grad_norm=38.484, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.316e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:29:45,120 (trainer:737) INFO: 37epoch:train:5801-5900batch: iter_time=1.202e-04, forward_time=0.104, loss_ctc=42.411, loss_att=45.184, acc=0.731, loss=44.352, backward_time=0.096, grad_norm=40.746, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.316e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 22:30:27,050 (trainer:737) INFO: 37epoch:train:5901-6000batch: iter_time=1.030e-04, forward_time=0.104, loss_ctc=42.018, loss_att=53.874, acc=0.731, loss=50.317, backward_time=0.097, grad_norm=44.210, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.316e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:31:09,001 (trainer:737) INFO: 37epoch:train:6001-6100batch: iter_time=1.131e-04, forward_time=0.106, loss_ctc=44.080, loss_att=48.530, acc=0.748, loss=47.195, backward_time=0.097, grad_norm=46.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.315e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:31:50,743 (trainer:737) INFO: 37epoch:train:6101-6200batch: iter_time=1.135e-04, forward_time=0.104, loss_ctc=39.484, loss_att=42.550, acc=0.740, loss=41.630, backward_time=0.096, grad_norm=38.706, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.315e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:32:13,891 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-16 22:32:33,504 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 22:32:37,162 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4367ba7a0>)
+[gpuc04:0/16] 2024-01-16 22:32:37,162 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-16 22:32:37,165 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 22:36:50,187 (trainer:737) INFO: 37epoch:train:6201-6300batch: iter_time=2.284, forward_time=0.103, loss_ctc=42.230, loss_att=45.610, acc=0.741, loss=44.596, backward_time=0.096, grad_norm=45.996, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.315e-04, train_time=2.994
+[gpuc04:0/16] 2024-01-16 22:37:31,670 (trainer:737) INFO: 37epoch:train:6301-6400batch: iter_time=1.068e-04, forward_time=0.104, loss_ctc=43.984, loss_att=43.804, acc=0.738, loss=43.858, backward_time=0.096, grad_norm=42.411, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.314e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 22:38:13,340 (trainer:737) INFO: 37epoch:train:6401-6500batch: iter_time=1.107e-04, forward_time=0.104, loss_ctc=44.861, loss_att=55.785, acc=0.735, loss=52.507, backward_time=0.096, grad_norm=43.141, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.314e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:38:54,963 (trainer:737) INFO: 37epoch:train:6501-6600batch: iter_time=1.005e-04, forward_time=0.104, loss_ctc=46.049, loss_att=48.810, acc=0.751, loss=47.981, backward_time=0.096, grad_norm=47.337, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.314e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:39:36,591 (trainer:737) INFO: 37epoch:train:6601-6700batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=50.946, loss_att=56.029, acc=0.734, loss=54.504, backward_time=0.096, grad_norm=49.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.313e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:40:18,300 (trainer:737) INFO: 37epoch:train:6701-6800batch: iter_time=1.051e-04, forward_time=0.105, loss_ctc=46.364, loss_att=56.284, acc=0.724, loss=53.308, backward_time=0.096, grad_norm=46.100, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.313e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:40:59,806 (trainer:737) INFO: 37epoch:train:6801-6900batch: iter_time=9.373e-05, forward_time=0.104, loss_ctc=43.001, loss_att=53.619, acc=0.704, loss=50.434, backward_time=0.095, grad_norm=50.490, clip=100.000, loss_scale=2.700e+34, optim_step_time=0.038, optim0_lr0=3.313e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 22:41:41,387 (trainer:737) INFO: 37epoch:train:6901-7000batch: iter_time=9.584e-05, forward_time=0.105, loss_ctc=43.048, loss_att=47.956, acc=0.757, loss=46.484, backward_time=0.096, grad_norm=41.841, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.312e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:41:48,777 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 22:42:22,892 (trainer:737) INFO: 37epoch:train:7001-7100batch: iter_time=9.721e-05, forward_time=0.104, loss_ctc=44.930, loss_att=45.149, acc=0.742, loss=45.083, backward_time=0.095, grad_norm=42.434, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.038, optim0_lr0=3.312e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 22:43:04,439 (trainer:737) INFO: 37epoch:train:7101-7200batch: iter_time=9.305e-05, forward_time=0.104, loss_ctc=36.023, loss_att=41.266, acc=0.754, loss=39.693, backward_time=0.095, grad_norm=40.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.312e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 22:43:46,202 (trainer:737) INFO: 37epoch:train:7201-7300batch: iter_time=9.296e-05, forward_time=0.106, loss_ctc=46.679, loss_att=56.209, acc=0.743, loss=53.350, backward_time=0.096, grad_norm=50.304, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.312e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:44:27,661 (trainer:737) INFO: 37epoch:train:7301-7400batch: iter_time=1.033e-04, forward_time=0.104, loss_ctc=34.999, loss_att=37.758, acc=0.763, loss=36.931, backward_time=0.095, grad_norm=36.550, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.311e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 22:45:09,612 (trainer:737) INFO: 37epoch:train:7401-7500batch: iter_time=1.070e-04, forward_time=0.107, loss_ctc=44.509, loss_att=49.349, acc=0.716, loss=47.897, backward_time=0.095, grad_norm=45.815, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.311e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:45:12,130 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-16 22:45:31,810 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 22:45:35,435 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdca0600e50>)
+[gpuc04:0/16] 2024-01-16 22:45:35,435 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-16 22:45:35,438 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 22:50:12,441 (trainer:737) INFO: 37epoch:train:7501-7600batch: iter_time=2.241, forward_time=0.105, loss_ctc=43.952, loss_att=51.740, acc=0.748, loss=49.404, backward_time=0.096, grad_norm=46.809, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.311e-04, train_time=3.028
+[gpuc04:0/16] 2024-01-16 22:50:47,362 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 22:50:54,472 (trainer:737) INFO: 37epoch:train:7601-7700batch: iter_time=1.038e-04, forward_time=0.104, loss_ctc=45.184, loss_att=46.191, acc=0.747, loss=45.889, backward_time=0.095, grad_norm=41.934, clip=100.000, loss_scale=1.899e+34, optim_step_time=0.038, optim0_lr0=3.310e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 22:51:36,241 (trainer:737) INFO: 37epoch:train:7701-7800batch: iter_time=1.181e-04, forward_time=0.105, loss_ctc=43.826, loss_att=57.454, acc=0.743, loss=53.365, backward_time=0.096, grad_norm=45.282, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.310e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:52:18,142 (trainer:737) INFO: 37epoch:train:7801-7900batch: iter_time=1.063e-04, forward_time=0.104, loss_ctc=48.054, loss_att=50.275, acc=0.754, loss=49.608, backward_time=0.096, grad_norm=49.817, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.310e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 22:53:00,143 (trainer:737) INFO: 37epoch:train:7901-8000batch: iter_time=1.038e-04, forward_time=0.105, loss_ctc=48.127, loss_att=60.059, acc=0.723, loss=56.480, backward_time=0.096, grad_norm=51.010, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.309e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 22:53:42,540 (trainer:737) INFO: 37epoch:train:8001-8100batch: iter_time=1.029e-04, forward_time=0.105, loss_ctc=43.480, loss_att=55.892, acc=0.743, loss=52.168, backward_time=0.096, grad_norm=44.522, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.309e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-16 22:54:24,225 (trainer:737) INFO: 37epoch:train:8101-8200batch: iter_time=9.942e-05, forward_time=0.104, loss_ctc=44.894, loss_att=53.857, acc=0.733, loss=51.168, backward_time=0.096, grad_norm=46.283, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.309e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:55:05,919 (trainer:737) INFO: 37epoch:train:8201-8300batch: iter_time=9.848e-05, forward_time=0.103, loss_ctc=39.263, loss_att=38.277, acc=0.774, loss=38.573, backward_time=0.095, grad_norm=40.008, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.309e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:55:47,534 (trainer:737) INFO: 37epoch:train:8301-8400batch: iter_time=9.742e-05, forward_time=0.103, loss_ctc=42.147, loss_att=44.414, acc=0.742, loss=43.734, backward_time=0.095, grad_norm=42.803, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.308e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 22:56:29,242 (trainer:737) INFO: 37epoch:train:8401-8500batch: iter_time=1.067e-04, forward_time=0.105, loss_ctc=41.202, loss_att=54.015, acc=0.740, loss=50.171, backward_time=0.096, grad_norm=43.452, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.308e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:57:10,984 (trainer:737) INFO: 37epoch:train:8501-8600batch: iter_time=1.193e-04, forward_time=0.106, loss_ctc=43.536, loss_att=49.510, acc=0.753, loss=47.718, backward_time=0.097, grad_norm=45.748, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.308e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 22:57:52,443 (trainer:737) INFO: 37epoch:train:8601-8700batch: iter_time=1.106e-04, forward_time=0.105, loss_ctc=38.641, loss_att=41.448, acc=0.753, loss=40.606, backward_time=0.095, grad_norm=39.468, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.307e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 22:58:15,635 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-16 22:58:35,561 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 22:58:39,521 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdca6c35e70>)
+[gpuc04:0/16] 2024-01-16 22:58:39,521 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-16 22:58:39,525 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 23:02:55,410 (trainer:737) INFO: 37epoch:train:8701-8800batch: iter_time=2.263, forward_time=0.106, loss_ctc=42.236, loss_att=45.816, acc=0.752, loss=44.742, backward_time=0.096, grad_norm=44.655, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.307e-04, train_time=3.029
+[gpuc04:0/16] 2024-01-16 23:03:36,924 (trainer:737) INFO: 37epoch:train:8801-8900batch: iter_time=1.087e-04, forward_time=0.104, loss_ctc=43.628, loss_att=44.308, acc=0.744, loss=44.104, backward_time=0.096, grad_norm=42.820, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.307e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:04:19,191 (trainer:737) INFO: 37epoch:train:8901-9000batch: iter_time=1.000e-04, forward_time=0.105, loss_ctc=44.853, loss_att=56.242, acc=0.747, loss=52.825, backward_time=0.097, grad_norm=45.590, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.306e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 23:05:00,909 (trainer:737) INFO: 37epoch:train:9001-9100batch: iter_time=1.139e-04, forward_time=0.104, loss_ctc=45.722, loss_att=49.363, acc=0.761, loss=48.271, backward_time=0.097, grad_norm=48.335, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.306e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:05:42,621 (trainer:737) INFO: 37epoch:train:9101-9200batch: iter_time=1.038e-04, forward_time=0.104, loss_ctc=50.369, loss_att=56.298, acc=0.741, loss=54.519, backward_time=0.096, grad_norm=50.970, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.306e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:06:24,712 (trainer:737) INFO: 37epoch:train:9201-9300batch: iter_time=1.149e-04, forward_time=0.106, loss_ctc=45.731, loss_att=56.015, acc=0.735, loss=52.930, backward_time=0.097, grad_norm=44.999, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.306e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:07:06,323 (trainer:737) INFO: 37epoch:train:9301-9400batch: iter_time=1.077e-04, forward_time=0.104, loss_ctc=43.445, loss_att=53.718, acc=0.720, loss=50.636, backward_time=0.096, grad_norm=51.492, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.305e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:07:48,592 (trainer:737) INFO: 37epoch:train:9401-9500batch: iter_time=1.137e-04, forward_time=0.104, loss_ctc=44.033, loss_att=48.729, acc=0.761, loss=47.320, backward_time=0.096, grad_norm=40.842, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.305e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 23:08:30,716 (trainer:737) INFO: 37epoch:train:9501-9600batch: iter_time=1.063e-04, forward_time=0.104, loss_ctc=44.819, loss_att=45.085, acc=0.755, loss=45.006, backward_time=0.096, grad_norm=43.282, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.305e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:09:12,824 (trainer:737) INFO: 37epoch:train:9601-9700batch: iter_time=9.746e-05, forward_time=0.104, loss_ctc=35.998, loss_att=41.406, acc=0.754, loss=39.784, backward_time=0.096, grad_norm=41.104, clip=100.000, loss_scale=1.215e+34, optim_step_time=0.039, optim0_lr0=3.304e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:09:54,685 (trainer:737) INFO: 37epoch:train:9701-9800batch: iter_time=1.137e-04, forward_time=0.105, loss_ctc=46.062, loss_att=56.610, acc=0.747, loss=53.445, backward_time=0.097, grad_norm=49.205, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.304e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:10:36,149 (trainer:737) INFO: 37epoch:train:9801-9900batch: iter_time=1.000e-04, forward_time=0.104, loss_ctc=34.672, loss_att=38.409, acc=0.771, loss=37.287, backward_time=0.096, grad_norm=35.659, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.304e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 23:11:17,929 (trainer:737) INFO: 37epoch:train:9901-10000batch: iter_time=9.270e-05, forward_time=0.104, loss_ctc=44.938, loss_att=48.853, acc=0.729, loss=47.679, backward_time=0.096, grad_norm=47.087, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.303e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:11:20,322 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-16 23:11:39,987 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 23:11:43,587 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe423087550>)
+[gpuc04:0/16] 2024-01-16 23:11:43,587 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-16 23:11:43,590 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 23:16:28,047 (trainer:737) INFO: 37epoch:train:10001-10100batch: iter_time=2.279, forward_time=0.105, loss_ctc=43.251, loss_att=45.967, acc=0.758, loss=45.152, backward_time=0.097, grad_norm=44.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.303e-04, train_time=3.101
+[gpuc04:0/16] 2024-01-16 23:17:09,592 (trainer:737) INFO: 37epoch:train:10101-10200batch: iter_time=1.078e-04, forward_time=0.104, loss_ctc=44.909, loss_att=44.734, acc=0.751, loss=44.786, backward_time=0.096, grad_norm=43.261, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.303e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:17:51,348 (trainer:737) INFO: 37epoch:train:10201-10300batch: iter_time=1.046e-04, forward_time=0.106, loss_ctc=44.048, loss_att=55.583, acc=0.749, loss=52.122, backward_time=0.097, grad_norm=43.332, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.303e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:18:32,935 (trainer:737) INFO: 37epoch:train:10301-10400batch: iter_time=1.018e-04, forward_time=0.105, loss_ctc=48.241, loss_att=48.882, acc=0.756, loss=48.690, backward_time=0.097, grad_norm=50.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.302e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:19:14,715 (trainer:737) INFO: 37epoch:train:10401-10500batch: iter_time=1.024e-04, forward_time=0.105, loss_ctc=47.853, loss_att=59.291, acc=0.725, loss=55.859, backward_time=0.097, grad_norm=49.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.302e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:19:56,462 (trainer:737) INFO: 37epoch:train:10501-10600batch: iter_time=9.949e-05, forward_time=0.105, loss_ctc=43.560, loss_att=55.521, acc=0.745, loss=51.933, backward_time=0.097, grad_norm=44.954, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.302e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:20:38,129 (trainer:737) INFO: 37epoch:train:10601-10700batch: iter_time=1.114e-04, forward_time=0.105, loss_ctc=44.956, loss_att=54.287, acc=0.731, loss=51.488, backward_time=0.097, grad_norm=48.537, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.301e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:21:19,639 (trainer:737) INFO: 37epoch:train:10701-10800batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=39.410, loss_att=37.499, acc=0.777, loss=38.072, backward_time=0.096, grad_norm=39.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.301e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:22:01,573 (trainer:737) INFO: 37epoch:train:10801-10900batch: iter_time=1.107e-04, forward_time=0.104, loss_ctc=41.977, loss_att=44.231, acc=0.743, loss=43.555, backward_time=0.096, grad_norm=43.178, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.301e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 23:22:43,395 (trainer:737) INFO: 37epoch:train:10901-11000batch: iter_time=1.028e-04, forward_time=0.105, loss_ctc=41.241, loss_att=53.870, acc=0.739, loss=50.081, backward_time=0.097, grad_norm=44.992, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.300e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:23:25,152 (trainer:737) INFO: 37epoch:train:11001-11100batch: iter_time=1.009e-04, forward_time=0.106, loss_ctc=43.408, loss_att=48.971, acc=0.755, loss=47.302, backward_time=0.097, grad_norm=45.471, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.300e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:24:07,185 (trainer:737) INFO: 37epoch:train:11101-11200batch: iter_time=1.199e-04, forward_time=0.104, loss_ctc=38.535, loss_att=41.392, acc=0.752, loss=40.535, backward_time=0.096, grad_norm=38.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.300e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 23:24:32,346 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-16 23:24:52,186 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 23:24:55,842 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc5bbe0df0>)
+[gpuc04:0/16] 2024-01-16 23:24:55,842 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-16 23:24:55,845 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 23:29:31,671 (trainer:737) INFO: 37epoch:train:11201-11300batch: iter_time=2.464, forward_time=0.111, loss_ctc=41.422, loss_att=49.110, acc=0.741, loss=46.804, backward_time=0.097, grad_norm=45.605, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.300e-04, train_time=3.245
+[gpuc04:0/16] 2024-01-16 23:30:13,462 (trainer:737) INFO: 37epoch:train:11301-11400batch: iter_time=1.156e-04, forward_time=0.104, loss_ctc=43.420, loss_att=46.317, acc=0.733, loss=45.448, backward_time=0.095, grad_norm=43.079, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.299e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:30:55,693 (trainer:737) INFO: 37epoch:train:11401-11500batch: iter_time=1.024e-04, forward_time=0.104, loss_ctc=44.314, loss_att=56.785, acc=0.734, loss=53.043, backward_time=0.096, grad_norm=44.757, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.299e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 23:31:38,528 (trainer:737) INFO: 37epoch:train:11501-11600batch: iter_time=1.058e-04, forward_time=0.104, loss_ctc=45.120, loss_att=48.870, acc=0.753, loss=47.745, backward_time=0.096, grad_norm=48.834, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.299e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-16 23:32:20,165 (trainer:737) INFO: 37epoch:train:11601-11700batch: iter_time=1.059e-04, forward_time=0.104, loss_ctc=49.910, loss_att=56.446, acc=0.733, loss=54.485, backward_time=0.096, grad_norm=49.812, clip=100.000, loss_scale=2.430e+34, optim_step_time=0.039, optim0_lr0=3.298e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:33:01,881 (trainer:737) INFO: 37epoch:train:11701-11800batch: iter_time=1.059e-04, forward_time=0.105, loss_ctc=46.182, loss_att=57.169, acc=0.724, loss=53.873, backward_time=0.097, grad_norm=44.803, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.298e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:33:06,809 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 23:33:43,377 (trainer:737) INFO: 37epoch:train:11801-11900batch: iter_time=1.081e-04, forward_time=0.104, loss_ctc=42.665, loss_att=54.630, acc=0.702, loss=51.041, backward_time=0.096, grad_norm=49.900, clip=100.000, loss_scale=2.308e+34, optim_step_time=0.039, optim0_lr0=3.298e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:34:25,309 (trainer:737) INFO: 37epoch:train:11901-12000batch: iter_time=1.065e-04, forward_time=0.104, loss_ctc=43.098, loss_att=48.080, acc=0.758, loss=46.585, backward_time=0.096, grad_norm=41.236, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.297e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 23:35:06,896 (trainer:737) INFO: 37epoch:train:12001-12100batch: iter_time=1.147e-04, forward_time=0.104, loss_ctc=44.675, loss_att=45.146, acc=0.743, loss=45.005, backward_time=0.095, grad_norm=43.883, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.297e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:35:48,458 (trainer:737) INFO: 37epoch:train:12101-12200batch: iter_time=9.842e-05, forward_time=0.104, loss_ctc=35.747, loss_att=40.897, acc=0.755, loss=39.352, backward_time=0.095, grad_norm=39.123, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.297e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:36:30,271 (trainer:737) INFO: 37epoch:train:12201-12300batch: iter_time=1.016e-04, forward_time=0.106, loss_ctc=45.874, loss_att=56.358, acc=0.741, loss=53.213, backward_time=0.097, grad_norm=50.030, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.297e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:37:11,741 (trainer:737) INFO: 37epoch:train:12301-12400batch: iter_time=1.007e-04, forward_time=0.104, loss_ctc=34.766, loss_att=37.694, acc=0.763, loss=36.816, backward_time=0.095, grad_norm=35.827, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.296e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-16 23:37:55,364 (trainer:737) INFO: 37epoch:train:12401-12500batch: iter_time=2.396e-04, forward_time=0.108, loss_ctc=44.219, loss_att=49.571, acc=0.717, loss=47.965, backward_time=0.096, grad_norm=45.459, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.296e-04, train_time=0.436
+[gpuc04:0/16] 2024-01-16 23:37:58,569 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-16 23:38:18,372 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 23:38:22,031 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc4e897cd0>)
+[gpuc04:0/16] 2024-01-16 23:38:22,031 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-16 23:38:22,035 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 23:42:53,699 (trainer:737) INFO: 37epoch:train:12501-12600batch: iter_time=2.307, forward_time=0.104, loss_ctc=43.246, loss_att=49.698, acc=0.751, loss=47.762, backward_time=0.097, grad_norm=45.596, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.296e-04, train_time=2.983
+[gpuc04:0/16] 2024-01-16 23:43:35,196 (trainer:737) INFO: 37epoch:train:12601-12700batch: iter_time=1.024e-04, forward_time=0.104, loss_ctc=44.969, loss_att=45.812, acc=0.747, loss=45.559, backward_time=0.097, grad_norm=43.466, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.295e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:44:16,943 (trainer:737) INFO: 37epoch:train:12701-12800batch: iter_time=1.098e-04, forward_time=0.105, loss_ctc=43.496, loss_att=56.429, acc=0.747, loss=52.549, backward_time=0.098, grad_norm=42.849, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.295e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:44:58,827 (trainer:737) INFO: 37epoch:train:12801-12900batch: iter_time=1.166e-04, forward_time=0.108, loss_ctc=47.894, loss_att=49.760, acc=0.756, loss=49.200, backward_time=0.097, grad_norm=49.830, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.295e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-16 23:45:40,935 (trainer:737) INFO: 37epoch:train:12901-13000batch: iter_time=1.072e-04, forward_time=0.105, loss_ctc=47.275, loss_att=58.874, acc=0.728, loss=55.394, backward_time=0.098, grad_norm=51.512, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.294e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:46:22,728 (trainer:737) INFO: 37epoch:train:13001-13100batch: iter_time=1.014e-04, forward_time=0.105, loss_ctc=43.150, loss_att=55.158, acc=0.746, loss=51.556, backward_time=0.098, grad_norm=45.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.294e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:47:04,741 (trainer:737) INFO: 37epoch:train:13101-13200batch: iter_time=1.049e-04, forward_time=0.105, loss_ctc=44.517, loss_att=53.600, acc=0.735, loss=50.875, backward_time=0.097, grad_norm=47.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.294e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-16 23:47:46,539 (trainer:737) INFO: 37epoch:train:13201-13300batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=39.056, loss_att=37.886, acc=0.775, loss=38.237, backward_time=0.097, grad_norm=40.461, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.294e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:48:28,702 (trainer:737) INFO: 37epoch:train:13301-13400batch: iter_time=1.043e-04, forward_time=0.105, loss_ctc=41.913, loss_att=43.893, acc=0.743, loss=43.299, backward_time=0.097, grad_norm=40.687, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.293e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:48:35,303 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-16 23:49:10,294 (trainer:737) INFO: 37epoch:train:13401-13500batch: iter_time=1.118e-04, forward_time=0.105, loss_ctc=41.007, loss_att=53.662, acc=0.740, loss=49.866, backward_time=0.097, grad_norm=43.941, clip=100.000, loss_scale=1.196e+34, optim_step_time=0.039, optim0_lr0=3.293e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:49:52,565 (trainer:737) INFO: 37epoch:train:13501-13600batch: iter_time=9.524e-05, forward_time=0.104, loss_ctc=43.034, loss_att=49.002, acc=0.755, loss=47.212, backward_time=0.097, grad_norm=44.477, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.293e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-16 23:50:34,668 (trainer:737) INFO: 37epoch:train:13601-13700batch: iter_time=9.774e-05, forward_time=0.104, loss_ctc=38.605, loss_att=41.511, acc=0.754, loss=40.639, backward_time=0.096, grad_norm=39.179, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.292e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-16 23:50:58,114 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-16 23:51:18,532 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-16 23:51:22,196 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc7711ff40>)
+[gpuc04:0/16] 2024-01-16 23:51:22,196 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-16 23:51:22,199 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-16 23:55:47,323 (trainer:737) INFO: 37epoch:train:13701-13800batch: iter_time=2.317, forward_time=0.104, loss_ctc=41.608, loss_att=47.729, acc=0.744, loss=45.893, backward_time=0.096, grad_norm=45.725, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.292e-04, train_time=3.126
+[gpuc04:0/16] 2024-01-16 23:56:28,817 (trainer:737) INFO: 37epoch:train:13801-13900batch: iter_time=1.394e-04, forward_time=0.104, loss_ctc=43.597, loss_att=45.962, acc=0.734, loss=45.252, backward_time=0.096, grad_norm=43.500, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.292e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-16 23:57:10,659 (trainer:737) INFO: 37epoch:train:13901-14000batch: iter_time=1.172e-04, forward_time=0.105, loss_ctc=44.813, loss_att=56.731, acc=0.734, loss=53.156, backward_time=0.097, grad_norm=46.007, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.291e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-16 23:57:52,287 (trainer:737) INFO: 37epoch:train:14001-14100batch: iter_time=1.205e-04, forward_time=0.105, loss_ctc=46.007, loss_att=48.631, acc=0.753, loss=47.844, backward_time=0.097, grad_norm=50.219, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.291e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:58:33,951 (trainer:737) INFO: 37epoch:train:14101-14200batch: iter_time=1.507e-04, forward_time=0.105, loss_ctc=49.978, loss_att=56.256, acc=0.733, loss=54.372, backward_time=0.097, grad_norm=50.507, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.291e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-16 23:59:15,715 (trainer:737) INFO: 37epoch:train:14201-14300batch: iter_time=1.208e-04, forward_time=0.105, loss_ctc=46.021, loss_att=56.684, acc=0.724, loss=53.485, backward_time=0.097, grad_norm=51.386, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.291e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-16 23:59:57,236 (trainer:737) INFO: 37epoch:train:14301-14400batch: iter_time=1.243e-04, forward_time=0.104, loss_ctc=42.655, loss_att=54.060, acc=0.704, loss=50.639, backward_time=0.096, grad_norm=51.621, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.290e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:00:38,866 (trainer:737) INFO: 37epoch:train:14401-14500batch: iter_time=1.203e-04, forward_time=0.104, loss_ctc=42.526, loss_att=47.271, acc=0.759, loss=45.848, backward_time=0.096, grad_norm=42.592, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.290e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 00:01:20,420 (trainer:737) INFO: 37epoch:train:14501-14600batch: iter_time=1.135e-04, forward_time=0.105, loss_ctc=44.451, loss_att=45.066, acc=0.743, loss=44.882, backward_time=0.096, grad_norm=43.700, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.290e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:02:01,958 (trainer:737) INFO: 37epoch:train:14601-14700batch: iter_time=1.211e-04, forward_time=0.104, loss_ctc=35.774, loss_att=40.884, acc=0.754, loss=39.351, backward_time=0.096, grad_norm=37.795, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.289e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:02:43,706 (trainer:737) INFO: 37epoch:train:14701-14800batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=45.559, loss_att=55.927, acc=0.742, loss=52.816, backward_time=0.097, grad_norm=49.950, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.289e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 00:03:25,144 (trainer:737) INFO: 37epoch:train:14801-14900batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=34.556, loss_att=37.699, acc=0.762, loss=36.756, backward_time=0.096, grad_norm=36.163, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.289e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 00:04:07,214 (trainer:737) INFO: 37epoch:train:14901-15000batch: iter_time=1.024e-04, forward_time=0.103, loss_ctc=44.270, loss_att=49.556, acc=0.716, loss=47.971, backward_time=0.095, grad_norm=44.870, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.289e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 00:24:10,524 (trainer:343) INFO: 37epoch results: [train] iter_time=0.198, forward_time=0.105, loss_ctc=43.839, loss_att=49.702, acc=0.742, loss=47.943, backward_time=0.096, grad_norm=45.203, clip=100.000, loss_scale=1.929e+34, optim_step_time=0.039, optim0_lr0=3.311e-04, train_time=0.644, time=2 hours, 41 minutes and 9.25 seconds, total_count=555000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=49.405, cer_ctc=0.255, loss_att=51.889, acc=0.610, cer=0.354, wer=0.997, loss=51.144, time=19 minutes and 52.42 seconds, total_count=172827, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 00:24:15,919 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 00:24:15,968 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/29epoch.pth
+[gpuc04:0/16] 2024-01-17 00:24:15,968 (trainer:272) INFO: 38/45epoch started. Estimated time to finish: 1 day, 2 minutes and 39.55 seconds
+[gpuc04:0/16] 2024-01-17 00:24:15,978 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 00:24:34,395 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 00:24:37,855 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdbb5a311b0>)
+[gpuc04:0/16] 2024-01-17 00:24:37,856 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 00:24:37,859 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 00:29:02,697 (trainer:737) INFO: 38epoch:train:1-100batch: iter_time=2.277, forward_time=0.105, loss_ctc=47.484, loss_att=59.513, acc=0.711, loss=55.904, backward_time=0.098, grad_norm=51.575, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.288e-04, train_time=2.867
+[gpuc04:0/16] 2024-01-17 00:29:44,259 (trainer:737) INFO: 38epoch:train:101-200batch: iter_time=1.072e-04, forward_time=0.104, loss_ctc=35.804, loss_att=45.585, acc=0.751, loss=42.650, backward_time=0.097, grad_norm=38.604, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.288e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:30:28,101 (trainer:737) INFO: 38epoch:train:201-300batch: iter_time=1.103e-04, forward_time=0.105, loss_ctc=47.453, loss_att=62.661, acc=0.717, loss=58.099, backward_time=0.098, grad_norm=50.570, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.288e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-17 00:31:12,706 (trainer:737) INFO: 38epoch:train:301-400batch: iter_time=1.135e-04, forward_time=0.115, loss_ctc=52.418, loss_att=57.959, acc=0.729, loss=56.297, backward_time=0.112, grad_norm=57.749, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.287e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 00:31:54,981 (trainer:737) INFO: 38epoch:train:401-500batch: iter_time=1.174e-04, forward_time=0.105, loss_ctc=45.399, loss_att=55.402, acc=0.742, loss=52.401, backward_time=0.098, grad_norm=43.609, clip=100.000, loss_scale=1.911e+34, optim_step_time=0.039, optim0_lr0=3.287e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 00:32:37,252 (trainer:737) INFO: 38epoch:train:501-600batch: iter_time=1.206e-04, forward_time=0.105, loss_ctc=39.869, loss_att=46.529, acc=0.753, loss=44.531, backward_time=0.097, grad_norm=47.136, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.287e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 00:33:21,222 (trainer:737) INFO: 38epoch:train:601-700batch: iter_time=1.133e-04, forward_time=0.112, loss_ctc=59.326, loss_att=60.631, acc=0.719, loss=60.239, backward_time=0.102, grad_norm=65.947, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.286e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-17 00:34:04,842 (trainer:737) INFO: 38epoch:train:701-800batch: iter_time=1.085e-04, forward_time=0.119, loss_ctc=39.243, loss_att=44.466, acc=0.741, loss=42.899, backward_time=0.097, grad_norm=42.512, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.286e-04, train_time=0.437
+[gpuc04:0/16] 2024-01-17 00:34:46,670 (trainer:737) INFO: 38epoch:train:801-900batch: iter_time=9.915e-05, forward_time=0.105, loss_ctc=54.099, loss_att=53.563, acc=0.756, loss=53.724, backward_time=0.098, grad_norm=60.603, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.286e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 00:35:31,496 (trainer:737) INFO: 38epoch:train:901-1000batch: iter_time=1.105e-04, forward_time=0.111, loss_ctc=51.822, loss_att=54.099, acc=0.741, loss=53.416, backward_time=0.097, grad_norm=52.306, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.286e-04, train_time=0.448
+[gpuc04:0/16] 2024-01-17 00:36:13,340 (trainer:737) INFO: 38epoch:train:1001-1100batch: iter_time=2.133e-04, forward_time=0.105, loss_ctc=43.370, loss_att=53.518, acc=0.726, loss=50.474, backward_time=0.097, grad_norm=48.405, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.285e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 00:36:55,478 (trainer:737) INFO: 38epoch:train:1101-1200batch: iter_time=1.139e-04, forward_time=0.106, loss_ctc=49.455, loss_att=54.350, acc=0.731, loss=52.882, backward_time=0.101, grad_norm=50.970, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.285e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 00:37:30,318 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 00:37:50,174 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 00:37:53,745 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb966de860>)
+[gpuc04:0/16] 2024-01-17 00:37:53,745 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 00:37:53,748 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 00:43:24,238 (trainer:737) INFO: 38epoch:train:1201-1300batch: iter_time=3.182, forward_time=0.141, loss_ctc=45.136, loss_att=52.911, acc=0.729, loss=50.578, backward_time=0.101, grad_norm=45.870, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.285e-04, train_time=3.887
+[gpuc04:0/16] 2024-01-17 00:44:05,805 (trainer:737) INFO: 38epoch:train:1301-1400batch: iter_time=1.543e-04, forward_time=0.105, loss_ctc=40.466, loss_att=55.045, acc=0.714, loss=50.671, backward_time=0.097, grad_norm=44.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.284e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:44:47,565 (trainer:737) INFO: 38epoch:train:1401-1500batch: iter_time=1.595e-04, forward_time=0.105, loss_ctc=39.291, loss_att=51.210, acc=0.740, loss=47.634, backward_time=0.096, grad_norm=42.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.284e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 00:45:29,235 (trainer:737) INFO: 38epoch:train:1501-1600batch: iter_time=1.290e-04, forward_time=0.106, loss_ctc=47.590, loss_att=50.345, acc=0.745, loss=49.519, backward_time=0.097, grad_norm=54.644, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.284e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 00:46:11,916 (trainer:737) INFO: 38epoch:train:1601-1700batch: iter_time=1.530e-04, forward_time=0.104, loss_ctc=47.111, loss_att=57.667, acc=0.715, loss=54.500, backward_time=0.097, grad_norm=50.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.284e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-17 00:46:54,027 (trainer:737) INFO: 38epoch:train:1701-1800batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=41.702, loss_att=49.541, acc=0.745, loss=47.190, backward_time=0.097, grad_norm=41.805, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.283e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 00:47:35,583 (trainer:737) INFO: 38epoch:train:1801-1900batch: iter_time=1.431e-04, forward_time=0.104, loss_ctc=42.580, loss_att=44.980, acc=0.740, loss=44.260, backward_time=0.096, grad_norm=44.903, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.283e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:48:17,555 (trainer:737) INFO: 38epoch:train:1901-2000batch: iter_time=1.545e-04, forward_time=0.103, loss_ctc=52.572, loss_att=55.331, acc=0.726, loss=54.503, backward_time=0.096, grad_norm=60.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.283e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 00:48:59,312 (trainer:737) INFO: 38epoch:train:2001-2100batch: iter_time=1.419e-04, forward_time=0.104, loss_ctc=49.505, loss_att=45.769, acc=0.751, loss=46.890, backward_time=0.096, grad_norm=52.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.282e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 00:49:41,206 (trainer:737) INFO: 38epoch:train:2101-2200batch: iter_time=1.320e-04, forward_time=0.105, loss_ctc=50.598, loss_att=58.225, acc=0.727, loss=55.937, backward_time=0.097, grad_norm=60.998, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.282e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 00:50:22,655 (trainer:737) INFO: 38epoch:train:2201-2300batch: iter_time=1.594e-04, forward_time=0.104, loss_ctc=40.684, loss_att=40.557, acc=0.749, loss=40.595, backward_time=0.095, grad_norm=43.182, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.282e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 00:51:04,222 (trainer:737) INFO: 38epoch:train:2301-2400batch: iter_time=1.560e-04, forward_time=0.105, loss_ctc=44.903, loss_att=53.967, acc=0.715, loss=51.248, backward_time=0.096, grad_norm=49.825, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.281e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 00:51:45,800 (trainer:737) INFO: 38epoch:train:2401-2500batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=45.596, loss_att=52.041, acc=0.741, loss=50.108, backward_time=0.096, grad_norm=46.918, clip=100.000, loss_scale=3.822e+34, optim_step_time=0.039, optim0_lr0=3.281e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 00:51:48,180 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 00:52:07,628 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 00:52:11,426 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe42e87fdf0>)
+[gpuc04:0/16] 2024-01-17 00:52:11,426 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 00:52:11,429 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 00:56:55,093 (trainer:737) INFO: 38epoch:train:2501-2600batch: iter_time=2.314, forward_time=0.105, loss_ctc=45.900, loss_att=54.093, acc=0.715, loss=51.635, backward_time=0.097, grad_norm=45.237, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.281e-04, train_time=3.093
+[gpuc04:0/16] 2024-01-17 00:57:36,515 (trainer:737) INFO: 38epoch:train:2601-2700batch: iter_time=9.832e-05, forward_time=0.104, loss_ctc=34.893, loss_att=41.341, acc=0.750, loss=39.407, backward_time=0.096, grad_norm=36.682, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.281e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 00:58:18,107 (trainer:737) INFO: 38epoch:train:2701-2800batch: iter_time=9.969e-05, forward_time=0.105, loss_ctc=45.517, loss_att=59.838, acc=0.718, loss=55.542, backward_time=0.097, grad_norm=48.140, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.280e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 00:59:00,216 (trainer:737) INFO: 38epoch:train:2801-2900batch: iter_time=1.053e-04, forward_time=0.105, loss_ctc=50.245, loss_att=53.945, acc=0.730, loss=52.835, backward_time=0.097, grad_norm=54.927, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.280e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 00:59:43,825 (trainer:737) INFO: 38epoch:train:2901-3000batch: iter_time=1.140e-04, forward_time=0.121, loss_ctc=43.838, loss_att=52.622, acc=0.740, loss=49.987, backward_time=0.098, grad_norm=41.986, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.280e-04, train_time=0.436
+[gpuc04:0/16] 2024-01-17 01:00:25,877 (trainer:737) INFO: 38epoch:train:3001-3100batch: iter_time=1.176e-04, forward_time=0.105, loss_ctc=39.683, loss_att=46.027, acc=0.750, loss=44.124, backward_time=0.097, grad_norm=45.603, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.279e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 01:01:07,465 (trainer:737) INFO: 38epoch:train:3101-3200batch: iter_time=1.155e-04, forward_time=0.105, loss_ctc=53.787, loss_att=55.317, acc=0.716, loss=54.858, backward_time=0.097, grad_norm=61.128, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.279e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:01:14,904 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 01:01:49,646 (trainer:737) INFO: 38epoch:train:3201-3300batch: iter_time=1.144e-04, forward_time=0.105, loss_ctc=38.565, loss_att=43.153, acc=0.742, loss=41.776, backward_time=0.096, grad_norm=42.247, clip=100.000, loss_scale=2.434e+34, optim_step_time=0.039, optim0_lr0=3.279e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 01:02:31,864 (trainer:737) INFO: 38epoch:train:3301-3400batch: iter_time=1.043e-04, forward_time=0.106, loss_ctc=49.234, loss_att=49.510, acc=0.757, loss=49.427, backward_time=0.097, grad_norm=53.484, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.279e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 01:03:13,518 (trainer:737) INFO: 38epoch:train:3401-3500batch: iter_time=1.227e-04, forward_time=0.105, loss_ctc=49.606, loss_att=50.437, acc=0.744, loss=50.188, backward_time=0.097, grad_norm=51.029, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.278e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:03:55,427 (trainer:737) INFO: 38epoch:train:3501-3600batch: iter_time=1.244e-04, forward_time=0.105, loss_ctc=41.985, loss_att=51.837, acc=0.715, loss=48.882, backward_time=0.096, grad_norm=45.508, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.278e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 01:04:37,243 (trainer:737) INFO: 38epoch:train:3601-3700batch: iter_time=1.094e-04, forward_time=0.105, loss_ctc=47.748, loss_att=52.517, acc=0.727, loss=51.086, backward_time=0.097, grad_norm=52.816, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.278e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:05:00,671 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 01:05:20,327 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 01:05:24,066 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe3faf47610>)
+[gpuc04:0/16] 2024-01-17 01:05:24,066 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 01:05:24,069 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 01:10:14,234 (trainer:737) INFO: 38epoch:train:3701-3800batch: iter_time=2.510, forward_time=0.105, loss_ctc=45.030, loss_att=52.010, acc=0.735, loss=49.916, backward_time=0.097, grad_norm=45.646, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.277e-04, train_time=3.370
+[gpuc04:0/16] 2024-01-17 01:10:56,159 (trainer:737) INFO: 38epoch:train:3801-3900batch: iter_time=1.295e-04, forward_time=0.105, loss_ctc=39.825, loss_att=55.313, acc=0.727, loss=50.666, backward_time=0.097, grad_norm=42.941, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.277e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 01:11:37,997 (trainer:737) INFO: 38epoch:train:3901-4000batch: iter_time=1.219e-04, forward_time=0.104, loss_ctc=38.294, loss_att=53.109, acc=0.742, loss=48.664, backward_time=0.096, grad_norm=50.188, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.277e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:12:19,773 (trainer:737) INFO: 38epoch:train:4001-4100batch: iter_time=1.026e-04, forward_time=0.105, loss_ctc=46.253, loss_att=53.783, acc=0.746, loss=51.524, backward_time=0.098, grad_norm=50.204, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.276e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:13:01,578 (trainer:737) INFO: 38epoch:train:4101-4200batch: iter_time=1.024e-04, forward_time=0.105, loss_ctc=46.794, loss_att=58.507, acc=0.726, loss=54.993, backward_time=0.098, grad_norm=46.462, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.276e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:13:43,249 (trainer:737) INFO: 38epoch:train:4201-4300batch: iter_time=1.130e-04, forward_time=0.104, loss_ctc=41.193, loss_att=49.582, acc=0.754, loss=47.065, backward_time=0.097, grad_norm=42.951, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.276e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:14:24,940 (trainer:737) INFO: 38epoch:train:4301-4400batch: iter_time=1.084e-04, forward_time=0.104, loss_ctc=42.539, loss_att=45.041, acc=0.748, loss=44.290, backward_time=0.097, grad_norm=46.328, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.276e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:15:07,105 (trainer:737) INFO: 38epoch:train:4401-4500batch: iter_time=1.091e-04, forward_time=0.103, loss_ctc=50.574, loss_att=54.924, acc=0.735, loss=53.619, backward_time=0.097, grad_norm=59.532, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.275e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 01:15:49,302 (trainer:737) INFO: 38epoch:train:4501-4600batch: iter_time=1.215e-04, forward_time=0.104, loss_ctc=48.752, loss_att=47.311, acc=0.753, loss=47.743, backward_time=0.096, grad_norm=53.423, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.275e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 01:16:31,233 (trainer:737) INFO: 38epoch:train:4601-4700batch: iter_time=1.113e-04, forward_time=0.105, loss_ctc=49.476, loss_att=61.419, acc=0.732, loss=57.836, backward_time=0.098, grad_norm=58.904, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.275e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 01:17:13,311 (trainer:737) INFO: 38epoch:train:4701-4800batch: iter_time=1.140e-04, forward_time=0.104, loss_ctc=40.152, loss_att=42.747, acc=0.751, loss=41.968, backward_time=0.096, grad_norm=40.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.274e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 01:17:55,580 (trainer:737) INFO: 38epoch:train:4801-4900batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=44.609, loss_att=53.995, acc=0.730, loss=51.179, backward_time=0.097, grad_norm=50.842, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.274e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 01:18:37,800 (trainer:737) INFO: 38epoch:train:4901-5000batch: iter_time=1.056e-04, forward_time=0.104, loss_ctc=45.067, loss_att=52.590, acc=0.749, loss=50.333, backward_time=0.097, grad_norm=45.934, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.274e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 01:18:43,039 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 01:19:02,052 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 01:19:05,605 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdca4d47100>)
+[gpuc04:0/16] 2024-01-17 01:19:05,605 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 01:19:05,609 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 01:24:19,867 (trainer:737) INFO: 38epoch:train:5001-5100batch: iter_time=2.612, forward_time=0.105, loss_ctc=45.550, loss_att=57.102, acc=0.708, loss=53.637, backward_time=0.097, grad_norm=45.361, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.274e-04, train_time=3.420
+[gpuc04:0/16] 2024-01-17 01:25:01,531 (trainer:737) INFO: 38epoch:train:5101-5200batch: iter_time=1.058e-04, forward_time=0.104, loss_ctc=34.578, loss_att=41.393, acc=0.751, loss=39.348, backward_time=0.095, grad_norm=37.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.273e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:25:43,185 (trainer:737) INFO: 38epoch:train:5201-5300batch: iter_time=9.833e-05, forward_time=0.105, loss_ctc=45.144, loss_att=59.395, acc=0.720, loss=55.120, backward_time=0.097, grad_norm=48.222, clip=100.000, loss_scale=3.780e+34, optim_step_time=0.038, optim0_lr0=3.273e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:26:24,820 (trainer:737) INFO: 38epoch:train:5301-5400batch: iter_time=1.005e-04, forward_time=0.105, loss_ctc=50.026, loss_att=54.802, acc=0.728, loss=53.369, backward_time=0.096, grad_norm=52.848, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.273e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:27:06,629 (trainer:737) INFO: 38epoch:train:5401-5500batch: iter_time=1.041e-04, forward_time=0.105, loss_ctc=43.432, loss_att=53.765, acc=0.736, loss=50.665, backward_time=0.096, grad_norm=43.368, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.272e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:27:48,154 (trainer:737) INFO: 38epoch:train:5501-5600batch: iter_time=1.017e-04, forward_time=0.104, loss_ctc=39.651, loss_att=45.978, acc=0.752, loss=44.080, backward_time=0.095, grad_norm=45.004, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.272e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 01:27:54,380 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 01:28:29,946 (trainer:737) INFO: 38epoch:train:5601-5700batch: iter_time=1.134e-04, forward_time=0.105, loss_ctc=52.733, loss_att=55.133, acc=0.720, loss=54.413, backward_time=0.096, grad_norm=59.657, clip=100.000, loss_scale=2.371e+34, optim_step_time=0.038, optim0_lr0=3.272e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:29:11,761 (trainer:737) INFO: 38epoch:train:5701-5800batch: iter_time=1.207e-04, forward_time=0.104, loss_ctc=38.254, loss_att=42.568, acc=0.743, loss=41.274, backward_time=0.096, grad_norm=40.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.271e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:29:53,521 (trainer:737) INFO: 38epoch:train:5801-5900batch: iter_time=1.385e-04, forward_time=0.106, loss_ctc=48.282, loss_att=48.887, acc=0.758, loss=48.705, backward_time=0.097, grad_norm=53.418, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.271e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:30:35,506 (trainer:737) INFO: 38epoch:train:5901-6000batch: iter_time=1.063e-04, forward_time=0.108, loss_ctc=48.786, loss_att=49.905, acc=0.745, loss=49.569, backward_time=0.097, grad_norm=53.402, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.271e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 01:31:17,052 (trainer:737) INFO: 38epoch:train:6001-6100batch: iter_time=9.369e-05, forward_time=0.104, loss_ctc=41.559, loss_att=51.514, acc=0.719, loss=48.528, backward_time=0.096, grad_norm=45.497, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.271e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 01:31:58,601 (trainer:737) INFO: 38epoch:train:6101-6200batch: iter_time=9.884e-05, forward_time=0.104, loss_ctc=47.267, loss_att=53.059, acc=0.726, loss=51.321, backward_time=0.096, grad_norm=49.206, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.270e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 01:32:26,081 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 01:32:45,609 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 01:32:49,244 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc9d367be0>)
+[gpuc04:0/16] 2024-01-17 01:32:49,244 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 01:32:49,248 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 01:37:02,281 (trainer:737) INFO: 38epoch:train:6201-6300batch: iter_time=2.334, forward_time=0.105, loss_ctc=44.753, loss_att=50.423, acc=0.732, loss=48.722, backward_time=0.096, grad_norm=43.941, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.270e-04, train_time=3.037
+[gpuc04:0/16] 2024-01-17 01:37:44,145 (trainer:737) INFO: 38epoch:train:6301-6400batch: iter_time=9.604e-05, forward_time=0.105, loss_ctc=39.534, loss_att=51.599, acc=0.723, loss=47.980, backward_time=0.096, grad_norm=43.079, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.270e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:38:26,009 (trainer:737) INFO: 38epoch:train:6401-6500batch: iter_time=1.293e-04, forward_time=0.105, loss_ctc=38.168, loss_att=50.528, acc=0.743, loss=46.820, backward_time=0.095, grad_norm=40.934, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.269e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:39:08,108 (trainer:737) INFO: 38epoch:train:6501-6600batch: iter_time=1.115e-04, forward_time=0.106, loss_ctc=45.402, loss_att=48.590, acc=0.751, loss=47.634, backward_time=0.096, grad_norm=47.901, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.269e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 01:39:49,881 (trainer:737) INFO: 38epoch:train:6601-6700batch: iter_time=1.138e-04, forward_time=0.106, loss_ctc=46.328, loss_att=55.714, acc=0.718, loss=52.898, backward_time=0.096, grad_norm=47.269, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.269e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:40:31,534 (trainer:737) INFO: 38epoch:train:6701-6800batch: iter_time=1.316e-04, forward_time=0.106, loss_ctc=40.563, loss_att=49.126, acc=0.749, loss=46.557, backward_time=0.096, grad_norm=41.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.269e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:41:13,192 (trainer:737) INFO: 38epoch:train:6801-6900batch: iter_time=1.212e-04, forward_time=0.105, loss_ctc=42.303, loss_att=43.696, acc=0.744, loss=43.278, backward_time=0.096, grad_norm=45.237, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.268e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:41:54,862 (trainer:737) INFO: 38epoch:train:6901-7000batch: iter_time=1.057e-04, forward_time=0.105, loss_ctc=50.315, loss_att=55.935, acc=0.730, loss=54.249, backward_time=0.095, grad_norm=64.171, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.268e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:42:36,565 (trainer:737) INFO: 38epoch:train:7001-7100batch: iter_time=1.013e-04, forward_time=0.106, loss_ctc=47.992, loss_att=46.078, acc=0.754, loss=46.652, backward_time=0.095, grad_norm=51.756, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.268e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:43:18,492 (trainer:737) INFO: 38epoch:train:7101-7200batch: iter_time=1.025e-04, forward_time=0.106, loss_ctc=49.414, loss_att=58.030, acc=0.728, loss=55.445, backward_time=0.096, grad_norm=55.417, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.267e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 01:43:59,994 (trainer:737) INFO: 38epoch:train:7201-7300batch: iter_time=1.130e-04, forward_time=0.105, loss_ctc=39.887, loss_att=40.029, acc=0.751, loss=39.987, backward_time=0.095, grad_norm=41.702, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.267e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 01:44:41,702 (trainer:737) INFO: 38epoch:train:7301-7400batch: iter_time=1.101e-04, forward_time=0.106, loss_ctc=44.296, loss_att=53.332, acc=0.719, loss=50.621, backward_time=0.096, grad_norm=50.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.267e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:45:23,397 (trainer:737) INFO: 38epoch:train:7401-7500batch: iter_time=9.397e-05, forward_time=0.105, loss_ctc=44.992, loss_att=51.327, acc=0.744, loss=49.427, backward_time=0.096, grad_norm=47.287, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.267e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:45:25,786 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 01:45:45,462 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 01:45:49,120 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe424856aa0>)
+[gpuc04:0/16] 2024-01-17 01:45:49,120 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 01:45:49,124 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 01:50:31,006 (trainer:737) INFO: 38epoch:train:7501-7600batch: iter_time=2.408, forward_time=0.106, loss_ctc=44.770, loss_att=57.882, acc=0.718, loss=53.948, backward_time=0.097, grad_norm=46.519, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.266e-04, train_time=3.076
+[gpuc04:0/16] 2024-01-17 01:51:12,853 (trainer:737) INFO: 38epoch:train:7601-7700batch: iter_time=1.679e-04, forward_time=0.105, loss_ctc=33.876, loss_att=43.587, acc=0.757, loss=40.674, backward_time=0.096, grad_norm=36.864, clip=100.000, loss_scale=3.842e+34, optim_step_time=0.038, optim0_lr0=3.266e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:51:26,647 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 01:51:54,846 (trainer:737) INFO: 38epoch:train:7701-7800batch: iter_time=1.616e-04, forward_time=0.107, loss_ctc=45.217, loss_att=61.904, acc=0.723, loss=56.898, backward_time=0.097, grad_norm=47.795, clip=100.000, loss_scale=2.748e+34, optim_step_time=0.038, optim0_lr0=3.266e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 01:52:36,636 (trainer:737) INFO: 38epoch:train:7801-7900batch: iter_time=1.468e-04, forward_time=0.107, loss_ctc=49.037, loss_att=55.584, acc=0.735, loss=53.619, backward_time=0.097, grad_norm=52.399, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.265e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:53:18,645 (trainer:737) INFO: 38epoch:train:7901-8000batch: iter_time=1.520e-04, forward_time=0.106, loss_ctc=43.239, loss_att=54.936, acc=0.745, loss=51.427, backward_time=0.097, grad_norm=44.242, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.265e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 01:54:00,511 (trainer:737) INFO: 38epoch:train:8001-8100batch: iter_time=1.592e-04, forward_time=0.105, loss_ctc=39.280, loss_att=45.750, acc=0.757, loss=43.809, backward_time=0.096, grad_norm=44.952, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.265e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:54:42,822 (trainer:737) INFO: 38epoch:train:8101-8200batch: iter_time=1.378e-04, forward_time=0.107, loss_ctc=52.146, loss_att=56.503, acc=0.724, loss=55.196, backward_time=0.096, grad_norm=58.826, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.265e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 01:55:24,465 (trainer:737) INFO: 38epoch:train:8201-8300batch: iter_time=1.489e-04, forward_time=0.106, loss_ctc=37.983, loss_att=44.269, acc=0.745, loss=42.384, backward_time=0.096, grad_norm=39.049, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.264e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 01:56:06,280 (trainer:737) INFO: 38epoch:train:8301-8400batch: iter_time=1.533e-04, forward_time=0.106, loss_ctc=48.378, loss_att=51.283, acc=0.762, loss=50.411, backward_time=0.096, grad_norm=52.620, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.264e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 01:56:48,701 (trainer:737) INFO: 38epoch:train:8401-8500batch: iter_time=1.730e-04, forward_time=0.107, loss_ctc=48.920, loss_att=52.648, acc=0.745, loss=51.529, backward_time=0.097, grad_norm=53.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.264e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 01:57:30,389 (trainer:737) INFO: 38epoch:train:8501-8600batch: iter_time=1.627e-04, forward_time=0.106, loss_ctc=40.904, loss_att=52.495, acc=0.731, loss=49.018, backward_time=0.096, grad_norm=43.903, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.263e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:58:12,134 (trainer:737) INFO: 38epoch:train:8601-8700batch: iter_time=1.588e-04, forward_time=0.107, loss_ctc=47.285, loss_att=53.374, acc=0.736, loss=51.548, backward_time=0.096, grad_norm=49.444, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.263e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 01:58:38,676 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 01:58:58,050 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 01:59:01,550 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdca05424d0>)
+[gpuc04:0/16] 2024-01-17 01:59:01,551 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 01:59:01,554 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 02:03:29,634 (trainer:737) INFO: 38epoch:train:8701-8800batch: iter_time=2.491, forward_time=0.106, loss_ctc=44.573, loss_att=52.650, acc=0.734, loss=50.227, backward_time=0.096, grad_norm=44.678, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.263e-04, train_time=3.175
+[gpuc04:0/16] 2024-01-17 02:04:11,544 (trainer:737) INFO: 38epoch:train:8801-8900batch: iter_time=1.483e-04, forward_time=0.106, loss_ctc=39.590, loss_att=54.030, acc=0.718, loss=49.698, backward_time=0.096, grad_norm=42.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.262e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:04:53,123 (trainer:737) INFO: 38epoch:train:8901-9000batch: iter_time=1.581e-04, forward_time=0.106, loss_ctc=38.185, loss_att=51.239, acc=0.741, loss=47.323, backward_time=0.096, grad_norm=41.185, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.262e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:05:34,847 (trainer:737) INFO: 38epoch:train:9001-9100batch: iter_time=1.600e-04, forward_time=0.107, loss_ctc=45.285, loss_att=48.843, acc=0.750, loss=47.776, backward_time=0.097, grad_norm=48.867, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.262e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:06:16,564 (trainer:737) INFO: 38epoch:train:9101-9200batch: iter_time=1.608e-04, forward_time=0.107, loss_ctc=45.798, loss_att=55.796, acc=0.721, loss=52.796, backward_time=0.097, grad_norm=47.916, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.262e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:06:58,154 (trainer:737) INFO: 38epoch:train:9201-9300batch: iter_time=1.496e-04, forward_time=0.106, loss_ctc=40.806, loss_att=48.734, acc=0.750, loss=46.356, backward_time=0.096, grad_norm=41.633, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.261e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:07:39,925 (trainer:737) INFO: 38epoch:train:9301-9400batch: iter_time=1.954e-04, forward_time=0.106, loss_ctc=42.311, loss_att=44.988, acc=0.739, loss=44.185, backward_time=0.099, grad_norm=46.769, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.261e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:08:21,648 (trainer:737) INFO: 38epoch:train:9401-9500batch: iter_time=1.966e-04, forward_time=0.106, loss_ctc=48.764, loss_att=53.364, acc=0.731, loss=51.984, backward_time=0.099, grad_norm=60.862, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.261e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:09:04,034 (trainer:737) INFO: 38epoch:train:9501-9600batch: iter_time=1.784e-04, forward_time=0.107, loss_ctc=47.680, loss_att=48.088, acc=0.754, loss=47.965, backward_time=0.097, grad_norm=52.668, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.260e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 02:09:46,724 (trainer:737) INFO: 38epoch:train:9601-9700batch: iter_time=1.704e-04, forward_time=0.110, loss_ctc=49.090, loss_att=58.074, acc=0.726, loss=55.379, backward_time=0.097, grad_norm=55.431, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.260e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-17 02:10:01,629 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 02:10:28,159 (trainer:737) INFO: 38epoch:train:9701-9800batch: iter_time=1.400e-04, forward_time=0.106, loss_ctc=39.374, loss_att=39.821, acc=0.752, loss=39.687, backward_time=0.096, grad_norm=41.369, clip=100.000, loss_scale=2.119e+34, optim_step_time=0.038, optim0_lr0=3.260e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 02:11:10,085 (trainer:737) INFO: 38epoch:train:9801-9900batch: iter_time=1.741e-04, forward_time=0.106, loss_ctc=43.870, loss_att=52.717, acc=0.719, loss=50.063, backward_time=0.096, grad_norm=49.389, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.260e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:11:51,798 (trainer:737) INFO: 38epoch:train:9901-10000batch: iter_time=1.599e-04, forward_time=0.106, loss_ctc=44.385, loss_att=51.253, acc=0.745, loss=49.193, backward_time=0.097, grad_norm=44.012, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.259e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:11:57,686 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 02:12:16,854 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 02:12:20,489 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4248012d0>)
+[gpuc04:0/16] 2024-01-17 02:12:20,489 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 02:12:20,493 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 02:16:56,684 (trainer:737) INFO: 38epoch:train:10001-10100batch: iter_time=2.626, forward_time=0.107, loss_ctc=44.945, loss_att=57.083, acc=0.719, loss=53.442, backward_time=0.098, grad_norm=45.386, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.259e-04, train_time=3.049
+[gpuc04:0/16] 2024-01-17 02:17:38,384 (trainer:737) INFO: 38epoch:train:10101-10200batch: iter_time=1.643e-04, forward_time=0.105, loss_ctc=34.148, loss_att=43.657, acc=0.759, loss=40.804, backward_time=0.096, grad_norm=38.008, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.259e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:18:20,285 (trainer:737) INFO: 38epoch:train:10201-10300batch: iter_time=1.779e-04, forward_time=0.107, loss_ctc=44.718, loss_att=61.927, acc=0.724, loss=56.765, backward_time=0.097, grad_norm=48.938, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.258e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:19:02,164 (trainer:737) INFO: 38epoch:train:10301-10400batch: iter_time=1.845e-04, forward_time=0.107, loss_ctc=48.715, loss_att=54.937, acc=0.740, loss=53.071, backward_time=0.097, grad_norm=51.987, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.258e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:19:43,990 (trainer:737) INFO: 38epoch:train:10401-10500batch: iter_time=1.838e-04, forward_time=0.106, loss_ctc=43.046, loss_att=53.570, acc=0.751, loss=50.413, backward_time=0.097, grad_norm=44.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.258e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:20:25,650 (trainer:737) INFO: 38epoch:train:10501-10600batch: iter_time=1.535e-04, forward_time=0.105, loss_ctc=38.859, loss_att=45.437, acc=0.758, loss=43.464, backward_time=0.097, grad_norm=45.110, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.258e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:21:07,363 (trainer:737) INFO: 38epoch:train:10601-10700batch: iter_time=1.706e-04, forward_time=0.106, loss_ctc=51.275, loss_att=54.942, acc=0.728, loss=53.842, backward_time=0.097, grad_norm=64.944, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.257e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:21:49,057 (trainer:737) INFO: 38epoch:train:10701-10800batch: iter_time=1.781e-04, forward_time=0.106, loss_ctc=37.923, loss_att=44.449, acc=0.744, loss=42.491, backward_time=0.096, grad_norm=41.266, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.257e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:22:30,910 (trainer:737) INFO: 38epoch:train:10801-10900batch: iter_time=1.600e-04, forward_time=0.107, loss_ctc=47.447, loss_att=50.165, acc=0.767, loss=49.350, backward_time=0.097, grad_norm=52.469, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.257e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:23:13,563 (trainer:737) INFO: 38epoch:train:10901-11000batch: iter_time=1.412e-04, forward_time=0.106, loss_ctc=48.183, loss_att=51.621, acc=0.747, loss=50.590, backward_time=0.097, grad_norm=50.344, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.256e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 02:23:55,323 (trainer:737) INFO: 38epoch:train:11001-11100batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=41.155, loss_att=51.896, acc=0.731, loss=48.673, backward_time=0.097, grad_norm=44.958, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.256e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:24:37,036 (trainer:737) INFO: 38epoch:train:11101-11200batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=47.173, loss_att=53.439, acc=0.736, loss=51.559, backward_time=0.097, grad_norm=50.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.256e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:25:03,728 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 02:25:23,144 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 02:25:26,780 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc8787be80>)
+[gpuc04:0/16] 2024-01-17 02:25:26,780 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 02:25:26,784 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 02:29:54,574 (trainer:737) INFO: 38epoch:train:11201-11300batch: iter_time=2.517, forward_time=0.106, loss_ctc=44.276, loss_att=49.932, acc=0.743, loss=48.235, backward_time=0.097, grad_norm=43.553, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.256e-04, train_time=3.175
+[gpuc04:0/16] 2024-01-17 02:30:36,451 (trainer:737) INFO: 38epoch:train:11301-11400batch: iter_time=1.578e-04, forward_time=0.106, loss_ctc=39.631, loss_att=52.799, acc=0.733, loss=48.849, backward_time=0.097, grad_norm=43.304, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.255e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:31:18,972 (trainer:737) INFO: 38epoch:train:11401-11500batch: iter_time=1.256e-04, forward_time=0.105, loss_ctc=38.165, loss_att=52.400, acc=0.746, loss=48.130, backward_time=0.096, grad_norm=40.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.255e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 02:32:00,842 (trainer:737) INFO: 38epoch:train:11501-11600batch: iter_time=1.618e-04, forward_time=0.107, loss_ctc=45.309, loss_att=52.420, acc=0.748, loss=50.287, backward_time=0.097, grad_norm=48.090, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.255e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:32:43,272 (trainer:737) INFO: 38epoch:train:11601-11700batch: iter_time=1.571e-04, forward_time=0.107, loss_ctc=46.206, loss_att=56.077, acc=0.735, loss=53.115, backward_time=0.097, grad_norm=46.876, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.254e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 02:33:24,924 (trainer:737) INFO: 38epoch:train:11701-11800batch: iter_time=1.589e-04, forward_time=0.106, loss_ctc=40.641, loss_att=49.224, acc=0.756, loss=46.649, backward_time=0.096, grad_norm=41.626, clip=100.000, loss_scale=3.406e+34, optim_step_time=0.038, optim0_lr0=3.254e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:33:39,939 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 02:34:07,060 (trainer:737) INFO: 38epoch:train:11801-11900batch: iter_time=1.540e-04, forward_time=0.109, loss_ctc=41.608, loss_att=44.595, acc=0.751, loss=43.699, backward_time=0.098, grad_norm=45.179, clip=100.000, loss_scale=2.790e+34, optim_step_time=0.038, optim0_lr0=3.254e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 02:34:48,986 (trainer:737) INFO: 38epoch:train:11901-12000batch: iter_time=1.460e-04, forward_time=0.106, loss_ctc=48.559, loss_att=54.512, acc=0.736, loss=52.726, backward_time=0.096, grad_norm=57.496, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.254e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:35:30,934 (trainer:737) INFO: 38epoch:train:12001-12100batch: iter_time=1.577e-04, forward_time=0.105, loss_ctc=47.597, loss_att=45.568, acc=0.757, loss=46.176, backward_time=0.096, grad_norm=49.632, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.253e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:36:12,872 (trainer:737) INFO: 38epoch:train:12101-12200batch: iter_time=1.529e-04, forward_time=0.108, loss_ctc=48.651, loss_att=59.781, acc=0.737, loss=56.442, backward_time=0.097, grad_norm=55.298, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.253e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:36:54,412 (trainer:737) INFO: 38epoch:train:12201-12300batch: iter_time=1.748e-04, forward_time=0.106, loss_ctc=39.352, loss_att=41.823, acc=0.755, loss=41.082, backward_time=0.096, grad_norm=41.353, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.253e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 02:37:36,174 (trainer:737) INFO: 38epoch:train:12301-12400batch: iter_time=1.527e-04, forward_time=0.106, loss_ctc=43.548, loss_att=52.653, acc=0.732, loss=49.921, backward_time=0.096, grad_norm=48.152, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.252e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:38:19,473 (trainer:737) INFO: 38epoch:train:12401-12500batch: iter_time=3.795e-04, forward_time=0.107, loss_ctc=45.146, loss_att=52.201, acc=0.752, loss=50.084, backward_time=0.101, grad_norm=45.854, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.252e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-17 02:38:26,706 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 02:38:46,934 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 02:38:50,563 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdcb699c790>)
+[gpuc04:0/16] 2024-01-17 02:38:50,563 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 02:38:50,567 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 02:43:33,438 (trainer:737) INFO: 38epoch:train:12501-12600batch: iter_time=2.593, forward_time=0.107, loss_ctc=44.863, loss_att=57.529, acc=0.710, loss=53.729, backward_time=0.098, grad_norm=46.256, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.252e-04, train_time=3.139
+[gpuc04:0/16] 2024-01-17 02:44:15,034 (trainer:737) INFO: 38epoch:train:12601-12700batch: iter_time=1.370e-04, forward_time=0.105, loss_ctc=34.246, loss_att=42.209, acc=0.749, loss=39.820, backward_time=0.096, grad_norm=36.629, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.252e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:44:56,784 (trainer:737) INFO: 38epoch:train:12701-12800batch: iter_time=1.317e-04, forward_time=0.105, loss_ctc=43.976, loss_att=59.659, acc=0.719, loss=54.954, backward_time=0.097, grad_norm=47.719, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.251e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:45:38,599 (trainer:737) INFO: 38epoch:train:12801-12900batch: iter_time=1.305e-04, forward_time=0.106, loss_ctc=48.685, loss_att=55.327, acc=0.730, loss=53.334, backward_time=0.097, grad_norm=51.282, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.251e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:46:21,737 (trainer:737) INFO: 38epoch:train:12901-13000batch: iter_time=1.304e-04, forward_time=0.106, loss_ctc=43.453, loss_att=53.208, acc=0.739, loss=50.282, backward_time=0.097, grad_norm=42.009, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.251e-04, train_time=0.431
+[gpuc04:0/16] 2024-01-17 02:47:03,316 (trainer:737) INFO: 38epoch:train:13001-13100batch: iter_time=1.322e-04, forward_time=0.105, loss_ctc=39.257, loss_att=45.738, acc=0.753, loss=43.794, backward_time=0.096, grad_norm=45.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.250e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 02:47:45,087 (trainer:737) INFO: 38epoch:train:13101-13200batch: iter_time=1.576e-04, forward_time=0.106, loss_ctc=51.175, loss_att=55.308, acc=0.719, loss=54.068, backward_time=0.096, grad_norm=59.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.250e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 02:48:27,355 (trainer:737) INFO: 38epoch:train:13201-13300batch: iter_time=1.507e-04, forward_time=0.106, loss_ctc=37.528, loss_att=42.802, acc=0.745, loss=41.220, backward_time=0.096, grad_norm=39.921, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.250e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 02:49:09,274 (trainer:737) INFO: 38epoch:train:13301-13400batch: iter_time=1.550e-04, forward_time=0.107, loss_ctc=47.964, loss_att=49.405, acc=0.758, loss=48.973, backward_time=0.096, grad_norm=51.111, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.250e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 02:49:51,760 (trainer:737) INFO: 38epoch:train:13401-13500batch: iter_time=1.720e-04, forward_time=0.107, loss_ctc=47.933, loss_att=49.685, acc=0.745, loss=49.160, backward_time=0.098, grad_norm=49.639, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.249e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 02:50:33,533 (trainer:737) INFO: 38epoch:train:13501-13600batch: iter_time=1.624e-04, forward_time=0.107, loss_ctc=40.661, loss_att=51.835, acc=0.718, loss=48.483, backward_time=0.097, grad_norm=45.482, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.249e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:51:19,365 (trainer:737) INFO: 38epoch:train:13601-13700batch: iter_time=1.522e-04, forward_time=0.127, loss_ctc=46.780, loss_att=52.703, acc=0.728, loss=50.926, backward_time=0.108, grad_norm=50.365, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.249e-04, train_time=0.458
+[gpuc04:0/16] 2024-01-17 02:51:46,815 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 02:52:07,139 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 02:52:11,178 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc878011b0>)
+[gpuc04:0/16] 2024-01-17 02:52:11,179 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 02:52:11,182 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 02:56:21,582 (trainer:737) INFO: 38epoch:train:13701-13800batch: iter_time=2.603, forward_time=0.106, loss_ctc=43.873, loss_att=51.075, acc=0.740, loss=48.915, backward_time=0.097, grad_norm=43.404, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.248e-04, train_time=3.022
+[gpuc04:0/16] 2024-01-17 02:57:03,449 (trainer:737) INFO: 38epoch:train:13801-13900batch: iter_time=1.214e-04, forward_time=0.105, loss_ctc=39.262, loss_att=54.626, acc=0.730, loss=50.017, backward_time=0.097, grad_norm=42.904, clip=100.000, loss_scale=3.427e+34, optim_step_time=0.039, optim0_lr0=3.248e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:57:45,250 (trainer:737) INFO: 38epoch:train:13901-14000batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=37.663, loss_att=52.560, acc=0.747, loss=48.091, backward_time=0.096, grad_norm=40.209, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.248e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:57:51,482 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 02:58:27,061 (trainer:737) INFO: 38epoch:train:14001-14100batch: iter_time=1.117e-04, forward_time=0.107, loss_ctc=46.121, loss_att=53.920, acc=0.749, loss=51.580, backward_time=0.098, grad_norm=46.829, clip=100.000, loss_scale=2.371e+34, optim_step_time=0.039, optim0_lr0=3.248e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:59:08,918 (trainer:737) INFO: 38epoch:train:14101-14200batch: iter_time=1.252e-04, forward_time=0.107, loss_ctc=46.226, loss_att=55.971, acc=0.734, loss=53.047, backward_time=0.097, grad_norm=49.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.247e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 02:59:50,689 (trainer:737) INFO: 38epoch:train:14201-14300batch: iter_time=1.323e-04, forward_time=0.106, loss_ctc=40.674, loss_att=49.062, acc=0.757, loss=46.546, backward_time=0.097, grad_norm=40.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.247e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:00:32,445 (trainer:737) INFO: 38epoch:train:14301-14400batch: iter_time=1.130e-04, forward_time=0.106, loss_ctc=41.781, loss_att=44.479, acc=0.751, loss=43.670, backward_time=0.097, grad_norm=45.695, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.247e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:01:14,159 (trainer:737) INFO: 38epoch:train:14401-14500batch: iter_time=1.239e-04, forward_time=0.105, loss_ctc=48.727, loss_att=55.983, acc=0.736, loss=53.807, backward_time=0.097, grad_norm=59.415, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.246e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:01:55,843 (trainer:737) INFO: 38epoch:train:14501-14600batch: iter_time=1.151e-04, forward_time=0.105, loss_ctc=47.576, loss_att=45.501, acc=0.756, loss=46.123, backward_time=0.097, grad_norm=49.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.246e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:02:37,808 (trainer:737) INFO: 38epoch:train:14601-14700batch: iter_time=1.322e-04, forward_time=0.106, loss_ctc=48.413, loss_att=60.535, acc=0.737, loss=56.899, backward_time=0.098, grad_norm=56.651, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.246e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 03:03:19,406 (trainer:737) INFO: 38epoch:train:14701-14800batch: iter_time=1.268e-04, forward_time=0.106, loss_ctc=39.408, loss_att=42.193, acc=0.754, loss=41.357, backward_time=0.096, grad_norm=40.288, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.246e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 03:04:01,498 (trainer:737) INFO: 38epoch:train:14801-14900batch: iter_time=1.412e-04, forward_time=0.107, loss_ctc=44.528, loss_att=53.398, acc=0.731, loss=50.737, backward_time=0.097, grad_norm=52.055, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.245e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 03:04:43,739 (trainer:737) INFO: 38epoch:train:14901-15000batch: iter_time=1.391e-04, forward_time=0.107, loss_ctc=44.960, loss_att=52.027, acc=0.751, loss=49.907, backward_time=0.097, grad_norm=45.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.245e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 03:24:50,691 (trainer:343) INFO: 38epoch results: [train] iter_time=0.203, forward_time=0.106, loss_ctc=44.500, loss_att=51.589, acc=0.738, loss=49.463, backward_time=0.097, grad_norm=48.028, clip=100.000, loss_scale=2.269e+34, optim_step_time=0.039, optim0_lr0=3.266e-04, train_time=0.642, time=2 hours, 40 minutes and 40.59 seconds, total_count=570000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=49.233, cer_ctc=0.256, loss_att=50.201, acc=0.605, cer=0.358, wer=0.998, loss=49.911, time=19 minutes and 53.88 seconds, total_count=177498, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 03:24:55,579 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 03:24:55,589 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/33epoch.pth
+[gpuc04:0/16] 2024-01-17 03:24:55,589 (trainer:272) INFO: 39/45epoch started. Estimated time to finish: 21 hours, 2 minutes and 47.15 seconds
+[gpuc04:0/16] 2024-01-17 03:24:55,600 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 03:25:15,497 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 03:25:19,352 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe421d70e80>)
+[gpuc04:0/16] 2024-01-17 03:25:19,352 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 03:25:19,355 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 03:29:47,569 (trainer:737) INFO: 39epoch:train:1-100batch: iter_time=2.452, forward_time=0.106, loss_ctc=48.877, loss_att=51.655, acc=0.738, loss=50.822, backward_time=0.098, grad_norm=48.699, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.245e-04, train_time=2.919
+[gpuc04:0/16] 2024-01-17 03:30:30,631 (trainer:737) INFO: 39epoch:train:101-200batch: iter_time=1.042e-04, forward_time=0.116, loss_ctc=42.690, loss_att=41.853, acc=0.772, loss=42.105, backward_time=0.099, grad_norm=45.351, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.244e-04, train_time=0.430
+[gpuc04:0/16] 2024-01-17 03:31:12,922 (trainer:737) INFO: 39epoch:train:201-300batch: iter_time=1.033e-04, forward_time=0.106, loss_ctc=51.609, loss_att=61.421, acc=0.733, loss=58.477, backward_time=0.099, grad_norm=60.206, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.244e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 03:31:55,377 (trainer:737) INFO: 39epoch:train:301-400batch: iter_time=1.229e-04, forward_time=0.109, loss_ctc=55.540, loss_att=54.038, acc=0.741, loss=54.489, backward_time=0.098, grad_norm=53.380, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.244e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 03:32:40,005 (trainer:737) INFO: 39epoch:train:401-500batch: iter_time=1.242e-04, forward_time=0.115, loss_ctc=40.230, loss_att=42.162, acc=0.754, loss=41.583, backward_time=0.108, grad_norm=43.450, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.244e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 03:33:25,243 (trainer:737) INFO: 39epoch:train:501-600batch: iter_time=1.230e-04, forward_time=0.106, loss_ctc=47.953, loss_att=59.158, acc=0.710, loss=55.797, backward_time=0.098, grad_norm=49.848, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.243e-04, train_time=0.452
+[gpuc04:0/16] 2024-01-17 03:34:07,520 (trainer:737) INFO: 39epoch:train:601-700batch: iter_time=1.236e-04, forward_time=0.105, loss_ctc=36.792, loss_att=47.904, acc=0.760, loss=44.571, backward_time=0.097, grad_norm=39.526, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.243e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 03:34:51,154 (trainer:737) INFO: 39epoch:train:701-800batch: iter_time=1.281e-04, forward_time=0.106, loss_ctc=43.693, loss_att=50.244, acc=0.747, loss=48.278, backward_time=0.098, grad_norm=45.985, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.243e-04, train_time=0.436
+[gpuc04:0/16] 2024-01-17 03:35:38,807 (trainer:737) INFO: 39epoch:train:801-900batch: iter_time=1.248e-04, forward_time=0.125, loss_ctc=47.425, loss_att=40.679, acc=0.744, loss=42.703, backward_time=0.100, grad_norm=48.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.242e-04, train_time=0.476
+[gpuc04:0/16] 2024-01-17 03:36:23,654 (trainer:737) INFO: 39epoch:train:901-1000batch: iter_time=1.284e-04, forward_time=0.112, loss_ctc=47.188, loss_att=52.536, acc=0.743, loss=50.932, backward_time=0.098, grad_norm=56.377, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.242e-04, train_time=0.448
+[gpuc04:0/16] 2024-01-17 03:36:35,193 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 03:37:08,629 (trainer:737) INFO: 39epoch:train:1001-1100batch: iter_time=1.318e-04, forward_time=0.105, loss_ctc=36.016, loss_att=38.253, acc=0.756, loss=37.582, backward_time=0.096, grad_norm=40.428, clip=100.000, loss_scale=2.329e+34, optim_step_time=0.039, optim0_lr0=3.242e-04, train_time=0.450
+[gpuc04:0/16] 2024-01-17 03:37:51,181 (trainer:737) INFO: 39epoch:train:1101-1200batch: iter_time=1.351e-04, forward_time=0.106, loss_ctc=37.903, loss_att=49.473, acc=0.740, loss=46.002, backward_time=0.097, grad_norm=39.554, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.242e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 03:38:38,136 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 03:38:56,940 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 03:39:00,836 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4106b3580>)
+[gpuc04:0/16] 2024-01-17 03:39:00,837 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 03:39:00,840 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 03:44:20,204 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 03:44:20,624 (trainer:737) INFO: 39epoch:train:1201-1300batch: iter_time=3.452, forward_time=0.105, loss_ctc=41.170, loss_att=41.590, acc=0.773, loss=41.464, backward_time=0.096, grad_norm=42.603, clip=100.000, loss_scale=2.066e+34, optim_step_time=0.039, optim0_lr0=3.241e-04, train_time=3.894
+[gpuc04:0/16] 2024-01-17 03:45:02,991 (trainer:737) INFO: 39epoch:train:1301-1400batch: iter_time=1.356e-04, forward_time=0.105, loss_ctc=41.721, loss_att=50.435, acc=0.750, loss=47.821, backward_time=0.097, grad_norm=45.802, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.241e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 03:45:45,264 (trainer:737) INFO: 39epoch:train:1401-1500batch: iter_time=1.418e-04, forward_time=0.106, loss_ctc=43.994, loss_att=45.989, acc=0.768, loss=45.391, backward_time=0.097, grad_norm=45.402, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.241e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 03:46:27,192 (trainer:737) INFO: 39epoch:train:1501-1600batch: iter_time=1.542e-04, forward_time=0.106, loss_ctc=55.970, loss_att=59.888, acc=0.739, loss=58.713, backward_time=0.098, grad_norm=54.286, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.240e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 03:47:09,191 (trainer:737) INFO: 39epoch:train:1601-1700batch: iter_time=1.441e-04, forward_time=0.105, loss_ctc=49.304, loss_att=53.100, acc=0.743, loss=51.961, backward_time=0.097, grad_norm=52.924, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.240e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 03:47:50,769 (trainer:737) INFO: 39epoch:train:1701-1800batch: iter_time=1.501e-04, forward_time=0.105, loss_ctc=40.101, loss_att=46.658, acc=0.734, loss=44.691, backward_time=0.096, grad_norm=42.556, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.240e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 03:48:33,236 (trainer:737) INFO: 39epoch:train:1801-1900batch: iter_time=1.267e-04, forward_time=0.104, loss_ctc=42.899, loss_att=55.767, acc=0.734, loss=51.906, backward_time=0.096, grad_norm=45.348, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.240e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 03:49:15,269 (trainer:737) INFO: 39epoch:train:1901-2000batch: iter_time=1.383e-04, forward_time=0.105, loss_ctc=42.554, loss_att=52.290, acc=0.755, loss=49.369, backward_time=0.097, grad_norm=42.336, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.239e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 03:49:57,001 (trainer:737) INFO: 39epoch:train:2001-2100batch: iter_time=1.391e-04, forward_time=0.103, loss_ctc=38.076, loss_att=38.949, acc=0.746, loss=38.687, backward_time=0.095, grad_norm=41.381, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.239e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:50:38,585 (trainer:737) INFO: 39epoch:train:2101-2200batch: iter_time=1.457e-04, forward_time=0.104, loss_ctc=51.079, loss_att=50.863, acc=0.742, loss=50.928, backward_time=0.096, grad_norm=59.239, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.239e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 03:51:20,343 (trainer:737) INFO: 39epoch:train:2201-2300batch: iter_time=1.646e-04, forward_time=0.104, loss_ctc=36.156, loss_att=38.636, acc=0.764, loss=37.892, backward_time=0.096, grad_norm=38.177, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.238e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 03:52:02,332 (trainer:737) INFO: 39epoch:train:2301-2400batch: iter_time=1.599e-04, forward_time=0.104, loss_ctc=37.841, loss_att=43.263, acc=0.743, loss=41.636, backward_time=0.096, grad_norm=39.834, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.238e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 03:52:43,937 (trainer:737) INFO: 39epoch:train:2401-2500batch: iter_time=1.552e-04, forward_time=0.104, loss_ctc=37.466, loss_att=47.325, acc=0.769, loss=44.367, backward_time=0.096, grad_norm=37.972, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.238e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 03:53:00,471 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 03:53:20,007 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 03:53:23,629 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc3723ffd0>)
+[gpuc04:0/16] 2024-01-17 03:53:23,629 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 03:53:23,632 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 03:59:13,932 (trainer:737) INFO: 39epoch:train:2501-2600batch: iter_time=3.361, forward_time=0.138, loss_ctc=47.747, loss_att=55.585, acc=0.724, loss=53.234, backward_time=0.103, grad_norm=48.167, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.238e-04, train_time=3.900
+[gpuc04:0/16] 2024-01-17 03:59:55,541 (trainer:737) INFO: 39epoch:train:2601-2700batch: iter_time=1.280e-04, forward_time=0.105, loss_ctc=41.490, loss_att=42.471, acc=0.764, loss=42.177, backward_time=0.096, grad_norm=45.538, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.237e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:00:37,322 (trainer:737) INFO: 39epoch:train:2701-2800batch: iter_time=1.355e-04, forward_time=0.107, loss_ctc=49.522, loss_att=62.556, acc=0.728, loss=58.646, backward_time=0.097, grad_norm=55.515, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.237e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:01:21,939 (trainer:737) INFO: 39epoch:train:2801-2900batch: iter_time=3.862e-04, forward_time=0.120, loss_ctc=51.586, loss_att=52.219, acc=0.730, loss=52.029, backward_time=0.097, grad_norm=48.406, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.042, optim0_lr0=3.237e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 04:02:03,785 (trainer:737) INFO: 39epoch:train:2901-3000batch: iter_time=1.516e-04, forward_time=0.105, loss_ctc=38.943, loss_att=42.353, acc=0.749, loss=41.330, backward_time=0.096, grad_norm=40.150, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.236e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:02:45,652 (trainer:737) INFO: 39epoch:train:3001-3100batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=46.126, loss_att=57.816, acc=0.708, loss=54.309, backward_time=0.096, grad_norm=47.576, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.236e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:03:27,543 (trainer:737) INFO: 39epoch:train:3101-3200batch: iter_time=1.460e-04, forward_time=0.106, loss_ctc=36.053, loss_att=47.631, acc=0.756, loss=44.158, backward_time=0.096, grad_norm=38.412, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.236e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 04:04:09,803 (trainer:737) INFO: 39epoch:train:3201-3300batch: iter_time=1.328e-04, forward_time=0.106, loss_ctc=42.457, loss_att=51.094, acc=0.739, loss=48.503, backward_time=0.096, grad_norm=42.795, clip=100.000, loss_scale=1.049e+34, optim_step_time=0.039, optim0_lr0=3.236e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 04:04:51,246 (trainer:737) INFO: 39epoch:train:3301-3400batch: iter_time=1.398e-04, forward_time=0.104, loss_ctc=45.419, loss_att=40.597, acc=0.739, loss=42.044, backward_time=0.095, grad_norm=45.799, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.235e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 04:05:33,640 (trainer:737) INFO: 39epoch:train:3401-3500batch: iter_time=1.505e-04, forward_time=0.105, loss_ctc=45.289, loss_att=52.036, acc=0.741, loss=50.012, backward_time=0.096, grad_norm=52.825, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.235e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 04:06:15,040 (trainer:737) INFO: 39epoch:train:3501-3600batch: iter_time=1.394e-04, forward_time=0.104, loss_ctc=35.434, loss_att=37.405, acc=0.757, loss=36.814, backward_time=0.095, grad_norm=38.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.235e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 04:06:57,016 (trainer:737) INFO: 39epoch:train:3601-3700batch: iter_time=1.588e-04, forward_time=0.105, loss_ctc=36.858, loss_att=50.384, acc=0.724, loss=46.326, backward_time=0.096, grad_norm=38.770, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.234e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 04:07:25,831 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 04:07:45,729 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 04:07:49,341 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb587fceb0>)
+[gpuc04:0/16] 2024-01-17 04:07:49,341 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 04:07:49,344 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 04:12:16,980 (trainer:737) INFO: 39epoch:train:3701-3800batch: iter_time=2.766, forward_time=0.105, loss_ctc=41.205, loss_att=42.328, acc=0.770, loss=41.991, backward_time=0.096, grad_norm=42.523, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.234e-04, train_time=3.199
+[gpuc04:0/16] 2024-01-17 04:12:58,596 (trainer:737) INFO: 39epoch:train:3801-3900batch: iter_time=1.410e-04, forward_time=0.105, loss_ctc=40.855, loss_att=51.341, acc=0.746, loss=48.195, backward_time=0.097, grad_norm=44.729, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.234e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:13:40,640 (trainer:737) INFO: 39epoch:train:3901-4000batch: iter_time=1.358e-04, forward_time=0.106, loss_ctc=43.460, loss_att=45.913, acc=0.769, loss=45.177, backward_time=0.097, grad_norm=46.097, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.234e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 04:14:22,368 (trainer:737) INFO: 39epoch:train:4001-4100batch: iter_time=1.377e-04, forward_time=0.105, loss_ctc=54.417, loss_att=60.587, acc=0.736, loss=58.736, backward_time=0.097, grad_norm=56.917, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.233e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:15:03,998 (trainer:737) INFO: 39epoch:train:4101-4200batch: iter_time=1.460e-04, forward_time=0.106, loss_ctc=47.791, loss_att=52.665, acc=0.744, loss=51.203, backward_time=0.097, grad_norm=52.108, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.233e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:15:46,402 (trainer:737) INFO: 39epoch:train:4201-4300batch: iter_time=2.074e-04, forward_time=0.106, loss_ctc=39.956, loss_att=47.075, acc=0.732, loss=44.940, backward_time=0.100, grad_norm=41.439, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.233e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 04:16:28,185 (trainer:737) INFO: 39epoch:train:4301-4400batch: iter_time=1.982e-04, forward_time=0.107, loss_ctc=42.846, loss_att=55.138, acc=0.737, loss=51.450, backward_time=0.096, grad_norm=44.097, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.233e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:17:10,385 (trainer:737) INFO: 39epoch:train:4401-4500batch: iter_time=2.053e-04, forward_time=0.107, loss_ctc=42.220, loss_att=52.371, acc=0.757, loss=49.325, backward_time=0.099, grad_norm=43.369, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.232e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 04:17:52,643 (trainer:737) INFO: 39epoch:train:4501-4600batch: iter_time=1.875e-04, forward_time=0.108, loss_ctc=37.114, loss_att=38.447, acc=0.748, loss=38.047, backward_time=0.098, grad_norm=39.307, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.232e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 04:18:34,641 (trainer:737) INFO: 39epoch:train:4601-4700batch: iter_time=1.820e-04, forward_time=0.107, loss_ctc=49.853, loss_att=52.094, acc=0.739, loss=51.422, backward_time=0.096, grad_norm=58.590, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.232e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 04:19:16,535 (trainer:737) INFO: 39epoch:train:4701-4800batch: iter_time=1.691e-04, forward_time=0.106, loss_ctc=36.114, loss_att=39.231, acc=0.761, loss=38.296, backward_time=0.096, grad_norm=40.548, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.231e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 04:19:58,118 (trainer:737) INFO: 39epoch:train:4801-4900batch: iter_time=1.896e-04, forward_time=0.107, loss_ctc=37.492, loss_att=42.895, acc=0.746, loss=41.274, backward_time=0.096, grad_norm=38.515, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.231e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:20:39,827 (trainer:737) INFO: 39epoch:train:4901-5000batch: iter_time=2.096e-04, forward_time=0.106, loss_ctc=37.017, loss_att=47.866, acc=0.767, loss=44.612, backward_time=0.097, grad_norm=38.501, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.231e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:20:47,281 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 04:21:06,349 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 04:21:09,971 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd1ef996f0>)
+[gpuc04:0/16] 2024-01-17 04:21:09,971 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 04:21:09,974 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 04:26:06,454 (trainer:737) INFO: 39epoch:train:5001-5100batch: iter_time=2.524, forward_time=0.120, loss_ctc=47.157, loss_att=51.378, acc=0.743, loss=50.112, backward_time=0.098, grad_norm=48.189, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.231e-04, train_time=3.266
+[gpuc04:0/16] 2024-01-17 04:26:48,185 (trainer:737) INFO: 39epoch:train:5101-5200batch: iter_time=1.336e-04, forward_time=0.105, loss_ctc=40.550, loss_att=41.099, acc=0.776, loss=40.934, backward_time=0.097, grad_norm=44.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.230e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:27:30,031 (trainer:737) INFO: 39epoch:train:5201-5300batch: iter_time=1.881e-04, forward_time=0.107, loss_ctc=49.085, loss_att=62.403, acc=0.738, loss=58.408, backward_time=0.097, grad_norm=55.414, clip=100.000, loss_scale=2.098e+34, optim_step_time=0.039, optim0_lr0=3.230e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:28:11,621 (trainer:737) INFO: 39epoch:train:5301-5400batch: iter_time=1.888e-04, forward_time=0.106, loss_ctc=50.842, loss_att=53.090, acc=0.743, loss=52.416, backward_time=0.097, grad_norm=50.062, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.230e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:28:53,498 (trainer:737) INFO: 39epoch:train:5401-5500batch: iter_time=1.733e-04, forward_time=0.106, loss_ctc=39.256, loss_att=43.090, acc=0.754, loss=41.940, backward_time=0.097, grad_norm=41.109, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.229e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 04:29:35,328 (trainer:737) INFO: 39epoch:train:5501-5600batch: iter_time=1.647e-04, forward_time=0.108, loss_ctc=45.954, loss_att=57.900, acc=0.716, loss=54.317, backward_time=0.096, grad_norm=46.944, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.229e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:30:17,460 (trainer:737) INFO: 39epoch:train:5601-5700batch: iter_time=1.619e-04, forward_time=0.108, loss_ctc=35.620, loss_att=46.245, acc=0.766, loss=43.057, backward_time=0.096, grad_norm=37.286, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.229e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 04:30:59,268 (trainer:737) INFO: 39epoch:train:5701-5800batch: iter_time=1.721e-04, forward_time=0.107, loss_ctc=41.865, loss_att=49.891, acc=0.749, loss=47.484, backward_time=0.097, grad_norm=43.093, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.229e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:31:40,860 (trainer:737) INFO: 39epoch:train:5801-5900batch: iter_time=1.636e-04, forward_time=0.106, loss_ctc=44.914, loss_att=40.311, acc=0.746, loss=41.692, backward_time=0.095, grad_norm=44.576, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.228e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:32:22,590 (trainer:737) INFO: 39epoch:train:5901-6000batch: iter_time=1.448e-04, forward_time=0.106, loss_ctc=45.088, loss_att=52.073, acc=0.743, loss=49.977, backward_time=0.096, grad_norm=51.842, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.228e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:32:44,909 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 04:33:03,994 (trainer:737) INFO: 39epoch:train:6001-6100batch: iter_time=1.458e-04, forward_time=0.105, loss_ctc=35.435, loss_att=37.762, acc=0.760, loss=37.064, backward_time=0.095, grad_norm=38.704, clip=100.000, loss_scale=3.189e+34, optim_step_time=0.039, optim0_lr0=3.228e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 04:33:45,655 (trainer:737) INFO: 39epoch:train:6101-6200batch: iter_time=1.576e-04, forward_time=0.107, loss_ctc=36.733, loss_att=49.767, acc=0.740, loss=45.857, backward_time=0.096, grad_norm=39.320, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.227e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:34:11,427 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 04:34:31,211 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 04:34:35,235 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe43c7e1bd0>)
+[gpuc04:0/16] 2024-01-17 04:34:35,236 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 04:34:35,239 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 04:38:55,070 (trainer:737) INFO: 39epoch:train:6201-6300batch: iter_time=2.519, forward_time=0.118, loss_ctc=40.401, loss_att=42.003, acc=0.773, loss=41.523, backward_time=0.096, grad_norm=40.509, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.227e-04, train_time=3.094
+[gpuc04:0/16] 2024-01-17 04:39:37,032 (trainer:737) INFO: 39epoch:train:6301-6400batch: iter_time=1.171e-04, forward_time=0.105, loss_ctc=40.676, loss_att=52.921, acc=0.731, loss=49.247, backward_time=0.096, grad_norm=48.774, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.227e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 04:40:19,034 (trainer:737) INFO: 39epoch:train:6401-6500batch: iter_time=1.322e-04, forward_time=0.106, loss_ctc=43.430, loss_att=46.218, acc=0.761, loss=45.382, backward_time=0.097, grad_norm=45.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.227e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 04:41:01,179 (trainer:737) INFO: 39epoch:train:6501-6600batch: iter_time=1.517e-04, forward_time=0.106, loss_ctc=54.608, loss_att=62.564, acc=0.729, loss=60.177, backward_time=0.097, grad_norm=56.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.226e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 04:41:42,872 (trainer:737) INFO: 39epoch:train:6601-6700batch: iter_time=1.512e-04, forward_time=0.106, loss_ctc=46.679, loss_att=51.306, acc=0.734, loss=49.918, backward_time=0.096, grad_norm=51.622, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.226e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:42:24,459 (trainer:737) INFO: 39epoch:train:6701-6800batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=39.552, loss_att=45.753, acc=0.738, loss=43.892, backward_time=0.096, grad_norm=40.464, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.226e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:43:06,659 (trainer:737) INFO: 39epoch:train:6801-6900batch: iter_time=1.532e-04, forward_time=0.107, loss_ctc=42.321, loss_att=55.517, acc=0.731, loss=51.558, backward_time=0.096, grad_norm=43.598, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.226e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 04:43:48,460 (trainer:737) INFO: 39epoch:train:6901-7000batch: iter_time=1.386e-04, forward_time=0.107, loss_ctc=41.674, loss_att=52.328, acc=0.749, loss=49.132, backward_time=0.097, grad_norm=42.565, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.225e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:44:30,293 (trainer:737) INFO: 39epoch:train:7001-7100batch: iter_time=1.480e-04, forward_time=0.106, loss_ctc=37.027, loss_att=39.301, acc=0.735, loss=38.619, backward_time=0.095, grad_norm=39.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.225e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:45:11,957 (trainer:737) INFO: 39epoch:train:7101-7200batch: iter_time=1.436e-04, forward_time=0.107, loss_ctc=49.319, loss_att=50.440, acc=0.745, loss=50.104, backward_time=0.096, grad_norm=55.726, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.225e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:45:53,481 (trainer:737) INFO: 39epoch:train:7201-7300batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=35.563, loss_att=38.539, acc=0.756, loss=37.646, backward_time=0.095, grad_norm=39.638, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.224e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 04:46:35,254 (trainer:737) INFO: 39epoch:train:7301-7400batch: iter_time=1.583e-04, forward_time=0.106, loss_ctc=36.863, loss_att=41.906, acc=0.744, loss=40.393, backward_time=0.095, grad_norm=38.550, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.224e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:47:16,852 (trainer:737) INFO: 39epoch:train:7401-7500batch: iter_time=1.430e-04, forward_time=0.105, loss_ctc=36.628, loss_att=48.327, acc=0.757, loss=44.817, backward_time=0.096, grad_norm=40.540, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.224e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:47:21,867 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 04:47:41,829 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 04:47:45,482 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe46f27fd30>)
+[gpuc04:0/16] 2024-01-17 04:47:45,482 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 04:47:45,485 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 04:52:29,747 (trainer:737) INFO: 39epoch:train:7501-7600batch: iter_time=2.520, forward_time=0.105, loss_ctc=47.742, loss_att=51.252, acc=0.736, loss=50.199, backward_time=0.096, grad_norm=49.050, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.224e-04, train_time=3.129
+[gpuc04:0/16] 2024-01-17 04:53:11,384 (trainer:737) INFO: 39epoch:train:7601-7700batch: iter_time=1.517e-04, forward_time=0.105, loss_ctc=40.153, loss_att=40.966, acc=0.767, loss=40.722, backward_time=0.097, grad_norm=41.791, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.223e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 04:53:53,153 (trainer:737) INFO: 39epoch:train:7701-7800batch: iter_time=1.565e-04, forward_time=0.105, loss_ctc=48.269, loss_att=60.657, acc=0.732, loss=56.940, backward_time=0.097, grad_norm=54.304, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.223e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:54:34,997 (trainer:737) INFO: 39epoch:train:7801-7900batch: iter_time=1.718e-04, forward_time=0.105, loss_ctc=50.236, loss_att=50.686, acc=0.735, loss=50.551, backward_time=0.096, grad_norm=50.224, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.223e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 04:55:16,557 (trainer:737) INFO: 39epoch:train:7901-8000batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=39.070, loss_att=42.130, acc=0.751, loss=41.212, backward_time=0.096, grad_norm=40.577, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.222e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 04:55:58,470 (trainer:737) INFO: 39epoch:train:8001-8100batch: iter_time=1.424e-04, forward_time=0.108, loss_ctc=45.609, loss_att=57.879, acc=0.706, loss=54.198, backward_time=0.096, grad_norm=47.451, clip=100.000, loss_scale=3.032e+34, optim_step_time=0.039, optim0_lr0=3.222e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 04:56:40,824 (trainer:737) INFO: 39epoch:train:8101-8200batch: iter_time=1.364e-04, forward_time=0.105, loss_ctc=35.855, loss_att=46.504, acc=0.759, loss=43.309, backward_time=0.096, grad_norm=39.102, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.222e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 04:57:22,864 (trainer:737) INFO: 39epoch:train:8201-8300batch: iter_time=1.545e-04, forward_time=0.106, loss_ctc=41.509, loss_att=50.079, acc=0.743, loss=47.508, backward_time=0.097, grad_norm=44.048, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.222e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 04:58:04,604 (trainer:737) INFO: 39epoch:train:8301-8400batch: iter_time=1.760e-04, forward_time=0.106, loss_ctc=44.241, loss_att=40.461, acc=0.741, loss=41.595, backward_time=0.095, grad_norm=44.648, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.221e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 04:58:04,983 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 04:58:46,150 (trainer:737) INFO: 39epoch:train:8401-8500batch: iter_time=1.668e-04, forward_time=0.106, loss_ctc=43.603, loss_att=50.545, acc=0.741, loss=48.462, backward_time=0.095, grad_norm=50.646, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.221e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 04:59:08,031 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 04:59:27,768 (trainer:737) INFO: 39epoch:train:8501-8600batch: iter_time=1.575e-04, forward_time=0.106, loss_ctc=35.047, loss_att=37.389, acc=0.758, loss=36.686, backward_time=0.095, grad_norm=38.011, clip=100.000, loss_scale=1.584e+34, optim_step_time=0.038, optim0_lr0=3.221e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:00:09,374 (trainer:737) INFO: 39epoch:train:8601-8700batch: iter_time=1.502e-04, forward_time=0.107, loss_ctc=36.296, loss_att=49.439, acc=0.729, loss=45.497, backward_time=0.095, grad_norm=39.465, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.220e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:00:35,347 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 05:00:54,763 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 05:00:58,441 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd43392da0>)
+[gpuc04:0/16] 2024-01-17 05:00:58,441 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 05:00:58,444 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 05:05:15,958 (trainer:737) INFO: 39epoch:train:8701-8800batch: iter_time=2.563, forward_time=0.110, loss_ctc=40.931, loss_att=42.617, acc=0.769, loss=42.111, backward_time=0.095, grad_norm=42.096, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.220e-04, train_time=3.066
+[gpuc04:0/16] 2024-01-17 05:05:57,601 (trainer:737) INFO: 39epoch:train:8801-8900batch: iter_time=1.872e-04, forward_time=0.106, loss_ctc=39.899, loss_att=51.963, acc=0.745, loss=48.344, backward_time=0.096, grad_norm=46.175, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.220e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:06:39,433 (trainer:737) INFO: 39epoch:train:8901-9000batch: iter_time=1.932e-04, forward_time=0.106, loss_ctc=42.977, loss_att=46.038, acc=0.769, loss=45.120, backward_time=0.098, grad_norm=46.474, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.220e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 05:07:21,184 (trainer:737) INFO: 39epoch:train:9001-9100batch: iter_time=1.972e-04, forward_time=0.106, loss_ctc=53.761, loss_att=60.789, acc=0.737, loss=58.680, backward_time=0.098, grad_norm=56.868, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.219e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:08:02,883 (trainer:737) INFO: 39epoch:train:9101-9200batch: iter_time=1.333e-04, forward_time=0.106, loss_ctc=47.063, loss_att=52.873, acc=0.745, loss=51.130, backward_time=0.097, grad_norm=47.943, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.219e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:08:44,442 (trainer:737) INFO: 39epoch:train:9201-9300batch: iter_time=1.742e-04, forward_time=0.106, loss_ctc=38.731, loss_att=46.213, acc=0.736, loss=43.968, backward_time=0.096, grad_norm=40.279, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.219e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:09:26,104 (trainer:737) INFO: 39epoch:train:9301-9400batch: iter_time=1.636e-04, forward_time=0.106, loss_ctc=42.327, loss_att=55.511, acc=0.740, loss=51.556, backward_time=0.097, grad_norm=43.880, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.219e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:10:08,066 (trainer:737) INFO: 39epoch:train:9401-9500batch: iter_time=1.561e-04, forward_time=0.106, loss_ctc=41.870, loss_att=52.279, acc=0.759, loss=49.156, backward_time=0.098, grad_norm=46.393, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.218e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 05:10:50,140 (trainer:737) INFO: 39epoch:train:9501-9600batch: iter_time=1.740e-04, forward_time=0.105, loss_ctc=36.906, loss_att=38.816, acc=0.749, loss=38.243, backward_time=0.097, grad_norm=39.468, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.218e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 05:11:31,995 (trainer:737) INFO: 39epoch:train:9601-9700batch: iter_time=1.774e-04, forward_time=0.106, loss_ctc=49.054, loss_att=51.624, acc=0.741, loss=50.853, backward_time=0.096, grad_norm=55.440, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.218e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 05:12:13,519 (trainer:737) INFO: 39epoch:train:9701-9800batch: iter_time=1.656e-04, forward_time=0.106, loss_ctc=35.575, loss_att=39.302, acc=0.763, loss=38.184, backward_time=0.096, grad_norm=38.642, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.217e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:12:55,247 (trainer:737) INFO: 39epoch:train:9801-9900batch: iter_time=1.742e-04, forward_time=0.106, loss_ctc=37.094, loss_att=42.850, acc=0.748, loss=41.123, backward_time=0.095, grad_norm=39.876, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.217e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:13:36,926 (trainer:737) INFO: 39epoch:train:9901-10000batch: iter_time=1.477e-04, forward_time=0.105, loss_ctc=36.739, loss_att=48.565, acc=0.767, loss=45.017, backward_time=0.096, grad_norm=38.678, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.217e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:13:43,995 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 05:14:03,322 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 05:14:07,193 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd17abae00>)
+[gpuc04:0/16] 2024-01-17 05:14:07,193 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 05:14:07,197 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 05:18:52,068 (trainer:737) INFO: 39epoch:train:10001-10100batch: iter_time=2.724, forward_time=0.106, loss_ctc=46.547, loss_att=52.840, acc=0.733, loss=50.952, backward_time=0.096, grad_norm=48.404, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.217e-04, train_time=3.151
+[gpuc04:0/16] 2024-01-17 05:19:34,027 (trainer:737) INFO: 39epoch:train:10101-10200batch: iter_time=1.870e-04, forward_time=0.107, loss_ctc=40.435, loss_att=41.805, acc=0.766, loss=41.394, backward_time=0.096, grad_norm=45.564, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.216e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 05:20:15,738 (trainer:737) INFO: 39epoch:train:10201-10300batch: iter_time=1.856e-04, forward_time=0.106, loss_ctc=47.784, loss_att=60.623, acc=0.732, loss=56.771, backward_time=0.097, grad_norm=54.577, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.216e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:20:57,216 (trainer:737) INFO: 39epoch:train:10301-10400batch: iter_time=2.309e-04, forward_time=0.105, loss_ctc=49.944, loss_att=51.065, acc=0.734, loss=50.728, backward_time=0.096, grad_norm=52.123, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.216e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:21:38,945 (trainer:737) INFO: 39epoch:train:10401-10500batch: iter_time=1.703e-04, forward_time=0.105, loss_ctc=38.466, loss_att=42.491, acc=0.751, loss=41.284, backward_time=0.096, grad_norm=39.096, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.215e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:22:20,569 (trainer:737) INFO: 39epoch:train:10501-10600batch: iter_time=1.487e-04, forward_time=0.106, loss_ctc=45.092, loss_att=58.030, acc=0.707, loss=54.149, backward_time=0.097, grad_norm=48.692, clip=100.000, loss_scale=1.527e+34, optim_step_time=0.039, optim0_lr0=3.215e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:23:02,141 (trainer:737) INFO: 39epoch:train:10601-10700batch: iter_time=1.781e-04, forward_time=0.105, loss_ctc=35.958, loss_att=47.244, acc=0.759, loss=43.858, backward_time=0.096, grad_norm=40.024, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.215e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:23:44,100 (trainer:737) INFO: 39epoch:train:10701-10800batch: iter_time=1.662e-04, forward_time=0.106, loss_ctc=41.497, loss_att=50.369, acc=0.742, loss=47.708, backward_time=0.096, grad_norm=44.323, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.215e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 05:24:25,766 (trainer:737) INFO: 39epoch:train:10801-10900batch: iter_time=1.922e-04, forward_time=0.105, loss_ctc=44.086, loss_att=40.537, acc=0.742, loss=41.601, backward_time=0.095, grad_norm=44.186, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.214e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:25:07,346 (trainer:737) INFO: 39epoch:train:10901-11000batch: iter_time=1.629e-04, forward_time=0.105, loss_ctc=42.674, loss_att=50.414, acc=0.743, loss=48.092, backward_time=0.096, grad_norm=49.268, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.214e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:25:48,704 (trainer:737) INFO: 39epoch:train:11001-11100batch: iter_time=1.819e-04, forward_time=0.104, loss_ctc=35.084, loss_att=37.363, acc=0.760, loss=36.679, backward_time=0.095, grad_norm=36.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.214e-04, train_time=0.413
+[gpuc04:0/16] 2024-01-17 05:26:30,183 (trainer:737) INFO: 39epoch:train:11101-11200batch: iter_time=1.763e-04, forward_time=0.105, loss_ctc=35.721, loss_att=49.429, acc=0.730, loss=45.316, backward_time=0.095, grad_norm=38.551, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.214e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:26:56,770 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 05:27:16,476 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 05:27:20,098 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd227c3e20>)
+[gpuc04:0/16] 2024-01-17 05:27:20,098 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 05:27:20,101 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 05:31:31,662 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 05:31:45,858 (trainer:737) INFO: 39epoch:train:11201-11300batch: iter_time=2.654, forward_time=0.126, loss_ctc=40.422, loss_att=41.896, acc=0.771, loss=41.454, backward_time=0.099, grad_norm=41.389, clip=100.000, loss_scale=1.720e+34, optim_step_time=0.039, optim0_lr0=3.213e-04, train_time=3.156
+[gpuc04:0/16] 2024-01-17 05:32:27,528 (trainer:737) INFO: 39epoch:train:11301-11400batch: iter_time=1.602e-04, forward_time=0.104, loss_ctc=40.355, loss_att=51.558, acc=0.749, loss=48.197, backward_time=0.097, grad_norm=48.684, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.213e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:33:09,263 (trainer:737) INFO: 39epoch:train:11401-11500batch: iter_time=1.600e-04, forward_time=0.105, loss_ctc=42.525, loss_att=45.890, acc=0.770, loss=44.880, backward_time=0.097, grad_norm=44.475, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.213e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:33:51,283 (trainer:737) INFO: 39epoch:train:11501-11600batch: iter_time=1.582e-04, forward_time=0.105, loss_ctc=52.871, loss_att=59.382, acc=0.743, loss=57.429, backward_time=0.098, grad_norm=55.762, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.212e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 05:34:33,460 (trainer:737) INFO: 39epoch:train:11601-11700batch: iter_time=1.491e-04, forward_time=0.105, loss_ctc=46.811, loss_att=53.378, acc=0.745, loss=51.408, backward_time=0.097, grad_norm=49.107, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.212e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 05:35:15,314 (trainer:737) INFO: 39epoch:train:11701-11800batch: iter_time=1.558e-04, forward_time=0.104, loss_ctc=39.031, loss_att=46.295, acc=0.739, loss=44.116, backward_time=0.097, grad_norm=41.390, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.212e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 05:35:56,967 (trainer:737) INFO: 39epoch:train:11801-11900batch: iter_time=1.371e-04, forward_time=0.105, loss_ctc=42.065, loss_att=55.325, acc=0.737, loss=51.347, backward_time=0.097, grad_norm=43.453, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.212e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:36:39,113 (trainer:737) INFO: 39epoch:train:11901-12000batch: iter_time=1.369e-04, forward_time=0.106, loss_ctc=41.378, loss_att=52.356, acc=0.758, loss=49.062, backward_time=0.098, grad_norm=42.873, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.211e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 05:37:20,582 (trainer:737) INFO: 39epoch:train:12001-12100batch: iter_time=1.280e-04, forward_time=0.105, loss_ctc=36.563, loss_att=38.308, acc=0.749, loss=37.785, backward_time=0.097, grad_norm=38.145, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.211e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 05:38:02,762 (trainer:737) INFO: 39epoch:train:12101-12200batch: iter_time=1.108e-04, forward_time=0.108, loss_ctc=48.114, loss_att=50.436, acc=0.748, loss=49.739, backward_time=0.097, grad_norm=56.282, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.211e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 05:38:44,301 (trainer:737) INFO: 39epoch:train:12201-12300batch: iter_time=1.088e-04, forward_time=0.105, loss_ctc=35.463, loss_att=38.447, acc=0.768, loss=37.552, backward_time=0.097, grad_norm=40.437, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.211e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:39:27,539 (trainer:737) INFO: 39epoch:train:12301-12400batch: iter_time=1.138e-04, forward_time=0.104, loss_ctc=36.951, loss_att=42.634, acc=0.747, loss=40.929, backward_time=0.096, grad_norm=39.320, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.210e-04, train_time=0.432
+[gpuc04:0/16] 2024-01-17 05:40:09,414 (trainer:737) INFO: 39epoch:train:12401-12500batch: iter_time=1.115e-04, forward_time=0.105, loss_ctc=36.480, loss_att=47.060, acc=0.771, loss=43.886, backward_time=0.097, grad_norm=38.795, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.210e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 05:40:21,130 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 05:40:40,388 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 05:40:44,033 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd136eeef0>)
+[gpuc04:0/16] 2024-01-17 05:40:44,033 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 05:40:44,037 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 05:46:22,402 (trainer:737) INFO: 39epoch:train:12501-12600batch: iter_time=3.306, forward_time=0.108, loss_ctc=46.719, loss_att=53.497, acc=0.731, loss=51.464, backward_time=0.096, grad_norm=46.944, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.210e-04, train_time=3.730
+[gpuc04:0/16] 2024-01-17 05:47:04,109 (trainer:737) INFO: 39epoch:train:12601-12700batch: iter_time=1.227e-04, forward_time=0.104, loss_ctc=40.185, loss_att=41.494, acc=0.768, loss=41.101, backward_time=0.097, grad_norm=43.788, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.209e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 05:47:45,883 (trainer:737) INFO: 39epoch:train:12701-12800batch: iter_time=1.391e-04, forward_time=0.105, loss_ctc=47.186, loss_att=61.059, acc=0.733, loss=56.897, backward_time=0.098, grad_norm=54.833, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.209e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 05:48:27,441 (trainer:737) INFO: 39epoch:train:12801-12900batch: iter_time=1.494e-04, forward_time=0.105, loss_ctc=49.175, loss_att=51.319, acc=0.734, loss=50.676, backward_time=0.096, grad_norm=50.621, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.209e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:49:08,943 (trainer:737) INFO: 39epoch:train:12901-13000batch: iter_time=1.251e-04, forward_time=0.105, loss_ctc=38.429, loss_att=41.715, acc=0.754, loss=40.729, backward_time=0.097, grad_norm=40.907, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.209e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:49:50,518 (trainer:737) INFO: 39epoch:train:13001-13100batch: iter_time=1.201e-04, forward_time=0.105, loss_ctc=45.154, loss_att=56.949, acc=0.711, loss=53.411, backward_time=0.097, grad_norm=47.510, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.208e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:50:32,110 (trainer:737) INFO: 39epoch:train:13101-13200batch: iter_time=1.219e-04, forward_time=0.105, loss_ctc=35.399, loss_att=46.959, acc=0.759, loss=43.491, backward_time=0.097, grad_norm=38.194, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.208e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:51:14,404 (trainer:737) INFO: 39epoch:train:13201-13300batch: iter_time=1.178e-04, forward_time=0.105, loss_ctc=41.420, loss_att=50.148, acc=0.741, loss=47.530, backward_time=0.098, grad_norm=43.141, clip=100.000, loss_scale=1.392e+34, optim_step_time=0.039, optim0_lr0=3.208e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 05:51:56,483 (trainer:737) INFO: 39epoch:train:13301-13400batch: iter_time=1.113e-04, forward_time=0.103, loss_ctc=44.667, loss_att=40.415, acc=0.743, loss=41.691, backward_time=0.095, grad_norm=47.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.207e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 05:52:38,109 (trainer:737) INFO: 39epoch:train:13401-13500batch: iter_time=1.126e-04, forward_time=0.103, loss_ctc=42.716, loss_att=50.586, acc=0.744, loss=48.225, backward_time=0.096, grad_norm=48.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.207e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:53:19,764 (trainer:737) INFO: 39epoch:train:13501-13600batch: iter_time=1.168e-04, forward_time=0.103, loss_ctc=34.744, loss_att=36.976, acc=0.759, loss=36.306, backward_time=0.095, grad_norm=38.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.207e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 05:54:01,247 (trainer:737) INFO: 39epoch:train:13601-13700batch: iter_time=1.089e-04, forward_time=0.104, loss_ctc=35.749, loss_att=49.491, acc=0.729, loss=45.368, backward_time=0.096, grad_norm=39.277, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.207e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 05:54:33,133 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 05:54:52,484 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 05:54:56,225 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd28bc34f0>)
+[gpuc04:0/16] 2024-01-17 05:54:56,226 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 05:54:56,229 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 05:59:12,326 (trainer:737) INFO: 39epoch:train:13701-13800batch: iter_time=2.684, forward_time=0.109, loss_ctc=40.329, loss_att=40.645, acc=0.775, loss=40.550, backward_time=0.097, grad_norm=41.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.206e-04, train_time=3.111
+[gpuc04:0/16] 2024-01-17 05:59:54,664 (trainer:737) INFO: 39epoch:train:13801-13900batch: iter_time=2.703e-04, forward_time=0.107, loss_ctc=39.644, loss_att=49.461, acc=0.738, loss=46.516, backward_time=0.098, grad_norm=45.820, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.206e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 06:00:36,491 (trainer:737) INFO: 39epoch:train:13901-14000batch: iter_time=2.231e-04, forward_time=0.106, loss_ctc=43.386, loss_att=45.418, acc=0.764, loss=44.809, backward_time=0.098, grad_norm=45.056, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.206e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 06:01:18,141 (trainer:737) INFO: 39epoch:train:14001-14100batch: iter_time=1.756e-04, forward_time=0.105, loss_ctc=54.386, loss_att=61.396, acc=0.732, loss=59.293, backward_time=0.097, grad_norm=56.126, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.206e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 06:01:59,643 (trainer:737) INFO: 39epoch:train:14101-14200batch: iter_time=2.181e-04, forward_time=0.105, loss_ctc=45.702, loss_att=50.199, acc=0.739, loss=48.850, backward_time=0.096, grad_norm=50.192, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.205e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:02:41,455 (trainer:737) INFO: 39epoch:train:14201-14300batch: iter_time=2.147e-04, forward_time=0.105, loss_ctc=39.241, loss_att=46.414, acc=0.735, loss=44.262, backward_time=0.096, grad_norm=40.929, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.205e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 06:03:23,008 (trainer:737) INFO: 39epoch:train:14301-14400batch: iter_time=2.331e-04, forward_time=0.105, loss_ctc=41.780, loss_att=55.378, acc=0.728, loss=51.299, backward_time=0.097, grad_norm=44.209, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.205e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:04:04,910 (trainer:737) INFO: 39epoch:train:14401-14500batch: iter_time=2.058e-04, forward_time=0.106, loss_ctc=41.555, loss_att=51.728, acc=0.752, loss=48.676, backward_time=0.097, grad_norm=43.276, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.204e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 06:04:46,353 (trainer:737) INFO: 39epoch:train:14501-14600batch: iter_time=2.310e-04, forward_time=0.104, loss_ctc=36.295, loss_att=38.865, acc=0.737, loss=38.094, backward_time=0.096, grad_norm=38.817, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.204e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 06:05:27,846 (trainer:737) INFO: 39epoch:train:14601-14700batch: iter_time=1.958e-04, forward_time=0.104, loss_ctc=48.667, loss_att=50.624, acc=0.742, loss=50.037, backward_time=0.096, grad_norm=55.885, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.204e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:06:11,760 (trainer:737) INFO: 39epoch:train:14701-14800batch: iter_time=1.799e-04, forward_time=0.104, loss_ctc=35.258, loss_att=37.510, acc=0.759, loss=36.834, backward_time=0.096, grad_norm=38.111, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.204e-04, train_time=0.439
+[gpuc04:0/16] 2024-01-17 06:06:53,492 (trainer:737) INFO: 39epoch:train:14801-14900batch: iter_time=1.890e-04, forward_time=0.104, loss_ctc=36.767, loss_att=41.965, acc=0.745, loss=40.406, backward_time=0.096, grad_norm=39.172, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.203e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 06:07:35,030 (trainer:737) INFO: 39epoch:train:14901-15000batch: iter_time=1.831e-04, forward_time=0.104, loss_ctc=36.472, loss_att=48.002, acc=0.757, loss=44.543, backward_time=0.097, grad_norm=39.619, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.203e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:27:39,336 (trainer:343) INFO: 39epoch results: [train] iter_time=0.224, forward_time=0.106, loss_ctc=42.351, loss_att=48.381, acc=0.746, loss=46.572, backward_time=0.097, grad_norm=45.154, clip=100.000, loss_scale=1.815e+34, optim_step_time=0.039, optim0_lr0=3.224e-04, train_time=0.650, time=2 hours, 42 minutes and 52.81 seconds, total_count=585000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=51.773, cer_ctc=0.261, loss_att=55.145, acc=0.590, cer=0.372, wer=1.000, loss=54.133, time=19 minutes and 50.76 seconds, total_count=182169, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 06:27:44,200 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 06:27:44,208 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/34epoch.pth
+[gpuc04:0/16] 2024-01-17 06:27:44,208 (trainer:272) INFO: 40/45epoch started. Estimated time to finish: 18 hours, 4 minutes and 48.01 seconds
+[gpuc04:0/16] 2024-01-17 06:27:44,217 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 06:28:03,016 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 06:28:06,459 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdbb5af1e10>)
+[gpuc04:0/16] 2024-01-17 06:28:06,459 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 06:28:06,462 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 06:32:39,255 (trainer:737) INFO: 40epoch:train:1-100batch: iter_time=2.528, forward_time=0.108, loss_ctc=45.041, loss_att=42.798, acc=0.765, loss=43.471, backward_time=0.097, grad_norm=45.143, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.203e-04, train_time=2.950
+[gpuc04:0/16] 2024-01-17 06:33:23,031 (trainer:737) INFO: 40epoch:train:101-200batch: iter_time=1.380e-04, forward_time=0.116, loss_ctc=42.313, loss_att=57.449, acc=0.721, loss=52.908, backward_time=0.105, grad_norm=53.721, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.203e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-17 06:34:05,312 (trainer:737) INFO: 40epoch:train:201-300batch: iter_time=1.282e-04, forward_time=0.108, loss_ctc=36.310, loss_att=37.797, acc=0.770, loss=37.351, backward_time=0.100, grad_norm=40.059, clip=100.000, loss_scale=2.783e+34, optim_step_time=0.039, optim0_lr0=3.202e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 06:34:47,225 (trainer:737) INFO: 40epoch:train:301-400batch: iter_time=1.311e-04, forward_time=0.107, loss_ctc=56.689, loss_att=62.337, acc=0.710, loss=60.642, backward_time=0.098, grad_norm=58.267, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.202e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 06:35:29,403 (trainer:737) INFO: 40epoch:train:401-500batch: iter_time=1.271e-04, forward_time=0.108, loss_ctc=50.109, loss_att=57.470, acc=0.718, loss=55.262, backward_time=0.097, grad_norm=52.808, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=3.202e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 06:35:44,501 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 06:36:11,593 (trainer:737) INFO: 40epoch:train:501-600batch: iter_time=1.254e-04, forward_time=0.106, loss_ctc=42.573, loss_att=49.140, acc=0.770, loss=47.170, backward_time=0.098, grad_norm=43.364, clip=100.000, loss_scale=2.790e+34, optim_step_time=0.039, optim0_lr0=3.201e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 06:36:56,028 (trainer:737) INFO: 40epoch:train:601-700batch: iter_time=1.256e-04, forward_time=0.117, loss_ctc=47.440, loss_att=54.665, acc=0.743, loss=52.498, backward_time=0.103, grad_norm=52.989, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.201e-04, train_time=0.444
+[gpuc04:0/16] 2024-01-17 06:37:39,008 (trainer:737) INFO: 40epoch:train:701-800batch: iter_time=1.237e-04, forward_time=0.115, loss_ctc=42.118, loss_att=49.046, acc=0.746, loss=46.968, backward_time=0.100, grad_norm=43.655, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.201e-04, train_time=0.430
+[gpuc04:0/16] 2024-01-17 06:38:23,271 (trainer:737) INFO: 40epoch:train:801-900batch: iter_time=1.378e-04, forward_time=0.105, loss_ctc=43.605, loss_att=44.666, acc=0.750, loss=44.348, backward_time=0.097, grad_norm=43.976, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.201e-04, train_time=0.442
+[gpuc04:0/16] 2024-01-17 06:39:05,599 (trainer:737) INFO: 40epoch:train:901-1000batch: iter_time=1.423e-04, forward_time=0.107, loss_ctc=52.138, loss_att=60.745, acc=0.721, loss=58.163, backward_time=0.098, grad_norm=57.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.200e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 06:39:50,768 (trainer:737) INFO: 40epoch:train:1001-1100batch: iter_time=1.292e-04, forward_time=0.107, loss_ctc=48.152, loss_att=54.223, acc=0.738, loss=52.402, backward_time=0.098, grad_norm=52.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.200e-04, train_time=0.451
+[gpuc04:0/16] 2024-01-17 06:40:35,155 (trainer:737) INFO: 40epoch:train:1101-1200batch: iter_time=1.174e-04, forward_time=0.115, loss_ctc=46.738, loss_att=51.885, acc=0.742, loss=50.341, backward_time=0.102, grad_norm=46.523, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.200e-04, train_time=0.443
+[gpuc04:0/16] 2024-01-17 06:41:16,501 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 06:41:35,792 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 06:41:39,421 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe402a4f0d0>)
+[gpuc04:0/16] 2024-01-17 06:41:39,421 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 06:41:39,425 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 06:47:24,224 (trainer:737) INFO: 40epoch:train:1201-1300batch: iter_time=3.658, forward_time=0.105, loss_ctc=40.858, loss_att=45.504, acc=0.754, loss=44.110, backward_time=0.096, grad_norm=42.683, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.200e-04, train_time=4.091
+[gpuc04:0/16] 2024-01-17 06:48:05,934 (trainer:737) INFO: 40epoch:train:1301-1400batch: iter_time=1.376e-04, forward_time=0.106, loss_ctc=41.152, loss_att=52.964, acc=0.735, loss=49.421, backward_time=0.097, grad_norm=45.713, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.199e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 06:48:48,024 (trainer:737) INFO: 40epoch:train:1401-1500batch: iter_time=1.245e-04, forward_time=0.104, loss_ctc=38.221, loss_att=44.485, acc=0.736, loss=42.606, backward_time=0.095, grad_norm=47.418, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.199e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 06:49:29,717 (trainer:737) INFO: 40epoch:train:1501-1600batch: iter_time=1.460e-04, forward_time=0.105, loss_ctc=40.459, loss_att=44.402, acc=0.739, loss=43.219, backward_time=0.095, grad_norm=42.633, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.199e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 06:49:39,309 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 06:50:11,618 (trainer:737) INFO: 40epoch:train:1601-1700batch: iter_time=1.507e-04, forward_time=0.104, loss_ctc=47.621, loss_att=58.579, acc=0.722, loss=55.292, backward_time=0.096, grad_norm=54.356, clip=100.000, loss_scale=1.269e+34, optim_step_time=0.038, optim0_lr0=3.198e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 06:50:53,695 (trainer:737) INFO: 40epoch:train:1701-1800batch: iter_time=1.407e-04, forward_time=0.108, loss_ctc=51.024, loss_att=52.024, acc=0.729, loss=51.724, backward_time=0.096, grad_norm=51.930, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.198e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 06:51:35,423 (trainer:737) INFO: 40epoch:train:1801-1900batch: iter_time=1.428e-04, forward_time=0.106, loss_ctc=46.818, loss_att=53.378, acc=0.749, loss=51.410, backward_time=0.096, grad_norm=48.585, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.198e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 06:52:18,091 (trainer:737) INFO: 40epoch:train:1901-2000batch: iter_time=1.390e-04, forward_time=0.105, loss_ctc=40.661, loss_att=47.078, acc=0.748, loss=45.153, backward_time=0.096, grad_norm=43.194, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.198e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 06:52:59,583 (trainer:737) INFO: 40epoch:train:2001-2100batch: iter_time=1.589e-04, forward_time=0.106, loss_ctc=40.844, loss_att=42.836, acc=0.754, loss=42.238, backward_time=0.096, grad_norm=43.254, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.197e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:53:41,163 (trainer:737) INFO: 40epoch:train:2101-2200batch: iter_time=1.276e-04, forward_time=0.105, loss_ctc=44.919, loss_att=50.482, acc=0.740, loss=48.813, backward_time=0.097, grad_norm=47.467, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.197e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 06:54:22,691 (trainer:737) INFO: 40epoch:train:2201-2300batch: iter_time=1.426e-04, forward_time=0.106, loss_ctc=49.256, loss_att=55.408, acc=0.715, loss=53.562, backward_time=0.096, grad_norm=56.015, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.197e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 06:55:04,400 (trainer:737) INFO: 40epoch:train:2301-2400batch: iter_time=1.303e-04, forward_time=0.107, loss_ctc=47.832, loss_att=56.323, acc=0.733, loss=53.776, backward_time=0.097, grad_norm=50.108, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.197e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 06:55:46,299 (trainer:737) INFO: 40epoch:train:2401-2500batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=40.515, loss_att=48.751, acc=0.747, loss=46.281, backward_time=0.096, grad_norm=43.636, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.196e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 06:55:52,054 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 06:56:11,673 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 06:56:15,275 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdc79aef6d0>)
+[gpuc04:0/16] 2024-01-17 06:56:15,275 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 06:56:15,279 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 07:00:57,545 (trainer:737) INFO: 40epoch:train:2501-2600batch: iter_time=2.604, forward_time=0.105, loss_ctc=42.836, loss_att=40.292, acc=0.762, loss=41.055, backward_time=0.097, grad_norm=41.329, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.196e-04, train_time=3.112
+[gpuc04:0/16] 2024-01-17 07:01:39,159 (trainer:737) INFO: 40epoch:train:2601-2700batch: iter_time=1.798e-04, forward_time=0.105, loss_ctc=39.501, loss_att=51.779, acc=0.720, loss=48.096, backward_time=0.097, grad_norm=48.345, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.196e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:02:20,597 (trainer:737) INFO: 40epoch:train:2701-2800batch: iter_time=1.838e-04, forward_time=0.105, loss_ctc=35.764, loss_att=36.600, acc=0.769, loss=36.349, backward_time=0.096, grad_norm=40.129, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.195e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 07:03:02,304 (trainer:737) INFO: 40epoch:train:2801-2900batch: iter_time=1.683e-04, forward_time=0.106, loss_ctc=53.038, loss_att=61.442, acc=0.705, loss=58.921, backward_time=0.097, grad_norm=57.370, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.195e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:03:44,339 (trainer:737) INFO: 40epoch:train:2901-3000batch: iter_time=1.893e-04, forward_time=0.106, loss_ctc=47.615, loss_att=53.940, acc=0.721, loss=52.042, backward_time=0.096, grad_norm=50.278, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.195e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:04:26,509 (trainer:737) INFO: 40epoch:train:3001-3100batch: iter_time=1.856e-04, forward_time=0.106, loss_ctc=41.451, loss_att=48.042, acc=0.764, loss=46.065, backward_time=0.097, grad_norm=41.457, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.195e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 07:05:08,395 (trainer:737) INFO: 40epoch:train:3101-3200batch: iter_time=1.641e-04, forward_time=0.105, loss_ctc=45.544, loss_att=52.075, acc=0.742, loss=50.115, backward_time=0.097, grad_norm=46.787, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.194e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:05:50,254 (trainer:737) INFO: 40epoch:train:3201-3300batch: iter_time=1.646e-04, forward_time=0.106, loss_ctc=41.351, loss_att=47.997, acc=0.745, loss=46.003, backward_time=0.096, grad_norm=43.440, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.194e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 07:06:32,198 (trainer:737) INFO: 40epoch:train:3301-3400batch: iter_time=1.866e-04, forward_time=0.105, loss_ctc=41.172, loss_att=42.818, acc=0.748, loss=42.324, backward_time=0.096, grad_norm=43.736, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.194e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:07:13,946 (trainer:737) INFO: 40epoch:train:3401-3500batch: iter_time=1.744e-04, forward_time=0.106, loss_ctc=50.565, loss_att=58.027, acc=0.724, loss=55.788, backward_time=0.097, grad_norm=57.585, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.194e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:07:57,187 (trainer:737) INFO: 40epoch:train:3501-3600batch: iter_time=2.418e-04, forward_time=0.107, loss_ctc=46.679, loss_att=51.771, acc=0.730, loss=50.243, backward_time=0.097, grad_norm=53.104, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.193e-04, train_time=0.432
+[gpuc04:0/16] 2024-01-17 07:08:39,098 (trainer:737) INFO: 40epoch:train:3601-3700batch: iter_time=1.779e-04, forward_time=0.109, loss_ctc=45.834, loss_att=50.954, acc=0.745, loss=49.418, backward_time=0.097, grad_norm=45.007, clip=100.000, loss_scale=1.838e+34, optim_step_time=0.039, optim0_lr0=3.193e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:09:07,149 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 07:09:26,802 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 07:09:30,669 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe43fcebfa0>)
+[gpuc04:0/16] 2024-01-17 07:09:30,669 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 07:09:30,672 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 07:13:57,383 (trainer:737) INFO: 40epoch:train:3701-3800batch: iter_time=2.696, forward_time=0.132, loss_ctc=39.720, loss_att=43.463, acc=0.754, loss=42.340, backward_time=0.099, grad_norm=42.354, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.193e-04, train_time=3.183
+[gpuc04:0/16] 2024-01-17 07:14:39,642 (trainer:737) INFO: 40epoch:train:3801-3900batch: iter_time=1.739e-04, forward_time=0.105, loss_ctc=41.023, loss_att=50.687, acc=0.742, loss=47.788, backward_time=0.097, grad_norm=46.817, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.192e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:15:21,222 (trainer:737) INFO: 40epoch:train:3901-4000batch: iter_time=1.957e-04, forward_time=0.104, loss_ctc=38.197, loss_att=43.822, acc=0.736, loss=42.135, backward_time=0.095, grad_norm=46.272, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.192e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:16:02,873 (trainer:737) INFO: 40epoch:train:4001-4100batch: iter_time=1.582e-04, forward_time=0.104, loss_ctc=40.282, loss_att=44.221, acc=0.741, loss=43.040, backward_time=0.096, grad_norm=42.147, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.192e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:16:44,779 (trainer:737) INFO: 40epoch:train:4101-4200batch: iter_time=1.734e-04, forward_time=0.106, loss_ctc=45.391, loss_att=57.960, acc=0.726, loss=54.189, backward_time=0.096, grad_norm=53.490, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.192e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:17:26,813 (trainer:737) INFO: 40epoch:train:4201-4300batch: iter_time=1.767e-04, forward_time=0.106, loss_ctc=50.058, loss_att=50.999, acc=0.731, loss=50.717, backward_time=0.096, grad_norm=51.389, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.191e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:18:08,547 (trainer:737) INFO: 40epoch:train:4301-4400batch: iter_time=1.619e-04, forward_time=0.106, loss_ctc=45.829, loss_att=52.589, acc=0.752, loss=50.561, backward_time=0.097, grad_norm=45.840, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.191e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:18:50,245 (trainer:737) INFO: 40epoch:train:4401-4500batch: iter_time=1.690e-04, forward_time=0.105, loss_ctc=40.118, loss_att=46.078, acc=0.751, loss=44.290, backward_time=0.096, grad_norm=40.473, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.191e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:19:32,242 (trainer:737) INFO: 40epoch:train:4501-4600batch: iter_time=1.556e-04, forward_time=0.105, loss_ctc=40.439, loss_att=42.299, acc=0.756, loss=41.741, backward_time=0.096, grad_norm=41.780, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.191e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:20:14,479 (trainer:737) INFO: 40epoch:train:4601-4700batch: iter_time=1.633e-04, forward_time=0.105, loss_ctc=44.028, loss_att=50.113, acc=0.741, loss=48.287, backward_time=0.097, grad_norm=46.786, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.190e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:20:56,547 (trainer:737) INFO: 40epoch:train:4701-4800batch: iter_time=1.521e-04, forward_time=0.105, loss_ctc=48.841, loss_att=53.863, acc=0.717, loss=52.356, backward_time=0.096, grad_norm=55.055, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.190e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:21:38,628 (trainer:737) INFO: 40epoch:train:4801-4900batch: iter_time=1.446e-04, forward_time=0.106, loss_ctc=47.797, loss_att=55.265, acc=0.734, loss=53.025, backward_time=0.097, grad_norm=53.726, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.190e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 07:22:20,481 (trainer:737) INFO: 40epoch:train:4901-5000batch: iter_time=1.477e-04, forward_time=0.105, loss_ctc=39.883, loss_att=48.578, acc=0.749, loss=45.970, backward_time=0.096, grad_norm=43.843, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.190e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 07:22:25,413 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 07:22:46,223 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 07:22:50,028 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe11c86fa30>)
+[gpuc04:0/16] 2024-01-17 07:22:50,028 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 07:22:50,031 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 07:27:33,132 (trainer:737) INFO: 40epoch:train:5001-5100batch: iter_time=2.699, forward_time=0.105, loss_ctc=41.367, loss_att=41.920, acc=0.768, loss=41.754, backward_time=0.096, grad_norm=41.697, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.189e-04, train_time=3.126
+[gpuc04:0/16] 2024-01-17 07:28:14,975 (trainer:737) INFO: 40epoch:train:5101-5200batch: iter_time=1.344e-04, forward_time=0.105, loss_ctc=38.651, loss_att=53.099, acc=0.731, loss=48.764, backward_time=0.096, grad_norm=50.591, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.189e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 07:28:56,473 (trainer:737) INFO: 40epoch:train:5201-5300batch: iter_time=1.506e-04, forward_time=0.104, loss_ctc=35.684, loss_att=37.364, acc=0.773, loss=36.860, backward_time=0.095, grad_norm=38.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.189e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 07:29:38,295 (trainer:737) INFO: 40epoch:train:5301-5400batch: iter_time=1.617e-04, forward_time=0.105, loss_ctc=51.523, loss_att=60.487, acc=0.717, loss=57.798, backward_time=0.096, grad_norm=54.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.188e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 07:30:20,456 (trainer:737) INFO: 40epoch:train:5401-5500batch: iter_time=1.514e-04, forward_time=0.104, loss_ctc=46.813, loss_att=56.143, acc=0.724, loss=53.344, backward_time=0.096, grad_norm=49.441, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.188e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 07:31:02,254 (trainer:737) INFO: 40epoch:train:5501-5600batch: iter_time=1.508e-04, forward_time=0.105, loss_ctc=41.164, loss_att=48.138, acc=0.774, loss=46.046, backward_time=0.097, grad_norm=43.539, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.188e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 07:31:41,498 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 07:31:44,431 (trainer:737) INFO: 40epoch:train:5601-5700batch: iter_time=1.569e-04, forward_time=0.104, loss_ctc=45.202, loss_att=53.916, acc=0.748, loss=51.302, backward_time=0.096, grad_norm=51.125, clip=100.000, loss_scale=3.524e+34, optim_step_time=0.038, optim0_lr0=3.188e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:32:26,107 (trainer:737) INFO: 40epoch:train:5701-5800batch: iter_time=1.553e-04, forward_time=0.104, loss_ctc=40.597, loss_att=47.883, acc=0.751, loss=45.697, backward_time=0.096, grad_norm=40.238, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.187e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:33:07,671 (trainer:737) INFO: 40epoch:train:5801-5900batch: iter_time=1.393e-04, forward_time=0.104, loss_ctc=40.821, loss_att=43.983, acc=0.756, loss=43.034, backward_time=0.096, grad_norm=42.676, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.187e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 07:33:49,713 (trainer:737) INFO: 40epoch:train:5901-6000batch: iter_time=1.433e-04, forward_time=0.104, loss_ctc=49.156, loss_att=59.090, acc=0.725, loss=56.110, backward_time=0.096, grad_norm=61.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.187e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:34:31,720 (trainer:737) INFO: 40epoch:train:6001-6100batch: iter_time=1.390e-04, forward_time=0.105, loss_ctc=46.396, loss_att=53.264, acc=0.742, loss=51.203, backward_time=0.097, grad_norm=50.652, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.187e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:35:13,417 (trainer:737) INFO: 40epoch:train:6101-6200batch: iter_time=1.521e-04, forward_time=0.105, loss_ctc=45.173, loss_att=50.640, acc=0.746, loss=49.000, backward_time=0.096, grad_norm=43.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.186e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:35:40,490 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 07:36:00,518 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 07:36:04,319 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd513981f0>)
+[gpuc04:0/16] 2024-01-17 07:36:04,319 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 07:36:04,323 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 07:40:17,507 (trainer:737) INFO: 40epoch:train:6201-6300batch: iter_time=2.617, forward_time=0.105, loss_ctc=38.718, loss_att=43.902, acc=0.762, loss=42.347, backward_time=0.096, grad_norm=39.811, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.186e-04, train_time=3.041
+[gpuc04:0/16] 2024-01-17 07:40:59,405 (trainer:737) INFO: 40epoch:train:6301-6400batch: iter_time=1.615e-04, forward_time=0.105, loss_ctc=40.592, loss_att=50.272, acc=0.759, loss=47.368, backward_time=0.097, grad_norm=45.076, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.186e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:41:41,033 (trainer:737) INFO: 40epoch:train:6401-6500batch: iter_time=1.476e-04, forward_time=0.105, loss_ctc=37.976, loss_att=45.830, acc=0.743, loss=43.474, backward_time=0.095, grad_norm=46.151, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.185e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:42:22,700 (trainer:737) INFO: 40epoch:train:6501-6600batch: iter_time=1.630e-04, forward_time=0.106, loss_ctc=39.811, loss_att=43.696, acc=0.749, loss=42.530, backward_time=0.096, grad_norm=42.502, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.185e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:43:04,328 (trainer:737) INFO: 40epoch:train:6601-6700batch: iter_time=1.694e-04, forward_time=0.105, loss_ctc=45.206, loss_att=60.506, acc=0.729, loss=55.916, backward_time=0.096, grad_norm=52.746, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.185e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:43:46,060 (trainer:737) INFO: 40epoch:train:6701-6800batch: iter_time=1.705e-04, forward_time=0.106, loss_ctc=49.125, loss_att=50.242, acc=0.741, loss=49.907, backward_time=0.096, grad_norm=48.707, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.185e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:44:27,796 (trainer:737) INFO: 40epoch:train:6801-6900batch: iter_time=1.482e-04, forward_time=0.107, loss_ctc=45.241, loss_att=52.041, acc=0.759, loss=50.001, backward_time=0.096, grad_norm=46.242, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.184e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:45:09,768 (trainer:737) INFO: 40epoch:train:6901-7000batch: iter_time=1.477e-04, forward_time=0.106, loss_ctc=40.172, loss_att=48.502, acc=0.756, loss=46.003, backward_time=0.096, grad_norm=39.866, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.184e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:45:51,444 (trainer:737) INFO: 40epoch:train:7001-7100batch: iter_time=1.607e-04, forward_time=0.106, loss_ctc=40.308, loss_att=43.208, acc=0.761, loss=42.338, backward_time=0.096, grad_norm=42.284, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.184e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:46:33,499 (trainer:737) INFO: 40epoch:train:7101-7200batch: iter_time=1.290e-04, forward_time=0.108, loss_ctc=43.465, loss_att=50.535, acc=0.746, loss=48.414, backward_time=0.097, grad_norm=48.272, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.184e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 07:47:15,703 (trainer:737) INFO: 40epoch:train:7201-7300batch: iter_time=1.523e-04, forward_time=0.106, loss_ctc=48.776, loss_att=55.853, acc=0.726, loss=53.730, backward_time=0.096, grad_norm=58.124, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.183e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:47:57,626 (trainer:737) INFO: 40epoch:train:7301-7400batch: iter_time=1.468e-04, forward_time=0.106, loss_ctc=47.352, loss_att=55.897, acc=0.741, loss=53.334, backward_time=0.097, grad_norm=49.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.183e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:48:39,247 (trainer:737) INFO: 40epoch:train:7401-7500batch: iter_time=1.373e-04, forward_time=0.105, loss_ctc=39.480, loss_att=49.227, acc=0.749, loss=46.303, backward_time=0.096, grad_norm=42.494, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.183e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 07:48:44,199 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 07:49:03,422 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 07:49:06,977 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe11c81cdf0>)
+[gpuc04:0/16] 2024-01-17 07:49:06,977 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 07:49:06,980 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 07:53:43,723 (trainer:737) INFO: 40epoch:train:7501-7600batch: iter_time=2.620, forward_time=0.106, loss_ctc=41.048, loss_att=39.695, acc=0.776, loss=40.101, backward_time=0.097, grad_norm=39.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.182e-04, train_time=3.045
+[gpuc04:0/16] 2024-01-17 07:54:25,917 (trainer:737) INFO: 40epoch:train:7601-7700batch: iter_time=1.262e-04, forward_time=0.106, loss_ctc=38.617, loss_att=50.381, acc=0.740, loss=46.852, backward_time=0.096, grad_norm=49.534, clip=100.000, loss_scale=2.222e+34, optim_step_time=0.038, optim0_lr0=3.182e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:55:07,640 (trainer:737) INFO: 40epoch:train:7701-7800batch: iter_time=1.159e-04, forward_time=0.106, loss_ctc=35.367, loss_att=36.533, acc=0.775, loss=36.183, backward_time=0.096, grad_norm=38.073, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.182e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:55:49,888 (trainer:737) INFO: 40epoch:train:7801-7900batch: iter_time=1.376e-04, forward_time=0.107, loss_ctc=51.694, loss_att=60.070, acc=0.720, loss=57.557, backward_time=0.096, grad_norm=52.804, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.182e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 07:56:31,808 (trainer:737) INFO: 40epoch:train:7901-8000batch: iter_time=1.453e-04, forward_time=0.106, loss_ctc=46.735, loss_att=56.662, acc=0.722, loss=53.684, backward_time=0.096, grad_norm=48.605, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.181e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 07:56:35,120 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 07:57:13,895 (trainer:737) INFO: 40epoch:train:8001-8100batch: iter_time=1.376e-04, forward_time=0.106, loss_ctc=41.281, loss_att=47.665, acc=0.774, loss=45.749, backward_time=0.097, grad_norm=42.740, clip=100.000, loss_scale=2.224e+34, optim_step_time=0.038, optim0_lr0=3.181e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 07:57:56,279 (trainer:737) INFO: 40epoch:train:8101-8200batch: iter_time=1.534e-04, forward_time=0.106, loss_ctc=45.465, loss_att=54.526, acc=0.746, loss=51.808, backward_time=0.097, grad_norm=49.232, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.181e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 07:58:37,973 (trainer:737) INFO: 40epoch:train:8201-8300batch: iter_time=1.516e-04, forward_time=0.106, loss_ctc=40.439, loss_att=46.923, acc=0.755, loss=44.978, backward_time=0.096, grad_norm=41.657, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.181e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 07:59:19,633 (trainer:737) INFO: 40epoch:train:8301-8400batch: iter_time=1.416e-04, forward_time=0.106, loss_ctc=40.395, loss_att=43.017, acc=0.757, loss=42.230, backward_time=0.096, grad_norm=43.585, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.180e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:00:01,443 (trainer:737) INFO: 40epoch:train:8401-8500batch: iter_time=1.425e-04, forward_time=0.106, loss_ctc=49.183, loss_att=59.674, acc=0.726, loss=56.527, backward_time=0.096, grad_norm=59.624, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.180e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:00:43,292 (trainer:737) INFO: 40epoch:train:8501-8600batch: iter_time=1.352e-04, forward_time=0.106, loss_ctc=46.326, loss_att=52.964, acc=0.743, loss=50.973, backward_time=0.096, grad_norm=51.201, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.180e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:01:25,031 (trainer:737) INFO: 40epoch:train:8601-8700batch: iter_time=1.384e-04, forward_time=0.107, loss_ctc=44.823, loss_att=50.507, acc=0.749, loss=48.802, backward_time=0.096, grad_norm=42.868, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.180e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:01:49,757 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 08:02:09,237 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 08:02:12,914 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe04e98d180>)
+[gpuc04:0/16] 2024-01-17 08:02:12,914 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 08:02:12,917 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 08:06:35,797 (trainer:737) INFO: 40epoch:train:8701-8800batch: iter_time=2.688, forward_time=0.105, loss_ctc=38.362, loss_att=44.868, acc=0.758, loss=42.916, backward_time=0.096, grad_norm=40.206, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.179e-04, train_time=3.107
+[gpuc04:0/16] 2024-01-17 08:07:17,824 (trainer:737) INFO: 40epoch:train:8801-8900batch: iter_time=1.446e-04, forward_time=0.105, loss_ctc=40.226, loss_att=53.255, acc=0.739, loss=49.346, backward_time=0.096, grad_norm=45.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.179e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 08:07:59,254 (trainer:737) INFO: 40epoch:train:8901-9000batch: iter_time=1.502e-04, forward_time=0.104, loss_ctc=37.297, loss_att=43.949, acc=0.737, loss=41.953, backward_time=0.095, grad_norm=45.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.179e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 08:08:40,809 (trainer:737) INFO: 40epoch:train:9001-9100batch: iter_time=1.487e-04, forward_time=0.105, loss_ctc=39.623, loss_att=43.643, acc=0.743, loss=42.437, backward_time=0.095, grad_norm=42.189, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.178e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 08:09:22,725 (trainer:737) INFO: 40epoch:train:9101-9200batch: iter_time=1.654e-04, forward_time=0.106, loss_ctc=45.248, loss_att=58.552, acc=0.725, loss=54.561, backward_time=0.096, grad_norm=52.968, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.178e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 08:10:04,692 (trainer:737) INFO: 40epoch:train:9201-9300batch: iter_time=1.836e-04, forward_time=0.105, loss_ctc=49.361, loss_att=50.938, acc=0.734, loss=50.464, backward_time=0.096, grad_norm=48.629, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.178e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 08:10:46,346 (trainer:737) INFO: 40epoch:train:9301-9400batch: iter_time=1.507e-04, forward_time=0.105, loss_ctc=45.792, loss_att=53.017, acc=0.752, loss=50.850, backward_time=0.096, grad_norm=46.108, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.178e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:11:27,966 (trainer:737) INFO: 40epoch:train:9401-9500batch: iter_time=1.741e-04, forward_time=0.104, loss_ctc=40.158, loss_att=46.859, acc=0.750, loss=44.849, backward_time=0.096, grad_norm=39.442, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.177e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:12:09,557 (trainer:737) INFO: 40epoch:train:9501-9600batch: iter_time=1.734e-04, forward_time=0.104, loss_ctc=39.769, loss_att=42.485, acc=0.758, loss=41.670, backward_time=0.096, grad_norm=41.588, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.177e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:12:51,846 (trainer:737) INFO: 40epoch:train:9601-9700batch: iter_time=1.502e-04, forward_time=0.104, loss_ctc=42.921, loss_att=50.129, acc=0.743, loss=47.966, backward_time=0.096, grad_norm=46.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.177e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 08:13:33,617 (trainer:737) INFO: 40epoch:train:9701-9800batch: iter_time=1.615e-04, forward_time=0.103, loss_ctc=48.022, loss_att=55.385, acc=0.716, loss=53.176, backward_time=0.095, grad_norm=58.467, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.177e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:14:15,425 (trainer:737) INFO: 40epoch:train:9801-9900batch: iter_time=1.520e-04, forward_time=0.105, loss_ctc=47.227, loss_att=55.985, acc=0.733, loss=53.358, backward_time=0.097, grad_norm=48.051, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.176e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:14:57,364 (trainer:737) INFO: 40epoch:train:9901-10000batch: iter_time=1.457e-04, forward_time=0.103, loss_ctc=39.582, loss_att=48.667, acc=0.748, loss=45.942, backward_time=0.096, grad_norm=41.874, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.176e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 08:15:04,632 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 08:15:24,227 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 08:15:27,956 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe13d197a60>)
+[gpuc04:0/16] 2024-01-17 08:15:27,956 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 08:15:27,959 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 08:20:01,328 (trainer:737) INFO: 40epoch:train:10001-10100batch: iter_time=2.607, forward_time=0.104, loss_ctc=40.953, loss_att=40.751, acc=0.774, loss=40.812, backward_time=0.096, grad_norm=41.490, clip=100.000, loss_scale=3.988e+34, optim_step_time=0.039, optim0_lr0=3.176e-04, train_time=3.039
+[gpuc04:0/16] 2024-01-17 08:20:28,010 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 08:20:42,953 (trainer:737) INFO: 40epoch:train:10101-10200batch: iter_time=1.407e-04, forward_time=0.106, loss_ctc=38.318, loss_att=50.820, acc=0.741, loss=47.069, backward_time=0.097, grad_norm=48.796, clip=100.000, loss_scale=3.399e+34, optim_step_time=0.039, optim0_lr0=3.176e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:21:24,800 (trainer:737) INFO: 40epoch:train:10201-10300batch: iter_time=1.451e-04, forward_time=0.105, loss_ctc=35.087, loss_att=36.653, acc=0.776, loss=36.183, backward_time=0.096, grad_norm=38.455, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.175e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:22:06,556 (trainer:737) INFO: 40epoch:train:10301-10400batch: iter_time=1.640e-04, forward_time=0.105, loss_ctc=50.840, loss_att=59.476, acc=0.721, loss=56.885, backward_time=0.097, grad_norm=54.303, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.175e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:22:48,238 (trainer:737) INFO: 40epoch:train:10401-10500batch: iter_time=1.718e-04, forward_time=0.105, loss_ctc=46.134, loss_att=56.621, acc=0.722, loss=53.475, backward_time=0.097, grad_norm=49.166, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.175e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:23:30,393 (trainer:737) INFO: 40epoch:train:10501-10600batch: iter_time=1.746e-04, forward_time=0.107, loss_ctc=41.560, loss_att=47.949, acc=0.774, loss=46.032, backward_time=0.097, grad_norm=41.845, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.174e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 08:24:12,187 (trainer:737) INFO: 40epoch:train:10601-10700batch: iter_time=1.473e-04, forward_time=0.106, loss_ctc=44.479, loss_att=53.884, acc=0.748, loss=51.063, backward_time=0.097, grad_norm=47.502, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.174e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:24:54,245 (trainer:737) INFO: 40epoch:train:10701-10800batch: iter_time=1.607e-04, forward_time=0.106, loss_ctc=40.137, loss_att=46.815, acc=0.755, loss=44.812, backward_time=0.097, grad_norm=41.544, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.174e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 08:25:35,899 (trainer:737) INFO: 40epoch:train:10801-10900batch: iter_time=1.473e-04, forward_time=0.106, loss_ctc=39.930, loss_att=43.209, acc=0.757, loss=42.225, backward_time=0.097, grad_norm=41.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.174e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:26:18,009 (trainer:737) INFO: 40epoch:train:10901-11000batch: iter_time=1.739e-04, forward_time=0.106, loss_ctc=50.137, loss_att=61.319, acc=0.727, loss=57.965, backward_time=0.097, grad_norm=56.329, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.173e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 08:27:00,170 (trainer:737) INFO: 40epoch:train:11001-11100batch: iter_time=1.691e-04, forward_time=0.107, loss_ctc=45.369, loss_att=52.895, acc=0.743, loss=50.637, backward_time=0.097, grad_norm=50.113, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.173e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 08:27:42,359 (trainer:737) INFO: 40epoch:train:11101-11200batch: iter_time=1.949e-04, forward_time=0.110, loss_ctc=44.840, loss_att=50.979, acc=0.747, loss=49.138, backward_time=0.097, grad_norm=44.560, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.173e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 08:28:10,246 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 08:28:29,416 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 08:28:33,031 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd39f990c0>)
+[gpuc04:0/16] 2024-01-17 08:28:33,031 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 08:28:33,034 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 08:32:49,468 (trainer:737) INFO: 40epoch:train:11201-11300batch: iter_time=2.593, forward_time=0.105, loss_ctc=38.064, loss_att=44.295, acc=0.758, loss=42.426, backward_time=0.097, grad_norm=40.576, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.173e-04, train_time=3.071
+[gpuc04:0/16] 2024-01-17 08:33:31,240 (trainer:737) INFO: 40epoch:train:11301-11400batch: iter_time=1.412e-04, forward_time=0.106, loss_ctc=40.290, loss_att=52.490, acc=0.741, loss=48.830, backward_time=0.097, grad_norm=44.747, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.172e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:34:12,790 (trainer:737) INFO: 40epoch:train:11401-11500batch: iter_time=1.565e-04, forward_time=0.105, loss_ctc=37.085, loss_att=44.593, acc=0.737, loss=42.340, backward_time=0.096, grad_norm=46.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.172e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 08:34:54,618 (trainer:737) INFO: 40epoch:train:11501-11600batch: iter_time=1.414e-04, forward_time=0.104, loss_ctc=39.454, loss_att=43.276, acc=0.744, loss=42.129, backward_time=0.096, grad_norm=40.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.172e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:35:36,847 (trainer:737) INFO: 40epoch:train:11601-11700batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=44.665, loss_att=58.504, acc=0.726, loss=54.352, backward_time=0.096, grad_norm=52.602, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.172e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 08:36:18,609 (trainer:737) INFO: 40epoch:train:11701-11800batch: iter_time=1.675e-04, forward_time=0.106, loss_ctc=48.993, loss_att=50.414, acc=0.735, loss=49.988, backward_time=0.097, grad_norm=47.480, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.171e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:37:00,875 (trainer:737) INFO: 40epoch:train:11801-11900batch: iter_time=1.900e-04, forward_time=0.106, loss_ctc=45.410, loss_att=53.018, acc=0.752, loss=50.736, backward_time=0.096, grad_norm=48.393, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.171e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 08:37:42,516 (trainer:737) INFO: 40epoch:train:11901-12000batch: iter_time=1.841e-04, forward_time=0.105, loss_ctc=39.500, loss_att=46.590, acc=0.750, loss=44.463, backward_time=0.096, grad_norm=39.758, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.171e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:38:24,142 (trainer:737) INFO: 40epoch:train:12001-12100batch: iter_time=1.809e-04, forward_time=0.104, loss_ctc=39.861, loss_att=42.386, acc=0.758, loss=41.629, backward_time=0.096, grad_norm=40.451, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.170e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:38:48,669 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 08:39:05,775 (trainer:737) INFO: 40epoch:train:12101-12200batch: iter_time=1.560e-04, forward_time=0.105, loss_ctc=43.260, loss_att=50.084, acc=0.743, loss=48.037, backward_time=0.096, grad_norm=46.337, clip=100.000, loss_scale=1.647e+34, optim_step_time=0.038, optim0_lr0=3.170e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 08:39:47,325 (trainer:737) INFO: 40epoch:train:12201-12300batch: iter_time=1.940e-04, forward_time=0.104, loss_ctc=46.739, loss_att=54.158, acc=0.717, loss=51.932, backward_time=0.096, grad_norm=57.700, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.170e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 08:40:29,126 (trainer:737) INFO: 40epoch:train:12301-12400batch: iter_time=1.887e-04, forward_time=0.106, loss_ctc=46.781, loss_att=55.765, acc=0.735, loss=53.070, backward_time=0.097, grad_norm=50.045, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.170e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:41:10,663 (trainer:737) INFO: 40epoch:train:12401-12500batch: iter_time=1.409e-04, forward_time=0.104, loss_ctc=39.157, loss_att=48.647, acc=0.750, loss=45.800, backward_time=0.096, grad_norm=42.489, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.169e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 08:41:17,692 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 08:41:37,138 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 08:41:40,886 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe13d1e9240>)
+[gpuc04:0/16] 2024-01-17 08:41:40,886 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 08:41:40,890 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 08:46:25,495 (trainer:737) INFO: 40epoch:train:12501-12600batch: iter_time=2.708, forward_time=0.105, loss_ctc=40.405, loss_att=40.773, acc=0.773, loss=40.663, backward_time=0.096, grad_norm=42.412, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.169e-04, train_time=3.148
+[gpuc04:0/16] 2024-01-17 08:47:07,168 (trainer:737) INFO: 40epoch:train:12601-12700batch: iter_time=1.581e-04, forward_time=0.105, loss_ctc=37.979, loss_att=51.051, acc=0.739, loss=47.130, backward_time=0.096, grad_norm=49.250, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.169e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:47:48,977 (trainer:737) INFO: 40epoch:train:12701-12800batch: iter_time=1.427e-04, forward_time=0.107, loss_ctc=35.409, loss_att=36.483, acc=0.775, loss=36.161, backward_time=0.096, grad_norm=39.012, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.169e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:48:30,960 (trainer:737) INFO: 40epoch:train:12801-12900batch: iter_time=1.667e-04, forward_time=0.105, loss_ctc=51.117, loss_att=59.296, acc=0.722, loss=56.842, backward_time=0.097, grad_norm=55.306, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.168e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 08:49:12,905 (trainer:737) INFO: 40epoch:train:12901-13000batch: iter_time=1.571e-04, forward_time=0.105, loss_ctc=45.455, loss_att=56.313, acc=0.723, loss=53.056, backward_time=0.096, grad_norm=48.007, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.168e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 08:49:54,753 (trainer:737) INFO: 40epoch:train:13001-13100batch: iter_time=1.916e-04, forward_time=0.107, loss_ctc=40.884, loss_att=47.957, acc=0.775, loss=45.835, backward_time=0.097, grad_norm=42.968, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.168e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:50:36,497 (trainer:737) INFO: 40epoch:train:13101-13200batch: iter_time=1.742e-04, forward_time=0.106, loss_ctc=44.370, loss_att=54.059, acc=0.747, loss=51.152, backward_time=0.097, grad_norm=46.346, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.168e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 08:51:18,394 (trainer:737) INFO: 40epoch:train:13201-13300batch: iter_time=1.818e-04, forward_time=0.105, loss_ctc=40.514, loss_att=46.894, acc=0.755, loss=44.980, backward_time=0.097, grad_norm=41.127, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.167e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 08:52:00,232 (trainer:737) INFO: 40epoch:train:13301-13400batch: iter_time=1.652e-04, forward_time=0.106, loss_ctc=40.201, loss_att=43.113, acc=0.758, loss=42.239, backward_time=0.097, grad_norm=42.206, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.167e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 08:52:42,436 (trainer:737) INFO: 40epoch:train:13401-13500batch: iter_time=1.709e-04, forward_time=0.107, loss_ctc=50.088, loss_att=59.840, acc=0.727, loss=56.914, backward_time=0.097, grad_norm=59.196, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.167e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 08:53:24,557 (trainer:737) INFO: 40epoch:train:13501-13600batch: iter_time=1.786e-04, forward_time=0.106, loss_ctc=45.610, loss_att=52.541, acc=0.742, loss=50.461, backward_time=0.097, grad_norm=47.890, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.167e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 08:54:06,832 (trainer:737) INFO: 40epoch:train:13601-13700batch: iter_time=1.572e-04, forward_time=0.107, loss_ctc=45.160, loss_att=50.863, acc=0.747, loss=49.152, backward_time=0.097, grad_norm=44.798, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.166e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 08:54:36,861 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 08:54:56,286 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 08:54:59,954 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd38bc7460>)
+[gpuc04:0/16] 2024-01-17 08:54:59,954 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 08:54:59,957 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 08:59:42,339 (trainer:737) INFO: 40epoch:train:13701-13800batch: iter_time=2.676, forward_time=0.106, loss_ctc=38.051, loss_att=44.283, acc=0.759, loss=42.413, backward_time=0.096, grad_norm=42.709, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.166e-04, train_time=3.355
+[gpuc04:0/16] 2024-01-17 09:00:24,094 (trainer:737) INFO: 40epoch:train:13801-13900batch: iter_time=1.440e-04, forward_time=0.105, loss_ctc=39.993, loss_att=51.787, acc=0.741, loss=48.249, backward_time=0.098, grad_norm=43.517, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.166e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 09:01:05,585 (trainer:737) INFO: 40epoch:train:13901-14000batch: iter_time=1.489e-04, forward_time=0.104, loss_ctc=36.829, loss_att=43.352, acc=0.741, loss=41.395, backward_time=0.096, grad_norm=45.021, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.165e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 09:01:47,543 (trainer:737) INFO: 40epoch:train:14001-14100batch: iter_time=1.855e-04, forward_time=0.105, loss_ctc=39.198, loss_att=43.422, acc=0.744, loss=42.155, backward_time=0.096, grad_norm=40.721, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.165e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 09:02:29,227 (trainer:737) INFO: 40epoch:train:14101-14200batch: iter_time=2.195e-04, forward_time=0.107, loss_ctc=44.452, loss_att=58.514, acc=0.726, loss=54.295, backward_time=0.097, grad_norm=52.107, clip=100.000, loss_scale=1.464e+34, optim_step_time=0.039, optim0_lr0=3.165e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 09:03:11,015 (trainer:737) INFO: 40epoch:train:14201-14300batch: iter_time=1.723e-04, forward_time=0.107, loss_ctc=48.554, loss_att=50.645, acc=0.736, loss=50.017, backward_time=0.097, grad_norm=48.823, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.165e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 09:03:52,850 (trainer:737) INFO: 40epoch:train:14301-14400batch: iter_time=1.723e-04, forward_time=0.106, loss_ctc=45.216, loss_att=52.683, acc=0.753, loss=50.443, backward_time=0.097, grad_norm=77.835, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.164e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 09:04:34,630 (trainer:737) INFO: 40epoch:train:14401-14500batch: iter_time=1.903e-04, forward_time=0.106, loss_ctc=39.833, loss_att=46.507, acc=0.750, loss=44.505, backward_time=0.097, grad_norm=39.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.164e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 09:05:16,630 (trainer:737) INFO: 40epoch:train:14501-14600batch: iter_time=1.859e-04, forward_time=0.107, loss_ctc=39.627, loss_att=41.865, acc=0.759, loss=41.194, backward_time=0.096, grad_norm=41.978, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.164e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:05:58,497 (trainer:737) INFO: 40epoch:train:14601-14700batch: iter_time=1.670e-04, forward_time=0.108, loss_ctc=43.126, loss_att=50.459, acc=0.743, loss=48.259, backward_time=0.097, grad_norm=45.682, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.164e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 09:06:40,134 (trainer:737) INFO: 40epoch:train:14701-14800batch: iter_time=1.940e-04, forward_time=0.106, loss_ctc=48.136, loss_att=54.280, acc=0.719, loss=52.437, backward_time=0.097, grad_norm=57.723, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.163e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 09:07:22,331 (trainer:737) INFO: 40epoch:train:14801-14900batch: iter_time=1.776e-04, forward_time=0.108, loss_ctc=46.693, loss_att=56.087, acc=0.734, loss=53.269, backward_time=0.097, grad_norm=49.347, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.163e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 09:08:04,505 (trainer:737) INFO: 40epoch:train:14901-15000batch: iter_time=1.369e-04, forward_time=0.106, loss_ctc=38.822, loss_att=48.131, acc=0.751, loss=45.338, backward_time=0.096, grad_norm=41.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.163e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 09:28:06,965 (trainer:343) INFO: 40epoch results: [train] iter_time=0.218, forward_time=0.106, loss_ctc=43.352, loss_att=49.993, acc=0.744, loss=48.001, backward_time=0.097, grad_norm=46.993, clip=100.000, loss_scale=1.912e+34, optim_step_time=0.039, optim0_lr0=3.183e-04, train_time=0.641, time=2 hours, 40 minutes and 30.59 seconds, total_count=600000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=49.201, cer_ctc=0.259, loss_att=51.146, acc=0.601, cer=0.378, wer=0.999, loss=50.563, time=19 minutes and 52 seconds, total_count=186840, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 09:28:11,872 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 09:28:11,928 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/35epoch.pth
+[gpuc04:0/16] 2024-01-17 09:28:11,928 (trainer:272) INFO: 41/45epoch started. Estimated time to finish: 15 hours, 3 minutes and 45.52 seconds
+[gpuc04:0/16] 2024-01-17 09:28:11,938 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 09:28:31,483 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 09:28:35,074 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe7460c5b40>)
+[gpuc04:0/16] 2024-01-17 09:28:35,074 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 09:28:35,078 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 09:33:07,229 (trainer:737) INFO: 41epoch:train:1-100batch: iter_time=2.442, forward_time=0.105, loss_ctc=43.273, loss_att=50.474, acc=0.722, loss=48.314, backward_time=0.098, grad_norm=46.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.163e-04, train_time=2.953
+[gpuc04:0/16] 2024-01-17 09:33:49,269 (trainer:737) INFO: 41epoch:train:101-200batch: iter_time=9.775e-05, forward_time=0.106, loss_ctc=48.471, loss_att=51.739, acc=0.732, loss=50.759, backward_time=0.098, grad_norm=50.480, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.162e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:34:33,634 (trainer:737) INFO: 41epoch:train:201-300batch: iter_time=1.191e-04, forward_time=0.116, loss_ctc=41.070, loss_att=55.660, acc=0.733, loss=51.283, backward_time=0.104, grad_norm=48.984, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.162e-04, train_time=0.443
+[gpuc04:0/16] 2024-01-17 09:35:16,382 (trainer:737) INFO: 41epoch:train:301-400batch: iter_time=1.060e-04, forward_time=0.108, loss_ctc=40.625, loss_att=44.246, acc=0.748, loss=43.160, backward_time=0.098, grad_norm=42.787, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.162e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-17 09:36:00,583 (trainer:737) INFO: 41epoch:train:401-500batch: iter_time=1.098e-04, forward_time=0.108, loss_ctc=45.724, loss_att=51.204, acc=0.744, loss=49.560, backward_time=0.102, grad_norm=50.150, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.161e-04, train_time=0.442
+[gpuc04:0/16] 2024-01-17 09:36:45,241 (trainer:737) INFO: 41epoch:train:501-600batch: iter_time=1.032e-04, forward_time=0.111, loss_ctc=44.593, loss_att=43.741, acc=0.751, loss=43.996, backward_time=0.101, grad_norm=48.397, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.161e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 09:37:27,462 (trainer:737) INFO: 41epoch:train:601-700batch: iter_time=1.101e-04, forward_time=0.105, loss_ctc=42.339, loss_att=52.992, acc=0.719, loss=49.796, backward_time=0.097, grad_norm=45.485, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.161e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 09:38:09,517 (trainer:737) INFO: 41epoch:train:701-800batch: iter_time=1.039e-04, forward_time=0.106, loss_ctc=41.458, loss_att=45.407, acc=0.759, loss=44.223, backward_time=0.097, grad_norm=41.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.161e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:38:54,820 (trainer:737) INFO: 41epoch:train:801-900batch: iter_time=1.057e-04, forward_time=0.116, loss_ctc=42.803, loss_att=53.372, acc=0.730, loss=50.201, backward_time=0.104, grad_norm=47.115, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.160e-04, train_time=0.453
+[gpuc04:0/16] 2024-01-17 09:39:37,587 (trainer:737) INFO: 41epoch:train:901-1000batch: iter_time=1.120e-04, forward_time=0.106, loss_ctc=40.576, loss_att=52.034, acc=0.745, loss=48.597, backward_time=0.098, grad_norm=43.004, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.160e-04, train_time=0.427
+[gpuc04:0/16] 2024-01-17 09:40:22,366 (trainer:737) INFO: 41epoch:train:1001-1100batch: iter_time=1.029e-04, forward_time=0.119, loss_ctc=50.317, loss_att=56.874, acc=0.712, loss=54.907, backward_time=0.098, grad_norm=56.752, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.044, optim0_lr0=3.160e-04, train_time=0.448
+[gpuc04:0/16] 2024-01-17 09:40:58,315 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 09:41:07,071 (trainer:737) INFO: 41epoch:train:1101-1200batch: iter_time=1.092e-04, forward_time=0.107, loss_ctc=52.685, loss_att=55.496, acc=0.726, loss=54.653, backward_time=0.097, grad_norm=56.001, clip=100.000, loss_scale=2.476e+34, optim_step_time=0.039, optim0_lr0=3.160e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 09:41:32,890 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 09:41:52,877 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 09:41:56,539 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe40354f0d0>)
+[gpuc04:0/16] 2024-01-17 09:41:56,539 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 09:41:56,543 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 09:47:48,380 (trainer:737) INFO: 41epoch:train:1201-1300batch: iter_time=2.941, forward_time=0.105, loss_ctc=45.757, loss_att=52.190, acc=0.733, loss=50.260, backward_time=0.097, grad_norm=50.041, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.159e-04, train_time=4.013
+[gpuc04:0/16] 2024-01-17 09:48:30,015 (trainer:737) INFO: 41epoch:train:1301-1400batch: iter_time=1.472e-04, forward_time=0.105, loss_ctc=43.745, loss_att=48.821, acc=0.745, loss=47.298, backward_time=0.097, grad_norm=47.030, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.159e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 09:49:12,018 (trainer:737) INFO: 41epoch:train:1401-1500batch: iter_time=1.411e-04, forward_time=0.105, loss_ctc=39.943, loss_att=48.882, acc=0.750, loss=46.200, backward_time=0.096, grad_norm=44.146, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.159e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:49:53,700 (trainer:737) INFO: 41epoch:train:1501-1600batch: iter_time=1.713e-04, forward_time=0.106, loss_ctc=42.413, loss_att=57.519, acc=0.732, loss=52.987, backward_time=0.096, grad_norm=47.968, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.159e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 09:50:35,813 (trainer:737) INFO: 41epoch:train:1601-1700batch: iter_time=1.419e-04, forward_time=0.106, loss_ctc=43.735, loss_att=51.493, acc=0.745, loss=49.166, backward_time=0.096, grad_norm=46.278, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.158e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 09:51:17,883 (trainer:737) INFO: 41epoch:train:1701-1800batch: iter_time=1.680e-04, forward_time=0.106, loss_ctc=42.091, loss_att=47.226, acc=0.752, loss=45.685, backward_time=0.096, grad_norm=44.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.158e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:51:59,452 (trainer:737) INFO: 41epoch:train:1801-1900batch: iter_time=1.444e-04, forward_time=0.106, loss_ctc=45.075, loss_att=44.992, acc=0.753, loss=45.017, backward_time=0.096, grad_norm=45.043, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.158e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 09:52:41,454 (trainer:737) INFO: 41epoch:train:1901-2000batch: iter_time=1.567e-04, forward_time=0.106, loss_ctc=39.449, loss_att=46.051, acc=0.755, loss=44.070, backward_time=0.096, grad_norm=40.117, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.158e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:53:23,468 (trainer:737) INFO: 41epoch:train:2001-2100batch: iter_time=1.906e-04, forward_time=0.107, loss_ctc=42.200, loss_att=54.747, acc=0.755, loss=50.983, backward_time=0.097, grad_norm=45.079, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.157e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 09:54:05,166 (trainer:737) INFO: 41epoch:train:2101-2200batch: iter_time=1.790e-04, forward_time=0.105, loss_ctc=40.869, loss_att=47.253, acc=0.749, loss=45.337, backward_time=0.097, grad_norm=42.320, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.157e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 09:54:47,363 (trainer:737) INFO: 41epoch:train:2201-2300batch: iter_time=1.610e-04, forward_time=0.109, loss_ctc=49.316, loss_att=55.769, acc=0.742, loss=53.833, backward_time=0.097, grad_norm=48.082, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.157e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 09:55:29,059 (trainer:737) INFO: 41epoch:train:2301-2400batch: iter_time=1.472e-04, forward_time=0.106, loss_ctc=40.299, loss_att=52.106, acc=0.731, loss=48.564, backward_time=0.096, grad_norm=50.169, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.157e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 09:56:10,610 (trainer:737) INFO: 41epoch:train:2401-2500batch: iter_time=1.300e-04, forward_time=0.106, loss_ctc=54.085, loss_att=56.359, acc=0.737, loss=55.677, backward_time=0.096, grad_norm=63.299, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.156e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 09:56:18,379 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 09:56:37,581 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 09:56:41,246 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4105ffb80>)
+[gpuc04:0/16] 2024-01-17 09:56:41,246 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 09:56:41,249 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 10:02:00,776 (trainer:737) INFO: 41epoch:train:2501-2600batch: iter_time=2.558, forward_time=0.106, loss_ctc=42.264, loss_att=50.831, acc=0.724, loss=48.261, backward_time=0.096, grad_norm=46.255, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.156e-04, train_time=3.501
+[gpuc04:0/16] 2024-01-17 10:02:42,751 (trainer:737) INFO: 41epoch:train:2601-2700batch: iter_time=1.061e-04, forward_time=0.105, loss_ctc=47.189, loss_att=51.556, acc=0.738, loss=50.246, backward_time=0.096, grad_norm=49.449, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.156e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:03:24,393 (trainer:737) INFO: 41epoch:train:2701-2800batch: iter_time=1.043e-04, forward_time=0.104, loss_ctc=39.949, loss_att=56.161, acc=0.732, loss=51.297, backward_time=0.096, grad_norm=46.919, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.155e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:04:05,943 (trainer:737) INFO: 41epoch:train:2801-2900batch: iter_time=1.159e-04, forward_time=0.104, loss_ctc=39.564, loss_att=44.761, acc=0.748, loss=43.202, backward_time=0.096, grad_norm=40.863, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.155e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 10:04:47,640 (trainer:737) INFO: 41epoch:train:2901-3000batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=42.952, loss_att=50.768, acc=0.747, loss=48.423, backward_time=0.096, grad_norm=46.292, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.155e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:05:29,694 (trainer:737) INFO: 41epoch:train:3001-3100batch: iter_time=1.143e-04, forward_time=0.104, loss_ctc=43.007, loss_att=43.016, acc=0.755, loss=43.013, backward_time=0.096, grad_norm=44.289, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.155e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:06:11,709 (trainer:737) INFO: 41epoch:train:3101-3200batch: iter_time=1.020e-04, forward_time=0.105, loss_ctc=41.430, loss_att=52.634, acc=0.721, loss=49.273, backward_time=0.096, grad_norm=45.933, clip=100.000, loss_scale=2.513e+34, optim_step_time=0.039, optim0_lr0=3.154e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:06:31,964 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 10:06:53,601 (trainer:737) INFO: 41epoch:train:3201-3300batch: iter_time=1.202e-04, forward_time=0.104, loss_ctc=40.566, loss_att=44.912, acc=0.762, loss=43.608, backward_time=0.096, grad_norm=40.882, clip=100.000, loss_scale=3.084e+34, optim_step_time=0.039, optim0_lr0=3.154e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 10:07:35,739 (trainer:737) INFO: 41epoch:train:3301-3400batch: iter_time=1.081e-04, forward_time=0.105, loss_ctc=41.686, loss_att=53.031, acc=0.730, loss=49.627, backward_time=0.096, grad_norm=45.552, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.154e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 10:08:17,398 (trainer:737) INFO: 41epoch:train:3401-3500batch: iter_time=1.030e-04, forward_time=0.105, loss_ctc=39.810, loss_att=51.084, acc=0.749, loss=47.702, backward_time=0.096, grad_norm=43.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.154e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:08:59,430 (trainer:737) INFO: 41epoch:train:3501-3600batch: iter_time=1.129e-04, forward_time=0.104, loss_ctc=47.427, loss_att=55.309, acc=0.716, loss=52.945, backward_time=0.095, grad_norm=51.677, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.153e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:09:41,177 (trainer:737) INFO: 41epoch:train:3601-3700batch: iter_time=1.174e-04, forward_time=0.104, loss_ctc=50.911, loss_att=53.511, acc=0.725, loss=52.731, backward_time=0.096, grad_norm=61.074, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.153e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:10:08,350 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 10:10:27,699 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 10:10:31,587 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd6977f6a0>)
+[gpuc04:0/16] 2024-01-17 10:10:31,587 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 10:10:31,590 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 10:15:01,505 (trainer:737) INFO: 41epoch:train:3701-3800batch: iter_time=2.533, forward_time=0.109, loss_ctc=44.920, loss_att=52.028, acc=0.723, loss=49.896, backward_time=0.096, grad_norm=46.474, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.153e-04, train_time=3.203
+[gpuc04:0/16] 2024-01-17 10:15:43,241 (trainer:737) INFO: 41epoch:train:3801-3900batch: iter_time=1.663e-04, forward_time=0.106, loss_ctc=42.782, loss_att=48.736, acc=0.736, loss=46.950, backward_time=0.097, grad_norm=45.663, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.153e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:16:24,859 (trainer:737) INFO: 41epoch:train:3901-4000batch: iter_time=1.669e-04, forward_time=0.105, loss_ctc=39.170, loss_att=46.843, acc=0.755, loss=44.541, backward_time=0.095, grad_norm=41.621, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.152e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:17:06,491 (trainer:737) INFO: 41epoch:train:4001-4100batch: iter_time=1.763e-04, forward_time=0.106, loss_ctc=41.499, loss_att=56.141, acc=0.731, loss=51.749, backward_time=0.096, grad_norm=47.536, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.152e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:17:48,092 (trainer:737) INFO: 41epoch:train:4101-4200batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=43.214, loss_att=50.237, acc=0.743, loss=48.130, backward_time=0.096, grad_norm=46.291, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.152e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:18:29,902 (trainer:737) INFO: 41epoch:train:4201-4300batch: iter_time=1.446e-04, forward_time=0.104, loss_ctc=41.229, loss_att=46.336, acc=0.746, loss=44.804, backward_time=0.096, grad_norm=44.153, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.152e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 10:19:12,308 (trainer:737) INFO: 41epoch:train:4301-4400batch: iter_time=1.374e-04, forward_time=0.104, loss_ctc=44.842, loss_att=44.976, acc=0.754, loss=44.936, backward_time=0.096, grad_norm=46.818, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.151e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 10:19:53,843 (trainer:737) INFO: 41epoch:train:4401-4500batch: iter_time=1.475e-04, forward_time=0.104, loss_ctc=39.479, loss_att=46.706, acc=0.744, loss=44.538, backward_time=0.096, grad_norm=39.937, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.151e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 10:20:35,530 (trainer:737) INFO: 41epoch:train:4501-4600batch: iter_time=1.349e-04, forward_time=0.105, loss_ctc=41.859, loss_att=53.141, acc=0.745, loss=49.756, backward_time=0.097, grad_norm=44.436, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.151e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:21:17,132 (trainer:737) INFO: 41epoch:train:4601-4700batch: iter_time=1.265e-04, forward_time=0.104, loss_ctc=40.343, loss_att=48.148, acc=0.734, loss=45.806, backward_time=0.096, grad_norm=42.738, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.150e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:21:59,314 (trainer:737) INFO: 41epoch:train:4701-4800batch: iter_time=1.279e-04, forward_time=0.104, loss_ctc=47.830, loss_att=55.550, acc=0.733, loss=53.234, backward_time=0.096, grad_norm=46.833, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.150e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 10:22:40,792 (trainer:737) INFO: 41epoch:train:4801-4900batch: iter_time=1.491e-04, forward_time=0.104, loss_ctc=39.855, loss_att=50.965, acc=0.725, loss=47.632, backward_time=0.096, grad_norm=49.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.150e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 10:23:22,813 (trainer:737) INFO: 41epoch:train:4901-5000batch: iter_time=1.399e-04, forward_time=0.104, loss_ctc=51.518, loss_att=54.128, acc=0.727, loss=53.345, backward_time=0.096, grad_norm=61.707, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.150e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:23:30,223 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 10:23:49,546 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 10:23:53,038 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd92276890>)
+[gpuc04:0/16] 2024-01-17 10:23:53,038 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 10:23:53,041 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 10:28:40,886 (trainer:737) INFO: 41epoch:train:5001-5100batch: iter_time=2.643, forward_time=0.133, loss_ctc=42.015, loss_att=49.504, acc=0.729, loss=47.257, backward_time=0.099, grad_norm=46.580, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.149e-04, train_time=3.180
+[gpuc04:0/16] 2024-01-17 10:29:22,636 (trainer:737) INFO: 41epoch:train:5101-5200batch: iter_time=1.395e-04, forward_time=0.106, loss_ctc=46.701, loss_att=50.428, acc=0.741, loss=49.310, backward_time=0.097, grad_norm=47.900, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.149e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:30:04,221 (trainer:737) INFO: 41epoch:train:5201-5300batch: iter_time=1.596e-04, forward_time=0.105, loss_ctc=39.242, loss_att=55.905, acc=0.735, loss=50.906, backward_time=0.096, grad_norm=47.785, clip=100.000, loss_scale=3.136e+34, optim_step_time=0.039, optim0_lr0=3.149e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:30:46,272 (trainer:737) INFO: 41epoch:train:5301-5400batch: iter_time=1.255e-04, forward_time=0.107, loss_ctc=39.603, loss_att=44.104, acc=0.749, loss=42.754, backward_time=0.096, grad_norm=40.868, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.149e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:30:52,969 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 10:31:27,944 (trainer:737) INFO: 41epoch:train:5401-5500batch: iter_time=1.359e-04, forward_time=0.105, loss_ctc=42.108, loss_att=50.108, acc=0.750, loss=47.708, backward_time=0.097, grad_norm=45.720, clip=100.000, loss_scale=2.392e+34, optim_step_time=0.039, optim0_lr0=3.148e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:32:09,617 (trainer:737) INFO: 41epoch:train:5501-5600batch: iter_time=1.337e-04, forward_time=0.105, loss_ctc=42.929, loss_att=42.726, acc=0.757, loss=42.787, backward_time=0.096, grad_norm=43.403, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.148e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:32:51,723 (trainer:737) INFO: 41epoch:train:5601-5700batch: iter_time=1.322e-04, forward_time=0.105, loss_ctc=40.903, loss_att=52.123, acc=0.724, loss=48.757, backward_time=0.097, grad_norm=43.323, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.148e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 10:33:33,324 (trainer:737) INFO: 41epoch:train:5701-5800batch: iter_time=1.286e-04, forward_time=0.106, loss_ctc=40.454, loss_att=44.414, acc=0.765, loss=43.226, backward_time=0.096, grad_norm=39.488, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.148e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:34:14,957 (trainer:737) INFO: 41epoch:train:5801-5900batch: iter_time=1.584e-04, forward_time=0.106, loss_ctc=41.573, loss_att=52.967, acc=0.732, loss=49.549, backward_time=0.096, grad_norm=46.719, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.147e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:34:56,634 (trainer:737) INFO: 41epoch:train:5901-6000batch: iter_time=1.400e-04, forward_time=0.106, loss_ctc=39.420, loss_att=50.739, acc=0.750, loss=47.343, backward_time=0.096, grad_norm=42.463, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.147e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:35:39,091 (trainer:737) INFO: 41epoch:train:6001-6100batch: iter_time=1.467e-04, forward_time=0.105, loss_ctc=47.461, loss_att=56.613, acc=0.716, loss=53.868, backward_time=0.096, grad_norm=52.320, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.147e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 10:36:21,133 (trainer:737) INFO: 41epoch:train:6101-6200batch: iter_time=1.551e-04, forward_time=0.106, loss_ctc=48.764, loss_att=52.502, acc=0.728, loss=51.381, backward_time=0.096, grad_norm=58.354, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.147e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:37:00,320 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 10:37:20,043 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 10:37:23,776 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd6972c880>)
+[gpuc04:0/16] 2024-01-17 10:37:23,776 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 10:37:23,780 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 10:42:58,789 (trainer:737) INFO: 41epoch:train:6201-6300batch: iter_time=3.254, forward_time=0.105, loss_ctc=44.994, loss_att=52.549, acc=0.734, loss=50.283, backward_time=0.097, grad_norm=47.843, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.146e-04, train_time=3.976
+[gpuc04:0/16] 2024-01-17 10:43:41,051 (trainer:737) INFO: 41epoch:train:6301-6400batch: iter_time=1.079e-04, forward_time=0.105, loss_ctc=42.934, loss_att=48.946, acc=0.745, loss=47.143, backward_time=0.097, grad_norm=46.363, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.146e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 10:44:22,876 (trainer:737) INFO: 41epoch:train:6401-6500batch: iter_time=1.083e-04, forward_time=0.104, loss_ctc=39.261, loss_att=48.081, acc=0.754, loss=45.435, backward_time=0.096, grad_norm=43.396, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.146e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 10:45:04,620 (trainer:737) INFO: 41epoch:train:6501-6600batch: iter_time=1.113e-04, forward_time=0.105, loss_ctc=41.488, loss_att=56.176, acc=0.741, loss=51.769, backward_time=0.097, grad_norm=47.684, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.146e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:45:46,233 (trainer:737) INFO: 41epoch:train:6601-6700batch: iter_time=1.140e-04, forward_time=0.105, loss_ctc=42.527, loss_att=50.900, acc=0.750, loss=48.388, backward_time=0.096, grad_norm=44.283, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.145e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:46:27,853 (trainer:737) INFO: 41epoch:train:6701-6800batch: iter_time=9.785e-05, forward_time=0.104, loss_ctc=40.989, loss_att=47.765, acc=0.751, loss=45.732, backward_time=0.096, grad_norm=43.303, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.145e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 10:47:09,769 (trainer:737) INFO: 41epoch:train:6801-6900batch: iter_time=9.885e-05, forward_time=0.104, loss_ctc=43.787, loss_att=43.667, acc=0.761, loss=43.703, backward_time=0.096, grad_norm=46.871, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.145e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 10:47:51,825 (trainer:737) INFO: 41epoch:train:6901-7000batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=39.300, loss_att=46.125, acc=0.755, loss=44.078, backward_time=0.096, grad_norm=39.656, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.145e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 10:48:33,603 (trainer:737) INFO: 41epoch:train:7001-7100batch: iter_time=1.044e-04, forward_time=0.105, loss_ctc=42.176, loss_att=55.279, acc=0.756, loss=51.348, backward_time=0.097, grad_norm=47.138, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.144e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 10:49:15,295 (trainer:737) INFO: 41epoch:train:7101-7200batch: iter_time=9.544e-05, forward_time=0.104, loss_ctc=40.308, loss_att=47.293, acc=0.750, loss=45.197, backward_time=0.096, grad_norm=42.489, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.144e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:49:57,052 (trainer:737) INFO: 41epoch:train:7201-7300batch: iter_time=1.126e-04, forward_time=0.105, loss_ctc=47.920, loss_att=56.190, acc=0.742, loss=53.709, backward_time=0.096, grad_norm=46.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.144e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:50:38,563 (trainer:737) INFO: 41epoch:train:7301-7400batch: iter_time=1.075e-04, forward_time=0.103, loss_ctc=39.298, loss_att=52.158, acc=0.732, loss=48.300, backward_time=0.095, grad_norm=47.199, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.143e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 10:51:20,438 (trainer:737) INFO: 41epoch:train:7401-7500batch: iter_time=9.705e-05, forward_time=0.107, loss_ctc=50.877, loss_att=54.669, acc=0.744, loss=53.532, backward_time=0.097, grad_norm=58.740, clip=100.000, loss_scale=3.822e+34, optim_step_time=0.039, optim0_lr0=3.143e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 10:51:22,786 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 10:51:42,436 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 10:51:46,004 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd72333280>)
+[gpuc04:0/16] 2024-01-17 10:51:46,004 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 10:51:46,007 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 10:56:29,510 (trainer:737) INFO: 41epoch:train:7501-7600batch: iter_time=2.407, forward_time=0.104, loss_ctc=41.586, loss_att=51.039, acc=0.725, loss=48.203, backward_time=0.096, grad_norm=45.405, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.143e-04, train_time=3.090
+[gpuc04:0/16] 2024-01-17 10:57:11,456 (trainer:737) INFO: 41epoch:train:7601-7700batch: iter_time=1.581e-04, forward_time=0.104, loss_ctc=46.891, loss_att=52.366, acc=0.738, loss=50.724, backward_time=0.096, grad_norm=47.262, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.143e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 10:57:53,164 (trainer:737) INFO: 41epoch:train:7701-7800batch: iter_time=1.417e-04, forward_time=0.105, loss_ctc=39.115, loss_att=55.377, acc=0.736, loss=50.498, backward_time=0.096, grad_norm=45.829, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.142e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 10:58:34,943 (trainer:737) INFO: 41epoch:train:7801-7900batch: iter_time=1.351e-04, forward_time=0.104, loss_ctc=39.182, loss_att=43.817, acc=0.751, loss=42.427, backward_time=0.096, grad_norm=39.565, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.142e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 10:59:16,805 (trainer:737) INFO: 41epoch:train:7901-8000batch: iter_time=1.143e-04, forward_time=0.104, loss_ctc=42.187, loss_att=50.362, acc=0.749, loss=47.909, backward_time=0.096, grad_norm=44.954, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.142e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 10:59:58,654 (trainer:737) INFO: 41epoch:train:8001-8100batch: iter_time=1.179e-04, forward_time=0.104, loss_ctc=42.163, loss_att=42.167, acc=0.758, loss=42.166, backward_time=0.096, grad_norm=44.381, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.142e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:00:06,527 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 11:00:40,152 (trainer:737) INFO: 41epoch:train:8101-8200batch: iter_time=1.220e-04, forward_time=0.104, loss_ctc=40.437, loss_att=51.733, acc=0.725, loss=48.345, backward_time=0.096, grad_norm=44.926, clip=100.000, loss_scale=2.455e+34, optim_step_time=0.039, optim0_lr0=3.141e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:01:21,961 (trainer:737) INFO: 41epoch:train:8201-8300batch: iter_time=1.095e-04, forward_time=0.104, loss_ctc=40.287, loss_att=44.958, acc=0.762, loss=43.557, backward_time=0.096, grad_norm=39.853, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.141e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:02:04,452 (trainer:737) INFO: 41epoch:train:8301-8400batch: iter_time=1.242e-04, forward_time=0.105, loss_ctc=41.144, loss_att=52.505, acc=0.735, loss=49.097, backward_time=0.096, grad_norm=44.639, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.141e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 11:02:46,378 (trainer:737) INFO: 41epoch:train:8401-8500batch: iter_time=1.126e-04, forward_time=0.103, loss_ctc=39.257, loss_att=51.061, acc=0.748, loss=47.520, backward_time=0.096, grad_norm=40.754, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.141e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 11:03:27,827 (trainer:737) INFO: 41epoch:train:8501-8600batch: iter_time=1.098e-04, forward_time=0.104, loss_ctc=45.821, loss_att=54.982, acc=0.719, loss=52.234, backward_time=0.096, grad_norm=49.102, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.140e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 11:04:09,321 (trainer:737) INFO: 41epoch:train:8601-8700batch: iter_time=1.113e-04, forward_time=0.104, loss_ctc=48.022, loss_att=51.626, acc=0.729, loss=50.545, backward_time=0.096, grad_norm=56.775, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.140e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:04:32,445 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 11:04:52,526 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 11:04:56,193 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd93a3f9a0>)
+[gpuc04:0/16] 2024-01-17 11:04:56,193 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 11:04:56,196 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 11:09:10,403 (trainer:737) INFO: 41epoch:train:8701-8800batch: iter_time=2.440, forward_time=0.105, loss_ctc=44.372, loss_att=51.532, acc=0.737, loss=49.384, backward_time=0.096, grad_norm=47.755, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.140e-04, train_time=3.011
+[gpuc04:0/16] 2024-01-17 11:09:52,421 (trainer:737) INFO: 41epoch:train:8801-8900batch: iter_time=1.193e-04, forward_time=0.108, loss_ctc=42.432, loss_att=48.161, acc=0.748, loss=46.443, backward_time=0.096, grad_norm=46.543, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.140e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 11:10:33,971 (trainer:737) INFO: 41epoch:train:8901-9000batch: iter_time=1.159e-04, forward_time=0.104, loss_ctc=39.281, loss_att=48.588, acc=0.754, loss=45.796, backward_time=0.096, grad_norm=44.035, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.139e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:11:15,666 (trainer:737) INFO: 41epoch:train:9001-9100batch: iter_time=1.208e-04, forward_time=0.104, loss_ctc=41.189, loss_att=56.297, acc=0.736, loss=51.765, backward_time=0.096, grad_norm=48.286, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.139e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:11:57,296 (trainer:737) INFO: 41epoch:train:9101-9200batch: iter_time=1.309e-04, forward_time=0.105, loss_ctc=42.081, loss_att=50.704, acc=0.748, loss=48.117, backward_time=0.096, grad_norm=44.020, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.139e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:12:39,160 (trainer:737) INFO: 41epoch:train:9201-9300batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=40.818, loss_att=46.385, acc=0.755, loss=44.714, backward_time=0.096, grad_norm=42.472, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.139e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:13:21,288 (trainer:737) INFO: 41epoch:train:9301-9400batch: iter_time=1.252e-04, forward_time=0.105, loss_ctc=43.559, loss_att=44.126, acc=0.758, loss=43.956, backward_time=0.096, grad_norm=42.686, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.138e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 11:14:02,781 (trainer:737) INFO: 41epoch:train:9401-9500batch: iter_time=1.358e-04, forward_time=0.104, loss_ctc=39.142, loss_att=45.631, acc=0.757, loss=43.684, backward_time=0.096, grad_norm=39.943, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.138e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:14:44,579 (trainer:737) INFO: 41epoch:train:9501-9600batch: iter_time=1.254e-04, forward_time=0.105, loss_ctc=41.540, loss_att=54.382, acc=0.756, loss=50.529, backward_time=0.096, grad_norm=43.285, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.138e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:15:26,299 (trainer:737) INFO: 41epoch:train:9601-9700batch: iter_time=1.235e-04, forward_time=0.105, loss_ctc=39.632, loss_att=46.880, acc=0.752, loss=44.706, backward_time=0.096, grad_norm=41.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.138e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:16:08,275 (trainer:737) INFO: 41epoch:train:9701-9800batch: iter_time=1.207e-04, forward_time=0.105, loss_ctc=46.999, loss_att=55.433, acc=0.745, loss=52.902, backward_time=0.096, grad_norm=45.756, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.137e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 11:16:50,041 (trainer:737) INFO: 41epoch:train:9801-9900batch: iter_time=1.309e-04, forward_time=0.104, loss_ctc=38.273, loss_att=51.486, acc=0.734, loss=47.522, backward_time=0.096, grad_norm=47.374, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.137e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:17:31,589 (trainer:737) INFO: 41epoch:train:9901-10000batch: iter_time=1.155e-04, forward_time=0.103, loss_ctc=50.871, loss_att=53.974, acc=0.742, loss=53.043, backward_time=0.096, grad_norm=57.914, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.137e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:17:36,097 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 11:17:55,394 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 11:17:59,008 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe130abb6d0>)
+[gpuc04:0/16] 2024-01-17 11:17:59,008 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 11:17:59,011 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 11:22:36,249 (trainer:737) INFO: 41epoch:train:10001-10100batch: iter_time=2.446, forward_time=0.106, loss_ctc=41.630, loss_att=48.426, acc=0.747, loss=46.387, backward_time=0.096, grad_norm=45.404, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.137e-04, train_time=3.046
+[gpuc04:0/16] 2024-01-17 11:22:48,806 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 11:23:18,260 (trainer:737) INFO: 41epoch:train:10101-10200batch: iter_time=1.406e-04, forward_time=0.106, loss_ctc=46.566, loss_att=51.702, acc=0.742, loss=50.161, backward_time=0.096, grad_norm=46.715, clip=100.000, loss_scale=2.287e+34, optim_step_time=0.039, optim0_lr0=3.136e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 11:23:59,920 (trainer:737) INFO: 41epoch:train:10201-10300batch: iter_time=1.593e-04, forward_time=0.106, loss_ctc=39.009, loss_att=54.474, acc=0.751, loss=49.835, backward_time=0.096, grad_norm=44.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.136e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:24:41,811 (trainer:737) INFO: 41epoch:train:10301-10400batch: iter_time=1.183e-04, forward_time=0.104, loss_ctc=38.930, loss_att=43.396, acc=0.756, loss=42.056, backward_time=0.096, grad_norm=38.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.136e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 11:25:23,532 (trainer:737) INFO: 41epoch:train:10401-10500batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=41.581, loss_att=49.463, acc=0.760, loss=47.098, backward_time=0.096, grad_norm=43.725, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.136e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:26:05,226 (trainer:737) INFO: 41epoch:train:10501-10600batch: iter_time=1.087e-04, forward_time=0.105, loss_ctc=42.670, loss_att=42.737, acc=0.761, loss=42.717, backward_time=0.096, grad_norm=43.670, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.135e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:26:46,889 (trainer:737) INFO: 41epoch:train:10601-10700batch: iter_time=1.045e-04, forward_time=0.105, loss_ctc=40.115, loss_att=51.210, acc=0.735, loss=47.882, backward_time=0.096, grad_norm=42.759, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.135e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:27:28,532 (trainer:737) INFO: 41epoch:train:10701-10800batch: iter_time=1.126e-04, forward_time=0.105, loss_ctc=39.838, loss_att=44.376, acc=0.774, loss=43.015, backward_time=0.096, grad_norm=39.647, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.135e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:28:10,304 (trainer:737) INFO: 41epoch:train:10801-10900batch: iter_time=1.150e-04, forward_time=0.105, loss_ctc=40.648, loss_att=51.812, acc=0.749, loss=48.463, backward_time=0.096, grad_norm=43.445, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.134e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:28:52,095 (trainer:737) INFO: 41epoch:train:10901-11000batch: iter_time=1.140e-04, forward_time=0.105, loss_ctc=38.959, loss_att=50.622, acc=0.763, loss=47.123, backward_time=0.096, grad_norm=41.586, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.134e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:29:33,761 (trainer:737) INFO: 41epoch:train:11001-11100batch: iter_time=9.539e-05, forward_time=0.104, loss_ctc=45.436, loss_att=55.935, acc=0.724, loss=52.785, backward_time=0.096, grad_norm=48.156, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.134e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:30:15,404 (trainer:737) INFO: 41epoch:train:11101-11200batch: iter_time=9.831e-05, forward_time=0.104, loss_ctc=47.183, loss_att=51.512, acc=0.744, loss=50.213, backward_time=0.096, grad_norm=58.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.134e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:30:38,710 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 11:30:58,592 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 11:31:02,524 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd93cd8c10>)
+[gpuc04:0/16] 2024-01-17 11:31:02,525 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 11:31:02,528 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 11:35:24,384 (trainer:737) INFO: 41epoch:train:11201-11300batch: iter_time=2.491, forward_time=0.105, loss_ctc=44.226, loss_att=50.602, acc=0.748, loss=48.689, backward_time=0.096, grad_norm=48.276, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.133e-04, train_time=3.090
+[gpuc04:0/16] 2024-01-17 11:36:06,084 (trainer:737) INFO: 41epoch:train:11301-11400batch: iter_time=9.447e-05, forward_time=0.105, loss_ctc=42.313, loss_att=47.355, acc=0.750, loss=45.842, backward_time=0.096, grad_norm=42.954, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.133e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:36:47,555 (trainer:737) INFO: 41epoch:train:11401-11500batch: iter_time=1.194e-04, forward_time=0.104, loss_ctc=38.888, loss_att=47.229, acc=0.756, loss=44.727, backward_time=0.096, grad_norm=43.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.133e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 11:37:29,216 (trainer:737) INFO: 41epoch:train:11501-11600batch: iter_time=1.226e-04, forward_time=0.105, loss_ctc=40.897, loss_att=55.156, acc=0.739, loss=50.879, backward_time=0.097, grad_norm=45.964, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.133e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:38:11,017 (trainer:737) INFO: 41epoch:train:11601-11700batch: iter_time=1.269e-04, forward_time=0.105, loss_ctc=41.886, loss_att=50.342, acc=0.748, loss=47.805, backward_time=0.096, grad_norm=44.003, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.132e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:38:52,550 (trainer:737) INFO: 41epoch:train:11701-11800batch: iter_time=1.160e-04, forward_time=0.105, loss_ctc=40.557, loss_att=46.226, acc=0.756, loss=44.525, backward_time=0.096, grad_norm=41.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.132e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:39:34,352 (trainer:737) INFO: 41epoch:train:11801-11900batch: iter_time=1.084e-04, forward_time=0.104, loss_ctc=43.540, loss_att=44.325, acc=0.758, loss=44.090, backward_time=0.096, grad_norm=44.159, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.132e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:40:16,632 (trainer:737) INFO: 41epoch:train:11901-12000batch: iter_time=1.297e-04, forward_time=0.104, loss_ctc=38.864, loss_att=45.216, acc=0.757, loss=43.310, backward_time=0.095, grad_norm=39.319, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.132e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 11:40:58,920 (trainer:737) INFO: 41epoch:train:12001-12100batch: iter_time=1.388e-04, forward_time=0.106, loss_ctc=41.694, loss_att=54.060, acc=0.758, loss=50.351, backward_time=0.096, grad_norm=45.299, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.131e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 11:41:20,522 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 11:41:40,537 (trainer:737) INFO: 41epoch:train:12101-12200batch: iter_time=1.303e-04, forward_time=0.106, loss_ctc=39.844, loss_att=46.466, acc=0.753, loss=44.479, backward_time=0.096, grad_norm=42.675, clip=100.000, loss_scale=2.517e+34, optim_step_time=0.039, optim0_lr0=3.131e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:42:23,359 (trainer:737) INFO: 41epoch:train:12201-12300batch: iter_time=1.290e-04, forward_time=0.105, loss_ctc=46.489, loss_att=54.852, acc=0.746, loss=52.343, backward_time=0.096, grad_norm=47.579, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.131e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-17 11:43:04,859 (trainer:737) INFO: 41epoch:train:12301-12400batch: iter_time=1.426e-04, forward_time=0.104, loss_ctc=38.139, loss_att=51.079, acc=0.737, loss=47.197, backward_time=0.095, grad_norm=46.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.131e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:43:46,659 (trainer:737) INFO: 41epoch:train:12401-12500batch: iter_time=1.274e-04, forward_time=0.104, loss_ctc=50.269, loss_att=55.273, acc=0.742, loss=53.772, backward_time=0.095, grad_norm=61.224, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.130e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:43:49,068 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 11:44:09,995 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 11:44:13,730 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe1314c37c0>)
+[gpuc04:0/16] 2024-01-17 11:44:13,730 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 11:44:13,733 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 11:48:47,309 (trainer:737) INFO: 41epoch:train:12501-12600batch: iter_time=2.481, forward_time=0.105, loss_ctc=41.064, loss_att=51.286, acc=0.728, loss=48.219, backward_time=0.097, grad_norm=46.686, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.130e-04, train_time=3.006
+[gpuc04:0/16] 2024-01-17 11:49:29,107 (trainer:737) INFO: 41epoch:train:12601-12700batch: iter_time=1.384e-04, forward_time=0.105, loss_ctc=46.204, loss_att=52.043, acc=0.739, loss=50.291, backward_time=0.097, grad_norm=54.040, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.130e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:50:10,758 (trainer:737) INFO: 41epoch:train:12701-12800batch: iter_time=1.441e-04, forward_time=0.104, loss_ctc=39.465, loss_att=56.681, acc=0.734, loss=51.516, backward_time=0.096, grad_norm=48.727, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.130e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:50:52,417 (trainer:737) INFO: 41epoch:train:12801-12900batch: iter_time=1.630e-04, forward_time=0.105, loss_ctc=38.985, loss_att=44.290, acc=0.750, loss=42.699, backward_time=0.097, grad_norm=40.483, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.129e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:51:34,202 (trainer:737) INFO: 41epoch:train:12901-13000batch: iter_time=1.580e-04, forward_time=0.105, loss_ctc=41.662, loss_att=50.510, acc=0.752, loss=47.855, backward_time=0.097, grad_norm=44.609, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.129e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:52:16,359 (trainer:737) INFO: 41epoch:train:13001-13100batch: iter_time=1.266e-04, forward_time=0.107, loss_ctc=42.555, loss_att=42.889, acc=0.759, loss=42.789, backward_time=0.096, grad_norm=44.656, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.129e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 11:52:58,515 (trainer:737) INFO: 41epoch:train:13101-13200batch: iter_time=1.190e-04, forward_time=0.104, loss_ctc=40.217, loss_att=52.230, acc=0.724, loss=48.626, backward_time=0.096, grad_norm=44.011, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.129e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 11:53:40,115 (trainer:737) INFO: 41epoch:train:13201-13300batch: iter_time=1.406e-04, forward_time=0.105, loss_ctc=39.997, loss_att=44.782, acc=0.764, loss=43.347, backward_time=0.096, grad_norm=39.193, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.128e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 11:54:21,923 (trainer:737) INFO: 41epoch:train:13301-13400batch: iter_time=1.516e-04, forward_time=0.105, loss_ctc=41.126, loss_att=53.169, acc=0.732, loss=49.556, backward_time=0.096, grad_norm=46.151, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.128e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 11:55:03,611 (trainer:737) INFO: 41epoch:train:13401-13500batch: iter_time=1.721e-04, forward_time=0.106, loss_ctc=38.933, loss_att=50.663, acc=0.751, loss=47.144, backward_time=0.096, grad_norm=41.951, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.128e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 11:55:45,175 (trainer:737) INFO: 41epoch:train:13501-13600batch: iter_time=1.471e-04, forward_time=0.104, loss_ctc=44.708, loss_att=55.523, acc=0.717, loss=52.278, backward_time=0.095, grad_norm=52.533, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.128e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 11:56:27,297 (trainer:737) INFO: 41epoch:train:13601-13700batch: iter_time=1.246e-04, forward_time=0.104, loss_ctc=47.044, loss_att=51.574, acc=0.729, loss=50.215, backward_time=0.095, grad_norm=55.742, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.127e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 11:56:54,091 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 11:57:13,850 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 11:57:17,536 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd717eee90>)
+[gpuc04:0/16] 2024-01-17 11:57:17,536 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 11:57:17,539 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 12:01:41,983 (trainer:737) INFO: 41epoch:train:13701-13800batch: iter_time=2.455, forward_time=0.104, loss_ctc=43.929, loss_att=52.032, acc=0.736, loss=49.601, backward_time=0.096, grad_norm=46.419, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.127e-04, train_time=3.147
+[gpuc04:0/16] 2024-01-17 12:02:23,776 (trainer:737) INFO: 41epoch:train:13801-13900batch: iter_time=1.240e-04, forward_time=0.105, loss_ctc=42.380, loss_att=48.176, acc=0.748, loss=46.437, backward_time=0.096, grad_norm=46.737, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.127e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:03:05,573 (trainer:737) INFO: 41epoch:train:13901-14000batch: iter_time=1.366e-04, forward_time=0.104, loss_ctc=38.796, loss_att=47.304, acc=0.758, loss=44.751, backward_time=0.095, grad_norm=44.550, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.127e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:03:47,303 (trainer:737) INFO: 41epoch:train:14001-14100batch: iter_time=1.515e-04, forward_time=0.105, loss_ctc=41.176, loss_att=55.797, acc=0.743, loss=51.410, backward_time=0.096, grad_norm=46.936, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.126e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 12:04:14,649 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 12:04:29,229 (trainer:737) INFO: 41epoch:train:14101-14200batch: iter_time=1.515e-04, forward_time=0.104, loss_ctc=41.661, loss_att=50.035, acc=0.752, loss=47.522, backward_time=0.096, grad_norm=42.230, clip=100.000, loss_scale=2.329e+34, optim_step_time=0.039, optim0_lr0=3.126e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 12:05:10,848 (trainer:737) INFO: 41epoch:train:14201-14300batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=40.721, loss_att=46.843, acc=0.753, loss=45.007, backward_time=0.096, grad_norm=42.435, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.126e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 12:05:52,502 (trainer:737) INFO: 41epoch:train:14301-14400batch: iter_time=1.287e-04, forward_time=0.105, loss_ctc=43.249, loss_att=43.450, acc=0.762, loss=43.390, backward_time=0.096, grad_norm=43.187, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.126e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 12:06:34,036 (trainer:737) INFO: 41epoch:train:14401-14500batch: iter_time=1.409e-04, forward_time=0.105, loss_ctc=38.522, loss_att=45.332, acc=0.757, loss=43.289, backward_time=0.095, grad_norm=54.005, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.125e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:07:15,893 (trainer:737) INFO: 41epoch:train:14501-14600batch: iter_time=1.293e-04, forward_time=0.106, loss_ctc=41.556, loss_att=54.019, acc=0.757, loss=50.280, backward_time=0.096, grad_norm=45.083, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.125e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:07:57,605 (trainer:737) INFO: 41epoch:train:14601-14700batch: iter_time=1.545e-04, forward_time=0.104, loss_ctc=39.734, loss_att=46.690, acc=0.753, loss=44.603, backward_time=0.096, grad_norm=43.140, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.125e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 12:08:39,371 (trainer:737) INFO: 41epoch:train:14701-14800batch: iter_time=1.749e-04, forward_time=0.105, loss_ctc=46.972, loss_att=55.613, acc=0.743, loss=53.021, backward_time=0.097, grad_norm=46.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.125e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 12:09:20,942 (trainer:737) INFO: 41epoch:train:14801-14900batch: iter_time=1.796e-04, forward_time=0.104, loss_ctc=37.731, loss_att=51.406, acc=0.736, loss=47.303, backward_time=0.096, grad_norm=46.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.124e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:10:03,207 (trainer:737) INFO: 41epoch:train:14901-15000batch: iter_time=1.396e-04, forward_time=0.103, loss_ctc=50.968, loss_att=55.135, acc=0.746, loss=53.885, backward_time=0.098, grad_norm=63.221, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.124e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 12:30:05,228 (trainer:343) INFO: 41epoch results: [train] iter_time=0.207, forward_time=0.105, loss_ctc=42.674, loss_att=50.380, acc=0.743, loss=48.068, backward_time=0.096, grad_norm=46.314, clip=100.000, loss_scale=2.215e+34, optim_step_time=0.039, optim0_lr0=3.143e-04, train_time=0.647, time=2 hours, 42 minutes and 2.21 seconds, total_count=615000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=52.007, cer_ctc=0.264, loss_att=52.793, acc=0.595, cer=0.396, wer=1.000, loss=52.558, time=19 minutes and 50.83 seconds, total_count=191511, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 12:30:10,304 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 12:30:10,314 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/36epoch.pth
+[gpuc04:0/16] 2024-01-17 12:30:10,314 (trainer:272) INFO: 42/45epoch started. Estimated time to finish: 12 hours, 3 minutes and 37.06 seconds
+[gpuc04:0/16] 2024-01-17 12:30:10,325 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 12:30:29,158 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 12:30:32,704 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdbb5af1e10>)
+[gpuc04:0/16] 2024-01-17 12:30:32,704 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 12:30:32,708 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 12:34:54,721 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 12:34:57,228 (trainer:737) INFO: 42epoch:train:1-100batch: iter_time=2.328, forward_time=0.103, loss_ctc=49.811, loss_att=48.798, acc=0.732, loss=49.102, backward_time=0.096, grad_norm=55.116, clip=100.000, loss_scale=2.014e+34, optim_step_time=0.039, optim0_lr0=3.124e-04, train_time=2.869
+[gpuc04:0/16] 2024-01-17 12:35:39,077 (trainer:737) INFO: 42epoch:train:101-200batch: iter_time=1.041e-04, forward_time=0.104, loss_ctc=45.516, loss_att=57.388, acc=0.719, loss=53.826, backward_time=0.097, grad_norm=47.595, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.124e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:36:21,037 (trainer:737) INFO: 42epoch:train:201-300batch: iter_time=1.065e-04, forward_time=0.104, loss_ctc=40.510, loss_att=48.839, acc=0.760, loss=46.340, backward_time=0.098, grad_norm=41.507, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.123e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 12:37:03,178 (trainer:737) INFO: 42epoch:train:301-400batch: iter_time=1.140e-04, forward_time=0.106, loss_ctc=44.409, loss_att=58.460, acc=0.721, loss=54.245, backward_time=0.098, grad_norm=52.997, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.123e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 12:37:47,477 (trainer:737) INFO: 42epoch:train:401-500batch: iter_time=1.128e-04, forward_time=0.105, loss_ctc=44.477, loss_att=59.557, acc=0.708, loss=55.033, backward_time=0.097, grad_norm=46.229, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.123e-04, train_time=0.443
+[gpuc04:0/16] 2024-01-17 12:38:30,271 (trainer:737) INFO: 42epoch:train:501-600batch: iter_time=1.189e-04, forward_time=0.104, loss_ctc=40.901, loss_att=52.923, acc=0.734, loss=49.316, backward_time=0.097, grad_norm=43.116, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.122e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-17 12:39:14,842 (trainer:737) INFO: 42epoch:train:601-700batch: iter_time=1.164e-04, forward_time=0.122, loss_ctc=31.279, loss_att=31.295, acc=0.787, loss=31.290, backward_time=0.101, grad_norm=35.374, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.122e-04, train_time=0.445
+[gpuc04:0/16] 2024-01-17 12:39:57,856 (trainer:737) INFO: 42epoch:train:701-800batch: iter_time=1.157e-04, forward_time=0.105, loss_ctc=47.221, loss_att=53.446, acc=0.740, loss=51.578, backward_time=0.097, grad_norm=50.755, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.122e-04, train_time=0.430
+[gpuc04:0/16] 2024-01-17 12:40:41,542 (trainer:737) INFO: 42epoch:train:801-900batch: iter_time=1.169e-04, forward_time=0.109, loss_ctc=50.551, loss_att=57.618, acc=0.711, loss=55.498, backward_time=0.106, grad_norm=51.555, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.122e-04, train_time=0.437
+[gpuc04:0/16] 2024-01-17 12:41:26,302 (trainer:737) INFO: 42epoch:train:901-1000batch: iter_time=1.038e-04, forward_time=0.107, loss_ctc=36.015, loss_att=42.650, acc=0.751, loss=40.659, backward_time=0.099, grad_norm=40.186, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.121e-04, train_time=0.447
+[gpuc04:0/16] 2024-01-17 12:42:07,762 (trainer:737) INFO: 42epoch:train:1001-1100batch: iter_time=1.101e-04, forward_time=0.104, loss_ctc=37.238, loss_att=41.883, acc=0.756, loss=40.490, backward_time=0.096, grad_norm=42.159, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.121e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 12:42:51,126 (trainer:737) INFO: 42epoch:train:1101-1200batch: iter_time=1.153e-04, forward_time=0.105, loss_ctc=46.304, loss_att=56.522, acc=0.728, loss=53.456, backward_time=0.097, grad_norm=59.146, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.121e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-17 12:43:17,449 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 12:43:37,289 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 12:43:41,062 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe1311c3070>)
+[gpuc04:0/16] 2024-01-17 12:43:41,062 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 12:43:41,066 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 12:50:17,034 (trainer:737) INFO: 42epoch:train:1201-1300batch: iter_time=2.591, forward_time=0.104, loss_ctc=48.814, loss_att=48.789, acc=0.738, loss=48.797, backward_time=0.096, grad_norm=51.413, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.121e-04, train_time=4.459
+[gpuc04:0/16] 2024-01-17 12:50:58,802 (trainer:737) INFO: 42epoch:train:1301-1400batch: iter_time=1.431e-04, forward_time=0.105, loss_ctc=47.649, loss_att=45.561, acc=0.749, loss=46.188, backward_time=0.097, grad_norm=47.085, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.120e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 12:51:40,610 (trainer:737) INFO: 42epoch:train:1401-1500batch: iter_time=1.252e-04, forward_time=0.106, loss_ctc=42.963, loss_att=59.700, acc=0.743, loss=54.679, backward_time=0.098, grad_norm=46.934, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.120e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:52:22,549 (trainer:737) INFO: 42epoch:train:1501-1600batch: iter_time=1.483e-04, forward_time=0.105, loss_ctc=38.917, loss_att=51.809, acc=0.748, loss=47.941, backward_time=0.097, grad_norm=43.103, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.120e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 12:53:04,724 (trainer:737) INFO: 42epoch:train:1601-1700batch: iter_time=1.629e-04, forward_time=0.106, loss_ctc=41.447, loss_att=55.725, acc=0.727, loss=51.442, backward_time=0.097, grad_norm=48.226, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.120e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 12:53:46,254 (trainer:737) INFO: 42epoch:train:1701-1800batch: iter_time=1.787e-04, forward_time=0.105, loss_ctc=43.081, loss_att=56.021, acc=0.720, loss=52.139, backward_time=0.097, grad_norm=45.465, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.119e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:54:28,434 (trainer:737) INFO: 42epoch:train:1801-1900batch: iter_time=1.598e-04, forward_time=0.106, loss_ctc=38.396, loss_att=46.999, acc=0.783, loss=44.418, backward_time=0.097, grad_norm=39.385, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.119e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 12:55:09,984 (trainer:737) INFO: 42epoch:train:1901-2000batch: iter_time=1.529e-04, forward_time=0.105, loss_ctc=38.332, loss_att=40.104, acc=0.761, loss=39.572, backward_time=0.096, grad_norm=42.087, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.119e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:55:51,741 (trainer:737) INFO: 42epoch:train:2001-2100batch: iter_time=1.363e-04, forward_time=0.106, loss_ctc=47.024, loss_att=52.807, acc=0.741, loss=51.072, backward_time=0.097, grad_norm=47.347, clip=100.000, loss_scale=1.101e+34, optim_step_time=0.040, optim0_lr0=3.119e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 12:56:33,311 (trainer:737) INFO: 42epoch:train:2101-2200batch: iter_time=1.530e-04, forward_time=0.105, loss_ctc=42.554, loss_att=52.956, acc=0.730, loss=49.836, backward_time=0.097, grad_norm=44.791, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.118e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:57:14,815 (trainer:737) INFO: 42epoch:train:2201-2300batch: iter_time=1.343e-04, forward_time=0.105, loss_ctc=37.567, loss_att=46.286, acc=0.755, loss=43.670, backward_time=0.097, grad_norm=41.690, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.118e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 12:58:01,598 (trainer:737) INFO: 42epoch:train:2301-2400batch: iter_time=1.453e-04, forward_time=0.132, loss_ctc=39.710, loss_att=47.694, acc=0.748, loss=45.299, backward_time=0.100, grad_norm=51.127, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.045, optim0_lr0=3.118e-04, train_time=0.468
+[gpuc04:0/16] 2024-01-17 12:58:43,419 (trainer:737) INFO: 42epoch:train:2401-2500batch: iter_time=1.460e-04, forward_time=0.105, loss_ctc=48.314, loss_att=50.976, acc=0.750, loss=50.177, backward_time=0.097, grad_norm=49.785, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.118e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 12:58:57,128 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 12:59:16,804 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 12:59:20,722 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb8eaf1120>)
+[gpuc04:0/16] 2024-01-17 12:59:20,722 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 12:59:20,726 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 13:04:31,559 (trainer:737) INFO: 42epoch:train:2501-2600batch: iter_time=2.781, forward_time=0.122, loss_ctc=47.382, loss_att=47.880, acc=0.736, loss=47.731, backward_time=0.097, grad_norm=48.003, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.117e-04, train_time=3.481
+[gpuc04:0/16] 2024-01-17 13:05:13,198 (trainer:737) INFO: 42epoch:train:2601-2700batch: iter_time=1.509e-04, forward_time=0.106, loss_ctc=44.907, loss_att=57.172, acc=0.721, loss=53.492, backward_time=0.097, grad_norm=48.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.117e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:05:54,916 (trainer:737) INFO: 42epoch:train:2701-2800batch: iter_time=1.658e-04, forward_time=0.106, loss_ctc=39.946, loss_att=48.470, acc=0.761, loss=45.913, backward_time=0.097, grad_norm=41.432, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.117e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:06:37,097 (trainer:737) INFO: 42epoch:train:2801-2900batch: iter_time=1.711e-04, forward_time=0.106, loss_ctc=43.247, loss_att=58.283, acc=0.723, loss=53.772, backward_time=0.097, grad_norm=50.308, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.117e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 13:07:18,861 (trainer:737) INFO: 42epoch:train:2901-3000batch: iter_time=1.544e-04, forward_time=0.106, loss_ctc=43.386, loss_att=58.191, acc=0.712, loss=53.749, backward_time=0.096, grad_norm=43.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.116e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:08:00,639 (trainer:737) INFO: 42epoch:train:3001-3100batch: iter_time=1.513e-04, forward_time=0.105, loss_ctc=40.254, loss_att=51.897, acc=0.740, loss=48.404, backward_time=0.096, grad_norm=41.349, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.116e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 13:08:42,031 (trainer:737) INFO: 42epoch:train:3101-3200batch: iter_time=1.435e-04, forward_time=0.105, loss_ctc=31.092, loss_att=31.272, acc=0.788, loss=31.218, backward_time=0.096, grad_norm=34.382, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.116e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 13:09:24,107 (trainer:737) INFO: 42epoch:train:3201-3300batch: iter_time=1.292e-04, forward_time=0.105, loss_ctc=45.983, loss_att=52.625, acc=0.740, loss=50.632, backward_time=0.096, grad_norm=45.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.116e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 13:10:05,603 (trainer:737) INFO: 42epoch:train:3301-3400batch: iter_time=1.401e-04, forward_time=0.105, loss_ctc=49.612, loss_att=56.770, acc=0.713, loss=54.623, backward_time=0.096, grad_norm=52.474, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.115e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:10:47,164 (trainer:737) INFO: 42epoch:train:3401-3500batch: iter_time=1.668e-04, forward_time=0.105, loss_ctc=35.147, loss_att=42.436, acc=0.754, loss=40.249, backward_time=0.096, grad_norm=38.510, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.115e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:11:28,581 (trainer:737) INFO: 42epoch:train:3501-3600batch: iter_time=1.431e-04, forward_time=0.105, loss_ctc=36.670, loss_att=41.345, acc=0.759, loss=39.943, backward_time=0.096, grad_norm=41.067, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.115e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 13:12:10,200 (trainer:737) INFO: 42epoch:train:3601-3700batch: iter_time=1.084e-04, forward_time=0.105, loss_ctc=44.643, loss_att=55.657, acc=0.730, loss=52.353, backward_time=0.096, grad_norm=52.930, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.115e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:12:35,394 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 13:12:55,178 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 13:12:58,918 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe405c51690>)
+[gpuc04:0/16] 2024-01-17 13:12:58,918 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 13:12:58,921 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 13:17:18,590 (trainer:737) INFO: 42epoch:train:3701-3800batch: iter_time=2.428, forward_time=0.104, loss_ctc=47.090, loss_att=47.531, acc=0.742, loss=47.399, backward_time=0.095, grad_norm=48.738, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.114e-04, train_time=3.084
+[gpuc04:0/16] 2024-01-17 13:18:00,143 (trainer:737) INFO: 42epoch:train:3801-3900batch: iter_time=1.073e-04, forward_time=0.104, loss_ctc=46.601, loss_att=45.226, acc=0.753, loss=45.639, backward_time=0.095, grad_norm=46.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.114e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:18:42,127 (trainer:737) INFO: 42epoch:train:3901-4000batch: iter_time=1.001e-04, forward_time=0.107, loss_ctc=41.948, loss_att=59.221, acc=0.744, loss=54.039, backward_time=0.097, grad_norm=45.429, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.114e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 13:19:24,002 (trainer:737) INFO: 42epoch:train:4001-4100batch: iter_time=1.298e-04, forward_time=0.104, loss_ctc=38.497, loss_att=51.341, acc=0.750, loss=47.488, backward_time=0.096, grad_norm=43.203, clip=100.000, loss_scale=2.202e+34, optim_step_time=0.039, optim0_lr0=3.114e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:20:05,910 (trainer:737) INFO: 42epoch:train:4101-4200batch: iter_time=1.349e-04, forward_time=0.105, loss_ctc=40.799, loss_att=55.422, acc=0.729, loss=51.035, backward_time=0.096, grad_norm=46.608, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.113e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:20:21,408 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 13:20:47,675 (trainer:737) INFO: 42epoch:train:4201-4300batch: iter_time=1.221e-04, forward_time=0.104, loss_ctc=42.653, loss_att=55.637, acc=0.723, loss=51.742, backward_time=0.096, grad_norm=43.112, clip=100.000, loss_scale=2.832e+34, optim_step_time=0.039, optim0_lr0=3.113e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:21:29,660 (trainer:737) INFO: 42epoch:train:4301-4400batch: iter_time=1.118e-04, forward_time=0.105, loss_ctc=37.963, loss_att=46.922, acc=0.785, loss=44.234, backward_time=0.096, grad_norm=38.911, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.113e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 13:22:11,145 (trainer:737) INFO: 42epoch:train:4401-4500batch: iter_time=1.168e-04, forward_time=0.104, loss_ctc=38.302, loss_att=39.999, acc=0.762, loss=39.490, backward_time=0.095, grad_norm=41.427, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.113e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:22:53,306 (trainer:737) INFO: 42epoch:train:4501-4600batch: iter_time=1.356e-04, forward_time=0.104, loss_ctc=46.353, loss_att=51.881, acc=0.743, loss=50.222, backward_time=0.096, grad_norm=46.610, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.112e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 13:23:35,004 (trainer:737) INFO: 42epoch:train:4601-4700batch: iter_time=1.164e-04, forward_time=0.104, loss_ctc=41.739, loss_att=52.870, acc=0.731, loss=49.531, backward_time=0.096, grad_norm=45.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.112e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:24:16,422 (trainer:737) INFO: 42epoch:train:4701-4800batch: iter_time=1.015e-04, forward_time=0.104, loss_ctc=37.295, loss_att=46.527, acc=0.753, loss=43.757, backward_time=0.095, grad_norm=41.485, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.112e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 13:24:58,179 (trainer:737) INFO: 42epoch:train:4801-4900batch: iter_time=1.115e-04, forward_time=0.104, loss_ctc=38.563, loss_att=47.548, acc=0.750, loss=44.852, backward_time=0.095, grad_norm=47.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.112e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:25:39,876 (trainer:737) INFO: 42epoch:train:4901-5000batch: iter_time=9.744e-05, forward_time=0.103, loss_ctc=47.711, loss_att=50.277, acc=0.751, loss=49.507, backward_time=0.096, grad_norm=48.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.111e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:25:46,328 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 13:26:05,940 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 13:26:09,725 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe1154a1210>)
+[gpuc04:0/16] 2024-01-17 13:26:09,725 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 13:26:09,728 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 13:30:48,897 (trainer:737) INFO: 42epoch:train:5001-5100batch: iter_time=2.483, forward_time=0.110, loss_ctc=48.081, loss_att=46.432, acc=0.745, loss=46.927, backward_time=0.096, grad_norm=49.921, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.111e-04, train_time=3.090
+[gpuc04:0/16] 2024-01-17 13:31:30,599 (trainer:737) INFO: 42epoch:train:5101-5200batch: iter_time=1.721e-04, forward_time=0.105, loss_ctc=44.265, loss_att=58.316, acc=0.724, loss=54.100, backward_time=0.097, grad_norm=46.253, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.111e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:32:12,297 (trainer:737) INFO: 42epoch:train:5201-5300batch: iter_time=1.571e-04, forward_time=0.106, loss_ctc=39.550, loss_att=47.302, acc=0.770, loss=44.976, backward_time=0.097, grad_norm=40.691, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.111e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:32:54,074 (trainer:737) INFO: 42epoch:train:5301-5400batch: iter_time=1.390e-04, forward_time=0.107, loss_ctc=42.748, loss_att=57.966, acc=0.737, loss=53.401, backward_time=0.097, grad_norm=48.225, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.110e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 13:33:35,744 (trainer:737) INFO: 42epoch:train:5401-5500batch: iter_time=1.331e-04, forward_time=0.106, loss_ctc=43.496, loss_att=59.951, acc=0.716, loss=55.014, backward_time=0.096, grad_norm=44.220, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.110e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:34:17,551 (trainer:737) INFO: 42epoch:train:5501-5600batch: iter_time=1.381e-04, forward_time=0.105, loss_ctc=39.414, loss_att=53.186, acc=0.744, loss=49.055, backward_time=0.096, grad_norm=43.549, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.110e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 13:34:59,199 (trainer:737) INFO: 42epoch:train:5601-5700batch: iter_time=1.230e-04, forward_time=0.105, loss_ctc=31.189, loss_att=31.074, acc=0.792, loss=31.109, backward_time=0.096, grad_norm=35.296, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.110e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:35:40,842 (trainer:737) INFO: 42epoch:train:5701-5800batch: iter_time=1.318e-04, forward_time=0.106, loss_ctc=45.753, loss_att=51.717, acc=0.754, loss=49.928, backward_time=0.096, grad_norm=47.258, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.109e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:36:22,691 (trainer:737) INFO: 42epoch:train:5801-5900batch: iter_time=1.402e-04, forward_time=0.106, loss_ctc=48.386, loss_att=57.451, acc=0.715, loss=54.731, backward_time=0.097, grad_norm=50.372, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.109e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 13:37:04,346 (trainer:737) INFO: 42epoch:train:5901-6000batch: iter_time=1.636e-04, forward_time=0.105, loss_ctc=34.801, loss_att=42.170, acc=0.766, loss=39.959, backward_time=0.096, grad_norm=38.779, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.109e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:37:46,316 (trainer:737) INFO: 42epoch:train:6001-6100batch: iter_time=1.613e-04, forward_time=0.108, loss_ctc=36.659, loss_att=41.751, acc=0.762, loss=40.223, backward_time=0.096, grad_norm=40.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.109e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:38:28,506 (trainer:737) INFO: 42epoch:train:6101-6200batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=43.673, loss_att=55.535, acc=0.735, loss=51.977, backward_time=0.097, grad_norm=52.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.108e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 13:38:58,223 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 13:39:17,242 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 13:39:20,762 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe115db01c0>)
+[gpuc04:0/16] 2024-01-17 13:39:20,762 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 13:39:20,766 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 13:43:34,152 (trainer:737) INFO: 42epoch:train:6201-6300batch: iter_time=2.496, forward_time=0.105, loss_ctc=47.208, loss_att=47.896, acc=0.738, loss=47.690, backward_time=0.096, grad_norm=49.373, clip=100.000, loss_scale=3.385e+34, optim_step_time=0.039, optim0_lr0=3.108e-04, train_time=3.056
+[gpuc04:0/16] 2024-01-17 13:44:16,062 (trainer:737) INFO: 42epoch:train:6301-6400batch: iter_time=1.769e-04, forward_time=0.105, loss_ctc=46.791, loss_att=46.357, acc=0.744, loss=46.487, backward_time=0.096, grad_norm=43.889, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.108e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:44:57,833 (trainer:737) INFO: 42epoch:train:6401-6500batch: iter_time=1.837e-04, forward_time=0.107, loss_ctc=42.278, loss_att=57.671, acc=0.739, loss=53.053, backward_time=0.097, grad_norm=46.070, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.108e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:45:39,522 (trainer:737) INFO: 42epoch:train:6501-6600batch: iter_time=1.839e-04, forward_time=0.105, loss_ctc=38.292, loss_att=51.515, acc=0.744, loss=47.548, backward_time=0.096, grad_norm=41.777, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.107e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:45:52,831 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 13:46:21,007 (trainer:737) INFO: 42epoch:train:6601-6700batch: iter_time=1.422e-04, forward_time=0.105, loss_ctc=40.323, loss_att=54.012, acc=0.727, loss=49.905, backward_time=0.096, grad_norm=43.322, clip=100.000, loss_scale=2.727e+34, optim_step_time=0.038, optim0_lr0=3.107e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:47:02,440 (trainer:737) INFO: 42epoch:train:6701-6800batch: iter_time=1.607e-04, forward_time=0.105, loss_ctc=41.931, loss_att=55.138, acc=0.714, loss=51.176, backward_time=0.096, grad_norm=43.813, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.107e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 13:47:44,322 (trainer:737) INFO: 42epoch:train:6801-6900batch: iter_time=2.194e-04, forward_time=0.106, loss_ctc=37.337, loss_att=46.206, acc=0.783, loss=43.545, backward_time=0.097, grad_norm=38.469, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.107e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:48:25,887 (trainer:737) INFO: 42epoch:train:6901-7000batch: iter_time=1.967e-04, forward_time=0.106, loss_ctc=37.501, loss_att=39.743, acc=0.760, loss=39.070, backward_time=0.096, grad_norm=40.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.106e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:49:07,474 (trainer:737) INFO: 42epoch:train:7001-7100batch: iter_time=1.739e-04, forward_time=0.106, loss_ctc=45.937, loss_att=51.119, acc=0.740, loss=49.565, backward_time=0.096, grad_norm=48.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.106e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:49:49,061 (trainer:737) INFO: 42epoch:train:7101-7200batch: iter_time=2.055e-04, forward_time=0.106, loss_ctc=41.593, loss_att=52.415, acc=0.725, loss=49.168, backward_time=0.096, grad_norm=44.778, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.106e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:50:30,566 (trainer:737) INFO: 42epoch:train:7201-7300batch: iter_time=1.653e-04, forward_time=0.105, loss_ctc=36.736, loss_att=46.682, acc=0.746, loss=43.698, backward_time=0.095, grad_norm=40.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.106e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 13:51:12,335 (trainer:737) INFO: 42epoch:train:7301-7400batch: iter_time=1.303e-04, forward_time=0.105, loss_ctc=38.698, loss_att=45.395, acc=0.757, loss=43.386, backward_time=0.095, grad_norm=43.740, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.105e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 13:51:54,197 (trainer:737) INFO: 42epoch:train:7401-7500batch: iter_time=1.296e-04, forward_time=0.105, loss_ctc=47.380, loss_att=48.992, acc=0.751, loss=48.508, backward_time=0.096, grad_norm=48.362, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.105e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 13:51:59,814 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 13:52:19,383 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 13:52:23,076 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe47e02a980>)
+[gpuc04:0/16] 2024-01-17 13:52:23,076 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 13:52:23,080 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 13:57:46,328 (trainer:737) INFO: 42epoch:train:7501-7600batch: iter_time=3.091, forward_time=0.105, loss_ctc=46.844, loss_att=48.591, acc=0.743, loss=48.067, backward_time=0.096, grad_norm=48.985, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.105e-04, train_time=3.521
+[gpuc04:0/16] 2024-01-17 13:58:27,993 (trainer:737) INFO: 42epoch:train:7601-7700batch: iter_time=1.034e-04, forward_time=0.104, loss_ctc=43.951, loss_att=58.672, acc=0.724, loss=54.255, backward_time=0.097, grad_norm=47.031, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.105e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 13:59:09,943 (trainer:737) INFO: 42epoch:train:7701-7800batch: iter_time=1.191e-04, forward_time=0.104, loss_ctc=39.600, loss_att=48.420, acc=0.770, loss=45.774, backward_time=0.097, grad_norm=42.289, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.104e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 13:59:51,666 (trainer:737) INFO: 42epoch:train:7801-7900batch: iter_time=1.265e-04, forward_time=0.105, loss_ctc=42.656, loss_att=57.893, acc=0.737, loss=53.322, backward_time=0.097, grad_norm=52.246, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.104e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:00:33,552 (trainer:737) INFO: 42epoch:train:7901-8000batch: iter_time=1.185e-04, forward_time=0.104, loss_ctc=43.676, loss_att=60.536, acc=0.715, loss=55.478, backward_time=0.097, grad_norm=45.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.104e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 14:01:15,126 (trainer:737) INFO: 42epoch:train:8001-8100batch: iter_time=1.302e-04, forward_time=0.105, loss_ctc=39.702, loss_att=53.443, acc=0.745, loss=49.321, backward_time=0.096, grad_norm=43.960, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.104e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:01:56,825 (trainer:737) INFO: 42epoch:train:8101-8200batch: iter_time=1.350e-04, forward_time=0.105, loss_ctc=30.622, loss_att=31.039, acc=0.791, loss=30.914, backward_time=0.095, grad_norm=34.336, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.103e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:02:38,425 (trainer:737) INFO: 42epoch:train:8201-8300batch: iter_time=1.363e-04, forward_time=0.107, loss_ctc=45.506, loss_att=51.740, acc=0.753, loss=49.870, backward_time=0.096, grad_norm=44.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.103e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:03:18,947 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 14:03:20,203 (trainer:737) INFO: 42epoch:train:8301-8400batch: iter_time=1.426e-04, forward_time=0.106, loss_ctc=48.274, loss_att=58.468, acc=0.713, loss=55.410, backward_time=0.096, grad_norm=48.913, clip=100.000, loss_scale=2.045e+34, optim_step_time=0.039, optim0_lr0=3.103e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:04:01,616 (trainer:737) INFO: 42epoch:train:8401-8500batch: iter_time=1.569e-04, forward_time=0.106, loss_ctc=34.531, loss_att=42.201, acc=0.766, loss=39.900, backward_time=0.096, grad_norm=38.250, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.103e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 14:04:43,028 (trainer:737) INFO: 42epoch:train:8501-8600batch: iter_time=1.499e-04, forward_time=0.106, loss_ctc=36.145, loss_att=41.557, acc=0.764, loss=39.933, backward_time=0.096, grad_norm=39.741, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.102e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 14:05:24,713 (trainer:737) INFO: 42epoch:train:8601-8700batch: iter_time=1.567e-04, forward_time=0.107, loss_ctc=43.418, loss_att=55.245, acc=0.735, loss=51.697, backward_time=0.097, grad_norm=52.039, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.102e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:05:50,779 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 14:06:10,346 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 14:06:14,022 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe48502bcd0>)
+[gpuc04:0/16] 2024-01-17 14:06:14,022 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 14:06:14,025 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 14:10:42,738 (trainer:737) INFO: 42epoch:train:8701-8800batch: iter_time=2.518, forward_time=0.104, loss_ctc=46.953, loss_att=47.520, acc=0.738, loss=47.350, backward_time=0.096, grad_norm=47.605, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.102e-04, train_time=3.180
+[gpuc04:0/16] 2024-01-17 14:11:24,408 (trainer:737) INFO: 42epoch:train:8801-8900batch: iter_time=1.226e-04, forward_time=0.104, loss_ctc=46.326, loss_att=45.143, acc=0.749, loss=45.498, backward_time=0.096, grad_norm=46.922, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.102e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:12:06,588 (trainer:737) INFO: 42epoch:train:8901-9000batch: iter_time=1.259e-04, forward_time=0.106, loss_ctc=41.780, loss_att=56.270, acc=0.743, loss=51.923, backward_time=0.097, grad_norm=45.730, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.101e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 14:12:48,570 (trainer:737) INFO: 42epoch:train:9001-9100batch: iter_time=1.239e-04, forward_time=0.104, loss_ctc=38.185, loss_att=51.125, acc=0.744, loss=47.243, backward_time=0.096, grad_norm=43.344, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.101e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 14:13:30,500 (trainer:737) INFO: 42epoch:train:9101-9200batch: iter_time=1.591e-04, forward_time=0.106, loss_ctc=40.001, loss_att=53.421, acc=0.728, loss=49.395, backward_time=0.096, grad_norm=45.921, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.101e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 14:14:12,184 (trainer:737) INFO: 42epoch:train:9201-9300batch: iter_time=1.387e-04, forward_time=0.105, loss_ctc=41.967, loss_att=54.951, acc=0.715, loss=51.056, backward_time=0.095, grad_norm=43.268, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.101e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:14:54,413 (trainer:737) INFO: 42epoch:train:9301-9400batch: iter_time=1.181e-04, forward_time=0.105, loss_ctc=37.475, loss_att=45.827, acc=0.784, loss=43.321, backward_time=0.096, grad_norm=39.091, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.100e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 14:15:35,956 (trainer:737) INFO: 42epoch:train:9401-9500batch: iter_time=1.242e-04, forward_time=0.105, loss_ctc=37.563, loss_att=39.863, acc=0.762, loss=39.173, backward_time=0.095, grad_norm=41.658, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.100e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:16:18,300 (trainer:737) INFO: 42epoch:train:9501-9600batch: iter_time=1.225e-04, forward_time=0.105, loss_ctc=45.859, loss_att=50.857, acc=0.741, loss=49.358, backward_time=0.095, grad_norm=46.764, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.100e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 14:16:59,999 (trainer:737) INFO: 42epoch:train:9601-9700batch: iter_time=1.392e-04, forward_time=0.105, loss_ctc=41.306, loss_att=51.832, acc=0.728, loss=48.675, backward_time=0.095, grad_norm=45.496, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.100e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:17:41,652 (trainer:737) INFO: 42epoch:train:9701-9800batch: iter_time=1.317e-04, forward_time=0.105, loss_ctc=36.737, loss_att=46.064, acc=0.747, loss=43.266, backward_time=0.095, grad_norm=42.394, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.099e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:18:23,144 (trainer:737) INFO: 42epoch:train:9801-9900batch: iter_time=1.251e-04, forward_time=0.105, loss_ctc=38.406, loss_att=45.374, acc=0.757, loss=43.284, backward_time=0.095, grad_norm=47.967, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.099e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:19:04,869 (trainer:737) INFO: 42epoch:train:9901-10000batch: iter_time=1.165e-04, forward_time=0.105, loss_ctc=46.669, loss_att=48.892, acc=0.749, loss=48.225, backward_time=0.096, grad_norm=46.891, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.099e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:19:11,139 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 14:19:30,557 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 14:19:34,583 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe47e080fd0>)
+[gpuc04:0/16] 2024-01-17 14:19:34,583 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 14:19:34,586 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 14:24:24,926 (trainer:737) INFO: 42epoch:train:10001-10100batch: iter_time=2.758, forward_time=0.117, loss_ctc=46.645, loss_att=47.622, acc=0.744, loss=47.329, backward_time=0.098, grad_norm=50.272, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.099e-04, train_time=3.200
+[gpuc04:0/16] 2024-01-17 14:25:06,624 (trainer:737) INFO: 42epoch:train:10101-10200batch: iter_time=1.244e-04, forward_time=0.104, loss_ctc=43.699, loss_att=58.626, acc=0.726, loss=54.148, backward_time=0.096, grad_norm=49.360, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.098e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:25:48,429 (trainer:737) INFO: 42epoch:train:10201-10300batch: iter_time=1.295e-04, forward_time=0.106, loss_ctc=39.003, loss_att=48.024, acc=0.771, loss=45.318, backward_time=0.097, grad_norm=41.481, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.098e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:26:30,181 (trainer:737) INFO: 42epoch:train:10301-10400batch: iter_time=1.583e-04, forward_time=0.107, loss_ctc=42.148, loss_att=58.493, acc=0.737, loss=53.589, backward_time=0.097, grad_norm=51.698, clip=100.000, loss_scale=1.070e+34, optim_step_time=0.038, optim0_lr0=3.098e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:27:11,800 (trainer:737) INFO: 42epoch:train:10401-10500batch: iter_time=1.701e-04, forward_time=0.106, loss_ctc=43.289, loss_att=59.923, acc=0.715, loss=54.933, backward_time=0.096, grad_norm=45.279, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.098e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:27:53,348 (trainer:737) INFO: 42epoch:train:10501-10600batch: iter_time=1.629e-04, forward_time=0.105, loss_ctc=39.346, loss_att=52.749, acc=0.746, loss=48.728, backward_time=0.096, grad_norm=42.501, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.097e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:28:34,871 (trainer:737) INFO: 42epoch:train:10601-10700batch: iter_time=1.511e-04, forward_time=0.105, loss_ctc=30.311, loss_att=30.788, acc=0.793, loss=30.645, backward_time=0.095, grad_norm=33.341, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.097e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:29:16,668 (trainer:737) INFO: 42epoch:train:10701-10800batch: iter_time=1.459e-04, forward_time=0.106, loss_ctc=45.361, loss_att=52.009, acc=0.753, loss=50.014, backward_time=0.096, grad_norm=46.524, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.097e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:29:58,278 (trainer:737) INFO: 42epoch:train:10801-10900batch: iter_time=1.283e-04, forward_time=0.105, loss_ctc=47.641, loss_att=57.330, acc=0.715, loss=54.423, backward_time=0.096, grad_norm=49.650, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.097e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:30:40,240 (trainer:737) INFO: 42epoch:train:10901-11000batch: iter_time=1.406e-04, forward_time=0.105, loss_ctc=34.348, loss_att=42.440, acc=0.766, loss=40.013, backward_time=0.095, grad_norm=38.313, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.096e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 14:31:21,730 (trainer:737) INFO: 42epoch:train:11001-11100batch: iter_time=1.502e-04, forward_time=0.105, loss_ctc=35.845, loss_att=40.958, acc=0.765, loss=39.424, backward_time=0.095, grad_norm=38.450, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.096e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:32:03,756 (trainer:737) INFO: 42epoch:train:11101-11200batch: iter_time=1.514e-04, forward_time=0.106, loss_ctc=43.586, loss_att=55.508, acc=0.736, loss=51.932, backward_time=0.096, grad_norm=53.491, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.096e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 14:32:32,026 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 14:32:52,122 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 14:32:55,807 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe2cc2e2a10>)
+[gpuc04:0/16] 2024-01-17 14:32:55,808 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 14:32:55,811 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 14:37:50,297 (trainer:737) INFO: 42epoch:train:11201-11300batch: iter_time=2.959, forward_time=0.104, loss_ctc=47.262, loss_att=47.301, acc=0.739, loss=47.289, backward_time=0.096, grad_norm=50.463, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.096e-04, train_time=3.465
+[gpuc04:0/16] 2024-01-17 14:38:31,867 (trainer:737) INFO: 42epoch:train:11301-11400batch: iter_time=1.325e-04, forward_time=0.104, loss_ctc=45.973, loss_att=44.694, acc=0.750, loss=45.078, backward_time=0.095, grad_norm=46.029, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.095e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:39:13,733 (trainer:737) INFO: 42epoch:train:11401-11500batch: iter_time=1.224e-04, forward_time=0.106, loss_ctc=42.065, loss_att=56.822, acc=0.743, loss=52.395, backward_time=0.096, grad_norm=45.925, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.095e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:39:55,593 (trainer:737) INFO: 42epoch:train:11501-11600batch: iter_time=1.358e-04, forward_time=0.105, loss_ctc=37.425, loss_att=50.878, acc=0.747, loss=46.842, backward_time=0.096, grad_norm=43.782, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.095e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:40:37,459 (trainer:737) INFO: 42epoch:train:11601-11700batch: iter_time=1.379e-04, forward_time=0.109, loss_ctc=39.748, loss_att=53.205, acc=0.728, loss=49.168, backward_time=0.096, grad_norm=45.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.095e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:41:19,221 (trainer:737) INFO: 42epoch:train:11701-11800batch: iter_time=1.603e-04, forward_time=0.105, loss_ctc=42.179, loss_att=55.023, acc=0.715, loss=51.170, backward_time=0.095, grad_norm=46.316, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.094e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:42:00,796 (trainer:737) INFO: 42epoch:train:11801-11900batch: iter_time=1.324e-04, forward_time=0.106, loss_ctc=37.519, loss_att=46.126, acc=0.784, loss=43.544, backward_time=0.096, grad_norm=38.898, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.094e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:42:42,525 (trainer:737) INFO: 42epoch:train:11901-12000batch: iter_time=1.469e-04, forward_time=0.106, loss_ctc=37.649, loss_att=39.015, acc=0.763, loss=38.605, backward_time=0.095, grad_norm=39.953, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.094e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:43:24,124 (trainer:737) INFO: 42epoch:train:12001-12100batch: iter_time=1.359e-04, forward_time=0.107, loss_ctc=45.772, loss_att=50.662, acc=0.742, loss=49.195, backward_time=0.096, grad_norm=46.662, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.094e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:44:05,611 (trainer:737) INFO: 42epoch:train:12101-12200batch: iter_time=1.341e-04, forward_time=0.106, loss_ctc=40.920, loss_att=51.333, acc=0.729, loss=48.209, backward_time=0.095, grad_norm=44.483, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.093e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:44:47,533 (trainer:737) INFO: 42epoch:train:12201-12300batch: iter_time=1.329e-04, forward_time=0.106, loss_ctc=36.453, loss_att=45.636, acc=0.749, loss=42.881, backward_time=0.095, grad_norm=41.055, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.093e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 14:45:29,392 (trainer:737) INFO: 42epoch:train:12301-12400batch: iter_time=1.433e-04, forward_time=0.106, loss_ctc=38.157, loss_att=45.487, acc=0.757, loss=43.288, backward_time=0.095, grad_norm=47.564, clip=100.000, loss_scale=2.139e+34, optim_step_time=0.038, optim0_lr0=3.093e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:46:10,998 (trainer:737) INFO: 42epoch:train:12401-12500batch: iter_time=1.230e-04, forward_time=0.106, loss_ctc=47.750, loss_att=48.789, acc=0.750, loss=48.477, backward_time=0.096, grad_norm=50.084, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=3.093e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:46:15,825 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 14:46:35,253 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 14:46:38,767 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe13292b460>)
+[gpuc04:0/16] 2024-01-17 14:46:38,767 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 14:46:38,770 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 14:51:23,936 (trainer:737) INFO: 42epoch:train:12501-12600batch: iter_time=2.711, forward_time=0.104, loss_ctc=46.613, loss_att=45.003, acc=0.742, loss=45.486, backward_time=0.095, grad_norm=48.672, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.092e-04, train_time=3.129
+[gpuc04:0/16] 2024-01-17 14:51:57,884 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 14:52:05,414 (trainer:737) INFO: 42epoch:train:12601-12700batch: iter_time=1.814e-04, forward_time=0.104, loss_ctc=43.213, loss_att=54.603, acc=0.730, loss=51.186, backward_time=0.095, grad_norm=45.402, clip=100.000, loss_scale=3.776e+34, optim_step_time=0.039, optim0_lr0=3.092e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:52:47,020 (trainer:737) INFO: 42epoch:train:12701-12800batch: iter_time=1.923e-04, forward_time=0.105, loss_ctc=39.249, loss_att=47.880, acc=0.764, loss=45.291, backward_time=0.096, grad_norm=40.986, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.092e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:53:28,889 (trainer:737) INFO: 42epoch:train:12801-12900batch: iter_time=1.884e-04, forward_time=0.105, loss_ctc=42.127, loss_att=56.880, acc=0.728, loss=52.454, backward_time=0.096, grad_norm=47.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.092e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:54:10,460 (trainer:737) INFO: 42epoch:train:12901-13000batch: iter_time=1.746e-04, forward_time=0.106, loss_ctc=42.972, loss_att=57.476, acc=0.717, loss=53.125, backward_time=0.096, grad_norm=46.333, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.092e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:54:52,003 (trainer:737) INFO: 42epoch:train:13001-13100batch: iter_time=1.783e-04, forward_time=0.106, loss_ctc=39.497, loss_att=51.946, acc=0.739, loss=48.211, backward_time=0.095, grad_norm=44.207, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.091e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 14:55:33,667 (trainer:737) INFO: 42epoch:train:13101-13200batch: iter_time=1.617e-04, forward_time=0.105, loss_ctc=30.588, loss_att=30.899, acc=0.792, loss=30.806, backward_time=0.095, grad_norm=34.867, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.091e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:56:15,973 (trainer:737) INFO: 42epoch:train:13201-13300batch: iter_time=1.655e-04, forward_time=0.106, loss_ctc=45.200, loss_att=50.871, acc=0.749, loss=49.169, backward_time=0.096, grad_norm=46.192, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.091e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 14:56:57,635 (trainer:737) INFO: 42epoch:train:13301-13400batch: iter_time=1.643e-04, forward_time=0.106, loss_ctc=48.071, loss_att=56.931, acc=0.716, loss=54.273, backward_time=0.095, grad_norm=49.710, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.091e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 14:57:39,376 (trainer:737) INFO: 42epoch:train:13401-13500batch: iter_time=1.906e-04, forward_time=0.106, loss_ctc=34.445, loss_att=41.670, acc=0.757, loss=39.503, backward_time=0.095, grad_norm=38.348, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.090e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 14:58:21,299 (trainer:737) INFO: 42epoch:train:13501-13600batch: iter_time=2.026e-04, forward_time=0.106, loss_ctc=35.539, loss_att=41.985, acc=0.757, loss=40.051, backward_time=0.095, grad_norm=38.622, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.090e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 14:59:03,158 (trainer:737) INFO: 42epoch:train:13601-13700batch: iter_time=1.683e-04, forward_time=0.105, loss_ctc=43.242, loss_att=54.365, acc=0.733, loss=51.028, backward_time=0.095, grad_norm=52.960, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.090e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 14:59:30,175 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 14:59:50,122 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 14:59:53,888 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd63a4b7c0>)
+[gpuc04:0/16] 2024-01-17 14:59:53,888 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 14:59:53,892 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 15:04:19,040 (trainer:737) INFO: 42epoch:train:13701-13800batch: iter_time=2.741, forward_time=0.105, loss_ctc=46.778, loss_att=47.574, acc=0.743, loss=47.335, backward_time=0.096, grad_norm=48.332, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.090e-04, train_time=3.159
+[gpuc04:0/16] 2024-01-17 15:05:00,991 (trainer:737) INFO: 42epoch:train:13801-13900batch: iter_time=1.648e-04, forward_time=0.105, loss_ctc=45.573, loss_att=44.753, acc=0.755, loss=44.999, backward_time=0.096, grad_norm=43.538, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.089e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 15:05:42,926 (trainer:737) INFO: 42epoch:train:13901-14000batch: iter_time=1.993e-04, forward_time=0.106, loss_ctc=41.574, loss_att=58.635, acc=0.746, loss=53.517, backward_time=0.097, grad_norm=45.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.089e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 15:06:24,576 (trainer:737) INFO: 42epoch:train:14001-14100batch: iter_time=1.753e-04, forward_time=0.105, loss_ctc=37.628, loss_att=50.933, acc=0.753, loss=46.942, backward_time=0.097, grad_norm=42.359, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.089e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 15:07:06,279 (trainer:737) INFO: 42epoch:train:14101-14200batch: iter_time=1.534e-04, forward_time=0.106, loss_ctc=39.741, loss_att=55.494, acc=0.731, loss=50.768, backward_time=0.097, grad_norm=45.587, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.089e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 15:07:47,905 (trainer:737) INFO: 42epoch:train:14201-14300batch: iter_time=1.299e-04, forward_time=0.105, loss_ctc=41.835, loss_att=55.458, acc=0.725, loss=51.371, backward_time=0.096, grad_norm=43.350, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.088e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 15:08:29,613 (trainer:737) INFO: 42epoch:train:14301-14400batch: iter_time=1.586e-04, forward_time=0.104, loss_ctc=36.999, loss_att=46.579, acc=0.787, loss=43.705, backward_time=0.096, grad_norm=37.525, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.088e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 15:09:11,163 (trainer:737) INFO: 42epoch:train:14401-14500batch: iter_time=1.465e-04, forward_time=0.104, loss_ctc=37.262, loss_att=39.517, acc=0.764, loss=38.840, backward_time=0.096, grad_norm=39.570, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.088e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:09:52,795 (trainer:737) INFO: 42epoch:train:14501-14600batch: iter_time=1.555e-04, forward_time=0.104, loss_ctc=45.698, loss_att=51.941, acc=0.744, loss=50.068, backward_time=0.097, grad_norm=44.932, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.088e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 15:10:34,354 (trainer:737) INFO: 42epoch:train:14601-14700batch: iter_time=1.576e-04, forward_time=0.105, loss_ctc=40.781, loss_att=52.901, acc=0.733, loss=49.265, backward_time=0.096, grad_norm=44.310, clip=100.000, loss_scale=2.451e+34, optim_step_time=0.039, optim0_lr0=3.087e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:11:15,912 (trainer:737) INFO: 42epoch:train:14701-14800batch: iter_time=1.472e-04, forward_time=0.106, loss_ctc=36.421, loss_att=45.425, acc=0.757, loss=42.724, backward_time=0.096, grad_norm=41.478, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.087e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:11:53,677 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 15:11:57,475 (trainer:737) INFO: 42epoch:train:14801-14900batch: iter_time=1.559e-04, forward_time=0.106, loss_ctc=38.108, loss_att=47.408, acc=0.751, loss=44.618, backward_time=0.096, grad_norm=45.049, clip=100.000, loss_scale=3.965e+34, optim_step_time=0.039, optim0_lr0=3.087e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:12:39,092 (trainer:737) INFO: 42epoch:train:14901-15000batch: iter_time=1.246e-04, forward_time=0.105, loss_ctc=47.060, loss_att=49.438, acc=0.754, loss=48.725, backward_time=0.097, grad_norm=48.560, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.087e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 15:32:47,700 (trainer:343) INFO: 42epoch results: [train] iter_time=0.213, forward_time=0.106, loss_ctc=41.648, loss_att=49.894, acc=0.745, loss=47.421, backward_time=0.096, grad_norm=44.979, clip=100.000, loss_scale=1.942e+34, optim_step_time=0.039, optim0_lr0=3.105e-04, train_time=0.650, time=2 hours, 42 minutes and 37.75 seconds, total_count=630000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=49.929, cer_ctc=0.254, loss_att=53.622, acc=0.594, cer=0.418, wer=1.000, loss=52.514, time=19 minutes and 59.4 seconds, total_count=196182, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 15:32:53,298 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 15:32:53,623 (trainer:272) INFO: 43/45epoch started. Estimated time to finish: 9 hours, 3 minutes and 19.14 seconds
+[gpuc04:0/16] 2024-01-17 15:32:53,634 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 15:33:12,217 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 15:33:15,823 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe77fc7e350>)
+[gpuc04:0/16] 2024-01-17 15:33:15,823 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 15:33:15,826 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 15:38:00,902 (trainer:737) INFO: 43epoch:train:1-100batch: iter_time=2.645, forward_time=0.110, loss_ctc=54.977, loss_att=68.001, acc=0.713, loss=64.093, backward_time=0.099, grad_norm=62.799, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.086e-04, train_time=3.072
+[gpuc04:0/16] 2024-01-17 15:38:42,865 (trainer:737) INFO: 43epoch:train:101-200batch: iter_time=1.231e-04, forward_time=0.106, loss_ctc=48.459, loss_att=53.374, acc=0.730, loss=51.899, backward_time=0.097, grad_norm=50.581, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.086e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 15:39:24,736 (trainer:737) INFO: 43epoch:train:201-300batch: iter_time=1.258e-04, forward_time=0.106, loss_ctc=44.075, loss_att=48.077, acc=0.744, loss=46.877, backward_time=0.098, grad_norm=44.853, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.086e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 15:40:06,847 (trainer:737) INFO: 43epoch:train:301-400batch: iter_time=1.275e-04, forward_time=0.107, loss_ctc=56.482, loss_att=55.160, acc=0.724, loss=55.556, backward_time=0.098, grad_norm=59.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.086e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 15:40:50,129 (trainer:737) INFO: 43epoch:train:401-500batch: iter_time=1.320e-04, forward_time=0.106, loss_ctc=52.209, loss_att=52.318, acc=0.743, loss=52.285, backward_time=0.098, grad_norm=50.653, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.085e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-17 15:41:34,738 (trainer:737) INFO: 43epoch:train:501-600batch: iter_time=1.299e-04, forward_time=0.106, loss_ctc=46.846, loss_att=56.421, acc=0.750, loss=53.548, backward_time=0.099, grad_norm=53.527, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.085e-04, train_time=0.446
+[gpuc04:0/16] 2024-01-17 15:42:25,668 (trainer:737) INFO: 43epoch:train:601-700batch: iter_time=1.220e-04, forward_time=0.105, loss_ctc=38.185, loss_att=40.897, acc=0.781, loss=40.083, backward_time=0.098, grad_norm=47.825, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.085e-04, train_time=0.509
+[gpuc04:0/16] 2024-01-17 15:43:08,243 (trainer:737) INFO: 43epoch:train:701-800batch: iter_time=1.295e-04, forward_time=0.105, loss_ctc=38.357, loss_att=43.604, acc=0.748, loss=42.030, backward_time=0.097, grad_norm=43.154, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.085e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 15:43:50,700 (trainer:737) INFO: 43epoch:train:801-900batch: iter_time=1.375e-04, forward_time=0.107, loss_ctc=54.054, loss_att=57.180, acc=0.757, loss=56.242, backward_time=0.099, grad_norm=52.802, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.084e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 15:44:32,581 (trainer:737) INFO: 43epoch:train:901-1000batch: iter_time=1.241e-04, forward_time=0.106, loss_ctc=40.270, loss_att=47.199, acc=0.745, loss=45.120, backward_time=0.098, grad_norm=46.887, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.084e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 15:45:16,992 (trainer:737) INFO: 43epoch:train:1001-1100batch: iter_time=1.290e-04, forward_time=0.106, loss_ctc=45.480, loss_att=56.276, acc=0.736, loss=53.037, backward_time=0.098, grad_norm=52.174, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.084e-04, train_time=0.444
+[gpuc04:0/16] 2024-01-17 15:46:03,137 (trainer:737) INFO: 43epoch:train:1101-1200batch: iter_time=1.249e-04, forward_time=0.107, loss_ctc=40.369, loss_att=41.960, acc=0.757, loss=41.482, backward_time=0.098, grad_norm=46.272, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.084e-04, train_time=0.461
+[gpuc04:0/16] 2024-01-17 15:46:36,344 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 15:46:55,766 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 15:46:59,584 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd66ed6740>)
+[gpuc04:0/16] 2024-01-17 15:46:59,584 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 15:46:59,587 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 15:52:28,903 (trainer:737) INFO: 43epoch:train:1201-1300batch: iter_time=3.037, forward_time=0.177, loss_ctc=43.320, loss_att=56.270, acc=0.719, loss=52.385, backward_time=0.118, grad_norm=51.145, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.083e-04, train_time=3.858
+[gpuc04:0/16] 2024-01-17 15:53:11,002 (trainer:737) INFO: 43epoch:train:1301-1400batch: iter_time=1.449e-04, forward_time=0.106, loss_ctc=52.410, loss_att=64.474, acc=0.701, loss=60.854, backward_time=0.096, grad_norm=61.530, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.083e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 15:53:53,042 (trainer:737) INFO: 43epoch:train:1401-1500batch: iter_time=1.716e-04, forward_time=0.106, loss_ctc=47.459, loss_att=51.335, acc=0.741, loss=50.172, backward_time=0.096, grad_norm=48.465, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.083e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 15:54:34,748 (trainer:737) INFO: 43epoch:train:1501-1600batch: iter_time=2.029e-04, forward_time=0.105, loss_ctc=42.058, loss_att=42.015, acc=0.737, loss=42.028, backward_time=0.096, grad_norm=43.989, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.083e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 15:55:16,539 (trainer:737) INFO: 43epoch:train:1601-1700batch: iter_time=1.614e-04, forward_time=0.106, loss_ctc=62.886, loss_att=58.243, acc=0.705, loss=59.636, backward_time=0.097, grad_norm=59.521, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.082e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 15:55:58,347 (trainer:737) INFO: 43epoch:train:1701-1800batch: iter_time=1.954e-04, forward_time=0.106, loss_ctc=46.353, loss_att=57.723, acc=0.743, loss=54.312, backward_time=0.097, grad_norm=50.232, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.082e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 15:56:40,286 (trainer:737) INFO: 43epoch:train:1801-1900batch: iter_time=1.556e-04, forward_time=0.109, loss_ctc=38.533, loss_att=44.719, acc=0.759, loss=42.863, backward_time=0.097, grad_norm=41.772, clip=100.000, loss_scale=2.264e+34, optim_step_time=0.039, optim0_lr0=3.082e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 15:57:21,814 (trainer:737) INFO: 43epoch:train:1901-2000batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=38.284, loss_att=40.430, acc=0.761, loss=39.786, backward_time=0.097, grad_norm=44.515, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.082e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:58:03,371 (trainer:737) INFO: 43epoch:train:2001-2100batch: iter_time=1.609e-04, forward_time=0.105, loss_ctc=44.007, loss_att=45.403, acc=0.755, loss=44.984, backward_time=0.097, grad_norm=46.849, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.081e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 15:58:45,005 (trainer:737) INFO: 43epoch:train:2101-2200batch: iter_time=1.715e-04, forward_time=0.107, loss_ctc=48.983, loss_att=57.006, acc=0.732, loss=54.599, backward_time=0.097, grad_norm=51.304, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.081e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 15:59:03,686 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 15:59:26,563 (trainer:737) INFO: 43epoch:train:2201-2300batch: iter_time=1.749e-04, forward_time=0.106, loss_ctc=37.875, loss_att=48.530, acc=0.746, loss=45.333, backward_time=0.097, grad_norm=44.052, clip=100.000, loss_scale=3.000e+34, optim_step_time=0.039, optim0_lr0=3.081e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:00:08,299 (trainer:737) INFO: 43epoch:train:2301-2400batch: iter_time=1.719e-04, forward_time=0.106, loss_ctc=44.175, loss_att=48.882, acc=0.735, loss=47.470, backward_time=0.096, grad_norm=50.431, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.081e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:00:49,740 (trainer:737) INFO: 43epoch:train:2401-2500batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=37.900, loss_att=40.101, acc=0.763, loss=39.441, backward_time=0.097, grad_norm=41.327, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.080e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 16:00:55,531 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 16:01:14,941 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 16:01:18,604 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4106bef80>)
+[gpuc04:0/16] 2024-01-17 16:01:18,604 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 16:01:18,607 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 16:06:14,887 (trainer:737) INFO: 43epoch:train:2501-2600batch: iter_time=2.797, forward_time=0.106, loss_ctc=53.411, loss_att=68.307, acc=0.696, loss=63.838, backward_time=0.096, grad_norm=60.674, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.080e-04, train_time=3.251
+[gpuc04:0/16] 2024-01-17 16:06:56,251 (trainer:737) INFO: 43epoch:train:2601-2700batch: iter_time=1.373e-04, forward_time=0.105, loss_ctc=46.362, loss_att=51.539, acc=0.728, loss=49.986, backward_time=0.095, grad_norm=47.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.080e-04, train_time=0.413
+[gpuc04:0/16] 2024-01-17 16:07:37,898 (trainer:737) INFO: 43epoch:train:2701-2800batch: iter_time=1.553e-04, forward_time=0.106, loss_ctc=42.878, loss_att=45.891, acc=0.747, loss=44.987, backward_time=0.095, grad_norm=42.649, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.080e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:08:19,482 (trainer:737) INFO: 43epoch:train:2801-2900batch: iter_time=1.522e-04, forward_time=0.107, loss_ctc=54.903, loss_att=53.608, acc=0.716, loss=53.997, backward_time=0.096, grad_norm=57.505, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.080e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:09:01,194 (trainer:737) INFO: 43epoch:train:2901-3000batch: iter_time=1.521e-04, forward_time=0.106, loss_ctc=48.407, loss_att=49.441, acc=0.742, loss=49.131, backward_time=0.096, grad_norm=46.342, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.079e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:09:43,268 (trainer:737) INFO: 43epoch:train:3001-3100batch: iter_time=1.386e-04, forward_time=0.107, loss_ctc=44.922, loss_att=53.904, acc=0.740, loss=51.209, backward_time=0.096, grad_norm=54.065, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.079e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 16:10:24,894 (trainer:737) INFO: 43epoch:train:3101-3200batch: iter_time=1.388e-04, forward_time=0.106, loss_ctc=37.496, loss_att=40.725, acc=0.772, loss=39.756, backward_time=0.096, grad_norm=41.887, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.079e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:11:06,688 (trainer:737) INFO: 43epoch:train:3201-3300batch: iter_time=1.540e-04, forward_time=0.105, loss_ctc=37.303, loss_att=43.466, acc=0.743, loss=41.617, backward_time=0.095, grad_norm=42.985, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.079e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 16:11:48,643 (trainer:737) INFO: 43epoch:train:3301-3400batch: iter_time=1.470e-04, forward_time=0.106, loss_ctc=53.019, loss_att=54.961, acc=0.754, loss=54.379, backward_time=0.097, grad_norm=50.532, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.078e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 16:12:30,063 (trainer:737) INFO: 43epoch:train:3401-3500batch: iter_time=1.454e-04, forward_time=0.106, loss_ctc=38.646, loss_att=45.541, acc=0.748, loss=43.473, backward_time=0.096, grad_norm=45.580, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.078e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 16:13:11,583 (trainer:737) INFO: 43epoch:train:3501-3600batch: iter_time=1.835e-04, forward_time=0.107, loss_ctc=44.230, loss_att=53.248, acc=0.741, loss=50.542, backward_time=0.096, grad_norm=50.328, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.078e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:13:53,061 (trainer:737) INFO: 43epoch:train:3601-3700batch: iter_time=1.630e-04, forward_time=0.108, loss_ctc=38.445, loss_att=40.514, acc=0.755, loss=39.893, backward_time=0.095, grad_norm=43.457, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.078e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:14:20,086 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 16:14:39,642 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 16:14:43,482 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4481b8c40>)
+[gpuc04:0/16] 2024-01-17 16:14:43,482 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 16:14:43,486 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 16:20:36,378 (trainer:737) INFO: 43epoch:train:3701-3800batch: iter_time=3.516, forward_time=0.175, loss_ctc=42.125, loss_att=53.495, acc=0.723, loss=50.084, backward_time=0.114, grad_norm=48.912, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.077e-04, train_time=4.033
+[gpuc04:0/16] 2024-01-17 16:21:17,930 (trainer:737) INFO: 43epoch:train:3801-3900batch: iter_time=1.708e-04, forward_time=0.106, loss_ctc=50.930, loss_att=61.661, acc=0.707, loss=58.442, backward_time=0.096, grad_norm=56.110, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.077e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:21:59,526 (trainer:737) INFO: 43epoch:train:3901-4000batch: iter_time=1.573e-04, forward_time=0.106, loss_ctc=46.373, loss_att=48.917, acc=0.747, loss=48.154, backward_time=0.097, grad_norm=46.014, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.077e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:22:41,368 (trainer:737) INFO: 43epoch:train:4001-4100batch: iter_time=2.006e-04, forward_time=0.106, loss_ctc=41.894, loss_att=41.005, acc=0.740, loss=41.272, backward_time=0.096, grad_norm=44.443, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.077e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 16:23:23,576 (trainer:737) INFO: 43epoch:train:4101-4200batch: iter_time=1.728e-04, forward_time=0.107, loss_ctc=60.505, loss_att=56.405, acc=0.712, loss=57.635, backward_time=0.097, grad_norm=58.794, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.076e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 16:24:05,317 (trainer:737) INFO: 43epoch:train:4201-4300batch: iter_time=1.869e-04, forward_time=0.107, loss_ctc=45.690, loss_att=56.439, acc=0.745, loss=53.215, backward_time=0.097, grad_norm=52.819, clip=100.000, loss_scale=3.219e+34, optim_step_time=0.039, optim0_lr0=3.076e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:24:46,848 (trainer:737) INFO: 43epoch:train:4301-4400batch: iter_time=1.979e-04, forward_time=0.106, loss_ctc=38.249, loss_att=44.448, acc=0.761, loss=42.588, backward_time=0.097, grad_norm=41.654, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.076e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:25:28,748 (trainer:737) INFO: 43epoch:train:4401-4500batch: iter_time=1.738e-04, forward_time=0.106, loss_ctc=37.072, loss_att=39.296, acc=0.765, loss=38.629, backward_time=0.096, grad_norm=42.212, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.076e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 16:26:10,391 (trainer:737) INFO: 43epoch:train:4501-4600batch: iter_time=1.897e-04, forward_time=0.106, loss_ctc=44.375, loss_att=44.795, acc=0.760, loss=44.669, backward_time=0.098, grad_norm=46.819, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=3.075e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:26:52,423 (trainer:737) INFO: 43epoch:train:4601-4700batch: iter_time=1.703e-04, forward_time=0.105, loss_ctc=47.943, loss_att=56.020, acc=0.733, loss=53.597, backward_time=0.099, grad_norm=49.844, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=3.075e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 16:27:18,732 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 16:27:34,600 (trainer:737) INFO: 43epoch:train:4701-4800batch: iter_time=1.937e-04, forward_time=0.106, loss_ctc=37.300, loss_att=47.832, acc=0.748, loss=44.672, backward_time=0.098, grad_norm=45.358, clip=100.000, loss_scale=3.378e+34, optim_step_time=0.041, optim0_lr0=3.075e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 16:28:16,385 (trainer:737) INFO: 43epoch:train:4801-4900batch: iter_time=1.923e-04, forward_time=0.106, loss_ctc=43.325, loss_att=47.258, acc=0.740, loss=46.078, backward_time=0.097, grad_norm=46.755, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.075e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 16:28:58,161 (trainer:737) INFO: 43epoch:train:4901-5000batch: iter_time=1.703e-04, forward_time=0.106, loss_ctc=38.084, loss_att=39.214, acc=0.767, loss=38.875, backward_time=0.096, grad_norm=42.465, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.074e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 16:29:10,052 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 16:29:30,436 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 16:29:34,200 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd66df60b0>)
+[gpuc04:0/16] 2024-01-17 16:29:34,201 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 16:29:34,204 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 16:34:49,265 (trainer:737) INFO: 43epoch:train:5001-5100batch: iter_time=3.084, forward_time=0.111, loss_ctc=52.449, loss_att=70.775, acc=0.707, loss=65.277, backward_time=0.099, grad_norm=61.190, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.074e-04, train_time=3.511
+[gpuc04:0/16] 2024-01-17 16:35:30,994 (trainer:737) INFO: 43epoch:train:5101-5200batch: iter_time=2.231e-04, forward_time=0.107, loss_ctc=45.509, loss_att=52.789, acc=0.735, loss=50.605, backward_time=0.098, grad_norm=48.246, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.074e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:36:12,651 (trainer:737) INFO: 43epoch:train:5201-5300batch: iter_time=2.228e-04, forward_time=0.106, loss_ctc=42.844, loss_att=47.943, acc=0.748, loss=46.413, backward_time=0.097, grad_norm=43.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.074e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:36:54,367 (trainer:737) INFO: 43epoch:train:5301-5400batch: iter_time=2.300e-04, forward_time=0.107, loss_ctc=54.762, loss_att=54.803, acc=0.728, loss=54.791, backward_time=0.097, grad_norm=57.885, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.073e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:37:36,000 (trainer:737) INFO: 43epoch:train:5401-5500batch: iter_time=1.877e-04, forward_time=0.107, loss_ctc=47.864, loss_att=51.454, acc=0.746, loss=50.377, backward_time=0.097, grad_norm=51.171, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.073e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:38:18,181 (trainer:737) INFO: 43epoch:train:5501-5600batch: iter_time=2.074e-04, forward_time=0.106, loss_ctc=44.419, loss_att=56.251, acc=0.753, loss=52.701, backward_time=0.097, grad_norm=51.336, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.073e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 16:38:59,753 (trainer:737) INFO: 43epoch:train:5601-5700batch: iter_time=2.010e-04, forward_time=0.106, loss_ctc=37.128, loss_att=40.125, acc=0.785, loss=39.226, backward_time=0.097, grad_norm=41.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.073e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:39:41,271 (trainer:737) INFO: 43epoch:train:5701-5800batch: iter_time=1.833e-04, forward_time=0.105, loss_ctc=37.395, loss_att=43.436, acc=0.750, loss=41.624, backward_time=0.096, grad_norm=42.720, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.072e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:40:23,741 (trainer:737) INFO: 43epoch:train:5801-5900batch: iter_time=1.684e-04, forward_time=0.108, loss_ctc=52.612, loss_att=57.099, acc=0.759, loss=55.753, backward_time=0.098, grad_norm=51.626, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.072e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 16:41:05,878 (trainer:737) INFO: 43epoch:train:5901-6000batch: iter_time=1.821e-04, forward_time=0.107, loss_ctc=37.985, loss_att=46.743, acc=0.748, loss=44.116, backward_time=0.097, grad_norm=43.783, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.072e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 16:41:47,548 (trainer:737) INFO: 43epoch:train:6001-6100batch: iter_time=1.895e-04, forward_time=0.107, loss_ctc=43.878, loss_att=55.796, acc=0.741, loss=52.221, backward_time=0.097, grad_norm=48.903, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.072e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:42:29,060 (trainer:737) INFO: 43epoch:train:6101-6200batch: iter_time=1.927e-04, forward_time=0.106, loss_ctc=38.350, loss_att=41.723, acc=0.759, loss=40.711, backward_time=0.096, grad_norm=43.884, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.072e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:43:00,499 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 16:43:20,252 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 16:43:24,219 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4481c1ea0>)
+[gpuc04:0/16] 2024-01-17 16:43:24,219 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 16:43:24,223 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 16:47:56,084 (trainer:737) INFO: 43epoch:train:6201-6300batch: iter_time=2.849, forward_time=0.106, loss_ctc=41.814, loss_att=53.581, acc=0.732, loss=50.051, backward_time=0.097, grad_norm=48.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.071e-04, train_time=3.270
+[gpuc04:0/16] 2024-01-17 16:48:37,799 (trainer:737) INFO: 43epoch:train:6301-6400batch: iter_time=1.645e-04, forward_time=0.106, loss_ctc=49.998, loss_att=62.411, acc=0.721, loss=58.687, backward_time=0.096, grad_norm=56.159, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.071e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:49:19,414 (trainer:737) INFO: 43epoch:train:6401-6500batch: iter_time=1.420e-04, forward_time=0.106, loss_ctc=46.451, loss_att=49.568, acc=0.755, loss=48.633, backward_time=0.096, grad_norm=47.430, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.071e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:50:00,949 (trainer:737) INFO: 43epoch:train:6501-6600batch: iter_time=1.673e-04, forward_time=0.106, loss_ctc=41.967, loss_att=42.069, acc=0.742, loss=42.038, backward_time=0.095, grad_norm=44.821, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.071e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 16:50:42,706 (trainer:737) INFO: 43epoch:train:6601-6700batch: iter_time=1.610e-04, forward_time=0.106, loss_ctc=60.589, loss_att=60.014, acc=0.717, loss=60.187, backward_time=0.096, grad_norm=58.488, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.070e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:51:24,426 (trainer:737) INFO: 43epoch:train:6701-6800batch: iter_time=1.629e-04, forward_time=0.106, loss_ctc=45.016, loss_att=57.309, acc=0.752, loss=53.621, backward_time=0.096, grad_norm=50.427, clip=100.000, loss_scale=2.845e+34, optim_step_time=0.038, optim0_lr0=3.070e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:51:46,667 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 16:52:06,200 (trainer:737) INFO: 43epoch:train:6801-6900batch: iter_time=1.590e-04, forward_time=0.106, loss_ctc=37.531, loss_att=44.193, acc=0.779, loss=42.195, backward_time=0.096, grad_norm=40.094, clip=100.000, loss_scale=3.168e+34, optim_step_time=0.038, optim0_lr0=3.070e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 16:52:47,825 (trainer:737) INFO: 43epoch:train:6901-7000batch: iter_time=1.611e-04, forward_time=0.106, loss_ctc=37.496, loss_att=39.546, acc=0.774, loss=38.931, backward_time=0.096, grad_norm=43.152, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.070e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:53:29,470 (trainer:737) INFO: 43epoch:train:7001-7100batch: iter_time=1.488e-04, forward_time=0.106, loss_ctc=43.495, loss_att=45.862, acc=0.760, loss=45.152, backward_time=0.096, grad_norm=44.409, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.069e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 16:54:11,226 (trainer:737) INFO: 43epoch:train:7101-7200batch: iter_time=1.468e-04, forward_time=0.106, loss_ctc=47.223, loss_att=55.910, acc=0.743, loss=53.304, backward_time=0.096, grad_norm=50.808, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.069e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:54:52,997 (trainer:737) INFO: 43epoch:train:7201-7300batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=36.903, loss_att=49.075, acc=0.750, loss=45.423, backward_time=0.096, grad_norm=44.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.069e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 16:55:34,974 (trainer:737) INFO: 43epoch:train:7301-7400batch: iter_time=1.357e-04, forward_time=0.107, loss_ctc=43.228, loss_att=49.100, acc=0.739, loss=47.338, backward_time=0.096, grad_norm=47.465, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.069e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 16:56:17,132 (trainer:737) INFO: 43epoch:train:7401-7500batch: iter_time=1.503e-04, forward_time=0.104, loss_ctc=37.172, loss_att=40.807, acc=0.770, loss=39.716, backward_time=0.095, grad_norm=41.328, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.068e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 16:56:22,213 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 16:56:41,650 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 16:56:45,343 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe11a62c640>)
+[gpuc04:0/16] 2024-01-17 16:56:45,344 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 16:56:45,347 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 17:01:40,855 (trainer:737) INFO: 43epoch:train:7501-7600batch: iter_time=2.761, forward_time=0.108, loss_ctc=52.322, loss_att=71.770, acc=0.689, loss=65.935, backward_time=0.097, grad_norm=59.120, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.068e-04, train_time=3.237
+[gpuc04:0/16] 2024-01-17 17:02:22,674 (trainer:737) INFO: 43epoch:train:7601-7700batch: iter_time=1.739e-04, forward_time=0.107, loss_ctc=44.873, loss_att=52.017, acc=0.728, loss=49.874, backward_time=0.096, grad_norm=47.784, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.068e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 17:03:05,008 (trainer:737) INFO: 43epoch:train:7701-7800batch: iter_time=1.639e-04, forward_time=0.107, loss_ctc=42.710, loss_att=46.541, acc=0.748, loss=45.392, backward_time=0.096, grad_norm=42.106, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.068e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 17:03:46,719 (trainer:737) INFO: 43epoch:train:7801-7900batch: iter_time=1.812e-04, forward_time=0.108, loss_ctc=53.651, loss_att=53.738, acc=0.719, loss=53.712, backward_time=0.096, grad_norm=56.627, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.067e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:04:28,326 (trainer:737) INFO: 43epoch:train:7901-8000batch: iter_time=1.998e-04, forward_time=0.107, loss_ctc=47.307, loss_att=49.050, acc=0.744, loss=48.528, backward_time=0.096, grad_norm=46.178, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.067e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:05:12,385 (trainer:737) INFO: 43epoch:train:8001-8100batch: iter_time=1.801e-04, forward_time=0.107, loss_ctc=44.162, loss_att=54.287, acc=0.740, loss=51.250, backward_time=0.097, grad_norm=51.649, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.067e-04, train_time=0.440
+[gpuc04:0/16] 2024-01-17 17:05:55,268 (trainer:737) INFO: 43epoch:train:8101-8200batch: iter_time=1.532e-04, forward_time=0.108, loss_ctc=36.623, loss_att=40.607, acc=0.772, loss=39.412, backward_time=0.097, grad_norm=41.618, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.067e-04, train_time=0.429
+[gpuc04:0/16] 2024-01-17 17:06:38,583 (trainer:737) INFO: 43epoch:train:8201-8300batch: iter_time=3.930e-04, forward_time=0.105, loss_ctc=36.874, loss_att=42.746, acc=0.747, loss=40.985, backward_time=0.099, grad_norm=41.839, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.043, optim0_lr0=3.066e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-17 17:07:03,438 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 17:07:20,553 (trainer:737) INFO: 43epoch:train:8301-8400batch: iter_time=1.344e-04, forward_time=0.107, loss_ctc=51.791, loss_att=54.600, acc=0.754, loss=53.758, backward_time=0.098, grad_norm=52.057, clip=100.000, loss_scale=1.647e+34, optim_step_time=0.039, optim0_lr0=3.066e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 17:08:02,059 (trainer:737) INFO: 43epoch:train:8401-8500batch: iter_time=1.288e-04, forward_time=0.104, loss_ctc=37.266, loss_att=46.182, acc=0.746, loss=43.507, backward_time=0.095, grad_norm=46.155, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.066e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:08:43,759 (trainer:737) INFO: 43epoch:train:8501-8600batch: iter_time=1.445e-04, forward_time=0.106, loss_ctc=43.602, loss_att=53.291, acc=0.740, loss=50.384, backward_time=0.096, grad_norm=49.099, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.066e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:09:25,189 (trainer:737) INFO: 43epoch:train:8601-8700batch: iter_time=1.500e-04, forward_time=0.105, loss_ctc=37.120, loss_att=39.847, acc=0.757, loss=39.029, backward_time=0.095, grad_norm=41.677, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.066e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 17:10:18,518 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 17:10:38,661 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 17:10:43,467 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd9cd4fa30>)
+[gpuc04:0/16] 2024-01-17 17:10:43,467 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 17:10:43,470 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 17:17:31,968 (trainer:737) INFO: 43epoch:train:8701-8800batch: iter_time=4.436, forward_time=0.106, loss_ctc=41.595, loss_att=52.637, acc=0.727, loss=49.324, backward_time=0.097, grad_norm=46.908, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.065e-04, train_time=4.868
+[gpuc04:0/16] 2024-01-17 17:18:13,684 (trainer:737) INFO: 43epoch:train:8801-8900batch: iter_time=1.305e-04, forward_time=0.106, loss_ctc=49.168, loss_att=61.415, acc=0.710, loss=57.741, backward_time=0.097, grad_norm=53.690, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.065e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:18:55,619 (trainer:737) INFO: 43epoch:train:8901-9000batch: iter_time=1.439e-04, forward_time=0.106, loss_ctc=45.819, loss_att=48.894, acc=0.746, loss=47.972, backward_time=0.098, grad_norm=46.883, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.065e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 17:19:37,632 (trainer:737) INFO: 43epoch:train:9001-9100batch: iter_time=1.275e-04, forward_time=0.106, loss_ctc=41.948, loss_att=41.435, acc=0.741, loss=41.589, backward_time=0.097, grad_norm=45.761, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.065e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 17:20:19,524 (trainer:737) INFO: 43epoch:train:9101-9200batch: iter_time=1.275e-04, forward_time=0.106, loss_ctc=59.008, loss_att=56.160, acc=0.712, loss=57.015, backward_time=0.097, grad_norm=54.316, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.064e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 17:21:01,149 (trainer:737) INFO: 43epoch:train:9201-9300batch: iter_time=1.246e-04, forward_time=0.106, loss_ctc=45.108, loss_att=56.658, acc=0.747, loss=53.193, backward_time=0.098, grad_norm=51.571, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.064e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:21:42,910 (trainer:737) INFO: 43epoch:train:9301-9400batch: iter_time=1.142e-04, forward_time=0.105, loss_ctc=37.756, loss_att=44.473, acc=0.761, loss=42.458, backward_time=0.097, grad_norm=41.014, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.064e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:22:24,344 (trainer:737) INFO: 43epoch:train:9401-9500batch: iter_time=1.196e-04, forward_time=0.106, loss_ctc=36.875, loss_att=39.454, acc=0.765, loss=38.680, backward_time=0.096, grad_norm=41.374, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.064e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 17:23:05,874 (trainer:737) INFO: 43epoch:train:9501-9600batch: iter_time=1.509e-04, forward_time=0.106, loss_ctc=43.641, loss_att=44.724, acc=0.759, loss=44.399, backward_time=0.096, grad_norm=44.329, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.063e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:23:47,548 (trainer:737) INFO: 43epoch:train:9601-9700batch: iter_time=1.446e-04, forward_time=0.106, loss_ctc=47.029, loss_att=55.974, acc=0.735, loss=53.291, backward_time=0.096, grad_norm=50.449, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.063e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:24:29,642 (trainer:737) INFO: 43epoch:train:9701-9800batch: iter_time=1.511e-04, forward_time=0.105, loss_ctc=36.952, loss_att=47.463, acc=0.750, loss=44.310, backward_time=0.096, grad_norm=44.117, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.063e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 17:25:11,399 (trainer:737) INFO: 43epoch:train:9801-9900batch: iter_time=1.592e-04, forward_time=0.106, loss_ctc=42.294, loss_att=47.332, acc=0.741, loss=45.820, backward_time=0.096, grad_norm=47.013, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.063e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:25:53,076 (trainer:737) INFO: 43epoch:train:9901-10000batch: iter_time=1.614e-04, forward_time=0.104, loss_ctc=36.871, loss_att=38.883, acc=0.768, loss=38.279, backward_time=0.096, grad_norm=40.479, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.062e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:25:58,859 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 17:26:18,196 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 17:26:21,860 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd650e45b0>)
+[gpuc04:0/16] 2024-01-17 17:26:21,860 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 17:26:21,863 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 17:31:27,136 (trainer:737) INFO: 43epoch:train:10001-10100batch: iter_time=2.899, forward_time=0.126, loss_ctc=51.120, loss_att=67.414, acc=0.699, loss=62.526, backward_time=0.098, grad_norm=57.889, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.041, optim0_lr0=3.062e-04, train_time=3.340
+[gpuc04:0/16] 2024-01-17 17:32:08,619 (trainer:737) INFO: 43epoch:train:10101-10200batch: iter_time=1.383e-04, forward_time=0.104, loss_ctc=45.068, loss_att=50.316, acc=0.732, loss=48.741, backward_time=0.096, grad_norm=46.469, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.062e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:32:52,006 (trainer:737) INFO: 43epoch:train:10201-10300batch: iter_time=1.561e-04, forward_time=0.105, loss_ctc=42.687, loss_att=45.637, acc=0.750, loss=44.752, backward_time=0.096, grad_norm=42.885, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.062e-04, train_time=0.434
+[gpuc04:0/16] 2024-01-17 17:33:33,686 (trainer:737) INFO: 43epoch:train:10301-10400batch: iter_time=1.820e-04, forward_time=0.105, loss_ctc=53.922, loss_att=53.184, acc=0.719, loss=53.406, backward_time=0.098, grad_norm=56.084, clip=100.000, loss_scale=1.464e+34, optim_step_time=0.039, optim0_lr0=3.061e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:34:15,338 (trainer:737) INFO: 43epoch:train:10401-10500batch: iter_time=1.672e-04, forward_time=0.105, loss_ctc=46.839, loss_att=48.970, acc=0.743, loss=48.331, backward_time=0.097, grad_norm=47.679, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.061e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:34:56,999 (trainer:737) INFO: 43epoch:train:10501-10600batch: iter_time=1.862e-04, forward_time=0.106, loss_ctc=43.921, loss_att=52.655, acc=0.743, loss=50.035, backward_time=0.097, grad_norm=51.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.061e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:35:38,556 (trainer:737) INFO: 43epoch:train:10601-10700batch: iter_time=2.307e-04, forward_time=0.106, loss_ctc=36.773, loss_att=40.141, acc=0.775, loss=39.131, backward_time=0.097, grad_norm=40.761, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.061e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:36:20,284 (trainer:737) INFO: 43epoch:train:10701-10800batch: iter_time=2.432e-04, forward_time=0.106, loss_ctc=37.161, loss_att=43.146, acc=0.745, loss=41.351, backward_time=0.098, grad_norm=41.974, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.060e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:37:02,640 (trainer:737) INFO: 43epoch:train:10801-10900batch: iter_time=2.231e-04, forward_time=0.107, loss_ctc=51.536, loss_att=54.168, acc=0.757, loss=53.379, backward_time=0.098, grad_norm=48.676, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.060e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 17:37:44,314 (trainer:737) INFO: 43epoch:train:10901-11000batch: iter_time=2.443e-04, forward_time=0.106, loss_ctc=37.735, loss_att=45.276, acc=0.750, loss=43.014, backward_time=0.098, grad_norm=44.939, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.060e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:38:26,385 (trainer:737) INFO: 43epoch:train:11001-11100batch: iter_time=2.214e-04, forward_time=0.107, loss_ctc=43.546, loss_att=53.684, acc=0.742, loss=50.642, backward_time=0.098, grad_norm=50.578, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.060e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 17:39:08,560 (trainer:737) INFO: 43epoch:train:11101-11200batch: iter_time=2.219e-04, forward_time=0.106, loss_ctc=37.214, loss_att=39.763, acc=0.758, loss=38.998, backward_time=0.097, grad_norm=45.000, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.060e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 17:39:35,904 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 17:39:55,546 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 17:39:59,729 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd754db730>)
+[gpuc04:0/16] 2024-01-17 17:39:59,729 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 17:39:59,732 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 17:45:30,151 (trainer:737) INFO: 43epoch:train:11201-11300batch: iter_time=2.890, forward_time=0.107, loss_ctc=40.847, loss_att=53.980, acc=0.731, loss=50.040, backward_time=0.097, grad_norm=48.391, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.059e-04, train_time=3.816
+[gpuc04:0/16] 2024-01-17 17:46:11,844 (trainer:737) INFO: 43epoch:train:11301-11400batch: iter_time=1.995e-04, forward_time=0.106, loss_ctc=49.391, loss_att=64.574, acc=0.717, loss=60.019, backward_time=0.098, grad_norm=56.810, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.059e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:46:53,772 (trainer:737) INFO: 43epoch:train:11401-11500batch: iter_time=1.709e-04, forward_time=0.105, loss_ctc=46.308, loss_att=50.215, acc=0.755, loss=49.043, backward_time=0.097, grad_norm=46.891, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.059e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 17:47:35,315 (trainer:737) INFO: 43epoch:train:11501-11600batch: iter_time=2.309e-04, forward_time=0.106, loss_ctc=41.686, loss_att=42.779, acc=0.743, loss=42.451, backward_time=0.096, grad_norm=43.318, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.059e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:48:06,555 (trainer:668) WARNING: The grad norm is inf. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 17:48:16,945 (trainer:737) INFO: 43epoch:train:11601-11700batch: iter_time=1.694e-04, forward_time=0.106, loss_ctc=58.741, loss_att=60.363, acc=0.716, loss=59.877, backward_time=0.097, grad_norm=57.296, clip=100.000, loss_scale=1.815e+34, optim_step_time=0.039, optim0_lr0=3.058e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:48:58,940 (trainer:737) INFO: 43epoch:train:11701-11800batch: iter_time=1.881e-04, forward_time=0.106, loss_ctc=44.501, loss_att=57.069, acc=0.750, loss=53.298, backward_time=0.096, grad_norm=51.134, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.058e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 17:49:40,489 (trainer:737) INFO: 43epoch:train:11801-11900batch: iter_time=1.573e-04, forward_time=0.106, loss_ctc=37.774, loss_att=44.920, acc=0.779, loss=42.776, backward_time=0.096, grad_norm=41.635, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.058e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:50:22,279 (trainer:737) INFO: 43epoch:train:11901-12000batch: iter_time=1.675e-04, forward_time=0.106, loss_ctc=36.944, loss_att=39.578, acc=0.775, loss=38.788, backward_time=0.096, grad_norm=42.415, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.058e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 17:51:03,883 (trainer:737) INFO: 43epoch:train:12001-12100batch: iter_time=1.892e-04, forward_time=0.106, loss_ctc=43.069, loss_att=46.185, acc=0.761, loss=45.250, backward_time=0.096, grad_norm=45.200, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.057e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:51:45,623 (trainer:737) INFO: 43epoch:train:12101-12200batch: iter_time=1.824e-04, forward_time=0.107, loss_ctc=47.058, loss_att=56.661, acc=0.745, loss=53.780, backward_time=0.097, grad_norm=51.826, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.057e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 17:52:27,269 (trainer:737) INFO: 43epoch:train:12201-12300batch: iter_time=1.896e-04, forward_time=0.106, loss_ctc=36.628, loss_att=49.525, acc=0.749, loss=45.656, backward_time=0.096, grad_norm=48.818, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.057e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 17:53:08,793 (trainer:737) INFO: 43epoch:train:12301-12400batch: iter_time=2.369e-04, forward_time=0.106, loss_ctc=41.737, loss_att=48.366, acc=0.742, loss=46.377, backward_time=0.096, grad_norm=44.837, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.057e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 17:53:50,256 (trainer:737) INFO: 43epoch:train:12401-12500batch: iter_time=1.870e-04, forward_time=0.105, loss_ctc=36.486, loss_att=40.728, acc=0.771, loss=39.455, backward_time=0.095, grad_norm=40.662, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.056e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 17:53:56,447 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 17:54:16,157 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 17:54:20,013 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe12f54a5f0>)
+[gpuc04:0/16] 2024-01-17 17:54:20,013 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 17:54:20,016 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 17:59:12,256 (trainer:737) INFO: 43epoch:train:12501-12600batch: iter_time=2.791, forward_time=0.107, loss_ctc=50.376, loss_att=67.359, acc=0.715, loss=62.264, backward_time=0.098, grad_norm=59.034, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.056e-04, train_time=3.220
+[gpuc04:0/16] 2024-01-17 17:59:53,866 (trainer:737) INFO: 43epoch:train:12601-12700batch: iter_time=1.957e-04, forward_time=0.106, loss_ctc=44.650, loss_att=51.294, acc=0.739, loss=49.301, backward_time=0.097, grad_norm=46.920, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.056e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:00:35,684 (trainer:737) INFO: 43epoch:train:12701-12800batch: iter_time=1.542e-04, forward_time=0.106, loss_ctc=42.256, loss_att=46.520, acc=0.753, loss=45.241, backward_time=0.096, grad_norm=43.396, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.056e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 18:01:17,441 (trainer:737) INFO: 43epoch:train:12801-12900batch: iter_time=1.895e-04, forward_time=0.106, loss_ctc=53.694, loss_att=53.879, acc=0.732, loss=53.824, backward_time=0.097, grad_norm=56.962, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.055e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 18:01:59,415 (trainer:737) INFO: 43epoch:train:12901-13000batch: iter_time=1.861e-04, forward_time=0.106, loss_ctc=46.413, loss_att=51.618, acc=0.745, loss=50.057, backward_time=0.097, grad_norm=47.033, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.055e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 18:02:41,550 (trainer:737) INFO: 43epoch:train:13001-13100batch: iter_time=2.010e-04, forward_time=0.106, loss_ctc=42.759, loss_att=54.295, acc=0.760, loss=50.834, backward_time=0.097, grad_norm=48.360, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.055e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 18:03:23,513 (trainer:737) INFO: 43epoch:train:13101-13200batch: iter_time=1.990e-04, forward_time=0.110, loss_ctc=36.174, loss_att=39.208, acc=0.787, loss=38.298, backward_time=0.097, grad_norm=39.034, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.055e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 18:04:05,088 (trainer:737) INFO: 43epoch:train:13201-13300batch: iter_time=1.655e-04, forward_time=0.105, loss_ctc=36.903, loss_att=42.750, acc=0.754, loss=40.996, backward_time=0.096, grad_norm=43.296, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.055e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:04:47,037 (trainer:737) INFO: 43epoch:train:13301-13400batch: iter_time=1.845e-04, forward_time=0.107, loss_ctc=51.542, loss_att=56.227, acc=0.761, loss=54.821, backward_time=0.097, grad_norm=51.410, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.054e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 18:05:28,736 (trainer:737) INFO: 43epoch:train:13401-13500batch: iter_time=1.905e-04, forward_time=0.105, loss_ctc=37.517, loss_att=45.493, acc=0.753, loss=43.100, backward_time=0.096, grad_norm=44.369, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.054e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 18:06:11,251 (trainer:737) INFO: 43epoch:train:13501-13600batch: iter_time=1.991e-04, forward_time=0.106, loss_ctc=43.190, loss_att=55.780, acc=0.741, loss=52.003, backward_time=0.097, grad_norm=47.907, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.054e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 18:06:53,102 (trainer:737) INFO: 43epoch:train:13601-13700batch: iter_time=1.843e-04, forward_time=0.104, loss_ctc=37.095, loss_att=40.797, acc=0.763, loss=39.687, backward_time=0.096, grad_norm=43.152, clip=100.000, loss_scale=1.298e+34, optim_step_time=0.039, optim0_lr0=3.054e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 18:07:20,454 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 18:07:40,469 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 18:07:44,296 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd69558ac0>)
+[gpuc04:0/16] 2024-01-17 18:07:44,297 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 18:07:44,300 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 18:12:14,024 (trainer:737) INFO: 43epoch:train:13701-13800batch: iter_time=2.740, forward_time=0.106, loss_ctc=41.043, loss_att=53.090, acc=0.734, loss=49.476, backward_time=0.096, grad_norm=46.041, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.053e-04, train_time=3.209
+[gpuc04:0/16] 2024-01-17 18:12:55,703 (trainer:737) INFO: 43epoch:train:13801-13900batch: iter_time=2.048e-04, forward_time=0.106, loss_ctc=49.124, loss_att=61.923, acc=0.724, loss=58.084, backward_time=0.096, grad_norm=56.126, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.053e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 18:13:37,795 (trainer:737) INFO: 43epoch:train:13901-14000batch: iter_time=2.372e-04, forward_time=0.105, loss_ctc=45.827, loss_att=49.216, acc=0.757, loss=48.199, backward_time=0.096, grad_norm=46.487, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.053e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 18:14:19,231 (trainer:737) INFO: 43epoch:train:14001-14100batch: iter_time=2.424e-04, forward_time=0.105, loss_ctc=41.494, loss_att=42.723, acc=0.740, loss=42.354, backward_time=0.095, grad_norm=44.033, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.053e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 18:15:00,891 (trainer:737) INFO: 43epoch:train:14101-14200batch: iter_time=2.106e-04, forward_time=0.106, loss_ctc=58.414, loss_att=58.381, acc=0.723, loss=58.391, backward_time=0.096, grad_norm=56.570, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.052e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:15:42,539 (trainer:737) INFO: 43epoch:train:14201-14300batch: iter_time=2.139e-04, forward_time=0.106, loss_ctc=44.932, loss_att=56.713, acc=0.752, loss=53.179, backward_time=0.097, grad_norm=52.532, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.052e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:16:24,127 (trainer:737) INFO: 43epoch:train:14301-14400batch: iter_time=2.250e-04, forward_time=0.106, loss_ctc=37.259, loss_att=44.776, acc=0.778, loss=42.521, backward_time=0.096, grad_norm=40.058, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.052e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:17:05,769 (trainer:737) INFO: 43epoch:train:14401-14500batch: iter_time=2.221e-04, forward_time=0.106, loss_ctc=36.817, loss_att=39.115, acc=0.777, loss=38.426, backward_time=0.096, grad_norm=41.452, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.052e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:17:47,402 (trainer:737) INFO: 43epoch:train:14501-14600batch: iter_time=2.158e-04, forward_time=0.106, loss_ctc=43.426, loss_att=46.029, acc=0.764, loss=45.248, backward_time=0.096, grad_norm=46.153, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.051e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 18:18:29,207 (trainer:737) INFO: 43epoch:train:14601-14700batch: iter_time=2.274e-04, forward_time=0.107, loss_ctc=46.597, loss_att=55.277, acc=0.746, loss=52.673, backward_time=0.097, grad_norm=49.674, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.051e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 18:19:11,089 (trainer:737) INFO: 43epoch:train:14701-14800batch: iter_time=2.022e-04, forward_time=0.106, loss_ctc=36.725, loss_att=50.496, acc=0.745, loss=46.365, backward_time=0.097, grad_norm=44.013, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.051e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 18:19:53,097 (trainer:737) INFO: 43epoch:train:14801-14900batch: iter_time=2.070e-04, forward_time=0.106, loss_ctc=42.330, loss_att=47.832, acc=0.744, loss=46.181, backward_time=0.096, grad_norm=48.567, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.051e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 18:20:35,247 (trainer:737) INFO: 43epoch:train:14901-15000batch: iter_time=1.894e-04, forward_time=0.106, loss_ctc=36.771, loss_att=40.727, acc=0.771, loss=39.540, backward_time=0.096, grad_norm=40.267, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.050e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 18:40:37,568 (trainer:343) INFO: 43epoch results: [train] iter_time=0.243, forward_time=0.107, loss_ctc=44.176, loss_att=50.152, acc=0.745, loss=48.359, backward_time=0.097, grad_norm=48.099, clip=100.000, loss_scale=1.933e+34, optim_step_time=0.039, optim0_lr0=3.068e-04, train_time=0.671, time=2 hours, 47 minutes and 52.08 seconds, total_count=645000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=50.032, cer_ctc=0.253, loss_att=52.116, acc=0.603, cer=0.387, wer=0.998, loss=51.491, time=19 minutes and 51.66 seconds, total_count=200853, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 18:40:42,838 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 18:40:42,899 (trainer:272) INFO: 44/45epoch started. Estimated time to finish: 6 hours, 3 minutes and 33.34 seconds
+[gpuc04:0/16] 2024-01-17 18:40:42,909 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 18:41:01,797 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 18:41:05,482 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe4215d1b40>)
+[gpuc04:0/16] 2024-01-17 18:41:05,482 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 18:41:05,485 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 18:46:01,879 (trainer:737) INFO: 44epoch:train:1-100batch: iter_time=2.631, forward_time=0.105, loss_ctc=40.743, loss_att=43.583, acc=0.754, loss=42.731, backward_time=0.097, grad_norm=45.262, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.050e-04, train_time=3.189
+[gpuc04:0/16] 2024-01-17 18:46:43,609 (trainer:737) INFO: 44epoch:train:101-200batch: iter_time=1.086e-04, forward_time=0.105, loss_ctc=43.014, loss_att=50.658, acc=0.748, loss=48.365, backward_time=0.098, grad_norm=43.844, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.050e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 18:47:31,008 (trainer:737) INFO: 44epoch:train:201-300batch: iter_time=1.136e-04, forward_time=0.105, loss_ctc=43.287, loss_att=56.999, acc=0.726, loss=52.885, backward_time=0.098, grad_norm=49.456, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.050e-04, train_time=0.474
+[gpuc04:0/16] 2024-01-17 18:48:15,175 (trainer:737) INFO: 44epoch:train:301-400batch: iter_time=1.355e-04, forward_time=0.105, loss_ctc=43.509, loss_att=60.076, acc=0.716, loss=55.106, backward_time=0.097, grad_norm=45.731, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.050e-04, train_time=0.441
+[gpuc04:0/16] 2024-01-17 18:48:58,382 (trainer:737) INFO: 44epoch:train:401-500batch: iter_time=1.215e-04, forward_time=0.105, loss_ctc=38.447, loss_att=47.469, acc=0.737, loss=44.762, backward_time=0.097, grad_norm=45.721, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.049e-04, train_time=0.432
+[gpuc04:0/16] 2024-01-17 18:49:40,878 (trainer:737) INFO: 44epoch:train:501-600batch: iter_time=1.348e-04, forward_time=0.106, loss_ctc=46.816, loss_att=56.486, acc=0.736, loss=53.585, backward_time=0.098, grad_norm=134.999, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.049e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 18:50:23,436 (trainer:737) INFO: 44epoch:train:601-700batch: iter_time=1.262e-04, forward_time=0.105, loss_ctc=47.075, loss_att=53.434, acc=0.740, loss=51.527, backward_time=0.098, grad_norm=53.619, clip=100.000, loss_scale=2.596e+34, optim_step_time=0.039, optim0_lr0=3.049e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 18:50:56,723 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 18:51:05,116 (trainer:737) INFO: 44epoch:train:701-800batch: iter_time=1.130e-04, forward_time=0.105, loss_ctc=43.897, loss_att=46.589, acc=0.738, loss=45.782, backward_time=0.097, grad_norm=53.185, clip=100.000, loss_scale=3.734e+34, optim_step_time=0.039, optim0_lr0=3.049e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 18:51:48,893 (trainer:737) INFO: 44epoch:train:801-900batch: iter_time=1.359e-04, forward_time=0.116, loss_ctc=52.683, loss_att=64.822, acc=0.717, loss=61.180, backward_time=0.100, grad_norm=56.870, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.048e-04, train_time=0.438
+[gpuc04:0/16] 2024-01-17 18:52:31,796 (trainer:737) INFO: 44epoch:train:901-1000batch: iter_time=1.170e-04, forward_time=0.111, loss_ctc=42.658, loss_att=46.556, acc=0.737, loss=45.387, backward_time=0.097, grad_norm=45.558, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.048e-04, train_time=0.429
+[gpuc04:0/16] 2024-01-17 18:53:17,025 (trainer:737) INFO: 44epoch:train:1001-1100batch: iter_time=1.167e-04, forward_time=0.108, loss_ctc=40.319, loss_att=38.875, acc=0.759, loss=39.308, backward_time=0.099, grad_norm=45.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.048e-04, train_time=0.452
+[gpuc04:0/16] 2024-01-17 18:53:59,252 (trainer:737) INFO: 44epoch:train:1101-1200batch: iter_time=1.186e-04, forward_time=0.106, loss_ctc=43.668, loss_att=53.416, acc=0.708, loss=50.492, backward_time=0.097, grad_norm=53.446, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.048e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 18:54:42,502 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 18:55:02,587 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 18:55:06,410 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdd392f6e90>)
+[gpuc04:0/16] 2024-01-17 18:55:06,410 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-17 18:55:06,414 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 19:03:13,934 (trainer:737) INFO: 44epoch:train:1201-1300batch: iter_time=3.304, forward_time=0.125, loss_ctc=45.358, loss_att=48.884, acc=0.747, loss=47.826, backward_time=0.098, grad_norm=43.974, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.047e-04, train_time=5.547
+[gpuc04:0/16] 2024-01-17 19:03:55,691 (trainer:737) INFO: 44epoch:train:1301-1400batch: iter_time=1.474e-04, forward_time=0.106, loss_ctc=41.567, loss_att=50.569, acc=0.749, loss=47.868, backward_time=0.096, grad_norm=45.385, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.047e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:04:37,387 (trainer:737) INFO: 44epoch:train:1401-1500batch: iter_time=1.675e-04, forward_time=0.106, loss_ctc=38.818, loss_att=45.652, acc=0.742, loss=43.602, backward_time=0.097, grad_norm=41.352, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.047e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:05:19,191 (trainer:737) INFO: 44epoch:train:1501-1600batch: iter_time=1.783e-04, forward_time=0.106, loss_ctc=44.613, loss_att=65.457, acc=0.710, loss=59.204, backward_time=0.097, grad_norm=49.673, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.047e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:06:00,765 (trainer:737) INFO: 44epoch:train:1601-1700batch: iter_time=2.047e-04, forward_time=0.106, loss_ctc=40.145, loss_att=50.011, acc=0.730, loss=47.051, backward_time=0.096, grad_norm=47.029, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.046e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 19:06:43,211 (trainer:737) INFO: 44epoch:train:1701-1800batch: iter_time=1.771e-04, forward_time=0.106, loss_ctc=42.699, loss_att=54.128, acc=0.745, loss=50.699, backward_time=0.097, grad_norm=47.713, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.046e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 19:07:25,733 (trainer:737) INFO: 44epoch:train:1801-1900batch: iter_time=2.062e-04, forward_time=0.105, loss_ctc=48.625, loss_att=52.568, acc=0.734, loss=51.385, backward_time=0.096, grad_norm=52.202, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.046e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 19:08:07,351 (trainer:737) INFO: 44epoch:train:1901-2000batch: iter_time=1.960e-04, forward_time=0.106, loss_ctc=39.869, loss_att=44.550, acc=0.752, loss=43.146, backward_time=0.096, grad_norm=50.765, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.046e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:08:49,196 (trainer:737) INFO: 44epoch:train:2001-2100batch: iter_time=1.641e-04, forward_time=0.107, loss_ctc=44.101, loss_att=50.932, acc=0.738, loss=48.883, backward_time=0.097, grad_norm=48.186, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.046e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:09:31,023 (trainer:737) INFO: 44epoch:train:2101-2200batch: iter_time=2.148e-04, forward_time=0.107, loss_ctc=50.131, loss_att=62.579, acc=0.717, loss=58.844, backward_time=0.097, grad_norm=49.990, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.045e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:10:12,811 (trainer:737) INFO: 44epoch:train:2201-2300batch: iter_time=1.731e-04, forward_time=0.105, loss_ctc=40.403, loss_att=40.311, acc=0.751, loss=40.338, backward_time=0.096, grad_norm=45.580, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.045e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:10:54,329 (trainer:737) INFO: 44epoch:train:2301-2400batch: iter_time=2.234e-04, forward_time=0.106, loss_ctc=42.880, loss_att=46.513, acc=0.727, loss=45.423, backward_time=0.096, grad_norm=47.868, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.045e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 19:11:35,961 (trainer:737) INFO: 44epoch:train:2401-2500batch: iter_time=1.793e-04, forward_time=0.105, loss_ctc=45.384, loss_att=55.601, acc=0.727, loss=52.536, backward_time=0.097, grad_norm=49.500, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.045e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:11:45,554 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 19:12:05,204 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 19:12:09,315 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fdb8ea3eb90>)
+[gpuc04:0/16] 2024-01-17 19:12:09,316 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 19:12:09,319 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 19:17:09,203 (trainer:737) INFO: 44epoch:train:2501-2600batch: iter_time=2.913, forward_time=0.105, loss_ctc=38.749, loss_att=46.178, acc=0.743, loss=43.949, backward_time=0.095, grad_norm=44.817, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.044e-04, train_time=3.332
+[gpuc04:0/16] 2024-01-17 19:17:50,821 (trainer:737) INFO: 44epoch:train:2601-2700batch: iter_time=1.942e-04, forward_time=0.106, loss_ctc=41.902, loss_att=48.104, acc=0.752, loss=46.243, backward_time=0.097, grad_norm=45.286, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.044e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:18:32,684 (trainer:737) INFO: 44epoch:train:2701-2800batch: iter_time=1.858e-04, forward_time=0.106, loss_ctc=42.155, loss_att=57.571, acc=0.722, loss=52.947, backward_time=0.097, grad_norm=47.938, clip=100.000, loss_scale=2.492e+34, optim_step_time=0.040, optim0_lr0=3.044e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:19:14,522 (trainer:737) INFO: 44epoch:train:2801-2900batch: iter_time=1.967e-04, forward_time=0.106, loss_ctc=42.187, loss_att=57.837, acc=0.716, loss=53.142, backward_time=0.096, grad_norm=45.267, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.044e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:19:56,192 (trainer:737) INFO: 44epoch:train:2901-3000batch: iter_time=1.699e-04, forward_time=0.105, loss_ctc=36.984, loss_att=47.420, acc=0.733, loss=44.289, backward_time=0.095, grad_norm=43.592, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.043e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:19:58,238 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 19:20:37,699 (trainer:737) INFO: 44epoch:train:3001-3100batch: iter_time=1.522e-04, forward_time=0.106, loss_ctc=45.183, loss_att=55.829, acc=0.730, loss=52.635, backward_time=0.095, grad_norm=50.428, clip=100.000, loss_scale=2.161e+34, optim_step_time=0.039, optim0_lr0=3.043e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 19:21:19,261 (trainer:737) INFO: 44epoch:train:3101-3200batch: iter_time=1.854e-04, forward_time=0.106, loss_ctc=45.866, loss_att=52.612, acc=0.736, loss=50.588, backward_time=0.096, grad_norm=52.615, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.043e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 19:22:00,947 (trainer:737) INFO: 44epoch:train:3201-3300batch: iter_time=1.647e-04, forward_time=0.106, loss_ctc=43.170, loss_att=45.557, acc=0.733, loss=44.841, backward_time=0.095, grad_norm=53.225, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.043e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:22:43,533 (trainer:737) INFO: 44epoch:train:3301-3400batch: iter_time=1.805e-04, forward_time=0.107, loss_ctc=51.139, loss_att=59.807, acc=0.719, loss=57.207, backward_time=0.097, grad_norm=52.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.042e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 19:23:25,565 (trainer:737) INFO: 44epoch:train:3401-3500batch: iter_time=1.742e-04, forward_time=0.105, loss_ctc=41.468, loss_att=46.755, acc=0.728, loss=45.169, backward_time=0.096, grad_norm=43.800, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.042e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 19:24:07,243 (trainer:737) INFO: 44epoch:train:3501-3600batch: iter_time=1.659e-04, forward_time=0.105, loss_ctc=38.885, loss_att=39.488, acc=0.750, loss=39.307, backward_time=0.095, grad_norm=45.606, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.042e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:24:48,799 (trainer:737) INFO: 44epoch:train:3601-3700batch: iter_time=1.716e-04, forward_time=0.105, loss_ctc=42.142, loss_att=52.353, acc=0.710, loss=49.290, backward_time=0.096, grad_norm=51.064, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.042e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 19:25:14,572 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 19:25:34,508 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 19:25:38,318 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe40c112cb0>)
+[gpuc04:0/16] 2024-01-17 19:25:38,318 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 19:25:38,322 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 19:31:03,364 (trainer:737) INFO: 44epoch:train:3701-3800batch: iter_time=2.915, forward_time=0.161, loss_ctc=44.907, loss_att=49.090, acc=0.743, loss=47.835, backward_time=0.111, grad_norm=44.862, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.042, optim0_lr0=3.042e-04, train_time=3.745
+[gpuc04:0/16] 2024-01-17 19:31:45,094 (trainer:737) INFO: 44epoch:train:3801-3900batch: iter_time=1.462e-04, forward_time=0.105, loss_ctc=41.425, loss_att=53.007, acc=0.743, loss=49.533, backward_time=0.096, grad_norm=47.388, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.041e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:32:27,044 (trainer:737) INFO: 44epoch:train:3901-4000batch: iter_time=1.439e-04, forward_time=0.107, loss_ctc=38.910, loss_att=46.870, acc=0.742, loss=44.482, backward_time=0.096, grad_norm=42.330, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.041e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 19:33:08,910 (trainer:737) INFO: 44epoch:train:4001-4100batch: iter_time=1.758e-04, forward_time=0.106, loss_ctc=44.309, loss_att=65.819, acc=0.711, loss=59.366, backward_time=0.096, grad_norm=48.571, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.041e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:33:51,224 (trainer:737) INFO: 44epoch:train:4101-4200batch: iter_time=1.772e-04, forward_time=0.106, loss_ctc=38.965, loss_att=49.939, acc=0.730, loss=46.647, backward_time=0.096, grad_norm=43.509, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.041e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 19:34:32,833 (trainer:737) INFO: 44epoch:train:4201-4300batch: iter_time=1.874e-04, forward_time=0.106, loss_ctc=42.237, loss_att=53.429, acc=0.746, loss=50.071, backward_time=0.096, grad_norm=51.368, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.040e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:35:14,454 (trainer:737) INFO: 44epoch:train:4301-4400batch: iter_time=1.681e-04, forward_time=0.105, loss_ctc=47.654, loss_att=52.392, acc=0.737, loss=50.971, backward_time=0.095, grad_norm=49.437, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.040e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:35:56,308 (trainer:737) INFO: 44epoch:train:4401-4500batch: iter_time=1.694e-04, forward_time=0.105, loss_ctc=39.100, loss_att=44.462, acc=0.753, loss=42.853, backward_time=0.095, grad_norm=48.179, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.040e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:36:01,674 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 19:36:38,046 (trainer:737) INFO: 44epoch:train:4501-4600batch: iter_time=1.656e-04, forward_time=0.106, loss_ctc=43.858, loss_att=50.734, acc=0.739, loss=48.671, backward_time=0.096, grad_norm=47.680, clip=100.000, loss_scale=1.164e+34, optim_step_time=0.039, optim0_lr0=3.040e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:37:20,165 (trainer:737) INFO: 44epoch:train:4601-4700batch: iter_time=1.571e-04, forward_time=0.106, loss_ctc=49.645, loss_att=62.404, acc=0.716, loss=58.576, backward_time=0.097, grad_norm=52.122, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.039e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 19:38:01,780 (trainer:737) INFO: 44epoch:train:4701-4800batch: iter_time=1.930e-04, forward_time=0.105, loss_ctc=39.929, loss_att=40.381, acc=0.752, loss=40.245, backward_time=0.096, grad_norm=46.893, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.039e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:38:43,434 (trainer:737) INFO: 44epoch:train:4801-4900batch: iter_time=1.665e-04, forward_time=0.106, loss_ctc=42.040, loss_att=45.891, acc=0.728, loss=44.735, backward_time=0.096, grad_norm=49.327, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.039e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:39:25,408 (trainer:737) INFO: 44epoch:train:4901-5000batch: iter_time=1.483e-04, forward_time=0.105, loss_ctc=44.445, loss_att=54.907, acc=0.730, loss=51.769, backward_time=0.097, grad_norm=48.254, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.039e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 19:39:31,499 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 19:39:51,173 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 19:39:54,921 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe128ba7e80>)
+[gpuc04:0/16] 2024-01-17 19:39:54,921 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-17 19:39:54,924 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 19:46:08,355 (trainer:737) INFO: 44epoch:train:5001-5100batch: iter_time=3.458, forward_time=0.108, loss_ctc=38.315, loss_att=45.002, acc=0.745, loss=42.996, backward_time=0.096, grad_norm=41.663, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.039e-04, train_time=4.029
+[gpuc04:0/16] 2024-01-17 19:46:50,104 (trainer:737) INFO: 44epoch:train:5101-5200batch: iter_time=1.485e-04, forward_time=0.105, loss_ctc=41.600, loss_att=47.266, acc=0.755, loss=45.566, backward_time=0.096, grad_norm=43.670, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.038e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:47:32,094 (trainer:737) INFO: 44epoch:train:5201-5300batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=41.698, loss_att=56.068, acc=0.724, loss=51.757, backward_time=0.096, grad_norm=45.177, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.038e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 19:48:13,785 (trainer:737) INFO: 44epoch:train:5301-5400batch: iter_time=1.554e-04, forward_time=0.105, loss_ctc=41.572, loss_att=56.816, acc=0.720, loss=52.243, backward_time=0.096, grad_norm=43.128, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.038e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 19:48:55,369 (trainer:737) INFO: 44epoch:train:5401-5500batch: iter_time=1.816e-04, forward_time=0.105, loss_ctc=36.601, loss_att=46.599, acc=0.737, loss=43.600, backward_time=0.096, grad_norm=44.704, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.038e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:49:37,194 (trainer:737) INFO: 44epoch:train:5501-5600batch: iter_time=1.849e-04, forward_time=0.106, loss_ctc=45.053, loss_att=55.964, acc=0.729, loss=52.691, backward_time=0.099, grad_norm=47.874, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.037e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:50:19,221 (trainer:737) INFO: 44epoch:train:5601-5700batch: iter_time=1.975e-04, forward_time=0.105, loss_ctc=45.284, loss_att=52.421, acc=0.737, loss=50.279, backward_time=0.099, grad_norm=51.435, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.037e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 19:51:00,866 (trainer:737) INFO: 44epoch:train:5701-5800batch: iter_time=2.333e-04, forward_time=0.105, loss_ctc=42.252, loss_att=45.171, acc=0.735, loss=44.295, backward_time=0.099, grad_norm=50.178, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.040, optim0_lr0=3.037e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:51:42,652 (trainer:737) INFO: 44epoch:train:5801-5900batch: iter_time=1.870e-04, forward_time=0.107, loss_ctc=50.599, loss_att=60.432, acc=0.718, loss=57.482, backward_time=0.097, grad_norm=52.566, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.037e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:52:25,068 (trainer:737) INFO: 44epoch:train:5901-6000batch: iter_time=1.486e-04, forward_time=0.109, loss_ctc=41.135, loss_att=46.551, acc=0.729, loss=44.926, backward_time=0.095, grad_norm=45.582, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.036e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 19:53:06,868 (trainer:737) INFO: 44epoch:train:6001-6100batch: iter_time=1.570e-04, forward_time=0.105, loss_ctc=38.548, loss_att=39.530, acc=0.750, loss=39.236, backward_time=0.095, grad_norm=43.521, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.036e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 19:53:48,457 (trainer:737) INFO: 44epoch:train:6101-6200batch: iter_time=1.593e-04, forward_time=0.106, loss_ctc=41.495, loss_att=51.279, acc=0.715, loss=48.344, backward_time=0.095, grad_norm=50.107, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=3.036e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 19:54:16,034 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 19:54:35,643 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 19:54:39,231 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe04e576a10>)
+[gpuc04:0/16] 2024-01-17 19:54:39,231 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 19:54:39,234 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 19:59:06,420 (trainer:737) INFO: 44epoch:train:6201-6300batch: iter_time=2.506, forward_time=0.109, loss_ctc=44.194, loss_att=48.522, acc=0.741, loss=47.223, backward_time=0.097, grad_norm=43.739, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.036e-04, train_time=3.179
+[gpuc04:0/16] 2024-01-17 19:59:48,432 (trainer:737) INFO: 44epoch:train:6301-6400batch: iter_time=1.272e-04, forward_time=0.105, loss_ctc=40.933, loss_att=48.002, acc=0.747, loss=45.881, backward_time=0.097, grad_norm=45.828, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.035e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:00:30,078 (trainer:737) INFO: 44epoch:train:6401-6500batch: iter_time=1.470e-04, forward_time=0.106, loss_ctc=38.324, loss_att=44.719, acc=0.743, loss=42.801, backward_time=0.096, grad_norm=41.582, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=3.035e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:01:11,764 (trainer:737) INFO: 44epoch:train:6501-6600batch: iter_time=1.406e-04, forward_time=0.106, loss_ctc=43.465, loss_att=61.862, acc=0.710, loss=56.343, backward_time=0.097, grad_norm=46.441, clip=100.000, loss_scale=1.942e+34, optim_step_time=0.039, optim0_lr0=3.035e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:01:53,300 (trainer:737) INFO: 44epoch:train:6601-6700batch: iter_time=1.825e-04, forward_time=0.105, loss_ctc=38.787, loss_att=48.768, acc=0.729, loss=45.774, backward_time=0.096, grad_norm=45.188, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.035e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:02:34,938 (trainer:737) INFO: 44epoch:train:6701-6800batch: iter_time=1.473e-04, forward_time=0.107, loss_ctc=41.915, loss_att=52.869, acc=0.745, loss=49.583, backward_time=0.097, grad_norm=46.451, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.035e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:03:16,711 (trainer:737) INFO: 44epoch:train:6801-6900batch: iter_time=1.432e-04, forward_time=0.105, loss_ctc=47.446, loss_att=52.784, acc=0.728, loss=51.183, backward_time=0.096, grad_norm=51.870, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.034e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:03:58,310 (trainer:737) INFO: 44epoch:train:6901-7000batch: iter_time=1.848e-04, forward_time=0.106, loss_ctc=38.799, loss_att=43.206, acc=0.747, loss=41.884, backward_time=0.096, grad_norm=50.112, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.034e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:04:40,288 (trainer:737) INFO: 44epoch:train:7001-7100batch: iter_time=1.523e-04, forward_time=0.107, loss_ctc=43.509, loss_att=48.677, acc=0.732, loss=47.127, backward_time=0.096, grad_norm=47.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.034e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:05:22,313 (trainer:737) INFO: 44epoch:train:7101-7200batch: iter_time=1.388e-04, forward_time=0.107, loss_ctc=48.569, loss_att=59.341, acc=0.712, loss=56.109, backward_time=0.097, grad_norm=49.748, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.034e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:06:03,848 (trainer:737) INFO: 44epoch:train:7201-7300batch: iter_time=1.401e-04, forward_time=0.106, loss_ctc=39.507, loss_att=41.694, acc=0.736, loss=41.038, backward_time=0.096, grad_norm=47.203, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.033e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:06:45,408 (trainer:737) INFO: 44epoch:train:7301-7400batch: iter_time=1.418e-04, forward_time=0.105, loss_ctc=41.847, loss_att=44.817, acc=0.731, loss=43.926, backward_time=0.096, grad_norm=49.118, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.033e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:07:27,134 (trainer:737) INFO: 44epoch:train:7401-7500batch: iter_time=1.163e-04, forward_time=0.106, loss_ctc=44.262, loss_att=54.148, acc=0.724, loss=51.183, backward_time=0.096, grad_norm=48.892, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.033e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:07:31,436 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 20:07:51,619 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 20:07:55,426 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe11fd13670>)
+[gpuc04:0/16] 2024-01-17 20:07:55,426 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 20:07:55,429 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 20:12:52,924 (trainer:737) INFO: 44epoch:train:7501-7600batch: iter_time=2.799, forward_time=0.105, loss_ctc=37.833, loss_att=42.420, acc=0.751, loss=41.044, backward_time=0.096, grad_norm=42.265, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.033e-04, train_time=3.258
+[gpuc04:0/16] 2024-01-17 20:13:34,622 (trainer:737) INFO: 44epoch:train:7601-7700batch: iter_time=2.015e-04, forward_time=0.106, loss_ctc=41.255, loss_att=46.265, acc=0.757, loss=44.762, backward_time=0.097, grad_norm=42.070, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.032e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:14:16,667 (trainer:737) INFO: 44epoch:train:7701-7800batch: iter_time=1.944e-04, forward_time=0.106, loss_ctc=41.384, loss_att=54.553, acc=0.728, loss=50.602, backward_time=0.097, grad_norm=43.930, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.032e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:14:58,291 (trainer:737) INFO: 44epoch:train:7801-7900batch: iter_time=1.996e-04, forward_time=0.105, loss_ctc=41.302, loss_att=56.561, acc=0.721, loss=51.984, backward_time=0.096, grad_norm=45.339, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.032e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:15:39,824 (trainer:737) INFO: 44epoch:train:7901-8000batch: iter_time=1.941e-04, forward_time=0.105, loss_ctc=36.408, loss_att=46.889, acc=0.735, loss=43.745, backward_time=0.096, grad_norm=45.877, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.032e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:16:21,604 (trainer:737) INFO: 44epoch:train:8001-8100batch: iter_time=1.763e-04, forward_time=0.107, loss_ctc=44.234, loss_att=54.790, acc=0.732, loss=51.623, backward_time=0.097, grad_norm=48.926, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.032e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:17:04,062 (trainer:737) INFO: 44epoch:train:8101-8200batch: iter_time=2.004e-04, forward_time=0.106, loss_ctc=45.674, loss_att=51.892, acc=0.740, loss=50.027, backward_time=0.096, grad_norm=50.475, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.031e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 20:17:46,615 (trainer:737) INFO: 44epoch:train:8201-8300batch: iter_time=1.593e-04, forward_time=0.105, loss_ctc=42.163, loss_att=44.499, acc=0.738, loss=43.798, backward_time=0.097, grad_norm=50.100, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.031e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 20:18:28,552 (trainer:737) INFO: 44epoch:train:8301-8400batch: iter_time=1.851e-04, forward_time=0.107, loss_ctc=50.072, loss_att=59.102, acc=0.722, loss=56.393, backward_time=0.099, grad_norm=51.063, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.031e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 20:19:10,320 (trainer:737) INFO: 44epoch:train:8401-8500batch: iter_time=2.099e-04, forward_time=0.105, loss_ctc=41.170, loss_att=46.052, acc=0.730, loss=44.587, backward_time=0.099, grad_norm=44.897, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.031e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:19:51,947 (trainer:737) INFO: 44epoch:train:8501-8600batch: iter_time=2.100e-04, forward_time=0.106, loss_ctc=38.604, loss_att=39.037, acc=0.753, loss=38.907, backward_time=0.096, grad_norm=42.196, clip=100.000, loss_scale=3.884e+34, optim_step_time=0.039, optim0_lr0=3.030e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:20:33,565 (trainer:737) INFO: 44epoch:train:8601-8700batch: iter_time=1.976e-04, forward_time=0.105, loss_ctc=41.177, loss_att=51.699, acc=0.713, loss=48.542, backward_time=0.097, grad_norm=50.357, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.030e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:20:59,290 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 20:21:19,993 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 20:21:23,926 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe04e5b6f50>)
+[gpuc04:0/16] 2024-01-17 20:21:23,926 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 20:21:23,929 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 20:25:49,871 (trainer:737) INFO: 44epoch:train:8701-8800batch: iter_time=2.739, forward_time=0.106, loss_ctc=44.442, loss_att=48.691, acc=0.741, loss=47.416, backward_time=0.097, grad_norm=44.381, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.040, optim0_lr0=3.030e-04, train_time=3.163
+[gpuc04:0/16] 2024-01-17 20:25:56,677 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 20:26:31,670 (trainer:737) INFO: 44epoch:train:8801-8900batch: iter_time=1.588e-04, forward_time=0.105, loss_ctc=40.354, loss_att=47.472, acc=0.749, loss=45.337, backward_time=0.097, grad_norm=42.294, clip=100.000, loss_scale=2.392e+34, optim_step_time=0.040, optim0_lr0=3.030e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:27:13,224 (trainer:737) INFO: 44epoch:train:8901-9000batch: iter_time=1.625e-04, forward_time=0.105, loss_ctc=37.920, loss_att=44.432, acc=0.745, loss=42.478, backward_time=0.096, grad_norm=40.646, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.029e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:27:54,869 (trainer:737) INFO: 44epoch:train:9001-9100batch: iter_time=1.592e-04, forward_time=0.105, loss_ctc=43.973, loss_att=62.176, acc=0.709, loss=56.715, backward_time=0.096, grad_norm=49.799, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.029e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:28:36,336 (trainer:737) INFO: 44epoch:train:9101-9200batch: iter_time=1.281e-04, forward_time=0.104, loss_ctc=38.435, loss_att=49.320, acc=0.727, loss=46.054, backward_time=0.095, grad_norm=46.068, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.029e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 20:29:18,683 (trainer:737) INFO: 44epoch:train:9201-9300batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=41.301, loss_att=52.289, acc=0.746, loss=48.992, backward_time=0.096, grad_norm=47.704, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.029e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 20:30:00,151 (trainer:737) INFO: 44epoch:train:9301-9400batch: iter_time=1.398e-04, forward_time=0.105, loss_ctc=47.116, loss_att=51.808, acc=0.731, loss=50.400, backward_time=0.096, grad_norm=51.195, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.029e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 20:30:41,622 (trainer:737) INFO: 44epoch:train:9401-9500batch: iter_time=1.580e-04, forward_time=0.105, loss_ctc=38.815, loss_att=43.087, acc=0.750, loss=41.805, backward_time=0.095, grad_norm=47.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.028e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 20:31:23,630 (trainer:737) INFO: 44epoch:train:9501-9600batch: iter_time=1.819e-04, forward_time=0.105, loss_ctc=43.025, loss_att=47.923, acc=0.737, loss=46.454, backward_time=0.096, grad_norm=48.075, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.028e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:32:05,391 (trainer:737) INFO: 44epoch:train:9601-9700batch: iter_time=1.911e-04, forward_time=0.107, loss_ctc=48.772, loss_att=60.025, acc=0.709, loss=56.649, backward_time=0.096, grad_norm=51.426, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.028e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:32:47,222 (trainer:737) INFO: 44epoch:train:9701-9800batch: iter_time=1.830e-04, forward_time=0.105, loss_ctc=39.688, loss_att=41.096, acc=0.739, loss=40.674, backward_time=0.096, grad_norm=44.851, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.028e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:33:28,722 (trainer:737) INFO: 44epoch:train:9801-9900batch: iter_time=1.855e-04, forward_time=0.105, loss_ctc=41.517, loss_att=44.406, acc=0.732, loss=43.539, backward_time=0.095, grad_norm=48.247, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.027e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:34:10,594 (trainer:737) INFO: 44epoch:train:9901-10000batch: iter_time=1.454e-04, forward_time=0.107, loss_ctc=43.991, loss_att=54.411, acc=0.721, loss=51.285, backward_time=0.096, grad_norm=47.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.027e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 20:34:16,458 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 20:34:35,985 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 20:34:39,796 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe1341078e0>)
+[gpuc04:0/16] 2024-01-17 20:34:39,796 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 20:34:39,799 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 20:39:39,649 (trainer:737) INFO: 44epoch:train:10001-10100batch: iter_time=2.809, forward_time=0.148, loss_ctc=37.668, loss_att=44.367, acc=0.758, loss=42.358, backward_time=0.103, grad_norm=41.364, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.027e-04, train_time=3.290
+[gpuc04:0/16] 2024-01-17 20:40:21,576 (trainer:737) INFO: 44epoch:train:10101-10200batch: iter_time=1.289e-04, forward_time=0.105, loss_ctc=41.083, loss_att=50.609, acc=0.752, loss=47.751, backward_time=0.098, grad_norm=44.066, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.027e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 20:41:03,380 (trainer:737) INFO: 44epoch:train:10201-10300batch: iter_time=1.244e-04, forward_time=0.106, loss_ctc=41.261, loss_att=57.612, acc=0.728, loss=52.707, backward_time=0.097, grad_norm=45.718, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.026e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:41:45,108 (trainer:737) INFO: 44epoch:train:10301-10400batch: iter_time=1.255e-04, forward_time=0.105, loss_ctc=41.374, loss_att=60.608, acc=0.718, loss=54.838, backward_time=0.097, grad_norm=46.859, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.026e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:42:27,220 (trainer:737) INFO: 44epoch:train:10401-10500batch: iter_time=1.202e-04, forward_time=0.105, loss_ctc=35.925, loss_att=46.820, acc=0.742, loss=43.551, backward_time=0.096, grad_norm=46.506, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.026e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 20:43:09,041 (trainer:737) INFO: 44epoch:train:10501-10600batch: iter_time=1.296e-04, forward_time=0.106, loss_ctc=44.898, loss_att=55.546, acc=0.740, loss=52.351, backward_time=0.096, grad_norm=49.460, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.026e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:43:50,674 (trainer:737) INFO: 44epoch:train:10601-10700batch: iter_time=1.133e-04, forward_time=0.105, loss_ctc=45.088, loss_att=52.878, acc=0.746, loss=50.541, backward_time=0.096, grad_norm=49.766, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.026e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:44:32,183 (trainer:737) INFO: 44epoch:train:10701-10800batch: iter_time=1.106e-04, forward_time=0.105, loss_ctc=41.938, loss_att=45.582, acc=0.745, loss=44.489, backward_time=0.096, grad_norm=49.274, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.025e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:45:14,080 (trainer:737) INFO: 44epoch:train:10801-10900batch: iter_time=1.045e-04, forward_time=0.106, loss_ctc=49.579, loss_att=63.357, acc=0.724, loss=59.224, backward_time=0.097, grad_norm=52.323, clip=100.000, loss_scale=3.822e+34, optim_step_time=0.039, optim0_lr0=3.025e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 20:45:55,929 (trainer:737) INFO: 44epoch:train:10901-11000batch: iter_time=1.052e-04, forward_time=0.105, loss_ctc=40.334, loss_att=46.828, acc=0.739, loss=44.880, backward_time=0.096, grad_norm=42.628, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.025e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 20:46:29,558 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 20:46:37,412 (trainer:737) INFO: 44epoch:train:11001-11100batch: iter_time=1.231e-04, forward_time=0.105, loss_ctc=38.564, loss_att=39.337, acc=0.760, loss=39.105, backward_time=0.096, grad_norm=41.814, clip=100.000, loss_scale=3.755e+34, optim_step_time=0.039, optim0_lr0=3.025e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:47:19,122 (trainer:737) INFO: 44epoch:train:11101-11200batch: iter_time=1.156e-04, forward_time=0.103, loss_ctc=40.555, loss_att=51.341, acc=0.719, loss=48.106, backward_time=0.095, grad_norm=49.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.024e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:47:47,537 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 20:48:07,082 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 20:48:10,857 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe11b347940>)
+[gpuc04:0/16] 2024-01-17 20:48:10,858 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 20:48:10,861 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 20:52:51,136 (trainer:737) INFO: 44epoch:train:11201-11300batch: iter_time=2.809, forward_time=0.107, loss_ctc=44.361, loss_att=49.574, acc=0.746, loss=48.010, backward_time=0.097, grad_norm=44.343, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.024e-04, train_time=3.320
+[gpuc04:0/16] 2024-01-17 20:53:32,857 (trainer:737) INFO: 44epoch:train:11301-11400batch: iter_time=1.553e-04, forward_time=0.105, loss_ctc=40.155, loss_att=49.757, acc=0.746, loss=46.876, backward_time=0.097, grad_norm=46.800, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.024e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:54:14,489 (trainer:737) INFO: 44epoch:train:11401-11500batch: iter_time=1.445e-04, forward_time=0.105, loss_ctc=37.791, loss_att=44.853, acc=0.746, loss=42.735, backward_time=0.097, grad_norm=40.533, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.024e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 20:54:56,487 (trainer:737) INFO: 44epoch:train:11501-11600batch: iter_time=1.335e-04, forward_time=0.109, loss_ctc=43.201, loss_att=62.621, acc=0.711, loss=56.795, backward_time=0.097, grad_norm=49.490, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.023e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 20:55:38,047 (trainer:737) INFO: 44epoch:train:11601-11700batch: iter_time=1.472e-04, forward_time=0.105, loss_ctc=38.431, loss_att=48.426, acc=0.730, loss=45.427, backward_time=0.097, grad_norm=44.370, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.023e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 20:56:21,034 (trainer:737) INFO: 44epoch:train:11701-11800batch: iter_time=1.407e-04, forward_time=0.106, loss_ctc=41.251, loss_att=53.195, acc=0.743, loss=49.612, backward_time=0.097, grad_norm=47.139, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.023e-04, train_time=0.430
+[gpuc04:0/16] 2024-01-17 20:57:03,179 (trainer:737) INFO: 44epoch:train:11801-11900batch: iter_time=1.401e-04, forward_time=0.105, loss_ctc=46.873, loss_att=52.550, acc=0.731, loss=50.847, backward_time=0.096, grad_norm=49.523, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.023e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 20:57:44,611 (trainer:737) INFO: 44epoch:train:11901-12000batch: iter_time=1.501e-04, forward_time=0.105, loss_ctc=38.512, loss_att=43.310, acc=0.748, loss=41.871, backward_time=0.097, grad_norm=48.903, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.023e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 20:58:26,287 (trainer:737) INFO: 44epoch:train:12001-12100batch: iter_time=1.562e-04, forward_time=0.105, loss_ctc=43.306, loss_att=48.563, acc=0.734, loss=46.986, backward_time=0.098, grad_norm=47.590, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.022e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:59:07,977 (trainer:737) INFO: 44epoch:train:12101-12200batch: iter_time=1.415e-04, forward_time=0.106, loss_ctc=48.829, loss_att=60.293, acc=0.712, loss=56.854, backward_time=0.097, grad_norm=53.727, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.022e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 20:59:50,202 (trainer:737) INFO: 44epoch:train:12201-12300batch: iter_time=1.271e-04, forward_time=0.105, loss_ctc=39.418, loss_att=41.695, acc=0.737, loss=41.012, backward_time=0.097, grad_norm=43.443, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.022e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 21:00:32,219 (trainer:737) INFO: 44epoch:train:12301-12400batch: iter_time=1.259e-04, forward_time=0.105, loss_ctc=41.268, loss_att=44.659, acc=0.734, loss=43.642, backward_time=0.101, grad_norm=57.940, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.022e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 21:01:13,780 (trainer:737) INFO: 44epoch:train:12401-12500batch: iter_time=1.200e-04, forward_time=0.105, loss_ctc=43.558, loss_att=53.826, acc=0.725, loss=50.746, backward_time=0.097, grad_norm=47.796, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.021e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 21:01:30,683 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-17 21:01:50,249 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 21:01:54,014 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe1350ec730>)
+[gpuc04:0/16] 2024-01-17 21:01:54,014 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 21:01:54,018 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 21:07:03,976 (trainer:737) INFO: 44epoch:train:12501-12600batch: iter_time=3.082, forward_time=0.106, loss_ctc=37.410, loss_att=43.252, acc=0.762, loss=41.499, backward_time=0.097, grad_norm=43.107, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.021e-04, train_time=3.502
+[gpuc04:0/16] 2024-01-17 21:07:45,804 (trainer:737) INFO: 44epoch:train:12601-12700batch: iter_time=1.347e-04, forward_time=0.106, loss_ctc=40.561, loss_att=49.648, acc=0.755, loss=46.922, backward_time=0.098, grad_norm=42.323, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.021e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 21:08:27,630 (trainer:737) INFO: 44epoch:train:12701-12800batch: iter_time=1.203e-04, forward_time=0.106, loss_ctc=41.427, loss_att=57.378, acc=0.730, loss=52.592, backward_time=0.098, grad_norm=50.247, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.021e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 21:09:09,451 (trainer:737) INFO: 44epoch:train:12801-12900batch: iter_time=1.543e-04, forward_time=0.106, loss_ctc=41.417, loss_att=60.664, acc=0.718, loss=54.890, backward_time=0.098, grad_norm=46.048, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.020e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 21:09:51,463 (trainer:737) INFO: 44epoch:train:12901-13000batch: iter_time=1.511e-04, forward_time=0.106, loss_ctc=36.068, loss_att=47.110, acc=0.742, loss=43.797, backward_time=0.097, grad_norm=46.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.020e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 21:10:33,721 (trainer:737) INFO: 44epoch:train:13001-13100batch: iter_time=1.443e-04, forward_time=0.106, loss_ctc=44.392, loss_att=55.008, acc=0.742, loss=51.823, backward_time=0.098, grad_norm=48.709, clip=100.000, loss_scale=2.472e+34, optim_step_time=0.040, optim0_lr0=3.020e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 21:10:58,250 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 21:11:15,423 (trainer:737) INFO: 44epoch:train:13101-13200batch: iter_time=1.448e-04, forward_time=0.106, loss_ctc=45.044, loss_att=52.309, acc=0.746, loss=50.130, backward_time=0.097, grad_norm=49.892, clip=100.000, loss_scale=3.294e+34, optim_step_time=0.040, optim0_lr0=3.020e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 21:11:57,014 (trainer:737) INFO: 44epoch:train:13201-13300batch: iter_time=1.581e-04, forward_time=0.105, loss_ctc=42.955, loss_att=45.813, acc=0.745, loss=44.956, backward_time=0.097, grad_norm=52.883, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.020e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:12:39,156 (trainer:737) INFO: 44epoch:train:13301-13400batch: iter_time=1.606e-04, forward_time=0.106, loss_ctc=49.682, loss_att=62.708, acc=0.726, loss=58.801, backward_time=0.098, grad_norm=52.156, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.019e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 21:13:20,786 (trainer:737) INFO: 44epoch:train:13401-13500batch: iter_time=1.727e-04, forward_time=0.105, loss_ctc=40.200, loss_att=46.346, acc=0.740, loss=44.502, backward_time=0.097, grad_norm=42.600, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.019e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:14:02,285 (trainer:737) INFO: 44epoch:train:13501-13600batch: iter_time=1.441e-04, forward_time=0.105, loss_ctc=38.101, loss_att=38.778, acc=0.760, loss=38.575, backward_time=0.097, grad_norm=41.963, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.019e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 21:14:43,790 (trainer:737) INFO: 44epoch:train:13601-13700batch: iter_time=1.489e-04, forward_time=0.105, loss_ctc=40.697, loss_att=51.401, acc=0.718, loss=48.190, backward_time=0.096, grad_norm=50.597, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.019e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 21:15:11,556 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-17 21:15:30,938 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 21:15:34,743 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe04c7f8a90>)
+[gpuc04:0/16] 2024-01-17 21:15:34,744 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 21:15:34,747 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 21:20:23,923 (trainer:737) INFO: 44epoch:train:13701-13800batch: iter_time=2.970, forward_time=0.117, loss_ctc=44.034, loss_att=47.793, acc=0.756, loss=46.666, backward_time=0.097, grad_norm=43.190, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.018e-04, train_time=3.401
+[gpuc04:0/16] 2024-01-17 21:21:05,748 (trainer:737) INFO: 44epoch:train:13801-13900batch: iter_time=1.801e-04, forward_time=0.106, loss_ctc=40.294, loss_att=50.093, acc=0.752, loss=47.153, backward_time=0.098, grad_norm=46.556, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.018e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 21:21:47,505 (trainer:737) INFO: 44epoch:train:13901-14000batch: iter_time=1.414e-04, forward_time=0.106, loss_ctc=37.827, loss_att=46.161, acc=0.744, loss=43.661, backward_time=0.097, grad_norm=40.929, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.018e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 21:22:29,275 (trainer:737) INFO: 44epoch:train:14001-14100batch: iter_time=1.894e-04, forward_time=0.106, loss_ctc=43.347, loss_att=64.938, acc=0.712, loss=58.460, backward_time=0.097, grad_norm=46.540, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.018e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 21:23:11,169 (trainer:737) INFO: 44epoch:train:14101-14200batch: iter_time=1.997e-04, forward_time=0.106, loss_ctc=37.913, loss_att=50.224, acc=0.732, loss=46.531, backward_time=0.096, grad_norm=47.026, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.017e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 21:23:52,815 (trainer:737) INFO: 44epoch:train:14201-14300batch: iter_time=1.760e-04, forward_time=0.106, loss_ctc=41.441, loss_att=52.269, acc=0.751, loss=49.020, backward_time=0.097, grad_norm=47.356, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.017e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:24:34,701 (trainer:737) INFO: 44epoch:train:14301-14400batch: iter_time=1.507e-04, forward_time=0.105, loss_ctc=46.781, loss_att=52.409, acc=0.736, loss=50.721, backward_time=0.096, grad_norm=50.643, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.017e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 21:25:16,292 (trainer:737) INFO: 44epoch:train:14401-14500batch: iter_time=1.465e-04, forward_time=0.106, loss_ctc=38.060, loss_att=43.763, acc=0.758, loss=42.052, backward_time=0.097, grad_norm=46.993, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.017e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:25:58,097 (trainer:737) INFO: 44epoch:train:14501-14600batch: iter_time=1.502e-04, forward_time=0.106, loss_ctc=43.317, loss_att=49.768, acc=0.745, loss=47.833, backward_time=0.098, grad_norm=48.428, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.017e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 21:26:39,997 (trainer:737) INFO: 44epoch:train:14601-14700batch: iter_time=1.694e-04, forward_time=0.106, loss_ctc=48.219, loss_att=61.618, acc=0.720, loss=57.598, backward_time=0.097, grad_norm=51.841, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.016e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 21:27:21,574 (trainer:737) INFO: 44epoch:train:14701-14800batch: iter_time=1.605e-04, forward_time=0.106, loss_ctc=39.119, loss_att=40.964, acc=0.748, loss=40.411, backward_time=0.097, grad_norm=43.168, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.016e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:28:03,143 (trainer:737) INFO: 44epoch:train:14801-14900batch: iter_time=1.423e-04, forward_time=0.105, loss_ctc=40.987, loss_att=45.379, acc=0.732, loss=44.062, backward_time=0.097, grad_norm=46.572, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.016e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 21:28:44,739 (trainer:737) INFO: 44epoch:train:14901-15000batch: iter_time=1.171e-04, forward_time=0.105, loss_ctc=43.468, loss_att=53.621, acc=0.736, loss=50.575, backward_time=0.097, grad_norm=46.211, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.016e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 21:48:46,657 (trainer:343) INFO: 44epoch results: [train] iter_time=0.233, forward_time=0.107, loss_ctc=42.349, loss_att=50.644, acc=0.735, loss=48.155, backward_time=0.097, grad_norm=47.886, clip=100.000, loss_scale=2.073e+34, optim_step_time=0.039, optim0_lr0=3.033e-04, train_time=0.672, time=2 hours, 48 minutes and 11.73 seconds, total_count=660000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=48.206, cer_ctc=0.249, loss_att=50.951, acc=0.604, cer=0.397, wer=1.000, loss=50.128, time=19 minutes and 51.82 seconds, total_count=205524, gpu_max_cached_mem_GB=27.988
+[gpuc04:0/16] 2024-01-17 21:48:52,289 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-17 21:48:52,362 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/39epoch.pth
+[gpuc04:0/16] 2024-01-17 21:48:52,363 (trainer:272) INFO: 45/45epoch started. Estimated time to finish: 3 hours, 2 minutes and 21.47 seconds
+[gpuc04:0/16] 2024-01-17 21:48:52,373 (multiple_iter_factory:32) INFO: Building 0th iter-factory...
+[gpuc04:0/16] 2024-01-17 21:49:12,120 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 21:49:15,976 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.8", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.8", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.8", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.8", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe3d83489a0>)
+[gpuc04:0/16] 2024-01-17 21:49:15,976 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.8, 
+[gpuc04:0/16] 2024-01-17 21:49:15,980 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 21:54:07,886 (trainer:737) INFO: 45epoch:train:1-100batch: iter_time=2.703, forward_time=0.128, loss_ctc=42.727, loss_att=54.401, acc=0.736, loss=50.899, backward_time=0.100, grad_norm=51.251, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.015e-04, train_time=3.155
+[gpuc04:0/16] 2024-01-17 21:54:49,615 (trainer:737) INFO: 45epoch:train:101-200batch: iter_time=1.179e-04, forward_time=0.105, loss_ctc=42.197, loss_att=43.333, acc=0.760, loss=42.992, backward_time=0.097, grad_norm=45.577, clip=100.000, loss_scale=2.928e+34, optim_step_time=0.039, optim0_lr0=3.015e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 21:55:13,752 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 21:55:31,334 (trainer:737) INFO: 45epoch:train:201-300batch: iter_time=1.210e-04, forward_time=0.105, loss_ctc=47.445, loss_att=50.453, acc=0.741, loss=49.551, backward_time=0.098, grad_norm=45.615, clip=100.000, loss_scale=3.273e+34, optim_step_time=0.039, optim0_lr0=3.015e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 21:56:13,349 (trainer:737) INFO: 45epoch:train:301-400batch: iter_time=1.204e-04, forward_time=0.105, loss_ctc=35.415, loss_att=37.673, acc=0.760, loss=36.996, backward_time=0.097, grad_norm=43.608, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.015e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 21:56:55,925 (trainer:737) INFO: 45epoch:train:401-500batch: iter_time=1.304e-04, forward_time=0.107, loss_ctc=44.500, loss_att=53.163, acc=0.773, loss=50.564, backward_time=0.099, grad_norm=47.423, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.014e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 21:57:37,874 (trainer:737) INFO: 45epoch:train:501-600batch: iter_time=1.252e-04, forward_time=0.106, loss_ctc=60.032, loss_att=58.236, acc=0.742, loss=58.775, backward_time=0.099, grad_norm=56.087, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.014e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 21:58:20,965 (trainer:737) INFO: 45epoch:train:601-700batch: iter_time=1.361e-04, forward_time=0.107, loss_ctc=50.468, loss_att=51.467, acc=0.752, loss=51.167, backward_time=0.099, grad_norm=52.978, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.014e-04, train_time=0.431
+[gpuc04:0/16] 2024-01-17 21:59:03,794 (trainer:737) INFO: 45epoch:train:701-800batch: iter_time=1.237e-04, forward_time=0.107, loss_ctc=48.609, loss_att=45.623, acc=0.749, loss=46.519, backward_time=0.099, grad_norm=50.898, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.014e-04, train_time=0.426
+[gpuc04:0/16] 2024-01-17 21:59:51,655 (trainer:737) INFO: 45epoch:train:801-900batch: iter_time=1.239e-04, forward_time=0.106, loss_ctc=43.824, loss_att=49.525, acc=0.740, loss=47.815, backward_time=0.098, grad_norm=46.016, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=3.014e-04, train_time=0.480
+[gpuc04:0/16] 2024-01-17 22:00:35,009 (trainer:737) INFO: 45epoch:train:901-1000batch: iter_time=1.167e-04, forward_time=0.106, loss_ctc=41.019, loss_att=51.379, acc=0.757, loss=48.271, backward_time=0.099, grad_norm=42.716, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.013e-04, train_time=0.433
+[gpuc04:0/16] 2024-01-17 22:01:25,060 (trainer:737) INFO: 45epoch:train:1001-1100batch: iter_time=1.141e-04, forward_time=0.140, loss_ctc=38.045, loss_att=49.598, acc=0.735, loss=46.132, backward_time=0.127, grad_norm=44.554, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.051, optim0_lr0=3.013e-04, train_time=0.500
+[gpuc04:0/16] 2024-01-17 22:02:09,388 (trainer:737) INFO: 45epoch:train:1101-1200batch: iter_time=1.345e-04, forward_time=0.106, loss_ctc=48.696, loss_att=51.991, acc=0.718, loss=51.002, backward_time=0.098, grad_norm=59.619, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.013e-04, train_time=0.443
+[gpuc04:0/16] 2024-01-17 22:02:59,723 (multiple_iter_factory:32) INFO: Building 1th iter-factory...
+[gpuc04:0/16] 2024-01-17 22:03:20,108 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 22:03:24,069 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.5", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.5", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.5", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.5", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe7460574c0>)
+[gpuc04:0/16] 2024-01-17 22:03:24,069 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.5, 
+[gpuc04:0/16] 2024-01-17 22:03:24,072 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 22:11:10,637 (trainer:737) INFO: 45epoch:train:1201-1300batch: iter_time=4.922, forward_time=0.109, loss_ctc=41.084, loss_att=55.661, acc=0.732, loss=51.288, backward_time=0.099, grad_norm=50.447, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.013e-04, train_time=5.412
+[gpuc04:0/16] 2024-01-17 22:11:54,258 (trainer:737) INFO: 45epoch:train:1301-1400batch: iter_time=1.797e-04, forward_time=0.107, loss_ctc=41.741, loss_att=50.278, acc=0.731, loss=47.717, backward_time=0.097, grad_norm=46.092, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.012e-04, train_time=0.436
+[gpuc04:0/16] 2024-01-17 22:12:36,603 (trainer:737) INFO: 45epoch:train:1401-1500batch: iter_time=1.822e-04, forward_time=0.104, loss_ctc=45.014, loss_att=44.796, acc=0.752, loss=44.862, backward_time=0.096, grad_norm=46.395, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.012e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 22:13:21,636 (trainer:737) INFO: 45epoch:train:1501-1600batch: iter_time=2.069e-04, forward_time=0.104, loss_ctc=41.719, loss_att=43.352, acc=0.751, loss=42.862, backward_time=0.096, grad_norm=45.795, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.012e-04, train_time=0.450
+[gpuc04:0/16] 2024-01-17 22:14:04,154 (trainer:737) INFO: 45epoch:train:1601-1700batch: iter_time=1.902e-04, forward_time=0.104, loss_ctc=38.637, loss_att=45.410, acc=0.757, loss=43.378, backward_time=0.096, grad_norm=43.317, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.012e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 22:14:46,464 (trainer:737) INFO: 45epoch:train:1701-1800batch: iter_time=1.601e-04, forward_time=0.105, loss_ctc=44.322, loss_att=55.405, acc=0.754, loss=52.080, backward_time=0.097, grad_norm=45.521, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.012e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 22:15:28,140 (trainer:737) INFO: 45epoch:train:1801-1900batch: iter_time=1.544e-04, forward_time=0.105, loss_ctc=57.212, loss_att=53.059, acc=0.741, loss=54.305, backward_time=0.097, grad_norm=54.859, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.011e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:16:09,850 (trainer:737) INFO: 45epoch:train:1901-2000batch: iter_time=1.524e-04, forward_time=0.106, loss_ctc=56.297, loss_att=52.320, acc=0.747, loss=53.513, backward_time=0.097, grad_norm=63.371, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.011e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:16:51,310 (trainer:737) INFO: 45epoch:train:2001-2100batch: iter_time=1.849e-04, forward_time=0.104, loss_ctc=44.541, loss_att=51.714, acc=0.712, loss=49.562, backward_time=0.096, grad_norm=47.668, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.011e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 22:17:32,769 (trainer:737) INFO: 45epoch:train:2101-2200batch: iter_time=1.597e-04, forward_time=0.104, loss_ctc=37.060, loss_att=42.873, acc=0.756, loss=41.129, backward_time=0.096, grad_norm=41.946, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.011e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 22:18:14,275 (trainer:737) INFO: 45epoch:train:2201-2300batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=39.436, loss_att=57.256, acc=0.727, loss=51.910, backward_time=0.096, grad_norm=46.040, clip=100.000, loss_scale=2.949e+34, optim_step_time=0.039, optim0_lr0=3.010e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 22:18:57,187 (trainer:737) INFO: 45epoch:train:2301-2400batch: iter_time=1.986e-04, forward_time=0.112, loss_ctc=41.815, loss_att=44.190, acc=0.751, loss=43.478, backward_time=0.097, grad_norm=45.503, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.010e-04, train_time=0.429
+[gpuc04:0/16] 2024-01-17 22:19:39,146 (trainer:737) INFO: 45epoch:train:2401-2500batch: iter_time=1.611e-04, forward_time=0.104, loss_ctc=46.685, loss_att=52.821, acc=0.713, loss=50.980, backward_time=0.096, grad_norm=55.230, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.010e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:19:46,076 (multiple_iter_factory:32) INFO: Building 2th iter-factory...
+[gpuc04:0/16] 2024-01-17 22:20:05,711 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 22:20:09,496 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.2", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.2", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.2", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.2", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe74608b8b0>)
+[gpuc04:0/16] 2024-01-17 22:20:09,497 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.2, 
+[gpuc04:0/16] 2024-01-17 22:20:09,500 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 22:25:02,335 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 22:25:33,618 (trainer:737) INFO: 45epoch:train:2501-2600batch: iter_time=2.853, forward_time=0.106, loss_ctc=40.819, loss_att=55.215, acc=0.738, loss=50.896, backward_time=0.097, grad_norm=44.105, clip=100.000, loss_scale=2.643e+34, optim_step_time=0.039, optim0_lr0=3.010e-04, train_time=3.544
+[gpuc04:0/16] 2024-01-17 22:26:15,246 (trainer:737) INFO: 45epoch:train:2601-2700batch: iter_time=1.571e-04, forward_time=0.106, loss_ctc=39.698, loss_att=44.149, acc=0.763, loss=42.814, backward_time=0.097, grad_norm=41.908, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.009e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:26:56,897 (trainer:737) INFO: 45epoch:train:2701-2800batch: iter_time=1.530e-04, forward_time=0.106, loss_ctc=46.572, loss_att=49.687, acc=0.744, loss=48.753, backward_time=0.097, grad_norm=45.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.009e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:27:38,565 (trainer:737) INFO: 45epoch:train:2801-2900batch: iter_time=1.414e-04, forward_time=0.105, loss_ctc=34.551, loss_att=37.121, acc=0.764, loss=36.350, backward_time=0.096, grad_norm=40.904, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.009e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:28:21,026 (trainer:737) INFO: 45epoch:train:2901-3000batch: iter_time=1.790e-04, forward_time=0.107, loss_ctc=43.650, loss_att=52.811, acc=0.776, loss=50.062, backward_time=0.098, grad_norm=45.196, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.009e-04, train_time=0.424
+[gpuc04:0/16] 2024-01-17 22:29:03,357 (trainer:737) INFO: 45epoch:train:3001-3100batch: iter_time=1.880e-04, forward_time=0.106, loss_ctc=57.243, loss_att=57.212, acc=0.744, loss=57.221, backward_time=0.097, grad_norm=54.961, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.009e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 22:29:45,188 (trainer:737) INFO: 45epoch:train:3101-3200batch: iter_time=1.657e-04, forward_time=0.106, loss_ctc=48.821, loss_att=50.848, acc=0.752, loss=50.240, backward_time=0.097, grad_norm=49.869, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.008e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:30:27,362 (trainer:737) INFO: 45epoch:train:3201-3300batch: iter_time=1.571e-04, forward_time=0.110, loss_ctc=47.066, loss_att=45.693, acc=0.750, loss=46.105, backward_time=0.097, grad_norm=49.665, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.008e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 22:31:09,221 (trainer:737) INFO: 45epoch:train:3301-3400batch: iter_time=1.283e-04, forward_time=0.105, loss_ctc=41.827, loss_att=50.249, acc=0.736, loss=47.723, backward_time=0.096, grad_norm=45.322, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.008e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:31:50,933 (trainer:737) INFO: 45epoch:train:3401-3500batch: iter_time=1.561e-04, forward_time=0.106, loss_ctc=40.005, loss_att=50.554, acc=0.761, loss=47.389, backward_time=0.097, grad_norm=41.790, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.008e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:32:32,499 (trainer:737) INFO: 45epoch:train:3501-3600batch: iter_time=1.509e-04, forward_time=0.104, loss_ctc=37.828, loss_att=50.348, acc=0.735, loss=46.592, backward_time=0.096, grad_norm=43.667, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.007e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 22:33:14,310 (trainer:737) INFO: 45epoch:train:3601-3700batch: iter_time=1.576e-04, forward_time=0.106, loss_ctc=47.361, loss_att=50.399, acc=0.725, loss=49.488, backward_time=0.097, grad_norm=55.718, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.007e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:33:40,111 (multiple_iter_factory:32) INFO: Building 3th iter-factory...
+[gpuc04:0/16] 2024-01-17 22:33:59,432 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 22:34:03,104 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.9", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.9", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.9", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.9", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe40dfe6860>)
+[gpuc04:0/16] 2024-01-17 22:34:03,104 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.9, 
+[gpuc04:0/16] 2024-01-17 22:34:03,108 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 22:38:37,531 (trainer:737) INFO: 45epoch:train:3701-3800batch: iter_time=2.779, forward_time=0.125, loss_ctc=39.681, loss_att=53.458, acc=0.737, loss=49.325, backward_time=0.098, grad_norm=49.434, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.007e-04, train_time=3.232
+[gpuc04:0/16] 2024-01-17 22:39:19,142 (trainer:737) INFO: 45epoch:train:3801-3900batch: iter_time=1.609e-04, forward_time=0.105, loss_ctc=41.425, loss_att=49.306, acc=0.735, loss=46.942, backward_time=0.096, grad_norm=42.974, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.007e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:40:00,924 (trainer:737) INFO: 45epoch:train:3901-4000batch: iter_time=1.428e-04, forward_time=0.105, loss_ctc=43.905, loss_att=44.390, acc=0.754, loss=44.244, backward_time=0.096, grad_norm=45.454, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.007e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:40:42,406 (trainer:737) INFO: 45epoch:train:4001-4100batch: iter_time=1.543e-04, forward_time=0.105, loss_ctc=41.062, loss_att=43.159, acc=0.752, loss=42.530, backward_time=0.096, grad_norm=43.583, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.006e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 22:41:24,408 (trainer:737) INFO: 45epoch:train:4101-4200batch: iter_time=1.787e-04, forward_time=0.105, loss_ctc=38.637, loss_att=45.584, acc=0.758, loss=43.500, backward_time=0.096, grad_norm=42.506, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=3.006e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 22:42:06,197 (trainer:737) INFO: 45epoch:train:4201-4300batch: iter_time=1.643e-04, forward_time=0.106, loss_ctc=44.045, loss_att=55.459, acc=0.755, loss=52.035, backward_time=0.097, grad_norm=50.212, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.006e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:42:48,031 (trainer:737) INFO: 45epoch:train:4301-4400batch: iter_time=1.602e-04, forward_time=0.106, loss_ctc=56.521, loss_att=52.859, acc=0.742, loss=53.958, backward_time=0.097, grad_norm=54.406, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.006e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:43:29,864 (trainer:737) INFO: 45epoch:train:4401-4500batch: iter_time=1.583e-04, forward_time=0.106, loss_ctc=55.715, loss_att=52.153, acc=0.747, loss=53.222, backward_time=0.097, grad_norm=57.714, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.005e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:44:11,900 (trainer:737) INFO: 45epoch:train:4501-4600batch: iter_time=1.514e-04, forward_time=0.105, loss_ctc=43.280, loss_att=51.363, acc=0.714, loss=48.938, backward_time=0.096, grad_norm=45.808, clip=100.000, loss_scale=3.572e+34, optim_step_time=0.039, optim0_lr0=3.005e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 22:44:53,726 (trainer:737) INFO: 45epoch:train:4601-4700batch: iter_time=1.575e-04, forward_time=0.106, loss_ctc=36.751, loss_att=42.429, acc=0.758, loss=40.726, backward_time=0.096, grad_norm=41.947, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.005e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 22:45:06,500 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 22:45:35,639 (trainer:737) INFO: 45epoch:train:4701-4800batch: iter_time=1.503e-04, forward_time=0.106, loss_ctc=38.836, loss_att=56.603, acc=0.728, loss=51.273, backward_time=0.096, grad_norm=44.290, clip=100.000, loss_scale=2.685e+34, optim_step_time=0.038, optim0_lr0=3.005e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:46:17,528 (trainer:737) INFO: 45epoch:train:4801-4900batch: iter_time=1.785e-04, forward_time=0.106, loss_ctc=40.556, loss_att=43.010, acc=0.755, loss=42.273, backward_time=0.096, grad_norm=44.612, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.005e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:46:59,184 (trainer:737) INFO: 45epoch:train:4901-5000batch: iter_time=1.359e-04, forward_time=0.105, loss_ctc=45.480, loss_att=51.441, acc=0.716, loss=49.653, backward_time=0.096, grad_norm=56.793, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.004e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:47:05,659 (multiple_iter_factory:32) INFO: Building 4th iter-factory...
+[gpuc04:0/16] 2024-01-17 22:47:25,801 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 22:47:30,077 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.7", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.7", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.7", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.7", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe49c99b9a0>)
+[gpuc04:0/16] 2024-01-17 22:47:30,077 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.7, 
+[gpuc04:0/16] 2024-01-17 22:47:30,081 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 22:52:32,364 (trainer:737) INFO: 45epoch:train:5001-5100batch: iter_time=2.659, forward_time=0.137, loss_ctc=40.644, loss_att=50.889, acc=0.735, loss=47.815, backward_time=0.102, grad_norm=44.568, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.004e-04, train_time=3.332
+[gpuc04:0/16] 2024-01-17 22:53:14,297 (trainer:737) INFO: 45epoch:train:5101-5200batch: iter_time=1.565e-04, forward_time=0.104, loss_ctc=39.094, loss_att=43.303, acc=0.754, loss=42.041, backward_time=0.096, grad_norm=41.559, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.004e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:53:56,040 (trainer:737) INFO: 45epoch:train:5201-5300batch: iter_time=1.624e-04, forward_time=0.105, loss_ctc=46.383, loss_att=50.065, acc=0.732, loss=48.960, backward_time=0.096, grad_norm=44.996, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.004e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:54:37,617 (trainer:737) INFO: 45epoch:train:5301-5400batch: iter_time=1.907e-04, forward_time=0.104, loss_ctc=34.166, loss_att=35.761, acc=0.770, loss=35.283, backward_time=0.095, grad_norm=40.646, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.003e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 22:55:19,890 (trainer:737) INFO: 45epoch:train:5401-5500batch: iter_time=1.802e-04, forward_time=0.106, loss_ctc=43.097, loss_att=52.019, acc=0.768, loss=49.342, backward_time=0.097, grad_norm=44.798, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.003e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 22:56:01,635 (trainer:737) INFO: 45epoch:train:5501-5600batch: iter_time=1.464e-04, forward_time=0.106, loss_ctc=56.571, loss_att=56.892, acc=0.740, loss=56.796, backward_time=0.097, grad_norm=54.165, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.003e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:56:43,390 (trainer:737) INFO: 45epoch:train:5601-5700batch: iter_time=1.434e-04, forward_time=0.105, loss_ctc=48.566, loss_att=50.317, acc=0.750, loss=49.792, backward_time=0.097, grad_norm=48.764, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.003e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 22:57:25,346 (trainer:737) INFO: 45epoch:train:5701-5800batch: iter_time=1.622e-04, forward_time=0.105, loss_ctc=46.785, loss_att=45.593, acc=0.746, loss=45.950, backward_time=0.097, grad_norm=52.274, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.002e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:58:06,852 (trainer:737) INFO: 45epoch:train:5801-5900batch: iter_time=1.802e-04, forward_time=0.104, loss_ctc=40.894, loss_att=48.372, acc=0.731, loss=46.129, backward_time=0.096, grad_norm=45.468, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.002e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 22:58:48,728 (trainer:737) INFO: 45epoch:train:5901-6000batch: iter_time=1.520e-04, forward_time=0.105, loss_ctc=39.903, loss_att=50.341, acc=0.753, loss=47.209, backward_time=0.096, grad_norm=41.269, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.002e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 22:59:30,272 (trainer:737) INFO: 45epoch:train:6001-6100batch: iter_time=1.802e-04, forward_time=0.105, loss_ctc=37.420, loss_att=48.287, acc=0.733, loss=45.027, backward_time=0.096, grad_norm=42.526, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.002e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:00:11,930 (trainer:737) INFO: 45epoch:train:6101-6200batch: iter_time=1.413e-04, forward_time=0.105, loss_ctc=46.997, loss_att=50.930, acc=0.718, loss=49.750, backward_time=0.096, grad_norm=56.421, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.002e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:00:40,098 (multiple_iter_factory:32) INFO: Building 5th iter-factory...
+[gpuc04:0/16] 2024-01-17 23:00:59,519 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 23:01:03,257 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.4", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.4", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.4", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.4", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fde2b6df490>)
+[gpuc04:0/16] 2024-01-17 23:01:03,257 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.4, 
+[gpuc04:0/16] 2024-01-17 23:01:03,260 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 23:05:33,732 (trainer:737) INFO: 45epoch:train:6201-6300batch: iter_time=2.797, forward_time=0.105, loss_ctc=38.720, loss_att=52.238, acc=0.744, loss=48.183, backward_time=0.097, grad_norm=47.208, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.001e-04, train_time=3.218
+[gpuc04:0/16] 2024-01-17 23:06:15,867 (trainer:737) INFO: 45epoch:train:6301-6400batch: iter_time=2.075e-04, forward_time=0.106, loss_ctc=41.302, loss_att=51.915, acc=0.743, loss=48.731, backward_time=0.098, grad_norm=44.025, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=3.001e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 23:06:57,803 (trainer:737) INFO: 45epoch:train:6401-6500batch: iter_time=1.939e-04, forward_time=0.108, loss_ctc=44.049, loss_att=43.562, acc=0.765, loss=43.708, backward_time=0.096, grad_norm=44.269, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.001e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:07:39,493 (trainer:737) INFO: 45epoch:train:6501-6600batch: iter_time=2.001e-04, forward_time=0.105, loss_ctc=40.567, loss_att=42.822, acc=0.763, loss=42.145, backward_time=0.096, grad_norm=43.809, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.001e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:08:21,210 (trainer:737) INFO: 45epoch:train:6601-6700batch: iter_time=2.185e-04, forward_time=0.105, loss_ctc=38.076, loss_att=46.563, acc=0.758, loss=44.017, backward_time=0.096, grad_norm=41.671, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=3.000e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:09:03,121 (trainer:737) INFO: 45epoch:train:6701-6800batch: iter_time=1.853e-04, forward_time=0.106, loss_ctc=42.926, loss_att=55.563, acc=0.765, loss=51.772, backward_time=0.097, grad_norm=63.898, clip=100.000, loss_scale=3.531e+34, optim_step_time=0.039, optim0_lr0=3.000e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:09:45,200 (trainer:737) INFO: 45epoch:train:6801-6900batch: iter_time=1.809e-04, forward_time=0.106, loss_ctc=55.800, loss_att=53.362, acc=0.746, loss=54.093, backward_time=0.097, grad_norm=54.697, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.000e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 23:10:27,694 (trainer:737) INFO: 45epoch:train:6901-7000batch: iter_time=1.895e-04, forward_time=0.107, loss_ctc=54.521, loss_att=52.359, acc=0.756, loss=53.007, backward_time=0.098, grad_norm=56.071, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.000e-04, train_time=0.425
+[gpuc04:0/16] 2024-01-17 23:11:09,423 (trainer:737) INFO: 45epoch:train:7001-7100batch: iter_time=2.115e-04, forward_time=0.106, loss_ctc=42.951, loss_att=51.125, acc=0.731, loss=48.673, backward_time=0.097, grad_norm=46.154, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=3.000e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:11:51,067 (trainer:737) INFO: 45epoch:train:7101-7200batch: iter_time=2.032e-04, forward_time=0.105, loss_ctc=36.330, loss_att=42.083, acc=0.768, loss=40.357, backward_time=0.097, grad_norm=41.992, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.999e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:12:33,087 (trainer:737) INFO: 45epoch:train:7201-7300batch: iter_time=2.002e-04, forward_time=0.105, loss_ctc=38.894, loss_att=57.875, acc=0.737, loss=52.180, backward_time=0.099, grad_norm=58.023, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.041, optim0_lr0=2.999e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 23:13:03,636 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 23:13:15,415 (trainer:737) INFO: 45epoch:train:7301-7400batch: iter_time=2.210e-04, forward_time=0.106, loss_ctc=40.675, loss_att=43.139, acc=0.758, loss=42.400, backward_time=0.099, grad_norm=45.294, clip=100.000, loss_scale=3.566e+34, optim_step_time=0.041, optim0_lr0=2.999e-04, train_time=0.423
+[gpuc04:0/16] 2024-01-17 23:13:57,136 (trainer:737) INFO: 45epoch:train:7401-7500batch: iter_time=2.177e-04, forward_time=0.105, loss_ctc=45.718, loss_att=54.024, acc=0.719, loss=51.532, backward_time=0.099, grad_norm=56.932, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.041, optim0_lr0=2.999e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:14:01,572 (multiple_iter_factory:32) INFO: Building 6th iter-factory...
+[gpuc04:0/16] 2024-01-17 23:14:21,927 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 23:14:25,852 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.1", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.1", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.1", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.1", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe486d67700>)
+[gpuc04:0/16] 2024-01-17 23:14:25,852 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.1, 
+[gpuc04:0/16] 2024-01-17 23:14:25,855 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 23:19:11,855 (trainer:737) INFO: 45epoch:train:7501-7600batch: iter_time=2.722, forward_time=0.105, loss_ctc=40.811, loss_att=53.293, acc=0.731, loss=49.549, backward_time=0.097, grad_norm=44.643, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.998e-04, train_time=3.147
+[gpuc04:0/16] 2024-01-17 23:19:53,461 (trainer:737) INFO: 45epoch:train:7601-7700batch: iter_time=1.749e-04, forward_time=0.104, loss_ctc=39.245, loss_att=43.911, acc=0.755, loss=42.511, backward_time=0.096, grad_norm=44.417, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.998e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:20:35,332 (trainer:737) INFO: 45epoch:train:7701-7800batch: iter_time=1.769e-04, forward_time=0.104, loss_ctc=46.025, loss_att=50.103, acc=0.733, loss=48.880, backward_time=0.096, grad_norm=44.239, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.998e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:21:16,941 (trainer:737) INFO: 45epoch:train:7801-7900batch: iter_time=1.825e-04, forward_time=0.103, loss_ctc=33.753, loss_att=35.826, acc=0.769, loss=35.204, backward_time=0.095, grad_norm=40.750, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.998e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:21:59,047 (trainer:737) INFO: 45epoch:train:7901-8000batch: iter_time=1.589e-04, forward_time=0.105, loss_ctc=42.991, loss_att=51.925, acc=0.769, loss=49.245, backward_time=0.096, grad_norm=44.547, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.998e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 23:22:40,878 (trainer:737) INFO: 45epoch:train:8001-8100batch: iter_time=1.560e-04, forward_time=0.106, loss_ctc=56.842, loss_att=56.690, acc=0.739, loss=56.736, backward_time=0.097, grad_norm=53.900, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.997e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:23:22,658 (trainer:737) INFO: 45epoch:train:8101-8200batch: iter_time=1.772e-04, forward_time=0.107, loss_ctc=48.414, loss_att=49.898, acc=0.752, loss=49.452, backward_time=0.097, grad_norm=51.477, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.997e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:24:04,679 (trainer:737) INFO: 45epoch:train:8201-8300batch: iter_time=1.542e-04, forward_time=0.105, loss_ctc=46.425, loss_att=45.668, acc=0.746, loss=45.895, backward_time=0.096, grad_norm=52.170, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.997e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-17 23:24:46,159 (trainer:737) INFO: 45epoch:train:8301-8400batch: iter_time=1.665e-04, forward_time=0.105, loss_ctc=40.800, loss_att=49.785, acc=0.727, loss=47.089, backward_time=0.095, grad_norm=44.811, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.997e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:25:27,791 (trainer:737) INFO: 45epoch:train:8401-8500batch: iter_time=1.773e-04, forward_time=0.106, loss_ctc=39.688, loss_att=50.674, acc=0.752, loss=47.378, backward_time=0.096, grad_norm=42.922, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.996e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:26:09,292 (trainer:737) INFO: 45epoch:train:8501-8600batch: iter_time=1.645e-04, forward_time=0.105, loss_ctc=36.969, loss_att=48.194, acc=0.734, loss=44.827, backward_time=0.095, grad_norm=41.839, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.996e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:26:51,009 (trainer:737) INFO: 45epoch:train:8601-8700batch: iter_time=1.379e-04, forward_time=0.106, loss_ctc=46.663, loss_att=51.126, acc=0.720, loss=49.787, backward_time=0.097, grad_norm=56.671, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.996e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:27:17,175 (multiple_iter_factory:32) INFO: Building 7th iter-factory...
+[gpuc04:0/16] 2024-01-17 23:27:36,410 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 23:27:39,972 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.0", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.0", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.0", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.0", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fde243b37f0>)
+[gpuc04:0/16] 2024-01-17 23:27:39,972 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.0, 
+[gpuc04:0/16] 2024-01-17 23:27:39,975 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 23:32:08,394 (trainer:737) INFO: 45epoch:train:8701-8800batch: iter_time=2.751, forward_time=0.106, loss_ctc=37.983, loss_att=51.588, acc=0.743, loss=47.506, backward_time=0.097, grad_norm=46.981, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.996e-04, train_time=3.174
+[gpuc04:0/16] 2024-01-17 23:32:50,056 (trainer:737) INFO: 45epoch:train:8801-8900batch: iter_time=1.819e-04, forward_time=0.104, loss_ctc=40.968, loss_att=51.867, acc=0.743, loss=48.597, backward_time=0.097, grad_norm=43.589, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.996e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:33:31,622 (trainer:737) INFO: 45epoch:train:8901-9000batch: iter_time=1.445e-04, forward_time=0.103, loss_ctc=43.278, loss_att=43.035, acc=0.767, loss=43.108, backward_time=0.096, grad_norm=45.253, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.995e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:34:13,213 (trainer:737) INFO: 45epoch:train:9001-9100batch: iter_time=1.451e-04, forward_time=0.103, loss_ctc=40.789, loss_att=42.791, acc=0.764, loss=42.191, backward_time=0.096, grad_norm=44.814, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.995e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:34:54,792 (trainer:737) INFO: 45epoch:train:9101-9200batch: iter_time=1.798e-04, forward_time=0.104, loss_ctc=37.876, loss_att=46.818, acc=0.759, loss=44.136, backward_time=0.097, grad_norm=40.878, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.995e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:35:36,736 (trainer:737) INFO: 45epoch:train:9201-9300batch: iter_time=1.590e-04, forward_time=0.105, loss_ctc=43.216, loss_att=56.174, acc=0.764, loss=52.287, backward_time=0.097, grad_norm=45.394, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.995e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:36:18,487 (trainer:737) INFO: 45epoch:train:9301-9400batch: iter_time=1.513e-04, forward_time=0.104, loss_ctc=55.209, loss_att=53.186, acc=0.746, loss=53.793, backward_time=0.097, grad_norm=53.534, clip=100.000, loss_scale=2.658e+34, optim_step_time=0.038, optim0_lr0=2.994e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:37:00,302 (trainer:737) INFO: 45epoch:train:9401-9500batch: iter_time=1.837e-04, forward_time=0.106, loss_ctc=54.249, loss_att=52.243, acc=0.758, loss=52.844, backward_time=0.097, grad_norm=54.045, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.994e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:37:41,801 (trainer:737) INFO: 45epoch:train:9501-9600batch: iter_time=1.565e-04, forward_time=0.104, loss_ctc=42.428, loss_att=50.101, acc=0.729, loss=47.799, backward_time=0.096, grad_norm=46.223, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.994e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:38:23,983 (trainer:737) INFO: 45epoch:train:9601-9700batch: iter_time=1.791e-04, forward_time=0.105, loss_ctc=36.468, loss_att=42.242, acc=0.766, loss=40.510, backward_time=0.096, grad_norm=42.856, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.994e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 23:39:05,586 (trainer:737) INFO: 45epoch:train:9701-9800batch: iter_time=1.517e-04, forward_time=0.104, loss_ctc=38.401, loss_att=57.508, acc=0.737, loss=51.775, backward_time=0.097, grad_norm=42.716, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.993e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:39:47,498 (trainer:737) INFO: 45epoch:train:9801-9900batch: iter_time=1.552e-04, forward_time=0.104, loss_ctc=40.180, loss_att=42.690, acc=0.759, loss=41.937, backward_time=0.097, grad_norm=44.165, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.039, optim0_lr0=2.993e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:40:29,013 (trainer:737) INFO: 45epoch:train:9901-10000batch: iter_time=1.327e-04, forward_time=0.102, loss_ctc=44.190, loss_att=52.194, acc=0.719, loss=49.793, backward_time=0.097, grad_norm=54.994, clip=100.000, loss_scale=4.154e+34, optim_step_time=0.038, optim0_lr0=2.993e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-17 23:40:36,039 (multiple_iter_factory:32) INFO: Building 8th iter-factory...
+[gpuc04:0/16] 2024-01-17 23:40:55,521 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 23:40:59,329 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.3", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.3", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.3", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.3", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe13b21b5b0>)
+[gpuc04:0/16] 2024-01-17 23:40:59,329 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.3, 
+[gpuc04:0/16] 2024-01-17 23:40:59,332 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 23:45:08,281 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-17 23:45:46,696 (trainer:737) INFO: 45epoch:train:10001-10100batch: iter_time=2.754, forward_time=0.108, loss_ctc=40.781, loss_att=53.604, acc=0.731, loss=49.757, backward_time=0.098, grad_norm=45.469, clip=100.000, loss_scale=2.245e+34, optim_step_time=0.040, optim0_lr0=2.993e-04, train_time=3.177
+[gpuc04:0/16] 2024-01-17 23:46:28,673 (trainer:737) INFO: 45epoch:train:10101-10200batch: iter_time=2.033e-04, forward_time=0.104, loss_ctc=38.878, loss_att=44.086, acc=0.754, loss=42.524, backward_time=0.098, grad_norm=43.006, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=2.993e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:47:10,782 (trainer:737) INFO: 45epoch:train:10201-10300batch: iter_time=2.170e-04, forward_time=0.107, loss_ctc=45.836, loss_att=50.332, acc=0.731, loss=48.983, backward_time=0.096, grad_norm=45.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.992e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 23:47:52,361 (trainer:737) INFO: 45epoch:train:10301-10400batch: iter_time=2.208e-04, forward_time=0.103, loss_ctc=33.531, loss_att=35.834, acc=0.770, loss=35.143, backward_time=0.095, grad_norm=39.959, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.992e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:48:34,015 (trainer:737) INFO: 45epoch:train:10401-10500batch: iter_time=2.286e-04, forward_time=0.105, loss_ctc=42.990, loss_att=51.578, acc=0.769, loss=49.002, backward_time=0.096, grad_norm=45.355, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.992e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-17 23:49:15,697 (trainer:737) INFO: 45epoch:train:10501-10600batch: iter_time=2.037e-04, forward_time=0.104, loss_ctc=56.377, loss_att=56.588, acc=0.740, loss=56.524, backward_time=0.097, grad_norm=55.191, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.992e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-17 23:49:57,544 (trainer:737) INFO: 45epoch:train:10601-10700batch: iter_time=1.722e-04, forward_time=0.103, loss_ctc=47.884, loss_att=50.323, acc=0.751, loss=49.591, backward_time=0.097, grad_norm=48.867, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.991e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:50:39,411 (trainer:737) INFO: 45epoch:train:10701-10800batch: iter_time=1.565e-04, forward_time=0.104, loss_ctc=46.096, loss_att=45.450, acc=0.746, loss=45.644, backward_time=0.097, grad_norm=51.152, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.991e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-17 23:51:20,833 (trainer:737) INFO: 45epoch:train:10801-10900batch: iter_time=1.756e-04, forward_time=0.103, loss_ctc=40.049, loss_att=48.161, acc=0.734, loss=45.728, backward_time=0.095, grad_norm=42.705, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.991e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-17 23:52:02,970 (trainer:737) INFO: 45epoch:train:10901-11000batch: iter_time=1.428e-04, forward_time=0.103, loss_ctc=39.537, loss_att=50.905, acc=0.753, loss=47.495, backward_time=0.096, grad_norm=41.689, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.991e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-17 23:52:44,908 (trainer:737) INFO: 45epoch:train:11001-11100batch: iter_time=1.468e-04, forward_time=0.102, loss_ctc=37.066, loss_att=48.319, acc=0.734, loss=44.943, backward_time=0.095, grad_norm=43.229, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.991e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-17 23:53:27,136 (trainer:737) INFO: 45epoch:train:11101-11200batch: iter_time=1.704e-04, forward_time=0.109, loss_ctc=46.401, loss_att=50.674, acc=0.719, loss=49.393, backward_time=0.098, grad_norm=55.546, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.990e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-17 23:54:03,787 (multiple_iter_factory:32) INFO: Building 9th iter-factory...
+[gpuc04:0/16] 2024-01-17 23:54:24,067 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-17 23:54:27,829 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.10", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.10", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.10", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.10", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fde21f74490>)
+[gpuc04:0/16] 2024-01-17 23:54:27,829 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.10, 
+[gpuc04:0/16] 2024-01-17 23:54:27,832 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-17 23:59:17,244 (trainer:737) INFO: 45epoch:train:11201-11300batch: iter_time=3.068, forward_time=0.117, loss_ctc=37.664, loss_att=50.978, acc=0.748, loss=46.984, backward_time=0.098, grad_norm=46.556, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.990e-04, train_time=3.501
+[gpuc04:0/16] 2024-01-17 23:59:59,248 (trainer:737) INFO: 45epoch:train:11301-11400batch: iter_time=1.422e-04, forward_time=0.106, loss_ctc=40.798, loss_att=51.129, acc=0.746, loss=48.030, backward_time=0.097, grad_norm=43.013, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.990e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-18 00:00:40,948 (trainer:737) INFO: 45epoch:train:11401-11500batch: iter_time=1.456e-04, forward_time=0.106, loss_ctc=43.377, loss_att=43.772, acc=0.764, loss=43.654, backward_time=0.097, grad_norm=46.826, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.040, optim0_lr0=2.990e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-18 00:00:44,631 (trainer:668) WARNING: The grad norm is nan. Skipping updating the model.
+[gpuc04:0/16] 2024-01-18 00:01:22,537 (trainer:737) INFO: 45epoch:train:11501-11600batch: iter_time=1.566e-04, forward_time=0.105, loss_ctc=40.352, loss_att=41.997, acc=0.765, loss=41.503, backward_time=0.097, grad_norm=41.611, clip=100.000, loss_scale=1.122e+34, optim_step_time=0.040, optim0_lr0=2.989e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:02:04,486 (trainer:737) INFO: 45epoch:train:11601-11700batch: iter_time=1.550e-04, forward_time=0.103, loss_ctc=38.051, loss_att=46.869, acc=0.758, loss=44.223, backward_time=0.097, grad_norm=40.907, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.989e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-18 00:02:46,333 (trainer:737) INFO: 45epoch:train:11701-11800batch: iter_time=1.521e-04, forward_time=0.104, loss_ctc=43.430, loss_att=55.041, acc=0.766, loss=51.558, backward_time=0.098, grad_norm=44.474, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.989e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-18 00:03:28,138 (trainer:737) INFO: 45epoch:train:11801-11900batch: iter_time=1.245e-04, forward_time=0.104, loss_ctc=55.338, loss_att=53.289, acc=0.745, loss=53.904, backward_time=0.098, grad_norm=53.551, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.989e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-18 00:04:10,049 (trainer:737) INFO: 45epoch:train:11901-12000batch: iter_time=1.550e-04, forward_time=0.105, loss_ctc=54.012, loss_att=52.140, acc=0.758, loss=52.701, backward_time=0.099, grad_norm=54.664, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.989e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-18 00:04:51,602 (trainer:737) INFO: 45epoch:train:12001-12100batch: iter_time=1.429e-04, forward_time=0.104, loss_ctc=42.206, loss_att=50.420, acc=0.728, loss=47.955, backward_time=0.096, grad_norm=44.310, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.988e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:05:33,111 (trainer:737) INFO: 45epoch:train:12101-12200batch: iter_time=1.518e-04, forward_time=0.104, loss_ctc=36.492, loss_att=42.681, acc=0.765, loss=40.824, backward_time=0.097, grad_norm=40.959, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.988e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:06:14,717 (trainer:737) INFO: 45epoch:train:12201-12300batch: iter_time=1.533e-04, forward_time=0.103, loss_ctc=38.386, loss_att=57.894, acc=0.738, loss=52.042, backward_time=0.096, grad_norm=43.699, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.988e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:06:56,272 (trainer:737) INFO: 45epoch:train:12301-12400batch: iter_time=1.453e-04, forward_time=0.104, loss_ctc=39.893, loss_att=43.404, acc=0.756, loss=42.350, backward_time=0.096, grad_norm=43.758, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.988e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:07:38,447 (trainer:737) INFO: 45epoch:train:12401-12500batch: iter_time=1.208e-04, forward_time=0.103, loss_ctc=44.673, loss_att=52.768, acc=0.726, loss=50.340, backward_time=0.096, grad_norm=56.410, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.987e-04, train_time=0.422
+[gpuc04:0/16] 2024-01-18 00:07:43,633 (multiple_iter_factory:32) INFO: Building 10th iter-factory...
+[gpuc04:0/16] 2024-01-18 00:08:03,901 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-18 00:08:07,877 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.11", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.11", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.11", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.11", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fe133f977c0>)
+[gpuc04:0/16] 2024-01-18 00:08:07,877 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.11, 
+[gpuc04:0/16] 2024-01-18 00:08:07,880 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-18 00:12:55,715 (trainer:737) INFO: 45epoch:train:12501-12600batch: iter_time=2.756, forward_time=0.105, loss_ctc=40.073, loss_att=53.089, acc=0.732, loss=49.184, backward_time=0.096, grad_norm=44.801, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.987e-04, train_time=3.172
+[gpuc04:0/16] 2024-01-18 00:13:37,089 (trainer:737) INFO: 45epoch:train:12601-12700batch: iter_time=2.137e-04, forward_time=0.103, loss_ctc=39.070, loss_att=44.044, acc=0.754, loss=42.552, backward_time=0.095, grad_norm=43.891, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.039, optim0_lr0=2.987e-04, train_time=0.413
+[gpuc04:0/16] 2024-01-18 00:14:18,683 (trainer:737) INFO: 45epoch:train:12701-12800batch: iter_time=1.800e-04, forward_time=0.104, loss_ctc=45.586, loss_att=49.965, acc=0.732, loss=48.651, backward_time=0.096, grad_norm=45.479, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.987e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:14:59,919 (trainer:737) INFO: 45epoch:train:12801-12900batch: iter_time=2.195e-04, forward_time=0.102, loss_ctc=33.241, loss_att=35.641, acc=0.771, loss=34.921, backward_time=0.094, grad_norm=40.205, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.987e-04, train_time=0.412
+[gpuc04:0/16] 2024-01-18 00:15:41,564 (trainer:737) INFO: 45epoch:train:12901-13000batch: iter_time=1.885e-04, forward_time=0.104, loss_ctc=42.793, loss_att=51.700, acc=0.769, loss=49.028, backward_time=0.096, grad_norm=45.112, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.986e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:16:23,486 (trainer:737) INFO: 45epoch:train:13001-13100batch: iter_time=1.865e-04, forward_time=0.104, loss_ctc=56.410, loss_att=56.590, acc=0.740, loss=56.536, backward_time=0.096, grad_norm=52.296, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.986e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-18 00:17:05,151 (trainer:737) INFO: 45epoch:train:13101-13200batch: iter_time=1.592e-04, forward_time=0.104, loss_ctc=47.645, loss_att=50.141, acc=0.751, loss=49.392, backward_time=0.096, grad_norm=50.381, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.986e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:17:46,768 (trainer:737) INFO: 45epoch:train:13201-13300batch: iter_time=1.609e-04, forward_time=0.103, loss_ctc=46.408, loss_att=46.004, acc=0.746, loss=46.125, backward_time=0.096, grad_norm=52.226, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.986e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:18:28,209 (trainer:737) INFO: 45epoch:train:13301-13400batch: iter_time=1.707e-04, forward_time=0.102, loss_ctc=40.122, loss_att=48.214, acc=0.733, loss=45.787, backward_time=0.095, grad_norm=42.442, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.985e-04, train_time=0.414
+[gpuc04:0/16] 2024-01-18 00:19:09,764 (trainer:737) INFO: 45epoch:train:13401-13500batch: iter_time=1.524e-04, forward_time=0.103, loss_ctc=39.351, loss_att=50.402, acc=0.753, loss=47.087, backward_time=0.096, grad_norm=42.216, clip=100.000, loss_scale=1.038e+34, optim_step_time=0.038, optim0_lr0=2.985e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:19:51,801 (trainer:737) INFO: 45epoch:train:13501-13600batch: iter_time=1.438e-04, forward_time=0.103, loss_ctc=36.462, loss_att=48.078, acc=0.735, loss=44.593, backward_time=0.096, grad_norm=42.871, clip=100.000, loss_scale=1.983e+34, optim_step_time=0.038, optim0_lr0=2.985e-04, train_time=0.420
+[gpuc04:0/16] 2024-01-18 00:20:34,651 (trainer:737) INFO: 45epoch:train:13601-13700batch: iter_time=1.681e-04, forward_time=0.103, loss_ctc=45.507, loss_att=50.196, acc=0.719, loss=48.789, backward_time=0.096, grad_norm=54.768, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.985e-04, train_time=0.428
+[gpuc04:0/16] 2024-01-18 00:21:00,082 (multiple_iter_factory:32) INFO: Building 11th iter-factory...
+[gpuc04:0/16] 2024-01-18 00:21:20,600 (s2t:445) INFO: Optional Data Names: ('text_prev', 'text_ctc', 'text_spk2', 'text_spk3', 'text_spk4')
+[gpuc04:0/16] 2024-01-18 00:21:24,258 (abs_task:1616) INFO: [train] dataset:
+ESPnetDataset(
+  speech: {"path": "exp/s2t_stats_raw_bpe50000/splits12/wav.scp/split.6", "type": "kaldi_ark"}
+  text_prev: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.prev/split.6", "type": "text"}
+  text_ctc: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text.ctc/split.6", "type": "text"}
+  text: {"path": "exp/s2t_stats_raw_bpe50000/splits12/text/split.6", "type": "text"}
+  preprocess: <espnet2.train.preprocessor.S2TPreprocessor object at 0x7fde21fd5990>)
+[gpuc04:0/16] 2024-01-18 00:21:24,259 (abs_task:1617) INFO: [train] Batch sampler: UnsortedBatchSampler(N-batch=19027, batch_size=256, key_file=exp/s2t_stats_raw_bpe50000/splits12/speech_shape/split.6, 
+[gpuc04:0/16] 2024-01-18 00:21:24,262 (abs_task:1618) INFO: [train] mini-batch sizes summary: N-batch=19027, mean=256.0, min=256, max=257
+[gpuc04:0/16] 2024-01-18 00:25:44,977 (trainer:737) INFO: 45epoch:train:13701-13800batch: iter_time=2.686, forward_time=0.104, loss_ctc=38.193, loss_att=51.572, acc=0.747, loss=47.559, backward_time=0.096, grad_norm=48.115, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.985e-04, train_time=3.103
+[gpuc04:0/16] 2024-01-18 00:26:26,928 (trainer:737) INFO: 45epoch:train:13801-13900batch: iter_time=1.485e-04, forward_time=0.103, loss_ctc=40.701, loss_att=51.059, acc=0.747, loss=47.951, backward_time=0.096, grad_norm=44.231, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.984e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-18 00:27:08,896 (trainer:737) INFO: 45epoch:train:13901-14000batch: iter_time=1.323e-04, forward_time=0.102, loss_ctc=43.018, loss_att=43.845, acc=0.764, loss=43.597, backward_time=0.096, grad_norm=44.410, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.039, optim0_lr0=2.984e-04, train_time=0.419
+[gpuc04:0/16] 2024-01-18 00:27:50,466 (trainer:737) INFO: 45epoch:train:14001-14100batch: iter_time=1.276e-04, forward_time=0.102, loss_ctc=40.473, loss_att=42.348, acc=0.767, loss=41.785, backward_time=0.096, grad_norm=42.069, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.984e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:28:31,993 (trainer:737) INFO: 45epoch:train:14101-14200batch: iter_time=1.188e-04, forward_time=0.102, loss_ctc=37.831, loss_att=46.616, acc=0.758, loss=43.980, backward_time=0.096, grad_norm=41.982, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.984e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:29:13,718 (trainer:737) INFO: 45epoch:train:14201-14300batch: iter_time=1.260e-04, forward_time=0.103, loss_ctc=43.014, loss_att=54.837, acc=0.767, loss=51.290, backward_time=0.096, grad_norm=44.281, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.983e-04, train_time=0.417
+[gpuc04:0/16] 2024-01-18 00:29:55,814 (trainer:737) INFO: 45epoch:train:14301-14400batch: iter_time=1.231e-04, forward_time=0.103, loss_ctc=55.654, loss_att=53.455, acc=0.745, loss=54.115, backward_time=0.097, grad_norm=56.694, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.983e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-18 00:30:37,679 (trainer:737) INFO: 45epoch:train:14401-14500batch: iter_time=1.238e-04, forward_time=0.103, loss_ctc=54.340, loss_att=52.127, acc=0.757, loss=52.791, backward_time=0.097, grad_norm=53.393, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.983e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-18 00:31:19,224 (trainer:737) INFO: 45epoch:train:14501-14600batch: iter_time=1.385e-04, forward_time=0.102, loss_ctc=42.087, loss_att=50.241, acc=0.731, loss=47.795, backward_time=0.096, grad_norm=45.985, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.983e-04, train_time=0.415
+[gpuc04:0/16] 2024-01-18 00:32:01,342 (trainer:737) INFO: 45epoch:train:14601-14700batch: iter_time=1.226e-04, forward_time=0.105, loss_ctc=35.914, loss_att=42.087, acc=0.767, loss=40.235, backward_time=0.096, grad_norm=41.056, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.983e-04, train_time=0.421
+[gpuc04:0/16] 2024-01-18 00:32:42,964 (trainer:737) INFO: 45epoch:train:14701-14800batch: iter_time=1.117e-04, forward_time=0.102, loss_ctc=38.358, loss_att=58.090, acc=0.737, loss=52.170, backward_time=0.096, grad_norm=42.858, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.982e-04, train_time=0.416
+[gpuc04:0/16] 2024-01-18 00:33:24,815 (trainer:737) INFO: 45epoch:train:14801-14900batch: iter_time=1.202e-04, forward_time=0.102, loss_ctc=39.802, loss_att=43.384, acc=0.755, loss=42.309, backward_time=0.096, grad_norm=44.602, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.982e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-18 00:34:06,628 (trainer:737) INFO: 45epoch:train:14901-15000batch: iter_time=1.252e-04, forward_time=0.102, loss_ctc=44.404, loss_att=51.468, acc=0.727, loss=49.349, backward_time=0.096, grad_norm=56.659, clip=100.000, loss_scale=2.077e+34, optim_step_time=0.038, optim0_lr0=2.982e-04, train_time=0.418
+[gpuc04:0/16] 2024-01-18 00:54:01,035 (trainer:343) INFO: 45epoch results: [train] iter_time=0.236, forward_time=0.106, loss_ctc=43.342, loss_att=49.292, acc=0.747, loss=47.507, backward_time=0.097, grad_norm=47.254, clip=100.000, loss_scale=2.194e+34, optim_step_time=0.039, optim0_lr0=2.999e-04, train_time=0.661, time=2 hours, 45 minutes and 23.13 seconds, total_count=675000, gpu_max_cached_mem_GB=27.988, [valid] loss_ctc=46.933, cer_ctc=0.249, loss_att=51.428, acc=0.603, cer=0.381, wer=0.999, loss=50.080, time=19 minutes and 45.36 seconds, total_count=210195, gpu_max_cached_mem_GB=27.988
+gpuc04:3119859:3120002 [3] NCCL INFO [Service thread] Connection closed by localRank 3
+gpuc04:3119862:3119997 [6] NCCL INFO [Service thread] Connection closed by localRank 6
+gpuc04:3119859:3119859 [3] NCCL INFO comm 0x12119740 rank 3 nranks 16 cudaDev 3 busId 4c000 - Abort COMPLETE
+gpuc06:1624907:1625016 [1] NCCL INFO [Service thread] Connection closed by localRank 1
+gpuc06:1624911:1625011 [5] NCCL INFO [Service thread] Connection closed by localRank 5
+gpuc04:3119862:3119862 [6] NCCL INFO comm 0x125d1780 rank 6 nranks 16 cudaDev 6 busId c8000 - Abort COMPLETE
+gpuc06:1624913:1625015 [7] NCCL INFO [Service thread] Connection closed by localRank 7
+gpuc06:1624911:1624911 [5] NCCL INFO comm 0xf0ace60 rank 13 nranks 16 cudaDev 5 busId 8b000 - Abort COMPLETE
+gpuc06:1624908:1625012 [2] NCCL INFO [Service thread] Connection closed by localRank 2
+gpuc06:1624907:1624907 [1] NCCL INFO comm 0xfa6c470 rank 9 nranks 16 cudaDev 1 busId b000 - Abort COMPLETE
+gpuc04:3119861:3119998 [5] NCCL INFO [Service thread] Connection closed by localRank 5
+gpuc06:1624909:1625017 [3] NCCL INFO [Service thread] Connection closed by localRank 3
+gpuc06:1624913:1624913 [7] NCCL INFO comm 0x52e33e00 rank 15 nranks 16 cudaDev 7 busId cb000 - Abort COMPLETE
+gpuc06:1624909:1624909 [3] NCCL INFO comm 0x5305cf80 rank 11 nranks 16 cudaDev 3 busId 4c000 - Abort COMPLETE
+gpuc04:3119861:3119861 [5] NCCL INFO comm 0x7fc3350 rank 5 nranks 16 cudaDev 5 busId 8b000 - Abort COMPLETE
+gpuc06:1624908:1624908 [2] NCCL INFO comm 0x574b7630 rank 10 nranks 16 cudaDev 2 busId 48000 - Abort COMPLETE
+gpuc04:3119857:3119999 [1] NCCL INFO [Service thread] Connection closed by localRank 1
+gpuc04:3119860:3120000 [4] NCCL INFO [Service thread] Connection closed by localRank 4
+gpuc06:1624910:1625014 [4] NCCL INFO [Service thread] Connection closed by localRank 4
+gpuc06:1624912:1625018 [6] NCCL INFO [Service thread] Connection closed by localRank 6
+gpuc04:3119860:3119860 [4] NCCL INFO comm 0xed6dc00 rank 4 nranks 16 cudaDev 4 busId 88000 - Abort COMPLETE
+gpuc04:3119857:3119857 [1] NCCL INFO comm 0x58d82f80 rank 1 nranks 16 cudaDev 1 busId b000 - Abort COMPLETE
+gpuc06:1624912:1624912 [6] NCCL INFO comm 0x109e2e20 rank 14 nranks 16 cudaDev 6 busId c8000 - Abort COMPLETE
+gpuc06:1624906:1625013 [0] NCCL INFO [Service thread] Connection closed by localRank 0
+gpuc06:1624910:1624910 [4] NCCL INFO comm 0xb764400 rank 12 nranks 16 cudaDev 4 busId 88000 - Abort COMPLETE
+gpuc04:3119858:3119995 [2] NCCL INFO [Service thread] Connection closed by localRank 2
+gpuc06:1624906:1624906 [0] NCCL INFO comm 0xf7092c0 rank 8 nranks 16 cudaDev 0 busId 7000 - Abort COMPLETE
+gpuc04:3119858:3119858 [2] NCCL INFO comm 0x119f4e30 rank 2 nranks 16 cudaDev 2 busId 48000 - Abort COMPLETE
+gpuc04:3119863:3120001 [7] NCCL INFO [Service thread] Connection closed by localRank 7
+gpuc04:3119863:3119863 [7] NCCL INFO comm 0x577eca40 rank 7 nranks 16 cudaDev 7 busId cb000 - Abort COMPLETE
+[gpuc04:0/16] 2024-01-18 00:54:06,226 (trainer:391) INFO: The best model has been updated: valid.total_count
+[gpuc04:0/16] 2024-01-18 00:54:06,286 (trainer:445) INFO: The model files were removed: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/40epoch.pth
+[gpuc04:0/16] 2024-01-18 00:54:06,286 (trainer:463) INFO: The training was finished at 45 epochs 
+[gpuc04:0/16] 2024-01-18 00:54:06,316 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.acc": exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.acc.ave_5best.pth
+[gpuc04:0/16] 2024-01-18 00:54:18,872 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.total_count": exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.total_count.ave_5best.pth
+gpuc04:3119856:3119996 [0] NCCL INFO [Service thread] Connection closed by localRank 0
+gpuc04:3119856:3119856 [0] NCCL INFO comm 0xacef5c0 rank 0 nranks 16 cudaDev 0 busId 7000 - Abort COMPLETE
+# Accounting: begin_time=1705429183
+# Accounting: end_time=1705560872
+# Accounting: time=131689 threads=1
+# Finished at Thu Jan 18 00:54:32 CST 2024 with status 0
diff --git a/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.total_count.ave_5best.pth b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.total_count.ave_5best.pth
new file mode 100644
index 0000000000000000000000000000000000000000..148b02fe72577ecb24b63f5be8b3a740dd5249fb
--- /dev/null
+++ b/exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.total_count.ave_5best.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99e5de1865e2c98308b41ce6f28b7f658bec7b274da60f37b219a99279d43f3a
+size 404971245
diff --git a/meta.yaml b/meta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..50a93104030ae684cc9c7c577a1babdaf111ebff
--- /dev/null
+++ b/meta.yaml
@@ -0,0 +1,8 @@
+espnet: '202308'
+files:
+  s2t_model_file: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/valid.total_count.ave_5best.pth
+python: 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0]
+timestamp: 1705960184.857766
+torch: 1.13.1
+yaml_files:
+  s2t_train_config: exp/s2t_train_s2t_ebf_conv2d_size384_e6_d6_piecewise_lr1e-3_warmup60k_flashattn_lessreg_raw_bpe50000/config.yaml