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--- |
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tags: |
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- espnet |
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- audio |
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- automatic-speech-recognition |
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language: noinfo |
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datasets: |
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- librispeech_100 |
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license: cc-by-4.0 |
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--- |
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## ESPnet2 ASR model |
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### `jkang/espnet2_librispeech_100_conformer_char` |
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This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). |
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### Demo: How to use in ESPnet2 |
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```bash |
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cd espnet |
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git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504 |
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pip install -e . |
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cd egs2/librispeech_100/asr1 |
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./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer_char |
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``` |
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<!-- Generated by scripts/utils/show_asr_result.sh --> |
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# RESULTS |
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## Environments |
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- date: `Thu Feb 24 17:47:04 KST 2022` |
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- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` |
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- espnet version: `espnet 0.10.7a1` |
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- pytorch version: `pytorch 1.10.1` |
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- Git hash: `82a0a0fa97b8a4a578f0a2c031ec49b3afec1504` |
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- Commit date: `Wed Feb 23 08:06:47 2022 +0900` |
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## asr_conformer_lr2e-3_warmup15k_amp_nondeterministic_char |
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### WER |
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |
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|---|---|---|---|---|---|---|---|---| |
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|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|93.9|5.6|0.5|0.7|6.8|57.1| |
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|decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|82.5|15.7|1.8|1.9|19.3|82.6| |
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|decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|93.8|5.7|0.6|0.7|6.9|58.4| |
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|decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|82.2|15.9|2.0|1.7|19.5|83.6| |
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### CER |
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |
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|---|---|---|---|---|---|---|---|---| |
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|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.3|1.0|0.7|0.7|2.4|57.1| |
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|decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.3|4.1|2.6|1.9|8.7|82.6| |
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|decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98.3|1.0|0.7|0.6|2.3|58.4| |
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|decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|93.2|4.1|2.7|1.8|8.6|83.6| |
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### TER |
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |
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|---|---|---|---|---|---|---|---|---| |
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## ASR config |
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<details><summary>expand</summary> |
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``` |
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config: conf/train_asr_char.yaml |
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print_config: false |
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log_level: INFO |
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dry_run: false |
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iterator_type: sequence |
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output_dir: exp/asr_conformer_lr2e-3_warmup15k_amp_nondeterministic_char |
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ngpu: 1 |
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seed: 2022 |
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num_workers: 4 |
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num_att_plot: 0 |
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dist_backend: nccl |
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dist_init_method: env:// |
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dist_world_size: null |
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dist_rank: null |
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local_rank: 0 |
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dist_master_addr: null |
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dist_master_port: null |
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dist_launcher: null |
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multiprocessing_distributed: false |
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unused_parameters: false |
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sharded_ddp: false |
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cudnn_enabled: true |
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cudnn_benchmark: false |
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cudnn_deterministic: false |
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collect_stats: false |
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write_collected_feats: false |
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max_epoch: 70 |
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patience: null |
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val_scheduler_criterion: |
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- valid |
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- loss |
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early_stopping_criterion: |
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- valid |
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- loss |
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- min |
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best_model_criterion: |
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- - valid |
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- acc |
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- max |
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keep_nbest_models: 10 |
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nbest_averaging_interval: 0 |
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grad_clip: 5.0 |
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grad_clip_type: 2.0 |
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grad_noise: false |
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accum_grad: 4 |
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no_forward_run: false |
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resume: true |
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train_dtype: float32 |
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use_amp: true |
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log_interval: 400 |
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use_matplotlib: true |
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use_tensorboard: true |
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use_wandb: false |
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wandb_project: null |
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wandb_id: null |
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wandb_entity: null |
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wandb_name: null |
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wandb_model_log_interval: -1 |
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detect_anomaly: false |
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pretrain_path: null |
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init_param: [] |
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ignore_init_mismatch: false |
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freeze_param: [] |
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num_iters_per_epoch: null |
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batch_size: 20 |
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valid_batch_size: null |
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batch_bins: 1600000 |
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valid_batch_bins: null |
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train_shape_file: |
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- exp/asr_stats_raw_en_char_sp/train/speech_shape |
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- exp/asr_stats_raw_en_char_sp/train/text_shape.char |
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valid_shape_file: |
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- exp/asr_stats_raw_en_char_sp/valid/speech_shape |
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- exp/asr_stats_raw_en_char_sp/valid/text_shape.char |
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batch_type: numel |
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valid_batch_type: null |
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fold_length: |
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- 80000 |
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- 150 |
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sort_in_batch: descending |
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sort_batch: descending |
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multiple_iterator: false |
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chunk_length: 500 |
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chunk_shift_ratio: 0.5 |
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num_cache_chunks: 1024 |
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train_data_path_and_name_and_type: |
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- - dump/raw/train_clean_100_sp/wav.scp |
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- speech |
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- kaldi_ark |
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- - dump/raw/train_clean_100_sp/text |
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- text |
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- text |
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valid_data_path_and_name_and_type: |
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- - dump/raw/dev/wav.scp |
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- speech |
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- kaldi_ark |
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- - dump/raw/dev/text |
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- text |
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- text |
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allow_variable_data_keys: false |
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max_cache_size: 0.0 |
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max_cache_fd: 32 |
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valid_max_cache_size: null |
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optim: adam |
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optim_conf: |
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lr: 0.002 |
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weight_decay: 1.0e-06 |
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scheduler: warmuplr |
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scheduler_conf: |
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warmup_steps: 15000 |
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token_list: |
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- <blank> |
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- <unk> |
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- <space> |
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- E |
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- T |
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- A |
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- O |
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- N |
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- I |
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- H |
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- S |
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- R |
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- D |
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- L |
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- U |
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- M |
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- C |
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- W |
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- F |
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- G |
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- Y |
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- P |
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- B |
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- V |
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- K |
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- '''' |
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- X |
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- J |
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- Q |
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- Z |
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- <sos/eos> |
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init: null |
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input_size: null |
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ctc_conf: |
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dropout_rate: 0.0 |
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ctc_type: builtin |
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reduce: true |
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ignore_nan_grad: true |
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joint_net_conf: null |
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model_conf: |
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ctc_weight: 0.3 |
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lsm_weight: 0.1 |
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length_normalized_loss: false |
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use_preprocessor: true |
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token_type: char |
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bpemodel: null |
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non_linguistic_symbols: null |
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cleaner: null |
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g2p: null |
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speech_volume_normalize: null |
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rir_scp: null |
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rir_apply_prob: 1.0 |
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noise_scp: null |
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noise_apply_prob: 1.0 |
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noise_db_range: '13_15' |
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frontend: default |
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frontend_conf: |
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n_fft: 512 |
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win_length: 400 |
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hop_length: 160 |
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fs: 16k |
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specaug: specaug |
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specaug_conf: |
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apply_time_warp: true |
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time_warp_window: 5 |
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time_warp_mode: bicubic |
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apply_freq_mask: true |
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freq_mask_width_range: |
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- 0 |
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- 27 |
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num_freq_mask: 2 |
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apply_time_mask: true |
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time_mask_width_ratio_range: |
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- 0.0 |
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- 0.05 |
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num_time_mask: 5 |
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normalize: global_mvn |
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normalize_conf: |
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stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz |
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preencoder: null |
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preencoder_conf: {} |
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encoder: conformer |
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encoder_conf: |
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output_size: 256 |
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attention_heads: 4 |
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linear_units: 1024 |
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num_blocks: 12 |
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dropout_rate: 0.1 |
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positional_dropout_rate: 0.1 |
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attention_dropout_rate: 0.1 |
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input_layer: conv2d |
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normalize_before: true |
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macaron_style: true |
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rel_pos_type: latest |
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pos_enc_layer_type: rel_pos |
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selfattention_layer_type: rel_selfattn |
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activation_type: swish |
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use_cnn_module: true |
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cnn_module_kernel: 31 |
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postencoder: null |
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postencoder_conf: {} |
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decoder: transformer |
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decoder_conf: |
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attention_heads: 4 |
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linear_units: 2048 |
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num_blocks: 6 |
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dropout_rate: 0.1 |
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positional_dropout_rate: 0.1 |
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self_attention_dropout_rate: 0.1 |
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src_attention_dropout_rate: 0.1 |
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required: |
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- output_dir |
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- token_list |
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version: 0.10.7a1 |
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distributed: false |
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``` |
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</details> |
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### Citing ESPnet |
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```BibTex |
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@inproceedings{watanabe2018espnet, |
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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}, |
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title={{ESPnet}: End-to-End Speech Processing Toolkit}, |
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year={2018}, |
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booktitle={Proceedings of Interspeech}, |
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pages={2207--2211}, |
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doi={10.21437/Interspeech.2018-1456}, |
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456} |
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} |
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``` |
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or arXiv: |
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```bibtex |
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@misc{watanabe2018espnet, |
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title={ESPnet: End-to-End Speech Processing Toolkit}, |
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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}, |
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year={2018}, |
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eprint={1804.00015}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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