Update model
Browse files- README.md +439 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/config.yaml +361 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_backward_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_fake_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_forward_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_optim_step_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_real_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_train_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_adv_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_backward_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_dur_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_feat_match_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_forward_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_kl_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_mel_loss.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_optim_step_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_train_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/gpu_max_cached_mem_GB.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/iter_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/optim0_lr0.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/optim1_lr0.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/train_time.png +0 -0
- exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/train.total_count.ave_10best.pth +3 -0
- meta.yaml +8 -0
README.md
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---
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license: cc-by-4.0
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---
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---
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tags:
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- espnet
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- audio
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- text-to-speech
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language: jp
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datasets:
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- studies
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license: cc-by-4.0
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---
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## ESPnet2 TTS model
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### `fujie/fujie_studies_tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody`
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This model was trained by Shinya Fujie using studies recipe in [espnet](https://github.com/espnet/espnet/).
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### Demo: How to use in ESPnet2
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Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
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if you haven't done that already.
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```bash
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cd espnet
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git checkout 2219358fbd064d79214b12540afd498feaf49596
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pip install -e .
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cd egs2/studies/tts1
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./run.sh --skip_data_prep false --skip_train true --download_model fujie/fujie_studies_tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody
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```
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## TTS config
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<details><summary>expand</summary>
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```
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config: ./conf/tuning/finetune_vits.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/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody
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ngpu: 1
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seed: 777
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num_workers: 4
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num_att_plot: 3
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dist_backend: nccl
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dist_init_method: env://
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dist_world_size: 2
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dist_rank: 0
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local_rank: 0
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dist_master_addr: localhost
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dist_master_port: 57369
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dist_launcher: null
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multiprocessing_distributed: true
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unused_parameters: true
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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: 100
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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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- - train
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- total_count
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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: -1
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grad_clip_type: 2.0
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grad_noise: false
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accum_grad: 1
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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: false
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log_interval: 50
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use_matplotlib: true
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use_tensorboard: true
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create_graph_in_tensorboard: false
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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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- downloads/models--espnet--kan-bayashi_jsut_vits_prosody/snapshots/3a859bfd2c9710846fa6244598000f0578a2d3e4/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody/train.total_count.ave_10best.pth
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ignore_init_mismatch: false
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freeze_param: []
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num_iters_per_epoch: 1000
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batch_size: 20
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valid_batch_size: null
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batch_bins: 1000000
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valid_batch_bins: null
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train_shape_file:
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- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/train/text_shape.phn
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- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/train/speech_shape
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valid_shape_file:
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- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/valid/text_shape.phn
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- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/valid/speech_shape
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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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- 150
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- 204800
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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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chunk_excluded_key_prefixes: []
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train_data_path_and_name_and_type:
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- - dump/22k/raw/ITA_tr_no_dev/text
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- text
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- text
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- - dump/22k/raw/ITA_tr_no_dev/wav.scp
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- speech
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- sound
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valid_data_path_and_name_and_type:
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- - dump/22k/raw/ITA_dev/text
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- text
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- text
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- - dump/22k/raw/ITA_dev/wav.scp
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- speech
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- sound
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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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exclude_weight_decay: false
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exclude_weight_decay_conf: {}
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optim: adamw
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optim_conf:
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lr: 0.0001
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betas:
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- 0.8
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- 0.99
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eps: 1.0e-09
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weight_decay: 0.0
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scheduler: exponentiallr
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scheduler_conf:
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gamma: 0.999875
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optim2: adamw
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optim2_conf:
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lr: 0.0001
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betas:
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- 0.8
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- 0.99
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eps: 1.0e-09
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weight_decay: 0.0
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scheduler2: exponentiallr
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scheduler2_conf:
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gamma: 0.999875
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generator_first: false
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token_list:
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- <blank>
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- <unk>
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- a
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- o
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- i
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- '['
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- '#'
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- u
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- ']'
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- e
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- k
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- n
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- t
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- r
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- s
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- N
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- m
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- _
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- sh
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- d
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- g
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- ^
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- $
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- w
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- cl
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- h
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- y
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- b
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- j
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- ts
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- ch
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- z
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- p
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- f
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- ky
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- ry
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- gy
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- hy
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- ny
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- by
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- my
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- py
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- v
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- dy
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- '?'
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- ty
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- <sos/eos>
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odim: null
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model_conf: {}
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use_preprocessor: true
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token_type: phn
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bpemodel: null
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non_linguistic_symbols: null
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cleaner: jaconv
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g2p: pyopenjtalk_prosody
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feats_extract: linear_spectrogram
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feats_extract_conf:
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n_fft: 1024
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hop_length: 256
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win_length: null
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normalize: null
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normalize_conf: {}
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tts: vits
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tts_conf:
|
234 |
+
generator_type: vits_generator
|
235 |
+
generator_params:
|
236 |
+
hidden_channels: 192
|
237 |
+
spks: -1
|
238 |
+
global_channels: -1
|
239 |
+
segment_size: 32
|
240 |
+
text_encoder_attention_heads: 2
|
241 |
+
text_encoder_ffn_expand: 4
|
242 |
+
text_encoder_blocks: 6
|
243 |
+
text_encoder_positionwise_layer_type: conv1d
|
244 |
+
text_encoder_positionwise_conv_kernel_size: 3
|
245 |
+
text_encoder_positional_encoding_layer_type: rel_pos
|
246 |
+
text_encoder_self_attention_layer_type: rel_selfattn
|
247 |
+
text_encoder_activation_type: swish
|
248 |
+
text_encoder_normalize_before: true
|
249 |
+
text_encoder_dropout_rate: 0.1
|
250 |
+
text_encoder_positional_dropout_rate: 0.0
|
251 |
+
text_encoder_attention_dropout_rate: 0.1
|
252 |
+
use_macaron_style_in_text_encoder: true
|
253 |
+
use_conformer_conv_in_text_encoder: false
|
254 |
+
text_encoder_conformer_kernel_size: -1
|
255 |
+
decoder_kernel_size: 7
|
256 |
+
decoder_channels: 512
|
257 |
+
decoder_upsample_scales:
|
258 |
+
- 8
|
259 |
+
- 8
|
260 |
+
- 2
|
261 |
+
- 2
|
262 |
+
decoder_upsample_kernel_sizes:
|
263 |
+
- 16
|
264 |
+
- 16
|
265 |
+
- 4
|
266 |
+
- 4
|
267 |
+
decoder_resblock_kernel_sizes:
|
268 |
+
- 3
|
269 |
+
- 7
|
270 |
+
- 11
|
271 |
+
decoder_resblock_dilations:
|
272 |
+
- - 1
|
273 |
+
- 3
|
274 |
+
- 5
|
275 |
+
- - 1
|
276 |
+
- 3
|
277 |
+
- 5
|
278 |
+
- - 1
|
279 |
+
- 3
|
280 |
+
- 5
|
281 |
+
use_weight_norm_in_decoder: true
|
282 |
+
posterior_encoder_kernel_size: 5
|
283 |
+
posterior_encoder_layers: 16
|
284 |
+
posterior_encoder_stacks: 1
|
285 |
+
posterior_encoder_base_dilation: 1
|
286 |
+
posterior_encoder_dropout_rate: 0.0
|
287 |
+
use_weight_norm_in_posterior_encoder: true
|
288 |
+
flow_flows: 4
|
289 |
+
flow_kernel_size: 5
|
290 |
+
flow_base_dilation: 1
|
291 |
+
flow_layers: 4
|
292 |
+
flow_dropout_rate: 0.0
|
293 |
+
use_weight_norm_in_flow: true
|
294 |
+
use_only_mean_in_flow: true
|
295 |
+
stochastic_duration_predictor_kernel_size: 3
|
296 |
+
stochastic_duration_predictor_dropout_rate: 0.5
|
297 |
+
stochastic_duration_predictor_flows: 4
|
298 |
+
stochastic_duration_predictor_dds_conv_layers: 3
|
299 |
+
vocabs: 47
|
300 |
+
aux_channels: 513
|
301 |
+
discriminator_type: hifigan_multi_scale_multi_period_discriminator
|
302 |
+
discriminator_params:
|
303 |
+
scales: 1
|
304 |
+
scale_downsample_pooling: AvgPool1d
|
305 |
+
scale_downsample_pooling_params:
|
306 |
+
kernel_size: 4
|
307 |
+
stride: 2
|
308 |
+
padding: 2
|
309 |
+
scale_discriminator_params:
|
310 |
+
in_channels: 1
|
311 |
+
out_channels: 1
|
312 |
+
kernel_sizes:
|
313 |
+
- 15
|
314 |
+
- 41
|
315 |
+
- 5
|
316 |
+
- 3
|
317 |
+
channels: 128
|
318 |
+
max_downsample_channels: 1024
|
319 |
+
max_groups: 16
|
320 |
+
bias: true
|
321 |
+
downsample_scales:
|
322 |
+
- 2
|
323 |
+
- 2
|
324 |
+
- 4
|
325 |
+
- 4
|
326 |
+
- 1
|
327 |
+
nonlinear_activation: LeakyReLU
|
328 |
+
nonlinear_activation_params:
|
329 |
+
negative_slope: 0.1
|
330 |
+
use_weight_norm: true
|
331 |
+
use_spectral_norm: false
|
332 |
+
follow_official_norm: false
|
333 |
+
periods:
|
334 |
+
- 2
|
335 |
+
- 3
|
336 |
+
- 5
|
337 |
+
- 7
|
338 |
+
- 11
|
339 |
+
period_discriminator_params:
|
340 |
+
in_channels: 1
|
341 |
+
out_channels: 1
|
342 |
+
kernel_sizes:
|
343 |
+
- 5
|
344 |
+
- 3
|
345 |
+
channels: 32
|
346 |
+
downsample_scales:
|
347 |
+
- 3
|
348 |
+
- 3
|
349 |
+
- 3
|
350 |
+
- 3
|
351 |
+
- 1
|
352 |
+
max_downsample_channels: 1024
|
353 |
+
bias: true
|
354 |
+
nonlinear_activation: LeakyReLU
|
355 |
+
nonlinear_activation_params:
|
356 |
+
negative_slope: 0.1
|
357 |
+
use_weight_norm: true
|
358 |
+
use_spectral_norm: false
|
359 |
+
generator_adv_loss_params:
|
360 |
+
average_by_discriminators: false
|
361 |
+
loss_type: mse
|
362 |
+
discriminator_adv_loss_params:
|
363 |
+
average_by_discriminators: false
|
364 |
+
loss_type: mse
|
365 |
+
feat_match_loss_params:
|
366 |
+
average_by_discriminators: false
|
367 |
+
average_by_layers: false
|
368 |
+
include_final_outputs: true
|
369 |
+
mel_loss_params:
|
370 |
+
fs: 22050
|
371 |
+
n_fft: 1024
|
372 |
+
hop_length: 256
|
373 |
+
win_length: null
|
374 |
+
window: hann
|
375 |
+
n_mels: 80
|
376 |
+
fmin: 0
|
377 |
+
fmax: null
|
378 |
+
log_base: null
|
379 |
+
lambda_adv: 1.0
|
380 |
+
lambda_mel: 45.0
|
381 |
+
lambda_feat_match: 2.0
|
382 |
+
lambda_dur: 1.0
|
383 |
+
lambda_kl: 1.0
|
384 |
+
sampling_rate: 22050
|
385 |
+
cache_generator_outputs: true
|
386 |
+
pitch_extract: null
|
387 |
+
pitch_extract_conf: {}
|
388 |
+
pitch_normalize: null
|
389 |
+
pitch_normalize_conf: {}
|
390 |
+
energy_extract: null
|
391 |
+
energy_extract_conf: {}
|
392 |
+
energy_normalize: null
|
393 |
+
energy_normalize_conf: {}
|
394 |
+
required:
|
395 |
+
- output_dir
|
396 |
+
- token_list
|
397 |
+
version: '202304'
|
398 |
+
distributed: true
|
399 |
+
```
|
400 |
+
|
401 |
+
</details>
|
402 |
+
|
403 |
+
|
404 |
+
|
405 |
+
### Citing ESPnet
|
406 |
+
|
407 |
+
```BibTex
|
408 |
+
@inproceedings{watanabe2018espnet,
|
409 |
+
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},
|
410 |
+
title={{ESPnet}: End-to-End Speech Processing Toolkit},
|
411 |
+
year={2018},
|
412 |
+
booktitle={Proceedings of Interspeech},
|
413 |
+
pages={2207--2211},
|
414 |
+
doi={10.21437/Interspeech.2018-1456},
|
415 |
+
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
|
416 |
+
}
|
417 |
+
|
418 |
+
|
419 |
+
|
420 |
+
|
421 |
+
@inproceedings{hayashi2020espnet,
|
422 |
+
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
|
423 |
+
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
|
424 |
+
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
|
425 |
+
pages={7654--7658},
|
426 |
+
year={2020},
|
427 |
+
organization={IEEE}
|
428 |
+
}
|
429 |
+
```
|
430 |
+
|
431 |
+
or arXiv:
|
432 |
+
|
433 |
+
```bibtex
|
434 |
+
@misc{watanabe2018espnet,
|
435 |
+
title={ESPnet: End-to-End Speech Processing Toolkit},
|
436 |
+
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},
|
437 |
+
year={2018},
|
438 |
+
eprint={1804.00015},
|
439 |
+
archivePrefix={arXiv},
|
440 |
+
primaryClass={cs.CL}
|
441 |
+
}
|
442 |
+
```
|
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/config.yaml
ADDED
@@ -0,0 +1,361 @@
|
|
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|
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|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
config: ./conf/tuning/finetune_vits.yaml
|
2 |
+
print_config: false
|
3 |
+
log_level: INFO
|
4 |
+
dry_run: false
|
5 |
+
iterator_type: sequence
|
6 |
+
output_dir: exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody
|
7 |
+
ngpu: 1
|
8 |
+
seed: 777
|
9 |
+
num_workers: 4
|
10 |
+
num_att_plot: 3
|
11 |
+
dist_backend: nccl
|
12 |
+
dist_init_method: env://
|
13 |
+
dist_world_size: 2
|
14 |
+
dist_rank: 0
|
15 |
+
local_rank: 0
|
16 |
+
dist_master_addr: localhost
|
17 |
+
dist_master_port: 57369
|
18 |
+
dist_launcher: null
|
19 |
+
multiprocessing_distributed: true
|
20 |
+
unused_parameters: true
|
21 |
+
sharded_ddp: false
|
22 |
+
cudnn_enabled: true
|
23 |
+
cudnn_benchmark: false
|
24 |
+
cudnn_deterministic: false
|
25 |
+
collect_stats: false
|
26 |
+
write_collected_feats: false
|
27 |
+
max_epoch: 100
|
28 |
+
patience: null
|
29 |
+
val_scheduler_criterion:
|
30 |
+
- valid
|
31 |
+
- loss
|
32 |
+
early_stopping_criterion:
|
33 |
+
- valid
|
34 |
+
- loss
|
35 |
+
- min
|
36 |
+
best_model_criterion:
|
37 |
+
- - train
|
38 |
+
- total_count
|
39 |
+
- max
|
40 |
+
keep_nbest_models: 10
|
41 |
+
nbest_averaging_interval: 0
|
42 |
+
grad_clip: -1
|
43 |
+
grad_clip_type: 2.0
|
44 |
+
grad_noise: false
|
45 |
+
accum_grad: 1
|
46 |
+
no_forward_run: false
|
47 |
+
resume: true
|
48 |
+
train_dtype: float32
|
49 |
+
use_amp: false
|
50 |
+
log_interval: 50
|
51 |
+
use_matplotlib: true
|
52 |
+
use_tensorboard: true
|
53 |
+
create_graph_in_tensorboard: false
|
54 |
+
use_wandb: false
|
55 |
+
wandb_project: null
|
56 |
+
wandb_id: null
|
57 |
+
wandb_entity: null
|
58 |
+
wandb_name: null
|
59 |
+
wandb_model_log_interval: -1
|
60 |
+
detect_anomaly: false
|
61 |
+
pretrain_path: null
|
62 |
+
init_param:
|
63 |
+
- downloads/models--espnet--kan-bayashi_jsut_vits_prosody/snapshots/3a859bfd2c9710846fa6244598000f0578a2d3e4/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody/train.total_count.ave_10best.pth
|
64 |
+
ignore_init_mismatch: false
|
65 |
+
freeze_param: []
|
66 |
+
num_iters_per_epoch: 1000
|
67 |
+
batch_size: 20
|
68 |
+
valid_batch_size: null
|
69 |
+
batch_bins: 1000000
|
70 |
+
valid_batch_bins: null
|
71 |
+
train_shape_file:
|
72 |
+
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/train/text_shape.phn
|
73 |
+
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/train/speech_shape
|
74 |
+
valid_shape_file:
|
75 |
+
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/valid/text_shape.phn
|
76 |
+
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_prosody/valid/speech_shape
|
77 |
+
batch_type: numel
|
78 |
+
valid_batch_type: null
|
79 |
+
fold_length:
|
80 |
+
- 150
|
81 |
+
- 204800
|
82 |
+
sort_in_batch: descending
|
83 |
+
sort_batch: descending
|
84 |
+
multiple_iterator: false
|
85 |
+
chunk_length: 500
|
86 |
+
chunk_shift_ratio: 0.5
|
87 |
+
num_cache_chunks: 1024
|
88 |
+
chunk_excluded_key_prefixes: []
|
89 |
+
train_data_path_and_name_and_type:
|
90 |
+
- - dump/22k/raw/ITA_tr_no_dev/text
|
91 |
+
- text
|
92 |
+
- text
|
93 |
+
- - dump/22k/raw/ITA_tr_no_dev/wav.scp
|
94 |
+
- speech
|
95 |
+
- sound
|
96 |
+
valid_data_path_and_name_and_type:
|
97 |
+
- - dump/22k/raw/ITA_dev/text
|
98 |
+
- text
|
99 |
+
- text
|
100 |
+
- - dump/22k/raw/ITA_dev/wav.scp
|
101 |
+
- speech
|
102 |
+
- sound
|
103 |
+
allow_variable_data_keys: false
|
104 |
+
max_cache_size: 0.0
|
105 |
+
max_cache_fd: 32
|
106 |
+
valid_max_cache_size: null
|
107 |
+
exclude_weight_decay: false
|
108 |
+
exclude_weight_decay_conf: {}
|
109 |
+
optim: adamw
|
110 |
+
optim_conf:
|
111 |
+
lr: 0.0001
|
112 |
+
betas:
|
113 |
+
- 0.8
|
114 |
+
- 0.99
|
115 |
+
eps: 1.0e-09
|
116 |
+
weight_decay: 0.0
|
117 |
+
scheduler: exponentiallr
|
118 |
+
scheduler_conf:
|
119 |
+
gamma: 0.999875
|
120 |
+
optim2: adamw
|
121 |
+
optim2_conf:
|
122 |
+
lr: 0.0001
|
123 |
+
betas:
|
124 |
+
- 0.8
|
125 |
+
- 0.99
|
126 |
+
eps: 1.0e-09
|
127 |
+
weight_decay: 0.0
|
128 |
+
scheduler2: exponentiallr
|
129 |
+
scheduler2_conf:
|
130 |
+
gamma: 0.999875
|
131 |
+
generator_first: false
|
132 |
+
token_list:
|
133 |
+
- <blank>
|
134 |
+
- <unk>
|
135 |
+
- a
|
136 |
+
- o
|
137 |
+
- i
|
138 |
+
- '['
|
139 |
+
- '#'
|
140 |
+
- u
|
141 |
+
- ']'
|
142 |
+
- e
|
143 |
+
- k
|
144 |
+
- n
|
145 |
+
- t
|
146 |
+
- r
|
147 |
+
- s
|
148 |
+
- N
|
149 |
+
- m
|
150 |
+
- _
|
151 |
+
- sh
|
152 |
+
- d
|
153 |
+
- g
|
154 |
+
- ^
|
155 |
+
- $
|
156 |
+
- w
|
157 |
+
- cl
|
158 |
+
- h
|
159 |
+
- y
|
160 |
+
- b
|
161 |
+
- j
|
162 |
+
- ts
|
163 |
+
- ch
|
164 |
+
- z
|
165 |
+
- p
|
166 |
+
- f
|
167 |
+
- ky
|
168 |
+
- ry
|
169 |
+
- gy
|
170 |
+
- hy
|
171 |
+
- ny
|
172 |
+
- by
|
173 |
+
- my
|
174 |
+
- py
|
175 |
+
- v
|
176 |
+
- dy
|
177 |
+
- '?'
|
178 |
+
- ty
|
179 |
+
- <sos/eos>
|
180 |
+
odim: null
|
181 |
+
model_conf: {}
|
182 |
+
use_preprocessor: true
|
183 |
+
token_type: phn
|
184 |
+
bpemodel: null
|
185 |
+
non_linguistic_symbols: null
|
186 |
+
cleaner: jaconv
|
187 |
+
g2p: pyopenjtalk_prosody
|
188 |
+
feats_extract: linear_spectrogram
|
189 |
+
feats_extract_conf:
|
190 |
+
n_fft: 1024
|
191 |
+
hop_length: 256
|
192 |
+
win_length: null
|
193 |
+
normalize: null
|
194 |
+
normalize_conf: {}
|
195 |
+
tts: vits
|
196 |
+
tts_conf:
|
197 |
+
generator_type: vits_generator
|
198 |
+
generator_params:
|
199 |
+
hidden_channels: 192
|
200 |
+
spks: -1
|
201 |
+
global_channels: -1
|
202 |
+
segment_size: 32
|
203 |
+
text_encoder_attention_heads: 2
|
204 |
+
text_encoder_ffn_expand: 4
|
205 |
+
text_encoder_blocks: 6
|
206 |
+
text_encoder_positionwise_layer_type: conv1d
|
207 |
+
text_encoder_positionwise_conv_kernel_size: 3
|
208 |
+
text_encoder_positional_encoding_layer_type: rel_pos
|
209 |
+
text_encoder_self_attention_layer_type: rel_selfattn
|
210 |
+
text_encoder_activation_type: swish
|
211 |
+
text_encoder_normalize_before: true
|
212 |
+
text_encoder_dropout_rate: 0.1
|
213 |
+
text_encoder_positional_dropout_rate: 0.0
|
214 |
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text_encoder_attention_dropout_rate: 0.1
|
215 |
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use_macaron_style_in_text_encoder: true
|
216 |
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use_conformer_conv_in_text_encoder: false
|
217 |
+
text_encoder_conformer_kernel_size: -1
|
218 |
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decoder_kernel_size: 7
|
219 |
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decoder_channels: 512
|
220 |
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decoder_upsample_scales:
|
221 |
+
- 8
|
222 |
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- 8
|
223 |
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- 2
|
224 |
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- 2
|
225 |
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decoder_upsample_kernel_sizes:
|
226 |
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- 16
|
227 |
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- 16
|
228 |
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- 4
|
229 |
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- 4
|
230 |
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decoder_resblock_kernel_sizes:
|
231 |
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- 3
|
232 |
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- 7
|
233 |
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- 11
|
234 |
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decoder_resblock_dilations:
|
235 |
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- - 1
|
236 |
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- 3
|
237 |
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- 5
|
238 |
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- - 1
|
239 |
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- 3
|
240 |
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- 5
|
241 |
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- - 1
|
242 |
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- 3
|
243 |
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- 5
|
244 |
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use_weight_norm_in_decoder: true
|
245 |
+
posterior_encoder_kernel_size: 5
|
246 |
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posterior_encoder_layers: 16
|
247 |
+
posterior_encoder_stacks: 1
|
248 |
+
posterior_encoder_base_dilation: 1
|
249 |
+
posterior_encoder_dropout_rate: 0.0
|
250 |
+
use_weight_norm_in_posterior_encoder: true
|
251 |
+
flow_flows: 4
|
252 |
+
flow_kernel_size: 5
|
253 |
+
flow_base_dilation: 1
|
254 |
+
flow_layers: 4
|
255 |
+
flow_dropout_rate: 0.0
|
256 |
+
use_weight_norm_in_flow: true
|
257 |
+
use_only_mean_in_flow: true
|
258 |
+
stochastic_duration_predictor_kernel_size: 3
|
259 |
+
stochastic_duration_predictor_dropout_rate: 0.5
|
260 |
+
stochastic_duration_predictor_flows: 4
|
261 |
+
stochastic_duration_predictor_dds_conv_layers: 3
|
262 |
+
vocabs: 47
|
263 |
+
aux_channels: 513
|
264 |
+
discriminator_type: hifigan_multi_scale_multi_period_discriminator
|
265 |
+
discriminator_params:
|
266 |
+
scales: 1
|
267 |
+
scale_downsample_pooling: AvgPool1d
|
268 |
+
scale_downsample_pooling_params:
|
269 |
+
kernel_size: 4
|
270 |
+
stride: 2
|
271 |
+
padding: 2
|
272 |
+
scale_discriminator_params:
|
273 |
+
in_channels: 1
|
274 |
+
out_channels: 1
|
275 |
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kernel_sizes:
|
276 |
+
- 15
|
277 |
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- 41
|
278 |
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- 5
|
279 |
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- 3
|
280 |
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channels: 128
|
281 |
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max_downsample_channels: 1024
|
282 |
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max_groups: 16
|
283 |
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bias: true
|
284 |
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downsample_scales:
|
285 |
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- 2
|
286 |
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- 2
|
287 |
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- 4
|
288 |
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- 4
|
289 |
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- 1
|
290 |
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nonlinear_activation: LeakyReLU
|
291 |
+
nonlinear_activation_params:
|
292 |
+
negative_slope: 0.1
|
293 |
+
use_weight_norm: true
|
294 |
+
use_spectral_norm: false
|
295 |
+
follow_official_norm: false
|
296 |
+
periods:
|
297 |
+
- 2
|
298 |
+
- 3
|
299 |
+
- 5
|
300 |
+
- 7
|
301 |
+
- 11
|
302 |
+
period_discriminator_params:
|
303 |
+
in_channels: 1
|
304 |
+
out_channels: 1
|
305 |
+
kernel_sizes:
|
306 |
+
- 5
|
307 |
+
- 3
|
308 |
+
channels: 32
|
309 |
+
downsample_scales:
|
310 |
+
- 3
|
311 |
+
- 3
|
312 |
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- 3
|
313 |
+
- 3
|
314 |
+
- 1
|
315 |
+
max_downsample_channels: 1024
|
316 |
+
bias: true
|
317 |
+
nonlinear_activation: LeakyReLU
|
318 |
+
nonlinear_activation_params:
|
319 |
+
negative_slope: 0.1
|
320 |
+
use_weight_norm: true
|
321 |
+
use_spectral_norm: false
|
322 |
+
generator_adv_loss_params:
|
323 |
+
average_by_discriminators: false
|
324 |
+
loss_type: mse
|
325 |
+
discriminator_adv_loss_params:
|
326 |
+
average_by_discriminators: false
|
327 |
+
loss_type: mse
|
328 |
+
feat_match_loss_params:
|
329 |
+
average_by_discriminators: false
|
330 |
+
average_by_layers: false
|
331 |
+
include_final_outputs: true
|
332 |
+
mel_loss_params:
|
333 |
+
fs: 22050
|
334 |
+
n_fft: 1024
|
335 |
+
hop_length: 256
|
336 |
+
win_length: null
|
337 |
+
window: hann
|
338 |
+
n_mels: 80
|
339 |
+
fmin: 0
|
340 |
+
fmax: null
|
341 |
+
log_base: null
|
342 |
+
lambda_adv: 1.0
|
343 |
+
lambda_mel: 45.0
|
344 |
+
lambda_feat_match: 2.0
|
345 |
+
lambda_dur: 1.0
|
346 |
+
lambda_kl: 1.0
|
347 |
+
sampling_rate: 22050
|
348 |
+
cache_generator_outputs: true
|
349 |
+
pitch_extract: null
|
350 |
+
pitch_extract_conf: {}
|
351 |
+
pitch_normalize: null
|
352 |
+
pitch_normalize_conf: {}
|
353 |
+
energy_extract: null
|
354 |
+
energy_extract_conf: {}
|
355 |
+
energy_normalize: null
|
356 |
+
energy_normalize_conf: {}
|
357 |
+
required:
|
358 |
+
- output_dir
|
359 |
+
- token_list
|
360 |
+
version: '202304'
|
361 |
+
distributed: true
|
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_backward_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_fake_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_forward_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_optim_step_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_real_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/discriminator_train_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_adv_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_backward_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_dur_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_feat_match_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_forward_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_kl_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_mel_loss.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_optim_step_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/generator_train_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/gpu_max_cached_mem_GB.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/iter_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/optim0_lr0.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/optim1_lr0.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/images/train_time.png
ADDED
exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/train.total_count.ave_10best.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:f6f3779d002e9faa88d506fe9c7fa41560830e8bdca027476e2cb0d4efe7d7c1
|
3 |
+
size 372534219
|
meta.yaml
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
espnet: '202304'
|
2 |
+
files:
|
3 |
+
model_file: exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/train.total_count.ave_10best.pth
|
4 |
+
python: 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0]
|
5 |
+
timestamp: 1684334640.290265
|
6 |
+
torch: 1.13.1
|
7 |
+
yaml_files:
|
8 |
+
train_config: exp/tts_finetune_vits_raw_phn_jaconv_pyopenjtalk_prosody/config.yaml
|