```BibTex
@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}
}

```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  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},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp`


This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```bash
cd espnet
git checkout b0ff60946ada6753af79423a2e6063984bec2926
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
```


## ASR config


<details><summary>expand</summary>


</details>


### Citing ESPnet


or arXiv:


model name: simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`


♻ Imported from https://zenodo.org/record/5499066/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave


model author: espnet


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: es
datasets:
- mls
license: cc-by-4.0


## Example ESPnet2 ASR model


### `ftshijt/mls_asr_transformer_valid.acc.best`


♻ Imported from https://zenodo.org/record/4458452/


This model was trained by ftshijt using mls/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


### Citing ESPnet


or arXiv:


model name: ftshijt_mls_asr_transformer_valid.acc.best


task: automatic-speech-recognition


tags: es, dataset:mls, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest`


♻ Imported from https://zenodo.org/record/5432566/


This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```python
# coming soon
```


model name: kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest


model author: espnet


task: text-to-speech


tags: ja, dataset:jvs, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- aishell
license: cc-by-4.0


## Example ESPnet2 ASR model


### `Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4604023/


This model was trained by Emiru Tsunoo using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: Emiru_Tsunoo_aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave


task: automatic-speech-recognition


tags: zh, dataset:aishell, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- audio-source-separation
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
inference: false


# ESPnet2 ENH pretrained model


## `Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en`


♻ Imported from <https://zenodo.org/record/4498562#.YOAOApozZH4>.


This model was trained by Chenda Li using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).


### Python API


```text
See https://github.com/espnet/espnet_model_zoo
```


### Evaluate in the recipe


```python
# coming soon
```


### Results


```bash
# RESULTS
## Environments
- date: `Thu Feb  4 01:16:18 CST 2021`
- python version: `3.7.6 (default, Jan  8 2020, 19:59:22)  [GCC 7.3.0]`
- espnet version: `espnet 0.9.7`
- pytorch version: `pytorch 1.5.0`
- Git hash: `a3334220b0352931677946d178fade3313cf82bb`
  - Commit date: `Fri Jan 29 23:35:47 2021 +0800`

## enh_train_enh_conv_tasnet_raw

config: ./conf/tuning/train_enh_conv_tasnet.yaml

|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_min_8k|0.949205|17.3785|16.8028|26.9785|
|enhanced_tt_min_8k|0.95349|16.6221|15.9494|25.9032|
```


### Training config


See full config in [`config.yaml`](./exp/enh_train_enh_conv_tasnet_raw/config.yaml)


```yaml
config: ./conf/tuning/train_enh_conv_tasnet.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_conv_tasnet_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: chenda-li-wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave


model author: espnet


task: audio-to-audio


tags: en, dataset:wsj0_2mix, espnet, audio, audio-source-separation, audio-to-audio, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best`


♻ Imported from https://zenodo.org/record/3969118/


This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best


task: text-to-speech


tags: zh, dataset:csmsc, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: jv
datasets:
- jv_openslr35
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `jv_openslr35`


♻ Imported from https://zenodo.org/record/5090139/


This model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}

```


model name: jv_openslr35


model author: espnet


task: automatic-speech-recognition


tags: jv, dataset:jv_openslr35, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0


### `byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp`


♻ Imported from https://huggingface.co/


This model was trained by byan using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: byan_librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp


tags: en, dataset:librispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_fastspeech2`


♻ Imported from https://zenodo.org/record/4032224/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_fastspeech2


model author: espnet


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


### `kan-bayashi/ljspeech_fastspeech`


♻ Imported from https://zenodo.org/record/3986231/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_ljspeech_fastspeech


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- speaker-diarization
language: en
datasets:
- mini_librispeech
license: cc-by-4.0


## ESPnet2 DIAR pretrained model


### `Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best`


♻ Imported from https://zenodo.org/record/5264020/


This model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
```


model name: Yushi_Ueda_mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best


model author: espnet


tags: en, dataset:mini_librispeech, arxiv:1804.00015, espnet, audio, speaker-diarization, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`


♻ Imported from https://zenodo.org/record/4381096/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5521416/


This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: kan-bayashi_libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave


model author: espnet


tags: en, dataset:libritts, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


### `kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5521340/


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave


task: text-to-speech


## Example ESPnet2 TTS model


### `kan-bayashi/libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss`


♻ Imported from https://zenodo.org/record/4418754/


model name: kan-bayashi_libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


### `kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4394608/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_tts_train_gst_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


### `kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave`


♻ Imported from https://zenodo.org/record/4031953/


This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave


task: text-to-speech


tags: zh, dataset:csmsc, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- catslu
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/sujay_catslu_map`


This model was trained by Sujay S Kumar using catslu recipe in [espnet](https://github.com/espnet/espnet/).


```bash
cd espnet
git checkout e31965d55993766461f0964216a0bb9aea3cfb7a
pip install -e .
cd egs2/catslu/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/sujay_catslu_map
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Sun Oct  3 12:53:16 EDT 2021`
- python version: `3.9.5 (default, Jun  4 2021, 12:28:51)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a3`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c`
  - Commit date: `Wed Sep 22 10:02:03 2021 -0400`


## asr_train_asr_smaller_aishell_xlsr_raw_zh_word


### WER


### CER


### TER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|


## ASR config


<details><summary>expand</summary>


```
config: conf/train_asr_smaller_aishell_xlsr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp_train_asr_smaller_aishell_xlsr/asr_train_asr_smaller_aishell_xlsr_raw_zh_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - train
    - loss
    - min
-   - valid
    - loss
    - min
-   - train
    - acc
    - max
-   - valid
    - acc
    - max
keep_nbest_models: 5
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: false
log_interval: null
use_tensorboard: true
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:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/speech_shape
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/text_shape.word
valid_shape_file:
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/speech_shape
- exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech
    - sound
-   - dump/raw/train/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/valid/wav.scp
    - speech
    - sound
-   - dump/raw/valid/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.0001
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 2500
token_list:
- <blank>
- <unk>
- 航
- 导
- inform_操作_none
- inform_终点名称_none
- 去
- none_none_none
- 我
- 到
- inform_poi名称_none
- unknown
- 要
- 市
- side
- 一
- 个
- 路
- 区
- 第
- 大
- 县
- 你
- inform_序列号_none
- 小
- 城
- 站
- 家
- 南
- 中
- 山
- 州
- 好
- 镇
- 场
- 的
- 院
- 西
- 店
- 东
- 车
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- 学
- 北
- 园
- dialect
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- 人
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- 十
- 里
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- 关
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- robot
- 出
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- 乐
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- 来
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- inform_请求类型_none
- 最
- noise
- 兰
- 湾
- 台
- 所
- 保
- 什
- 福
- 建
- 说
- 就
- 沙
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- inform_页码_none
- 六
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- 成
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- 限
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- 云
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- 马
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- 务
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- 岛
- 没
- 生
- 常
- 理
- 会
- 们
- 重
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- 合
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- 顺
- 美
- 儿
- 头
- 乌
- 设
- 厦
- 化
- 郑
- 时
- inform_poi目标_none
- 现
- 农
- 港
- 泰
- 停
- 宜
- 昆
- 九
- 对
- 管
- 看
- 界
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- 文
- 博
- 嘉
- 零
- 苏
- 能
- 面
- 客
- 红
- 搜
- 远
- 古
- 津
- 始
- 王
- 呃
- 用
- 瑞
- 后
- 雅
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- 木
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- 汇
- 夏
- 他
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- 清
- 临
- 服
- 渡
- 日
- 幺
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- 吉
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- 利
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- 香
- 太
- 双
- 永
- 图
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- request_位置_none
- 技
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- inform_对象_none
- 义
- 多
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- 气
- 老
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- 池
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- 营
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- 置
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- 辉
- 批
- 音
- 康
- 威
- 幼
- 斯
- 库
- 拉
- 星
- 团
- 风
- 岗
- 话
- 放
- 泽
- 晋
- 部
- 知
- 外
- 塔
- 沈
- 奇
- 卫
- 月
- 庭
- 眼
- 总
- 梅
- 房
- 千
- 哈
- 自
- 字
- 呢
- 豪
- 直
- 盘
- 屯
- 超
- 祥
- 佳
- 恒
- 过
- 以
- 两
- 蓝
- 修
- 入
- 松
- 铁
- 职
- 珠
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- 当
- 李
- 钱
- s
- 货
- 惠
- 格
- 岳
- 淮
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- 社
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- 内
- 蒙
- 分
- 柏
- 富
- 碧
- 凰
- 陵
- 桐
- 边
- 坡
- 胶
- 得
- 力
- 滚
- 喀
- 旗
- 料
- 歌
- 块
- 滩
- 查
- 虹
- 续
- 为
- 驾
- 许
- 峰
- 问
- 真
- 视
- 选
- 接
- 语
- 洪
- 众
- 全
- 徽
- 鄂
- 实
- 未
- 杭
- 尚
- 胜
- 塘
- 产
- 鱼
- 叉
- 岸
- 洛
- 随
- 哎
- 配
- 丁
- 继
- 迪
- 牛
- 坪
- 无
- 深
- 圳
- 韩
- 法
- 灵
- 迁
- 间
- 逼
- 步
- 咸
- 期
- 菜
- 紫
- 邢
- 赣
- 横
- 播
- 鼎
- 进
- 止
- 铜
- 便
- 鸡
- 巴
- 仁
- 财
- 佛
- 桂
- 官
- 英
- 绵
- 奥
- 矿
- 波
- 治
- 元
- 首
- 钟
- 计
- 飞
- 坊
- 阿
- 代
- 周
- 朝
- 固
- 错
- 向
- 潭
- 隆
- 装
- 纳
- 伊
- 将
- 军
- 师
- 途
- 影
- 怀
- 择
- 药
- 术
- 手
- 于
- 离
- 族
- 莲
- 布
- 呼
- 峡
- 迈
- 委
- 叮
- 咚
- 阴
- 宏
- 郡
- 健
- 本
- 洋
- 再
- 支
- 划
- 郊
- 绿
- 妈
- 旅
- 堰
- 肥
- 玛
- 左
- 网
- inform_途经点名称_none
- 拜
- 材
- inform_终点修饰_none
- 辽
- 煤
- 谢
- 则
- 土
- 草
- 埠
- 伦
- 堂
- 卡
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- 底
- 灯
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- 寻
- 掉
- 展
- 庙
- 赵
- 余
- 见
- 望
- 故
- 事
- 相
- 杨
- inform_终点目标_none
- 馨
- 税
- 属
- 资
- 井
- 艺
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- 微
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- 阜
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- 甲
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- 休
- 啥
- 锡
- 渝
- 岩
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- 柳
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- request_路况_none
- 靖
- 沃
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- 防
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- 球
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- 坝
- 翠
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- 榆
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- 目
- t
- 凌
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- 轻
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- 黔
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- 像
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- 然
- 潍
- 基
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- 聊
- 驶
- inform_路线偏好_none
- 澄
- 几
- 等
- 塑
- 监
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- 螺
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- 奎
- 优
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- 唐
- 写
- 今
- 慢
- 傻
- 反
- 次
- 甘
- 肃
- 它
- 泗
- 贺
- 拍
- 咱
- 留
- ktv
- 察
- 顶
- 啦
- 别
- 润
- 谷
- 仙
- 慧
- 朱
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- 座
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- 邑
- 右
- 铺
- 梁
- 宣
- 幸
- 哥
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- 员
- 招
- 番
- 徐
- 检
- 巷
- 私
- 堡
- 跟
- 器
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- 立
- 氏
- 教
- 圣
- 购
- 印
- 黑
- 完
- 条
- 唉
- 燕
- 屿
- 闸
- 茶
- 任
- 种
- 蛋
- 荆
- 岔
- inform_value_none
- 黎
- 奉
- 准
- 熟
- 薛
- 朔
- 范
- 械
- 菲
- 雪
- 腾
- 备
- 琼
- 尹
- 垣
- 吴
- 示
- 嫖
- 宫
- 冲
- 毛
- 绘
- 菏
- 嘞
- 浙
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- 各
- 饶
- 嗷
- 简
- 施
- 俱
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- 豆
- 栋
- 险
- 岘
- 滇
- 叶
- 卓
- 荔
- 刘
- 滕
- 系
- 统
- e
- 做
- 巡
- 坐
- 研
- 究
- 盐
- 冀
- 象
- 斗
- 娄
- 先
- 陆
- deny_操作_none
- 户
- 额
- 价
- 更
- 拆
- 溧
- 量
- 帝
- 断
- 态
- 智
- 蜀
- 庐
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- inform_poi修饰_none
- 础
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- 创
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- 昔
- 衣
- 铭
- 浏
- 喆
- 垦
- 墓
- 戍
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
    extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
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'
frontend: s3prl
frontend_conf:
    frontend_conf:
        upstream: wav2vec2_xlsr
    download_dir: ./hub
    multilayer_feature: true
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
    input_size: 1024
    output_size: 80
encoder: conformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d
    normalize_before: true
    macaron_style: true
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    use_cnn_module: true
    cnn_module_kernel: 15
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 2048
    num_blocks: 4
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: 0.10.3a3
distributed: false
```


</details>


## LM config


```
NONE
```


model name: sujay_catslu_map


model author: espnet


task: automatic-speech-recognition


tags: zh, dataset:catslu, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_gst_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4036264/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_gst_conformer_fastspeech2


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- iwslt22_dialect
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug`


This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).


```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install -e .
cd egs2/iwslt22_dialect/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Wed Feb  2 05:32:30 EST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1`
- Git hash: `99581e0f5af3ad68851d556645e7292771436df9`
  - Commit date: `Sat Jan 29 11:32:38 2022 -0500`


## asr_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe1000_sp


### WER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|27370|54.7|39.5|5.8|8.8|54.2|87.9|


### CER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|145852|84.1|7.1|8.8|11.5|27.4|87.9|


### TER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|64424|63.8|22.8|13.4|12.2|48.3|87.9|


## ASR config


<details><summary>expand</summary>


```
config: conf/tuning/train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe1000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 55101
dist_launcher: null
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: 80
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
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: null
batch_size: 20
valid_batch_size: null
batch_bins: 25000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe1000_sp/train/speech_shape
- exp/asr_stats_raw_bpe1000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe1000_sp/valid/speech_shape
- exp/asr_stats_raw_bpe1000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - /scratch/iwslt22asrdump/raw/train_sp/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22asrdump/raw/train_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /scratch/iwslt22asrdump/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22asrdump/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.002
    weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 15000
token_list:
- <blank>
- <unk>
- ّ
- ي
- ا
- ِ
- ل
- َ
- و
- ه
- ة
- م
- ر
- ك
- ▁ما
- ُ
- ب
- ش
- د
- ت
- ▁في
- َّ
- ▁ن
- ▁ي
- ▁ت
- ن
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- ح
- ▁ه
- س
- وا
- ▁م
- ف
- ▁إي
- ع
- ▁ب
- ها
- ط
- ى
- ق
- ▁الل
- ▁أ
- ج
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- ▁و
- ▁إيه
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- ▁يا
- ز
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- ص
- ▁أه
- خ
- ات
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- ▁أنا
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- ▁ك
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- ▁وال
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- ▁الحمدلله
- ▁ياسر
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- ▁تقول
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- ▁بالحق
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- وز
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- ▁قول
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- هار
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- مون
- وق
- ▁نورمال
- ▁عندها
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- تني
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- ▁لو
- ▁ست
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- ▁أمور
- رات
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- ▁حم
- ▁الإ
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- ▁ث
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- رين
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- آ
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- طف
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- ▁بار
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- نتر
- ▁نبدا
- ▁حال
- ▁مريم
- الم
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- رجول
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- ▁تخرج
- ▁باس
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- ▁عندهم
- ▁نتفر
- مسة
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- بعين
- ▁أكاهو
- ▁ميش
- مراة
- ▁خذا
- ▁ظ
- ▁سيدي
- ▁معاي
- ▁شبيه
- ▁حكا
- ▁سف
- ▁بعضنا
- ▁بالض
- ▁ليلة
- ▁زعما
- ▁الحق
- مضان
- ▁صعيب
- ▁قالتلك
- ً
- ملة
- ▁بق
- عرف
- لاطة
- ▁خرج
- ▁أخت
- ▁تقوللي
- ▁معانا
- ▁صغير
- ▁إسمه
- ▁بعض
- ▁العام
- ▁علينا
- ▁يتع
- ▁فاش
- ▁شع
- ▁معاهم
- ▁يسالش
- ▁لهنا
- ▁سمعت
- ▁البار
- ▁نتصو
- ▁الاخ
- ▁وكان
- وبة
- دمة
- ▁كون
- ▁مبعد
- ▁تسمع
- ▁بعيد
- ▁تاكل
- ▁نلقا
- لامة
- لاثة
- ▁ذ
- ▁تحس
- ▁الواح
- ▁لدار
- ▁فاتت
- ▁تاو
- ▁أحوالك
- ▁عاملين
- ▁كبيرة
- عجب
- ▁بنت
- ▁بيدي
- ▁حكيت
- ▁تحط
- ▁مسكينة
- ▁هاذوكم
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- لاث
- ▁عشرة
- ▁عيني
- ▁تعب
- ▁ياكل
- ▁وزيد
- ▁طول
- ▁حمدلله
- ▁وقتاه
- ▁معناه
- ▁وآش
- ▁ووه
- ▁وواحد
- ▁نشوفوا
- ▁عيد
- ▁بصراحة
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- ▁راجل
- ▁وحدي
- ▁وعشرين
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- ▁قالتله
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- عيد
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- ▁الحمد
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- ▁دورة
- ▁عالأقل
- ▁آذاك
- ▁وبال
- ▁الجاي
- عطيني
- ▁ياخذ
- ▁احكيلي
- ▁نهبط
- ▁رقدت
- بلاصة
- ▁عزيز
- ▁صغار
- ▁أقسم
- ▁جيب
- ▁وصلت
- ▁أحوال
- ▁جيست
- ▁جماعة
- سئل
- ▁خوذ
- ▁يهز
- ▁الأخرى
- ▁آلاف
- ▁إسمع
- ▁الحقيقة
- ▁ناقص
- ▁حاط
- ▁موجود
- عباد
- ▁آذيك
- ▁خارج
- ▁الخير
- ▁البنات
- بقى
- ▁طرف
- ▁سينون
- ▁ماذاب
- ▁البحر
- ▁نرقد
- مدلله
- ▁إيجى
- ▁خالتي
- ▁فازة
- ▁بريك
- ▁شريبتك
- ▁تطلع
- ؤ
- ▁المشكلة
- ▁طري
- ▁مادام
- ▁طلبت
- ▁يلعب
- ▁نعاود
- ▁وحدك
- ▁ظاهر
- ٱ
- ژ
- ٍ
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
joint_net_conf: null
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram1000/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'
frontend: default
frontend_conf:
    n_fft: 512
    hop_length: 256
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 27
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_ratio_range:
    - 0.0
    - 0.05
    num_time_mask: 5
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_bpe1000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 1024
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    normalize_before: true
    macaron_style: true
    rel_pos_type: latest
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    use_cnn_module: true
    cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: true
```


</details>


### Citing ESPnet


```BibTex
@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}
}

```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  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},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug


model author: espnet


task: automatic-speech-recognition


tags: noinfo, dataset:iwslt22_dialect, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/vctk_full_band_multi_spk_vits`


♻ Imported from https://zenodo.org/record/5521431/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_full_band_multi_spk_vits


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- mini-an4
license: cc-by-4.0


# ESPnet2 ASR pretrained model


## `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`


♻ Imported from <https://zenodo.org/record/3957940#.YN7zwJozZH4>
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


### Training config


See full config in [`config.yaml`](./config.yaml)


```yaml
config: null
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_raw_bpe
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:mini-an4, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- aishell4
license: cc-by-4.0


## ESPnet2 ASR model


### `Dan_Berrebbi_aishell4_asr`


This model was trained by dan_berrebbi using aishell4 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```bash
cd espnet
git checkout da1a26652f7d5a019cc24ad1e0e6e844f2b57e1b
pip install -e .
cd egs2/aishell4/asr1
./run.sh --skip_data_prep false --skip_train true --download_model Dan_Berrebbi_aishell4_asr
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Tue Sep 21 09:36:01 EDT 2021`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: `7887faeabbc2299922267928e190ed89cb032a36`
  - Commit date: `Mon Sep 20 16:25:02 2021 -0400`


## asr_fine_tune5_100ep


### WER


### CER


### TER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|


## ASR config


expand
```
config: conf/tuning/train_asr_conformer5.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_fine_tune5_100ep
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
grad_clip: 3
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
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: null
batch_size: 20
valid_batch_size: null
batch_bins: 10000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_zh_char/train/speech_shape
- exp/asr_stats_raw_zh_char/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_zh_char/valid/speech_shape
- exp/asr_stats_raw_zh_char/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 51200
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train_nodev/wav.scp
    - speech
    - sound
-   - dump/raw/train_nodev/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech
    - sound
-   - dump/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 4.0
scheduler: noamlr
scheduler_conf:
    model_size: 256
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ，
- 的
- 是
- 个
- 这
- 一
- 。
- 就
- 儿
- 嗯
- 们
- 呃
- 我
- 有
- <sil>
- 那
- 说
- 不
- 些
- 也
- 他
- 你
- 要
- 后
- 以
- 咱
- 在
- 啊
- 了
- 然
- 家
- 都
- 来
- 还
- 可
- 子
- 下
- 上
- 时
- 比
- 话
- 孩
- 呢
- 去
- 人
- 好
- 对
- 能
- 么
- 吧
- 学
- 多
- 到
- 看
- 为
- 进
- 把
- 大
- 做
- 生
- 种
- 品
- 给
- 没
- 行
- 现
- 小
- 会
- 作
- 较
- 方
- 块
- 业
- 让
- 点
- 定
- 因
- 什
- 长
- 面
- 如
- 安
- 客
- 问
- 过
- 车
- 出
- 啦
- 边
- 候
- 主
- 所
- 题
- 买
- 销
- 天
- 意
- 自
- 全
- 动
- 工
- '&'
- 老
- 或
- 者
- 年
- 着
- 实
- 活
- 理
- 包
- 样
- 再
- 区
- 用
- 呀
- 零
- 员
- 发
- 先
- 部
- 放
- 门
- 情
- 像
- 分
- 售
- 很
- 开
- 己
- 十
- 括
- 跟
- 事
- 需
- 更
- 其
- 装
- 市
- 成
- 里
- 物
- 别
- 间
- 第
- 次
- 中
- 提
- 超
- 顾
- 保
- 感
- 加
- 量
- 二
- 和
- 各
- 嘛
- 新
- 每
- 完
- 力
- 消
- 得
- 店
- 本
- 通
- 习
- 觉
- 道
- 心
- 校
- 菜
- 交
- 哪
- 产
- 于
- 位
- 电
- 想
- 三
- 况
- 度
- 期
- 应
- 但
- 教
- 体
- 常
- 师
- 它
- 高
- 前
- 之
- 西
- 特
- 商
- 果
- 场
- 重
- 防
- 管
- 起
- 地
- 该
- 东
- 少
- 打
- 费
- 当
- 带
- 服
- 口
- 购
- 知
- 回
- 同
- 钱
- 外
- 户
- 注
- 促
- 价
- 解
- <#>
- 水
- 百
- 今
- 太
- 最
- 报
- 怎
- 才
- 等
- 及
- 关
- <->
- 肯
- 火
- 机
- 流
- 制
- 送
- 手
- 确
- 法
- 写
- 玩
- 传
- 路
- 班
- 查
- 招
- 卖
- 几
- 正
- 合
- 够
- 五
- 引
- 容
- 只
- 男
- 日
- 四
- 宣
- 反
- 两
- 清
- 处
- 周
- 单
- 首
- 课
- 衣
- 便
- 身
- 气
- 针
- 奶
- 六
- 经
- 接
- 女
- 育
- 鲜
- 赠
- 试
- 停
- 晚
- 类
- 故
- 入
- 性
- 增
- 食
- 满
- 格
- 基
- 备
- 洗
- 培
- 质
- 美
- 明
- 整
- 化
- 公
- 案
- 哎
- 吸
- 原
- 易
- 幺
- 总
- 尽
- 优
- 而
- 建
- 责
- 啥
- 干
- 月
- 使
- 找
- 季
- 望
- 器
- 目
- 识
- 低
- 听
- 烟
- 相
- 早
- 检
- 护
- 摆
- 住
- 直
- 从
- 务
- 希
- 导
- 内
- 八
- 持
- 近
- 配
- 叫
- 见
- 设
- 吗
- 非
- 调
- 程
- 拿
- 训
- <%>
- 结
- 标
- 挺
- 花
- <$>
- 受
- 式
- 求
- 平
- 换
- 具
- 愿
- 货
- 牌
- 专
- 轻
- 推
- 妈
- 司
- 辆
- 存
- 名
- 且
- 欢
- 喜
- 吃
- 数
- 段
- 议
- 控
- 往
- 礼
- 决
- 走
- 养
- 免
- 惠
- 园
- 档
- 谁
- 真
- 快
- 置
- 幼
- 乐
- 证
- 向
- 厂
- 简
- 声
- 视
- 划
- 绩
- 适
- 集
- 搞
- 办
- 规
- 灾
- 造
- 准
- 必
- 任
- 险
- 响
- 毕
- 群
- 鞋
- 九
- 嘞
- 信
- 库
- 计
- 认
- 奖
- 表
- 无
- 影
- 头
- 卡
- 告
- 考
- 抽
- 竟
- 选
- 帮
- 何
- 修
- 酒
- 尤
- 线
- 穿
- 讲
- 光
- 留
- 讨
- 随
- 请
- 卫
- 系
- 队
- 失
- 双
- 庭
- 强
- 微
- 折
- 色
- 半
- 否
- 立
- 差
- 沟
- 冬
- 批
- 害
- 已
- 危
- 白
- 爆
- 节
- 参
- 逛
- 搭
- 风
- 朋
- 友
- 环
- 验
- 评
- 严
- 般
- 效
- 舞
- 饭
- 境
- 负
- 又
- 底
- 术
- 刚
- 件
- 罚
- 助
- 态
- 状
- 室
- 房
- 游
- 息
- 领
- 难
- 警
- 按
- 级
- 错
- 利
- 与
- 餐
- 陪
- 蹈
- 论
- 记
- 许
- 马
- 算
- 楼
- 型
- 排
- 广
- 值
- 油
- 糕
- 楚
- 步
- 至
- 拉
- 紧
- 灯
- 升
- 七
- 共
- 努
- 除
- 展
- 形
- 元
- 网
- 宜
- 营
- 兴
- 互
- 蛋
- 燃
- 冷
- 条
- 思
- 巡
- 净
- 须
- 遇
- 落
- 禁
- 科
- 款
- 哦
- 止
- 采
- 材
- 介
- 套
- 围
- 维
- 旦
- 切
- 显
- 汇
- 损
- 速
- 越
- 模
- 假
- 精
- 稍
- 书
- 绍
- 父
- 积
- 策
- 示
- 骑
- 改
- 跑
- 运
- 变
- 洁
- 仓
- 鱼
- <space>
- 绝
- 诶
- 伤
- 细
- 职
- 离
- 慢
- 素
- 料
- 睡
- 趣
- 爱
- 母
- 眼
- 味
- 列
- 督
- 张
- 率
- 被
- 域
- 语
- 坏
- 资
- 红
- 减
- 励
- 择
- 预
- 层
- 陈
- 根
- 休
- 毒
- 球
- 爸
- 登
- 足
- 取
- 指
- 柜
- 限
- 降
- 概
- 院
- 供
- 支
- 额
- 源
- 始
- 盘
- 饮
- 项
- 液
- 童
- 爷
- 号
- 抓
- 台
- 转
- 观
- 金
- 照
- 滑
- 岁
- 致
- 文
- 她
- 弄
- 站
- 酸
- 音
- 胎
- 投
- 疏
- 乱
- 临
- 允
- 狗
- 疫
- 询
- 、
- 象
- 占
- 坐
- 倒
- 争
- 午
- 亲
- 读
- 演
- 退
- 惯
- 贵
- 达
- 监
- 志
- 绿
- 醒
- 急
- 驾
- 违
- 诉
- 片
- 空
- 势
- 极
- 豆
- 独
- 钟
- 代
- 瓶
- 纸
- 并
- 企
- 映
- 统
- 属
- 省
- 夜
- 障
- 谈
- 避
- 由
- 终
- 频
- 掉
- 估
- 激
- 仅
- 布
- 谢
- 灭
- 忙
- 码
- 伙
- 缺
- 叶
- 功
- 析
- 赖
- 架
- 范
- 签
- D
- 待
- 神
- 龄
- 画
- 券
- 居
- 杜
- 堵
- 您
- 勤
- 扫
- 技
- 财
- 隐
- 患
- 例
- 乘
- 摩
- 戏
- 鼓
- 份
- 杂
- 散
- 热
- 铺
- 据
- 肤
- 怕
- 依
- 拖
- 充
- 智
- 偷
- 远
- 挂
- 盗
- 附
- 梯
- 冰
- 联
- 借
- 蹭
- 异
- 蔬
- 绑
- 堂
- 将
- 厨
- 帽
- 破
- 戴
- 皮
- 粉
- 氛
- 仪
- 国
- 益
- 闯
- 惩
- 逃
- 刻
- 突
- 申
- 略
- 顿
- 毛
- 召
- 海
- 黄
- 青
- 士
- 移
- 喝
- 板
- 练
- 歌
- 千
- 床
- 享
- 磨
- 构
- 收
- 万
- 摸
- 圈
- 亮
- 刹
- 逆
- 驶
- 赶
- 松
- 呐
- 压
- 拥
- 辅
- 协
- 托
- 断
- 轮
- 善
- 哈
- 捆
- 座
- 病
- 健
- 牛
- 草
- 释
- 似
- 土
- 补
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- 堆
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- 密
- 背
- 言
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- 尚
- 窗
- C
- 艺
- 纠
- 纷
- 忽
- 句
- 另
- 施
- 政
- 温
- 某
- 翻
- 章
- 守
- 熟
- 民
- 续
- 良
- 挤
- 础
- 字
- 瓜
- 乎
- 竞
- 距
- 际
- 暖
- 凭
- 董
- 碗
- 短
- 渠
- 康
- 藏
- 香
- 虽
- 露
- 厉
- 忘
- 误
- 冒
- 窃
- 络
- 淡
- 腐
- 颜
- 播
- 默
- 锻
- 炼
- 宝
- 组
- 淘
- 则
- 逻
- 垃
- 圾
- 复
- 贴
- 靠
- 潜
- 察
- 晨
- 碰
- 剩
- 峰
- 深
- 偏
- 虑
- 念
- 初
- 闹
- 幸
- 跳
- 米
- 旧
- 蛤
- 虾
- 汽
- 苦
- 螃
- 蟹
- 冲
- 固
- 隔
- 懂
- 卷
- 镜
- 罩
- 暴
- 闭
- 野
- 玻
- 璃
- 义
- B
- 煤
- 富
- 踩
- 途
- 闲
- 紫
- 北
- 欲
- 曲
- 榜
- 垒
- 伴
- 累
- 判
- 搜
- 困
- 租
- 键
- 肥
- 社
- 弯
- 角
- 纪
- 律
- 详
- 右
- 刮
- 继
- 撤
- 输
- 普
- 未
- 稳
- 摔
- 访
- 扩
- 扣
- 末
- 票
- 承
- 担
- 丢
- 涉
- 欠
- 创
- 获
- 摊
- 疑
- 蓝
- 答
- 霜
- 录
- 齐
- 烦
- 治
- 粗
- 叛
- 污
- 址
- 若
- 染
- 含
- 药
- 雨
- 此
- 陌
- 研
- 催
- 拨
- 页
- 磕
- 呆
- 脸
- 墙
- 夫
- A
- 棉
- 袜
- 填
- 死
- 懒
- 植
- 扇
- 捡
- 遍
- 操
- 摄
- 箱
- ？
- 繁
- 城
- 咯
- 左
- 拐
- 悉
- 犯
- 宽
- 伞
- 余
- 糊
- 巧
- 透
- 贪
- 顺
- 局
- 妇
- 私
- 浪
- 岗
- 棋
- 序
- 辛
- V
- 握
- 擦
- 扔
- 斤
- 付
- 剐
- 锁
- 麻
- 敢
- 桶
- 佩
- 坠
- 封
- 替
- 塞
- 斗
- 攀
- 爽
- 沉
- 混
- 滋
- 刺
- 潮
- 皿
- 端
- 刷
- 刀
- 巾
- 烫
- 木
- 漏
- 迅
- 织
- 救
- 吹
- 仔
- 称
- 返
- 景
- 聚
- 阶
- 秀
- 涨
- P
- 颈
- 肩
- 泥
- I
- 侣
- 尔
- 伍
- 甚
- 皂
- 蒙
- 世
- 界
- 嘻
- 辈
- Q
- 审
- 尾
- 浇
- 遛
- 馨
- 措
- 邻
- 撒
- 挥
- 遵
- 予
- 击
- 鉴
- 殊
- 哇
- 载
- 添
- 盈
- 盯
- 惊
- 喷
- 荷
- 怠
- 抢
- 喂
- 饱
- 谅
- 团
- 龙
- 冻
- 图
- 掺
- 扑
- 刊
- 葱
- 薄
- 萝
- 卜
- 麦
- 苹
- 触
- 飞
- 艳
- 畅
- 鸡
- 权
- 趟
- 连
- 哭
- 旁
- 漂
- 焊
- 敞
- 叉
- 钢
- 氧
- 溺
- 聊
- 巢
- 衡
- 淀
- 劣
- 虫
- 符
- 均
- 辨
- 菌
- 彻
- 烂
- 厅
- 皱
- 妥
- 拾
- 插
- 携
- 竹
- 碍
- 湿
- 灵
- 忌
- 旅
- 勿
- 宿
- 迷
- 探
- 春
- 劵
- 星
- 耐
- 裤
- 颖
- 韩
- 艾
- 灸
- 邀
- 婚
- 乳
- 芽
- 挑
- 摘
- 阿
- 姨
- 伊
- 慕
- 纯
- 貌
- 嘴
- 偶
- 睛
- 献
- 坚
- 账
- 典
- 唱
- L
- E
- 贡
- 寒
- 唧
- Y
- 尝
- 抹
- 汰
- 腾
- 哼
- 仿
- 英
- 舒
- 扰
- 拒
- 剪
- 夏
- 宠
- 咬
- 派
- 委
- 婉
- 执
- 呗
- 悄
- 搬
- 雪
- 盐
- 暂
- 奸
- 耍
- 僻
- 却
- 署
- 寻
- 串
- 援
- 亏
- 烈
- 印
- 捎
- 幅
- 绘
- 锈
- 闸
- 罪
- 嫌
- 俗
- 歹
- 劳
- 兜
- 喽
- 谓
- 鹤
- 舍
- 克
- 徇
- 倍
- 敏
- 丝
- 纺
- 拭
- 融
- 蔫
- 掂
- 测
- T
- 众
- 卸
- 暗
- 赔
- 偿
- 举
- 劲
- 篮
- 储
- 乙
- 炔
- 软
- 侵
- 诱
- 浊
- 蚀
- 秽
- 炸
- 泽
- 闻
- 鼻
- 甜
- 澈
- 脏
- 官
- 凝
- 芳
- 灰
- 卵
- 农
- 烧
- 肉
- 桌
- 椅
- 垫
- 硬
- 叠
- 瓷
- 碎
- 柄
- 屉
- 拳
- 撞
- 铝
- 歇
- 遗
- 炮
- 掌
- 妨
- 静
- 浸
- 涂
- 凉
- 炫
- 耀
- 姓
- 究
- 奏
- 缆
- 脚
- 酿
- 抄
- 慌
- 戚
- 燥
- 毯
- 挽
- 诺
- 济
- 旺
- 抖
- 郊
- 疗
- 巴
- 痧
- 脊
- 膜
- 晒
- 润
- 掏
- 笔
- 鞭
- 博
- 捧
- 函
- 胡
- 锅
- 雾
- 疯
- 狂
- 趋
- 膏
- 妆
- 尘
- 袋
- 贝
- 俺
- 耽
- 怀
- 恐
- 赋
- 脑
- 焉
- 愣
- 呵
- 噼
- 啪
- 虚
- 河
- 归
- 绊
- 械
- 扬
- 筒
- 靴
- 束
- 彩
- 荐
- 沙
- 迎
- 荡
- 凌
- 昂
- 碑
- 蹦
- 扉
- 泼
- 丰
- 滴
- 沾
- 亭
- 粘
- 奇
- 饼
- 牙
- 娃
- 杯
- 踢
- 嘿
- 抛
- 枯
- 剔
- 苗
- 纹
- 永
- 津
- 唉
- 趁
- 屡
- 逮
- 戒
- 肃
- 仁
- 肇
- 醉
- 糟
- 馈
- 横
- 扭
- 盔
- 侧
- 鲁
- 莽
- 飙
- 稿
- 逐
- 谋
- 京
- 苏
- 宁
- 驻
- 咨
- 旷
- 拓
- 杆
- 秤
- 叮
- 嘱
- 咋
- 炊
- 怪
- 婆
- 阎
- 王
- 饿
- 鬼
- 惨
- 渡
- 坎
- 囤
- 甲
- 蛙
- 鲤
- 桂
- 石
- 玉
- 溪
- 华
- 窝
- 截
- 秩
- 嗨
- 芹
- 梨
- 蕉
- S
- 煲
- 汤
- 鲫
- 揽
- 挡
- 柚
- 瑞
- 匹
- '2'
- 踹
- 吵
- 凶
- 矩
- 迟
- 脾
- 纳
- 朵
- 墨
- 袖
- 链
- 钩
- 笼
- 熄
- 盆
- 殴
- 欺
- 诈
- 厕
- 娱
- 爬
- 威
- 胁
- 阅
- 赌
- 拢
- 症
- 伪
- 脂
- 堪
- 盛
- 蚊
- 蝇
- 煎
- 晰
- 柔
- 涩
- 汁
- 腹
- 胃
- 痉
- 挛
- 颗
- 粒
- 匀
- 败
- 历
- 佳
- 乏
- 寄
- 残
- 杀
- 剂
- 疾
- 衍
- 溅
- 倘
- 褶
- 席
- 启
- 遮
- 槽
- 递
- 橱
- 迹
- 镁
- 泄
- 阀
- 柴
- 阻
- 恋
- 盲
- 浓
- 捂
- 腰
- 姿
- 缝
- 肿
- 焦
- 骗
- 伺
- 嘘
- 掩
- 褥
- 帘
- 籍
- 锥
- 锋
- 尖
- 锐
- 祸
- 秒
- 李
- 伸
- 浏
- 览
- 航
- 讯
- 谨
- 慎
- 匪
- 劫
- 医
- 族
- 忧
- 孤
- 拜
- 窄
- 唯
- 搁
- 朝
- 尺
- 盟
- 波
- 隆
- 词
- 村
- 娶
- 媳
- 县
- 聘
- 醇
- 泡
- 坨
- 淋
- 延
- 柱
- 肾
- 蒸
- 槛
- 赚
- 凡
- 恩
- 厚
- 赞
- 茎
- 蒜
- 苔
- 甘
- 菠
- 涮
- 霾
- 仍
- 云
- 追
- 丽
- 盖
- 欧
- 莱
- 雅
- 婴
- 孕
- 敲
- 约
- 惰
- 谱
- 射
- 惑
- 睹
- 奉
- 诚
- 惶
- 卓
- 勉
- 聪
- 疼
- 弃
- 奴
- 隶
- 嚷
- 眠
- 躺
- 乒
- 乓
- 琴
- 挖
- 掘
- 阵
- 浆
- 索
- 呼
- 古
- 弥
- 熔
- 抱
- 怨
- 猫
- 笑
- 挣
- 黑
- 猛
- 令
- 核
- 磊
- 橙
- 吨
- 吊
- 蘸
- 氮
- 罐
- 战
- 懈
- 渐
- 胜
- 命
- 抬
- 缘
- 睦
- 扮
- 珠
- 颁
- 蔼
- 凳
- 饰
- 缤
- 晶
- 抵
- 遥
- 腿
- 拍
- 妻
- 羽
- 绒
- 梳
- 袄
- 述
- 跆
- 屈
- 脱
- 朗
- 劝
- 胆
- 腔
- 圆
- 亚
- 宴
- 编
- 肢
- 壶
- 暑
- 怒
- 描
- 绕
- 悦
- 忆
- 嗓
- 胖
- 疙
- 瘩
- 哒
- 碴
- 棱
- 炒
- 井
- 漫
- 烘
- 焙
- 涤
- 船
- 纱
- 君
- 茉
- 莉
- 钙
- 瞩
- <_>
- 塌
- 嗷
- 屁
- 股
- 绪
- 勇
- 奋
- 荣
- 诲
- 卑
- 挫
- 昧
- 疲
- 惫
- 册
- 呈
- 僵
- 熬
- 敬
- 呦
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: /ocean/projects/cis210027p/berrebbi/espnet/egs2/aishell4/asr1/data/nlsyms.txt
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'
frontend: default
frontend_conf:
    n_fft: 512
    win_length: 400
    hop_length: 160
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_zh_char/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 12
    linear_units: 2048
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
    attention_dropout_rate: 0.0
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    macaron_style: true
    use_cnn_module: true
    cnn_module_kernel: 15
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    input_layer: embed
    num_blocks: 6
    linear_units: 2048
    dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.3a1
distributed: false
```

## LM config
expand
```
config: conf/train_lm_transformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/lm_nuit
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - loss
    - min
keep_nbest_models: 10
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: false
log_interval: null
use_tensorboard: true
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: null
batch_size: 20
valid_batch_size: null
batch_bins: 2000000
valid_batch_bins: null
train_shape_file:
- exp/lm_stats_zh_char/train/text_shape.char
valid_shape_file:
- exp/lm_stats_zh_char/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/lm_train.txt
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.005
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ，
- 的
- 是
- 个
- 这
- 一
- 。
- 就
- 儿
- 嗯
- 们
- 呃
- 我
- 有
- <sil>
- 那
- 说
- 不
- 些
- 也
- 他
- 你
- 要
- 后
- 以
- 咱
- 在
- 啊
- 了
- 然
- 家
- 都
- 来
- 还
- 可
- 子
- 下
- 上
- 时
- 比
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- 扉
- 泼
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- 亭
- 粘
- 奇
- 饼
- 牙
- 娃
- 杯
- 踢
- 嘿
- 抛
- 枯
- 剔
- 苗
- 纹
- 永
- 津
- 唉
- 趁
- 屡
- 逮
- 戒
- 肃
- 仁
- 肇
- 醉
- 糟
- 馈
- 横
- 扭
- 盔
- 侧
- 鲁
- 莽
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- 稿
- 逐
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- 驻
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- 旷
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- 杆
- 秤
- 叮
- 嘱
- 咋
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- '2'
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- 跆
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- 编
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- 暑
- 怒
- 描
- 绕
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- 忆
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- 君
- 茉
- 莉
- 钙
- 瞩
- <_>
- 塌
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- 绪
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- 奋
- 荣
- 诲
- 卑
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- 册
- 呈
- 僵
- 熬
- 敬
- 呦
- <sos/eos>
init: null
model_conf:
    ignore_id: 0
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: /ocean/projects/cis210027p/berrebbi/espnet/egs2/aishell4/asr1/data/nlsyms.txt
cleaner: null
g2p: null
lm: transformer
lm_conf:
    pos_enc: null
    embed_unit: 128
    att_unit: 512
    head: 8
    unit: 2048
    layer: 16
    dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.3a1
distributed: false
```


model name: Dan_Berrebbi_aishell4_asr


model author: espnet


task: automatic-speech-recognition


tags: zh, dataset:aishell4, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`


♻ Imported from https://zenodo.org/record/4433198/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slurp
license: cc-by-4.0


## ESPnet2 SLU pretrained model


### `siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`


♻ Imported from https://zenodo.org/record/5590384


This model was trained by siddhana using slurp/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}

```


model name: siddhana_slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:slurp, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best`


♻ Imported from https://zenodo.org/record/3986225/


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best


task: text-to-speech


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/ljspeech_vits`


♻ Imported from https://zenodo.org/record/5443814/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_ljspeech_vits


model author: espnet


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`


♻ Imported from https://zenodo.org/record/4391409/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0


### `kan-bayashi/libritts_gst+xvector_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4418774/


This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: kan-bayashi_libritts_gst_xvector_conformer_fastspeech2


model author: espnet


tags: en, dataset:libritts, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5431984/


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave


task: text-to-speech


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5521354/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave


model author: espnet


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4393279/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: kr
datasets:
- zeroth_korean
license: cc-by-4.0


## Example ESPnet2 ASR model


### `Hoon_Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4014588/


This model was trained by Hoon Chung using zeroth_korean/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: Hoon_Chung_zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave


model author: espnet


task: automatic-speech-recognition


tags: kr, dataset:zeroth_korean, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_vits_accent_with_pause`


♻ Imported from https://zenodo.org/record/5414980/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_vits_accent_with_pause


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_gst_fastspeech`


♻ Imported from https://zenodo.org/record/3986241/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: kan-bayashi_vctk_gst_fastspeech


model author: espnet


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0


### `kan-bayashi/libritts_xvector_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4418754/


This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  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},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_libritts_xvector_conformer_fastspeech2


task: text-to-speech


tags: en, dataset:libritts, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp`


This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).


```bash
cd espnet
git checkout b0ff60946ada6753af79423a2e6063984bec2926
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp
```


## ASR config


<details><summary>expand</summary>


</details>


```BibTex
@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}
}

```


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  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},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- yolo_mixtec
license: cc-by-4.0


### `espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer`


This model was trained by ftshijt using yolo_mixtec recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```bash
cd espnet

pip install -e .
cd els/yolo_mixtec/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Wed Nov 10 02:59:39 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
  - Commit date: ``


## asr_train_asr_transformer_specaug_raw_bpe500


### WER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|81348|84.1|11.8|4.1|2.5|18.3|82.5|


### CER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|626187|93.4|2.2|4.4|2.4|9.0|82.5|


### TER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|325684|90.7|5.2|4.1|2.2|11.5|82.5|


## ASR config


<details><summary>expand</summary>