---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_jvs010_vits_accent_with_pause`
♻ 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/).
### 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}
}
```


---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- hkust
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave`
♻ Imported from https://zenodo.org/record/4430974/

This model was trained by kamo-naoyuki using hkust/asr1 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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/vctk_xvector_tacotron2`
♻ Imported from https://zenodo.org/record/4394600/

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}
}
```


---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave`
♻ Imported from https://zenodo.org/record/4541452/

This model was trained by kamo-naoyuki using librispeech/asr1 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}
}
```


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

# ESPnet2 ASR pretrained model

## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en`

This model was trained by Takashi Maekaku using librispeech 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: `Fri Aug  6 11:44:39 JST 2021`
- python version: `3.7.9 (default, Apr 23 2021, 13:48:31)  [GCC 5.5.0 20171010]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.0`
- Git hash: `0f7558a716ab830d0c29da8785840124f358d47b`
  - Commit date: `Tue Jun 8 15:33:49 2021 -0400`

## asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|96.8|2.8|0.4|0.3|3.4|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.4|1.4|0.2|0.2|1.8|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|96.8|2.8|0.4|0.4|3.6|36.0|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.8|0.6|0.6|0.3|1.5|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.9|0.5|0.5|0.4|1.4|36.0|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|98.2|1.3|0.5|0.4|2.2|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|96.0|2.8|1.2|0.6|4.6|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|98.1|1.3|0.6|0.4|2.3|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|96.0|2.7|1.3|0.6|4.6|36.0|
```

### Training config

See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml)

```yaml
config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
ngpu: 3
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


---
tags:
- espnet
- audio
- automatic-speech-recognition
language: jp
datasets:
- csj
license: cc-by-4.0
---

## ESPnet2 ASR model

### `espnet/kan-bayashi_csj_asr_train_asr_conformer`

This model was trained by Nelson Yalta using csj recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

```bash
cd espnet
git checkout 0d8cd47dd3572248b502bc831cd305e648170233
pip install -e .
cd egs2/csj/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/kan-bayashi_csj_asr_train_asr_conformer
```

## ASR config

<details><summary>expand</summary>

```
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_char_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: 47308
dist_launcher: null
multiprocessing_distributed: true
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: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 6
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
pretrain_path: []
pretrain_key: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 15000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_sp/train/speech_shape
- exp/asr_stats_raw_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_sp/valid/speech_shape
- exp/asr_stats_raw_sp/valid/text_shape.char
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:
-   - dump/raw/train_nodup_sp/wav.scp
    - speech
    - sound
-   - dump/raw/train_nodup_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/train_dev/wav.scp
    - speech
    - sound
-   - dump/raw/train_dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.002
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- "\u306E"
- "\u3044"
- "\u3067"
- "\u3068"
- "\u30FC"
- "\u3066"
- "\u3046"
- "\u307E"
- "\u3059"
- "\u3057"
- "\u306B"
- "\u3063"
- "\u306A"
- "\u3048"
- "\u305F"
- "\u3053"
- "\u304C"
- "\u304B"
- "\u306F"
- "\u308B"
- "\u3042"
- "\u3093"
- "\u308C"
- "\u3082"
- "\u3092"
- "\u305D"
- "\u308A"
- "\u3089"
- "\u3051"
- "\u304F"
- "\u3069"
- "\u3088"
- "\u304D"
- "\u3060"
- "\u304A"
- "\u30F3"
- "\u306D"
- "\u4E00"
- "\u3055"
- "\u30B9"
- "\u8A00"
- "\u3061"
- "\u3064"
- "\u5206"
- "\u30C8"
- "\u3084"
- "\u4EBA"
- "\u30EB"
- "\u601D"
- "\u308F"
- "\u6642"
- "\u65B9"
- "\u3058"
- "\u30A4"
- "\u884C"
- "\u4F55"
- "\u307F"
- "\u5341"
- "\u30E9"
- "\u4E8C"
- "\u672C"
- "\u8A9E"
- "\u5927"
- "\u7684"
- "\u30AF"
- "\u30BF"
- "\u308D"
- "\u3070"
- "\u3087"
- "\u3083"
- "\u97F3"
- "\u51FA"
- "\u305B"
- "\u30C3"
- "\u5408"
- "\u65E5"
- "\u4E2D"
- "\u751F"
- "\u4ECA"
- "\u898B"
- "\u30EA"
- "\u9593"
- "\u8A71"
- "\u3081"
- "\u30A2"
- "\u5F8C"
- "\u81EA"
- "\u305A"
- "\u79C1"
- "\u30C6"
- "\u4E0A"
- "\u5E74"
- "\u5B66"
- "\u4E09"
- "\u30B7"
- "\u5834"
- "\u30C7"
- "\u5B9F"
- "\u5B50"
- "\u4F53"
- "\u8003"
- "\u5BFE"
- "\u7528"
- "\u6587"
- "\u30D1"
- "\u5F53"
- "\u7D50"
- "\u5EA6"
- "\u5165"
- "\u8A33"
- "\u30D5"
- "\u98A8"
- "\u30E0"
- "\u30D7"
- "\u6700"
- "\u30C9"
- "\u30EC"
- "\u30ED"
- "\u4F5C"
- "\u6570"
- "\u76EE"
- "\u30B8"
- "\u95A2"
- "\u30B0"
- "\u767A"
- "\u8005"
- "\u5B9A"
- "\u3005"
- "\u3050"
- "\u30B3"
- "\u4E8B"
- "\u624B"
- "\u5168"
- "\u5909"
- "\u30DE"
- "\u6027"
- "\u8868"
- "\u4F8B"
- "\u52D5"
- "\u8981"
- "\u5148"
- "\u524D"
- "\u610F"
- "\u90E8"
- "\u4F1A"
- "\u6301"
- "\u30E1"
- "\u5316"
- "\u9054"
- "\u4ED8"
- "\u5F62"
- "\u73FE"
- "\u4E94"
- "\u30AB"
- "\u3079"
- "\u53D6"
- "\u56DE"
- "\u5E38"
- "\u4F7F"
- "\u611F"
- "\u66F8"
- "\u6C17"
- "\u6CD5"
- "\u7A0B"
- "\u3071"
- "\u56DB"
- "\u591A"
- "\u8272"
- "\u30BB"
- "\u7406"
- "\u975E"
- "\u30D0"
- "\u58F0"
- "\u5358"
- "\u756A"
- "\uFF21"
- "\u6210"
- "\u540C"
- "\u901A"
- "\u30A3"
- "\u679C"
- "\u30AD"
- "\u554F"
- "\u984C"
- "\u69CB"
- "\u56FD"
- "\u6765"
- "\u9AD8"
- "\u6B21"
- "\u9A13"
- "\u3052"
- "\u30C1"
- "\u4EE5"
- "\u3054"
- "\u4EE3"
- "\u30E2"
- "\u30AA"
- "\u51C4"
- "\u7279"
- "\u77E5"
- "\u30E5"
- "\u7269"
- "\u660E"
- "\u70B9"
- "\u5473"
- "\u767E"
- "\u89E3"
- "\u8FD1"
- "\u8B58"
- "\u5730"
- "\u540D"
- "\u805E"
- "\u4E0B"
- "\u5C0F"
- "\u6559"
- "\u30B5"
- "\u70BA"
- "\u4E5D"
- "\u30D6"
- "\u5BB6"
- "\u30CB"
- "\u521D"
- "\u30D9"
- "\u30E7"
- "\u5C11"
- "\u8A8D"
- "\u8AD6"
- "\u529B"
- "\u516D"
- "\u30D3"
- "\u60C5"
- "\u7FD2"
- "\u30A6"
- "\u7ACB"
- "\u5FC3"
- "\u8ABF"
- "\u5831"
- "\u30A8"
- "\uFF24"
- "\uFF2E"
- "\u793A"
- "\u793E"
- "\u9055"
- "\u969B"
- "\u3056"
- "\u8AAC"
- "\u5FDC"
- "\u98DF"
- "\u72B6"
- "\u9577"
- "\u7814"
- "\u6821"
- "\u5185"
- "\u639B"
- "\u30DF"
- "\u5916"
- "\u5411"
- "\u80FD"
- "\u516B"
- "\u9762"
- "\u7A76"
- "\u7136"
- "\u3073"
- "\u30D4"
- "\u4E3B"
- "\u4FC2"
- "\u5024"
- "\u91CD"
- "\u8A5E"
- "\u4F9B"
- "\u5F97"
- "\u5FC5"
- "\u5973"
- "\u78BA"
- "\u7D42"
- "\u30BA"
- "\u6BCD"
- "\u696D"
- "\u7387"
- "\u65B0"
- "\u6D3B"
- "\u697D"
- "\u8449"
- "\u8A08"
- "\u30CA"
- "\u3080"
- "\u6240"
- "\u4E16"
- "\u6B63"
- "\u30E3"
- "\u8A18"
- "\u671F"
- "\u5207"
- "\u3078"
- "\u6A5F"
- "\u30DA"
- "\u5343"
- "\u985E"
- "\u5143"
- "\u614B"
- "\u826F"
- "\u5728"
- "\u6709"
- "\u30C0"
- "\u4E03"
- "\uFF23"
- "\u5225"
- "\u30EF"
- "\u691C"
- "\u7D9A"
- "\u9078"
- "\u57FA"
- "\u76F8"
- "\u6708"
- "\u4FA1"
- "\u7D20"
- "\u4ED6"
- "\u6BD4"
- "\u9023"
- "\u96C6"
- "\u30A7"
- "\u307B"
- "\u4F4D"
- "\u597D"
- "\uFF2D"
- "\u5F37"
- "\u4E0D"
- "\u5FA1"
- "\u6790"
- "\u30DD"
- "\u7121"
- "\u89AA"
- "\u53D7"
- "\u3086"
- "\u7F6E"
- "\u8C61"
- "\u4ED5"
- "\u5F0F"
- "\u30CD"
- "\u6307"
- "\u8AAD"
- "\u6C7A"
- "\u8ECA"
- "\u96FB"
- "\u904E"
- "\u30B1"
- "\u8A55"
- "\u5229"
- "\u6B8B"
- "\u8D77"
- "\u30CE"
- "\u7D4C"
- "\u56F3"
- "\u4F1D"
- "\u500B"
- "\u30C4"
- "\u7BC0"
- "\u9053"
- "\u5E73"
- "\u91D1"
- "\u899A"
- "\uFF34"
- "\u4F4F"
- "\u59CB"
- "\u63D0"
- "\u5B58"
- "\u5171"
- "\u30DB"
- "\u7B2C"
- "\u7D44"
- "\u89B3"
- "\u80B2"
- "\u6771"
- "\u305E"
- "\u958B"
- "\u52A0"
- "\u5F15"
- "\uFF33"
- "\u53E3"
- "\u6C34"
- "\u5BB9"
- "\u5468"
- "\u5B87"
- "\u7D04"
- "\u5B57"
- "\u3076"
- "\u9803"
- "\u3072"
- "\u5B99"
- "\u6BB5"
- "\u30BD"
- "\u97FF"
- "\u30DC"
- "\u53CB"
- "\u91CF"
- "\u6599"
- "\u3085"
- "\u5CF6"
- "\u8EAB"
- "\u76F4"
- "\u753B"
- "\u7DDA"
- "\u54C1"
- "\u5DEE"
- "\u4EF6"
- "\u9069"
- "\u5F35"
- "\u8FBA"
- "\u8FBC"
- "\u91CE"
- "\u69D8"
- "\u578B"
- "\u4E88"
- "\u7A2E"
- "\u5074"
- "\u8FF0"
- "\u5C71"
- "\u5C4B"
- "\u5E30"
- "\u30CF"
- "\u4E57"
- "\u539F"
- "\u683C"
- "\u8CEA"
- "\u666E"
- "\uFF30"
- "\u9020"
- "\u753A"
- "\u30B4"
- "\u82F1"
- "\u63A5"
- "\u304E"
- "\u6E2C"
- "\u3075"
- "\u7FA9"
- "\u4EAC"
- "\u5272"
- "\u5236"
- "\u7B54"
- "\u5404"
- "\u4FE1"
- "\u754C"
- "\u6211"
- "\u7A7A"
- "\uFF0E"
- "\u7740"
- "\u53EF"
- "\u66F4"
- "\u6D77"
- "\u4E0E"
- "\u9032"
- "\u52B9"
- "\u5F7C"
- "\u771F"
- "\u7530"
- "\u5FB4"
- "\u6D41"
- "\u5177"
- "\uFF32"
- "\u5E02"
- "\u67FB"
- "\u5B89"
- "\uFF22"
- "\u5E83"
- "\u50D5"
- "\u6CE2"
- "\u5C40"
- "\u8A2D"
- "\u7537"
- "\u767D"
- "\u30B6"
- "\u53CD"
- "\u6226"
- "\u533A"
- "\u6C42"
- "\u96D1"
- "\uFF29"
- "\u6B69"
- "\u8CB7"
- "\u982D"
- "\u7B97"
- "\u534A"
- "\u4FDD"
- "\u5E03"
- "\u96E3"
- "\uFF2C"
- "\u5224"
- "\u843D"
- "\u8DB3"
- "\u5E97"
- "\u7533"
- "\u8FD4"
- "\u30AE"
- "\u4E07"
- "\u6728"
- "\u6614"
- "\u8F03"
- "\u7D22"
- "\uFF26"
- "\u30B2"
- "\u6B86"
- "\u60AA"
- "\u5883"
- "\u548C"
- "\u907A"
- "\u57DF"
- "\u968E"
- "\u542B"
- "\u305C"
- "\u30BC"
- "\u65AD"
- "\u9650"
- "\u63A8"
- "\u4F4E"
- "\u5F71"
- "\u898F"
- "\u6319"
- "\u90FD"
- "\u307C"
- "\u6848"
- "\u4EEE"
- "\u88AB"
- "\u547C"
- "\u30A1"
- "\u96E2"
- "\u7CFB"
- "\u79FB"
- "\u30AC"
- "\u5DDD"
- "\u6E96"
- "\u904B"
- "\u6761"
- "\u5FF5"
- "\u6C11"
- "\uFF27"
- "\u7236"
- "\u75C5"
- "\u79D1"
- "\u4E21"
- "\u7531"
- "\u8A66"
- "\u56E0"
- "\u547D"
- "\u795E"
- "\uFF28"
- "\u7570"
- "\u7C21"
- "\u53E4"
- "\u6F14"
- "\u5897"
- "\u51E6"
- "\u8B70"
- "\u7DD2"
- "\u7CBE"
- "\u6613"
- "\u53F7"
- "\u65CF"
- "\u52FF"
- "\u60F3"
- "\u5217"
- "\u5C0E"
- "\u8EE2"
- "\u54E1"
- "\u30E6"
- "\u6BCE"
- "\u8996"
- "\u4E26"
- "\u98DB"
- "\u4F3C"
- "\u6620"
- "\u7D71"
- "\u4EA4"
- "\u30D2"
- "\u6B4C"
- "\u5F85"
- "\u8CC7"
- "\u8907"
- "\u8AA4"
- "\u63DB"
- "\u6A19"
- "\u6CC1"
- "\u914D"
- "\u62BD"
- "\u822C"
- "\u7403"
- "\u9006"
- "\u65C5"
- "\u6628"
- "\u9662"
- "\u99C5"
- "\u74B0"
- "\u5BDF"
- "\u516C"
- "\u6B73"
- "\u5C5E"
- "\u8F9E"
- "\u5947"
- "\u6CBB"
- "\u5E7E"
- "\u82E5"
- "\u58F2"
- "\u632F"
- "\u7686"
- "\u6CE8"
- "\u6B74"
- "\u9805"
- "\u5F93"
- "\u5747"
- "\u5F79"
- "\u9806"
- "\u53BB"
- "\u56E3"
- "\u8853"
- "\u7DF4"
- "\u6FC0"
- "\u6982"
- "\u66FF"
- "\u7B49"
- "\u98F2"
- "\u53F2"
- "\u88DC"
- "\u901F"
- "\u53C2"
- "\u65E9"
- "\u53CE"
- "\u9332"
- "\u671D"
- "\u5186"
- "\u5370"
- "\u5668"
- "\u63A2"
- "\u7D00"
- "\u9001"
- "\u6E1B"
- "\u571F"
- "\u5929"
- "\uFF2F"
- "\u50BE"
- "\u72AC"
- "\u9060"
- "\u5E2F"
- "\u52A9"
- "\u6A2A"
- "\u591C"
- "\u7523"
- "\u8AB2"
- "\u5BA2"
- "\u629E"
- "\u5712"
- "\u4E38"
- "\u50CF"
- "\u50CD"
- "\u6750"
- "\u5DE5"
- "\u904A"
- "\u544A"
- "\u523A"
- "\u6539"
- "\u8D64"
- "\u8074"
- "\u4ECB"
- "\u8077"
- "\u53F0"
- "\u77ED"
- "\u8AB0"
- "\u7D30"
- "\u672A"
- "\u770C"
- "\u9928"
- "\u6B62"
- "\u53F3"
- "\u306C"
- "\u3065"
- "\u56F2"
- "\u8A0E"
- "\u6B7B"
- "\u5EFA"
- "\u592B"
- "\u7AE0"
- "\u964D"
- "\u666F"
- "\u706B"
- "\u30A9"
- "\u9E97"
- "\u8B1B"
- "\u72EC"
- "\u5DE6"
- "\u5C64"
- "\uFF25"
- "\u5C55"
- "\u653F"
- "\u5099"
- "\u4F59"
- "\u7D76"
- "\u5065"
- "\u518D"
- "\u9580"
- "\u5546"
- "\u52DD"
- "\u52C9"
- "\u82B1"
- "\u30E4"
- "\u8EF8"
- "\u97FB"
- "\u66F2"
- "\u6574"
- "\u652F"
- "\u6271"
- "\u53E5"
- "\u6280"
- "\u5317"
- "\u30D8"
- "\u897F"
- "\u5247"
- "\u4FEE"
- "\u6388"
- "\u9031"
- "\u5BA4"
- "\u52D9"
- "\u9664"
- "\u533B"
- "\u6563"
- "\u56FA"
- "\u7AEF"
- "\u653E"
- "\u99AC"
- "\u7A4D"
- "\u8208"
- "\u592A"
- "\u5ACC"
- "\u9F62"
- "\u672B"
- "\u7D05"
- "\u6E90"
- "\u6E80"
- "\u5931"
- "\u5BDD"
- "\u6D88"
- "\u6E08"
- "\u4FBF"
- "\u983C"
- "\u4F01"
- "\u5B8C"
- "\u4F11"
- "\u9752"
- "\u7591"
- "\u8D70"
- "\u6975"
- "\u767B"
- "\u8AC7"
- "\u6839"
- "\u6025"
- "\u512A"
- "\u7D75"
- "\u623B"
- "\u5E2B"
- "\u5F59"
- "\u6DF7"
- "\u8DEF"
- "\u7E70"
- "\uFF2B"
- "\u8A3C"
- "\u713C"
- "\u6562"
- "\u5BB3"
- "\u96F6"
- "\u6253"
- "\u82E6"
- "\u7701"
- "\u7D19"
- "\u5C02"
- "\u8DDD"
- "\u9854"
- "\u8D8A"
- "\u4E89"
- "\u56F0"
- "\u5BC4"
- "\u5199"
- "\u4E92"
- "\u6DF1"
- "\u5A5A"
- "\u7DCF"
- "\u89A7"
- "\u80CC"
- "\u7BC9"
- "\u6E29"
- "\u8336"
- "\u62EC"
- "\u8CA0"
- "\u590F"
- "\u89E6"
- "\u7D14"
- "\u9045"
- "\u58EB"
- "\u96A3"
- "\u6050"
- "\u91C8"
- "\u967A"
- "\u5150"
- "\u5BBF"
- "\u6A21"
- "\u77F3"
- "\u983B"
- "\u5B09"
- "\u5EA7"
- "\u7642"
- "\u7E4B"
- "\uFF38"
- "\u5C06"
- "\u8FFD"
- "\u5EAD"
- "\u6238"
- "\u5371"
- "\u5BC6"
- "\u5DF1"
- "\u9014"
- "\u7BC4"
- "\u99C4"
- "\u7D39"
- "\u4EFB"
- "\u968F"
- "\u5357"
- "\uFF11"
- "\u5EB7"
- "\u9818"
- "\u5FD8"
- "\u3045"
- "\u59FF"
- "\u7F8E"
- "\u55B6"
- "\u6349"
- "\u65E2"
- "\u7167"
- "\uFF2A"
- "\u4EF2"
- "\u9152"
- "\u52E2"
- "\u9ED2"
- "\u5149"
- "\u6E21"
- "\u75DB"
- "\u62C5"
- "\u5F31"
- "\u307D"
- "\uFF36"
- "\u7D0D"
- "\u629C"
- "\u5E45"
- "\u6D17"
- "\u7A81"
- "\u671B"
- "\u5373"
- "\u9858"
- "\u7565"
- "\uFF12"
- "\u9811"
- "\u5FD7"
- "\u5B85"
- "\u7247"
- "\u656C"
- "\u6751"
- "\u60B2"
- "\u81A8"
- "\u89D2"
- "\u30E8"
- "\u4F9D"
- "\u8A73"
- "\u5F8B"
- "\u9B5A"
- "\u52B4"
- "\u5A66"
- "\u6163"
- "\u732B"
- "\u5019"
- "\u8001"
- "\u558B"
- "\u79F0"
- "\u796D"
- "\u7FA4"
- "\u7E2E"
- "\u6C38"
- "\u616E"
- "\u5EF6"
- "\u7A3F"
- "\u611B"
- "\u8089"
- "\u9589"
- "\u8CBB"
- "\u6295"
- "\u6D3E"
- "\u81F4"
- "\u7BA1"
- "\u7C73"
- "\u5E95"
- "\u7D99"
- "\u6C0F"
- "\u690D"
- "\u501F"
- "\u5727"
- "\u52E4"
- "\u6F22"
- "\u66AE"
- "\u5F27"
- "\u88C5"
- "\u57CE"
- "\u5287"
- "\u76DB"
- "\u63F4"
- "\u9244"
- "\u8C37"
- "\u5E72"
- "\u7E26"
- "\u8A31"
- "\u6016"
- "\u9A5A"
- "\u8A8C"
- "\uFF35"
- "\u8B77"
- "\u5B88"
- "\u8033"
- "\u6B32"
- "\u8239"
- "\uFF10"
- "\u5178"
- "\u67D3"
- "\u7D1A"
- "\u98FE"
- "\u5144"
- "\u71B1"
- "\u8F09"
- "\u88FD"
- "\u5BFA"
- "\u662D"
- "\u7FFB"
- "\u5426"
- "\u5584"
- "\u62BC"
- "\u53CA"
- "\u6A29"
- "\u559C"
- "\u670D"
- "\u8CB0"
- "\u8EFD"
- "\u677F"
- "\u61B6"
- "\u98FC"
- "\u5C3E"
- "\u5FA9"
- "\u5E78"
- "\u7389"
- "\u5354"
- "\u679A"
- "\u90CE"
- "\u8840"
- "\u524A"
- "\u5922"
- "\u63A1"
- "\u6674"
- "\u6B20"
- "\u602A"
- "\u65BD"
- "\u7DE8"
- "\u98EF"
- "\u7B56"
- "\u9000"
- "\uFF39"
- "\u8349"
- "\u61F8"
- "\u6458"
- "\u58CA"
- "\u4F38"
- "\u85AC"
- "\u9996"
- "\u5BFF"
- "\u53B3"
- "\u606F"
- "\u5C45"
- "\u643A"
- "\u9F3B"
- "\u9280"
- "\u4EA1"
- "\u6CCA"
- "\u8857"
- "\u9759"
- "\u9CE5"
- "\u677E"
- "\u5F92"
- "\u969C"
- "\u7B4B"
- "\u7559"
- "\u51B7"
- "\u5C24"
- "\u68EE"
- "\u5438"
- "\u5012"
- "\u68B0"
- "\u6D0B"
- "\u821E"
- "\u6A4B"
- "\u500D"
- "\u6255"
- "\u5352"
- "\u7E04"
- "\u6C5A"
- "\u53F8"
- "\u6625"
- "\u793C"
- "\u66DC"
- "\u6545"
- "\u526F"
- "\u5F01"
- "\u5439"
- "\u85E4"
- "\u8DE1"
- "\u962A"
- "\u4E86"
- "\u91E3"
- "\u9632"
- "\u7834"
- "\u6012"
- "\u662F"
- "\u30A5"
- "\u7AF6"
- "\u8179"
- "\u4E95"
- "\u4E08"
- "\u64AE"
- "\u72ED"
- "\u5BD2"
- "\u7B46"
- "\u5965"
- "\u8C4A"
- "\u732E"
- "\u5C31"
- "\u5A18"
- "\u79D2"
- "\u6C5F"
- "\u8E0F"
- "\u8A13"
- "\u7372"
- "\u96E8"
- "\u6BBA"
- "\u57CB"
- "\u64CD"
- "\u9AA8"
- "\u8D85"
- "\u6D5C"
- "\u8B66"
- "\u7DD1"
- "\u7D61"
- "\u8133"
- "\u7B11"
- "\u6D6E"
- "\u7D66"
- "\u7126"
- "\u8A70"
- "\u878D"
- "\u738B"
- "\u5C3A"
- "\u5E7C"
- "\u820C"
- "\u663C"
- "\u88CF"
- "\u6CE3"
- "\u67C4"
- "\u9396"
- "\u62E1"
- "\u8A3A"
- "\u7DE0"
- "\u5B98"
- "\u6697"
- "\u820E"
- "\u6298"
- "\u5264"
- "\u4E73"
- "\u6B6F"
- "\u7248"
- "\u5C04"
- "\u8108"
- "\u9707"
- "\u7802"
- "\u4F34"
- "\u72AF"
- "\u4F50"
- "\u5DDE"
- "\u8FB2"
- "\u8DA3"
- "\u990A"
- "\u675F"
- "\u6E2F"
- "\u8FEB"
- "\u5F3E"
- "\u798F"
- "\u51AC"
- "\u541B"
- "\u6B66"
- "\u77AC"
- "\u67A0"
- "\u6CA2"
- "\u661F"
- "\u5BCC"
- "\u6557"
- "\u5D0E"
- "\u6355"
- "\u8377"
- "\u5F1F"
- "\u95BE"
- "\u7E54"
- "\u7C89"
- "\u725B"
- "\u8DF5"
- "\u9999"
- "\u6797"
- "\u83DC"
- "\u62CD"
- "\u63CF"
- "\u888B"
- "\u6607"
- "\u91DD"
- "\u8FCE"
- "\u585A"
- "\u5A46"
- "\uFF49"
- "\u8ECD"
- "\uFF13"
- "\uFF37"
- "\u5BC2"
- "\u8F29"
- "\u3074"
- "\u5DFB"
- "\u4E01"
- "\u504F"
- "\u79CB"
- "\u5E9C"
- "\u6CC9"
- "\u81F3"
- "\u6368"
- "\u7956"
- "\u8584"
- "\u5B97"
- "\u5FB9"
- "\u93E1"
- "\u75C7"
- "\u6CB9"
- "\u8131"
- "\u9CF4"
- "\u7AE5"
- "\u6BDB"
- "\u9077"
- "\u84CB"
- "\u58C1"
- "\u5915"
- "\u5589"
- "\u907F"
- "\u984D"
- "\u6EA2"
- "\u96F0"
- "\u4EE4"
- "\u59C9"
- "\u63E1"
- "\u3077"
- "\u523B"
- "\u62E0"
- "\u8CA1"
- "\u8FF7"
- "\u9063"
- "\u82B8"
- "\u5E8F"
- "\u76E3"
- "\u8457"
- "\u5869"
- "\u5009"
- "\u7F6A"
- "\u6F5C"
- "\u7D5E"
- "\u764C"
- "\u5BAE"
- "\u5E2D"
- "\u8F2A"
- "\u594F"
- "\u846C"
- "\u6C60"
- "\u6CBF"
- "\u5FAE"
- "\u5305"
- "\u76CA"
- "\u76AE"
- "\u4FC3"
- "\u6297"
- "\u5FEB"
- "\u66AB"
- "\u52E7"
- "\u8CA9"
- "\u8C46"
- "\u5B63"
- "\u529F"
- "\u9A12"
- "\uFF54"
- "\u97D3"
- "\u6ED1"
- "\u75B2"
- "\u9003"
- "\u9061"
- "\u5E79"
- "\u60A9"
- "\u83D3"
- "\u672D"
- "\u6804"
- "\u9177"
- "\u8B1D"
- "\u6C96"
- "\u96EA"
- "\u5360"
- "\u60D1"
- "\u63FA"
- "\u866B"
- "\u62B1"
- "\uFF4B"
- "\u5CA1"
- "\u6E9C"
- "\u8535"
- "\u7763"
- "\u6838"
- "\u4E71"
- "\u4E45"
- "\u9EC4"
- "\u9670"
- "\u7720"
- "\u7B26"
- "\u6B8A"
- "\u628A"
- "\u6291"
- "\u5E0C"
- "\u63C3"
- "\u6483"
- "\u5EAB"
- "\u5409"
- "\u6E6F"
- "\u65CB"
- "\u640D"
- "\u52AA"
- "\u64E6"
- "\u9769"
- "\u6E0B"
- "\u773C"
- "\u592E"
- "\u8CDE"
- "\u5374"
- "\u5948"
- "\u539A"
- "\u59D4"
- "\u83EF"
- "\u96A0"
- "\uFF4E"
- "\u30CC"
- "\u9BAE"
- "\u515A"
- "\u5C65"
- "\u8A98"
- "\u6469"
- "\u6162"
- "\u5442"
- "\u7206"
- "\u7BB1"
- "\u6075"
- "\u9678"
- "\u7DCA"
- "\u7E3E"
- "\u5742"
- "\u7B52"
- "\u7532"
- "\u5348"
- "\u5230"
- "\u8CAC"
- "\u5C0A"
- "\u6CF3"
- "\u6279"
- "\u7518"
- "\u5B6B"
- "\u7159"
- "\u8A2A"
- "\u50B7"
- "\u6E05"
- "\u716E"
- "\u88C1"
- "\u9694"
- "\u8ED2"
- "\uFF31"
- "\u7FBD"
- "\u5D29"
- "\u7A74"
- "\u7CD6"
- "\u707D"
- "\u5275"
- "\u6F70"
- "\u6691"
- "\u87BA"
- "\u653B"
- "\u6577"
- "\u6575"
- "\u76E4"
- "\u9732"
- "\u7A93"
- "\u63B2"
- "\u81E8"
- "\u53E9"
- "\u5145"
- "\u4FFA"
- "\u8F38"
- "\u967D"
- "\u6B27"
- "\u6687"
- "\u6B6A"
- "\u6DFB"
- "\u60A3"
- "\u5FD9"
- "\u70AD"
- "\u829D"
- "\u8EDF"
- "\u88D5"
- "\u7E01"
- "\u6F2B"
- "\u7A1A"
- "\u7968"
- "\u8A69"
- "\u5CB8"
- "\u7687"
- "\uFF4A"
- "\u6627"
- "\u5100"
- "\u5857"
- "\u8E0A"
- "\u8AF8"
- "\u6D74"
- "\u904D"
- "\u66D6"
- "\u5BE7"
- "\u99B4"
- "\u5339"
- "\u03B1"
- "\u627F"
- "\u30BE"
- "\u6383"
- "\u5375"
- "\u5999"
- "\u3043"
- "\u66B4"
- "\u62B5"
- "\u604B"
- "\u8863"
- "\u6EB6"
- "\u7DAD"
- "\u514D"
- "\u6392"
- "\u685C"
- "\u7573"
- "\u7B87"
- "\u6398"
- "\u535A"
- "\u6FC3"
- "\u7FCC"
- "\u8056"
- "\u7DB2"
- "\u885B"
- "\u64EC"
- "\u5E8A"
- "\u9178"
- "\u6669"
- "\u4E7E"
- "\u90AA"
- "\u7551"
- "\u6EDE"
- "\u5802"
- "\u7E41"
- "\u4ECF"
- "\u5FB3"
- "\u7DE9"
- "\u6A39"
- "\u6551"
- "\u633F"
- "\u68D2"
- "\u906D"
- "\u676F"
- "\u6065"
- "\u6E56"
- "\u6E09"
- "\u81D3"
- "\u8CB4"
- "\u723A"
- "\u7981"
- "\u4F75"
- "\u5263"
- "\u786C"
- "\u58C7"
- "\u80A9"
- "\u6D78"
- "\u4F0A"
- "\u5B9D"
- "\u6094"
- "\u8E8D"
- "\u6DB2"
- "\u99C6"
- "\u6D25"
- "\u307A"
- "\u6D45"
- "\u8B72"
- "\u5CA9"
- "\u9B45"
- "\u587E"
- "\u03B8"
- "\u6696"
- "\u6CB3"
- "\u8A95"
- "\u7F36"
- "\u5507"
- "\u80A2"
- "\u6328"
- "\u62F6"
- "\u7A0E"
- "\u50AC"
- "\u8A34"
- "\uFF58"
- "\u968A"
- "\u659C"
- "\u770B"
- "\uFF50"
- "\u6D66"
- "\u8352"
- "\uFF41"
- "\u71C3"
- "\u52A3"
- "\u5BA3"
- "\u8FBF"
- "\u790E"
- "\u62FE"
- "\u5C4A"
- "\u6905"
- "\u5EC3"
- "\u6749"
- "\u9AEA"
- "\u77E2"
- "\u67D4"
- "\u55AB"
- "\u73CD"
- "\u57FC"
- "\u88C2"
- "\u63B4"
- "\u59BB"
- "\u8CA7"
- "\u934B"
- "\u59A5"
- "\u59B9"
- "\u5175"
- "\uFF14"
- "\u623F"
- "\u5951"
- "\u65E8"
- "\uFF44"
- "\u0394"
- "\u5DE1"
- "\u8A02"
- "\u5F90"
- "\u8CC0"
- "\u7BED"
- "\u9810"
- "\u84C4"
- "\u8846"
- "\u5DE8"
- "\u5506"
- "\u65E6"
- "\u5531"
- "\u9047"
- "\u6E67"
- "\u8010"
- "\u96C4"
- "\u6D99"
- "\u8CB8"
- "\u822A"
- "\u5104"
- "\u5618"
- "\u6C37"
- "\u78C1"
- "\u679D"
- "\u8CAB"
- "\u61D0"
- "\u52DF"
- "\u8155"
- "\u65E7"
- "\u7AF9"
- "\u99D0"
- "\u8A72"
- "\uFF52"
- "\u5893"
- "\u518A"
- "\u80F8"
- "\u758E"
- "\u773A"
- "\uFF45"
- "\u9855"
- "\u631F"
- "\u55A7"
- "\u520A"
- "\u68C4"
- "\u990C"
- "\u67F1"
- "\u5800"
- "\u8ACB"
- "\u79D8"
- "\u6717"
- "\u96F2"
- "\u8170"
- "\u7A32"
- "\u828B"
- "\u8C9D"
- "\u5C48"
- "\u91CC"
- "\u508D"
- "\u8102"
- "\u6FC1"
- "\u54B2"
- "\u6BD2"
- "\u6EC5"
- "\u5629"
- "\u6442"
- "\u6E7E"
- "\u83CC"
- "\u8150"
- "\u5211"
- "\u5F25"
- "\u5AC1"
- "\u61A7"
- "\u4E18"
- "\u5C90"
- "\u52B1"
- "\u8CA2"
- "\u6C41"
- "\u96C7"
- "\u5076"
- "\u9774"
- "\u72D9"
- "\u719F"
- "\u900F"
- "\uFF59"
- "\u8CFC"
- "\u5319"
- "\uFF46"
- "\uFF15"
- "\u92AD"
- "\u6D12"
- "\u8A17"
- "\u809D"
- "\u963F"
- "\u80C3"
- "\uFF53"
- "\u885D"
- "\u621A"
- "\uFF4D"
- "\u84B8"
- "\u4FF3"
- "\u8972"
- "\u5265"
- "\u5BE9"
- "\u6817"
- "\u8A87"
- "\u5237"
- "\u7CF8"
- "\u90F7"
- "\u5049"
- "\u6C57"
- "\u53CC"
- "\u98FD"
- "\u77DB"
- "\u984E"
- "\u552F"
- "\u6590"
- "\u7DB4"
- "\u5B64"
- "\u90F5"
- "\u76D7"
- "\u9E7F"
- "\u8CC3"
- "\u76FE"
- "\u682A"
- "\u9ED9"
- "\u7C8B"
- "\u63DA"
- "\u9808"
- "\u7092"
- "\u9285"
- "\u5E81"
- "\u9B54"
- "\u75E9"
- "\u9802"
- "\u76BF"
- "\u970A"
- "\u5E55"
- "\u570F"
- "\u574A"
- "\u72C2"
- "\u8912"
- "\u9451"
- "\u50B5"
- "\u77AD"
- "\u565B"
- "\u5E33"
- "\u5782"
- "\u8870"
- "\u4ED9"
- "\u9EA6"
- "\u8CA8"
- "\u7AAA"
- "\u6F6E"
- "\u6FEF"
- "\u5238"
- "\u7D1B"
- "\u7384"
- "\u7C4D"
- "\uFF43"
- "\u74F6"
- "\u5DE3"
- "\u5192"
- "\u6CBC"
- "\u99D2"
- "\u5C3D"
- "\u517C"
- "\u7C97"
- "\u63BB"
- "\u80BA"
- "\u9154"
- "\uFF4C"
- "\u702C"
- "\u505C"
- "\u6F20"
- "\u673A"
- "\u916C"
- "\u4FD7"
- "\u8986"
- "\u5C3B"
- "\u9375"
- "\u5805"
- "\u6F2C"
- "\u2212"
- "\u79C0"
- "\u6885"
- "\u9042"
- "\u57F9"
- "\u871C"
- "\uFF42"
- "\u30FB"
- "\u52C7"
- "\u8ECC"
- "\u7F85"
- "\uFF3A"
- "\u5BB4"
- "\u8C5A"
- "\u7A3C"
- "\u62AB"
- "\u8CAF"
- "\u9EBB"
- "\u6C4E"
- "\u51DD"
- "\u5FE0"
- "\uFF55"
- "\u5F80"
- "\u8AE6"
- "\u8B19"
- "\u6F0F"
- "\u5410"
- "\u3047"
- "\u7652"
- "\u9663"
- "\u6D6A"
- "\u52D8"
- "\u53D9"
- "\u5200"
- "\u67B6"
- "\u57F7"
- "\u5674"
- "\u5197"
- "\u4E4F"
- "\u837B"
- "\u81ED"
- "\u708A"
- "\u598A"
- "\u808C"
- "\u8CDB"
- "\u5C0B"
- "\u9175"
- "\u757F"
- "\u5270"
- "\u706F"
- "\u8C6A"
- "\u9685"
- "\u9905"
- "\u7949"
- "\u80AF"
- "\u62DB"
- "\u7A3D"
- "\u5F6B"
- "\u5F69"
- "\u03B2"
- "\u6B04"
- "\u718A"
- "\u68CB"
- "\u6CB8"
- "\u6C88"
- "\u8339"
- "\u7ABA"
- "\u5B9C"
- "\u8217"
- "\u7CA7"
- "\u683D"
- "\u80AA"
- "\u9665"
- "\u6CE1"
- "\u95D8"
- "\u8F3F"
- "\u5353"
- "\u7070"
- "\u8F9B"
- "\u6F01"
- "\u9F13"
- "\u585E"
- "\u8CD1"
- "\u76C6"
- "\u68FA"
- "\u6311"
- "\u54F2"
- "\u9867"
- "\u8B21"
- "\u8302"
- "\u90A3"
- "\u80DE"
- "\u4F3A"
- "\u5A92"
- "\u708E"
- "\u67D0"
- "\u564C"
- "\u5203"
- "\u6F5F"
- "\u7656"
- "\u4E80"
- "\u63EE"
- "\u511F"
- "\u4E39"
- "\u7DEF"
- "\u9DB4"
- "\u4E4B"
- "\u6BB4"
- "\u4EF0"
- "\u5949"
- "\u7E2B"
- "\u75F4"
- "\u8650"
- "\u61B2"
- "\u71E5"
- "\u6DC0"
- "\uFF57"
- "\u88F8"
- "\u82BD"
- "\u63A7"
- "\u95A3"
- "\u7587"
- "\u925B"
- "\u8178"
- "\u5642"
- "\u935B"
- "\u654F"
- "\u9162"
- "\u938C"
- "\u81E3"
- "\u8E74"
- "\u5A01"
- "\u6D44"
- "\u7965"
- "\u795D"
- "\u86C7"
- "\u811A"
- "\u4F0F"
- "\u6F54"
- "\u5510"
- "\u6955"
- "\u57A3"
- "\u932F"
- "\u514B"
- "\u614C"
- "\u6BBF"
- "\u819C"
- "\u61A9"
- "\u9065"
- "\u82DB"
- "\u9676"
- "\u8997"
- "\u78E8"
- "\u624D"
- "\u5E1D"
- "\u642C"
- "\u722A"
- "\u90CA"
- "\u80A5"
- "\u819D"
- "\u62D2"
- "\u868A"
- "\u5208"
- "\u5132"
- "\uFF48"
- "\u596E"
- "\u7761"
- "\u5BEE"
- "\uFF17"
- "\u4FB5"
- "\u9B31"
- "\u635C"
- "\u6DBC"
- "\u5A20"
- "\u7363"
- "\u7C92"
- "\u963B"
- "\u6CE5"
- "\u7ADC"
- "\u91A4"
- "\u92ED"
- "\u6606"
- "\u9234"
- "\u7DBF"
- "\u830E"
- "\u8107"
- "\u7948"
- "\u8A60"
- "\u6B53"
- "\u7F70"
- "\u68DA"
- "\u83CA"
- "\u6069"
- "\u7267"
- "\u540A"
- "\u8DF3"
- "\u6DE1"
- "\u7F72"
- "\u596A"
- "\u9038"
- "\u6170"
- "\u5EB6"
- "\u9262"
- "\u8B5C"
- "\u5ECA"
- "\u5606"
- "\u62ED"
- "\u8CED"
- "\u99C1"
- "\u7F8A"
- "\u5384"
- "\u7D10"
- "\u9673"
- "\u816B"
- "\u6841"
- "\u9298"
- "\u96CC"
- "\u636E"
- "\u62DD"
- "\u60E8"
- "\u96DB"
- "\u845B"
- "\u7FA8"
- "\u609F"
- "\u76DF"
- "\u7E4A"
- "\u9192"
- "\u65EC"
- "\u6DAF"
- "\u8CC4"
- "\u6E7F"
- "\u6F02"
- "\u7D2B"
- "\u30F4"
- "\u4E9C"
- "\u8AA0"
- "\u5854"
- "\u5E4C"
- "\u80C6"
- "\u64A5"
- "\u865A"
- "\u6F64"
- "\u9699"
- "\u5F84"
- "\u6C72"
- "\u8CE2"
- "\u5BF8"
- "\u8888"
- "\u88DF"
- "\u8266"
- "\uFF19"
- "\u62D8"
- "\uFF47"
- "\u5841"
- "\u5BDB"
- "\u51A0"
- "\u614E"
- "\u971E"
- "\u731B"
- "\u67CF"
- "\u733F"
- "\u9084"
- "\u50E7"
- "\u53EB"
- "\u53F1"
- "\u72E9"
- "\u63C9"
- "\u7D2F"
- "\u5982"
- "\u7897"
- "\u6BBB"
- "\u906E"
- "\u5FCD"
- "\u6EF4"
- "\u6B96"
- "\u8D08"
- "\u74A7"
- "\u6F38"
- "\u6589"
- "\u03BC"
- "\u9686"
- "\u6176"
- "\u72A0"
- "\u7272"
- "\u5146"
- "\u576A"
- "\u6284"
- "\u65D7"
- "\u50DA"
- "\u5C3F"
- "\u51CD"
- "\u902E"
- "\u7B39"
- "\u8F1D"
- "\u5C1A"
- "\u8015"
- "\u51CC"
- "\u632B"
- "\u4F10"
- "\u7BB8"
- "\u4E91"
- "\u5968"
- "\u819A"
- "\u9010"
- "\u03B3"
- "\u5F26"
- "\u9700"
- "\u5C01"
- "\u5E3D"
- "\u6F31"
- "\u9283"
- "\u507D"
- "\u5875"
- "\u7E1B"
- "\u58A8"
- "\u6020"
- "\u96F7"
- "\u5766"
- "\u68A8"
- "\u90ED"
- "\u7A4F"
- "\u67FF"
- "\u7AFF"
- "\u5E61"
- "\u5F81"
- "\u99B3"
- "\u9EBA"
- "\u03C4"
- "\u8154"
- "\u7C98"
- "\u7409"
- "\u731F"
- "\u4EC1"
- "\u8358"
- "\u6492"
- "\u7C3F"
- "\u90E1"
- "\u7B4C"
- "\u5D8B"
- "\u6FE1"
- "\u618E"
- "\u5446"
- "\u6F15"
- "\u5A29"
- "\u68DF"
- "\u6052"
- "\uFF18"
- "\u5553"
- "\u5B5D"
- "\u67F3"
- "\u64A4"
- "\u85CD"
- "\u95C7"
- "\u5B22"
- "\u67F4"
- "\u6734"
- "\u6D1E"
- "\u5CB3"
- "\u9B3C"
- "\u8DE8"
- "\u3049"
- "\u70C8"
- "\u559A"
- "\u6F84"
- "\u6FEB"
- "\u82A6"
- "\u62D3"
- "\u51FD"
- "\u6843"
- "\u76F2"
- "\u6CA1"
- "\u7A6B"
- "\u6212"
- "\u99FF"
- "\u8D05"
- "\u67AF"
- "\u6C70"
- "\u53F6"
- "\u90A6"
- "\u66C7"
- "\u9A30"
- "\u711A"
- "\u51F6"
- "\u5CF0"
- "\u69FD"
- "\u67DA"
- "\u5320"
- "\u9A19"
- "\u502B"
- "\u84EE"
- "\u634C"
- "\u61F2"
- "\u8B0E"
- "\u91B8"
- "\u56DA"
- "\u7344"
- "\u6EDD"
- "\u6795"
- "\u60DC"
- "\u7DB1"
- "\u8B33"
- "\u7089"
- "\u5DFE"
- "\u91DC"
- "\u9BAB"
- "\u6E58"
- "\u92F3"
- "\u5351"
- "\uFF51"
- "\u7DBB"
- "\u5EF7"
- "\u85A6"
- "\u667A"
- "\u6C99"
- "\u8CBF"
- "\u8098"
- "\uFF16"
- "\u5F0A"
- "\u66F0"
- "\u7881"
- "\u9DFA"
- "\u6676"
- "\u8D74"
- "\u8513"
- "\u75D2"
- "\u79E9"
- "\u5DE7"
- "\u9418"
- "\u7B1B"
- "\u638C"
- "\u53EC"
- "\u5347"
- "\u6249"
- "\u5A2F"
- "\u8A1F"
- "\u8247"
- "\u64B2"
- "\uFF56"
- "\u6182"
- "\u90B8"
- "\u5098"
- "\u7CDE"
- "\u03BB"
- "\u5C16"
- "\u723D"
- "\u7832"
- "\u55A9"
- "\u80CE"
- "\u84B2"
- "\u9DF9"
- "\u755C"
- "\u6897"
- "\uFF4F"
- "\u5023"
- "\u6247"
- "\u7DFB"
- "\u6756"
- "\u622F"
- "\u5D50"
- "\u6A3D"
- "\u6F06"
- "\u9CE9"
- "\u039B"
- "\u5FAA"
- "\u8896"
- "\u9784"
- "\u6851"
- "\u5D16"
- "\u59A8"
- "\u66A6"
- "\u59D3"
- "\u7A00"
- "\u3041"
- "\u920D"
- "\u9727"
- "\u9837"
- "\u8105"
- "\u7B20"
- "\u86CD"
- "\u8328"
- "\u69CD"
- "\u3062"
- "\u59EB"
- "\u6ABB"
- "\u8463"
- "\u6C7D"
- "\u541F"
- "\u807E"
- "\u73E0"
- "\u62B9"
- "\u9D28"
- "\u64AB"
- "\u8607"
- "\u7AC3"
- "\u864E"
- "\u78EF"
- "\u77E9"
- "\u7CCA"
- "\u55AA"
- "\u8A6E"
- "\u82D1"
- "\u98F4"
- "\u6089"
- "\u674F"
- "\u9B42"
- "\u914C"
- "\u9BC9"
- "\u8A50"
- "\u03A3"
- "\u7815"
- "\u55DC"
- "\u7FFC"
- "\u4F0E"
- "\u751A"
- "\u5F66"
- "\u961C"
- "\u8706"
- "\u6109"
- "\u80F4"
- "\u8776"
- "\u8B00"
- "\u9271"
- "\u75E2"
- "\u73ED"
- "\u9438"
- "\u92F8"
- "\u62D9"
- "\u6068"
- "\u4EAD"
- "\u4EAB"
- "\u75AB"
- "\u5F13"
- "\u74E6"
- "\u7D46"
- "\u814E"
- "\u62F3"
- "\u9A0E"
- "\u58B3"
- "\u83F1"
- "\u6813"
- "\u5256"
- "\u6D2A"
- "\u5484"
- "\u9591"
- "\u58EE"
- "\u9945"
- "\u65ED"
- "\u8987"
- "\u80A1"
- "\u86D9"
- "\u724C"
- "\u965B"
- "\u714E"
- "\u63AC"
- "\u9AED"
- "\u9019"
- "\u5E7B"
- "\u54B3"
- "\u6E26"
- "\u55C5"
- "\u7A42"
- "\u7434"
- "\u5FCC"
- "\u70CF"
- "\u5448"
- "\u91D8"
- "\u611A"
- "\u6C3E"
- "\u8AFE"
- "\u6E9D"
- "\u7336"
- "\u7AAF"
- "\u8ACF"
- "\u8CC2"
- "\u57C3"
- "\u51F8"
- "\u7D0B"
- "\u6ADB"
- "\u525B"
- "\u98E2"
- "\u4FCA"
- "\u54C0"
- "\u5BB0"
- "\u93AE"
- "\u7435"
- "\u7436"
- "\u96C5"
- "\u8494"
- "\u85AA"
- "\u8A93"
- "\u59EA"
- "\u62D7"
- "\u8778"
- "\u7169"
- "\u7B51"
- "\u690E"
- "\u4FB6"
- "\u553E"
- "\u7BAA"
- "\u5075"
- "\u8861"
- "\u03C3"
- "\u88FE"
- "\u95B2"
- "\u805A"
- "\u4E3C"
- "\u633D"
- "\u7E4D"
- "\u82D7"
- "\u9E93"
- "\u03C6"
- "\u03B4"
- "\u4E32"
- "\u51E1"
- "\u5F18"
- "\u85FB"
- "\u61C7"
- "\u817F"
- "\u7A9F"
- "\u6803"
- "\u6652"
- "\u5E84"
- "\u7891"
- "\u7B4F"
- "\u7B25"
- "\u5E06"
- "\u96B7"
- "\u8FB0"
- "\u75BE"
- "\u8FE6"
- "\u8A6B"
- "\u5617"
- "\u582A"
- "\u6842"
- "\u5B9B"
- "\u58F7"
- "\u8AED"
- "\u97AD"
- "\u9310"
- "\u6DF5"
- "\u79E4"
- "\u7525"
- "\u4F8D"
- "\u66FD"
- "\u6572"
- "\u63AA"
- "\u6168"
- "\u83E9"
- "\u5CE0"
- "\u901D"
- "\u5F70"
- "\u67F5"
- "\u82AF"
- "\u7C50"
- "\u57A2"
- "\u03BE"
- "\u77EF"
- "\u8C8C"
- "\u8F44"
- "\u8A89"
- "\u9813"
- "\u7D79"
- "\u9E78"
- "\u5E7D"
- "\u6881"
- "\u642D"
- "\u54BD"
- "\u82B3"
- "\u7729"
- "\u0393"
- "\u61A4"
- "\u7985"
- "\u6063"
- "\u5840"
- "\u7149"
- "\u75FA"
- "\uFF06"
- "\u7A40"
- "\u545F"
- "\u918D"
- "\u9190"
- "\u7901"
- "\u51F9"
- "\u86EE"
- "\u5974"
- "\u64AD"
- "\u7E79"
- "\u8499"
- "\u8A63"
- "\u4E5F"
- "\u5420"
- "\u4E59"
- "\u8E8A"
- "\u8E87"
- "\u9D2C"
- "\u7A92"
- "\u59E5"
- "\u9326"
- "\u694A"
- "\u8017"
- "\u6F09"
- "\u60E7"
- "\u4FE3"
- "\u6876"
- "\u5CFB"
- "\u905C"
- "\u65FA"
- "\u75D5"
- "\u03A6"
- "\u6234"
- "\u658E"
- "\u8CD3"
- "\u7BC7"
- "\u8429"
- "\u85E9"
- "\u7950"
- "\u8B83"
- "\u83AB"
- "\u9C39"
- "\u85A9"
- "\u5378"
- "\u4E9B"
- "\u75B9"
- "\u8E44"
- "\u4E56"
- "\uFF5A"
- "\u92FC"
- "\u6A3A"
- "\u5B8F"
- "\u7BE4"
- "\u8258"
- "\u81B3"
- "\u7A83"
- "\u7E82"
- "\u5598"
- "\u786B"
- "\u99D5"
- "\u7261"
- "\u732A"
- "\u62D0"
- "\u60DA"
- "\u60A0"
- "\u7CE7"
- "\u95A5"
- "\u03C0"
- "\u853D"
- "\u6850"
- "\u981A"
- "\u9214"
- "\u697C"
- "\u8C9E"
- "\u602F"
- "\u817A"
- "\u8305"
- "\u6CF0"
- "\u9913"
- "\u5C51"
- "\u9BDB"
- "\u929B"
- "\u9AB8"
- "\u9C57"
- "\u5824"
- "\u9675"
- "\u6DD8"
- "\u64C1"
- "\u81FC"
- "\u6D32"
- "\u8FBB"
- "\u8A23"
- "\u5C4F"
- "\u9BE8"
- "\u895F"
- "\u5CE1"
- "\u660C"
- "\u982C"
- "\u5806"
- "\u865C"
- "\u840E"
- "\u9EB9"
- "\u7CE0"
- "\u68B1"
- "\u8AFA"
- "\u5403"
- "\u66A2"
- "\u5B54"
- "\u5EB8"
- "\u5DF3"
- "\u589C"
- "\u85AE"
- "\u6101"
- "\u664B"
- "\u8236"
- "\u8FC5"
- "\u6B3A"
- "\u9640"
- "\u7709"
- "\u6CC4"
- "\u59FB"
- "\u9688"
- "\u58CC"
- "\u69D9"
- "\u5E87"
- "\u52D2"
- "\u6E07"
- "\u91E7"
- "\u4E43"
- "\u82D4"
- "\u9306"
- "\u58D5"
- "\u78D0"
- "\u6962"
- "\u65A7"
- "\u5E63"
- "\u03B7"
- "\u7E55"
- "\u83C5"
- "\u7109"
- "\u5112"
- "\u5D07"
- "\u8276"
- "\u5449"
- "\u7984"
- "\u54C9"
- "\u68AF"
- "\u5937"
- "\u546A"
- "\u56C3"
- "\u84BC"
- "\u9A28"
- "\u9D3B"
- "\u862D"
- "\u7CA5"
- "\u7D3A"
- "\u7D17"
- "\u7164"
- "\u03C9"
- "\u52FE"
- "\u97A0"
- "\u4F3D"
- "\u7AAE"
- "\u6E15"
- "\u0392"
- "\u8D66"
- "\u6597"
- "\u66F9"
- "\u8CE0"
- "\u5CAC"
- "\u847A"
- "\u7D33"
- "\u5B8D"
- "\u6191"
- "\u6357"
- "\u7C9B"
- "\u8CCA"
- "\u9F8D"
- "\u81C6"
- "\u6C8C"
- "\u52C5"
- "\u8096"
- "\u559D"
- "\u8CAA"
- "\u82AD"
- "\u8549"
- "\u919C"
- "\u64B9"
- "\u5740"
- "\u7BE0"
- "\u7D2C"
- "\u75B1"
- "\u52F2"
- "\u86FE"
- "\u88B4"
- "\u8749"
- "\u685F"
- "\u4FF5"
- "\u818F"
- "\u5DF7"
- "\u5072"
- "\u6148"
- "\u754F"
- "\u96BB"
- "\u606D"
- "\u64B0"
- "\u9D0E"
- "\u52AB"
- "\u63C6"
- "\u914E"
- "\u8106"
- "\u6241"
- "\u9761"
- "\u8511"
- "\u95CA"
- "\u96BC"
- "\u6CCC"
- "\u5996"
- "\u65A1"
- "\u52C3"
- "\u637B"
- "\u6E13"
- "\u937E"
- "\u5954"
- "\u6155"
- "\u5984"
- "\u6A0B"
- "\u936C"
- "\u502D"
- "\u8679"
- "\u03BD"
- "\u60A6"
- "\u8151"
- "\u62EE"
- "\u51E0"
- "\u80E1"
- "\u8FC2"
- "\u8EAF"
- "\u50ED"
- "\u6ECB"
- "\u7B8B"
- "\u75F0"
- "\u65AC"
- "\u85AB"
- "\u673D"
- "\u82A5"
- "\u9756"
- "\u907C"
- "\u6591"
- "\u7953"
- "\u5B95"
- "\u976D"
- "\u72D7"
- "\u81BF"
- "\u59AC"
- "\u5A7F"
- "\u7554"
- "\u7AEA"
- "\u9D5C"
- "\u8CE6"
- "\u7E1E"
- "\u6731"
- "\u7C95"
- "\u69FB"
- "\u6D69"
- "\u511A"
- "\u8CDC"
- "\u8B39"
- "\u68B5"
- "\u5A9B"
- "\u7947"
- "\u5516"
- "\u03C8"
- "\u03C1"
- "\u5A9A"
- "\u540E"
- "\u6FB1"
- "\u7DBE"
- "\u6372"
- "\u67E9"
- "\u6DF3"
- "\u74DC"
- "\u5631"
- "\u51B4"
- "\u6115"
- "\u9211"
- "\u51B6"
- "\u67A2"
- "\u03A9"
- "\u77B0"
- "\u6775"
- "\u5EB5"
- "\u4F2F"
- "\u840C"
- "\u5609"
- "\u4FC4"
- "\u7D06"
- "\u81A0"
- "\u7252"
- "\u8EB0"
- "\u543E"
- "\u50FB"
- "\u704C"
- "\u646F"
- "\u5091"
- "\u929A"
- "\u8B90"
- "\u8910"
- "\u8FB1"
- "\u7345"
- "\u7B94"
- "\u73A9"
- "\u4F43"
- "\u583A"
- "\u5504"
- "\u515C"
- "\u62CC"
- "\u5751"
- "\u75D8"
- "\u69CC"
- "\u77B3"
- "\u79BF"
- "\u66D9"
- "\u5DF2"
- "\u7FC1"
- "\u5C3C"
- "\u60BC"
- "\u7F77"
- "\u699C"
- "\u5451"
- "\u79E6"
- "\u533F"
- "\u03BA"
- "\u7259"
- "\u4F46"
- "\u572D"
- "\u548E"
- "\u745E"
- "\u7A1C"
- "\u785D"
- "\u6BC5"
- "\u7015"
- "\u8702"
- "\u978D"
- "\u6A2B"
- "\u7566"
- "\u660F"
- "\u755D"
- "\u4FAE"
- "\u548B"
- "\u6367"
- "\u7F9E"
- "\u803D"
- "\u60B8"
- "\u51E7"
- "\u4EAE"
- "\u9AC4"
- "\u54FA"
- "\u4FEF"
- "\u567A"
- "\u8058"
- "\u8654"
- "\u5B8B"
- "\u93A7"
- "\u968B"
- "\u51B3"
- "\u59D1"
- "\u7078"
- "\u927E"
- "\u8F5F"
- "\u60F0"
- "\u03C7"
- "\u643E"
- "\u6854"
- "\u7F6B"
- "\u8E4A"
- "\u68B6"
- "\u6893"
- "\u7F75"
- "\u65A5"
- "\u6276"
- "\u6147"
- "\u61C3"
- "\u9949"
- "\u6E25"
- "\u6AD3"
- "\u80E4"
- "\u56A2"
- "\u9CF3"
- "\u6A84"
- "\u8C79"
- "\u50B2"
- "\u50D1"
- "\u7586"
- "\u6134"
- "\u53A8"
- "\u6FB9"
- "\u9320"
- "\u64E2"
- "\u6EBA"
- "\u7624"
- "\u73CA"
- "\u5BC5"
- "\u6977"
- "\u9583"
- "\u9CF6"
- "\u7119"
- "\u6912"
- "\u9B4F"
- "\u9798"
- "\u68A2"
- "\u6900"
- "\u8ACC"
- "\u696B"
- "\u5F14"
- "\u65D2"
- "\u5957"
- "\u9F5F"
- "\u9F6C"
- "\u7D18"
- "\u810A"
- "\u536F"
- "\u727D"
- "\u6BD8"
- "\u6714"
- "\u514E"
- "\u721B"
- "\u6D9C"
- "\u5851"
- "\u5F04"
- "\u676D"
- "\u63A0"
- "\u80B4"
- "\u626E"
- "\u51F1"
- "\u798D"
- "\u8036"
- "\u808B"
- "\u7235"
- "\u61AB"
- "\u57D3"
- "\u5983"
- "\u9910"
- "\u7C7E"
- "\u7262"
- "\u6816"
- "\u9017"
- "\u7058"
- "\u5E5F"
- "\u68F2"
- "\u5687"
- "\u7827"
- "\u6E1A"
- "\u7C9F"
- "\u7A7F"
- "\u7F60"
- "\u68F9"
- "\u8594"
- "\u8587"
- "\u526A"
- "\u7B48"
- "\u936E"
- "\u892A"
- "\u7AA9"
- "\u58F1"
- "\u30F2"
- "\u7460"
- "\u7483"
- "\u61BE"
- "\u5E16"
- "\u6960"
- "\u03B5"
- "\u5480"
- "\u56BC"
- "\u56A5"
- "\u6D29"
- "\u6A58"
- "\u6867"
- "\u6A9C"
- "\u63F6"
- "\u63C4"
- "\u88E1"
- "\u6A80"
- "\u900D"
- "\u9081"
- "\u6028"
- "\u73B2"
- "\u90C1"
- "\u5815"
- "\u8AB9"
- "\u8B17"
- "\u8956"
- "\u51F0"
- "\u9B41"
- "\u5B75"
- "\u7766"
- "\u71FB"
- "\u5243"
- "\u53A9"
- "\u71D7"
- "\u84D1"
- "\u5EFB"
- "\u75D4"
- "\u837C"
- "\u6190"
- "\u6070"
- "\u8F9F"
- "\u5F98"
- "\u5F8A"
- "\u4FA0"
- "\u5830"
- "\u971C"
- "\u809B"
- "\u76E7"
- "\u5835"
- "\u72DB"
- "\u9D8F"
- "\u9119"
- "\u4F73"
- "\u916A"
- "\u8AE7"
- "\u6973"
- "\u7826"
- "\u5AC9"
- "\u5DEB"
- "\u53E1"
- "\u9716"
- "\u6E23"
- "\u5544"
- "\u798E"
- "\u6CAB"
- "\u821F"
- "\u6C5D"
- "\u5302"
- "\u99F1"
- "\u6C08"
- "\u308E"
- "\u714C"
- "\u7DAC"
- "\u5F1B"
- "\u586B"
- "\u84C1"
- "\u5039"
- "\u7CFE"
- "\u51A5"
- "\u674E"
- "\u966A"
- "\u8877"
- "\u59E6"
- "\u5962"
- "\u75BC"
- "\u8A54"
- "\u8599"
- "\u8B5A"
- "\u5CEF"
- "\u684E"
- "\u688F"
- "\u9B92"
- "\u8A1B"
- "\u55B0"
- "\u7960"
- "\u67A1"
- "\u6681"
- "\u4E5E"
- "\u91C7"
- "\u9739"
- "\u9742"
- "\u687F"
- "\u929C"
- "\u4F51"
- "\u79BE"
- "\u5944"
- "\u6930"
- "\u87F9"
- "\u8061"
- "\u98AF"
- "\u30C2"
- "\u8E81"
- "\u8E42"
- "\u8E99"
- "\u8695"
- "\u693F"
- "\u62F7"
- "\u9257"
- "\u8882"
- "\u78CB"
- "\u7422"
- "\u6B3D"
- "\u60B6"
- "\u53C9"
- "\u7E37"
- "\u8A36"
- "\u50C5"
- "\u5C6F"
- "\u5EEC"
- "\u5C41"
- "\u99A8"
- "\u6E20"
- "\u8568"
- "\u699B"
- "\u675C"
- "\u7791"
- "\u6A8E"
- "\u8ECB"
- "\u8F62"
- "\u8700"
- "\u8235"
- "\u82B9"
- "\u6B3E"
- "\u639F"
- "\u8E2A"
- "\u745A"
- "\u71E6"
- "\u7D21"
- "\u584A"
- "\u8171"
- "\u6753"
- "\u65A4"
- "\u786F"
- "\u55AC"
- "\u8B04"
- "\u79DF"
- "\u8180"
- "\u80F1"
- "\u6EC4"
- "\u9C10"
- "\u8475"
- "\u8471"
- "\u8461"
- "\u5A49"
- "\u88D4"
- "\u9F0E"
- "\u9187"
- "\u67EF"
- "\u991E"
- "\u96C1"
- "\u8AA6"
- "\u8A62"
- "\u633A"
- "\u7AFA"
- "\u8A82"
- "\u5191"
- "\u8718"
- "\u86DB"
- "\u70B8"
- "\u932B"
- "\u58C5"
- "\u8087"
- "\u54AC"
- "\u9B8E"
- "\u67D1"
- "\u7D9C"
- "\u5BE1"
- "\u7977"
- "\u522E"
- "\u8CCE"
- "\u9B18"
- "\u884D"
- "\u5FD6"
- "\u685D"
- "\u0398"
- "\u039A"
- "\u03A8"
- "\u53E2"
- "\u4FCE"
- "\u7396"
- "\u78A7"
- "\u8766"
- "\u8521"
- "\u649A"
- "\u7A14"
- "\u752B"
- "\u6D35"
- "\u7893"
- "\u9ECE"
- "\u5AE1"
- "\u8755"
- "\u725F"
- "\u6B89"
- "\u6C83"
- "\u7B50"
- "\u619A"
- "\u6E24"
- "\u9B4D"
- "\u9B4E"
- "\u71ED"
- "\u7940"
- "\u6D1B"
- "\u88F3"
- "\u4E11"
- "\u9846"
- "\u9952"
- "\u5EC9"
- "\u689F"
- "\u848B"
- "\u6DD1"
- "\u8737"
- "\u9644"
- "\u695A"
- "\u9F20"
- "\u5154"
- "\u61AC"
- "\u5F57"
- "\u66FC"
- "\u5D11"
- "\u57DC"
- "\u5F77"
- "\u5F7F"
- "\u5DF4"
- "\u831C"
- "\u6D9B"
- "\u57E0"
- "\u945A"
- "\u92D2"
- "\u5C09"
- "\u53AD"
- "\u7B75"
- "\u7AE3"
- "\u7E8F"
- "\u6194"
- "\u60B4"
- "\u8E5F"
- "\u675E"
- "\u7825"
- "\u8F14"
- "\u9C52"
- "\u4FAF"
- "\u7D62"
- "\u5475"
- "\u698E"
- "\u53EA"
- "\u71D5"
- "\u5C60"
- "\u5614"
- "\u74E2"
- "\u9291"
- "\u880D"
- "\u932C"
- "\u608C"
- "\u8A1D"
- "\u7DB8"
- "\u530D"
- "\u5310"
- "\u637A"
- "\u6A59"
- "\u5BB5"
- "\u9D60"
- "\u57F4"
- "\u7690"
- "\u9021"
- "\u4FF8"
- "\u7A63"
- "\u54A4"
- "\u8309"
- "\u8389"
- "\u6643"
- "\u6EF8"
- "\u5289"
- "\u5026"
- "\u8944"
- "\u7B4D"
- "\u5239"
- "\u83BD"
- "\u9041"
- "\u66F5"
- "\u79BD"
- "\u7B67"
- "\u7E0A"
- "\u7FD4"
- "\u5BF5"
- "\u834F"
- "\u758B"
- "\u84EC"
- "\u83B1"
- "\u8EAC"
- "\u696E"
- "\u76C8"
- "\u5C13"
- "\u72FC"
- "\u85C9"
- "\u965F"
- "\u620E"
- "\u4E8E"
- "\u6F58"
- "\u8012"
- "\u5F82"
- "\u5FA0"
- "\u99AE"
- "\u5F6D"
- "\u5E47"
- "\u9087"
- "\u6CD3"
- "\u80B1"
- "\u65BC"
- "\u6602"
- "\u8E64"
- "\u7463"
- "\u9A65"
- "\u4EA8"
- "\u8AEE"
- "\u77EE"
- "\u8569"
- "\u6566"
- "\u30EE"
- "\u6208"
- "\u8229"
- "\u9B6F"
- "\u65E0"
- "\u6159"
- "\u6127"
- "\u8340"
- "\u6309"
- "\u914B"
- "\u59F6"
- "\u723E"
- "\u8602"
- "\u986B"
- "\u593E"
- "\u59DA"
- "\u701D"
- "\u6FD8"
- "\u964B"
- "\u777E"
- "\u5B30"
- "\u5DBA"
- "\u821B"
- "\u7B65"
- "\u95A4"
- "\u68D8"
- "\u9812"
- "\u59BE"
- "\u8B2C"
- "\u4F0D"
- "\u537F"
- "\u8FEA"
- "\u5686"
- "\u60F9"
- "\u80DA"
- "\u6C6A"
- "\u543B"
- "\u9B51"
- "\u8F3B"
- "\u59C6"
- "\u84FC"
- "\u6AC2"
- "\u5315"
- "\u4F70"
- "\u7246"
- "\u5CD9"
- "\u725D"
- "\u9DF2"
- "\u7DCB"
- "\u7BAD"
- "\u82EB"
- "\u5366"
- "\u5B5F"
- "\u5323"
- "\u4ED4"
- "\u5D19"
- "\u6787"
- "\u6777"
- "\u81C0"
- "\u681E"
- "\u9E1E"
- "\u61FA"
- "\u55DA"
- "\u6DB8"
- "\u30C5"
- "\u8D16"
- "\u5E9A"
- "\u93D1"
- "\u9149"
- "\u670B"
- "\u70F9"
- "\u53C8"
- "\u7337"
- "\u7C00"
- "\u5B2C"
- "\u88B7"
- "\u6BB7"
- "\u51DB"
- "\u4EC0"
- "\u71FF"
- "\u5556"
- "\u7BC6"
- "\u7DD8"
- "\u5036"
- "\u6AC3"
- "\u8A03"
- "\u540F"
- "\u5CB1"
- "\u8A25"
- "\u958F"
- "\u5DBD"
- "\u722C"
- "\u618A"
- "\u7511"
- "\u6144"
- "\u5E25"
- "\u7704"
- "\u5A11"
- "\u50E5"
- "\u5016"
- "\u800C"
- "\u8F4D"
- "\u5583"
- "\u81BE"
- "\u7099"
- "\u85AF"
- "\u97EE"
- "\u4E99"
- "\u8B14"
- "\u86CE"
- "\u7425"
- "\u73C0"
- "\u698A"
- "\u7C3E"
- "\u8D6D"
- "\u8823"
- "\u8299"
- "\u8B01"
- "\u9022"
- "\u8466"
- "\u6670"
- "\u5398"
- "\u707C"
- "\u903C"
- "\u9328"
- "\u700B"
- "\u5FF8"
- "\u6029"
- "\u7165"
- "\u7B0F"
- "\u5FFD"
- "\u7708"
- "\u7DEC"
- "\u5C4D"
- "\u75BD"
- "\u6E5B"
- "\u788D"
- "\u8AE4"
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: 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: null
cleaner: null
g2p: null
frontend: default
frontend_conf:
    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_sp/train/feats_stats.npz
encoder: conformer
encoder_conf:
    output_size: 512
    attention_heads: 8
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d6
    normalize_before: true
    macaron_style: false
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    use_cnn_module: true
    cnn_module_kernel: 31
decoder: transformer
decoder_conf:
    attention_heads: 8
    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
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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_full_band_vits_accent_with_pause`
♻ Imported from https://zenodo.org/record/5431984/

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}
}
```


---
tags:
- espnet
- audio
- speech-enhancement
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
---
## Example ESPnet2 ENH model
### `Chenda_Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave`
♻ Imported from https://zenodo.org/record/4498562/

This model was trained by Chenda Li using wsj0_2mix/enh1 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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻ Imported from https://zenodo.org/record/4433200/

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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_transformer`
♻ Imported from https://zenodo.org/record/4034121/

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}
}
```


---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- mini_an4
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻ Imported from https://zenodo.org/record/3957940/

This model was trained by kamo-naoyuki using mini_an4/asr1 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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_transformer`
♻ Imported from https://zenodo.org/record/4037456/

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}
}
```


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

# ESPnet2 ASR pretrained model

## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en`

This model was trained by Takashi Maekaku using librispeech 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: `Sat Jul  3 23:10:19 JST 2021`
- python version: `3.7.9 (default, Apr 23 2021, 13:48:31)  [GCC 5.5.0 20171010]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.0`
- Git hash: `0f7558a716ab830d0c29da8785840124f358d47b`
  - Commit date: `Tue Jun 8 15:33:49 2021 -0400`

## asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.3|1.6|0.2|0.2|1.9|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|95.1|4.3|0.6|0.4|5.4|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.2|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|95.3|4.1|0.6|0.5|5.2|45.8|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.5|0.2|0.2|0.2|0.6|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.1|1.0|0.9|0.5|2.4|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.5|0.2|0.3|0.2|0.7|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.3|0.8|0.9|0.5|2.3|45.8|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.6|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|94.1|4.3|1.6|1.1|7.0|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|97.6|1.6|0.8|0.4|2.8|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|94.3|4.0|1.8|1.0|6.7|45.8|
```

### Training config

See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml)

```yaml
config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
ngpu: 3
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


---
tags:
- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave`
♻ Imported from https://zenodo.org/record/5499120/

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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻ 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
```
### 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}
}
```


---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.ave`
♻ Imported from https://zenodo.org/record/4604066/

This model was trained by kamo-naoyuki using librispeech/asr1 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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻ 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/).
### 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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest`
♻ Imported from https://zenodo.org/record/5432540/

This model was trained by kan-bayashi using jvs/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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻ Imported from https://zenodo.org/record/4381100/

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}
}
```


---
tags:
- 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_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en`

♻ Imported from <https://zenodo.org/record/4498554#.YOAOEpozZH4>.

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 01:08:19 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_rnn_tf_raw

config: conf/tuning/train_enh_rnn_tf.yaml

|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_min_8k|0.891065|11.556|10.3982|18.0655|
|enhanced_tt_min_8k|0.896373|11.4086|10.2433|18.0496|
```

### Training config

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

```yaml
config: conf/tuning/train_enh_rnn_tf.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_train_enh_rnn_tf_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
```


---
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_train.loss.ave`
♻ Imported from https://zenodo.org/record/4034121/

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}
}
```


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

## ESPnet2 ASR model

### `espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer`

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

### Demo: How to use in ESPnet2

```bash
cd espnet

pip install -e .
cd els/puebla_nahuatl/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer
```

<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Nov  7 18:16:55 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_hubert_raw_bpe500_sp
### 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|10576|90532|77.0|17.0|6.0|3.6|26.6|74.0|

### 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|10576|590273|92.2|2.1|5.7|3.0|10.8|74.0|

### 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|10576|242435|86.0|7.3|6.8|3.5|17.5|74.0|

## ASR config

<details><summary>expand</summary>

```
config: conf/tuning/train_asr_transformer_hubert.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_hubert_raw_bpe500_sp
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: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
grad_clip: 5
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_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: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_bpe500_sp/valid/text_shape.bpe
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:
-   - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /tmp/jiatong-150390.uytFFbyG/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/jiatong-150390.uytFFbyG/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: 1.0
scheduler: noamlr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ':'
- N
- ▁A
- ▁WA
- ▁KE
- ▁YO
- ▁NE
- ▁SE
- H
- MO
- WA
- ''''
- ▁NO
- ▁I
- ▁N
- S
- ▁KI
- K
- ▁
- MAH
- KA
- TA
- L
- ▁POS
- PA
- ▁KA
- ▁TA
- ▁MO
- T
- ▁YEHWA
- I
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- ▁YA
- ▁DE
- MA
- A
- ▁TE
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- TSI
- NI
- CHI
- ▁PERO
- KI
- LI
- TO
- WI
- ▁PARA
- KO
- E
- ▁O
- ▁IKA
- TE
- O
- W
- ▁NEH
- ▁NOCHI
- CH
- ▁TI
- ▁TIK
- LO
- ▁SAH
- ▁MAH
- NA
- LA
- ▁OMPA
- ▁IHKÓ
- YA
- ▁NI
- ▁PORQUE
- ▁MA
- YO
- ▁TEIN
- LIA
- ▁E
- MPA
- ▁NIKA
- X
- YAH
- ▁KWALTSI
- SA
- TSA
- ▁MOCHI
- ▁NIK
- ▁WE
- ▁TO
- TSÍ
- ▁SEMI
- ▁KITA
- WAK
- KWI
- MI
- ▁MM
- ▁XO
- ▁SEKI
- JÓ
- AH
- ▁KOMO
- R
- NE
- ▁OK
- ▁KWALI
- ▁CHI
- ▁YEH
- ▁NELI
- SE
- PO
- WAH
- PI
- ME
- KWA
- ▁PA
- ▁ONKAK
- KE
- ▁YE
- ▁T
- LTIK
- ▁TEHWA
- TAH
- ▁TIKI
- ▁QUE
- ▁NIKI
- PE
- ▁IWKI
- XI
- TOK
- ▁TAMAN
- ▁KO
- TSO
- LE
- RA
- SI
- WÍ
- MAN
- ▁TIMO
- 'NO'
- SO
- ▁MIAK
- U
- ▁TEH
- ▁KICHI
- ▁XA
- WE
- ▁KOW
- KEH
- NÍ
- LIK
- ▁ITECH
- TIH
- ▁PE
- ▁KIPIA
- ▁CUANDO
- ▁KWALTIA
- ▁HASTA
- LOWA
- ▁ENTÓ
- ▁NA
- XO
- RO
- TIA
- ▁NIKITA
- CHIHCHI
- ▁SEPA
- ▁MAHYÁ
- ▁PAHTI
- ▁K
- LIAH
- ▁SAYOH
- MATI
- ▁PI
- TS
- ▁MÁS
- XMATI
- KAH
- ▁XI
- M
- ▁ESTE
- HKO
- KOWIT
- MIKI
- CHO
- ▁TAK
- Á
- ▁KILIAH
- CHIO
- ▁KIHTOWA
- ▁KITE
- NEKI
- ▁ME
- XA
- ▁TEL
- B
- ▁KOWIT
- ▁ATA
- TIK
- ▁EKINTSI
- ▁IMA
- ▁KWA
- ▁OSO
- ▁NEHJÓ
- ▁ITEYO
- Y
- SKEH
- ▁ISTA
- ▁NIKILIA
- LIH
- ▁TIKWI
- ▁PANÉ
- KOWA
- ▁OX
- TEKI
- ▁SA
- NTE
- ▁KIKWI
- TSITSI
- NOH
- AHSI
- ▁IXO
- WIA
- LTSI
- ▁KIMA
- C
- ▁WEHWEI
- ▁TEPITSI
- ▁IHK
- ▁XIWIT
- YI
- LIS
- ▁CA
- XMATTOK
- SÁ
- ▁MOTA
- RE
- ▁TIKIHTO
- ▁MI
- ▁X
- D
- ▁SAN
- WIH
- ▁WEHKA
- KWE
- CHA
- ▁SI
- KTIK
- ▁YETOK
- ▁MOKA
- NEMI
- LILIA
- ▁¿
- TIW
- ▁KIHTOWAH
- LTI
- Ó
- MASÁ
- ▁POR
- ▁TIKITA
- KETSA
- ▁IWA
- METS
- YOH
- ▁TAKWA
- HKEH
- ▁KIKWIH
- ▁KIKWA
- NIA
- ▁ACHI
- ▁KIKWAH
- ▁KACHI
- ▁PO
- ▁IGUAL
- NAL
- ▁PILI
- ▁NIMAN
- YE
- ▁NIKMATI
- WIAH
- ▁KIPA
- ▁M
- J
- ▁KWI
- ▁WI
- WAYA
- Z
- ▁KITEKI
- G
- ▁'
- ▁IHKO
- CE
- ▁TONI
- ▁TSIKITSI
- P
- DO
- TOKEH
- NIK
- ▁TIKILIAH
- ▁KOWTAH
- ▁TAI
- ▁TATA
- TIAH
- CA
- PIL
- CHOWA
- ▁KIMATI
- ▁TAMA
- XKA
- XIWIT
- TOS
- KILIT
- ILWI
- SKI
- YEH
- DA
- WAYO
- ▁TAPA
- ▁NIMO
- CHIT
- ▁NIMITS
- ▁KINA
- PAHTI
- RI
- ▁BUENO
- ▁ESKI
- WAYAH
- PANO
- KOW
- WEYAK
- LPAN
- LTIA
- ▁KITO
- CO
- ▁TINE
- KIH
- JO
- ▁KATKA
- ▁TIKTA
- PAHTIA
- ▁XIWTSI
- ▁CHIKA
- ▁KANAH
- ▁KOYO
- MPI
- ▁IXIWYO
- IHTIK
- ▁KWE
- ▁XIW
- WILIA
- XTIK
- ▁VE
- ▁TIKMATI
- ▁KOKOLIS
- LKWI
- ▁AHKO
- MEKAT
- ▁TIKMA
- ▁NIMITSILIA
- ▁MITS
- XTA
- ▁CO
- ▁KOMA
- ▁KOMOHKÓ
- F
- ▁OKSEKI
- ▁TEISÁ
- ▁ESO
- ▁IKOWYO
- ▁ES
- TOHTO
- XTI
- ▁TSI
- ▁TIKO
- PIHPI
- ▁OKSÉ
- ▁WEHKAPAN
- KALAKI
- ▁WEL
- ▁MIGUEL
- TEKITI
- ▁TOKNI
- ROWA
- ▁MOSKALTIA
- Í
- XOKO
- ▁TIKCHI
- ▁EHE
- ▁KWO
- LPI
- HTOK
- TSTI
- TÍ
- ▁TEIHSÁ
- KILO
- ▁PUES
- SKIA
- HTIW
- LILIAH
- ▁IHWA
- ▁KOSTIK
- ▁TIKIHTOWAH
- ▁CHA
- ▁COMO
- ▁KIMANA
- CU
- TAMAN
- WITS
- ▁KOKO
- ILPIA
- ▁NIMONO
- ▁WELI
- ▁NIKWI
- WTOK
- ▁KINEKI
- KOKOH
- ▁P
- LTIAH
- XKO
- ▁ONKAYA
- TAPOWI
- MATTOK
- ▁MISMO
- ▁NIKIHTO
- ▁NIKMATTOK
- MESKIA
- ▁SOH
- KWOWIT
- XTIA
- WELITA
- ▁DESPUÉS
- ▁IXWA
- ZA
- TSAPOT
- SKAL
- ▁SIEMPRE
- TINEMI
- Ñ
- ▁ESKIA
- NELOWA
- ▁TZINACAPAN
- ▁DI
- XIWYO
- ▁AHA
- ▁AHWIA
- É
- ▁KIKWIAH
- MATTOKEH
- ▁ACHTO
- XTILIA
- TAPAL
- ▁KIHTO
- TEHTE
- ▁PORIN
- ▁TSOPE
- ▁KAHFE
- GU
- ▁NIMITSTAHTANI
- ▁TAHTA
- ▁KOWTATI
- ISWAT
- ▁TIKPIA
- ▁KOMEKAT
- TIOWIH
- ▁TIMONOHNO
- ▁TIEMPO
- WEHKA
- QUI
- ▁TIHTI
- ▁XOXOKTIK
- ▁TAXKAL
- EHE
- ▁AJÁ
- NANAKAT
- NIWKI
- ▁CI
- ▁ITSMOL
- ▁NIKPIA
- TEKPA
- ▁BO
- ▁TASOHKA
- Ú
- ¡
- '8'
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- '0'
- '1'
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- ¿
- Ò
- '4'
- À
- '7'
- '5'
- '3'
- ́
- V
- ̈
- Ï
- '6'
- Q
- Ì
- <sos/eos>
init: xavier_uniform
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: bpe
bpemodel: data/token_list/bpe_unigram500/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: s3prl
frontend_conf:
    frontend_conf:
        upstream: hubert_large_ll60k
    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: transformer
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
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: 0.10.4a1
distributed: false
```

</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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_fastspeech`
♻ Imported from https://zenodo.org/record/3986227/

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}
}
```


---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause`
♻ Imported from https://zenodo.org/record/4436448/

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}
}
```