```
config: conf/tuning/train_asr_transformer_specaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_specaug_raw_bpe500
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/train/speech_shape
- exp/asr_stats_raw_bpe500/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500/valid/speech_shape
- exp/asr_stats_raw_bpe500/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/st-jiatong-54826.tbQP9L0N/raw/train/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/st-jiatong-54826.tbQP9L0N/raw/train/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /tmp/st-jiatong-54826.tbQP9L0N/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/st-jiatong-54826.tbQP9L0N/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>
- '4'
- '3'
- '1'
- '2'
- A
- ▁NDI
- '''4'
- '''1'
- U
- ▁BA
- O
- ▁I
- E
- 4=
- ▁KU
- ▁TAN
- ▁KA
- '''3'
- NI
- ▁YA
- RA
- 3=
- 2=
- IN
- NA
- ▁TA
- AN
- ▁KAN
- ▁NI
- ▁NDA
- ▁NA
- ▁JI
- KAN
- CHI
- (3)=
- I
- UN
- 1-
- ▁SA
- (4)=
- ▁JA
- XI
- ▁KO
- ▁TI
- TA
- KU
- BI
- ▁YU
- ▁KWA
- KA
- XA
- 1=
- ▁YO
- RI
- NDO
- ▁XA
- TU
- ▁TU
- ▁ÑA
- ▁KI
- ▁XI
- YO
- NDU
- NDA
- ▁CHI
- (2)=
- ▁BI
- ▁NU
- KI
- (1)=
- YU
- 3-
- ▁MI
- 'ON'
- ▁A
- BA
- 4-
- KO
- ▁NDU
- ▁ÑU
- ▁NDO
- NU
- ÑU
- '143'
- ▁SI
- ▁SO
- 13-
- NDI
- ▁AN
- ▁SU
- TIN
- SA
- ▁BE
- TO
- RUN
- KWA
- KWI
- ▁NDE
- ▁KWI
- XIN
- ▁U
- SI
- SO
- ▁TUN
- EN
- ▁KWE
- YA
- (4)=2
- NDE
- TI
- TUN
- ▁TIN
- MA
- ▁SE
- ▁XU
- SU
- ▁LU
- ▁KE
- ▁
- MI
- ▁RAN
- (3)=2
- 14-
- ▁MA
- KUN
- LU
- N
- ▁O
- KE
- NGA
- ▁IS
- ▁JU
- '='
- ▁LA
- ÑA
- JA
- CHUN
- R
- TAN
- PU
- ▁TIEM
- LI
- LA
- CHIU
- ▁PA
- M
- ▁REY
- ▁BAN
- JI
- L
- SUN
- ▁SEÑOR
- ▁JO
- ▁TIO
- KWE
- CHU
- S
- ▁YE
- KIN
- XU
- BE
- ▁CUENTA
- ▁SAN
- RRU
- ▁¿
- CHA
- ▁TO
- RRA
- LO
- TE
- ▁AMIGU
- PA
- XAN
- ▁C
- C
- ▁CHA
- ▁TE
- ▁HIJO
- ▁MB
- ▁PI
- G
- ▁ÁNIMA
- ▁CHE
- ▁P
- B
- NDIO
- SE
- ▁SANTU
- MU
- ▁PADRE
- D
- JU
- Z
- ▁TORO
- ▁PO
- LE
- ▁LI
- RO
- ▁LO
- ▁MESA
- CA
- ▁CHIU
- DO
- ▁BU
- ▁BUTA
- JO
- T
- TRU
- RU
- ▁MBO
- ▁JUAN
- ▁MM
- ▁CA
- ▁M
- ▁MAS
- ▁DE
- V
- ▁MAÑA
- ▁UTA
- DA
- ▁MULA
- ▁YOLOXÓCHITL
- ▁CONSEJU
- ▁Y
- ▁LE
- ÓN
- ▁MISA
- TIU
- ▁CANDELA
- ▁PATRÓN
- ▁PADRINU
- ▁MARCU
- ▁V
- ▁G
- Í
- ▁XE
- ▁MU
- ▁XO
- NGUI
- ▁CO
- ▁HOMBRE
- ▁PESU
- ▁PE
- ▁D
- ▁MACHITI
- CO
- REN
- ▁RANCHU
- ▁MIS
- ▁MACHU
- J
- ▁PAN
- CHO
- H
- ▁CHU
- Y
- ▁TON
- GA
- X
- ▁VI
- ▁FE
- ▁TARRAYA
- ▁SANTÍSIMA
- ▁N
- ▁MAYÓ
- ▁CARRU
- ▁F
- ▁PAPÁ
- ▁PALOMA
- ▁MARÍA
- ▁PEDRU
- ▁CAFÉ
- ▁COMISARIO
- ▁PANELA
- ▁PELÓN
- É
- ▁POZO
- ▁CABRÓN
- ▁GUACHU
- ▁S
- RES
- ▁COSTUMBRE
- ▁SEÑA
- QUI
- ▁ORO
- CH
- ▁MAR
- SIN
- SAN
- ▁COSTA
- ▁MAMÁ
- ▁CINCUENTA
- ▁CHO
- ▁PEDR
- ▁JUNTA
- MÚ
- ▁TIENDA
- ▁JOSÉ
- NC
- ▁ES
- ▁SUERTE
- ▁FAMILIA
- ▁ZAPATU
- NTE
- ▁PASTO
- ▁CON
- Ñ
- ▁BOTE
- CIÓN
- ▁RE
- ▁BOLSA
- ▁MANGO
- ▁JWE
- ▁GASTU
- ▁T
- ▁B
- ▁KW
- ÍN
- ▁HIJA
- ▁CUARENT
- ▁VAQUERU
- ▁NECHITO
- ▁NOVIA
- ▁NOVIO
- JWE
- ▁PUENTE
- ▁SANDÍA
- ▁MALA
- Ó
- ▁ABONO
- ▁JESÚS
- ▁CUARTO
- ▁EFE
- ▁REINA
- ▁COMANDANTE
- ▁ESCUELA
- ▁MANZANA
- ▁MÁQUINA
- LLA
- ▁COR
- ▁JERÓNIMO
- ▁PISTOLA
- NGI
- CIO
- ▁FRANCISCU
- ▁TEODORO
- CER
- ▁SALUBI
- ▁MEZA
- ▁MÚSIC
- ▁RU
- ▁CONSTANTINO
- ▁GARCÍA
- ▁FRENU
- ▁ROSA
- ▁CERVEZA
- ▁CIGARRU
- ▁COMISIÓN
- ▁CUNIJO
- ▁FRANCISCO
- ▁HÍJOLE
- ▁NUEVE
- ▁MUL
- ▁PANTALÓN
- ▁CAMISA
- ▁CHINGADA
- ▁SEMANA
- ▁COM
- GAR
- ▁MARTÍN
- ▁SÁBADO
- ▁TRABAJO
- ▁CINCO
- ▁DIE
- ▁EST
- NDWA
- ▁LECHIN
- ▁COCO
- ILLU
- ▁CORRE
- ▁MADR
- ▁REC
- ▁BAUTISTA
- ▁VENTANA
- ▁CUÑAD
- ▁ANTONIU
- ▁COPALA
- LÍN
- ▁SECUND
- ▁COHETE
- ▁HISTORIA
- ▁POLICÍA
- ENCIA
- ▁CAD
- ▁LUIS
- ▁DOCTOR
- ▁GONZÁLEZ
- ▁JUEVE
- ▁LIBRU
- ▁QUESU
- ▁VIAJE
- ▁CART
- ▁LOCO
- ▁BOL
- ▁COMPADRE
- ▁JWI
- ▁METRU
- ▁BUENO
- ▁TRE
- ▁CASTILLO
- ▁COMITÉ
- ▁ETERNO
- ▁LÍQUIDO
- ▁MOLE
- ▁CAPULCU
- ▁DOMING
- ▁ROMA
- ▁CARAJU
- ▁RIATA
- ▁TRATU
- ▁SEIS
- ▁ADÁN
- ▁JUANCITO
- ▁HOR
- ''''
- ▁ARRÓ
- ▁COCINA
- ▁PALACIO
- ▁RÓMULO
- K
- ▁ALFONSO
- ▁BARTOLO
- ▁FELIPE
- ▁HERRER
- ▁PAULINO
- ▁YEGUA
- ▁LISTA
- Ú
- ▁ABRIL
- ▁CUATRO
- ▁DICIEMBRE
- ▁MARGARITO
- ▁MOJONERA
- ▁SOLEDAD
- ▁VESTIDO
- ▁PELOTA
- RRET
- ▁CAPITÁN
- ▁COMUNIÓN
- ▁CUCHARA
- ▁FERNANDO
- ▁GUADALUPE
- ▁MIGUEL
- ▁PELÚN
- ▁SECRETARIU
- ▁LENCHU
- ▁EVA
- ▁SEGUND
- ▁CANTOR
- ▁CHILPANCINGO
- ▁GABRIEL
- ▁QUINIENTO
- ▁RAÚL
- ▁SEVERIAN
- ▁TUMBADA
- ▁MALINCHI
- ▁PRIMU
- ▁MORAL
- ▁AGOSTO
- ▁CENTÍMETRO
- ▁FIRMA
- ▁HUEHUETÁN
- ▁MANGUERA
- ▁MEDI
- ▁MUERT
- ▁SALAZAR
- ▁VIERNI
- LILL
- ▁LL
- '-'
- ▁CAMPESINO
- ▁CIVIL
- ▁COMISARIADO
- )
- (
- Ã
- ‘
- ¿
- Ü
- ¡
- Q
- F
- Á
- P
- Ÿ
- W
- Ý
- <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
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: 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_bpe500/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 12
    linear_units: 2048
    dropout_rate: 0.1
    output_size: 512
    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}
}
```


model name: ftshijt_espnet2_asr_yolo_mixtec_transformer


model author: espnet


task: automatic-speech-recognition


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


tags:


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


## ESPnet2 TTS pretrained model


### `kan-bayashi/csmsc_full_band_vits`


♻ Imported from https://zenodo.org/record/5443852/


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
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


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


model name: kan-bayashi_csmsc_full_band_vits


task: text-to-speech


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


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


## Example ESPnet2 ASR model


### `kamo-naoyuki/timit_asr_train_asr_raw_word_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4284058/


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


### Citing ESPnet


or arXiv:


model name: kamo-naoyuki_timit_asr_train_asr_raw_word_valid.acc.ave


model author: espnet


task: automatic-speech-recognition


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


tags:


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


### `kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4278363/


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


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kamo-naoyuki_reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.acc.ave


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


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


## Example ESPnet2 TTS model


### `kan-bayashi/libritts_xvector_trasnformer`


♻ Imported from https://zenodo.org/record/4409704/


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


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


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


model name: kan-bayashi_libritts_xvector_trasnformer


model author: espnet


task: text-to-speech


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


tags:


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


### `kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave`


♻ Imported from https://zenodo.org/record/4034125/


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
```


model name: kan-bayashi_csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave


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


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


## ESPnet2 ASR pretrained model


### `siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`


♻ Imported from https://zenodo.org/record/5656007


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


### 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}
}

```


or arXiv:


model name: siddhana_fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best


model author: espnet


task: automatic-speech-recognition


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


tags:


## ESPnet2 TTS pretrained model


### `kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5443852/


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


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


model name: kan-bayashi_csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave


task: text-to-speech


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


### `kan-bayashi/jvs_jvs010_vits_prosody`


♻ Imported from https://zenodo.org/record/5521494/


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


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jvs_jvs010_vits_prosody


model author: espnet


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4039194/


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
```


model name: kan-bayashi_ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


task: text-to-speech


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


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


## ESPnet2 TTS pretrained model


### `kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest`


♻ Imported from https://zenodo.org/record/5521446/


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


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


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


model name: kan-bayashi_tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest


model author: espnet


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


tags:


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`


♻ Imported from https://zenodo.org/record/3989498/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best


task: text-to-speech


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


### `kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ 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/).


### Citing ESPnet


or arXiv:


model name: kan-bayashi_vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


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


tags:


- espnet
- audio
- text-to-speech
language: 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_train.loss.ave`


♻ Imported from https://zenodo.org/record/4391405/


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
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


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


model name: kan-bayashi_jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave


task: text-to-speech


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


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


### `kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best`


♻ 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/).


### Citing ESPnet


or arXiv:


model name: kan-bayashi_csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best


model author: espnet


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


tags:


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave`


♻ Imported from https://zenodo.org/record/4032246/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave


task: text-to-speech


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


### `kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


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


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


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


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


model name: kan-bayashi_vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave`


♻ Imported from https://zenodo.org/record/4031955/


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
```


model name: kan-bayashi_csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave


task: text-to-speech


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


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


## Example ESPnet2 ASR model


### `Shinji_Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`


♻ Imported from https://zenodo.org/record/4030677/


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


### Citing ESPnet


or arXiv:


model name: Shinji_Watanabe_librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best


model author: espnet


task: automatic-speech-recognition


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


tags:


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


## Example ESPnet2 TTS model


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


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


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


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


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


model name: kan-bayashi_vctk_gst_xvector_conformer_fastspeech2


task: text-to-speech


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


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


## Example ESPnet2 ASR model


♻ Imported from https://zenodo.org/record/4415021/


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


### Citing ESPnet


or arXiv:


model author: espnet


task: automatic-speech-recognition


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4036268/


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
```


model name: kan-bayashi_ljspeech_conformer_fastspeech2


task: text-to-speech


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


- espnet
- audio
- automatic-speech-recognition
language:
- en
- zh
- multilingual
datasets:
- seame
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1`


♻ Imported from https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1


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


### 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}
}

```


or arXiv:


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


model name: vectominist_seame_asr_conformer_bpe5626


model author: espnet


task: automatic-speech-recognition


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_gst+xvector_tacotron2`


♻ Imported from https://zenodo.org/record/4394598/


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


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


model name: kan-bayashi_vctk_gst_xvector_tacotron2


task: text-to-speech


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


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


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody`


♻ Imported from https://zenodo.org/record/5499050/


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


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_conformer_fastspeech2_tacotron2_prosody


model author: espnet


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


tags:


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_gst+xvector_transformer`


♻ Imported from https://zenodo.org/record/4393277/


### Demo: How to use in ESPnet2


```python
# coming soon
```


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


model name: kan-bayashi_vctk_gst_xvector_transformer


task: text-to-speech


- espnet
- audio
- audio-source-separation
- audio-to-audio
language: en
datasets:
- dns_ins20
license: cc-by-4.0
inference: false


# ESPnet2 ENH pretrained model


## `neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best, fs=16k, lang=en`


♻ Imported from <https://zenodo.org/record/4923697#.YOAOIpozZH4>.


This model was trained by neillu23 using dns_ins20 recipe in [espnet](https://github.com/espnet/espnet/).


### Python API


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


### Evaluate in the recipe


### Results


```bash
# RESULTS
## Environments
- date: `Wed Jun  9 09:49:34 CST 2021`
- python version: `3.8.10 (default, May 19 2021, 18:05:58)  [GCC 7.3.0]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.4.0`
- Git hash: `c1dfefb98bf59f654e0907b9681668eaca8ddfcc`
  - Commit date: `Tue Jun 8 17:23:26 2021 +0800`

## enh_train_enh_blstm_tf_raw

config: ./conf/tuning/train_enh_blstm_tf.yaml

|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_synthetic|0.98|23.87|23.87|0.00|
|enhanced_tt_synthetic_no_reverb|0.96|15.94|15.94|0.00|
|enhanced_tt_synthetic_with_reverb|0.84|11.86|11.86|0.00|
```


### Training config


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


```yaml
config: ./conf/tuning/train_enh_blstm_tf.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_train_enh_blstm_tf_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 45398
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: yen-ju-lu-dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best


model author: espnet


task: audio-to-audio


tags: en, dataset:dns_ins20, espnet, audio, audio-source-separation, audio-to-audio, license:cc-by-4.0


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4036272/


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}
}
```


model name: kan-bayashi_ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


task: text-to-speech


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


- espnet
- audio
- speech-enhancement
- audio-to-audio
language: en
datasets:
- librimix
license: cc-by-4.0


## Example ESPnet2 ENH model


### `anogkongda/librimix_enh_train_raw_valid.si_snr.ave`


♻ Imported from https://zenodo.org/record/4480771/


This model was trained by anogkongda using librimix/enh1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: anogkongda_librimix_enh_train_raw_valid.si_snr.ave


model author: espnet


task: audio-to-audio


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_fastspeech2_accent`


♻ 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


or arXiv:


model name: kan-bayashi_jsut_fastspeech2_accent


task: text-to-speech


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


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


## ESPnet2 ASR model


### `espnet/brianyan918_iwslt22_dialect_transformer_fisherlike`


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


```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install -e .
cd egs2/iwslt22_dialect/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_transformer_fisherlike
```


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


# RESULTS


## Environments


- date: `Mon Jan 31 10:15:38 EST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1`
- Git hash: `99581e0f5af3ad68851d556645e7292771436df9`
  - Commit date: `Sat Jan 29 11:32:38 2022 -0500`


## asr_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe1000_sp


### WER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|27370|53.4|41.1|5.5|9.5|56.1|88.2|


### CER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|145852|83.8|7.5|8.7|12.2|28.4|88.2|


### TER


|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|64424|62.9|23.9|13.3|13.4|50.5|88.2|


## ASR config


<details><summary>expand</summary>


```
config: conf/tuning/transformer_fisherlike_4gpu_bbins16m_fix.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe1000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 60761
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 3
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe1000_sp/train/speech_shape
- exp/asr_stats_raw_bpe1000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe1000_sp/valid/speech_shape
- exp/asr_stats_raw_bpe1000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - /scratch/iwslt22asrdump/raw/train_sp/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22asrdump/raw/train_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /scratch/iwslt22asrdump/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22asrdump/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 5.0
scheduler: noamlr
scheduler_conf:
    model_size: 256
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ّ
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- ▁إي
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- ▁الل
- ▁أ
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- ▁والل
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- ▁آخر
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- ▁ٱ
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- ابل
- ▁ماك
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- بار
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- ▁جديد
- ▁يز
- ▁كر
- ▁حاسيلو
- ▁شق
- ▁اه
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- رج
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- ▁صحيح
- ▁غير
- ▁يخدم
- مان
- وكا
- ▁عند
- ▁قاعدة
- ▁تس
- ربة
- ▁راس
- ▁حط
- ▁نكل
- تني
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- سيون
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- ▁لو
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- صف
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- ▁كامل
- ▁نخدم
- ▁يبدا
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- رات
- ▁تونس
- بدا
- ▁تحكي
- ▁سو
- ▁جاي
- ▁وحدة
- ▁ساعة
- حنا
- ▁بكري
- ▁إل
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- ▁كم
- ▁تبدا
- ارة
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- رق
- لوا
- ▁يمكن
- ▁خاط
- ▁وص
- جين
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- قد
- ▁قل
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- ▁ماشي
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- ▁خذيت
- امات
- ▁كنت
- خرج
- ▁لقيت
- رتاح
- كس
- ▁حاجات
- ▁مريق
- ▁مل
- ليفون
- اوا
- ▁شفت
- ▁عاملة
- ▁تن
- ▁والا
- سأل
- ▁حد
- ▁قاللك
- ▁العباد
- ▁عالاخ
- ▁وآك
- ▁ماني
- ▁ناخذ
- ▁حم
- ▁الإ
- ▁ماضي
- ▁ث
- الة
- ▁أخرى
- رين
- ▁تشوف
- ▁نخرج
- ▁أربعة
- ▁ألف
- نيش
- ▁هاي
- آ
- ▁فيك
- رشة
- ولة
- فلة
- ▁بابا
- ▁أما
- ▁روحي
- ▁فيهم
- ▁رج
- ▁ليك
- ونس
- يرة
- ▁وأكهو
- ندي
- ▁صار
- شك
- ▁نرو
- ▁آكهو
- ▁تش
- ▁غاديكا
- ▁معاها
- ▁لب
- ▁أذاكا
- ▁آني
- ▁يوم
- عملوا
- ▁نقعد
- دوا
- ▁عد
- سمع
- متني
- ▁الخدمة
- ▁مازلت
- ▁قعدت
- ايا
- ▁برك
- قعد
- ▁خرجت
- ضح
- ▁قالل
- ▁يقول
- ▁وفي
- ▁حق
- ختي
- ▁يعني
- خدم
- ▁جيت
- ▁نرمال
- طف
- ▁عجب
- ▁تقعد
- ▁مشينا
- اية
- ▁خدمة
- لدي
- روف
- ▁الفطر
- ▁مشكل
- ▁سل
- ▁وآنا
- الط
- ▁بالس
- ▁هانا
- ▁أوه
- ▁أذيكا
- ▁وإ
- ▁عليهم
- ▁حالة
- جت
- قضي
- ▁لق
- ▁ونصف
- سعة
- عطيه
- عاو
- خانة
- ▁مخ
- ▁شبيك
- بيعة
- ▁أهوك
- يني
- ▁تعد
- ▁خال
- ▁قريب
- ▁راك
- ▁قالت
- ▁لتو
- ▁أكثر
- اعة
- ▁يظهرلي
- ▁ماشية
- سمعني
- ▁نسيت
- ▁ينج
- ▁الحمدلل
- هدي
- ▁وشن
- ▁تطي
- ▁هنا
- ▁نسمع
- ▁إنتوما
- ▁نحكيلك
- ▁قاعد
- ▁اسمعني
- خرين
- إ
- ماعة
- ▁بالر
- ▁دا
- ▁عمر
- ▁نشري
- ▁قهوة
- ▁تبارك
- ▁صب
- ▁مشات
- غر
- ▁شريت
- ▁عامل
- ▁زوج
- ثنين
- ▁برب
- ريق
- ▁نكم
- ▁لم
- بيب
- ▁مياة
- ▁مالل
- ▁قعد
- ▁سخون
- قس
- ▁وحده
- ▁اسمع
- ▁خمسة
- ▁غالي
- ▁الأو
- رلي
- ▁العظيم
- ▁ترو
- تهم
- كري
- ▁نجيب
- ▁جملة
- قول
- ▁قلتلي
- ▁إيجا
- ▁يقعد
- ▁إيام
- ▁يعطيك
- ▁نخل
- ▁دب
- يمة
- رهبة
- ▁نهز
- ▁محم
- ▁بين
- غار
- ▁نحنا
- ▁بون
- ▁الغ
- ▁شهر
- ▁بار
- رقة
- ▁نطي
- ئ
- ترو
- ▁ملا
- ▁الكرهبة
- ▁باه
- ▁عالإخ
- ▁عباد
- ▁بلاصة
- ▁مشى
- بيع
- ▁نفس
- ▁عملنا
- ▁واح
- ▁أحلاه
- ▁بحذاك
- ▁لأ
- ▁دخ
- باب
- ▁ودر
- ▁غالب
- ▁ناكل
- ▁مثلا
- ء
- ▁راقد
- ▁تفر
- ▁الوقت
- ▁تاخذ
- حذا
- نتر
- ▁نبدا
- ▁حال
- ▁مريم
- الم
- ▁جمعة
- رجول
- ▁معايا
- ▁تخرج
- ▁باس
- ▁ساعات
- ▁عندهم
- ▁نتفر
- مسة
- ▁الجمعة
- بعين
- ▁أكاهو
- ▁ميش
- مراة
- ▁خذا
- ▁ظ
- ▁سيدي
- ▁معاي
- ▁شبيه
- ▁حكا
- ▁سف
- ▁بعضنا
- ▁بالض
- ▁ليلة
- ▁زعما
- ▁الحق
- مضان
- ▁صعيب
- ▁قالتلك
- ً
- ملة
- ▁بق
- عرف
- لاطة
- ▁خرج
- ▁أخت
- ▁تقوللي
- ▁معانا
- ▁صغير
- ▁إسمه
- ▁بعض
- ▁العام
- ▁علينا
- ▁يتع
- ▁فاش
- ▁شع
- ▁معاهم
- ▁يسالش
- ▁لهنا
- ▁سمعت
- ▁البار
- ▁نتصو
- ▁الاخ
- ▁وكان
- وبة
- دمة
- ▁كون
- ▁مبعد
- ▁تسمع
- ▁بعيد
- ▁تاكل
- ▁نلقا
- لامة
- لاثة
- ▁ذ
- ▁تحس
- ▁الواح
- ▁لدار
- ▁فاتت
- ▁تاو
- ▁أحوالك
- ▁عاملين
- ▁كبيرة
- عجب
- ▁بنت
- ▁بيدي
- ▁حكيت
- ▁تحط
- ▁مسكينة
- ▁هاذوكم
- ▁نزيد
- لاث
- ▁عشرة
- ▁عيني
- ▁تعب
- ▁ياكل
- ▁وزيد
- ▁طول
- ▁حمدلله
- ▁وقتاه
- ▁معناه
- ▁وآش
- ▁ووه
- ▁وواحد
- ▁نشوفوا
- ▁عيد
- ▁بصراحة
- ▁بحذانا
- ▁قاعدين
- ▁راجل
- ▁وحدي
- ▁وعشرين
- ▁لين
- ▁خايب
- ▁قالتله
- ▁تهز
- عيد
- ▁كبير
- ▁يعرف
- ▁عارف
- ▁الفلوس
- ▁زايد
- ▁خدمت
- ▁هاذوما
- ▁سلاطة
- ▁فارغة
- ▁ساعتين
- ▁تبد
- ▁راو
- ▁مائة
- ▁بعضهم
- ▁ظاهرلي
- ▁الفازة
- كتب
- ▁القهوة
- سبوك
- ▁زاد
- ▁ضرب
- حكيلي
- ▁فوق
- ▁عاود
- ▁راي
- ▁ومبعد
- ▁حوايج
- ▁دخلت
- ▁يقوللك
- ▁زيد
- ▁زلت
- لفزة
- ▁وقال
- ▁يهب
- ▁يلزمني
- ▁الحمد
- ▁أذي
- طبيعت
- ▁دورة
- ▁عالأقل
- ▁آذاك
- ▁وبال
- ▁الجاي
- عطيني
- ▁ياخذ
- ▁احكيلي
- ▁نهبط
- ▁رقدت
- بلاصة
- ▁عزيز
- ▁صغار
- ▁أقسم
- ▁جيب
- ▁وصلت
- ▁أحوال
- ▁جيست
- ▁جماعة
- سئل
- ▁خوذ
- ▁يهز
- ▁الأخرى
- ▁آلاف
- ▁إسمع
- ▁الحقيقة
- ▁ناقص
- ▁حاط
- ▁موجود
- عباد
- ▁آذيك
- ▁خارج
- ▁الخير
- ▁البنات
- بقى
- ▁طرف
- ▁سينون
- ▁ماذاب
- ▁البحر
- ▁نرقد
- مدلله
- ▁إيجى
- ▁خالتي
- ▁فازة
- ▁بريك
- ▁شريبتك
- ▁تطلع
- ؤ
- ▁المشكلة
- ▁طري
- ▁مادام
- ▁طلبت
- ▁يلعب
- ▁نعاود
- ▁وحدك
- ▁ظاهر
- ٱ
- ژ
- ٍ
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
joint_net_conf: null
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
    n_fft: 512
    win_length: 400
    hop_length: 160
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_bpe1000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 12
    linear_units: 2048
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
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: 0.10.6a1
distributed: true
```


</details>


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


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


model name: brianyan918_iwslt22_dialect_transformer_fisherlike


model author: espnet


task: automatic-speech-recognition


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


tags:


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


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4032246/


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
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


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


model name: kan-bayashi_jsut_conformer_fastspeech2


task: text-to-speech


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


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


### `kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4394602/


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


### Citing ESPnet


or arXiv:


model name: kan-bayashi_vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


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


tags:


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


## Example ESPnet2 ASR model


♻ Imported from https://zenodo.org/record/4543003/


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
```


task: automatic-speech-recognition


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


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


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave`


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


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


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


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


model name: kan-bayashi_jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave


model author: espnet


task: text-to-speech


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


tags:


## ESPnet2 TTS pretrained model


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


♻ Imported from https://zenodo.org/record/5499040/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave


language: en
tags:


- go-emotion
- text-classification
- pytorch
datasets:
- go_emotions
metrics:
- f1
widget:
- text: "Thanks for giving advice to the people who need it! 👌🙏"
license: mit


## Model Description


1. Based on the uncased BERT pretrained model with a linear output layer.
1. Added several commonly-used emoji and tokens to the special token list of the tokenizer.
1. Did label smoothing while training.
1. Used weighted loss and focal loss to help the cases which trained badly.


## Results


Best Result of `Macro F1` - 70%


## Tutorial Link


- [GitHub](https://github.com/justin871030/GoEmotions)


model name: bert-base-uncased-goemotions-group-finetuned


model author: justin871030


task: text-classification


tags: pytorch, bert, en, dataset:go_emotions, transformers, go-emotion, text-classification, license:mit, infinity_compatible


model name: bert-base-uncased-goemotions-ekman-finetuned


Best Result of `Macro F1` - 53%


model name: bert-base-uncased-goemotions-original-finetuned


tags:


- conversational


# Fawreez DialoGPT Model


model name: DialoGPT-small-Fawreez


model author: Fawreez


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


- generated_from_trainer
model-index:
- name: bertlet-base-uncased-for-sequence-classification
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# bertlet-base-uncased-for-sequence-classification


This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0


### Framework versions


- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3


model name: bertlet-base-uncased-for-sequence-classification


model author: mgreenbe


task: text-classification


tags: pytorch, bert, text-classification, transformers, generated_from_trainer, model-index, infinity_compatible


hello
hello
hello
hello


model name: bert-joke-identifier


model author: TaahaKazi


tags:


hello


model name: joke-identifier-bert


model name: joke-identifier-2


model name: joke-identifier-3


hello
hello


model name: joke-identifier-1


language: fr
widget:


- text: "Je m'appelle Hicham et je vis a Fès"


# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)


## Introduction


[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based the language model for French Camembert (based on the RoBERTa architecture).


For further information or requests, please visite our website at [typica.ai Website](https://typica.ai/) or send us an email at contactus@typica.ai


## How to use MagBERT-NER with HuggingFace


##### Load MagBERT-NER and its sub-word tokenizer :


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("TypicaAI/magbert-ner")
model = AutoModelForTokenClassification.from_pretrained("TypicaAI/magbert-ner")

##### Process text sample (from wikipedia about the current Prime Minister of Morocco) Using NER pipeline

from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)
nlp("Saad Dine El Otmani, né le 16 janvier 1956 à Inezgane, est un homme d'État marocain, chef du gouvernement du Maroc depuis le 5 avril 2017")

#[{'entity_group': 'I-PERSON',
#  'score': 0.8941445276141167,
#  'word': 'Saad Dine El Otmani'},
# {'entity_group': 'B-DATE',
#  'score': 0.5967703461647034,
#  'word': '16 janvier 1956'},
# {'entity_group': 'B-GPE', 'score': 0.7160899192094803, 'word': 'Inezgane'},
# {'entity_group': 'B-NORP', 'score': 0.7971733212471008, 'word': 'marocain'},
# {'entity_group': 'B-GPE', 'score': 0.8921478390693665, 'word': 'Maroc'},
# {'entity_group': 'B-DATE',
#  'score': 0.5760444005330404,
#  'word': '5 avril 2017'}]

```


## Authors


MagBert-NER Model was trained by Hicham Assoudi, Ph.D.
For any questions, comments you can contact me at assoudi@typica.ai


## Citation


If you use our work, please cite:
Hicham Assoudi, Ph.D., MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb), (2020)


model name: magbert-ner


model author: TypicaAI


task: token-classification


tags: pytorch, camembert, token-classification, fr, transformers, autonlp_compatible


## language: fi


# FinBERT fine-tuned with the FinnSentiment dataset


This is a FinBERT model fine-tuned with the [FinnSentiment dataset](https://arxiv.org/pdf/2012.02613.pdf).


model name: finbert-finnsentiment


model author: fergusq


task: text-classification


tags: pytorch, bert, text-classification, fi, arxiv:2012.02613, transformers, infinity_compatible


This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly 270,000 earth science-based publications.


The tokenizer used is AutoTokenizer, which is trained on the same corpus.


Stay tuned for further downstream task tests and updates to the model.


in the works


- MLM + NSP task loss
- Add more data sources for training
- Test using downstream tasks


model name: bert-e-base-mlm


model author: nasa-impact


task: feature-extraction


tags: pytorch, bert, feature-extraction, transformers, infinity_compatible


tags:


- conversational


# Dwight DialoGPT Model


You can find the code [here](https://github.com/sudo-apt-Abrar/BearsandBeets)


model name: DialoGPT-small-dwight


model author: sudoabrar


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


First-try


model name: Transformer


model author: LucasLi


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: Rubens XLSR Wav2Vec2 Large 53 Portuguese
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 19.30%


# Wav2Vec2-Large-XLSR-53-Portuguese


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-a-Portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-a-Portuguese")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Portuguese test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-a-Portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-a-Portuguese")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'  # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result(wer)**: 19.30 %


## Training


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found at: https://github.com/RubensZimbres/wav2vec2/blob/main/fine-tuning.py


model name: Wav2Vec2-Large-XLSR-53-a-Portuguese


model author: Rubens


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, pt, dataset:common_voice, transformers, audio, speech, apache-2.0, portuguese-speech-corpus, xlsr-fine-tuning-week, PyTorch, license:apache-2.0, model-index


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: Rubens XLSR Wav2Vec2 Large 53 Portuguese
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 20.41%


# Wav2Vec2-Large-XLSR-53-Portuguese


## Usage


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-Portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-Portuguese")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-Portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Rubens/Wav2Vec2-Large-XLSR-53-Portuguese")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'  # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 20.41 %


model name: Wav2Vec2-Large-XLSR-53-Portuguese


readme test


model name: test


model author: mazula


tags:


- generated_from_trainer
metrics:
- rouge
model-index:
- name: medium
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# medium


This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_paraphraser_on_T5) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.6025
- Rouge1: 81.6007
- Rouge2: 75.1196
- Rougel: 81.4213
- Rougelsum: 81.4956
- Gen Len: 32.4286


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 63   | 0.5775          | 65.0748 | 58.8985 | 64.5731 | 63.6249   | 19.0    |
| No log        | 2.0   | 126  | 0.5806          | 74.3055 | 69.2025 | 73.4922 | 73.0941   | 17.8571 |
| No log        | 3.0   | 189  | 0.6025          | 71.3808 | 66.0359 | 70.1235 | 69.4614   | 18.0    |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: medium


model author: Peter


task: text2text-generation


tags: pytorch, t5, text2text-generation, transformers, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
    - name: Matthews Correlation
type: matthews_correlation
value: 0.5285049056800905


# distilbert-base-uncased-finetuned-cola


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:


- Loss: 0.6015
- Matthews Correlation: 0.5285


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5266        | 1.0   | 535  | 0.5474          | 0.4015               |
| 0.3561        | 2.0   | 1070 | 0.4830          | 0.5214               |
| 0.2416        | 3.0   | 1605 | 0.6015          | 0.5285               |
| 0.1695        | 4.0   | 2140 | 0.7748          | 0.5162               |
| 0.1302        | 5.0   | 2675 | 0.8369          | 0.5268               |


### Framework versions


- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-cola


model author: Pkrawczak


task: text-classification


tags: pytorch, tensorboard, distilbert, text-classification, dataset:glue, transformers, license:apache-2.0, generated_from_trainer, model-index, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-de-en-finetuned-de-to-en-second
results:
  - task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: de-en
metrics:
    - name: Bleu
type: bleu
value: 37.9762


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# opus-mt-de-en-finetuned-de-to-en-second


This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset.
It achieves the following results on the evaluation set:


- Loss: 1.2282
- Bleu: 37.9762
- Gen Len: 25.3696


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


### Training results


| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 157  | 1.1837          | 38.8278 | 25.22   |
| No log        | 2.0   | 314  | 1.2057          | 38.3047 | 25.2908 |
| No log        | 3.0   | 471  | 1.2167          | 38.231  | 25.316  |
| 1.4808        | 4.0   | 628  | 1.2256          | 37.9871 | 25.3556 |
| 1.4808        | 5.0   | 785  | 1.2282          | 37.9762 | 25.3696 |


### Framework versions


- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: opus-mt-de-en-finetuned-de-to-en-second


model author: felipetanios


task: text2text-generation


tags: pytorch, tensorboard, marian, text2text-generation, dataset:wmt16, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


tags:


- conversational


# GPT


model name: rh-bender


model author: paladinx00


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


hello


model name: opsolBERT


model author: mukherjeearnab


task: fill-mask


tags: pytorch, jax, roberta, fill-mask, transformers, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []


# distilroberta-base-finetuned-wikitext2


This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0


### Training results


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: distilroberta-base-finetuned-wikitext2


model author: AT


task: fill-mask


tags: pytorch, tensorboard, roberta, fill-mask, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


language:


- mr
tags:
- fill-mask
license: apache-2.0
datasets:
- Oscar Corpus, News, Stories
widget:
- text: "हा खरोखर चांगला [MASK] आहे."


# Marathi DistilBERT


## Model description


This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.


```
DISCLAIMER

This model has not been thoroughly tested and may contain biased opinions or inappropriate language. User discretion is advised
```


## Training data


The training data has been extracted from a variety of sources, mainly including:


1. Oscar Corpus
1. Marathi Newspapers
1. Marathi storybooks and articles


The data is cleaned by removing all languages other than Marathi, while preserving common punctuations


## Training procedure


The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.


## Example


```python
from transformers import pipeline
fill_mask = pipeline(
    "fill-mask",
    model="DarshanDeshpande/marathi-distilbert",
    tokenizer="DarshanDeshpande/marathi-distilbert",
)
fill_mask("हा खरोखर चांगला [MASK] आहे.")
```


### BibTeX entry and citation info


```bibtex
@misc{sanh2020distilbert,
      title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
      author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
      year={2020},
      eprint={1910.01108},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


Authors

1. Darshan Deshpande: GitHub, LinkedIn


2. Harshavardhan Abichandani: GitHub, LinkedIn


model name: marathi-distilbert


model author: DarshanDeshpande


task: fill-mask


tags: pytorch, tf, distilbert, fill-mask, mr, dataset:Oscar Corpus, News, Stories, arxiv:1910.01108, transformers, license:apache-2.0, autonlp_compatible, infinity_compatible


language: es
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 Spanish by pcuenq
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice es
type: common_voice
args: es
metrics:
    - name: Test WER
type: wer
value: 10.50


# Wav2Vec2-Large-XLSR-53-Spanish


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset{s}.
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "es", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Spanish test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "es", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-es")
model.to("cuda")

## Text pre-processing

chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\￼\…\’\ː\'\‹\›\`\´\®\—\→]'
chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)

def remove_special_characters(batch):
    batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
    return batch

def replace_diacritics(batch):
    sentence = batch["sentence"]
    sentence = re.sub('ì', 'í', sentence)
    sentence = re.sub('ù', 'ú', sentence)
    sentence = re.sub('ò', 'ó', sentence)
    sentence = re.sub('à', 'á', sentence)
    batch["sentence"] = sentence
    return batch

def replace_additional(batch):
    sentence = batch["sentence"]
    sentence = re.sub('ã', 'a', sentence)   # Portuguese, as in São Paulo
    sentence = re.sub('ō', 'o', sentence)   # Japanese
    sentence = re.sub('ê', 'e', sentence)   # Português
    batch["sentence"] = sentence
    return batch

## Audio pre-processing

# I tried to perform the resampling using a `torchaudio` `Resampler` transform,
# but found that the process deadlocked when using multiple processes.
# Perhaps my torchaudio is using the wrong sox library under the hood, I'm not sure.
# Fortunately, `librosa` seems to work fine, so that's what I'll use for now.

import librosa
def speech_file_to_array_fn(batch):
    speech_array, sample_rate = torchaudio.load(batch["path"])
    batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
    return batch

# One-pass mapping function

# Text transformation and audio resampling
def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = replace_diacritics(batch)
    batch = replace_additional(batch)
    batch = speech_file_to_array_fn(batch)
    return batch

# Number of CPUs or None
num_proc = 16

test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

# WER Metric computation
# `wer.compute` crashes in my computer with more than ~10000 samples.
# Until I confirm in a different one, I created a "chunked" version of the computation.
# It gives the same results as `wer.compute` for smaller datasets.

import jiwer

def chunked_wer(targets, predictions, chunk_size=None):
    if chunk_size is None: return jiwer.wer(targets, predictions)
    start = 0
    end = chunk_size
    H, S, D, I = 0, 0, 0, 0
    while start < len(targets):
        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
        H = H + chunk_metrics["hits"]
        S = S + chunk_metrics["substitutions"]
        D = D + chunk_metrics["deletions"]
        I = I + chunk_metrics["insertions"]
        start += chunk_size
        end += chunk_size
    return float(S + D + I) / float(H + S + D)

print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))
#print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

```


**Test Result**: 10.50 %


## Text processing


The Common Voice `es` dataset has a lot of characters that don't belong to the Spanish language, even after discarding separators and punctuators. I made some translations and discarded most of the extraneous characters.


I decided to keep all the Spanish language diacritics. This is a difficult decision. Some times the diacritics are added just because of ortography rules, but they don't alter the meaning of the word. In other cases, however, the diacritics carry meaning, as they disambiguate among different senses. A better WER score would surely have been achieved using just the non-accented characters, and the resulting text would be understood by Spanish speakers. Nevertheless, I think keeping them is "more correct".


All the rules I applied are shown in the evaluation script.


## Training


The Common Voice `train` and `validation` datasets were used for training.


For dataset handling reasons, I initially split `train`+`validation` in 10% splits so I could see progress earlier and react if needed.


* I trained for 30 epochs on the first split only, using similar values as the ones proposed by Patrick in his demo notebook. I used a batch_size of 24 with 2 gradient accumulation steps. This gave a WER of about 16.3%on the full test set.
* I then trained the resulting model on the 9 remaining splits, for 3 epochs each, but with a faster warmup of 75 steps.
* Next, I trained 3 epochs on each of the 10 splits using a smaller learning rate of `1e-4`. A warmup of 75 steps was used in this case too. The final model had a WER of about 11.7%.
* By this time we had already figured out the reason for the initial delay in training time, and I decided to use the full dataset for training. However, in my tests I had seen that varying the learning rate seemed to work well, so I wanted to replicate that. I selected a cosine schedule with hard restarts, a reference learning rate of `3e-5` and 10 epochs. I configured the cosine schedule to have 10 cycles too, and used no warmup. This produced a WER of ~10.5%.


## Other things I tried


* Starting from the same fine-tuned model, I compared a constant lr of 1e-4 against a linear schedule with warmup. The linear schedule worked better (11.85 vs 12.72 WER%).
* I tried to use a Spanish model to improve a Basque one. I transformed the text to make ortography more similar to the target language, but the Basque model did not improve.
* Label smoothing did not work.


## Issues and other technical challenges


I had previously used the `transformers` library as an end user, just to try Bert on some tasks, but this is the first time I have needed to look into the code.


* The `Datasets` abstraction is great because, being based on memory-mapped files, it allows arbitrarily-sized datasets to be processed. However, it is important to understand its limitations and trade-offs. I found caching convenient, but disk usage explodes fast. I keep the datasets for my current projects in a 1 TB, fast SSD disk, and a couple of times I ran out of space. I had to understand how cache files are stored and learn when it's best to disable caching and manually save when you need to. I found that data exploration is better suited for smaller datasets or sampled ones, but actual processing is most efficient when you have identified the transformations you need and apply them in a single `map` operation.
* There was a noticeable delay before training started. Fortunately, we found the reason why, discussed it in Slack and the forums and created a workaround.
* The WER metric crashed on large datasets. I evaluated on a small sample (also, it's faster) and wrote an accumulative version of wer that runs on fixed memory. I'd like to verify whether this change makes sense to be used inside the training loop.
* `torchaudio` deadlocks when using multiple processes. `librosa` works fine. To be investigated.
* When using `num_proc` inside a notebook, I could not see progress bars. This is surely some permissions issue in my computer. I still need to find it out.


model name: wav2vec2-large-xlsr-53-es


model author: pcuenq


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, es, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


language: eu
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 Basque by pcuenq
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eu
type: common_voice
args: eu
metrics:
    - name: Test WER
type: wer
value: 15.34


# Wav2Vec2-Large-XLSR-53-EU


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Basque using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "eu", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")
model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Basque test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")

model_name = "pcuenq/wav2vec2-large-xlsr-53-eu"

processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.to("cuda")

## Text pre-processing

chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\￼\…\’\ː\'\‹\›\`\´\®\—\→]'
chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)

def remove_special_characters(batch):
    batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
    return batch

## Audio pre-processing

import librosa
def speech_file_to_array_fn(batch):
    speech_array, sample_rate = torchaudio.load(batch["path"])
    batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
    return batch

# Text transformation and audio resampling
def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = speech_file_to_array_fn(batch)
    return batch

# Number of CPUs or None
num_proc = 16
test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

# WER Metric computation
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result**: 15.34 %


## Training


The Common Voice `train` and `validation` datasets were used for training. Training was performed for 22 + 20 epochs with the following parameters:


- Batch size 16, 2 gradient accumulation steps.
- Learning rate: 2.5e-4
- Activation dropout: 0.05
- Attention dropout: 0.1
- Hidden dropout: 0.05
- Feature proj. dropout: 0.05
- Mask time probability: 0.08
- Layer dropout: 0.05


model name: wav2vec2-large-xlsr-53-eu


model author: pcuenq


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, eu, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


tags:


- generated_from_trainer
model-index:
- name: wav2vec2-common-voice-7.0-cs-augmented_v3
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-common-voice-7.0-cs-augmented_v3


This model was trained from scratch on the None dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training: