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espnet
|
## ESPnet2 DIAR model
### `espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best`
This model was trained by YushiUeda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 650472b45a67612eaac09c7fbd61dc25f8ff2405
pip install -e .
cd egs2/mini_librispeech/diar1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best
```
<!-- Generated by scripts/utils/show_diar_result.sh -->
# RESULTS
## Environments
- date: `Tue Jan 4 16:43:34 EST 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.5a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `0b2a6786b6f627f47defaee22911b3c2dc04af2a`
- Commit date: `Thu Dec 23 12:22:49 2021 -0500`
## diar_train_diar_raw
### DER
dev_clean_2_ns2_beta2_500
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med11_collar0.0|32.28|
|result_th0.3_med1_collar0.0|32.64|
|result_th0.4_med11_collar0.0|30.43|
|result_th0.4_med1_collar0.0|31.15|
|result_th0.5_med11_collar0.0|29.45|
|result_th0.5_med1_collar0.0|30.53|
|result_th0.6_med11_collar0.0|29.52|
|result_th0.6_med1_collar0.0|30.95|
|result_th0.7_med11_collar0.0|30.92|
|result_th0.7_med1_collar0.0|32.69|
## DIAR config
<details><summary>expand</summary>
```
config: conf/train_diar.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_train_diar_raw
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: 33757
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: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 3
nbest_averaging_interval: 0
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_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: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_stats_8k/train/speech_shape
- exp/diar_stats_8k/train/spk_labels_shape
valid_shape_file:
- exp/diar_stats_8k/valid/speech_shape
- exp/diar_stats_8k/valid/spk_labels_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 200000
chunk_shift_ratio: 0.5
num_cache_chunks: 64
train_data_path_and_name_and_type:
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
valid_data_path_and_name_and_type:
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp
- speech
- sound
- - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm
- spk_labels
- rttm
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.01
scheduler: noamlr
scheduler_conf:
warmup_steps: 1000
num_spk: 2
init: xavier_uniform
input_size: null
model_conf:
attractor_weight: 1.0
use_preprocessor: true
frontend: default
frontend_conf:
fs: 8k
hop_length: 128
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/diar_stats_8k/train/feats_stats.npz
encoder: transformer
encoder_conf:
input_layer: linear
num_blocks: 2
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
decoder: linear
decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf: {}
attractor: null
attractor_conf: {}
required:
- output_dir
version: 0.10.5a1
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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["mini_librispeech"]}
| null |
espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best
|
[
"espnet",
"audio",
"diarization",
"dataset:mini_librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 DIAR model
------------------
### 'espnet/YushiUeda\_mini\_librispeech\_diar\_train\_diar\_raw\_valid.URL'
This model was trained by YushiUeda using mini\_librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Jan 4 16:43:34 EST 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.5a1'
* pytorch version: 'pytorch 1.9.0+cu102'
* Git hash: '0b2a6786b6f627f47defaee22911b3c2dc04af2a'
+ Commit date: 'Thu Dec 23 12:22:49 2021 -0500'
diar\_train\_diar\_raw
----------------------
### DER
dev\_clean\_2\_ns2\_beta2\_500
DIAR config
-----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/YushiUeda\\_mini\\_librispeech\\_diar\\_train\\_diar\\_raw\\_valid.URL'\n\n\nThis model was trained by YushiUeda using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jan 4 16:43:34 EST 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.5a1'\n* pytorch version: 'pytorch 1.9.0+cu102'\n* Git hash: '0b2a6786b6f627f47defaee22911b3c2dc04af2a'\n\t+ Commit date: 'Thu Dec 23 12:22:49 2021 -0500'\n\n\ndiar\\_train\\_diar\\_raw\n----------------------",
"### DER\n\n\ndev\\_clean\\_2\\_ns2\\_beta2\\_500\n\n\n\nDIAR config\n-----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_mini\\_librispeech\\_diar\\_train\\_diar\\_raw\\_valid.URL'\n\n\nThis model was trained by YushiUeda using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jan 4 16:43:34 EST 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.5a1'\n* pytorch version: 'pytorch 1.9.0+cu102'\n* Git hash: '0b2a6786b6f627f47defaee22911b3c2dc04af2a'\n\t+ Commit date: 'Thu Dec 23 12:22:49 2021 -0500'\n\n\ndiar\\_train\\_diar\\_raw\n----------------------",
"### DER\n\n\ndev\\_clean\\_2\\_ns2\\_beta2\\_500\n\n\n\nDIAR config\n-----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
43,
63,
164,
30,
11
] |
[
"passage: TAGS\n#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/YushiUeda\\_mini\\_librispeech\\_diar\\_train\\_diar\\_raw\\_valid.URL'\n\n\nThis model was trained by YushiUeda using mini\\_librispeech recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jan 4 16:43:34 EST 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.5a1'\n* pytorch version: 'pytorch 1.9.0+cu102'\n* Git hash: '0b2a6786b6f627f47defaee22911b3c2dc04af2a'\n\t+ Commit date: 'Thu Dec 23 12:22:49 2021 -0500'\n\n\ndiar\\_train\\_diar\\_raw\n----------------------### DER\n\n\ndev\\_clean\\_2\\_ns2\\_beta2\\_500\n\n\n\nDIAR config\n-----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 ASR pretrained model
### `Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best`
♻️ Imported from https://zenodo.org/record/5154341/
This model was trained by Yushi Ueda using ksponspeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
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}
}
```
|
{"language": "kr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["ksponspeech"]}
|
automatic-speech-recognition
|
espnet/Yushi_Ueda_ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256-truncated-eb42e5
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"kr",
"dataset:ksponspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"kr"
] |
TAGS
#espnet #audio #automatic-speech-recognition #kr #dataset-ksponspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR pretrained model
### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'
️ Imported from URL
This model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 ASR pretrained model",
"### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #kr #dataset-ksponspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
50,
10,
82,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #kr #dataset-ksponspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 ASR pretrained model### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 DIAR pretrained model
### `Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best`
♻️ Imported from https://zenodo.org/record/5264020/
This model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speaker-diarization"], "datasets": ["mini_librispeech"]}
| null |
espnet/Yushi_Ueda_mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best
|
[
"espnet",
"audio",
"speaker-diarization",
"en",
"dataset:mini_librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #speaker-diarization #en #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 DIAR pretrained model
### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'
️ Imported from URL
This model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 DIAR pretrained model",
"### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #speaker-diarization #en #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 DIAR pretrained model",
"### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
10,
67,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #speaker-diarization #en #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 DIAR pretrained model### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `akreal/espnet2_swbd_da_hubert_conformer`
This model was trained by Pavel Denisov using swbd_da recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 08c6efbc6299c972301236625f9abafe087c9f9c
pip install -e .
cd egs2/swbd_da/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/akreal_swbd_da_hubert_conformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Jan 20 19:31:21 CET 2022`
- python version: `3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.10.1+cu113`
- Git hash: `08c6efbc6299c972301236625f9abafe087c9f9c`
- Commit date: `Tue Jan 4 13:40:33 2022 +0100`
## asr_train_asr_raw_en_word_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.loss.ave/test_context3|2379|2379|66.3|33.7|0.0|0.0|33.7|33.7|
|decode_asr_asr_model_valid.loss.ave/valid_context3|8116|8116|69.5|30.5|0.0|0.0|30.5|30.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.loss.ave/test_context3|2379|19440|76.1|17.7|6.2|8.1|32.0|33.7|
|decode_asr_asr_model_valid.loss.ave/valid_context3|8116|66353|79.5|16.1|4.4|8.0|28.5|30.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_hubert_context3.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_hubert_context3_raw_en_word_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 35
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 7
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_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:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 4000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_context3_raw_en_word_sp/train/speech_shape
- exp/asr_stats_context3_raw_en_word_sp/train/text_shape.word
valid_shape_file:
- exp/asr_stats_context3_raw_en_word_sp/valid/speech_shape
- exp/asr_stats_context3_raw_en_word_sp/valid/text_shape.word
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_context3_sp/wav.scp
- speech
- sound
- - dump/raw/train_context3_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid_context3/wav.scp
- speech
- sound
- - dump/raw/valid_context3/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- statement
- backchannel
- opinion
- abandon
- agree
- yn_q
- apprec
- 'yes'
- uninterp
- close
- wh_q
- acknowledge
- 'no'
- yn_decl_q
- hedge
- backchannel_q
- sum
- quote
- affirm
- other
- directive
- repeat
- open_q
- completion
- rhet_q
- hold
- reject
- answer
- neg
- ans_dispref
- repeat_q
- open
- or
- commit
- maybe
- decl_q
- third_pty
- self_talk
- thank
- apology
- tag_q
- downplay
- <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: 0.0
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.5a1
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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["swbd_da"]}
|
automatic-speech-recognition
|
espnet/akreal_swbd_da_hubert_conformer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:swbd_da",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-swbd_da #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'akreal/espnet2\_swbd\_da\_hubert\_conformer'
This model was trained by Pavel Denisov using swbd\_da recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Jan 20 19:31:21 CET 2022'
* python version: '3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]'
* espnet version: 'espnet 0.10.6a1'
* pytorch version: 'pytorch 1.10.1+cu113'
* Git hash: '08c6efbc6299c972301236625f9abafe087c9f9c'
+ Commit date: 'Tue Jan 4 13:40:33 2022 +0100'
asr\_train\_asr\_raw\_en\_word\_sp
----------------------------------
### WER
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'akreal/espnet2\\_swbd\\_da\\_hubert\\_conformer'\n\n\nThis model was trained by Pavel Denisov using swbd\\_da recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jan 20 19:31:21 CET 2022'\n* python version: '3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.10.1+cu113'\n* Git hash: '08c6efbc6299c972301236625f9abafe087c9f9c'\n\t+ Commit date: 'Tue Jan 4 13:40:33 2022 +0100'\n\n\nasr\\_train\\_asr\\_raw\\_en\\_word\\_sp\n----------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-swbd_da #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'akreal/espnet2\\_swbd\\_da\\_hubert\\_conformer'\n\n\nThis model was trained by Pavel Denisov using swbd\\_da recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jan 20 19:31:21 CET 2022'\n* python version: '3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.10.1+cu113'\n* Git hash: '08c6efbc6299c972301236625f9abafe087c9f9c'\n\t+ Commit date: 'Tue Jan 4 13:40:33 2022 +0100'\n\n\nasr\\_train\\_asr\\_raw\\_en\\_word\\_sp\n----------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
51,
48,
177,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-swbd_da #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'akreal/espnet2\\_swbd\\_da\\_hubert\\_conformer'\n\n\nThis model was trained by Pavel Denisov using swbd\\_da recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jan 20 19:31:21 CET 2022'\n* python version: '3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.10.1+cu113'\n* Git hash: '08c6efbc6299c972301236625f9abafe087c9f9c'\n\t+ Commit date: 'Tue Jan 4 13:40:33 2022 +0100'\n\n\nasr\\_train\\_asr\\_raw\\_en\\_word\\_sp\n----------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
# ESPnet2 ENH pretrained model
## `anogkongda/librimix_enh_train_raw_valid.si_snr.ave`
♻️ Imported from <https://zenodo.org/record/4480771#.YN70WJozZH4>
This model was trained by anogkongda using librimix 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}
}
```
### Training config
See full config in [`config.yaml`](./config.yaml)
```yaml
config: conf/tuning/train_conformer_fastspeech2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["librimix"], "inference": false}
|
audio-to-audio
|
espnet/anogkongda-librimix_enh_train_raw_valid.si_snr.ave
|
[
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'
️ Imported from <URL
This model was trained by anogkongda using librimix recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See full config in 'URL'
|
[
"# ESPnet2 ENH pretrained model",
"## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n\n️ Imported from <URL\nThis model was trained by anogkongda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'URL'"
] |
[
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n\n️ Imported from <URL\nThis model was trained by anogkongda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'URL'"
] |
[
55,
10,
53,
11,
11,
13
] |
[
"passage: TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n# ESPnet2 ENH pretrained model## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n\n️ Imported from <URL\nThis model was trained by anogkongda using librimix recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\n\n\nor arXiv:### Training config\n\nSee full config in 'URL'"
] |
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] |
null | null |
espnet
|
## 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/).
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement", "audio-to-audio"], "datasets": ["librimix"]}
|
audio-to-audio
|
espnet/anogkongda_librimix_enh_train_raw_valid.si_snr.ave
|
[
"espnet",
"audio",
"speech-enhancement",
"audio-to-audio",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ENH model
### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'
️ Imported from URL
This model was trained by anogkongda using librimix/enh1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ENH model",
"### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by anogkongda using librimix/enh1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ENH model",
"### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by anogkongda using librimix/enh1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
54,
9,
57,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ENH model### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by anogkongda using librimix/enh1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ST model
### `espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install -e .
cd egs2/iwslt22_dialect/st1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix
```
<!-- Generated by scripts/utils/show_st_results.sh -->
# RESULTS
## Environments
- date: `Tue Feb 8 13:29:21 EST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.8.1`
- Git hash: `77fce65312877a132bbae01917ad26b74f6e2e14`
- Commit date: `Tue Feb 8 10:48:10 2022 -0500`
## st_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe_tc1000_sp
### BLEU
|dataset|bleu_score|verbose_score|
|---|---|---|
p3_st_model_valid.acc.ave|12.0|37.4/17.3/8.6/4.5 (BP = 0.952 ratio = 0.953 hyp_len = 40192 ref_len = 42181)
## ST 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/st_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe_tc1000_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: 36641
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: 50
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/st_stats_raw_bpe1000_sp/train/speech_shape
- exp/st_stats_raw_bpe1000_sp/train/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/train/src_text_shape.bpe
valid_shape_file:
- exp/st_stats_raw_bpe1000_sp/valid/speech_shape
- exp/st_stats_raw_bpe1000_sp/valid/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
- 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/iwslt22dump//raw/train_sp/wav.scp
- speech
- kaldi_ark
- - /scratch/iwslt22dump//raw/train_sp/text.tc.en
- text
- text
- - /scratch/iwslt22dump//raw/train_sp/text.tc.rm.ta
- src_text
- text
valid_data_path_and_name_and_type:
- - /scratch/iwslt22dump//raw/dev/wav.scp
- speech
- kaldi_ark
- - /scratch/iwslt22dump//raw/dev/text.tc.en
- text
- text
- - /scratch/iwslt22dump//raw/dev/text.tc.rm.ta
- src_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: 12.5
scheduler: noamlr
scheduler_conf:
model_size: 256
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- s
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- apo
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- ▁i
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- ▁to
- ▁that
- ▁a
- n
- a
- ▁he
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- m
- d
- ▁yes
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- ▁no
- ▁in
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- ▁for
- ▁we
- ing
- ll
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- re
- ▁are
- ▁did
- ▁god
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- er
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- th
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- ▁oh
- ▁doing
- ▁everything
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- le
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- ▁two
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- ▁had
- ▁still
- ▁say
- ro
- ▁after
- ce
- ▁hello
- ▁ma
- ▁call
- w
- ▁listen
- il
- ▁should
- ▁girl
- ▁f
- z
- ▁too
- ▁let
- ▁understand
- ▁may
- ▁much
- ▁think
- ch
- ir
- ha
- ▁other
- ▁tomorrow
- ▁were
- ▁people
- es
- ▁year
- di
- ba
- ▁right
- el
- ▁things
- ▁house
- v
- ▁actually
- un
- ▁an
- ▁give
- ▁only
- ▁better
- pe
- ▁need
- ▁buy
- ▁de
- ne
- ▁ha
- ur
- ion
- ▁made
- la
- ▁willing
- ▁nothing
- ▁called
- ▁night
- ▁yesterday
- se
- ▁came
- ▁lot
- ter
- ▁g
- po
- ▁find
- ry
- ▁car
- ▁over
- ic
- ▁stay
- ▁eat
- ent
- ▁always
- ▁very
- 'on'
- ▁put
- ▁ramadan
- ▁those
- ▁hear
- is
- ▁talk
- ▁three
- ▁anything
- ▁mo
- ▁little
- ▁been
- ▁already
- fi
- ation
- ke
- ▁first
- ▁look
- it
- ▁won
- ▁mom
- ▁way
- ▁before
- ▁ok
- ▁last
- fa
- ▁cook
- vi
- ▁hi
- ▁same
- ▁thought
- ▁also
- um
- ate
- ▁money
- ▁start
- ▁place
- us
- ▁morning
- ▁could
- ▁ask
- ▁bring
- ▁bit
- ▁lo
- ▁leave
- ▁man
- ▁left
- ine
- ▁days
- ge
- ▁la
- ▁week
- ▁friend
- ▁problem
- ▁sister
- ▁allah
- ▁feel
- ▁every
- ▁more
- fe
- ▁long
- ▁hundred
- ▁j
- ▁eh
- ho
- ca
- em
- ▁talking
- ▁exam
- ▁next
- ▁new
- ▁fun
- ▁took
- ▁alright
- co
- ▁w
- ▁um
- ▁eid
- ▁brother
- ▁our
- gh
- ow
- ▁o
- ▁four
- ni
- wa
- ▁else
- ▁finish
- bo
- ▁sleep
- ▁bless
- ▁dear
- ▁since
- ▁play
- ▁name
- hi
- ▁coming
- ▁many
- et
- ▁usual
- ▁con
- ▁maybe
- ▁off
- bi
- ▁than
- ▁any
- ▁mother
- ▁son
- om
- ▁their
- ▁keep
- ▁dinner
- ▁ten
- ▁half
- ▁help
- ▁bad
- and
- ▁pass
- ▁hot
- ▁guy
- ▁least
- ▁down
- ▁bought
- ▁dinars
- ▁working
- ▁around
- ▁normal
- ▁poor
- ▁stuff
- ▁hope
- ▁used
- ▁again
- ▁bro
- ul
- ▁phone
- ▁ex
- ▁done
- ▁six
- ▁na
- ▁month
- ▁tired
- ▁check
- ▁show
- ▁together
- oo
- ▁later
- ▁past
- ▁five
- ▁watch
- ya
- ▁coffee
- ment
- ut
- ▁plan
- ▁great
- ▁daughter
- j
- ▁another
- side
- ▁change
- ▁yet
- ting
- ▁until
- ▁honestly
- ▁whole
- ol
- ▁care
- ▁sure
- able
- id
- ▁big
- ▁spend
- ▁exactly
- ▁boy
- ▁course
- ▁end
- ▁please
- ▁started
- he
- up
- ▁found
- ▁saw
- ▁family
- ▁asked
- ▁enough
- ▁during
- ▁rest
- ▁which
- ▁gave
- ▁true
- ▁while
- ▁job
- ▁el
- ▁each
- ▁away
- ▁kids
- ▁goes
- less
- ▁twenty
- ▁eight
- ▁someone
- ▁cha
- ▁clothes
- ah
- ▁myself
- ▁nice
- ▁late
- ▁old
- ▁real
- age
- ant
- ▁fast
- ▁add
- ▁hard
- ▁these
- ful
- im
- ▁close
- ive
- ▁dad
- ▁pay
- ies
- ▁dude
- ▁alone
- ▁far
- ance
- ▁dis
- ▁seven
- ▁isn
- ▁pro
- our
- ▁thousand
- ▁break
- ▁hour
- ▁wait
- ▁brought
- ▁open
- ▁un
- ▁wedding
- ▁walk
- ▁father
- ▁ka
- ▁second
- x
- ▁saturday
- ▁salad
- ▁win
- ▁everyone
- ▁water
- ▁tunis
- ▁remember
- ity
- ▁wake
- ▁minute
- ▁school
- ▁sunday
- ▁own
- ▁shop
- ▁cold
- ▁meet
- ▁wear
- ever
- ▁send
- ▁early
- ▁gra
- tic
- ▁short
- ▁use
- ▁sometimes
- hou
- ▁love
- ▁prepare
- ▁sea
- ▁study
- ure
- ▁com
- qui
- ▁hand
- ▁both
- ja
- ▁summer
- ▁wrong
- ▁wanted
- che
- ▁miss
- ▁try
- ▁iftar
- ▁yourself
- q
- ▁live
- war
- ▁expensive
- ▁getting
- ▁waiting
- ▁once
- ▁kh
- ▁forgot
- ▁nine
- ▁anymore
- ▁soup
- ▁uncle
- ▁beach
- ▁saying
- ▁into
- ▁having
- ▁brik
- ▁room
- ▁food
- ▁visit
- ▁matter
- ▁thirty
- ▁taking
- ▁rain
- ▁aunt
- ▁never
- ▁pick
- ▁tunisia
- ▁health
- ▁head
- ▁cut
- ▁fasting
- ▁sick
- ▁friday
- ▁forget
- ▁monday
- ▁become
- ▁dress
- ated
- ▁most
- wi
- ▁hang
- ▁life
- ▁fish
- ▁happy
- ▁delicious
- ▁deal
- ▁finished
- ble
- ▁studying
- ▁weather
- ▁making
- ▁cost
- ▁bl
- ▁stayed
- ▁guess
- ▁teach
- ▁stop
- ▁near
- ▁watching
- ▁without
- ▁imagine
- ▁seriously
- fl
- ▁speak
- ▁idea
- ▁must
- ▁normally
- ▁turn
- ize
- ▁clean
- ▁tv
- ▁meat
- ▁woke
- ▁example
- ▁easy
- ▁sent
- ▁sell
- over
- ▁fifty
- ▁amazing
- ▁beautiful
- ▁whatever
- ▁enjoy
- ▁talked
- ▁believe
- ▁thinking
- ▁count
- ▁almost
- ▁longer
- ▁afternoon
- ▁hair
- ▁front
- ▁earlier
- ▁mind
- ▁kind
- ▁tea
- ▁best
- ▁rent
- ▁picture
- ▁cooked
- ▁price
- ight
- ▁soon
- ▁woman
- ▁otherwise
- ▁happened
- ▁story
- ▁luck
- ▁high
- ▁happen
- ▁arrive
- ▁paper
- ga
- ▁quickly
- ▁looking
- ub
- ▁number
- ▁staying
- ▁sit
- man
- ack
- ▁important
- ▁either
- ▁person
- ▁small
- ▁free
- ▁crazy
- ▁playing
- ▁kept
- ▁part
- ▁game
- law
- ▁till
- uck
- ▁ready
- ▁might
- ▁gone
- ▁full
- ▁fix
- ▁subject
- ▁laugh
- ▁doctor
- ▁welcome
- ▁eleven
- ▁sleeping
- ▁heat
- ▁probably
- ▁such
- ▁café
- ▁fat
- ▁sweet
- ▁married
- ▁drink
- ▁move
- ▁outside
- ▁especially
- ▁group
- ji
- ▁market
- ▁through
- ▁train
- ▁protect
- ▁turned
- ▁red
- ▁busy
- ▁light
- ▁noise
- ▁street
- ▁manage
- ▁piece
- ▁sitting
- gue
- ▁sake
- ▁party
- ish
- ▁young
- ▁case
- ▁cool
- huh
- ▁marwa
- ▁drive
- ▁pray
- clock
- ▁couscous
- ▁spent
- ▁felt
- ▁hopefully
- ▁everybody
- ▁living
- ▁pain
- line
- ▁between
- ▁match
- ▁prayer
- que
- ian
- ▁facebook
- ▁spi
- ▁eye
- ▁children
- ▁tonight
- ▁mohamed
- ▁understood
- ▁black
- ▁husband
- ▁rid
- ▁kitchen
- ▁face
- ▁swim
- ▁kid
- ▁invite
- ▁cup
- ▁grilled
- ▁wife
- ▁cousin
- ▁drop
- ▁wow
- ▁table
- ▁du
- ▁bored
- ▁neighborhood
- ▁agree
- ▁bread
- ▁hamma
- ▁straight
- ▁tuesday
- ▁anyone
- ▁lunch
- ade
- ▁himself
- ▁gather
- ▁wish
- ▁fifteen
- ▁wednesday
- ▁die
- ▁thursday
- ▁color
- ▁asleep
- ▁different
- ▁whether
- ▁ago
- ▁middle
- ▁class
- ▁cake
- shirt
- ▁fight
- ▁clear
- ▁test
- ▁plus
- ▁sousse
- ▁beginning
- ▁result
- ▁learn
- ▁crowded
- ▁slept
- ▁shoes
- ▁august
- ▁pretty
- ▁white
- ▁apparently
- ▁reach
- ▁mariem
- ▁return
- ▁road
- ▁million
- ▁stand
- ▁paid
- ▁word
- ious
- ▁few
- ▁breakfast
- ▁post
- ▁kilo
- ▁chicken
- ▁grade
- ▁read
- ▁accept
- ▁birthday
- ▁exhaust
- ▁point
- ▁july
- ▁patience
- ▁studies
- ▁trouble
- ▁along
- ▁worry
- ▁follow
- ▁hurt
- ▁afraid
- ▁trip
- ▁ahmed
- ▁remain
- ▁succeed
- ▁mercy
- ▁difficult
- ▁weekend
- ▁answer
- ▁cheap
- ▁repeat
- ▁auntie
- ▁sign
- ▁hold
- ▁under
- ▁olive
- ▁mahdi
- ▁sfax
- ▁annoy
- ▁dishes
- ▁message
- ▁business
- ▁french
- ▁serious
- ▁travel
- ▁office
- ▁wonder
- ▁student
- ▁internship
- ▁pepper
- ▁knew
- ▁kill
- ▁sauce
- ▁herself
- ▁hammamet
- ▁damn
- ▁mix
- ▁suit
- ▁medicine
- ▁remove
- ▁gonna
- ▁company
- ▁quarter
- ▁shopping
- ▁correct
- ▁throw
- ▁grow
- ▁voice
- ▁series
- gotten
- ▁taste
- ▁driving
- ▁hospital
- ▁sorry
- ▁aziz
- ▁milk
- ▁green
- ▁baccalaureate
- ▁running
- ▁lord
- ▁explain
- ▁angry
- ▁build
- ▁fruit
- ▁photo
- é
- ▁crying
- ▁baby
- ▁store
- ▁project
- ▁france
- ▁twelve
- ▁decide
- ▁swimming
- ▁world
- ▁preparing
- ▁special
- ▁session
- ▁behind
- ▁vegetable
- ▁strong
- ▁fatma
- ▁treat
- ▁cream
- ▁situation
- ▁settle
- ▁totally
- ▁stopped
- ▁book
- ▁honest
- ▁solution
- ▁vacation
- ▁cheese
- ▁ahead
- ▁sami
- ▁focus
- ▁scared
- ▁club
- ▁consider
- ▁final
- ▁naturally
- ▁barely
- ▁issue
- ▁floor
- ▁birth
- ▁almighty
- ▁engagement
- ▁blue
- ▁empty
- ▁soccer
- ▁prophet
- ▁ticket
- ▁indeed
- ▁write
- ▁present
- ▁patient
- ▁available
- ▁holiday
- ▁leaving
- ▁became
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- ٱ
- ژ
- ٍ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
asr_weight: 0.3
mt_weight: 0.0
mtlalpha: 1.0
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram1000/bpe.model
src_bpemodel: data/token_list/src_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/st_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
extra_asr_decoder: transformer
extra_asr_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
dropout_rate: 0.1
extra_mt_decoder: transformer
extra_mt_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- src_token_list
- 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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-translation"], "datasets": ["iwslt22_dialect"]}
| null |
espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix
|
[
"espnet",
"audio",
"speech-translation",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ST model
----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_st\_transformer\_fisherlike\_4gpu\_bbins16m\_fix'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 8 13:29:21 EST 2022'
* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.8.1'
* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'
+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'
st\_transformer\_fisherlike\_4gpu\_bbins16m\_fix\_raw\_bpe\_tc1000\_sp
----------------------------------------------------------------------
### BLEU
dataset: p3\_st\_model\_valid.URL, bleu\_score: 12.0, verbose\_score: 37.4/17.3/8.6/4.5 (BP = 0.952 ratio = 0.953 hyp\_len = 40192 ref\_len = 42181)
ST config
---------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_st\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 13:29:21 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe\\_tc1000\\_sp\n----------------------------------------------------------------------",
"### BLEU\n\n\ndataset: p3\\_st\\_model\\_valid.URL, bleu\\_score: 12.0, verbose\\_score: 37.4/17.3/8.6/4.5 (BP = 0.952 ratio = 0.953 hyp\\_len = 40192 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_st\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 13:29:21 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe\\_tc1000\\_sp\n----------------------------------------------------------------------",
"### BLEU\n\n\ndataset: p3\\_st\\_model\\_valid.URL, bleu\\_score: 12.0, verbose\\_score: 37.4/17.3/8.6/4.5 (BP = 0.952 ratio = 0.953 hyp\\_len = 40192 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
48,
73,
184,
81,
11
] |
[
"passage: TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/brianyan918\\_iwslt22\\_dialect\\_st\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 13:29:21 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe\\_tc1000\\_sp\n----------------------------------------------------------------------### BLEU\n\n\ndataset: p3\\_st\\_model\\_valid.URL, bleu\\_score: 12.0, verbose\\_score: 37.4/17.3/8.6/4.5 (BP = 0.952 ratio = 0.953 hyp\\_len = 40192 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install -e .
cd egs2/iwslt22_dialect/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Feb 2 05:32:30 EST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1`
- Git hash: `99581e0f5af3ad68851d556645e7292771436df9`
- Commit date: `Sat Jan 29 11:32:38 2022 -0500`
## asr_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe1000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|27370|54.7|39.5|5.8|8.8|54.2|87.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|145852|84.1|7.1|8.8|11.5|27.4|87.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test1|4204|64424|63.8|22.8|13.4|12.2|48.3|87.9|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe1000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 55101
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 80
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 25000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe1000_sp/train/speech_shape
- exp/asr_stats_raw_bpe1000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe1000_sp/valid/speech_shape
- exp/asr_stats_raw_bpe1000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /scratch/iwslt22asrdump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - /scratch/iwslt22asrdump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - /scratch/iwslt22asrdump/raw/dev/wav.scp
- speech
- kaldi_ark
- - /scratch/iwslt22asrdump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
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- ▁نحب
- ▁عندك
- ▁وما
- ▁فر
- اني
- ▁قلتله
- ▁الط
- فر
- ▁دار
- ▁عليها
- ▁يعمل
- ▁نت
- ▁تح
- باح
- ▁ماهو
- ▁وكل
- ▁وع
- قت
- ▁فهمتك
- عر
- ▁وس
- ▁تر
- ▁سي
- يلة
- ▁قلت
- ▁رمضان
- صل
- ▁آما
- ▁الواحد
- ▁بيه
- ▁ثلاثة
- ▁فهمتني
- ▁ها
- بط
- ▁مازال
- قل
- ▁بالك
- ▁معناتها
- ▁ور
- ▁قلتلها
- ▁يس
- رب
- ▁ام
- ▁وبعد
- ▁الث
- ▁وإنت
- ▁بحذا
- ▁لازم
- ْ
- ▁بن
- قرا
- سك
- ▁يت
- خل
- ▁فه
- عت
- ▁هاك
- ▁تق
- ▁قبل
- ▁وك
- ▁نقول
- ▁الز
- حم
- ▁عادش
- حكي
- وها
- بة
- نس
- طل
- ▁علاه
- ذا
- ▁سا
- ▁طل
- الي
- ▁يق
- ▁دو
- حوا
- حد
- ▁نشوف
- نة
- ▁لي
- ▁تك
- ▁نا
- ▁هاذ
- ▁خويا
- ▁المر
- ▁وينك
- ▁البر
- ▁أتو
- ينا
- ▁حل
- ولي
- ▁ثم
- ▁عم
- ▁آي
- ▁قر
- از
- ▁وح
- كش
- بعة
- ▁كيفاه
- ▁نع
- ▁الحمدلله
- ▁ياسر
- ▁الخ
- ▁معاك
- ▁معاه
- ▁تقول
- دة
- ▁حكاية
- تش
- ▁حس
- ▁غدوا
- ▁بالحق
- روا
- وز
- ▁تخ
- ▁العيد
- رجع
- ▁بالي
- ▁جات
- ▁وج
- حة
- ▁وش
- ▁آخر
- ▁طا
- ▁مت
- لقا
- تك
- ▁مس
- ▁راني
- كون
- ▁صاحب
- ▁هاكا
- ▁قول
- ▁عر
- ▁عنده
- ▁يلزم
- ▁هاذا
- ▁يخ
- ▁وقتاش
- ▁وقت
- بع
- ▁العش
- ▁هاذي
- هاش
- ينة
- ▁هاذاكا
- عطي
- ▁تنج
- ▁باهية
- نيا
- فت
- ▁يحب
- ▁تف
- ▁أهلا
- وف
- ▁غدوة
- ▁بيك
- ▁بد
- عن
- ▁در
- ▁ننج
- هار
- ▁الحكاية
- مون
- وق
- ▁نورمال
- ▁عندها
- خر
- ▁بو
- ▁حب
- ▁آكا
- ▁وف
- ▁هاذيكا
- ▁ديجا
- ▁وق
- ▁طي
- لتل
- بعث
- ▁تص
- رك
- ▁مانيش
- ▁العادة
- ▁شوف
- ضر
- ▁يمشي
- ▁نعملوا
- ▁عرفت
- ▁زال
- ▁متع
- ▁عمل
- ▁بيها
- ▁نحكي
- اع
- ▁نج
- معة
- ▁والكل
- عناها
- ▁يعي
- ▁نجي
- ستن
- ▁هاذيك
- ▁عام
- ▁فلوس
- قة
- تين
- ▁بالقدا
- لهم
- ▁تخدم
- ▁ٱ
- ▁شيء
- ▁راهي
- ▁جاب
- ولاد
- ابل
- ▁ماك
- عة
- ▁نمشيوا
- وني
- شري
- بار
- انس
- ▁وقتها
- ▁جديد
- ▁يز
- ▁كر
- ▁حاسيلو
- ▁شق
- ▁اه
- ▁سايي
- ▁انشالل
- رج
- مني
- ▁بلا
- ▁صحيح
- ▁غير
- ▁يخدم
- مان
- وكا
- ▁عند
- ▁قاعدة
- ▁تس
- ربة
- ▁راس
- ▁حط
- ▁نكل
- تني
- ▁الو
- سيون
- ▁عندنا
- ▁لو
- ▁ست
- صف
- ▁ض
- ▁كامل
- ▁نخدم
- ▁يبدا
- ▁دونك
- ▁أمور
- رات
- ▁تونس
- بدا
- ▁تحكي
- ▁سو
- ▁جاي
- ▁وحدة
- ▁ساعة
- حنا
- ▁بكري
- ▁إل
- ▁وبر
- ▁كم
- ▁تبدا
- ارة
- ادي
- رق
- لوا
- ▁يمكن
- ▁خاط
- ▁وص
- جين
- ▁هاذاي
- ▁هز
- قد
- ▁قل
- ▁وكهو
- ▁نص
- ▁دي
- لقى
- ▁وأنا
- سين
- ▁يح
- ▁ماشي
- ▁شو
- ▁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
hop_length: 256
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_bpe1000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iwslt22_dialect"]}
|
automatic-speech-recognition
|
espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_train\_asr\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Feb 2 05:32:30 EST 2022'
* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.6a1'
* pytorch version: 'pytorch 1.8.1'
* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'
+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'
asr\_train\_asr\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug\_raw\_bpe1000\_sp
-----------------------------------------------------------------------------------
### WER
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Feb 2 05:32:30 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe1000\\_sp\n-----------------------------------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Feb 2 05:32:30 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe1000\\_sp\n-----------------------------------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
52,
82,
193,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Feb 2 05:32:30 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe1000\\_sp\n-----------------------------------------------------------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ST model
### `espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install -e .
cd egs2/iwslt22_dialect/st1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug
```
<!-- Generated by scripts/utils/show_st_results.sh -->
# RESULTS
## Environments
- date: `Tue Feb 8 12:54:12 EST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.8.1`
- Git hash: `77fce65312877a132bbae01917ad26b74f6e2e14`
- Commit date: `Tue Feb 8 10:48:10 2022 -0500`
## st_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe_tc1000_sp
### BLEU
|dataset|bleu_score|verbose_score|
|---|---|---|
pen2_st_model_valid.acc.ave|13.9|44.0/21.8/11.4/6.2 (BP = 0.859 ratio = 0.868 hyp_len = 36614 ref_len = 42181)
## ST config
<details><summary>expand</summary>
```
config: conf/tuning/train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/st_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe_tc1000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 80
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: true
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 25000000
valid_batch_bins: null
train_shape_file:
- exp/st_stats_raw_bpe1000_sp/train/speech_shape
- exp/st_stats_raw_bpe1000_sp/train/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/train/src_text_shape.bpe
valid_shape_file:
- exp/st_stats_raw_bpe1000_sp/valid/speech_shape
- exp/st_stats_raw_bpe1000_sp/valid/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_sp/text.tc.en
- text
- text
- - dump/raw/train_sp/text.tc.rm.ta
- src_text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text.tc.en
- text
- text
- - dump/raw/dev/text.tc.rm.ta
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
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- ne
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- ▁those
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- ▁hi
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- ▁feel
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- ▁tired
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- ▁watch
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- ▁great
- ▁daughter
- j
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- ▁yet
- ting
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- ▁whole
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- ▁walk
- ▁father
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- ▁minute
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- ▁short
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- ▁sea
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- ▁com
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- ▁hand
- ▁both
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- ▁summer
- ▁wrong
- ▁wanted
- che
- ▁miss
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- ▁yourself
- q
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- war
- ▁expensive
- ▁getting
- ▁waiting
- ▁once
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- ▁forgot
- ▁nine
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- ▁soup
- ▁uncle
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- ▁brik
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- ▁never
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- ▁health
- ▁head
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- ▁fasting
- ▁sick
- ▁friday
- ▁forget
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- over
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- ▁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
model_conf:
asr_weight: 0.3
mt_weight: 0.0
mtlalpha: 1.0
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram1000/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
n_fft: 512
hop_length: 256
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/st_stats_raw_bpe1000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
extra_asr_decoder: transformer
extra_asr_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
dropout_rate: 0.1
extra_mt_decoder: transformer
extra_mt_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- src_token_list
- token_list
version: 0.10.6a1
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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-translation"], "datasets": ["iwslt22_dialect"]}
| null |
espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug
|
[
"espnet",
"audio",
"speech-translation",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ST model
----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_train\_st\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 8 12:54:12 EST 2022'
* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.8.1'
* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'
+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'
st\_train\_st\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug\_raw\_bpe\_tc1000\_sp
-------------------------------------------------------------------------------------
### BLEU
dataset: pen2\_st\_model\_valid.URL, bleu\_score: 13.9, verbose\_score: 44.0/21.8/11.4/6.2 (BP = 0.859 ratio = 0.868 hyp\_len = 36614 ref\_len = 42181)
ST config
---------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 12:54:12 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe\\_tc1000\\_sp\n-------------------------------------------------------------------------------------",
"### BLEU\n\n\ndataset: pen2\\_st\\_model\\_valid.URL, bleu\\_score: 13.9, verbose\\_score: 44.0/21.8/11.4/6.2 (BP = 0.859 ratio = 0.868 hyp\\_len = 36614 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 12:54:12 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe\\_tc1000\\_sp\n-------------------------------------------------------------------------------------",
"### BLEU\n\n\ndataset: pen2\\_st\\_model\\_valid.URL, bleu\\_score: 13.9, verbose\\_score: 44.0/21.8/11.4/6.2 (BP = 0.859 ratio = 0.868 hyp\\_len = 36614 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
48,
81,
196,
80,
11
] |
[
"passage: TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 12:54:12 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.7a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '77fce65312877a132bbae01917ad26b74f6e2e14'\n\t+ Commit date: 'Tue Feb 8 10:48:10 2022 -0500'\n\n\nst\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug\\_raw\\_bpe\\_tc1000\\_sp\n-------------------------------------------------------------------------------------### BLEU\n\n\ndataset: pen2\\_st\\_model\\_valid.URL, bleu\\_score: 13.9, verbose\\_score: 44.0/21.8/11.4/6.2 (BP = 0.859 ratio = 0.868 hyp\\_len = 36614 ref\\_len = 42181)\n\n\nST config\n---------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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null | null |
espnet
|
## 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/).
### Demo: How to use in ESPnet2
```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>
- ّ
- ي
- ا
- ِ
- ل
- َ
- و
- ه
- ة
- م
- ر
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- ُ
- ب
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- ▁في
- َّ
- ▁ن
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- ح
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- ▁م
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- ▁إي
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- ▁ب
- ها
- ط
- ى
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- ▁الل
- ▁أ
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- ص
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- ▁آنا
- ▁الم
- ▁خ
- ▁الس
- ▁وال
- ون
- ور
- ▁أم
- ▁هك
- ▁آش
- ▁الد
- ▁عاد
- ▁ج
- ▁معناها
- ▁مع
- اش
- ▁الص
- ▁نهار
- ▁لل
- لها
- ▁تي
- ▁رب
- ▁خاطر
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- ▁شي
- الل
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- ▁ون
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- مشي
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- ▁الب
- ▁لاباس
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- ▁عال
- ذ
- ▁فم
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- ▁حاجة
- ▁شوي
- اكا
- ▁ياخي
- ▁هاني
- ▁صح
- اس
- ▁آه
- ▁برشة
- ▁الن
- ▁وت
- ▁الج
- لك
- ▁راهو
- سم
- ▁الح
- مت
- ▁الت
- ▁بعد
- اج
- عد
- ▁انشا
- وش
- لت
- ▁وين
- ث
- ▁ولا
- ▁باش
- ▁فيها
- نت
- ▁إ
- ▁الأ
- ▁الف
- ▁إم
- ▁واحد
- ▁ألو
- ▁عندي
- ▁أك
- ▁خل
- ▁وي
- ▁تعمل
- أ
- ▁ريت
- ▁وأ
- ▁تعرف
- بت
- ▁الع
- ▁مشيت
- ▁وه
- ▁حاصيلو
- ▁بالل
- ▁نعمل
- ▁غ
- ▁تجي
- ▁يجي
- ▁كيفاش
- ▁عملت
- ظ
- اك
- ▁هاو
- ▁اش
- ▁قد
- ▁نق
- ▁د
- ▁زادا
- ▁فيه
- رة
- ▁بر
- ▁الش
- ▁ز
- ▁كيما
- ▁الا
- ند
- عم
- ▁نح
- ▁بنتي
- ▁نمشي
- ▁عليك
- ▁نعرفش
- ▁كهو
- ▁وم
- ▁ط
- تي
- ▁خير
- ▁آ
- مش
- ▁عليه
- له
- حت
- ▁إيا
- ▁أحنا
- ▁تع
- الا
- عب
- ▁ديما
- ▁تت
- ▁جو
- ▁مالا
- ▁أو
- ▁قلتلك
- ▁معنتها
- لنا
- ▁شكون
- ▁تحب
- بر
- ▁الر
- ▁وا
- ▁الق
- اء
- ▁عل
- ▁البارح
- ▁وخ
- ▁سافا
- ▁هوما
- ▁ولدي
- ▁
- ▁نعرف
- يف
- رت
- ▁وب
- ▁روح
- ▁علاش
- ▁هاذاك
- ▁رو
- وس
- ▁جا
- ▁كيف
- طر
- ▁غادي
- يكا
- عمل
- ▁نحب
- ▁عندك
- ▁وما
- ▁فر
- اني
- ▁قلتله
- ▁الط
- فر
- ▁دار
- ▁عليها
- ▁يعمل
- ▁نت
- ▁تح
- باح
- ▁ماهو
- ▁وكل
- ▁وع
- قت
- ▁فهمتك
- عر
- ▁وس
- ▁تر
- ▁سي
- يلة
- ▁قلت
- ▁رمضان
- صل
- ▁آما
- ▁الواحد
- ▁بيه
- ▁ثلاثة
- ▁فهمتني
- ▁ها
- بط
- ▁مازال
- قل
- ▁بالك
- ▁معناتها
- ▁ور
- ▁قلتلها
- ▁يس
- رب
- ▁ام
- ▁وبعد
- ▁الث
- ▁وإنت
- ▁بحذا
- ▁لازم
- ْ
- ▁بن
- قرا
- سك
- ▁يت
- خل
- ▁فه
- عت
- ▁هاك
- ▁تق
- ▁قبل
- ▁وك
- ▁نقول
- ▁الز
- حم
- ▁عادش
- حكي
- وها
- بة
- نس
- طل
- ▁علاه
- ذا
- ▁سا
- ▁طل
- الي
- ▁يق
- ▁دو
- حوا
- حد
- ▁نشوف
- نة
- ▁لي
- ▁تك
- ▁نا
- ▁هاذ
- ▁خويا
- ▁المر
- ▁وينك
- ▁البر
- ▁أتو
- ينا
- ▁حل
- ولي
- ▁ثم
- ▁عم
- ▁آي
- ▁قر
- از
- ▁وح
- كش
- بعة
- ▁كيفاه
- ▁نع
- ▁الحمدلله
- ▁ياسر
- ▁الخ
- ▁معاك
- ▁معاه
- ▁تقول
- دة
- ▁حكاية
- تش
- ▁حس
- ▁غدوا
- ▁بالحق
- روا
- وز
- ▁تخ
- ▁العيد
- رجع
- ▁بالي
- ▁جات
- ▁وج
- حة
- ▁وش
- ▁آخر
- ▁طا
- ▁مت
- لقا
- تك
- ▁مس
- ▁راني
- كون
- ▁صاحب
- ▁هاكا
- ▁قول
- ▁عر
- ▁عنده
- ▁يلزم
- ▁هاذا
- ▁يخ
- ▁وقتاش
- ▁وقت
- بع
- ▁العش
- ▁هاذي
- هاش
- ينة
- ▁هاذاكا
- عطي
- ▁تنج
- ▁باهية
- نيا
- فت
- ▁يحب
- ▁تف
- ▁أهلا
- وف
- ▁غدوة
- ▁بيك
- ▁بد
- عن
- ▁در
- ▁ننج
- هار
- ▁الحكاية
- مون
- وق
- ▁نورمال
- ▁عندها
- خر
- ▁بو
- ▁حب
- ▁آكا
- ▁وف
- ▁هاذيكا
- ▁ديجا
- ▁وق
- ▁طي
- لتل
- بعث
- ▁تص
- رك
- ▁مانيش
- ▁العادة
- ▁شوف
- ضر
- ▁يمشي
- ▁نعملوا
- ▁عرفت
- ▁زال
- ▁متع
- ▁عمل
- ▁بيها
- ▁نحكي
- اع
- ▁نج
- معة
- ▁والكل
- عناها
- ▁يعي
- ▁نجي
- ستن
- ▁هاذيك
- ▁عام
- ▁فلوس
- قة
- تين
- ▁بالقدا
- لهم
- ▁تخدم
- ▁ٱ
- ▁شيء
- ▁راهي
- ▁جاب
- ولاد
- ابل
- ▁ماك
- عة
- ▁نمشيوا
- وني
- شري
- بار
- انس
- ▁وقتها
- ▁جديد
- ▁يز
- ▁كر
- ▁حاسيلو
- ▁شق
- ▁اه
- ▁سايي
- ▁انشالل
- رج
- مني
- ▁بلا
- ▁صحيح
- ▁غير
- ▁يخدم
- مان
- وكا
- ▁عند
- ▁قاعدة
- ▁تس
- ربة
- ▁راس
- ▁حط
- ▁نكل
- تني
- ▁الو
- سيون
- ▁عندنا
- ▁لو
- ▁ست
- صف
- ▁ض
- ▁كامل
- ▁نخدم
- ▁يبدا
- ▁دونك
- ▁أمور
- رات
- ▁تونس
- بدا
- ▁تحكي
- ▁سو
- ▁جاي
- ▁وحدة
- ▁ساعة
- حنا
- ▁بكري
- ▁إل
- ▁وبر
- ▁كم
- ▁تبدا
- ارة
- ادي
- رق
- لوا
- ▁يمكن
- ▁خاط
- ▁وص
- جين
- ▁هاذاي
- ▁هز
- قد
- ▁قل
- ▁وكهو
- ▁نص
- ▁دي
- لقى
- ▁وأنا
- سين
- ▁يح
- ▁ماشي
- ▁شو
- ▁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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iwslt22_dialect"]}
|
automatic-speech-recognition
|
espnet/brianyan918_iwslt22_dialect_transformer_fisherlike
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_transformer\_fisherlike'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
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
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_transformer\\_fisherlike'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jan 31 10:15:38 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe1000\\_sp\n-------------------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_transformer\\_fisherlike'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jan 31 10:15:38 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe1000\\_sp\n-------------------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
52,
56,
178,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/brianyan918\\_iwslt22\\_dialect\\_transformer\\_fisherlike'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jan 31 10:15:38 EST 2022'\n* python version: '3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.6a1'\n* pytorch version: 'pytorch 1.8.1'\n* Git hash: '99581e0f5af3ad68851d556645e7292771436df9'\n\t+ Commit date: 'Sat Jan 29 11:32:38 2022 -0500'\n\n\nasr\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix\\_raw\\_bpe1000\\_sp\n-------------------------------------------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 ASR pretrained model
### `byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp`
♻️ Imported from https://huggingface.co/
This model was trained by byan using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
espnet/byan_librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_ac-truncated-68a97b
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR pretrained model
### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'
️ Imported from URL
This model was trained by byan using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 ASR pretrained model",
"### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'\n️ Imported from URL\n\nThis model was trained by byan using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'\n️ Imported from URL\n\nThis model was trained by byan using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
10,
78,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 ASR pretrained model### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'\n️ Imported from URL\n\nThis model was trained by byan using librispeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
# ESPnet2 ENH pretrained model
## `Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en`
♻️ Imported from <https://zenodo.org/record/4498562#.YOAOApozZH4>.
This model was trained by Chenda Li using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://github.com/espnet/espnet_model_zoo
```
### Evaluate in the recipe
```python
# coming soon
```
### Results
```bash
# RESULTS
## Environments
- date: `Thu Feb 4 01:16:18 CST 2021`
- python version: `3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0]`
- espnet version: `espnet 0.9.7`
- pytorch version: `pytorch 1.5.0`
- Git hash: `a3334220b0352931677946d178fade3313cf82bb`
- Commit date: `Fri Jan 29 23:35:47 2021 +0800`
## enh_train_enh_conv_tasnet_raw
config: ./conf/tuning/train_enh_conv_tasnet.yaml
|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_min_8k|0.949205|17.3785|16.8028|26.9785|
|enhanced_tt_min_8k|0.95349|16.6221|15.9494|25.9032|
```
### Training config
See full config in [`config.yaml`](./exp/enh_train_enh_conv_tasnet_raw/config.yaml)
```yaml
config: ./conf/tuning/train_enh_conv_tasnet.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_conv_tasnet_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["wsj0_2mix"], "inference": false}
|
audio-to-audio
|
espnet/chenda-li-wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave
|
[
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'
️ Imported from <URL
This model was trained by Chenda Li using wsj0_2mix recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
See full config in 'URL'
|
[
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSee full config in 'URL'"
] |
[
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSee full config in 'URL'"
] |
[
51,
10,
79,
4,
7,
3,
13
] |
[
"passage: TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n# ESPnet2 ENH pretrained model## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.### Python API### Evaluate in the recipe### Results### Training config\n\nSee full config in 'URL'"
] |
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] |
null | null |
espnet
|
# ESPnet2 ENH pretrained model
## `Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en`
♻️ Imported from <https://zenodo.org/record/4498554#.YOAOEpozZH4>.
This model was trained by Chenda Li using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://github.com/espnet/espnet_model_zoo
```
### Evaluate in the recipe
```python
# coming soon
```
### Results
```bash
# RESULTS
## Environments
- date: `Thu Feb 4 01:08:19 CST 2021`
- python version: `3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0]`
- espnet version: `espnet 0.9.7`
- pytorch version: `pytorch 1.5.0`
- Git hash: `a3334220b0352931677946d178fade3313cf82bb`
- Commit date: `Fri Jan 29 23:35:47 2021 +0800`
## enh_train_enh_rnn_tf_raw
config: conf/tuning/train_enh_rnn_tf.yaml
|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_min_8k|0.891065|11.556|10.3982|18.0655|
|enhanced_tt_min_8k|0.896373|11.4086|10.2433|18.0496|
```
### Training config
See full config in [`config.yaml`](./exp/enh_train_enh_rnn_tf_raw/config.yaml)
```yaml
config: conf/tuning/train_enh_rnn_tf.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_train_enh_rnn_tf_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["wsj0_2mix"], "inference": false}
|
audio-to-audio
|
espnet/chenda-li-wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave
|
[
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'
️ Imported from <URL
This model was trained by Chenda Li using wsj0_2mix recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
See full config in 'URL'
|
[
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSee full config in 'URL'"
] |
[
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSee full config in 'URL'"
] |
[
51,
10,
79,
4,
7,
3,
13
] |
[
"passage: TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n# ESPnet2 ENH pretrained model## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.### Python API### Evaluate in the recipe### Results### Training config\n\nSee full config in 'URL'"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer`
This model was trained by ftshijt using puebla_nahuatl recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/puebla_nahuatl/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Nov 7 18:16:55 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_hubert_raw_bpe500_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|90532|77.0|17.0|6.0|3.6|26.6|74.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|590273|92.2|2.1|5.7|3.0|10.8|74.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|242435|86.0|7.3|6.8|3.5|17.5|74.0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transformer_hubert.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_hubert_raw_bpe500_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_bpe500_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - /tmp/jiatong-150390.uytFFbyG/raw/dev/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-150390.uytFFbyG/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
warmup_steps: 25000
token_list:
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- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 12
linear_units: 2048
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["puebla_nahuatl"]}
|
automatic-speech-recognition
|
espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:puebla_nahuatl",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #automatic-speech-recognition #dataset-puebla_nahuatl #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_puebla\_nahuatl\_transfer'
This model was trained by ftshijt using puebla\_nahuatl recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Nov 7 18:16:55 EST 2021'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.4a1'
* pytorch version: 'pytorch 1.9.0'
* Git hash: ''
+ Commit date: ''
asr\_train\_asr\_transformer\_hubert\_raw\_bpe500\_sp
-----------------------------------------------------
### WER
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/ftshijt\\_espnet2\\_asr\\_puebla\\_nahuatl\\_transfer'\n\n\nThis model was trained by ftshijt using puebla\\_nahuatl recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 18:16:55 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_hubert\\_raw\\_bpe500\\_sp\n-----------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-puebla_nahuatl #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_puebla\\_nahuatl\\_transfer'\n\n\nThis model was trained by ftshijt using puebla\\_nahuatl recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 18:16:55 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_hubert\\_raw\\_bpe500\\_sp\n-----------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
51,
58,
137,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #dataset-puebla_nahuatl #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/ftshijt\\_espnet2\\_asr\\_puebla\\_nahuatl\\_transfer'\n\n\nThis model was trained by ftshijt using puebla\\_nahuatl recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 18:16:55 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_hubert\\_raw\\_bpe500\\_sp\n-----------------------------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_totonac_transformer`
This model was trained by ftshijt using totonac recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/totonac/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_totonac_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Nov 7 09:22:09 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_specaug_raw_bpe250_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev|530|3547|59.8|32.9|7.3|6.5|46.7|87.4|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test|704|5018|55.5|35.7|8.8|6.1|50.6|92.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev|530|22510|88.1|4.4|7.4|3.9|15.8|87.4|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test|704|32990|86.9|4.3|8.8|4.0|17.1|92.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev|530|9360|70.3|15.8|13.8|4.3|34.0|87.4|
|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test|704|13835|70.5|16.0|13.6|4.4|33.9|92.0|
## ASR config
<details><summary>expand</summary>
```
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_bpe250_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe250_sp/train/speech_shape
- exp/asr_stats_raw_bpe250_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe250_sp/valid/speech_shape
- exp/asr_stats_raw_bpe250_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - /tmp/jiatong-7359.okvPvI3Z/raw/dev/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-7359.okvPvI3Z/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: 4000
token_list:
- <blank>
- <unk>
- ':'
- ▁N
- NI
- N
- ▁IYMA
- ▁NA
- NA
- ▁WA
- WA
- ▁
- ''''
- KA
- ▁MA
- MA
- T
- ▁XA
- TA
- NCHU
- WI
- ▁LI
- ▁NI
- PA
- YI
- ▁PUS
- K
- ▁PI
- ▁X
- S
- ▁TA
- YA
- ▁LA
- Q
- QA
- TI
- ▁KA
- QO
- W
- ▁KAH
- ▁PALA
- H
- X
- XA
- ▁KI
- A
- LH
- I
- LA
- ▁CHA
- ▁A
- ▁XLI
- ▁LHI
- U
- ▁K
- KANI
- KU
- Y
- ▁LU
- Á
- ▁CHU
- O
- KI
- ▁KIWI
- NTLA
- ▁TLA
- M
- ▁TAWA
- ▁TI
- ▁S
- WANI
- CHA
- LHI
- LI
- ▁TU
- ▁PALHA
- Í
- ▁CHANÁ
- ▁KILHWAMPA
- KÁN
- ▁WAYMA
- E
- SA
- ▁E
- ▁LHU
- LHA
- PU
- ▁LHA
- ▁PA
- ▁LAK
- ▁ANTA
- ▁KITI
- NCHÚ
- SI
- TLA
- PI
- ▁KINI
- CHI
- ▁PEROH
- ▁PU
- QÓ
- QALHCHIWINA
- TU
- ▁TLHA
- ▁WI
- NÁ
- ▁KAN
- ▁NAYI
- CH
- 'NO'
- ▁U
- TSA
- MÁ
- NQO
- ▁ANA
- ▁LIKWA
- ▁XTA
- J
- ▁QALH
- TO
- TÁ
- ▁USA
- ▁PORQUE
- ▁MI
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- ▁TAWÁ
- XI
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- P
- CHIWI
- WÁ
- NTI
- ▁JKA
- Ú
- NTLHA
- R
- TSI
- C
- STA
- ▁LH
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- MPI
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- TLHA
- YÁ
- KUCHAN
- XAQATLI
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- ▁LAQ
- QALH
- KATSI
- Ó
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- ▁J
- ▁QAMA
- NTU
- MI
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- ▁XANAT
- ▁CHI
- JA
- ▁IY
- ▁TSU
- MAKLAKAS
- ▁MAQA
- LÁ
- ▁KATSIYA
- ▁TLANKA
- ▁STAK
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- ▁LHIKWA
- ▁SQA
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- ▁TLAQ
- ▁JKATSI
- MAKLAKASKINKA
- YÁW
- WATIYA
- CHÁ
- ▁IPORQUEI
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- TSU
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- LAKASKINQO
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- <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_unigram250/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_bpe250_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
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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["totonac"]}
|
automatic-speech-recognition
|
espnet/ftshijt_espnet2_asr_totonac_transformer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:totonac",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #automatic-speech-recognition #dataset-totonac #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_totonac\_transformer'
This model was trained by ftshijt using totonac recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Nov 7 09:22:09 EST 2021'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.4a1'
* pytorch version: 'pytorch 1.9.0'
* Git hash: ''
+ Commit date: ''
asr\_train\_asr\_transformer\_specaug\_raw\_bpe250\_sp
------------------------------------------------------
### WER
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/ftshijt\\_espnet2\\_asr\\_totonac\\_transformer'\n\n\nThis model was trained by ftshijt using totonac recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 09:22:09 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe250\\_sp\n------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-totonac #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_totonac\\_transformer'\n\n\nThis model was trained by ftshijt using totonac recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 09:22:09 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe250\\_sp\n------------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
47,
48,
137,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #dataset-totonac #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/ftshijt\\_espnet2\\_asr\\_totonac\\_transformer'\n\n\nThis model was trained by ftshijt using totonac recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 09:22:09 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe250\\_sp\n------------------------------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer`
This model was trained by ftshijt using yolo_mixtec recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/yolo_mixtec/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Nov 10 02:59:39 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_specaug_raw_bpe500
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|81348|84.1|11.8|4.1|2.5|18.3|82.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|626187|93.4|2.2|4.4|2.4|9.0|82.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|325684|90.7|5.2|4.1|2.2|11.5|82.5|
## ASR config
<details><summary>expand</summary>
```
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'
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- 14-
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- '='
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- <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}
}
```
|
{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["yolo_mixtec"]}
|
automatic-speech-recognition
|
espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:yolo_mixtec",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"noinfo"
] |
TAGS
#espnet #audio #automatic-speech-recognition #dataset-yolo_mixtec #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_yolo\_mixtec\_transformer'
This model was trained by ftshijt using yolo\_mixtec recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Nov 10 02:59:39 EST 2021'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.4a1'
* pytorch version: 'pytorch 1.9.0'
* Git hash: ''
+ Commit date: ''
asr\_train\_asr\_transformer\_specaug\_raw\_bpe500
--------------------------------------------------
### WER
### CER
### TER
ASR config
----------
expand
### Citing ESPnet
or arXiv:
|
[
"### 'espnet/ftshijt\\_espnet2\\_asr\\_yolo\\_mixtec\\_transformer'\n\n\nThis model was trained by ftshijt using yolo\\_mixtec recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Nov 10 02:59:39 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe500\n--------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-yolo_mixtec #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_yolo\\_mixtec\\_transformer'\n\n\nThis model was trained by ftshijt using yolo\\_mixtec recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Nov 10 02:59:39 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe500\n--------------------------------------------------",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
[
49,
55,
134,
4,
3,
10,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #dataset-yolo_mixtec #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/ftshijt\\_espnet2\\_asr\\_yolo\\_mixtec\\_transformer'\n\n\nThis model was trained by ftshijt using yolo\\_mixtec recipe in espnet.### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Nov 10 02:59:39 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espnet version: 'espnet 0.10.4a1'\n* pytorch version: 'pytorch 1.9.0'\n* Git hash: ''\n\t+ Commit date: ''\n\n\nasr\\_train\\_asr\\_transformer\\_specaug\\_raw\\_bpe500\n--------------------------------------------------### WER### CER### TER\n\n\n\nASR config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `ftshijt/mls_asr_transformer_valid.acc.best`
♻️ Imported from https://zenodo.org/record/4458452/
This model was trained by ftshijt using mls/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```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}
}
```
|
{"language": "es", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mls"]}
|
automatic-speech-recognition
|
espnet/ftshijt_mls_asr_transformer_valid.acc.best
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"es",
"dataset:mls",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"es"
] |
TAGS
#espnet #audio #automatic-speech-recognition #es #dataset-mls #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'ftshijt/mls_asr_transformer_valid.URL'
️ Imported from URL
This model was trained by ftshijt using mls/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'ftshijt/mls_asr_transformer_valid.URL'\n️ Imported from URL\n\nThis model was trained by ftshijt using mls/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #es #dataset-mls #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'ftshijt/mls_asr_transformer_valid.URL'\n️ Imported from URL\n\nThis model was trained by ftshijt using mls/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #es #dataset-mls #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'ftshijt/mls_asr_transformer_valid.URL'\n️ Imported from URL\n\nThis model was trained by ftshijt using mls/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 ASR pretrained model
### `jv_openslr35`
♻️ Imported from https://zenodo.org/record/5090139/
This model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
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}
}
```
|
{"language": "jv", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["jv_openslr35"]}
|
automatic-speech-recognition
|
espnet/jv_openslr35
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"jv",
"dataset:jv_openslr35",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"jv"
] |
TAGS
#espnet #audio #automatic-speech-recognition #jv #dataset-jv_openslr35 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR pretrained model
### 'jv_openslr35'
️ Imported from URL
This model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 ASR pretrained model",
"### 'jv_openslr35'\n️ Imported from URL\n\nThis model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #jv #dataset-jv_openslr35 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'jv_openslr35'\n️ Imported from URL\n\nThis model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
54,
10,
47,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #jv #dataset-jv_openslr35 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 ASR pretrained model### 'jv_openslr35'\n️ Imported from URL\n\nThis model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
# ESPnet2 ASR pretrained model
## `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from <https://zenodo.org/record/3957940#.YN7zwJozZH4>
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
### Training config
See full config in [`config.yaml`](./config.yaml)
```yaml
config: null
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_raw_bpe
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mini-an4"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:mini-an4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-mini-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'
️ Imported from <URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See full config in 'URL'
|
[
"# ESPnet2 ASR pretrained model",
"## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from <URL\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'URL'"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from <URL\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'URL'"
] |
[
50,
10,
60,
11,
11,
13
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n# ESPnet2 ASR pretrained model## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from <URL\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\n\n\nor arXiv:### Training config\n\nSee full config in 'URL'"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/aishell_conformer`
♻️ Imported from https://zenodo.org/record/4105763/
This model was trained by kamo-naoyuki using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_aishell_conformer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/aishell_conformer'
️ Imported from URL
This model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/aishell_conformer'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/aishell_conformer'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
48,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/aishell_conformer'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4414883/
This model was trained by kamo-naoyuki using chime4/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["chime4"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:chime4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
9,
72,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.acc.ave`
♻️ 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/).
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["dirha_wsj"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scp-truncated-2fd1f8
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:dirha_wsj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-dirha_wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using dirha_wsj/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using dirha_wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-dirha_wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using dirha_wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
51,
9,
144,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-dirha_wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using dirha_wsj/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4430974/
This model was trained by kamo-naoyuki using hkust/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["hkust"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20-truncated-934e17
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:hkust",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-hkust #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-hkust #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
92,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-hkust #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave`
♻️ 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
```
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-55c091
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
9,
126,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4543018/
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-b76af5
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
9,
127,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4541452/
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_schedule-truncated-c8e5f9
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
9,
113,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4604066/
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer6_n_fft512_hop_length2-truncated-a63357
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
49,
9,
108,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3957940/
This model was trained by kamo-naoyuki using mini_an4/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mini_an4"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_mini_an4_asr_train_raw_bpe_valid.acc.best
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:mini_an4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-mini_an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini_an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
50,
9,
64,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini_an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4441309/
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
```
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["reverb"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdata-truncated-0e9753
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:reverb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
152,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `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
```
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["reverb"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_reverb_asr_train_asr_transformer4_raw_char_batch_bins1600-truncated-1b72bb
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:reverb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
83,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-reverb #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using reverb/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/).
### 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}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["timit"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_timit_asr_train_asr_raw_word_valid.acc.ave
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:timit",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-timit #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using timit/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using timit/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-timit #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using timit/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
47,
9,
62,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-timit #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/timit_asr_train_asr_raw_word_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using timit/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/wsj`
♻️ Imported from https://zenodo.org/record/4003381/
This model was trained by kamo-naoyuki using wsj/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["wsj"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_wsj
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:wsj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/wsj'
️ Imported from URL
This model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/wsj'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/wsj'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
46,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/wsj'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kamo-naoyuki/wsj_transformer2`
♻️ Imported from https://zenodo.org/record/4243201/
This model was trained by kamo-naoyuki using wsj/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["wsj"]}
|
automatic-speech-recognition
|
espnet/kamo-naoyuki_wsj_transformer2
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:wsj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kamo-naoyuki/wsj_transformer2'
️ Imported from URL
This model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/wsj_transformer2'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/wsj_transformer2'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
50,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kamo-naoyuki/wsj_transformer2'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using wsj/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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null | null |
espnet
|
## ESPnet2 ASR model
### `espnet/kan-bayashi_csj_asr_train_asr_conformer`
This model was trained by Nelson Yalta using csj recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0d8cd47dd3572248b502bc831cd305e648170233
pip install -e .
cd egs2/csj/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/kan-bayashi_csj_asr_train_asr_conformer
```
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_char_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 47308
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 6
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
pretrain_path: []
pretrain_key: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 15000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_sp/train/speech_shape
- exp/asr_stats_raw_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_sp/valid/speech_shape
- exp/asr_stats_raw_sp/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_nodup_sp/wav.scp
- speech
- sound
- - dump/raw/train_nodup_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev/wav.scp
- speech
- sound
- - dump/raw/train_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
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- "\u3044"
- "\u3067"
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- "\u5F62"
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- "\u7269"
- "\u660E"
- "\u70B9"
- "\u5473"
- "\u767E"
- "\u89E3"
- "\u8FD1"
- "\u8B58"
- "\u5730"
- "\u540D"
- "\u805E"
- "\u4E0B"
- "\u5C0F"
- "\u6559"
- "\u30B5"
- "\u70BA"
- "\u4E5D"
- "\u30D6"
- "\u5BB6"
- "\u30CB"
- "\u521D"
- "\u30D9"
- "\u30E7"
- "\u5C11"
- "\u8A8D"
- "\u8AD6"
- "\u529B"
- "\u516D"
- "\u30D3"
- "\u60C5"
- "\u7FD2"
- "\u30A6"
- "\u7ACB"
- "\u5FC3"
- "\u8ABF"
- "\u5831"
- "\u30A8"
- "\uFF24"
- "\uFF2E"
- "\u793A"
- "\u793E"
- "\u9055"
- "\u969B"
- "\u3056"
- "\u8AAC"
- "\u5FDC"
- "\u98DF"
- "\u72B6"
- "\u9577"
- "\u7814"
- "\u6821"
- "\u5185"
- "\u639B"
- "\u30DF"
- "\u5916"
- "\u5411"
- "\u80FD"
- "\u516B"
- "\u9762"
- "\u7A76"
- "\u7136"
- "\u3073"
- "\u30D4"
- "\u4E3B"
- "\u4FC2"
- "\u5024"
- "\u91CD"
- "\u8A5E"
- "\u4F9B"
- "\u5F97"
- "\u5FC5"
- "\u5973"
- "\u78BA"
- "\u7D42"
- "\u30BA"
- "\u6BCD"
- "\u696D"
- "\u7387"
- "\u65B0"
- "\u6D3B"
- "\u697D"
- "\u8449"
- "\u8A08"
- "\u30CA"
- "\u3080"
- "\u6240"
- "\u4E16"
- "\u6B63"
- "\u30E3"
- "\u8A18"
- "\u671F"
- "\u5207"
- "\u3078"
- "\u6A5F"
- "\u30DA"
- "\u5343"
- "\u985E"
- "\u5143"
- "\u614B"
- "\u826F"
- "\u5728"
- "\u6709"
- "\u30C0"
- "\u4E03"
- "\uFF23"
- "\u5225"
- "\u30EF"
- "\u691C"
- "\u7D9A"
- "\u9078"
- "\u57FA"
- "\u76F8"
- "\u6708"
- "\u4FA1"
- "\u7D20"
- "\u4ED6"
- "\u6BD4"
- "\u9023"
- "\u96C6"
- "\u30A7"
- "\u307B"
- "\u4F4D"
- "\u597D"
- "\uFF2D"
- "\u5F37"
- "\u4E0D"
- "\u5FA1"
- "\u6790"
- "\u30DD"
- "\u7121"
- "\u89AA"
- "\u53D7"
- "\u3086"
- "\u7F6E"
- "\u8C61"
- "\u4ED5"
- "\u5F0F"
- "\u30CD"
- "\u6307"
- "\u8AAD"
- "\u6C7A"
- "\u8ECA"
- "\u96FB"
- "\u904E"
- "\u30B1"
- "\u8A55"
- "\u5229"
- "\u6B8B"
- "\u8D77"
- "\u30CE"
- "\u7D4C"
- "\u56F3"
- "\u4F1D"
- "\u500B"
- "\u30C4"
- "\u7BC0"
- "\u9053"
- "\u5E73"
- "\u91D1"
- "\u899A"
- "\uFF34"
- "\u4F4F"
- "\u59CB"
- "\u63D0"
- "\u5B58"
- "\u5171"
- "\u30DB"
- "\u7B2C"
- "\u7D44"
- "\u89B3"
- "\u80B2"
- "\u6771"
- "\u305E"
- "\u958B"
- "\u52A0"
- "\u5F15"
- "\uFF33"
- "\u53E3"
- "\u6C34"
- "\u5BB9"
- "\u5468"
- "\u5B87"
- "\u7D04"
- "\u5B57"
- "\u3076"
- "\u9803"
- "\u3072"
- "\u5B99"
- "\u6BB5"
- "\u30BD"
- "\u97FF"
- "\u30DC"
- "\u53CB"
- "\u91CF"
- "\u6599"
- "\u3085"
- "\u5CF6"
- "\u8EAB"
- "\u76F4"
- "\u753B"
- "\u7DDA"
- "\u54C1"
- "\u5DEE"
- "\u4EF6"
- "\u9069"
- "\u5F35"
- "\u8FBA"
- "\u8FBC"
- "\u91CE"
- "\u69D8"
- "\u578B"
- "\u4E88"
- "\u7A2E"
- "\u5074"
- "\u8FF0"
- "\u5C71"
- "\u5C4B"
- "\u5E30"
- "\u30CF"
- "\u4E57"
- "\u539F"
- "\u683C"
- "\u8CEA"
- "\u666E"
- "\uFF30"
- "\u9020"
- "\u753A"
- "\u30B4"
- "\u82F1"
- "\u63A5"
- "\u304E"
- "\u6E2C"
- "\u3075"
- "\u7FA9"
- "\u4EAC"
- "\u5272"
- "\u5236"
- "\u7B54"
- "\u5404"
- "\u4FE1"
- "\u754C"
- "\u6211"
- "\u7A7A"
- "\uFF0E"
- "\u7740"
- "\u53EF"
- "\u66F4"
- "\u6D77"
- "\u4E0E"
- "\u9032"
- "\u52B9"
- "\u5F7C"
- "\u771F"
- "\u7530"
- "\u5FB4"
- "\u6D41"
- "\u5177"
- "\uFF32"
- "\u5E02"
- "\u67FB"
- "\u5B89"
- "\uFF22"
- "\u5E83"
- "\u50D5"
- "\u6CE2"
- "\u5C40"
- "\u8A2D"
- "\u7537"
- "\u767D"
- "\u30B6"
- "\u53CD"
- "\u6226"
- "\u533A"
- "\u6C42"
- "\u96D1"
- "\uFF29"
- "\u6B69"
- "\u8CB7"
- "\u982D"
- "\u7B97"
- "\u534A"
- "\u4FDD"
- "\u5E03"
- "\u96E3"
- "\uFF2C"
- "\u5224"
- "\u843D"
- "\u8DB3"
- "\u5E97"
- "\u7533"
- "\u8FD4"
- "\u30AE"
- "\u4E07"
- "\u6728"
- "\u6614"
- "\u8F03"
- "\u7D22"
- "\uFF26"
- "\u30B2"
- "\u6B86"
- "\u60AA"
- "\u5883"
- "\u548C"
- "\u907A"
- "\u57DF"
- "\u968E"
- "\u542B"
- "\u305C"
- "\u30BC"
- "\u65AD"
- "\u9650"
- "\u63A8"
- "\u4F4E"
- "\u5F71"
- "\u898F"
- "\u6319"
- "\u90FD"
- "\u307C"
- "\u6848"
- "\u4EEE"
- "\u88AB"
- "\u547C"
- "\u30A1"
- "\u96E2"
- "\u7CFB"
- "\u79FB"
- "\u30AC"
- "\u5DDD"
- "\u6E96"
- "\u904B"
- "\u6761"
- "\u5FF5"
- "\u6C11"
- "\uFF27"
- "\u7236"
- "\u75C5"
- "\u79D1"
- "\u4E21"
- "\u7531"
- "\u8A66"
- "\u56E0"
- "\u547D"
- "\u795E"
- "\uFF28"
- "\u7570"
- "\u7C21"
- "\u53E4"
- "\u6F14"
- "\u5897"
- "\u51E6"
- "\u8B70"
- "\u7DD2"
- "\u7CBE"
- "\u6613"
- "\u53F7"
- "\u65CF"
- "\u52FF"
- "\u60F3"
- "\u5217"
- "\u5C0E"
- "\u8EE2"
- "\u54E1"
- "\u30E6"
- "\u6BCE"
- "\u8996"
- "\u4E26"
- "\u98DB"
- "\u4F3C"
- "\u6620"
- "\u7D71"
- "\u4EA4"
- "\u30D2"
- "\u6B4C"
- "\u5F85"
- "\u8CC7"
- "\u8907"
- "\u8AA4"
- "\u63DB"
- "\u6A19"
- "\u6CC1"
- "\u914D"
- "\u62BD"
- "\u822C"
- "\u7403"
- "\u9006"
- "\u65C5"
- "\u6628"
- "\u9662"
- "\u99C5"
- "\u74B0"
- "\u5BDF"
- "\u516C"
- "\u6B73"
- "\u5C5E"
- "\u8F9E"
- "\u5947"
- "\u6CBB"
- "\u5E7E"
- "\u82E5"
- "\u58F2"
- "\u632F"
- "\u7686"
- "\u6CE8"
- "\u6B74"
- "\u9805"
- "\u5F93"
- "\u5747"
- "\u5F79"
- "\u9806"
- "\u53BB"
- "\u56E3"
- "\u8853"
- "\u7DF4"
- "\u6FC0"
- "\u6982"
- "\u66FF"
- "\u7B49"
- "\u98F2"
- "\u53F2"
- "\u88DC"
- "\u901F"
- "\u53C2"
- "\u65E9"
- "\u53CE"
- "\u9332"
- "\u671D"
- "\u5186"
- "\u5370"
- "\u5668"
- "\u63A2"
- "\u7D00"
- "\u9001"
- "\u6E1B"
- "\u571F"
- "\u5929"
- "\uFF2F"
- "\u50BE"
- "\u72AC"
- "\u9060"
- "\u5E2F"
- "\u52A9"
- "\u6A2A"
- "\u591C"
- "\u7523"
- "\u8AB2"
- "\u5BA2"
- "\u629E"
- "\u5712"
- "\u4E38"
- "\u50CF"
- "\u50CD"
- "\u6750"
- "\u5DE5"
- "\u904A"
- "\u544A"
- "\u523A"
- "\u6539"
- "\u8D64"
- "\u8074"
- "\u4ECB"
- "\u8077"
- "\u53F0"
- "\u77ED"
- "\u8AB0"
- "\u7D30"
- "\u672A"
- "\u770C"
- "\u9928"
- "\u6B62"
- "\u53F3"
- "\u306C"
- "\u3065"
- "\u56F2"
- "\u8A0E"
- "\u6B7B"
- "\u5EFA"
- "\u592B"
- "\u7AE0"
- "\u964D"
- "\u666F"
- "\u706B"
- "\u30A9"
- "\u9E97"
- "\u8B1B"
- "\u72EC"
- "\u5DE6"
- "\u5C64"
- "\uFF25"
- "\u5C55"
- "\u653F"
- "\u5099"
- "\u4F59"
- "\u7D76"
- "\u5065"
- "\u518D"
- "\u9580"
- "\u5546"
- "\u52DD"
- "\u52C9"
- "\u82B1"
- "\u30E4"
- "\u8EF8"
- "\u97FB"
- "\u66F2"
- "\u6574"
- "\u652F"
- "\u6271"
- "\u53E5"
- "\u6280"
- "\u5317"
- "\u30D8"
- "\u897F"
- "\u5247"
- "\u4FEE"
- "\u6388"
- "\u9031"
- "\u5BA4"
- "\u52D9"
- "\u9664"
- "\u533B"
- "\u6563"
- "\u56FA"
- "\u7AEF"
- "\u653E"
- "\u99AC"
- "\u7A4D"
- "\u8208"
- "\u592A"
- "\u5ACC"
- "\u9F62"
- "\u672B"
- "\u7D05"
- "\u6E90"
- "\u6E80"
- "\u5931"
- "\u5BDD"
- "\u6D88"
- "\u6E08"
- "\u4FBF"
- "\u983C"
- "\u4F01"
- "\u5B8C"
- "\u4F11"
- "\u9752"
- "\u7591"
- "\u8D70"
- "\u6975"
- "\u767B"
- "\u8AC7"
- "\u6839"
- "\u6025"
- "\u512A"
- "\u7D75"
- "\u623B"
- "\u5E2B"
- "\u5F59"
- "\u6DF7"
- "\u8DEF"
- "\u7E70"
- "\uFF2B"
- "\u8A3C"
- "\u713C"
- "\u6562"
- "\u5BB3"
- "\u96F6"
- "\u6253"
- "\u82E6"
- "\u7701"
- "\u7D19"
- "\u5C02"
- "\u8DDD"
- "\u9854"
- "\u8D8A"
- "\u4E89"
- "\u56F0"
- "\u5BC4"
- "\u5199"
- "\u4E92"
- "\u6DF1"
- "\u5A5A"
- "\u7DCF"
- "\u89A7"
- "\u80CC"
- "\u7BC9"
- "\u6E29"
- "\u8336"
- "\u62EC"
- "\u8CA0"
- "\u590F"
- "\u89E6"
- "\u7D14"
- "\u9045"
- "\u58EB"
- "\u96A3"
- "\u6050"
- "\u91C8"
- "\u967A"
- "\u5150"
- "\u5BBF"
- "\u6A21"
- "\u77F3"
- "\u983B"
- "\u5B09"
- "\u5EA7"
- "\u7642"
- "\u7E4B"
- "\uFF38"
- "\u5C06"
- "\u8FFD"
- "\u5EAD"
- "\u6238"
- "\u5371"
- "\u5BC6"
- "\u5DF1"
- "\u9014"
- "\u7BC4"
- "\u99C4"
- "\u7D39"
- "\u4EFB"
- "\u968F"
- "\u5357"
- "\uFF11"
- "\u5EB7"
- "\u9818"
- "\u5FD8"
- "\u3045"
- "\u59FF"
- "\u7F8E"
- "\u55B6"
- "\u6349"
- "\u65E2"
- "\u7167"
- "\uFF2A"
- "\u4EF2"
- "\u9152"
- "\u52E2"
- "\u9ED2"
- "\u5149"
- "\u6E21"
- "\u75DB"
- "\u62C5"
- "\u5F31"
- "\u307D"
- "\uFF36"
- "\u7D0D"
- "\u629C"
- "\u5E45"
- "\u6D17"
- "\u7A81"
- "\u671B"
- "\u5373"
- "\u9858"
- "\u7565"
- "\uFF12"
- "\u9811"
- "\u5FD7"
- "\u5B85"
- "\u7247"
- "\u656C"
- "\u6751"
- "\u60B2"
- "\u81A8"
- "\u89D2"
- "\u30E8"
- "\u4F9D"
- "\u8A73"
- "\u5F8B"
- "\u9B5A"
- "\u52B4"
- "\u5A66"
- "\u6163"
- "\u732B"
- "\u5019"
- "\u8001"
- "\u558B"
- "\u79F0"
- "\u796D"
- "\u7FA4"
- "\u7E2E"
- "\u6C38"
- "\u616E"
- "\u5EF6"
- "\u7A3F"
- "\u611B"
- "\u8089"
- "\u9589"
- "\u8CBB"
- "\u6295"
- "\u6D3E"
- "\u81F4"
- "\u7BA1"
- "\u7C73"
- "\u5E95"
- "\u7D99"
- "\u6C0F"
- "\u690D"
- "\u501F"
- "\u5727"
- "\u52E4"
- "\u6F22"
- "\u66AE"
- "\u5F27"
- "\u88C5"
- "\u57CE"
- "\u5287"
- "\u76DB"
- "\u63F4"
- "\u9244"
- "\u8C37"
- "\u5E72"
- "\u7E26"
- "\u8A31"
- "\u6016"
- "\u9A5A"
- "\u8A8C"
- "\uFF35"
- "\u8B77"
- "\u5B88"
- "\u8033"
- "\u6B32"
- "\u8239"
- "\uFF10"
- "\u5178"
- "\u67D3"
- "\u7D1A"
- "\u98FE"
- "\u5144"
- "\u71B1"
- "\u8F09"
- "\u88FD"
- "\u5BFA"
- "\u662D"
- "\u7FFB"
- "\u5426"
- "\u5584"
- "\u62BC"
- "\u53CA"
- "\u6A29"
- "\u559C"
- "\u670D"
- "\u8CB0"
- "\u8EFD"
- "\u677F"
- "\u61B6"
- "\u98FC"
- "\u5C3E"
- "\u5FA9"
- "\u5E78"
- "\u7389"
- "\u5354"
- "\u679A"
- "\u90CE"
- "\u8840"
- "\u524A"
- "\u5922"
- "\u63A1"
- "\u6674"
- "\u6B20"
- "\u602A"
- "\u65BD"
- "\u7DE8"
- "\u98EF"
- "\u7B56"
- "\u9000"
- "\uFF39"
- "\u8349"
- "\u61F8"
- "\u6458"
- "\u58CA"
- "\u4F38"
- "\u85AC"
- "\u9996"
- "\u5BFF"
- "\u53B3"
- "\u606F"
- "\u5C45"
- "\u643A"
- "\u9F3B"
- "\u9280"
- "\u4EA1"
- "\u6CCA"
- "\u8857"
- "\u9759"
- "\u9CE5"
- "\u677E"
- "\u5F92"
- "\u969C"
- "\u7B4B"
- "\u7559"
- "\u51B7"
- "\u5C24"
- "\u68EE"
- "\u5438"
- "\u5012"
- "\u68B0"
- "\u6D0B"
- "\u821E"
- "\u6A4B"
- "\u500D"
- "\u6255"
- "\u5352"
- "\u7E04"
- "\u6C5A"
- "\u53F8"
- "\u6625"
- "\u793C"
- "\u66DC"
- "\u6545"
- "\u526F"
- "\u5F01"
- "\u5439"
- "\u85E4"
- "\u8DE1"
- "\u962A"
- "\u4E86"
- "\u91E3"
- "\u9632"
- "\u7834"
- "\u6012"
- "\u662F"
- "\u30A5"
- "\u7AF6"
- "\u8179"
- "\u4E95"
- "\u4E08"
- "\u64AE"
- "\u72ED"
- "\u5BD2"
- "\u7B46"
- "\u5965"
- "\u8C4A"
- "\u732E"
- "\u5C31"
- "\u5A18"
- "\u79D2"
- "\u6C5F"
- "\u8E0F"
- "\u8A13"
- "\u7372"
- "\u96E8"
- "\u6BBA"
- "\u57CB"
- "\u64CD"
- "\u9AA8"
- "\u8D85"
- "\u6D5C"
- "\u8B66"
- "\u7DD1"
- "\u7D61"
- "\u8133"
- "\u7B11"
- "\u6D6E"
- "\u7D66"
- "\u7126"
- "\u8A70"
- "\u878D"
- "\u738B"
- "\u5C3A"
- "\u5E7C"
- "\u820C"
- "\u663C"
- "\u88CF"
- "\u6CE3"
- "\u67C4"
- "\u9396"
- "\u62E1"
- "\u8A3A"
- "\u7DE0"
- "\u5B98"
- "\u6697"
- "\u820E"
- "\u6298"
- "\u5264"
- "\u4E73"
- "\u6B6F"
- "\u7248"
- "\u5C04"
- "\u8108"
- "\u9707"
- "\u7802"
- "\u4F34"
- "\u72AF"
- "\u4F50"
- "\u5DDE"
- "\u8FB2"
- "\u8DA3"
- "\u990A"
- "\u675F"
- "\u6E2F"
- "\u8FEB"
- "\u5F3E"
- "\u798F"
- "\u51AC"
- "\u541B"
- "\u6B66"
- "\u77AC"
- "\u67A0"
- "\u6CA2"
- "\u661F"
- "\u5BCC"
- "\u6557"
- "\u5D0E"
- "\u6355"
- "\u8377"
- "\u5F1F"
- "\u95BE"
- "\u7E54"
- "\u7C89"
- "\u725B"
- "\u8DF5"
- "\u9999"
- "\u6797"
- "\u83DC"
- "\u62CD"
- "\u63CF"
- "\u888B"
- "\u6607"
- "\u91DD"
- "\u8FCE"
- "\u585A"
- "\u5A46"
- "\uFF49"
- "\u8ECD"
- "\uFF13"
- "\uFF37"
- "\u5BC2"
- "\u8F29"
- "\u3074"
- "\u5DFB"
- "\u4E01"
- "\u504F"
- "\u79CB"
- "\u5E9C"
- "\u6CC9"
- "\u81F3"
- "\u6368"
- "\u7956"
- "\u8584"
- "\u5B97"
- "\u5FB9"
- "\u93E1"
- "\u75C7"
- "\u6CB9"
- "\u8131"
- "\u9CF4"
- "\u7AE5"
- "\u6BDB"
- "\u9077"
- "\u84CB"
- "\u58C1"
- "\u5915"
- "\u5589"
- "\u907F"
- "\u984D"
- "\u6EA2"
- "\u96F0"
- "\u4EE4"
- "\u59C9"
- "\u63E1"
- "\u3077"
- "\u523B"
- "\u62E0"
- "\u8CA1"
- "\u8FF7"
- "\u9063"
- "\u82B8"
- "\u5E8F"
- "\u76E3"
- "\u8457"
- "\u5869"
- "\u5009"
- "\u7F6A"
- "\u6F5C"
- "\u7D5E"
- "\u764C"
- "\u5BAE"
- "\u5E2D"
- "\u8F2A"
- "\u594F"
- "\u846C"
- "\u6C60"
- "\u6CBF"
- "\u5FAE"
- "\u5305"
- "\u76CA"
- "\u76AE"
- "\u4FC3"
- "\u6297"
- "\u5FEB"
- "\u66AB"
- "\u52E7"
- "\u8CA9"
- "\u8C46"
- "\u5B63"
- "\u529F"
- "\u9A12"
- "\uFF54"
- "\u97D3"
- "\u6ED1"
- "\u75B2"
- "\u9003"
- "\u9061"
- "\u5E79"
- "\u60A9"
- "\u83D3"
- "\u672D"
- "\u6804"
- "\u9177"
- "\u8B1D"
- "\u6C96"
- "\u96EA"
- "\u5360"
- "\u60D1"
- "\u63FA"
- "\u866B"
- "\u62B1"
- "\uFF4B"
- "\u5CA1"
- "\u6E9C"
- "\u8535"
- "\u7763"
- "\u6838"
- "\u4E71"
- "\u4E45"
- "\u9EC4"
- "\u9670"
- "\u7720"
- "\u7B26"
- "\u6B8A"
- "\u628A"
- "\u6291"
- "\u5E0C"
- "\u63C3"
- "\u6483"
- "\u5EAB"
- "\u5409"
- "\u6E6F"
- "\u65CB"
- "\u640D"
- "\u52AA"
- "\u64E6"
- "\u9769"
- "\u6E0B"
- "\u773C"
- "\u592E"
- "\u8CDE"
- "\u5374"
- "\u5948"
- "\u539A"
- "\u59D4"
- "\u83EF"
- "\u96A0"
- "\uFF4E"
- "\u30CC"
- "\u9BAE"
- "\u515A"
- "\u5C65"
- "\u8A98"
- "\u6469"
- "\u6162"
- "\u5442"
- "\u7206"
- "\u7BB1"
- "\u6075"
- "\u9678"
- "\u7DCA"
- "\u7E3E"
- "\u5742"
- "\u7B52"
- "\u7532"
- "\u5348"
- "\u5230"
- "\u8CAC"
- "\u5C0A"
- "\u6CF3"
- "\u6279"
- "\u7518"
- "\u5B6B"
- "\u7159"
- "\u8A2A"
- "\u50B7"
- "\u6E05"
- "\u716E"
- "\u88C1"
- "\u9694"
- "\u8ED2"
- "\uFF31"
- "\u7FBD"
- "\u5D29"
- "\u7A74"
- "\u7CD6"
- "\u707D"
- "\u5275"
- "\u6F70"
- "\u6691"
- "\u87BA"
- "\u653B"
- "\u6577"
- "\u6575"
- "\u76E4"
- "\u9732"
- "\u7A93"
- "\u63B2"
- "\u81E8"
- "\u53E9"
- "\u5145"
- "\u4FFA"
- "\u8F38"
- "\u967D"
- "\u6B27"
- "\u6687"
- "\u6B6A"
- "\u6DFB"
- "\u60A3"
- "\u5FD9"
- "\u70AD"
- "\u829D"
- "\u8EDF"
- "\u88D5"
- "\u7E01"
- "\u6F2B"
- "\u7A1A"
- "\u7968"
- "\u8A69"
- "\u5CB8"
- "\u7687"
- "\uFF4A"
- "\u6627"
- "\u5100"
- "\u5857"
- "\u8E0A"
- "\u8AF8"
- "\u6D74"
- "\u904D"
- "\u66D6"
- "\u5BE7"
- "\u99B4"
- "\u5339"
- "\u03B1"
- "\u627F"
- "\u30BE"
- "\u6383"
- "\u5375"
- "\u5999"
- "\u3043"
- "\u66B4"
- "\u62B5"
- "\u604B"
- "\u8863"
- "\u6EB6"
- "\u7DAD"
- "\u514D"
- "\u6392"
- "\u685C"
- "\u7573"
- "\u7B87"
- "\u6398"
- "\u535A"
- "\u6FC3"
- "\u7FCC"
- "\u8056"
- "\u7DB2"
- "\u885B"
- "\u64EC"
- "\u5E8A"
- "\u9178"
- "\u6669"
- "\u4E7E"
- "\u90AA"
- "\u7551"
- "\u6EDE"
- "\u5802"
- "\u7E41"
- "\u4ECF"
- "\u5FB3"
- "\u7DE9"
- "\u6A39"
- "\u6551"
- "\u633F"
- "\u68D2"
- "\u906D"
- "\u676F"
- "\u6065"
- "\u6E56"
- "\u6E09"
- "\u81D3"
- "\u8CB4"
- "\u723A"
- "\u7981"
- "\u4F75"
- "\u5263"
- "\u786C"
- "\u58C7"
- "\u80A9"
- "\u6D78"
- "\u4F0A"
- "\u5B9D"
- "\u6094"
- "\u8E8D"
- "\u6DB2"
- "\u99C6"
- "\u6D25"
- "\u307A"
- "\u6D45"
- "\u8B72"
- "\u5CA9"
- "\u9B45"
- "\u587E"
- "\u03B8"
- "\u6696"
- "\u6CB3"
- "\u8A95"
- "\u7F36"
- "\u5507"
- "\u80A2"
- "\u6328"
- "\u62F6"
- "\u7A0E"
- "\u50AC"
- "\u8A34"
- "\uFF58"
- "\u968A"
- "\u659C"
- "\u770B"
- "\uFF50"
- "\u6D66"
- "\u8352"
- "\uFF41"
- "\u71C3"
- "\u52A3"
- "\u5BA3"
- "\u8FBF"
- "\u790E"
- "\u62FE"
- "\u5C4A"
- "\u6905"
- "\u5EC3"
- "\u6749"
- "\u9AEA"
- "\u77E2"
- "\u67D4"
- "\u55AB"
- "\u73CD"
- "\u57FC"
- "\u88C2"
- "\u63B4"
- "\u59BB"
- "\u8CA7"
- "\u934B"
- "\u59A5"
- "\u59B9"
- "\u5175"
- "\uFF14"
- "\u623F"
- "\u5951"
- "\u65E8"
- "\uFF44"
- "\u0394"
- "\u5DE1"
- "\u8A02"
- "\u5F90"
- "\u8CC0"
- "\u7BED"
- "\u9810"
- "\u84C4"
- "\u8846"
- "\u5DE8"
- "\u5506"
- "\u65E6"
- "\u5531"
- "\u9047"
- "\u6E67"
- "\u8010"
- "\u96C4"
- "\u6D99"
- "\u8CB8"
- "\u822A"
- "\u5104"
- "\u5618"
- "\u6C37"
- "\u78C1"
- "\u679D"
- "\u8CAB"
- "\u61D0"
- "\u52DF"
- "\u8155"
- "\u65E7"
- "\u7AF9"
- "\u99D0"
- "\u8A72"
- "\uFF52"
- "\u5893"
- "\u518A"
- "\u80F8"
- "\u758E"
- "\u773A"
- "\uFF45"
- "\u9855"
- "\u631F"
- "\u55A7"
- "\u520A"
- "\u68C4"
- "\u990C"
- "\u67F1"
- "\u5800"
- "\u8ACB"
- "\u79D8"
- "\u6717"
- "\u96F2"
- "\u8170"
- "\u7A32"
- "\u828B"
- "\u8C9D"
- "\u5C48"
- "\u91CC"
- "\u508D"
- "\u8102"
- "\u6FC1"
- "\u54B2"
- "\u6BD2"
- "\u6EC5"
- "\u5629"
- "\u6442"
- "\u6E7E"
- "\u83CC"
- "\u8150"
- "\u5211"
- "\u5F25"
- "\u5AC1"
- "\u61A7"
- "\u4E18"
- "\u5C90"
- "\u52B1"
- "\u8CA2"
- "\u6C41"
- "\u96C7"
- "\u5076"
- "\u9774"
- "\u72D9"
- "\u719F"
- "\u900F"
- "\uFF59"
- "\u8CFC"
- "\u5319"
- "\uFF46"
- "\uFF15"
- "\u92AD"
- "\u6D12"
- "\u8A17"
- "\u809D"
- "\u963F"
- "\u80C3"
- "\uFF53"
- "\u885D"
- "\u621A"
- "\uFF4D"
- "\u84B8"
- "\u4FF3"
- "\u8972"
- "\u5265"
- "\u5BE9"
- "\u6817"
- "\u8A87"
- "\u5237"
- "\u7CF8"
- "\u90F7"
- "\u5049"
- "\u6C57"
- "\u53CC"
- "\u98FD"
- "\u77DB"
- "\u984E"
- "\u552F"
- "\u6590"
- "\u7DB4"
- "\u5B64"
- "\u90F5"
- "\u76D7"
- "\u9E7F"
- "\u8CC3"
- "\u76FE"
- "\u682A"
- "\u9ED9"
- "\u7C8B"
- "\u63DA"
- "\u9808"
- "\u7092"
- "\u9285"
- "\u5E81"
- "\u9B54"
- "\u75E9"
- "\u9802"
- "\u76BF"
- "\u970A"
- "\u5E55"
- "\u570F"
- "\u574A"
- "\u72C2"
- "\u8912"
- "\u9451"
- "\u50B5"
- "\u77AD"
- "\u565B"
- "\u5E33"
- "\u5782"
- "\u8870"
- "\u4ED9"
- "\u9EA6"
- "\u8CA8"
- "\u7AAA"
- "\u6F6E"
- "\u6FEF"
- "\u5238"
- "\u7D1B"
- "\u7384"
- "\u7C4D"
- "\uFF43"
- "\u74F6"
- "\u5DE3"
- "\u5192"
- "\u6CBC"
- "\u99D2"
- "\u5C3D"
- "\u517C"
- "\u7C97"
- "\u63BB"
- "\u80BA"
- "\u9154"
- "\uFF4C"
- "\u702C"
- "\u505C"
- "\u6F20"
- "\u673A"
- "\u916C"
- "\u4FD7"
- "\u8986"
- "\u5C3B"
- "\u9375"
- "\u5805"
- "\u6F2C"
- "\u2212"
- "\u79C0"
- "\u6885"
- "\u9042"
- "\u57F9"
- "\u871C"
- "\uFF42"
- "\u30FB"
- "\u52C7"
- "\u8ECC"
- "\u7F85"
- "\uFF3A"
- "\u5BB4"
- "\u8C5A"
- "\u7A3C"
- "\u62AB"
- "\u8CAF"
- "\u9EBB"
- "\u6C4E"
- "\u51DD"
- "\u5FE0"
- "\uFF55"
- "\u5F80"
- "\u8AE6"
- "\u8B19"
- "\u6F0F"
- "\u5410"
- "\u3047"
- "\u7652"
- "\u9663"
- "\u6D6A"
- "\u52D8"
- "\u53D9"
- "\u5200"
- "\u67B6"
- "\u57F7"
- "\u5674"
- "\u5197"
- "\u4E4F"
- "\u837B"
- "\u81ED"
- "\u708A"
- "\u598A"
- "\u808C"
- "\u8CDB"
- "\u5C0B"
- "\u9175"
- "\u757F"
- "\u5270"
- "\u706F"
- "\u8C6A"
- "\u9685"
- "\u9905"
- "\u7949"
- "\u80AF"
- "\u62DB"
- "\u7A3D"
- "\u5F6B"
- "\u5F69"
- "\u03B2"
- "\u6B04"
- "\u718A"
- "\u68CB"
- "\u6CB8"
- "\u6C88"
- "\u8339"
- "\u7ABA"
- "\u5B9C"
- "\u8217"
- "\u7CA7"
- "\u683D"
- "\u80AA"
- "\u9665"
- "\u6CE1"
- "\u95D8"
- "\u8F3F"
- "\u5353"
- "\u7070"
- "\u8F9B"
- "\u6F01"
- "\u9F13"
- "\u585E"
- "\u8CD1"
- "\u76C6"
- "\u68FA"
- "\u6311"
- "\u54F2"
- "\u9867"
- "\u8B21"
- "\u8302"
- "\u90A3"
- "\u80DE"
- "\u4F3A"
- "\u5A92"
- "\u708E"
- "\u67D0"
- "\u564C"
- "\u5203"
- "\u6F5F"
- "\u7656"
- "\u4E80"
- "\u63EE"
- "\u511F"
- "\u4E39"
- "\u7DEF"
- "\u9DB4"
- "\u4E4B"
- "\u6BB4"
- "\u4EF0"
- "\u5949"
- "\u7E2B"
- "\u75F4"
- "\u8650"
- "\u61B2"
- "\u71E5"
- "\u6DC0"
- "\uFF57"
- "\u88F8"
- "\u82BD"
- "\u63A7"
- "\u95A3"
- "\u7587"
- "\u925B"
- "\u8178"
- "\u5642"
- "\u935B"
- "\u654F"
- "\u9162"
- "\u938C"
- "\u81E3"
- "\u8E74"
- "\u5A01"
- "\u6D44"
- "\u7965"
- "\u795D"
- "\u86C7"
- "\u811A"
- "\u4F0F"
- "\u6F54"
- "\u5510"
- "\u6955"
- "\u57A3"
- "\u932F"
- "\u514B"
- "\u614C"
- "\u6BBF"
- "\u819C"
- "\u61A9"
- "\u9065"
- "\u82DB"
- "\u9676"
- "\u8997"
- "\u78E8"
- "\u624D"
- "\u5E1D"
- "\u642C"
- "\u722A"
- "\u90CA"
- "\u80A5"
- "\u819D"
- "\u62D2"
- "\u868A"
- "\u5208"
- "\u5132"
- "\uFF48"
- "\u596E"
- "\u7761"
- "\u5BEE"
- "\uFF17"
- "\u4FB5"
- "\u9B31"
- "\u635C"
- "\u6DBC"
- "\u5A20"
- "\u7363"
- "\u7C92"
- "\u963B"
- "\u6CE5"
- "\u7ADC"
- "\u91A4"
- "\u92ED"
- "\u6606"
- "\u9234"
- "\u7DBF"
- "\u830E"
- "\u8107"
- "\u7948"
- "\u8A60"
- "\u6B53"
- "\u7F70"
- "\u68DA"
- "\u83CA"
- "\u6069"
- "\u7267"
- "\u540A"
- "\u8DF3"
- "\u6DE1"
- "\u7F72"
- "\u596A"
- "\u9038"
- "\u6170"
- "\u5EB6"
- "\u9262"
- "\u8B5C"
- "\u5ECA"
- "\u5606"
- "\u62ED"
- "\u8CED"
- "\u99C1"
- "\u7F8A"
- "\u5384"
- "\u7D10"
- "\u9673"
- "\u816B"
- "\u6841"
- "\u9298"
- "\u96CC"
- "\u636E"
- "\u62DD"
- "\u60E8"
- "\u96DB"
- "\u845B"
- "\u7FA8"
- "\u609F"
- "\u76DF"
- "\u7E4A"
- "\u9192"
- "\u65EC"
- "\u6DAF"
- "\u8CC4"
- "\u6E7F"
- "\u6F02"
- "\u7D2B"
- "\u30F4"
- "\u4E9C"
- "\u8AA0"
- "\u5854"
- "\u5E4C"
- "\u80C6"
- "\u64A5"
- "\u865A"
- "\u6F64"
- "\u9699"
- "\u5F84"
- "\u6C72"
- "\u8CE2"
- "\u5BF8"
- "\u8888"
- "\u88DF"
- "\u8266"
- "\uFF19"
- "\u62D8"
- "\uFF47"
- "\u5841"
- "\u5BDB"
- "\u51A0"
- "\u614E"
- "\u971E"
- "\u731B"
- "\u67CF"
- "\u733F"
- "\u9084"
- "\u50E7"
- "\u53EB"
- "\u53F1"
- "\u72E9"
- "\u63C9"
- "\u7D2F"
- "\u5982"
- "\u7897"
- "\u6BBB"
- "\u906E"
- "\u5FCD"
- "\u6EF4"
- "\u6B96"
- "\u8D08"
- "\u74A7"
- "\u6F38"
- "\u6589"
- "\u03BC"
- "\u9686"
- "\u6176"
- "\u72A0"
- "\u7272"
- "\u5146"
- "\u576A"
- "\u6284"
- "\u65D7"
- "\u50DA"
- "\u5C3F"
- "\u51CD"
- "\u902E"
- "\u7B39"
- "\u8F1D"
- "\u5C1A"
- "\u8015"
- "\u51CC"
- "\u632B"
- "\u4F10"
- "\u7BB8"
- "\u4E91"
- "\u5968"
- "\u819A"
- "\u9010"
- "\u03B3"
- "\u5F26"
- "\u9700"
- "\u5C01"
- "\u5E3D"
- "\u6F31"
- "\u9283"
- "\u507D"
- "\u5875"
- "\u7E1B"
- "\u58A8"
- "\u6020"
- "\u96F7"
- "\u5766"
- "\u68A8"
- "\u90ED"
- "\u7A4F"
- "\u67FF"
- "\u7AFF"
- "\u5E61"
- "\u5F81"
- "\u99B3"
- "\u9EBA"
- "\u03C4"
- "\u8154"
- "\u7C98"
- "\u7409"
- "\u731F"
- "\u4EC1"
- "\u8358"
- "\u6492"
- "\u7C3F"
- "\u90E1"
- "\u7B4C"
- "\u5D8B"
- "\u6FE1"
- "\u618E"
- "\u5446"
- "\u6F15"
- "\u5A29"
- "\u68DF"
- "\u6052"
- "\uFF18"
- "\u5553"
- "\u5B5D"
- "\u67F3"
- "\u64A4"
- "\u85CD"
- "\u95C7"
- "\u5B22"
- "\u67F4"
- "\u6734"
- "\u6D1E"
- "\u5CB3"
- "\u9B3C"
- "\u8DE8"
- "\u3049"
- "\u70C8"
- "\u559A"
- "\u6F84"
- "\u6FEB"
- "\u82A6"
- "\u62D3"
- "\u51FD"
- "\u6843"
- "\u76F2"
- "\u6CA1"
- "\u7A6B"
- "\u6212"
- "\u99FF"
- "\u8D05"
- "\u67AF"
- "\u6C70"
- "\u53F6"
- "\u90A6"
- "\u66C7"
- "\u9A30"
- "\u711A"
- "\u51F6"
- "\u5CF0"
- "\u69FD"
- "\u67DA"
- "\u5320"
- "\u9A19"
- "\u502B"
- "\u84EE"
- "\u634C"
- "\u61F2"
- "\u8B0E"
- "\u91B8"
- "\u56DA"
- "\u7344"
- "\u6EDD"
- "\u6795"
- "\u60DC"
- "\u7DB1"
- "\u8B33"
- "\u7089"
- "\u5DFE"
- "\u91DC"
- "\u9BAB"
- "\u6E58"
- "\u92F3"
- "\u5351"
- "\uFF51"
- "\u7DBB"
- "\u5EF7"
- "\u85A6"
- "\u667A"
- "\u6C99"
- "\u8CBF"
- "\u8098"
- "\uFF16"
- "\u5F0A"
- "\u66F0"
- "\u7881"
- "\u9DFA"
- "\u6676"
- "\u8D74"
- "\u8513"
- "\u75D2"
- "\u79E9"
- "\u5DE7"
- "\u9418"
- "\u7B1B"
- "\u638C"
- "\u53EC"
- "\u5347"
- "\u6249"
- "\u5A2F"
- "\u8A1F"
- "\u8247"
- "\u64B2"
- "\uFF56"
- "\u6182"
- "\u90B8"
- "\u5098"
- "\u7CDE"
- "\u03BB"
- "\u5C16"
- "\u723D"
- "\u7832"
- "\u55A9"
- "\u80CE"
- "\u84B2"
- "\u9DF9"
- "\u755C"
- "\u6897"
- "\uFF4F"
- "\u5023"
- "\u6247"
- "\u7DFB"
- "\u6756"
- "\u622F"
- "\u5D50"
- "\u6A3D"
- "\u6F06"
- "\u9CE9"
- "\u039B"
- "\u5FAA"
- "\u8896"
- "\u9784"
- "\u6851"
- "\u5D16"
- "\u59A8"
- "\u66A6"
- "\u59D3"
- "\u7A00"
- "\u3041"
- "\u920D"
- "\u9727"
- "\u9837"
- "\u8105"
- "\u7B20"
- "\u86CD"
- "\u8328"
- "\u69CD"
- "\u3062"
- "\u59EB"
- "\u6ABB"
- "\u8463"
- "\u6C7D"
- "\u541F"
- "\u807E"
- "\u73E0"
- "\u62B9"
- "\u9D28"
- "\u64AB"
- "\u8607"
- "\u7AC3"
- "\u864E"
- "\u78EF"
- "\u77E9"
- "\u7CCA"
- "\u55AA"
- "\u8A6E"
- "\u82D1"
- "\u98F4"
- "\u6089"
- "\u674F"
- "\u9B42"
- "\u914C"
- "\u9BC9"
- "\u8A50"
- "\u03A3"
- "\u7815"
- "\u55DC"
- "\u7FFC"
- "\u4F0E"
- "\u751A"
- "\u5F66"
- "\u961C"
- "\u8706"
- "\u6109"
- "\u80F4"
- "\u8776"
- "\u8B00"
- "\u9271"
- "\u75E2"
- "\u73ED"
- "\u9438"
- "\u92F8"
- "\u62D9"
- "\u6068"
- "\u4EAD"
- "\u4EAB"
- "\u75AB"
- "\u5F13"
- "\u74E6"
- "\u7D46"
- "\u814E"
- "\u62F3"
- "\u9A0E"
- "\u58B3"
- "\u83F1"
- "\u6813"
- "\u5256"
- "\u6D2A"
- "\u5484"
- "\u9591"
- "\u58EE"
- "\u9945"
- "\u65ED"
- "\u8987"
- "\u80A1"
- "\u86D9"
- "\u724C"
- "\u965B"
- "\u714E"
- "\u63AC"
- "\u9AED"
- "\u9019"
- "\u5E7B"
- "\u54B3"
- "\u6E26"
- "\u55C5"
- "\u7A42"
- "\u7434"
- "\u5FCC"
- "\u70CF"
- "\u5448"
- "\u91D8"
- "\u611A"
- "\u6C3E"
- "\u8AFE"
- "\u6E9D"
- "\u7336"
- "\u7AAF"
- "\u8ACF"
- "\u8CC2"
- "\u57C3"
- "\u51F8"
- "\u7D0B"
- "\u6ADB"
- "\u525B"
- "\u98E2"
- "\u4FCA"
- "\u54C0"
- "\u5BB0"
- "\u93AE"
- "\u7435"
- "\u7436"
- "\u96C5"
- "\u8494"
- "\u85AA"
- "\u8A93"
- "\u59EA"
- "\u62D7"
- "\u8778"
- "\u7169"
- "\u7B51"
- "\u690E"
- "\u4FB6"
- "\u553E"
- "\u7BAA"
- "\u5075"
- "\u8861"
- "\u03C3"
- "\u88FE"
- "\u95B2"
- "\u805A"
- "\u4E3C"
- "\u633D"
- "\u7E4D"
- "\u82D7"
- "\u9E93"
- "\u03C6"
- "\u03B4"
- "\u4E32"
- "\u51E1"
- "\u5F18"
- "\u85FB"
- "\u61C7"
- "\u817F"
- "\u7A9F"
- "\u6803"
- "\u6652"
- "\u5E84"
- "\u7891"
- "\u7B4F"
- "\u7B25"
- "\u5E06"
- "\u96B7"
- "\u8FB0"
- "\u75BE"
- "\u8FE6"
- "\u8A6B"
- "\u5617"
- "\u582A"
- "\u6842"
- "\u5B9B"
- "\u58F7"
- "\u8AED"
- "\u97AD"
- "\u9310"
- "\u6DF5"
- "\u79E4"
- "\u7525"
- "\u4F8D"
- "\u66FD"
- "\u6572"
- "\u63AA"
- "\u6168"
- "\u83E9"
- "\u5CE0"
- "\u901D"
- "\u5F70"
- "\u67F5"
- "\u82AF"
- "\u7C50"
- "\u57A2"
- "\u03BE"
- "\u77EF"
- "\u8C8C"
- "\u8F44"
- "\u8A89"
- "\u9813"
- "\u7D79"
- "\u9E78"
- "\u5E7D"
- "\u6881"
- "\u642D"
- "\u54BD"
- "\u82B3"
- "\u7729"
- "\u0393"
- "\u61A4"
- "\u7985"
- "\u6063"
- "\u5840"
- "\u7149"
- "\u75FA"
- "\uFF06"
- "\u7A40"
- "\u545F"
- "\u918D"
- "\u9190"
- "\u7901"
- "\u51F9"
- "\u86EE"
- "\u5974"
- "\u64AD"
- "\u7E79"
- "\u8499"
- "\u8A63"
- "\u4E5F"
- "\u5420"
- "\u4E59"
- "\u8E8A"
- "\u8E87"
- "\u9D2C"
- "\u7A92"
- "\u59E5"
- "\u9326"
- "\u694A"
- "\u8017"
- "\u6F09"
- "\u60E7"
- "\u4FE3"
- "\u6876"
- "\u5CFB"
- "\u905C"
- "\u65FA"
- "\u75D5"
- "\u03A6"
- "\u6234"
- "\u658E"
- "\u8CD3"
- "\u7BC7"
- "\u8429"
- "\u85E9"
- "\u7950"
- "\u8B83"
- "\u83AB"
- "\u9C39"
- "\u85A9"
- "\u5378"
- "\u4E9B"
- "\u75B9"
- "\u8E44"
- "\u4E56"
- "\uFF5A"
- "\u92FC"
- "\u6A3A"
- "\u5B8F"
- "\u7BE4"
- "\u8258"
- "\u81B3"
- "\u7A83"
- "\u7E82"
- "\u5598"
- "\u786B"
- "\u99D5"
- "\u7261"
- "\u732A"
- "\u62D0"
- "\u60DA"
- "\u60A0"
- "\u7CE7"
- "\u95A5"
- "\u03C0"
- "\u853D"
- "\u6850"
- "\u981A"
- "\u9214"
- "\u697C"
- "\u8C9E"
- "\u602F"
- "\u817A"
- "\u8305"
- "\u6CF0"
- "\u9913"
- "\u5C51"
- "\u9BDB"
- "\u929B"
- "\u9AB8"
- "\u9C57"
- "\u5824"
- "\u9675"
- "\u6DD8"
- "\u64C1"
- "\u81FC"
- "\u6D32"
- "\u8FBB"
- "\u8A23"
- "\u5C4F"
- "\u9BE8"
- "\u895F"
- "\u5CE1"
- "\u660C"
- "\u982C"
- "\u5806"
- "\u865C"
- "\u840E"
- "\u9EB9"
- "\u7CE0"
- "\u68B1"
- "\u8AFA"
- "\u5403"
- "\u66A2"
- "\u5B54"
- "\u5EB8"
- "\u5DF3"
- "\u589C"
- "\u85AE"
- "\u6101"
- "\u664B"
- "\u8236"
- "\u8FC5"
- "\u6B3A"
- "\u9640"
- "\u7709"
- "\u6CC4"
- "\u59FB"
- "\u9688"
- "\u58CC"
- "\u69D9"
- "\u5E87"
- "\u52D2"
- "\u6E07"
- "\u91E7"
- "\u4E43"
- "\u82D4"
- "\u9306"
- "\u58D5"
- "\u78D0"
- "\u6962"
- "\u65A7"
- "\u5E63"
- "\u03B7"
- "\u7E55"
- "\u83C5"
- "\u7109"
- "\u5112"
- "\u5D07"
- "\u8276"
- "\u5449"
- "\u7984"
- "\u54C9"
- "\u68AF"
- "\u5937"
- "\u546A"
- "\u56C3"
- "\u84BC"
- "\u9A28"
- "\u9D3B"
- "\u862D"
- "\u7CA5"
- "\u7D3A"
- "\u7D17"
- "\u7164"
- "\u03C9"
- "\u52FE"
- "\u97A0"
- "\u4F3D"
- "\u7AAE"
- "\u6E15"
- "\u0392"
- "\u8D66"
- "\u6597"
- "\u66F9"
- "\u8CE0"
- "\u5CAC"
- "\u847A"
- "\u7D33"
- "\u5B8D"
- "\u6191"
- "\u6357"
- "\u7C9B"
- "\u8CCA"
- "\u9F8D"
- "\u81C6"
- "\u6C8C"
- "\u52C5"
- "\u8096"
- "\u559D"
- "\u8CAA"
- "\u82AD"
- "\u8549"
- "\u919C"
- "\u64B9"
- "\u5740"
- "\u7BE0"
- "\u7D2C"
- "\u75B1"
- "\u52F2"
- "\u86FE"
- "\u88B4"
- "\u8749"
- "\u685F"
- "\u4FF5"
- "\u818F"
- "\u5DF7"
- "\u5072"
- "\u6148"
- "\u754F"
- "\u96BB"
- "\u606D"
- "\u64B0"
- "\u9D0E"
- "\u52AB"
- "\u63C6"
- "\u914E"
- "\u8106"
- "\u6241"
- "\u9761"
- "\u8511"
- "\u95CA"
- "\u96BC"
- "\u6CCC"
- "\u5996"
- "\u65A1"
- "\u52C3"
- "\u637B"
- "\u6E13"
- "\u937E"
- "\u5954"
- "\u6155"
- "\u5984"
- "\u6A0B"
- "\u936C"
- "\u502D"
- "\u8679"
- "\u03BD"
- "\u60A6"
- "\u8151"
- "\u62EE"
- "\u51E0"
- "\u80E1"
- "\u8FC2"
- "\u8EAF"
- "\u50ED"
- "\u6ECB"
- "\u7B8B"
- "\u75F0"
- "\u65AC"
- "\u85AB"
- "\u673D"
- "\u82A5"
- "\u9756"
- "\u907C"
- "\u6591"
- "\u7953"
- "\u5B95"
- "\u976D"
- "\u72D7"
- "\u81BF"
- "\u59AC"
- "\u5A7F"
- "\u7554"
- "\u7AEA"
- "\u9D5C"
- "\u8CE6"
- "\u7E1E"
- "\u6731"
- "\u7C95"
- "\u69FB"
- "\u6D69"
- "\u511A"
- "\u8CDC"
- "\u8B39"
- "\u68B5"
- "\u5A9B"
- "\u7947"
- "\u5516"
- "\u03C8"
- "\u03C1"
- "\u5A9A"
- "\u540E"
- "\u6FB1"
- "\u7DBE"
- "\u6372"
- "\u67E9"
- "\u6DF3"
- "\u74DC"
- "\u5631"
- "\u51B4"
- "\u6115"
- "\u9211"
- "\u51B6"
- "\u67A2"
- "\u03A9"
- "\u77B0"
- "\u6775"
- "\u5EB5"
- "\u4F2F"
- "\u840C"
- "\u5609"
- "\u4FC4"
- "\u7D06"
- "\u81A0"
- "\u7252"
- "\u8EB0"
- "\u543E"
- "\u50FB"
- "\u704C"
- "\u646F"
- "\u5091"
- "\u929A"
- "\u8B90"
- "\u8910"
- "\u8FB1"
- "\u7345"
- "\u7B94"
- "\u73A9"
- "\u4F43"
- "\u583A"
- "\u5504"
- "\u515C"
- "\u62CC"
- "\u5751"
- "\u75D8"
- "\u69CC"
- "\u77B3"
- "\u79BF"
- "\u66D9"
- "\u5DF2"
- "\u7FC1"
- "\u5C3C"
- "\u60BC"
- "\u7F77"
- "\u699C"
- "\u5451"
- "\u79E6"
- "\u533F"
- "\u03BA"
- "\u7259"
- "\u4F46"
- "\u572D"
- "\u548E"
- "\u745E"
- "\u7A1C"
- "\u785D"
- "\u6BC5"
- "\u7015"
- "\u8702"
- "\u978D"
- "\u6A2B"
- "\u7566"
- "\u660F"
- "\u755D"
- "\u4FAE"
- "\u548B"
- "\u6367"
- "\u7F9E"
- "\u803D"
- "\u60B8"
- "\u51E7"
- "\u4EAE"
- "\u9AC4"
- "\u54FA"
- "\u4FEF"
- "\u567A"
- "\u8058"
- "\u8654"
- "\u5B8B"
- "\u93A7"
- "\u968B"
- "\u51B3"
- "\u59D1"
- "\u7078"
- "\u927E"
- "\u8F5F"
- "\u60F0"
- "\u03C7"
- "\u643E"
- "\u6854"
- "\u7F6B"
- "\u8E4A"
- "\u68B6"
- "\u6893"
- "\u7F75"
- "\u65A5"
- "\u6276"
- "\u6147"
- "\u61C3"
- "\u9949"
- "\u6E25"
- "\u6AD3"
- "\u80E4"
- "\u56A2"
- "\u9CF3"
- "\u6A84"
- "\u8C79"
- "\u50B2"
- "\u50D1"
- "\u7586"
- "\u6134"
- "\u53A8"
- "\u6FB9"
- "\u9320"
- "\u64E2"
- "\u6EBA"
- "\u7624"
- "\u73CA"
- "\u5BC5"
- "\u6977"
- "\u9583"
- "\u9CF6"
- "\u7119"
- "\u6912"
- "\u9B4F"
- "\u9798"
- "\u68A2"
- "\u6900"
- "\u8ACC"
- "\u696B"
- "\u5F14"
- "\u65D2"
- "\u5957"
- "\u9F5F"
- "\u9F6C"
- "\u7D18"
- "\u810A"
- "\u536F"
- "\u727D"
- "\u6BD8"
- "\u6714"
- "\u514E"
- "\u721B"
- "\u6D9C"
- "\u5851"
- "\u5F04"
- "\u676D"
- "\u63A0"
- "\u80B4"
- "\u626E"
- "\u51F1"
- "\u798D"
- "\u8036"
- "\u808B"
- "\u7235"
- "\u61AB"
- "\u57D3"
- "\u5983"
- "\u9910"
- "\u7C7E"
- "\u7262"
- "\u6816"
- "\u9017"
- "\u7058"
- "\u5E5F"
- "\u68F2"
- "\u5687"
- "\u7827"
- "\u6E1A"
- "\u7C9F"
- "\u7A7F"
- "\u7F60"
- "\u68F9"
- "\u8594"
- "\u8587"
- "\u526A"
- "\u7B48"
- "\u936E"
- "\u892A"
- "\u7AA9"
- "\u58F1"
- "\u30F2"
- "\u7460"
- "\u7483"
- "\u61BE"
- "\u5E16"
- "\u6960"
- "\u03B5"
- "\u5480"
- "\u56BC"
- "\u56A5"
- "\u6D29"
- "\u6A58"
- "\u6867"
- "\u6A9C"
- "\u63F6"
- "\u63C4"
- "\u88E1"
- "\u6A80"
- "\u900D"
- "\u9081"
- "\u6028"
- "\u73B2"
- "\u90C1"
- "\u5815"
- "\u8AB9"
- "\u8B17"
- "\u8956"
- "\u51F0"
- "\u9B41"
- "\u5B75"
- "\u7766"
- "\u71FB"
- "\u5243"
- "\u53A9"
- "\u71D7"
- "\u84D1"
- "\u5EFB"
- "\u75D4"
- "\u837C"
- "\u6190"
- "\u6070"
- "\u8F9F"
- "\u5F98"
- "\u5F8A"
- "\u4FA0"
- "\u5830"
- "\u971C"
- "\u809B"
- "\u76E7"
- "\u5835"
- "\u72DB"
- "\u9D8F"
- "\u9119"
- "\u4F73"
- "\u916A"
- "\u8AE7"
- "\u6973"
- "\u7826"
- "\u5AC9"
- "\u5DEB"
- "\u53E1"
- "\u9716"
- "\u6E23"
- "\u5544"
- "\u798E"
- "\u6CAB"
- "\u821F"
- "\u6C5D"
- "\u5302"
- "\u99F1"
- "\u6C08"
- "\u308E"
- "\u714C"
- "\u7DAC"
- "\u5F1B"
- "\u586B"
- "\u84C1"
- "\u5039"
- "\u7CFE"
- "\u51A5"
- "\u674E"
- "\u966A"
- "\u8877"
- "\u59E6"
- "\u5962"
- "\u75BC"
- "\u8A54"
- "\u8599"
- "\u8B5A"
- "\u5CEF"
- "\u684E"
- "\u688F"
- "\u9B92"
- "\u8A1B"
- "\u55B0"
- "\u7960"
- "\u67A1"
- "\u6681"
- "\u4E5E"
- "\u91C7"
- "\u9739"
- "\u9742"
- "\u687F"
- "\u929C"
- "\u4F51"
- "\u79BE"
- "\u5944"
- "\u6930"
- "\u87F9"
- "\u8061"
- "\u98AF"
- "\u30C2"
- "\u8E81"
- "\u8E42"
- "\u8E99"
- "\u8695"
- "\u693F"
- "\u62F7"
- "\u9257"
- "\u8882"
- "\u78CB"
- "\u7422"
- "\u6B3D"
- "\u60B6"
- "\u53C9"
- "\u7E37"
- "\u8A36"
- "\u50C5"
- "\u5C6F"
- "\u5EEC"
- "\u5C41"
- "\u99A8"
- "\u6E20"
- "\u8568"
- "\u699B"
- "\u675C"
- "\u7791"
- "\u6A8E"
- "\u8ECB"
- "\u8F62"
- "\u8700"
- "\u8235"
- "\u82B9"
- "\u6B3E"
- "\u639F"
- "\u8E2A"
- "\u745A"
- "\u71E6"
- "\u7D21"
- "\u584A"
- "\u8171"
- "\u6753"
- "\u65A4"
- "\u786F"
- "\u55AC"
- "\u8B04"
- "\u79DF"
- "\u8180"
- "\u80F1"
- "\u6EC4"
- "\u9C10"
- "\u8475"
- "\u8471"
- "\u8461"
- "\u5A49"
- "\u88D4"
- "\u9F0E"
- "\u9187"
- "\u67EF"
- "\u991E"
- "\u96C1"
- "\u8AA6"
- "\u8A62"
- "\u633A"
- "\u7AFA"
- "\u8A82"
- "\u5191"
- "\u8718"
- "\u86DB"
- "\u70B8"
- "\u932B"
- "\u58C5"
- "\u8087"
- "\u54AC"
- "\u9B8E"
- "\u67D1"
- "\u7D9C"
- "\u5BE1"
- "\u7977"
- "\u522E"
- "\u8CCE"
- "\u9B18"
- "\u884D"
- "\u5FD6"
- "\u685D"
- "\u0398"
- "\u039A"
- "\u03A8"
- "\u53E2"
- "\u4FCE"
- "\u7396"
- "\u78A7"
- "\u8766"
- "\u8521"
- "\u649A"
- "\u7A14"
- "\u752B"
- "\u6D35"
- "\u7893"
- "\u9ECE"
- "\u5AE1"
- "\u8755"
- "\u725F"
- "\u6B89"
- "\u6C83"
- "\u7B50"
- "\u619A"
- "\u6E24"
- "\u9B4D"
- "\u9B4E"
- "\u71ED"
- "\u7940"
- "\u6D1B"
- "\u88F3"
- "\u4E11"
- "\u9846"
- "\u9952"
- "\u5EC9"
- "\u689F"
- "\u848B"
- "\u6DD1"
- "\u8737"
- "\u9644"
- "\u695A"
- "\u9F20"
- "\u5154"
- "\u61AC"
- "\u5F57"
- "\u66FC"
- "\u5D11"
- "\u57DC"
- "\u5F77"
- "\u5F7F"
- "\u5DF4"
- "\u831C"
- "\u6D9B"
- "\u57E0"
- "\u945A"
- "\u92D2"
- "\u5C09"
- "\u53AD"
- "\u7B75"
- "\u7AE3"
- "\u7E8F"
- "\u6194"
- "\u60B4"
- "\u8E5F"
- "\u675E"
- "\u7825"
- "\u8F14"
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- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_sp/train/feats_stats.npz
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d6
normalize_before: true
macaron_style: false
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "jp", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["csj"]}
|
automatic-speech-recognition
|
espnet/kan-bayashi_csj_asr_train_asr_conformer
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"jp",
"dataset:csj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"jp"
] |
TAGS
#espnet #audio #automatic-speech-recognition #jp #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR model
### 'espnet/kan-bayashi_csj_asr_train_asr_conformer'
This model was trained by Nelson Yalta using csj recipe in espnet.
### Demo: How to use in ESPnet2
## ASR config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 ASR model",
"### 'espnet/kan-bayashi_csj_asr_train_asr_conformer'\n\nThis model was trained by Nelson Yalta using csj recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #jp #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR model",
"### 'espnet/kan-bayashi_csj_asr_train_asr_conformer'\n\nThis model was trained by Nelson Yalta using csj recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
48,
7,
44,
11,
23,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #jp #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 ASR model### 'espnet/kan-bayashi_csj_asr_train_asr_conformer'\n\nThis model was trained by Nelson Yalta using csj recipe in espnet.### Demo: How to use in ESPnet2## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>### Citing ESPnet\n\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4037458/
This model was trained by kan-bayashi using csj/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["csj"]}
|
automatic-speech-recognition
|
espnet/kan-bayashi_csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"ja",
"dataset:csj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #automatic-speech-recognition #ja #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.URL'
️ Imported from URL
This model was trained by kan-bayashi using csj/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #ja #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csj/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
48,
9,
62,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #ja #dataset-csj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csj/asr1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_conformer_fastspeech2`
♻️ 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
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_conformer_fastspeech2
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/csmsc_conformer_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
10,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/csmsc_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_fastspeech`
♻️ Imported from https://zenodo.org/record/3986227/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_fastspeech
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_fastspeech'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
45,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_fastspeech2`
♻️ Imported from https://zenodo.org/record/4031953/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_fastspeech2
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
46,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_full_band_vits
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/csmsc_full_band_vits'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_full_band_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_full_band_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
10,
48,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/csmsc_full_band_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_tacotron2`
♻️ Imported from https://zenodo.org/record/3969118/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tacotron2
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
46,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_transformer`
♻️ 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
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_transformer
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_transformer'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
44,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
75,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4031953/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
72,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `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/).
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
71,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
10,
79,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best`
♻️ Imported from https://zenodo.org/record/3969118/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
You first need to import the following packages
```bash
pip install torch
pip install espnet_model_zoo
```
Then start using it!
```python
import soundfile
from espnet2.bin.tts_inference import Text2Speech
text2speech = Text2Speech.from_pretrained("espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best")
text = "春江潮水连海平，海上明月共潮生"
speech = text2speech(text)["wav"]
soundfile.write("out.wav", speech.numpy(), text2speech.fs, "PCM_16")
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
You first need to import the following packages
Then start using it!
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2\nYou first need to import the following packages\n\nThen start using it!",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2\nYou first need to import the following packages\n\nThen start using it!",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
72,
25,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2\nYou first need to import the following packages\n\nThen start using it!### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `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
```
### 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}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
9,
70,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5499120/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
10,
75,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_vits`
♻️ Imported from https://zenodo.org/record/5499120/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["csmsc"]}
|
text-to-speech
|
espnet/kan-bayashi_csmsc_vits
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"zh"
] |
TAGS
#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/csmsc_vits'
️ Imported from URL
This model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/csmsc_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
46,
10,
44,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #zh #dataset-csmsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/csmsc_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using csmsc/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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
```
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_conformer_fastspeech2
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_conformer_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
47,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_conformer_fastspeech2_accent`
♻️ Imported from https://zenodo.org/record/4381102/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_accent
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_conformer_fastspeech2_accent'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
50,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_conformer_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause`
♻️ Imported from https://zenodo.org/record/4436448/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
54,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_tacotron2_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
55,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody`
♻️ Imported from https://zenodo.org/record/5499066/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_conformer_fastspeech2_transformer_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
53,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_fastspeech`
♻️ Imported from https://zenodo.org/record/3986225/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_fastspeech
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_fastspeech'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
43,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_fastspeech2`
♻️ Imported from https://zenodo.org/record/4032224/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_fastspeech2
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
44,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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
```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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_fastspeech2_accent
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_fastspeech2_accent'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
47,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_fastspeech2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_fastspeech2_accent_with_pause`
♻️ Imported from https://zenodo.org/record/4436450/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_fastspeech2_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_fastspeech2_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
51,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_fastspeech2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_full_band_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5431984/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_full_band_vits_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_full_band_vits_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_full_band_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_full_band_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
53,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_full_band_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_full_band_vits_prosody`
♻️ Imported from https://zenodo.org/record/5521340/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_full_band_vits_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_full_band_vits_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_full_band_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_full_band_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_full_band_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tacotron2`
♻️ Imported from https://zenodo.org/record/3963886/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tacotron2
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
44,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tacotron2_accent`
♻️ Imported from https://zenodo.org/record/4381098/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tacotron2_accent
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tacotron2_accent'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
47,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tacotron2_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tacotron2_accent_with_pause`
♻️ Imported from https://zenodo.org/record/4433194/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tacotron2_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tacotron2_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tacotron2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
51,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tacotron2_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tacotron2_prosody`
♻️ Imported from https://zenodo.org/record/5499026/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tacotron2_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tacotron2_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
47,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tacotron2_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_transformer`
♻️ Imported from https://zenodo.org/record/4034121/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_transformer
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_transformer'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
42,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_transformer_accent`
♻️ Imported from https://zenodo.org/record/4381096/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_transformer_accent
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_transformer_accent'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
45,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_transformer_accent'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_transformer_accent_with_pause`
♻️ Imported from https://zenodo.org/record/4433196/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_transformer_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_transformer_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_transformer_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_transformer_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_transformer_prosody`
♻️ Imported from https://zenodo.org/record/5499040/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_transformer_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_transformer_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
45,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_transformer_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
72,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381102/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-15ef5f
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
83,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4436448/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-a7f080
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
87,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`
♻️ 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/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-569e81
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
83,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4391409/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-35ef5a
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
81,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4433198/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-74c1b4
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
85,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`
♻️ Imported from https://zenodo.org/record/5499066/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-f43d8f
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
81,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
69,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381100/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jacon-truncated-f45dcb
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
80,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4436450/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jacon-truncated-e5d906
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
84,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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
```
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jac-truncated-6f4cf5
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
78,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4433200/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jac-truncated-60fc24
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
82,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best`
♻️ Imported from https://zenodo.org/record/3986225/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
68,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5431984/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_a-truncated-d7d5d0
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
83,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5521340/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_p-truncated-66d5fc
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
79,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381098/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
72,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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-0.04192105680704117,
-0.1007988452911377
] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4433194/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
76,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`
♻️ Imported from https://zenodo.org/record/5499026/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
72,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best`
♻️ Imported from https://zenodo.org/record/3963886/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
69,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
[
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381096/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
70,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4433196/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_acce-truncated-be0f66
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
74,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## 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/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
70,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4034121/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 TTS model
### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
9,
67,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 TTS model### 'kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5414980/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with-truncated-ba3566
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
79,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5521354/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
75,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5414980/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_vits_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_vits_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jsut_vits_prosody`
♻️ Imported from https://zenodo.org/record/5521354/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jsut"]}
|
text-to-speech
|
espnet/kan-bayashi_jsut_vits_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jsut_vits_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jsut_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
45,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jsut_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_jvs001_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5432540/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jvs"]}
|
text-to-speech
|
espnet/kan-bayashi_jvs_jvs001_vits_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jvs_jvs001_vits_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jvs/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs001_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs001_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
53,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jvs_jvs001_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_jvs010_vits_accent_with_pause`
♻️ Imported from https://zenodo.org/record/5432566/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jvs"]}
|
text-to-speech
|
espnet/kan-bayashi_jvs_jvs010_vits_accent_with_pause
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jvs_jvs010_vits_accent_with_pause'
️ Imported from URL
This model was trained by kan-bayashi using jvs/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs010_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs010_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
53,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jvs_jvs010_vits_accent_with_pause'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `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/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jvs"]}
|
text-to-speech
|
espnet/kan-bayashi_jvs_jvs010_vits_prosody
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jvs_jvs010_vits_prosody'
️ Imported from URL
This model was trained by kan-bayashi using jvs/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs010_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_jvs010_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
49,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jvs_jvs010_vits_prosody'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest`
♻️ Imported from https://zenodo.org/record/5432540/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jvs"]}
|
text-to-speech
|
espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'
️ Imported from URL
This model was trained by kan-bayashi using jvs/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
79,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## ESPnet2 TTS pretrained model
### `kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest`
♻️ Imported from https://zenodo.org/record/5432566/
This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### 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}
}
```
|
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["jvs"]}
|
text-to-speech
|
espnet/kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-d57a28
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jvs",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"ja"
] |
TAGS
#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS pretrained model
### 'kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'
️ Imported from URL
This model was trained by kan-bayashi using jvs/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
[
45,
10,
79,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #text-to-speech #ja #dataset-jvs #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## ESPnet2 TTS pretrained model### 'kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using jvs/tts1 recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\nor arXiv:"
] |
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