
## ESPnet2 EnhS2T model 

### `espnet/simpleoier_chime4_enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char`

This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

```bash
cd espnet
git checkout 44971ff962aae30c962226f1ba3d87de057ac00e
pip install -e .
cd egs2/chime4/enh_asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_chime4_enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char
```

<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Apr 28 00:09:17 EDT 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16)  [GCC 7.5.0]`
- espnet version: `espnet 202204`
- pytorch version: `pytorch 1.8.1`
- Git hash: ``
  - Commit date: ``

## enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|27119|93.0|5.2|1.8|0.6|7.7|53.3|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|93.9|4.5|1.6|0.5|6.7|49.9|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|27119|91.8|6.0|2.2|0.8|9.0|57.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|27120|92.2|6.0|1.9|0.7|8.6|55.5|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|93.6|4.9|1.5|0.6|7.1|51.6|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|27120|89.9|7.6|2.4|1.0|11.1|59.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|21409|86.7|9.7|3.5|1.3|14.5|64.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|89.2|7.9|2.9|1.0|11.8|61.2|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|21409|84.6|11.4|4.0|1.5|17.0|69.4|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|21416|86.0|10.5|3.5|1.5|15.5|67.5|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|88.1|8.9|3.1|1.2|13.1|64.8|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|21416|82.8|13.1|4.1|1.9|19.1|69.4|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|160390|96.6|1.4|2.0|0.6|4.0|53.3|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|97.1|1.1|1.8|0.5|3.4|49.9|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|160390|95.9|1.7|2.3|0.8|4.8|57.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|160400|95.9|1.7|2.3|0.7|4.8|55.5|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|96.8|1.4|1.9|0.6|3.8|51.6|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|160400|94.7|2.5|2.9|1.0|6.3|59.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|126796|92.8|3.2|4.0|1.2|8.4|64.7|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|94.3|2.4|3.3|1.0|6.6|61.2|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|126796|91.5|3.8|4.6|1.6|10.0|69.4|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|126812|92.2|3.5|4.2|1.7|9.5|67.5|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|93.7|2.7|3.5|1.4|7.7|64.8|
|decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|126812|90.3|4.8|4.9|2.2|11.9|69.4|

### TER

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

## EnhS2T config

<details><summary>expand</summary>

```
config: conf/train_enh_asr_convtasnet_fbank_transformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 0
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: 50
patience: 5
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
-   - train
    - loss
    - min
keep_nbest_models: 10
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/enh_asr_stats_raw_en_char/train/speech_shape
- exp/enh_asr_stats_raw_en_char/train/speech_ref1_shape
- exp/enh_asr_stats_raw_en_char/train/text_shape.char
valid_shape_file:
- exp/enh_asr_stats_raw_en_char/valid/speech_shape
- exp/enh_asr_stats_raw_en_char/valid/speech_ref1_shape
- exp/enh_asr_stats_raw_en_char/valid/text_shape.char
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 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/tr05_multi_noisy_si284/wav.scp
    - speech
    - sound
-   - dump/raw/tr05_multi_noisy_si284/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/tr05_multi_noisy_si284/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dt05_multi_isolated_1ch_track/wav.scp
    - speech
    - sound
-   - dump/raw/dt05_multi_isolated_1ch_track/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/dt05_multi_isolated_1ch_track/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
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 20000
token_list: data/en_token_list/char/tokens.txt
src_token_list: null
init: xavier_uniform
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
enh_criterions:
-   name: si_snr
    conf:
        eps: 1e-7
    wrapper: fixed_order
    wrapper_conf:
        weight: 1.0
enh_model_conf:
    stft_consistency: false
    loss_type: mask_mse
    mask_type: null
asr_model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
    extract_feats_in_collect_stats: false
st_model_conf:
    stft_consistency: false
    loss_type: mask_mse
    mask_type: null
subtask_series:
- enh
- asr
model_conf:
    bypass_enh_prob: 0.0
use_preprocessor: true
token_type: char
bpemodel: null
src_token_type: bpe
src_bpemodel: null
non_linguistic_symbols: data/nlsyms.txt
cleaner: null
g2p: null
enh_encoder: conv
enh_encoder_conf:
    channel: 256
    kernel_size: 40
    stride: 20
enh_separator: tcn
enh_separator_conf:
    num_spk: 1
    layer: 4
    stack: 2
    bottleneck_dim: 256
    hidden_dim: 512
    kernel: 3
    causal: false
    norm_type: gLN
    nonlinear: relu
enh_decoder: conv
enh_decoder_conf:
    channel: 256
    kernel_size: 40
    stride: 20
frontend: default
frontend_conf:
    fs: 16k
    n_fft: 512
    win_length: 400
    hop_length: 160
    frontend_conf: null
    apply_stft: true
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: {}
asr_preencoder: null
asr_preencoder_conf: {}
asr_encoder: transformer
asr_encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d
    normalize_before: true
asr_postencoder: null
asr_postencoder_conf: {}
asr_decoder: transformer
asr_decoder_conf:
    input_layer: embed
    attention_heads: 4
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.0
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0
st_preencoder: null
st_preencoder_conf: {}
st_encoder: rnn
st_encoder_conf: {}
st_postencoder: null
st_postencoder_conf: {}
st_decoder: rnn
st_decoder_conf: {}
st_extra_asr_decoder: rnn
st_extra_asr_decoder_conf: {}
st_extra_mt_decoder: rnn
st_extra_mt_decoder_conf: {}
required:
- output_dir
- token_list
version: '202204'
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", "speech-enhancement-recognition"], "datasets": ["chime4"]}
espnet audio speech-enhancement-recognition en dataset:chime4 arxiv:1804.00015 license:cc-by-4.0 region:us
TAGS
#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us 

ESPnet2 EnhS2T model
--------------------


### 'espnet/simpleoier\_chime4\_enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char'


This model was trained by simpleoier using chime4 recipe in espnet.


### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Thu Apr 28 00:09:17 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 202204'
* pytorch version: 'pytorch 1.8.1'
* Git hash: ''
	+ Commit date: ''


enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char
------------------------------------------------------------------------


### WER



### CER



### TER



EnhS2T config
-------------


expand

### Citing ESPnet


or arXiv:



### 'espnet/simpleoier\_chime4\_enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char'


This model was trained by simpleoier using chime4 recipe in espnet. ### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Thu Apr 28 00:09:17 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 202204'
* pytorch version: 'pytorch 1.8.1'
* Git hash: ''
	+ Commit date: ''


enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char
------------------------------------------------------------------------ ### WER ### CER ### TER



EnhS2T config
-------------


expand ### Citing ESPnet


or arXiv:
TAGS
#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us 
 ### 'espnet/simpleoier\_chime4\_enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char'


This model was trained by simpleoier using chime4 recipe in espnet. ### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Thu Apr 28 00:09:17 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 202204'
* pytorch version: 'pytorch 1.8.1'
* Git hash: ''
	+ Commit date: ''


enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char
------------------------------------------------------------------------ ### WER ### CER ### TER



EnhS2T config
-------------


expand ### Citing ESPnet


or arXiv:
passage: TAGS
#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us 
### 'espnet/simpleoier\_chime4\_enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char'


This model was trained by simpleoier using chime4 recipe in espnet.### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Thu Apr 28 00:09:17 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 202204'
* pytorch version: 'pytorch 1.8.1'
* Git hash: ''
	+ Commit date: ''


enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char
------------------------------------------------------------------------### WER### CER### TER



EnhS2T config
-------------


expand### Citing ESPnet


or arXiv: