espnet audio automatic-speech-recognition en dataset:chime4 arxiv:1804.00015 license:cc-by-4.0 region:us
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- chime4
license: cc-by-4.0
---

## ESPnet2 ASR model

### `espnet/eason_chime4_asr2_e_branchformer12_conv1d1_raw_wavlm_large_21_km1k_bpe_rm2k_char_ts_sp`

This model was trained by yichenl5 using chime4 recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.

```bash
cd espnet
git checkout 83e687f3b41310a000f4a5b65857734709752bf6
pip install -e .
cd egs2/chime4/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/eason_chime4_asr2_e_branchformer12_conv1d1_raw_wavlm_large_21_km1k_bpe_rm2k_char_ts_sp
```

<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Sep 11 04:53:07 EDT 2023`
- python version: `3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0]`
- espnet version: `espnet 202308`
- pytorch version: `pytorch 1.13.1+cu117`
- Git hash: `83e687f3b41310a000f4a5b65857734709752bf6`
  - Commit date: `Tue Aug 15 18:31:02 2023 -0400`

## exp/asr_train_discrete_asr_e_branchformer_e12_mlp1024_linear1024_macaron_lr1e-4_warmup25k_conv1d1_raw_wavlm_large_21_km1000_bpe_rm2000_char_ts_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|27119|89.9|8.6|1.5|0.8|10.9|58.5|
|decode_asr_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|92.2|6.6|1.2|0.6|8.4|54.3|
|decode_asr_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|27119|89.0|9.6|1.4|0.9|11.9|63.5|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|27120|89.8|8.4|1.7|0.7|10.8|61.8|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|93.3|5.7|1.0|0.4|7.1|54.9|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|27120|84.4|12.8|2.8|0.8|16.5|67.1|
|decode_asr_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|21409|90.5|8.2|1.3|0.6|10.1|66.7|
|decode_asr_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|92.8|6.3|0.9|0.5|7.7|58.0|
|decode_asr_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|21409|88.3|10.0|1.7|0.8|12.5|71.5|
|decode_asr_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|21416|89.0|9.2|1.8|0.9|11.8|66.4|
|decode_asr_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|92.7|6.4|0.9|0.7|8.0|61.0|
|decode_asr_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|21416|83.3|13.2|3.5|1.1|17.8|69.4|
|decode_asr_asr_model_valid.acc.best/dt05_real_beamformit_2mics|1640|27119|89.5|9.2|1.3|0.9|11.4|60.2|
|decode_asr_asr_model_valid.acc.best/dt05_real_beamformit_5mics|1640|27119|91.7|7.1|1.2|0.6|8.9|57.6|
|decode_asr_asr_model_valid.acc.best/dt05_real_isolated_1ch_track|1640|27119|88.2|10.0|1.8|0.7|12.5|65.9|
|decode_asr_asr_model_valid.acc.best/dt05_simu_beamformit_2mics|1640|27120|89.3|9.1|1.6|0.7|11.4|64.9|
|decode_asr_asr_model_valid.acc.best/dt05_simu_beamformit_5mics|1640|27120|93.0|6.1|0.9|0.5|7.5|57.1|
|decode_asr_asr_model_valid.acc.best/dt05_simu_isolated_1ch_track|1640|27120|83.7|12.6|3.6|0.7|16.9|68.7|
|decode_asr_asr_model_valid.acc.best/et05_real_beamformit_2mics|1320|21409|89.7|8.9|1.3|0.6|10.9|70.1|
|decode_asr_asr_model_valid.acc.best/et05_real_beamformit_5mics|1320|21409|92.3|6.8|0.9|0.5|8.2|60.8|
|decode_asr_asr_model_valid.acc.best/et05_real_isolated_1ch_track|1320|21409|87.7|10.3|2.0|0.8|13.1|71.7|
|decode_asr_asr_model_valid.acc.best/et05_simu_beamformit_2mics|1320|21416|88.4|9.9|1.7|1.0|12.7|69.5|
|decode_asr_asr_model_valid.acc.best/et05_simu_beamformit_5mics|1320|21416|92.3|6.8|0.8|0.8|8.5|63.0|
|decode_asr_asr_model_valid.acc.best/et05_simu_isolated_1ch_track|1320|21416|82.6|13.6|3.8|1.0|18.4|71.4|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|160390|95.4|2.2|2.4|1.0|5.6|58.5|
|decode_asr_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|96.7|1.5|1.8|0.7|3.9|54.3|
|decode_asr_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|160390|95.1|2.6|2.3|1.2|6.0|63.5|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|160400|95.5|2.2|2.3|0.8|5.3|61.8|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|97.6|1.2|1.2|0.5|2.9|54.9|
|decode_asr_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|160400|91.8|4.1|4.2|1.3|9.5|67.1|
|decode_asr_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|126796|96.3|1.9|1.8|0.7|4.4|66.7|
|decode_asr_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|97.5|1.2|1.3|0.6|3.1|58.0|
|decode_asr_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|126796|95.3|2.4|2.3|0.9|5.6|71.5|
|decode_asr_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|126812|95.3|2.2|2.5|1.0|5.7|66.4|
|decode_asr_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|97.5|1.2|1.3|0.9|3.4|61.0|
|decode_asr_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|126812|91.1|4.1|4.8|1.5|10.4|69.4|
|decode_asr_asr_model_valid.acc.best/dt05_real_beamformit_2mics|1640|160390|95.4|2.4|2.2|1.1|5.7|60.2|
|decode_asr_asr_model_valid.acc.best/dt05_real_beamformit_5mics|1640|160390|96.6|1.7|1.7|0.8|4.1|57.6|
|decode_asr_asr_model_valid.acc.best/dt05_real_isolated_1ch_track|1640|160390|94.9|2.6|2.6|1.0|6.2|65.9|
|decode_asr_asr_model_valid.acc.best/dt05_simu_beamformit_2mics|1640|160400|95.4|2.3|2.3|0.9|5.6|64.9|
|decode_asr_asr_model_valid.acc.best/dt05_simu_beamformit_5mics|1640|160400|97.6|1.2|1.2|0.6|3.0|57.1|
|decode_asr_asr_model_valid.acc.best/dt05_simu_isolated_1ch_track|1640|160400|91.4|4.0|4.6|1.2|9.7|68.7|
|decode_asr_asr_model_valid.acc.best/et05_real_beamformit_2mics|1320|126796|96.1|1.9|2.0|0.7|4.6|70.1|
|decode_asr_asr_model_valid.acc.best/et05_real_beamformit_5mics|1320|126796|97.4|1.3|1.3|0.6|3.2|60.8|
|decode_asr_asr_model_valid.acc.best/et05_real_isolated_1ch_track|1320|126796|95.1|2.4|2.5|0.9|5.8|71.7|
|decode_asr_asr_model_valid.acc.best/et05_simu_beamformit_2mics|1320|126812|95.0|2.4|2.6|1.1|6.1|69.5|
|decode_asr_asr_model_valid.acc.best/et05_simu_beamformit_5mics|1320|126812|97.4|1.3|1.3|0.9|3.6|63.0|
|decode_asr_asr_model_valid.acc.best/et05_simu_isolated_1ch_track|1320|126812|90.8|4.0|5.1|1.5|10.6|71.4|

### TER

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

## ASR config

<details><summary>expand</summary>

```
config: conf/tuning/train_discrete_asr_e_branchformer_e12_mlp1024_linear1024_macaron_lr1e-4_warmup25k_conv1d1.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_discrete_asr_e_branchformer_e12_mlp1024_linear1024_macaron_lr1e-4_warmup25k_conv1d1_raw_wavlm_large_21_km1000_bpe_rm2000_char_ts_sp
ngpu: 1
seed: 2022
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
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 25
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: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 10000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_char_sp/train/text_shape.char
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_char_sp/train/src_text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_char_sp/valid/text_shape.char
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_char_sp/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
-   - dump/raw/tr05_multi_noisy_si284_sp/text.ts.en
    - text
    - text
-   - dump/raw/tr05_multi_noisy_si284_sp/text.rm.wavlm_large_21_km1000
    - src_text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dt05_multi_isolated_1ch_track/text.ts.en
    - text
    - text
-   - dump/raw/dt05_multi_isolated_1ch_track/text.rm.wavlm_large_21_km1000
    - src_text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.0001
    weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- <space>
- E
- T
- A
- O
- I
- N
- S
- R
- H
- D
- L
- C
- M
- U
- P
- F
- G
- Y
- B
- W
- V
- .
- K
- ''''
- X
- ','
- Q
- '-'
- J
- '"'
- <
- '>'
- Z
- '*'
- ':'
- (
- )
- '?'
- '&'
- ;
- '!'
- /
- '{'
- '}'
- '~'
- '`'
- _
- <sos/eos>
src_token_list:
- <blank>
- <unk>
- 僣
- 亯
- 仮
- 偁
- 冨
- 侐
- 僧
- 伧
- 儉
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- 冒
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- 不
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- 倊
- 仉
- 兇
- 凈
- 兕
- 么
- 傆
- 傥
- 乆
- 仢
- 兰
- 丟
- 乻
- 亴
- 丩
- 仩儰
- 俆
- 之
- 倭
- 儱
- 俤
- 为
- 伕
- 冉
- 僷
- 业
- 兼
- 乙
- 偶俸
- 乭
- 傞
- 仸
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- 俳
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- 仓
- 一
- 侀
- 像
- 凜
- 伖
- 傹
- 儭
- 亿
- 们
- 书
- 偛
- 亨
- 冭
- 儜
- 佱
- 丑
- 儺
- 丌像
- 佊亂
- 侫
- 仴儫
- 仱
- 丄
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- 侎
- 傤
- 伢
- 兗
- 佭
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- 九
- 冰
- 供
- 凁
- 偔
- 兝丆
- 儬
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- 凡
- 亏
- 乜
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- 兹
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- 儴
- 乞
- 亅
- 僘
- 兲
- 倠
- 东
- 几
- 偋
- 乂
- 侹亐俔
- 亐俔
- 乬
- 亸
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- 偎
- 処
- 倪
- 丸
- 偦
- 乨
- 僗
- 冿
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- 乕
- 严
- 传
- 丧
- 乚
- 令
- 僌
- 兤
- 光
- 儑
- 倜
- 佻
- 丆
- 乷主
- 倐
- 凔
- 保
- 傜凁
- 偡
- 佋
- 七一
- 兄
- 伣
- 儧
- 倛
- 丨
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- 临
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- 僠
- 兝
- 凋仂
- 兹么
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- 丣仙
- 习
- 倴
- 僸侜
- 偢僄
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- 侻
- 俚
- 仃
- 兝乗
- 伬
- 偺
- 亰
- 乪
- 俹
- 不儠
- 凈乏
- 凃
- 侱
- 乓倎
- 偱
- 佳
- 伩
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- 偹
- 乇
- 偮互
- 伄
- 凕
- 俟
- 仴
- 互
- 傇
- 僜
- 俭
- 俫
- 儿
- 丽儑
- 儭丵
- 佚
- 偊
- 侑丨
- 倱
- 侾
- 僿亸
- 伒
- 兊倐
- 免
- 乏
- 侳
- 伉
- 冬
- 俢
- 价
- 債
- 俆伩
- 仰亝
- 佝
- 乫
- 兜冖典
- 乐
- 两
- 乄
- 傭
- 乔
- 凈僌
- 倰
- 亓
- 倌
- 偳
- 侑
- 倅
- 償傿
- 俔
- 傩乘儮
- 偾
- 些
- 冂
- 傾
- 佒
- 佡亂
- 偷
- 伵
- 偏
- 倬
- 傽
- 仗
- 军
- 丷
- 丶
- 乣其
- 假
- 乜佝
- 仨
- 乡
- 倬仢
- 僸
- 伅
- 儂伊乴
- 僺
- 僾
- 乇伻
- 兮倨
- 凥侵
- 亢个仓
- 伀
- 偩
- 倅仚傕
- 偤
- 仄
- 亮
- ▁伋丕兙
- 儮
- 儣
- 丗
- 儞
- 倿
- 倎
- 侑丫
- 亟
- 僥
- 伿
- 僽
- 凛
- 仡
- 也
- 亂
- 側
- 亲
- 侌
- 俖
- 亐
- 俒
- 乃傺
- 今
- 偆
- 乄传
- 僫傤
- 丁
- 儲
- 兩
- 佉佤
- 亱
- 佒傘
- 兊
- 傉
- 儻
- 傝
- 仡倓俣
- 乩
- 儂佢伊乴
- 伇
- 侉
- 乾
- 丬
- 井侺
- 仟
- 乑
- 丌
- 丣
- 依
- 伴
- 佥
- 儻伀
- 伙佾
- 侹亐
- 乘
- 伎
- 人
- 俩
- 供僜億丅
- 儖丬
- 亨互
- 丢側
- 僺亓
- 仦义
- 倕
- 仙
- 享
- 佡
- 傜
- 乯侊
- 丽
- 冡
- 仐会
- 乩傹
- 兙
- 偈
- ▁並作丰
- 伶兕
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- 佊亂休倂
- 儃
- 僃仰
- 僒
- 冏仫冏
- 倝仲兀兢侌
- 亖倝偸兀兢侌三乺仆二
- 傔义僸
- 偋偷
- 仡乾
- 俉倕丶
- 他
- 丌像公侴
- 伙
- 丞乎余丈
- 倵傤
- 仉亖乪
- 仦
- 佥于
- 任佻
- 冭允书
- 例佧
- 云
- 八傃便
- 億丅
- 义
- 亻兣亻
- 亱俬
- 余
- 充
- 俬
- 先
- 一佹亱
- 佛儧
- 佊佡倱
- 仭
- 兰主
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- 伥
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- 佸俨傝儖丬
- ▁具兆
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- 乁
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- 冣
- 住
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- 俙
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- 倫
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- 他儁
- 乩傹乩傹
- 倣乣其
- 伳什偯伺亽
- 倵亠
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- 凜仚傕
- 伄俦
- 伕伏允
- 俳亖傏
- 佀儒冿
- 佄乞亴
- 傫
- 亭
- 倧亚倆
- 亀亵
- 供俌些
- 倜伖
- 冪
- 個儧乆
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- 伎僔侻
- 伛乃傺
- 从侔乮
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- 僜億
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- 傾偮
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- 乄佉
- 丑偁丏
- 侤
- 凑
- 侌军偦
- 停
- 仼亃八傃便亀
- 侥
- 佅亲
- 京偁京
- 僱
- 亀
- 伂兛僵
- 乄侑丨
- 倧亶倆僉傷亵
- 乧伥凓
- 俶
- 乁侪冲们倬
- 代
- 举
- 傭偧乆
- 侫亯侫
- ▁伋丕偁兙
- 俚亻
- 光倓俣
- 倬俕丳偣丷伂兛僵伖
- 人働
- 丷傇
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- 俷
- 仏俍佴仅
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- 儻亗佁兽且偵乊倘乳予么亸
- 冉俚
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- 冠
- 位
- 具
- 並
- 伋
- 僡
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
use_preprocessor: true
token_type: char
src_token_type: bpe
bpemodel: null
src_bpemodel: data/token_list/src_bpe_unigram2000_rm_wavlm_large_21_km1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
tokenizer_encode_conf: null
src_tokenizer_encode_conf:
    enable_sampling: true
    alpha: 0.4
    nbest_size: -1
frontend: embed
frontend_conf:
    embed_dim: 512
    positional_dropout_rate: 0.1
specaug: specaug
specaug_conf:
    apply_time_warp: false
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: false
    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
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    attention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    cgmlp_linear_units: 1024
    cgmlp_conv_kernel: 31
    use_linear_after_conv: false
    gate_activation: identity
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv1d1
    layer_drop_rate: 0.0
    linear_units: 1024
    positionwise_layer_type: linear
    use_ffn: true
    macaron_ffn: true
    merge_conv_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
model: discrete_asr
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
required:
- output_dir
- src_token_list
- token_list
version: '202308'
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}
}
```
