ESPnet2 ASR model

`fujie/espnet_asr_cejc_pron_aux_cbs_transducer_081616_hop132`

This model was trained by Shinya Fujie using cejc_pron_aux recipe in espnet.

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already.

cd espnet
git checkout 03f77b8db5be7503c3a540b113178b34cfbccee8
pip install -e .
cd egs2/cejc_pron_aux/asr1
./run.sh --skip_data_prep false --skip_train true --download_model fujie/espnet_asr_cejc_pron_aux_cbs_transducer_081616_hop132

RESULTS

Environments

date: Wed Jun 11 12:40:31 JST 2025
python version: 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0]
espnet version: espnet 202503
pytorch version: pytorch 2.3.0+cu121
Git hash: 03f77b8db5be7503c3a540b113178b34cfbccee8
- Commit date: Tue Jun 10 16:01:35 2025 +0900

exp/asr_train_asr_cbs_transducer_081616_hop132

WER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10f	953	11908	84.6	8.4	7.0	4.0	19.4	63.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10m	957	16092	86.8	8.0	5.2	4.3	17.5	66.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20f	1466	18326	87.3	7.3	5.5	3.3	16.1	62.3
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20m	1772	23756	82.6	10.2	7.2	4.2	21.6	64.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30f	1734	24116	90.6	5.4	4.0	3.6	13.0	55.6
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30m	1688	20116	69.3	17.9	12.8	4.3	35.0	66.9
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40f	1477	20717	85.9	6.9	7.2	3.6	17.7	58.4
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40m	1498	24747	87.1	6.7	6.2	3.5	16.3	63.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50f	1450	26584	90.1	5.1	4.8	3.4	13.4	62.5
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50m	1499	22572	87.9	6.8	5.3	3.6	15.7	62.3
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60f	1335	21810	81.7	10.4	7.8	4.4	22.7	65.0
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60m	1621	24151	83.1	9.4	7.5	3.3	20.2	68.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70f	906	9542	83.8	8.8	7.3	4.2	20.4	57.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70m	894	12490	88.1	7.0	4.9	4.6	16.4	58.3

CER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10f	953	24583	88.0	5.0	7.0	4.0	15.9	63.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10m	957	33749	90.1	4.6	5.3	4.1	14.0	66.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20f	1466	37024	89.8	4.6	5.7	3.1	13.3	62.3
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20m	1772	47838	86.9	6.4	6.7	4.1	17.2	64.1
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30f	1734	48968	92.5	3.3	4.2	3.7	11.2	55.6
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30m	1688	41067	77.2	11.0	11.8	4.3	27.1	66.9
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40f	1477	42609	88.3	4.3	7.4	3.4	15.2	58.4
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40m	1498	51748	88.8	4.1	7.0	3.3	14.5	63.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50f	1450	54181	91.8	3.5	4.7	3.4	11.6	62.5
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50m	1499	46031	90.5	4.2	5.3	3.4	13.0	62.3
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60f	1335	45028	86.1	6.3	7.6	4.0	17.8	65.0
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60m	1621	49442	86.7	5.5	7.8	3.2	16.5	68.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70f	906	19386	87.2	5.4	7.4	4.2	17.0	57.2
decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70m	894	26203	90.8	4.4	4.9	4.6	13.8	58.3

TER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err

ASR config

expand

config: myconf/train_asr_cbs_transducer_081616_hop132.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_asr_cbs_transducer_081616_hop132
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 48739
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
gradient_as_bucket_view: true
ddp_comm_hook: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
use_tf32: false
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
    - cer_transducer
    - min
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
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
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: espnet_ninjal
wandb_id: null
wandb_entity: null
wandb_name: cejc_cbs_td_081616_hop132
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
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: 1500000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- exp/asr_stats_raw_jp_word/train/speech_shape
- exp/asr_stats_raw_jp_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_jp_word/valid/speech_shape
- exp/asr_stats_raw_jp_word/valid/text_shape.word
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 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: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
-   - dump/raw/train_nodup/wav.scp
    - speech
    - sound
-   - dump/raw/train_nodup/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
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.002
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- '|'
- ー
- ン
- イ
- ッ
- カ
- ナ
- テ
- ト
- タ
- ア
- ノ
- デ
- ダ
- ソ
- コ
- <sp>
- ウ
- シ
- モ
- ラ
- ネ
- ル
- ス
- ワ
- オ
- マ
- ニ
- ク
- レ
- ガ
- ヨ
- キ
- サ
- ケ
- ヤ
- エ
- ド
- ツ
- ハ
- リ
- チ
- ミ
- ユ
- ジ
- ホ
- ジャ
- ロ
- セ
- ゴ
- ノ+F
- メ
- バ
- ア+F
- ブ
- チャ
- ショ
- フ
- <mask>
- ヒ
- ー+F
- チョ
- パ
- ジュ
- ゼ
- ベ
- ズ
- グ
- ム
- ヘ
- ゲ
- キョ
- ジョ
- ビ
- ン+D
- ボ
- シュ
- ギ
- シャ
- エ+F
- ザ
- プ
- ト+F
- イ+D
- ソ+F
- キュ
- チュ
- ポ
- リョ
- ゾ
- ス+D
- キャ
- ピ
- ン+F
- ー+D
- ッ+F
- ウ+D
- ヒャ
- ア+D
- ナ+D
- オ+D
- ヌ
- コ+D
- カ+D
- ギョ
- シ+D
- ペ
- リャ
- ニュ
- ソ+D
- ッ+D
- ティ
- ツ+D
- ク+D
- ハ+D
- タ+D
- ギャ
- ト+D
- ヒョ
- ディ
- エ+D
- キ+D
- ダ+D
- ド+D
- ヤ+D
- マ+D
- ファ
- ヒ+D
- ホ+D
- サ+D
- モ+D
- フ+D
- ヨ+D
- ワ+D
- ミ+D
- ウ+F
- ビョ
- チ+D
- フォ
- ム+D
- リュ
- フェ
- ジ+D
- ユ+D
- ニ+D
- ジェ
- ケ+D
- ガ+D
- セ+D
- デ+D
- ラ+D
- フィ
- ノ+D
- テ+D
- ギュ
- チェ
- リ+D
- ズ+D
- ミョ
- ゼ+D
- ニャ
- ル+D
- ビャ
- メ+D
- シュ+D
- ピョ
- ロ+D
- ブ+D
- バ+D
- ショ+D
- ウェ
- ゴ+D
- ヘ+D
- トゥ
- ピャ
- ネ+D
- チョ+D
- ジュ+D
- グ+D
- ウィ
- レ+D
- ビュ
- ヌ+D
- ニョ
- ゲ+D
- ボ+D
- ビ+D
- シェ
- キョ+D
- パ+D
- チュ+D
- オ+F
- ベ+D
- ウォ
- ミュ
- チャ+D
- シャ+D
- キュ+D
- ピュ
- ザ+D
- ギ+D
- ドゥ
- プ+D
- ジョ+D
- ジャ+D
- ヒュ
- リョ+D
- ポ+D
- ミャ
- デュ
- ヒャ+D
- ゾ+D
- キャ+D
- ピ+D
- ペ+D
- ファ+D
- ヒョ+D
- ツォ
- ツァ
- チェ+D
- ギョ+D
- ディ+D
- トゥ+D
- イ+F
- フォ+D
- ニュ+D
- ツェ
- ビョ+D
- ドゥ+D
- フェ+D
- ギャ+D
- ジェ+D
- ティ+D
- ナ+F
- リュ+D
- ヤ+F
- フィ+D
- ニャ+D
- テュ
- ビュ+D
- ニェ
- ヌ+F
- ウェ+D
- ヴィ
- ミャ+D
- ヴォ
- ド+F
- シェ+D
- ギュ+D
- ホ+F
- ツィ
- ワ+F
- ル+F
- ダ+F
- マ+F
- ウィ+D
- ガ+F
- ピャ+D
- メ+F
- スィ
- ビャ+D
- ニョ+F
- ロ+F
- ウォ+D
- ネ+F
- ヴァ
- ヴ
- フュ
- ミョ+D
- ズ+F
- ヴィ+D
- ニャ+F
- ミュ+F
- デュ+D
- ニョ+D
- ミュ+D
- ツォ+F
- ヒェ+D
- ヒュ+D
- カ+F
- ユ+F
- リャ+D
- ブ+F
- ヘ+F
- ハ+F
- ツォ+D
- <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
    brctc_risk_strategy: exp
    brctc_group_strategy: end
    brctc_risk_factor: 0.0
joint_net_conf:
    joint_space_size: 640
use_preprocessor: true
use_lang_prompt: false
use_nlp_prompt: false
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'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: default
frontend_conf:
    hop_length: 132
    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_jp_word/train/feats_stats.npz
model: espnet
model_conf:
    ctc_weight: 0.0
    report_cer: true
    report_wer: true
preencoder: null
preencoder_conf: {}
encoder: contextual_block_conformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d
    normalize_before: true
    activation_type: swish
    macaron_style: true
    use_cnn_module: true
    cnn_module_kernel: 15
    block_size: 40
    hop_size: 16
    look_ahead: 16
    init_average: true
    ctx_pos_enc: true
postencoder: null
postencoder_conf: {}
decoder: transducer
decoder_conf:
    rnn_type: lstm
    num_layers: 1
    hidden_size: 512
    dropout: 0.1
    dropout_embed: 0.2
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202503'
distributed: true

Citing ESPnet

@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:

@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}
}