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fujielab-asr (>= 0.1.4) —
no checkout of an old ESPnet commit is required.pip install "fujielab-asr>=0.1.4"1import numpy as np, soundfile as sf
2from fujielab.asr.espnet_ext.espnet2.bin.asr_transducer_inference_cbs import Speech2Text
3
4s2t = Speech2Text.from_pretrained(
5 "fujie/espnet_asr_cbs_transducer_120303_hop132_cc0105",
6 streaming=True, lm_weight=0.0,
7 beam_size=20, beam_search_config=dict(search_type="maes"),
8)
9
10audio, fs = sf.read("utterance.wav") # 16 kHz mono
11chunk = int(16000 * 0.1) # 100 ms
12for i in range(0, len(audio), chunk):
13 c = audio[i:i + chunk]
14 is_final = len(c) < chunk
15 if is_final:
16 c = np.pad(c, (0, chunk - len(c)))
17 hyps = s2t.streaming_decode(c, is_final=is_final)
18 res = s2t.hypotheses_to_results(hyps)
19 if res:
20 print(res[0][0]) # current best transcriptexamples/run_streaming_asr.py in fujielab-asr. For a model that predicts a
separate per-token auxiliary label sequence (N/F/D) instead of composite tokens,
see fujie/espnet_asr_csj_pron_aux_cbs_ctc_120300_hop132.fujie/espnet_asr_cbs_transducer_120303_hop132_cc01051cd espnet
2git checkout 4c1c38f2c9c6a105ff4cffa8c833b0eb47f501a4
3pip install -e .
4cd egs2/cejc_alt/asr1
5./run.sh --skip_data_prep false --skip_train true --download_model fujie/espnet_asr_cbs_transducer_120303_hop132_cc0105Sun Mar 10 16:16:24 JST 20243.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0]espnet 202402pytorch 2.1.0+cu121bf3653d6bd16c10a1df83f1db07e681374453f75
Wed Mar 6 17:25:02 2024 +0900| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|---|---|---|---|---|---|---|---|
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10f | 953 | 11908 | 89.2 | 5.7 | 5.1 | 3.0 | 13.8 | 58.0 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10m | 957 | 16092 | 93.8 | 2.9 | 3.3 | 2.1 | 8.3 | 55.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval1_csj | 1400 | 63362 | 94.9 | 3.0 | 2.1 | 1.2 | 6.3 | 69.5 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20f | 1466 | 18326 | 90.5 | 5.1 | 4.4 | 2.5 | 12.0 | 55.0 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20m | 1772 | 23756 | 89.0 | 5.8 | 5.2 | 2.8 | 13.8 | 56.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval2_csj | 1413 | 64151 | 96.2 | 2.3 | 1.5 | 0.9 | 4.7 | 67.9 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30f | 1734 | 24116 | 93.6 | 3.4 | 3.0 | 2.3 | 8.8 | 48.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30m | 1688 | 20116 | 85.2 | 8.0 | 6.8 | 3.5 | 18.3 | 59.4 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval3_csj | 1437 | 40131 | 96.3 | 2.0 | 1.8 | 1.2 | 4.9 | 52.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40f | 1477 | 20717 | 90.3 | 4.2 | 5.4 | 2.5 | 12.2 | 53.2 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40m | 1498 | 24747 | 92.4 | 3.5 | 4.1 | 2.3 | 9.9 | 55.7 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50f | 1450 | 26584 | 95.4 | 2.0 | 2.6 | 1.8 | 6.4 | 49.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50m | 1499 | 22572 | 92.0 | 4.1 | 4.0 | 2.4 | 10.4 | 54.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60f | 1335 | 21810 | 92.6 | 3.5 | 3.9 | 2.5 | 9.8 | 54.9 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60m | 1621 | 24151 | 89.5 | 5.0 | 5.4 | 2.3 | 12.8 | 62.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70f | 906 | 9542 | 88.7 | 5.7 | 5.6 | 3.4 | 14.7 | 53.4 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70m | 894 | 12490 | 92.9 | 3.5 | 3.5 | 2.6 | 9.7 | 51.6 |
| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|---|---|---|---|---|---|---|---|
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10f | 953 | 24583 | 91.5 | 3.5 | 5.0 | 3.1 | 11.6 | 58.0 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval10m | 957 | 33749 | 94.9 | 1.6 | 3.5 | 2.4 | 7.5 | 55.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval1_csj | 1400 | 139085 | 96.0 | 1.5 | 2.5 | 1.4 | 5.4 | 69.5 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20f | 1466 | 37024 | 92.3 | 3.1 | 4.6 | 2.6 | 10.4 | 55.0 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval20m | 1772 | 47838 | 91.4 | 3.6 | 5.1 | 2.8 | 11.4 | 56.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval2_csj | 1413 | 140081 | 97.0 | 1.0 | 2.0 | 1.2 | 4.2 | 67.9 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30f | 1734 | 48968 | 94.6 | 2.1 | 3.3 | 2.7 | 8.0 | 48.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval30m | 1688 | 41067 | 88.4 | 4.9 | 6.7 | 3.5 | 15.1 | 59.4 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval3_csj | 1437 | 86583 | 96.8 | 0.8 | 2.3 | 1.5 | 4.7 | 52.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40f | 1477 | 42609 | 91.7 | 2.8 | 5.5 | 2.4 | 10.7 | 53.2 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval40m | 1498 | 51748 | 93.2 | 2.1 | 4.7 | 2.5 | 9.3 | 55.7 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50f | 1450 | 54181 | 95.8 | 1.4 | 2.8 | 1.9 | 6.1 | 49.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval50m | 1499 | 46031 | 93.4 | 2.6 | 4.0 | 2.4 | 9.0 | 54.6 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60f | 1335 | 45028 | 93.9 | 2.0 | 4.2 | 2.7 | 8.9 | 54.9 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval60m | 1621 | 49442 | 91.4 | 3.0 | 5.6 | 2.5 | 11.1 | 62.1 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70f | 906 | 19386 | 90.7 | 3.7 | 5.6 | 3.6 | 12.9 | 53.4 |
| decode_cbs_transducer_asr_model_valid.cer_transducer.ave_10best/eval70m | 894 | 26203 | 94.1 | 2.1 | 3.7 | 3.0 | 8.9 | 51.6 |
| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|
config: myconf/train_asr_cbs_transducer_120303_hop132_silver11.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_120303_hop132_cc0105
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: 100
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_120303_hop132_cc0105
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param:
- ./exp/asr_train_asr_cbs_transducer_081616_hop132/valid.cer_transducer.ave_10best.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 2000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_jp_word_cc0105/train/speech_shape
- exp/asr_stats_raw_jp_word_cc0105/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_jp_word_cc0105/valid/speech_shape
- exp/asr_stats_raw_jp_word_cc0105/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
train_data_path_and_name_and_type:
- - dump/raw/train_nodup_cc_01_05/wav.scp
- speech
- sound
- - dump/raw/train_nodup_cc_01_05/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev_cc/wav.scp
- speech
- sound
- - dump/raw/train_dev_cc/text
- text
- text
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>
- <mask>
- '|'
- ー
- ン
- イ
- ト
- カ
- ノ
- <sp>
- テ
- デ
- タ
- シ
- ス
- ナ
- ッ
- コ
- オ
- ニ
- マ
- ワ
- ガ
- ク
- モ
- ー+F
- ル
- キ
- レ
- エ+F
- ラ
- リ
- ア
- ケ
- ツ
- ソ
- ユ
- ド
- サ
- セ
- ヨ
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- チ
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- ア+F
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- ネ
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- ハ
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- ブ
- バ
- ヤ
- ヒ
- メ
- ウ
- フ
- ショ
- ジョ
- ジュ
- ズ
- ゲ
- シュ
- ム
- チョ
- ト+F
- キョ
- グ
- パ
- ベ
- シャ
- ゼ
- ソ+F
- ン+F
- ギ
- ザ
- ビ
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- ボ
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- ヘ
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- ン+D
- チュ
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- ウ+F
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- ヒョ
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- イ+D
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- オ+D
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- ヒャ
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- ヨ+F
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- ズィ+D
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- ル+F
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- テュ+F
- ミェ+D
- ボ+F
- ジャ+F
- ヴァ+D
- ジ+F
- チ+F
- ゴ+F
- ピョ+D
- ヒェ
- ニ+F
- シュ+F
- ミュ+F
- <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_cc0105/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: 18
hop_size: 3
look_ahead: 3
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: '202402'
distributed: false1@inproceedings{watanabe2018espnet,
2 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},
3 title={{ESPnet}: End-to-End Speech Processing Toolkit},
4 year={2018},
5 booktitle={Proceedings of Interspeech},
6 pages={2207--2211},
7 doi={10.21437/Interspeech.2018-1456},
8 url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
9}
10
11
12
13
14
151@misc{watanabe2018espnet,
2 title={ESPnet: End-to-End Speech Processing Toolkit},
3 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},
4 year={2018},
5 eprint={1804.00015},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}