ESPnet2 ASR model
espnet/kohei0209_ted3_asr2_e_branchformer1_raw_wavlm_large_21_km1000_bpe_rm2000_bpe_ts500_sp
This model was trained by kohei0209 using tedlium3 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 7bcdab47ff7f47e55d52061e55db4128913f32b6
pip install -e .
cd egs2/tedlium3/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/kohei0209_ted3_asr2_e_branchformer1_raw_wavlm_large_21_km1000_bpe_rm2000_bpe_ts500_sp
RESULTS
Environments
- date:
Thu Oct 19 22:11:12 JST 2023
- python version:
3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]
- espnet version:
espnet 202308
- pytorch version:
pytorch 1.13.1
- Git hash:
7bcdab47ff7f47e55d52061e55db4128913f32b6
- Commit date:
Thu Aug 31 20:42:18 2023 +0900
exp/asr_train_discrete_asr_e_branchformer1_raw_wavlm_large_21_km1000_bpe_rm2000_bpe_ts500_sp/
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_model_valid.acc.ave/test |
1155 |
27500 |
94.6 |
3.4 |
2.0 |
3.5 |
8.9 |
79.0 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_model_valid.acc.ave/test |
1155 |
145066 |
97.4 |
0.9 |
1.7 |
4.2 |
6.7 |
79.0 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_model_valid.acc.ave/test |
1155 |
54206 |
96.1 |
2.2 |
1.7 |
3.8 |
7.7 |
79.0 |
exp/asr_train_discrete_asr_e_branchformer1_raw_wavlm_large_21_km1000_bpe_rm2000_bpe_ts500_sp/decode_asr_model_valid.acc.ave
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
507 |
17783 |
94.2 |
3.7 |
2.2 |
3.2 |
9.0 |
84.8 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
507 |
95429 |
97.2 |
0.9 |
1.9 |
3.6 |
6.3 |
84.8 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
507 |
36002 |
95.8 |
2.3 |
1.9 |
3.2 |
7.4 |
84.8 |
ASR config
expand
config: ./conf/tuning/train_discrete_asr_e_branchformer1_1gpu_bins80M.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_branchformer1_1gpu_bins80M_raw_wavlm_large_21_km1000_bpe_rm2000_bpe_ts500_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 1
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
- 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: true
log_interval: 500
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
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: 80000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_bpe500_sp/train/src_text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_bpe500_sp/valid/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe2000_bpe500_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/train_sp/text.rm.wavlm_large_21_km1000
- src_text
- text
- - dump/raw/train_sp/text.ts.en
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/text.ts.en
- text
- text
- - dump/raw/dev/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: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 10000
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- <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: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram500_ts_en/bpe.model
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
- 10
num_freq_mask: 0
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
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: conv1d2
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
layer_drop_rate: 0.0
model: discrete_asr
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
share_decoder_input_output_embed: false
share_encoder_decoder_input_embed: false
extract_feats_in_collect_stats: false
required:
- output_dir
- src_token_list
- token_list
version: "202308"
distributed: false
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}
}
