metadata
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- A
license: cc-by-4.0
ESPnet2 ASR model
espnet/jiyang_tang_aphsiabank_english_asr_ebranchformer_wavlm_aph_en_both
This model was trained by Jiyang Tang using A 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 edf949f535938da8c705c1d26cc561b2d4cb4778
pip install -e .
cd jtang1/A/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/jiyang_tang_aphsiabank_english_asr_ebranchformer_wavlm_aph_en_both
RESULTS
Environments
- date:
Tue Mar 7 12:06:32 EST 2023
- python version:
3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]
- espnet version:
espnet 202301
- pytorch version:
pytorch 1.8.1
- Git hash:
b0b2a0aa9c335267046e83036b87e88af30698da
- Commit date:
Tue Feb 7 14:56:31 2023 -0500
- Commit date:
asr_ebranchformer_wavlm_aph_en_both
WER
dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
---|---|---|---|---|---|---|---|---|
decode_asr_model_valid.acc.ave/test | 28424 | 296887 | 83.0 | 12.4 | 4.6 | 2.7 | 19.6 | 71.1 |
CER
dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
---|---|---|---|---|---|---|---|---|
decode_asr_model_valid.acc.ave/test | 28424 | 1507391 | 91.9 | 3.0 | 5.1 | 3.0 | 11.1 | 71.1 |
TER
dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
---|
ASR config
expand
config: conf/tuning/train_asr_ebranchformer_small_wavlm_large1.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_ebranchformer_wavlm_aph_en_both
ngpu: 1
seed: 2022
num_workers: 4
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: 44175
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 30
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
grad_clip_type: 2.0
grad_noise: false
accum_grad: 8
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 200
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:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 6000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_char_sp/train/speech_shape
- exp/asr_stats_raw_en_char_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_en_char_sp/valid/speech_shape
- exp/asr_stats_raw_en_char_sp/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 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/train_sp/wav.scp
- speech
- sound
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/val/wav.scp
- speech
- sound
- - dump/raw/val/text
- 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: 2500
token_list:
- <blank>
- <unk>
- '[APH]'
- '[NONAPH]'
- <space>
- e
- t
- a
- h
- o
- A
- n
- '['
- P
- H
- ']'
- i
- s
- N
- d
- r
- u
- l
- m
- w
- O
- y
- g
- c
- b
- f
- p
- k
- ''''
- v
- j
- <
- L
- U
- '>'
- ɪ
- x
- ə
- z
- ɛ
- ɑ
- q
- ɹ
- æ
- ˞
- ʌ
- ʃ
- ʊ
- ɔ
- ŋ
- ɚ
- ɾ
- ʒ
- ð
- θ
- ɜ
- ɝ
- ɡ
- '0'
- ː
- ʔ
- ɒ
- é
- ɸ
- ̩
- ʤ
- ʧ
- <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
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: local/nlsyms.txt
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: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
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
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
layer_drop_rate: 0.1
input_layer: conv2d1
macaron_ffn: true
pos_enc_layer_type: rel_pos
attention_layer_type: rel_selfattn
rel_pos_type: latest
cgmlp_linear_units: 3072
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
positionwise_layer_type: linear
use_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: 0.2
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202301'
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}
}