ESPnet2 ASR model
balaji1312/jibo_kids_wavlm_aed_transformer
This model was trained by using recipe in espnet.
Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
cd espnet
pip install -e .
cd egs2/jibo_kids/asr1
./run.sh --skip_data_prep false --skip_train true --download_model balaji1312/jibo_kids_wavlm_aed_transformer
RESULTS
Environments
- date:
Thu Jan 30 06:18:01 EST 2025
- python version:
3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0]
- espnet version:
espnet 202402
- pytorch version:
pytorch 2.4.0
- Git hash:
c46aa9a594ff83d52cbf61d84c5650325d1ce527
- Commit date:
Sun Oct 13 14:39:31 2024 -0400
exp/asr_train_asr_wavlm_transformer_raw_en_bpe1024
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_asr_model_valid.acc.best/test |
1044 |
3686 |
56.1 |
31.4 |
12.5 |
8.1 |
52.0 |
62.3 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_asr_model_valid.acc.best/test |
1044 |
16215 |
75.4 |
8.1 |
16.6 |
9.4 |
34.1 |
62.3 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_asr_model_valid.acc.best/test |
1044 |
5220 |
64.5 |
18.0 |
17.5 |
10.4 |
45.9 |
62.3 |
exp/asr_train_asr_wavlm_transformer_raw_en_bpe1024/decode_asr_asr_model_valid.acc.best
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
853 |
2372 |
59.8 |
31.2 |
8.9 |
7.2 |
47.3 |
64.0 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
853 |
9855 |
78.3 |
7.3 |
14.3 |
8.4 |
30.1 |
64.0 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/dev |
853 |
3590 |
68.2 |
16.2 |
15.6 |
6.4 |
38.3 |
64.0 |
ASR config
expand
config: conf/tuning/train_asr_wavlm_transformer.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_wavlm_transformer_raw_en_bpe1024
ngpu: 1
seed: 2022
num_workers: 4
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
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
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
- acc
- max
keep_nbest_models: 4
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 400
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
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
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: 1200000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- exp/asr_stats_raw_en_bpe1024/train/speech_shape
- exp/asr_stats_raw_en_bpe1024/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe1024/valid/speech_shape
- exp/asr_stats_raw_en_bpe1024/valid/text_shape.bpe
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/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/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
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
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- ▁CLOS
- ▁SEVE
- ▁DIRT
- ▁NUMB
- YOU
- PRI
- ▁JIB
- ETTIER
- FFERENT
- ERCASE
- ROOM
- ▁DIFF
- ▁JELLY
- ▁SEVENEIGHT
- ORGE
- ▁YELL
- DRA
- ▁SLOW
- ▁MON
- ▁BUG
- YPE
- ▁BRU
- COL
- PUS
- WO
- INET
- NGRY
- BRUSHING
- ▁CUB
- OCTO
- HIC
- UDE
- RUB
- MOR
- LOCK
- ▁BR
- YOUR
- ▁STR
- ▁KNE
- ▁CRO
- ▁BO
- UALLY
- ▁TOOTHB
- ▁ANYMO
- UKU
- ▁GUE
- MA
- ENTY
- PHA
- ▁QUE
- PF
- KE
- NOW
- ▁LAS
- ▁SHIN
- ARN
- GE
- ▁MAIL
- RUSHING
- Q
- <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: null
use_preprocessor: true
use_lang_prompt: false
use_nlp_prompt: false
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram1024/bpe.model
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: 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: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 18
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d2
normalize_before: true
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
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202402'
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}
}
