ESPnet2 ASR model

`pyf98/swbd_e_branchformer`

This model was trained by Yifan Peng using swbd recipe in espnet.

References:

E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)

Demo: How to use in ESPnet2

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

cd espnet
git checkout ee573bc6f5de4309c1e29137294a7305d9175e65
pip install -e .
cd egs2/swbd/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/swbd_e_branchformer

RESULTS

Environments

date: Tue Dec 27 05:05:40 CST 2022
python version: 3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]
espnet version: espnet 202211
pytorch version: pytorch 1.12.1
Git hash: ef3ce328551c12c03284defc757f42df47c46170
- Commit date: Mon Dec 26 20:34:28 2022 -0500

asr_train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron_raw_en_bpe2000_sp

WER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_asr_asr_model_valid.acc.ave/eval2000/hyp.callhm.ctm.filt.sys	2628	21594	88.7	8.4	2.9	2.1	13.4	46.2
decode_asr_asr_model_valid.acc.ave/eval2000/hyp.ctm.filt.sys	4459	42989	91.2	6.1	2.8	1.5	10.4	41.5
decode_asr_asr_model_valid.acc.ave/eval2000/hyp.swbd.ctm.filt.sys	1831	21395	93.7	3.7	2.6	1.0	7.3	34.8

CER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_asr_asr_model_valid.acc.ave/eval2000	4322	181952	93.1	3.3	3.6	10.7	17.7	67.7

TER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_asr_asr_model_valid.acc.ave/eval2000	4322	47227	83.0	12.6	4.4	15.3	32.3	67.7

ASR config

expand

config: conf/tuning/train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron_raw_en_bpe2000_sp
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37983
dist_launcher: null
multiprocessing_distributed: true
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
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
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: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 40000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe2000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe2000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe2000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe2000_sp/valid/text_shape.bpe
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_nodup_sp/wav.scp
    - speech
    - kaldi_ark
-   - dump/raw/train_nodup_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/train_dev/wav.scp
    - speech
    - kaldi_ark
-   - dump/raw/train_dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.002
    weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 50000
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- ▁cost
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- ▁child
- ▁government
- ▁crime
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- ▁days
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- ▁terms
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- ▁send
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- ▁level
- ▁phone
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- ▁woman
- ▁oil
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- ▁changed
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- ▁exp
- ▁particular
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- ▁station
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- ▁choice
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- ▁personal
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- ▁dollar
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- ▁die
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- ▁general
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- ▁awful
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- ible
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- ▁lives
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- ner
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- ized
- ▁acc
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- ▁bag
- ton
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- ▁jo
- ▁bother
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init: null
input_size: null
ctc_conf:
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    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram2000/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
frontend: default
frontend_conf:
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    fs: 16k
specaug: specaug
specaug_conf:
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normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_en_bpe2000_sp/train/feats_stats.npz
model: espnet
model_conf:
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preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
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postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
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preprocessor: default
preprocessor_conf: {}
required:
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version: '202211'
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}
}