msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000

Usage

import librosa
from espnet2.bin.asr_inference import Speech2Text

speech2text = Speech2Text.from_pretrained(model_tag="espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000")
# librosa resamples and mixes to one channel, so any file works; 16000 is
# what nearly every espnet recogniser is trained on - check this model's
# config if its audio is not 16 kHz
speech, rate = librosa.load("audio.wav", sr=16000, mono=True)
text, *_ = speech2text(speech)[0]
print(text)

ESPnet2 AVSR model

espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000

This model was trained by ms-dot-k using lrs3 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/lrs3/avsr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/msk_lrs3_train_avsr_avhubert_large_extracted_en_bpe1000

<!-- Generated by scripts/utils/show_asr_result.sh -->

RESULTS

Environments

• date: Thu Sep 28 23:59:06 KST 2023
• python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]
• espnet version: espnet 202308
• pytorch version: pytorch 1.12.0
• Git hash: 5d0758e2a7063b82d1f10a8ac2de98eb6cf8a352
  • Commit date: Wed Aug 30 18:03:42 2023 -0400

exp/asr_train_avsr_avhubert_large_extracted_en_bpe1000

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ASR config

<details><summary>expand</summary>

config: conf/train_avsr_avhubert_large.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_avsr_avhubert_large_extracted_en_bpe1000
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 54927
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 20
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: false
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: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_extracted_en_bpe1000/train/speech_shape
- exp/asr_stats_extracted_en_bpe1000/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_extracted_en_bpe1000/valid/speech_shape
- exp/asr_stats_extracted_en_bpe1000/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 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/extracted/train/feats.scp
    - speech
    - kaldi_ark
-   - dump/extracted/train/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/extracted/val/feats.scp
    - speech
    - kaldi_ark
-   - dump/extracted/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: 0.0003
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 8000
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- ▁TRULY
- '{'
- <sos/eos>
init: xavier_uniform
input_size: 2048
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: bpe
bpemodel: data/en_token_list/bpe_unigram1000/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: null
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_extracted_en_bpe1000/train/feats_stats.npz
model: espnet
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: avhubert
encoder_conf:
    avhubert_url: https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/noise-pretrain/large_vox_iter5.pt
    avhubert_dir_path: ./local/pre-trained
    encoder_embed_dim: 1024
    encoder_attention_heads: 16
    encoder_ffn_embed_dim: 4096
    encoder_layers: 24
    dropout: 0.1
    dropout_features: 0.1
    encoder_layerdrop: 0.05
    attention_dropout: 0.1
    extracted: true
    freeze_finetune_updates: 10000
    feature_grad_mult: 1.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 4096
    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: '202308'
distributed: true

</details>

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}
}