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BEATs-ESC-FinetunedFold2

Automatic Speech Recognition
ESPnet
audio
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ESPnet2 ASR model

shikhar7ssu/BEATs-ESC-FinetunedFold2

This model was trained by Shikhar Bharadwaj using esc50 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 ca9ca1ef8bc86753238ca7a0de05d87b8f57abb3
pip install -e .
cd egs2/esc50/asr1
./run.sh --skip_data_prep false --skip_train true --download_model shikhar7ssu/BEATs-ESC-FinetunedFold2

RESULTS

Environments

  • date: Sat Dec 14 18:44:26 EST 2024
  • python version: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0]
  • espnet version: espnet 202412
  • pytorch version: pytorch 2.4.0
  • Git hash: cb80e61a15d6a13dc342ae5a413d2b870dd869c6
    • Commit date: Fri Dec 13 11:57:16 2024 -0500

/compute/babel-13-33/sbharad2/expdir/asr_fast.fold2/inference_ctc_weight0.0_maxlenratio-1_asr_model_valid.acc.best

WER

dataset Snt Wrd Corr Sub Del Ins Err S.Err
org/val2 400 400 97.0 3.0 0.0 0.0 3.0 3.0

CER

dataset Snt Wrd Corr Sub Del Ins Err S.Err
org/val2 400 5520 99.7 0.3 0.0 0.0 0.3 3.0

TER

dataset Snt Wrd Corr Sub Del Ins Err S.Err

ASR config

expand 
config: conf/beats_classification.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: /compute/babel-13-33/sbharad2/expdir/asr_fast.fold2
ngpu: 1
seed: 0
num_workers: 2
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: true
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 1000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 1
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: true
wandb_project: BEATs-ESC
wandb_id: null
wandb_entity: shikhar
wandb_name: fast.fold2
wandb_model_log_interval: 0
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- /compute/babel-13-33/sbharad2/expdir/asr_stats_raw_2_word/train/speech_shape
- /compute/babel-13-33/sbharad2/expdir/asr_stats_raw_2_word/train/text_shape.word
valid_shape_file:
- /compute/babel-13-33/sbharad2/expdir/asr_stats_raw_2_word/valid/speech_shape
- /compute/babel-13-33/sbharad2/expdir/asr_stats_raw_2_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 100000
- 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:
-   - /compute/babel-13-33/sbharad2/dumpdir/raw/train2/wav.scp
    - speech
    - sound
-   - /compute/babel-13-33/sbharad2/dumpdir/raw/train2/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /compute/babel-13-33/sbharad2/dumpdir/raw/val2/wav.scp
    - speech
    - sound
-   - /compute/babel-13-33/sbharad2/dumpdir/raw/val2/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: adamw
optim_conf:
    lr: 0.0001
    weight_decay: 0.01
    betas:
    - 0.9
    - 0.98
scheduler: cosineannealingwarmuprestarts
scheduler_conf:
    first_cycle_steps: 6000
    warmup_steps: 300
    max_lr: 0.0001
    min_lr: 5.0e-06
token_list:
- <blank>
- <unk>
- audio_class:0
- audio_class:14
- audio_class:36
- audio_class:19
- audio_class:30
- audio_class:34
- audio_class:9
- audio_class:22
- audio_class:48
- audio_class:41
- audio_class:47
- audio_class:31
- audio_class:17
- audio_class:45
- audio_class:8
- audio_class:15
- audio_class:46
- audio_class:37
- audio_class:32
- audio_class:16
- audio_class:25
- audio_class:4
- audio_class:3
- audio_class:27
- audio_class:43
- audio_class:12
- audio_class:40
- audio_class:29
- audio_class:10
- audio_class:7
- audio_class:26
- audio_class:6
- audio_class:44
- audio_class:23
- audio_class:20
- audio_class:49
- audio_class:24
- audio_class:39
- audio_class:28
- audio_class:18
- audio_class:2
- audio_class:35
- audio_class:38
- audio_class:21
- audio_class:1
- audio_class:11
- audio_class:42
- audio_class:5
- audio_class:33
- audio_class:13
- <sos/eos>
init: xavier_normal
input_size: 1
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: word
bpemodel: null
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: null
normalize_conf: {}
model: espnet
model_conf:
    ctc_weight: 0.0
    lsm_weight: 0.1
    length_normalized_loss: true
preencoder: null
preencoder_conf: {}
encoder: beats
encoder_conf:
    beats_ckpt_path: /compute/babel-13-33/sbharad2/models/BEATs/BEATs_iter3.pt
    fbank_mean: 11.72215
    fbank_std: 10.60431
    beats_config:
        layer_wise_gradient_decay_ratio: 0.2
        encoder_layerdrop: 0.1
        dropout: 0.0
    specaug_config:
        apply_time_warp: true
        apply_freq_mask: false
        freq_mask_width_range:
        - 0
        - 32
        num_freq_mask: 1
        apply_time_mask: true
        time_mask_width_ratio_range:
        - 0
        - 0.06
        num_time_mask: 1
    roll_augment: true
    roll_interval: 16000
    use_weighted_representation: false
postencoder: null
postencoder_conf: {}
decoder: linear_decoder
decoder_conf:
    pooling: mean
    dropout: 0.1
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202412'
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}
}
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