roshansh_how2_asr_raw_ft_sum_valid.acc

Usage

import librosa
from espnet2.bin.asr_inference import Speech2Text

speech2text = Speech2Text.from_pretrained(model_tag="espnet/roshansh_how2_asr_raw_ft_sum_valid.acc")
# 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 ASR model

espnet/roshansh_how2_asr_raw_ft_sum_valid.acc

This model was trained by roshansh-cmu using how2 recipe in espnet.

Demo: How to use in ESPnet2

cd espnet
git checkout e6f42a9783a5d9eba0687c19417f933e890722d7
pip install -e .
cd egs2/how2/sum1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/roshansh_how2_asr_raw_ft_sum_valid.acc

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

RESULTS

Environments

• date: Mon Feb 7 15:24:21 EST 2022
• python version: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]
• espnet version: espnet 0.10.6a1
• pytorch version: pytorch 1.10.1
• Git hash: 04561cdf3b6c3bc1d51edb04c93b953759ef551d
  • Commit date: Mon Feb 7 09:06:12 2022 -0500

asr_raw_ft_sum

ASR config

<details><summary>expand</summary>

config: conf/train_asr_conformer_vid_lf.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_raw_ft_sum
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 8
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 45875
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: 100
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 10
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 5000
use_tensorboard: true
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:
- exp/asr_raw_utt_conformer/valid.acc.ave_10best.pth:::ctc
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 60000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_vid_sum/train/speech_shape
- exp/asr_stats_raw_vid_sum/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_vid_sum/valid/speech_shape
- exp/asr_stats_raw_vid_sum/valid/text_shape.bpe
batch_type: length
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/tr_2000h_sum_trim/wav.scp
    - speech
    - sound
-   - dump/raw/tr_2000h_sum_trim/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/cv05_sum_trim/wav.scp
    - speech
    - sound
-   - dump/raw/cv05_sum_trim/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: 0.001
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.5
    patience: 1
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- Q
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- '*'
- $
- '~'
- <sos/eos>
init: null
input_size: null
ctc_conf:
    ignore_nan_grad: true
model_conf:
    ctc_weight: 0.0
    lsm_weight: 0.15
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram1000/bpe.model
non_linguistic_symbols: data/nlsyms
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'
frontend: default
frontend_conf:
    n_fft: 512
    hop_length: 256
    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
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_vid_sum/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
    output_size: 512
    attention_heads: 8
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    normalize_before: true
    macaron_style: true
    pos_enc_layer_type: abs_pos
    selfattention_layer_type: lf_selfattn
    activation_type: swish
    use_cnn_module: true
    cnn_module_kernel: 31
    attention_windows:
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    - 40
    attention_dilation:
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    - 1
    attention_mode: tvm
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 512
    num_blocks: 6
    dropout_rate: 0.15
    positional_dropout_rate: 0.15
    self_attention_dropout_rate: 0.15
    src_attention_dropout_rate: 0.15
required:
- output_dir
- token_list
version: 0.10.0
distributed: true

</details>

Please cite the following paper if you use this recipe:

@misc{sharma2022speech,
      title={Speech Summarization using Restricted Self-Attention}, 
      author={Roshan Sharma and Shruti Palaskar and Alan W Black and Florian Metze},
      year={2022},
      eprint={2110.06263},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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##3={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}