readme: ---
  tags:
  - espnet
  - audio
  - speech-recognition
  language: zh
  datasets:
  - commonvoice
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ASR model
  
  ### `espnet/shihlun-asr-commonvoice-zh-TW`
  This model was trained by Shih-Lun Wu using the commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
  ### Demo: How to use in ESPnet2
  ```bash
  cd espnet
  pip install -e .
  cd egs2/commonvoice/asr1
  ./asr.sh \
    --stage 1 \
    --stop_stage 13 \
    --nj 32 \
    --inference_nj 32 \
    --skip_train true \
    --train_set "train_zh_TW" \
    --valid_set "dev_zh_TW" \
    --test_sets "dev_zh_TW test_zh_TW" \
    --lang "zh_TW" \
    --local_data_opts "--lang zh-TW" \
    --speed_perturb_factors "0.9 1.0 1.1" \
    --lm_train_text "data/train_zh_TW/text" \
    --token_type bpe \
    --nbpe 2542 \
    --bpemode "unigram" \
    --bpe_train_text "data/train_zh_TW/text" \
    --use_lm false \
    --inference_asr_model "valid.acc.best.pth" \
    --download_model "espnet/shihlun-asr-commonvoice-zh-TW"
  ```
  
  <!-- Generated by scripts/utils/show_asr_result.sh -->
  ## RESULTS
  ### Environments
  - date: `Thu Sep  1 21:49:10 UTC 2022`
  - python version: `3.9.12 (main, Jun  1 2022, 11:38:51)  [GCC 7.5.0]`
  - espnet version: `espnet 202207`
  - pytorch version: `pytorch 1.12.1+cu102`
  - Git hash: `13db69d3befc3c82a5ff5a11e28bf79d5030603f`
    - Commit date: `Mon Aug 29 13:44:35 2022 +0000`
  
  ### asr_train_asr_conformer5_raw_zh_TW_bpe2542_sp_lr1.0
  #### CER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |inference_asr_model_valid.acc.best/dev_zh_TW|2627|22200|97.7|2.1|0.2|0.0|2.4|9.5|
  |inference_asr_model_valid.acc.best/test_zh_TW|2627|21991|98.0|1.6|0.4|0.1|2.1|7.7|
  
  #### TER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |inference_asr_model_valid.acc.best/dev_zh_TW|2627|24827|98.6|1.2|0.2|0.0|1.5|4.0|
  |inference_asr_model_valid.acc.best/test_zh_TW|2627|24618|98.8|0.9|0.4|0.1|1.3|3.4|