readme: ---
  tags:
  - espnet
  - audio
  - speech-recognition
  - openai-whisper
  language: en
  datasets:
  - librispeech
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ASR model
  
  ### `espnet/shihlun_asr_whisper_medium_finetuned_librispeech100`
  This model was trained by Shih-Lun Wu (slseanwu) using the librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  ```bash
  cd espnet
  pip install -e .
  cd egs2/librispeech_100/asr1
  
  train_set="train_clean_100"
  valid_set="dev"
  test_sets="test_clean test_other dev_clean dev_other"
  asr_tag=whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs
  asr_config=conf/tuning/train_asr_whisper_full.yaml
  inference_config=conf/decode_asr_whisper_noctc_greedy.yaml
  
  ./asr.sh \
      --skip_data_prep false \
      --skip_train true \
      --skip_eval false \
      --lang en \
      --ngpu 1 \
      --nj 4 \
      --stage 1 \
      --stop_stage 13 \
      --gpu_inference true \
      --inference_nj 1 \
      --token_type whisper_multilingual \
      --feats_normalize '' \
      --max_wav_duration 30 \
      --speed_perturb_factors "0.9 1.0 1.1" \
      --audio_format "flac.ark" \
      --feats_type raw \
      --use_lm false \
      --cleaner whisper_en \
      --asr_tag "${asr_tag}" \
      --asr_config "${asr_config}" \
      --inference_config "${inference_config}" \
      --inference_asr_model valid.acc.ave.pth \
      --train_set "${train_set}" \
      --valid_set "${valid_set}" \
      --test_sets "${test_sets}" "$@"
  ```
  
  <!-- Generated by scripts/utils/show_asr_result.sh -->
  # RESULTS
  ## Environments
  - date: `Mon Jan  9 23:06:34 CST 2023`
  - python version: `3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0]`
  - espnet version: `espnet 202211`
  - pytorch version: `pytorch 1.12.1`
  - Git hash: `d89be931dcc8f61437ac49cbe39a773f2054c50c`
    - Commit date: `Mon Jan 9 11:06:45 2023 -0600`
  
  ## asr_whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs
  ### WER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dev_clean|2703|54798|97.7|1.9|0.3|0.3|2.6|30.1|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dev_other|2864|51528|95.3|4.3|0.4|0.6|5.3|45.4|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/test_clean|2620|53027|97.6|2.1|0.3|0.4|2.7|30.9|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/test_other|2939|52882|95.1|4.4|0.5|0.7|5.6|47.5|
  
  ### CER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dev_clean|2703|287287|99.3|0.3|0.4|0.3|1.0|30.1|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dev_other|2864|265648|98.3|1.0|0.7|0.6|2.3|45.4|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/test_clean|2620|280691|99.3|0.3|0.3|0.3|1.0|30.9|
  |decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/test_other|2939|271738|98.3|1.0|0.7|0.7|2.4|47.5|
  