---
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|