---
license: apache-2.0
datasets:
  - google/fleurs
  - mozilla-foundation/common_voice_16_1
  - vivos
  - doof-ferb/vlsp2020_vinai_100h
  - doof-ferb/fpt_fosd
  - doof-ferb/infore1_25hours
language: ["vi"]
library_name: peft
base_model: openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
metrics: ["wer"]
model-index:
- name: doof-ferb/whisper-large-peft-lora-vi
  results:
  - task:
      type: automatic-speech-recognition
    dataset:
      type: mozilla-foundation/common_voice_16_1
      name: Mozilla CommonVoice (Vietnamese) v16.1
      config: vi
      split: test
    metrics:
      - type: wer
        value: 14.7
        verified: false
  - task:
      type: automatic-speech-recognition
    dataset:
      type: google/fleurs
      name: Google FLEURS (Vietnamese)
      config: vi_vn
      split: test
    metrics:
      - type: wer
        value: 14.7
        verified: false
  - task:
      type: automatic-speech-recognition
    dataset:
      type: vivos
      name: ĐHQG TPHCM VIVOS
      split: test
    metrics:
      - type: wer
        value: 9.4
        verified: false
---

whisper large v3 PEFT LoRA trained on a big collection of vietnamese speech datasets

TODO:
- [x] training then publish checkpoint
- [x] evaluate WER on Common Voice & FLEURS & VIVOS

3.6k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2), train 3.6% of 1.6B params

manually evaluate WER on test set - vietnamese part:
| @ `float16` | `CommonVoice v16.1` | `FLEURS` | `VIVOS` |
|---|---|---|---|
| original `whisper-large-v3` | 16.2% | 8.3% | 12.3% |
| this LoRA | 14.7% | 14.7% | 9.4% |

all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi