---
license: apache-2.0
datasets:
  - doof-ferb/vlsp2020_vinai_100h
  - doof-ferb/fpt_fosd
  - doof-ferb/infore1_25hours
  - doof-ferb/infore2_audiobooks
  - quocanh34/viet_vlsp
  - linhtran92/final_dataset_500hrs_wer0
  - linhtran92/viet_youtube_asr_corpus_v2
  - google/fleurs
  - mozilla-foundation/common_voice_16_1
  - vivos
language: ["vi"]
metrics: ["wer"]
library_name: transformers
base_model: openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
model-index:
- name: doof-ferb/whisper-tiny-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: 26.6
        verified: false
  - task:
      type: automatic-speech-recognition
    dataset:
      type: google/fleurs
      name: Google FLEURS (Vietnamese)
      config: vi_vn
      split: test
    metrics:
      - type: wer
        value: 37.1
        verified: false
  - task:
      type: automatic-speech-recognition
    dataset:
      type: vivos
      name: ĐHQG TPHCM VIVOS
      split: test
    metrics:
      - type: wer
        value: 18.7
        verified: false
---

whisper tiny fine-tuned on a very big collection of vietnamese speech datasets

TODO:
- [x] training then publish checkpoint
- [x] evaluate WER on Common Voice & FLEURS & VIVOS
- [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper`
- [ ] convert to ONNX: to try https://github.com/k2-fsa/sherpa-onnx & https://github.com/zhuzilin/whisper-openvino
- [ ] convert to TensorRT: https://github.com/openai/whisper/discussions/169

21k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2)

manually evaluate WER on test set - vietnamese part:
| @ `float16` | `CommonVoice v16.1` | `FLEURS` | `VIVOS` |
|---|---|---|---|
| original `whisper-tiny` | >100% | 88.6% | 62.5% |
| this model | 26.6% | 37.1% | 18.7% |

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

usage example:
```python
import torch
from transformers import pipeline

PIPE = pipeline(task="automatic-speech-recognition", model="doof-ferb/whisper-tiny-vi", device="cuda:0", torch_dtype=torch.float16)
PIPE_KWARGS = {"language": "vi", "task": "transcribe"}

PIPE("audio.mp3", generate_kwargs=PIPE_KWARGS)["text"]
```