metadata
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:
- training then publish checkpoint
- 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