File size: 1,722 Bytes
8960503 828e0c2 8960503 828e0c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
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
|