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--- |
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license: apache-2.0 |
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datasets: |
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- doof-ferb/vlsp2020_vinai_100h |
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- doof-ferb/fpt_fosd |
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- doof-ferb/infore1_25hours |
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- doof-ferb/infore2_audiobooks |
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- quocanh34/viet_vlsp |
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- linhtran92/final_dataset_500hrs_wer0 |
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- linhtran92/viet_youtube_asr_corpus_v2 |
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- google/fleurs |
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- mozilla-foundation/common_voice_16_1 |
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- vivos |
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language: ["vi"] |
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metrics: ["wer"] |
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library_name: transformers |
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base_model: openai/whisper-tiny |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: doof-ferb/whisper-tiny-vi |
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results: |
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- task: |
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type: automatic-speech-recognition |
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dataset: |
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type: mozilla-foundation/common_voice_16_1 |
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name: Mozilla CommonVoice (Vietnamese) v16.1 |
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config: vi |
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split: test |
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metrics: |
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- type: wer |
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value: 26.6 |
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verified: false |
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- task: |
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type: automatic-speech-recognition |
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dataset: |
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type: google/fleurs |
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name: Google FLEURS (Vietnamese) |
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config: vi_vn |
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split: test |
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metrics: |
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- type: wer |
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value: 37.1 |
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verified: false |
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- task: |
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type: automatic-speech-recognition |
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dataset: |
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type: vivos |
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name: ĐHQG TPHCM VIVOS |
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split: test |
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metrics: |
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- type: wer |
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value: 18.7 |
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verified: false |
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--- |
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whisper tiny fine-tuned on a very big collection of vietnamese speech datasets |
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TODO: |
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- [x] training then publish checkpoint (*no ETA*) |
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- [x] evaluate WER on Common Voice & FLEURS |
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- [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper` |
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- [ ] convert to ONNX: to try `k2-fsa/sherpa-onnx` & `zhuzilin/whisper-openvino` |
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21k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2) |
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all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi |
