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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
 
 
 
 
 
 
 
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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
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+ - [x] evaluate WER on Common Voice & FLEURS & VIVOS
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  - [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper`
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+ - [ ] convert to ONNX: to try https://github.com/k2-fsa/sherpa-onnx & https://github.com/zhuzilin/whisper-openvino
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+ - [ ] convert to TensorRT: https://github.com/openai/whisper/discussions/169
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  21k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2)
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+ manually evaluate WER on test set - vietnamese part:
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+ | @ `float16` | `CommonVoice v16.1` | `FLEURS` | `VIVOS` |
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+ |---|---|---|---|
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+ | original `whisper-tiny` | >100% | 88.6% | 62.5% |
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+ | this model | 26.6% | 37.1% | 18.7% |
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+
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+ all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi