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Welcome

If you find this model helpful, please like this model and star us on https://github.com/LianjiaTech/BELLE and https://github.com/shuaijiang/Whisper-Finetune

Belle-whisper-large-v2-zh

Fine tune whisper-large-v2 to enhance Chinese speech recognition capabilities, Belle-whisper-large-v2-zh demonstrates a 30-70% relative improvement in performance on Chinese ASR benchmarks, including AISHELL1, AISHELL2, WENETSPEECH, and HKUST.

Usage


from transformers import pipeline

transcriber = pipeline(
  "automatic-speech-recognition", 
  model="BELLE-2/Belle-whisper-large-v2-zh"
)

transcriber.model.config.forced_decoder_ids = (
  transcriber.tokenizer.get_decoder_prompt_ids(
    language="zh", 
    task="transcribe"
  )
)

transcription = transcriber("my_audio.wav") 

Fine-tuning

Model (Re)Sample Rate Train Datasets Fine-tuning (full or peft)
Belle-whisper-large-v2-zh 16KHz AISHELL-1 AISHELL-2 WenetSpeech HKUST full fine-tuning

If you want to fine-thuning the model on your datasets, please reference to the github repo

CER(%) ↓

Model Language Tag aishell_1_test(↓) aishell_2_test(↓) wenetspeech_net(↓) wenetspeech_meeting(↓) HKUST_dev(↓)
whisper-large-v2 Chinese 8.818 6.183 12.343 26.413 31.917
Belle-whisper-large-v2-zh Chinese 2.549 3.746 8.503 14.598 16.289

Citation

Please cite our paper and github when using our code, data or model.

@misc{BELLE,
  author = {BELLEGroup},
  title = {BELLE: Be Everyone's Large Language model Engine},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
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