BELLE-2/Belle-whisper-large-v3-zh-punct

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-v3-zh-punct

Fine tune whisper-large-v3-zh to enhance Chinese punctuation mark capabilities while maintaining comparable performance, Belle-whisper-large-v3-zh-punct demonstrates similar performance to Belle-whisper-large-v3-zh on Chinese ASR benchmarks, including AISHELL1, AISHELL2, WENETSPEECH, and HKUST.

The punctuation marks come from model punc_ct-transformer_cn-en-common-vocab471067-large, and are added to the training datasets.

Usage

from transformers import pipeline

transcriber = pipeline(
  "automatic-speech-recognition", 
  model="BELLE-2/Belle-whisper-large-v3-zh-punct"
)

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

transcription = transcriber("my_audio.wav") 

Fine-tuning

To incorporate punctuation marks without compromising performance, Lora fine-tuning was employed. If you want to fine-thuning the model on your datasets, please reference to the github repo

CER(%) ↓

It is worth mentioning that compared to Belle-whisper-large-v3-zh, Belle-whisper-large-v3-zh-punct even has a slight improvement in complex acoustic scenes(such as wenetspeech_meeting). And the punctation marks of Belle-whisper-large-v3-zh-punct are removed to compute the CER.

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}},
}