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

python

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

Model	(Re)Sample Rate	Train Datasets	Fine-tuning (full or peft)
Belle-whisper-large-v3-zh-punct	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	lora 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(%) ↓

Model	Language Tag	aishell_1_test(↓)	aishell_2_test(↓)	wenetspeech_net(↓)	wenetspeech_meeting(↓)	HKUST_dev(↓)
whisper-large-v3	Chinese	8.085	5.475	11.72	20.15	28.597
Belle-whisper-large-v3-zh	Chinese	2.781	3.786	8.865	11.246	16.440
Belle-whisper-large-v3-zh-punct	Chinese	2.945	3.808	8.998	10.973	17.196

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}},
}