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
| 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}},
}