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}},
}