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---
license: apache-2.0
metrics:
- cer
---
# Belle-whisper-large-v2-zh
Fine tune whisper-large-v2 to improve Chinese speech recognition, Belle-whisper-large-v2-zh has 30-70% relative improvements on Chinese ASR benchmark(AISHELL1, AISHELL2, WENETSPEECH, HKUST).
## Usage
```python
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](https://openslr.magicdatatech.com/resources/33/) [AISHELL-2](https://www.aishelltech.com/aishell_2) [WenetSpeech](https://wenet.org.cn/WenetSpeech/) [HKUST](https://catalog.ldc.upenn.edu/LDC2005S15) | [full fine-tuning](https://github.com/shuaijiang/Whisper-Finetune) |
## CER
| Model | Language Tag | aishell_1_test |aishell_2_test| wenetspeech_net | wenetspeech_meeting | HKUST_dev|
|:----------------:|:-------:|:-----------:|:-----------:|:--------:|:-----------:|:-------:|
| whisper-large-v2 | Chinese | 0.08818 | 0.06183 | 0.12343 | 0.26413 | 0.31917 |
| Belle-whisper-large-v2-zh | Chinese | 0.02549 | 0.03746 | 0.08503 | 0.14598 | 0.16289 |
|