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--- |
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license: apache-2.0 |
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metrics: |
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- cer |
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--- |
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# Belle-whisper-large-v2-zh |
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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). |
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## Usage |
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```python |
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from transformers import pipeline |
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transcriber = pipeline( |
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"automatic-speech-recognition", |
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model="BELLE-2/Belle-whisper-large-v2-zh" |
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) |
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transcriber.model.config.forced_decoder_ids = ( |
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transcriber.tokenizer.get_decoder_prompt_ids( |
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language="zh", |
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task="transcribe" |
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) |
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) |
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transcription = transcriber("my_audio.wav") |
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``` |
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## Fine-tuning |
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| Model | (Re)Sample Rate | Train Datasets | Fine-tuning (full or peft) | |
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|:----------------:|:-------:|:----------------------------------------------------------:|:-----------:| |
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| 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) | |
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## CER |
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| Model | Language Tag | aishell_1_test |aishell_2_test| wenetspeech_net | wenetspeech_meeting | HKUST_dev| |
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|:----------------:|:-------:|:-----------:|:-----------:|:--------:|:-----------:|:-------:| |
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| whisper-large-v2 | Chinese | 0.08818 | 0.06183 | 0.12343 | 0.26413 | 0.31917 | |
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| Belle-whisper-large-v2-zh | Chinese | 0.02549 | 0.03746 | 0.08503 | 0.14598 | 0.16289 | |
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