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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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## Welcome |
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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 |
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# Belle-whisper-large-v3-zh-punct |
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Fine tune whisper-large-v3-zh to enhance Chinese punctuation mark capabilities while maintaining comparable performance, |
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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. |
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The punctuation marks come from model [punc_ct-transformer_cn-en-common-vocab471067-large](https://www.modelscope.cn/models/iic/punc_ct-transformer_cn-en-common-vocab471067-large/), |
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and are added to the training datasets. |
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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-v3-zh-punct" |
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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-v3-zh-punct | 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) | [lora fine-tuning](https://github.com/shuaijiang/Whisper-Finetune) | |
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To incorporate punctuation marks without compromising performance, Lora fine-tuning was employed. |
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If you want to fine-thuning the model on your datasets, please reference to the [github repo](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-v3 | Chinese | 8.085 | 5.475 | 11.72 | 20.15 | 28.597 | |
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| Belle-whisper-large-v3-zh | Chinese | 2.781 | 3.786 | 8.865 | 11.246 | 16.440 | |
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| Belle-whisper-large-v3-zh-punct | Chinese | 2.945 | 3.808 | 8.998 | **10.973** | 17.196 | |
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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). |
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And the punctation marks of Belle-whisper-large-v3-zh-punct are removed to compute the CER. |
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## Citation |
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Please cite our paper and github when using our code, data or model. |
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``` |
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@misc{BELLE, |
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author = {BELLEGroup}, |
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title = {BELLE: Be Everyone's Large Language model Engine}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/LianjiaTech/BELLE}}, |
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} |
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``` |
