Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+metrics:
+- cer
+---
+
+## 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-turbo-zh
+Fine tune whisper-large-v3-turbo-zh to enhance Chinese speech recognition capabilities,
+Belle-whisper-large-v3-zh-punct demonstrates a x-y% relative improvement in performance to whisper-large-v3-turbo on Chinese ASR benchmarks, including AISHELL1, AISHELL2, WENETSPEECH, and HKUST.
+
+Same to Belle-whisper-large-v3-zh-punct, 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/),
+and are added to the training datasets.
+
+## Usage
+```python
+
+from transformers import pipeline
+
+transcriber = pipeline(
+  "automatic-speech-recognition", 
+  model="BELLE-2/Belle-whisper-large-v3-turbo-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-v3-turbo-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)  |   [lora fine-tuning](https://github.com/shuaijiang/Whisper-Finetune)   |    
+
+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](https://github.com/shuaijiang/Whisper-Finetune)
+
+    
+## 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 |
+| whisper-large-v3-turbo | Chinese |  8.639 | 6.014  |  13.507   |  20.313 | 37.324 |
+| Belle-whisper-large-v3-turbo-zh | Chinese |   2.x    | 3.x  |   8.x    | 11.x | 16.x |
+
+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}},
+}
+```