Update README.md

README.md

@@ -38,7 +38,7 @@ 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)   |    
+| 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)  |   [full 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)
@@ -51,8 +51,7 @@ If you want to fine-thuning the model on your datasets, please reference to the
 | 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.
+It is worth mentioning that compared to whisper-large-v3-turbo, Belle-whisper-large-v3-turbo-zh even has a significant improvement in complex acoustic scenes(such as wenetspeech_meeting).
 
 ## Citation