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@@ -38,11 +38,11 @@ transcription = transcriber("my_audio.wav")
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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-v2 | Chinese | 8.818% | 6.183% | 12.343% | 26.413% | 31.917% |
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- | Belle-whisper-large-v2-zh | Chinese | 2.549% | 3.746% | 8.503% | 14.598% | 16.289% |
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  ## Citation
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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-v2 | Chinese | 8.818 | 6.183 | 12.343 | 26.413 | 31.917 |
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+ | Belle-whisper-large-v2-zh | Chinese | 2.549 | 3.746 | 8.503 | 14.598 | 16.289 |
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  ## Citation
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