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@@ -44,8 +44,8 @@ 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 | Parameters(M) |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 |1550 |Chinese | 8.818% | 6.183% | 12.343% | 26.413% | 31.917% |
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  | distilwhisper-large-v2 |756| Chinese | - | - | - | - | - |
 
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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 | Parameters(M) |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 |1550 |Chinese | 8.818% | 6.183% | 12.343% | 26.413% | 31.917% |
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  | distilwhisper-large-v2 |756| Chinese | - | - | - | - | - |