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@@ -13,7 +13,7 @@ Similar to distilwhisper-large-v2, Belle-distilwhisper-large-v2-zh is **5.8 time
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  Despite having 51% fewer parameters, Belle-distilwhisper-large-v2-zh achieves a relative improvement of **-3% to 35%** over whisper-large-v2.
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- It's important to note that distilwhisper-large-v2 cannot transcribe Chinese (it only outputs English) in the Chinese ASR benchmarks AISHELL1, AISHELL2, WENETSPEECH, and HKUST.
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  ## Usage
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  ```python
@@ -47,9 +47,9 @@ If you want to fine-thuning the model on your datasets, please reference to the
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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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- | Belle-distilwhisper-large-v2-zh| 756 | Chinese | 5.958 | 6.477 | 12.786 | 17.039 | 20.771 |
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  ## Citation
 
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  Despite having 51% fewer parameters, Belle-distilwhisper-large-v2-zh achieves a relative improvement of **-3% to 35%** over whisper-large-v2.
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+ It's important to note that the original distilwhisper-large-v2 cannot transcribe Chinese (it only outputs English).
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  ## Usage
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  ```python
 
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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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+ | Belle-distilwhisper-large-v2-zh| 756 | Chinese | 5.958% | 6.477% | 12.786% | 17.039% | 20.771% |
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  ## Citation