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@@ -52,4 +52,13 @@ transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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  transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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  # ['ζœ¨ζ‘γ•γ‚“γ«ι›»θ©±γ‚’θ²Έγ—γ¦γ‚‚γ‚‰γ„γΎγ—γŸγ€‚']
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- ```
 
 
 
 
 
 
 
 
 
 
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  transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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  # ['ζœ¨ζ‘γ•γ‚“γ«ι›»θ©±γ‚’θ²Έγ—γ¦γ‚‚γ‚‰γ„γΎγ—γŸγ€‚']
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+ ```
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+
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+ ## Test Result
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+
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+ In the table below I report the Character Error Rate (CER) of the model tested on [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K) dataset.
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+ | Model | CER |
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+ | ------------- | ------------- |
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+ | Ivydata/whisper-small-japanese | **23.10%** |
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+ | Ivydata/wav2vec2-large-xlsr-53-japanese | **27.87%** |
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+ | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | 34.18% |