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README.md
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@@ -40,19 +40,19 @@ See [https://github.com/kotoba-tech/kotoba-whisper](https://github.com/kotoba-te
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Due to the nature of cascaded approach, the pipeline has additional complexity compared to the single end2end OpenAI whisper models for the sake of high accuracy.
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Following table shows the mean inference time in second averaged over 10 trials on audio sample with different durations.
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| model | 10 | 30 | 60 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B)) | 0.173 | 0.247 | 0.352 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B)) | 0.173 | 0.24 | 0.348 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B)) | 0.17 | 0.245 | 0.348 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M)) | 0.108 | 0.179 | 0.283 |
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| [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 0.061 | 0.184 | 0.372 |
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| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 0.062 | 0.199 | 0.415 |
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| [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 0.062 | 0.183 | 0.363 |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 0.045 | 0.132 | 0.266 |
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| [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 0.135 | 0.376 | 0.631 |
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| [openai/whisper-base](https://huggingface.co/openai/whisper-base) | 0.054 | 0.108 | 0.231 |
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| [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 0.045 | 0.124 | 0.208 |
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## Usage
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Here is an example to translate Japanese speech into English text translation.
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Due to the nature of cascaded approach, the pipeline has additional complexity compared to the single end2end OpenAI whisper models for the sake of high accuracy.
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Following table shows the mean inference time in second averaged over 10 trials on audio sample with different durations.
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| model | 10 | 30 | 60 | 300 |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|------:|------:|------:|
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B)) | 0.173 | 0.247 | 0.352 | 1.772 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B)) | 0.173 | 0.24 | 0.348 | 1.515 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B)) | 0.17 | 0.245 | 0.348 | 1.882 |
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| [japanese-asr/ja-cascaded-s2t-translation](https://huggingface.co/japanese-asr/ja-cascaded-s2t-translation) ([facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M)) | 0.108 | 0.179 | 0.283 | 1.33 |
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| [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 0.061 | 0.184 | 0.372 | 1.804 |
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| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 0.062 | 0.199 | 0.415 | 1.854 |
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| [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 0.062 | 0.183 | 0.363 | 1.899 |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 0.045 | 0.132 | 0.266 | 1.368 |
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| [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 0.135 | 0.376 | 0.631 | 3.495 |
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| [openai/whisper-base](https://huggingface.co/openai/whisper-base) | 0.054 | 0.108 | 0.231 | 1.019 |
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| [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 0.045 | 0.124 | 0.208 | 0.838 |
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## Usage
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Here is an example to translate Japanese speech into English text translation.
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