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large-v2 figure and arxiv url update

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Jong Wook Kim
2022-12-09 00:12:39 -05:00
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README.md
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# Whisper
[[Blog]](https://openai.com/blog/whisper)
[[Paper]](https://cdn.openai.com/papers/whisper.pdf)
[[Paper]](https://arxiv.org/abs/2212.04356)
[[Model card]](model-card.md)
[[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)
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For English-only applications, the `.en` models tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
Whisper's performance varies widely depending on the language. The figure below shows a WER breakdown by languages of Fleurs dataset, using the `large` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://cdn.openai.com/papers/whisper.pdf).
Whisper's performance varies widely depending on the language. The figure below shows a WER breakdown by languages of Fleurs dataset, using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356).
![WER breakdown by language](language-breakdown.svg)