large-v2 figure and arxiv url update

README.md

@@ -1,7 +1,7 @@
 # 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)
 
@@ -66,7 +66,7 @@ There are five model sizes, four with English-only versions, offering speed and
 
 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)