From 0f39c89d9212e4d0c64b915cf7ba3c1f0b59fecc Mon Sep 17 00:00:00 2001 From: Mikko Vedru Date: Tue, 17 Jan 2023 09:46:42 +0200 Subject: [PATCH] Update README.md (#804) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 74c434d..281561b 100644 --- a/README.md +++ b/README.md @@ -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-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). +Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) 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). The smaller is better. ![WER breakdown by language](language-breakdown.svg)