wasertech commited on
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Update constants.py

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  1. constants.py +1 -1
constants.py CHANGED
@@ -101,7 +101,7 @@ CUSTOM_MESSAGE = """## Using CommonVoice to approximate average WER for open dom
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  This space is a fork of the original [hf-audio/open_asr_leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard). It aims to demonstrate how the CommonVoice Test Set provides a relatively accurate approximation of the average WER/CER (Word Error Rate/Character Error Rate) at a significantly lower computational cost.
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  #### Why is this useful?
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- This opens way the to achieve standardized test set for most languages, enabling us to programmatically select a reasonably effective model for any language supported by CommonVoice.
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  For more context, [here](https://gist.github.com/wasertech/400ca3dd61f2d6f7f4f5495afbb32ef3) is the output of my ASR server when running without any specified model to load for various languages. It tries to score the most suitable model for any given language. Since metrics are mostly self-reported, sometimes in different format, it consistently picks an unadequate model.
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  This space is a fork of the original [hf-audio/open_asr_leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard). It aims to demonstrate how the CommonVoice Test Set provides a relatively accurate approximation of the average WER/CER (Word Error Rate/Character Error Rate) at a significantly lower computational cost.
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  #### Why is this useful?
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+ This opens the way to achieve standardized test set for most languages, enabling us to programmatically select a reasonably effective model for any language supported by CommonVoice.
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  For more context, [here](https://gist.github.com/wasertech/400ca3dd61f2d6f7f4f5495afbb32ef3) is the output of my ASR server when running without any specified model to load for various languages. It tries to score the most suitable model for any given language. Since metrics are mostly self-reported, sometimes in different format, it consistently picks an unadequate model.
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