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Browse files- constants.py +1 -1
constants.py
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@@ -97,7 +97,7 @@ are ranked based on their average WER scores, from lowest to highest.
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For more details on the individual datasets and how models are evaluated to give the ESB score, refer to the [ESB paper](https://arxiv.org/abs/2210.13352).
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"""
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CUSTOM_MESSAGE = """## Using CommonVoice to approximate average WER for open domain
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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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For more details on the individual datasets and how models are evaluated to give the ESB score, refer to the [ESB paper](https://arxiv.org/abs/2210.13352).
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"""
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CUSTOM_MESSAGE = """## Using CommonVoice to approximate average WER for open domain transcription
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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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