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Browse filesCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
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title: CER
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sdk: gradio
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tags:
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- metric
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# Metric Card for CER
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## Metric description
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---
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title: CER
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emoji: 🤗
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colorFrom: blue
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colorTo: red
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sdk: gradio
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app_file: app.py
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pinned: false
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tags:
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- evaluate
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- metric
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description: >-
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Character error rate (CER) is a common metric of the performance of an
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automatic speech recognition system.
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CER is similar to Word Error Rate (WER), but operates on character instead of
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word. Please refer to docs of WER for further information.
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Character error rate can be computed as:
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CER = (S + D + I) / N = (S + D + I) / (S + D + C)
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where
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S is the number of substitutions,
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D is the number of deletions,
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I is the number of insertions,
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C is the number of correct characters,
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N is the number of characters in the reference (N=S+D+C).
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CER's output is not always a number between 0 and 1, in particular when there
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is a high number of insertions. This value is often associated to the
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percentage of characters that were incorrectly predicted. The lower the value,
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the better the
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performance of the ASR system with a CER of 0 being a perfect score.
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---
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# Metric Card for CER
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## Metric description
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