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Error rate calculation for handwriting recognition. pip install editdistance==0.4
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| import editdistance | |
| def cer(y_true: str, y_pred: str) -> float: | |
| assert isinstance(y_true, str) and isinstance(y_pred, str) | |
| n_err = editdistance.eval(y_true.rstrip(), y_pred.rstrip()) | |
| return n_err / len(y_true) | |
| def wer(transcript: str, reference: str) -> float: | |
| assert isinstance(transcript, str) and isinstance(reference, str) | |
| transcript = transcript.split() | |
| reference = reference.split() | |
| n_err = editdistance.eval(transcript, reference) | |
| return n_err / len(reference) | |
| if __name__ == "__main__": | |
| # exact case | |
| cer_score = cer("hello world", "hello world") | |
| wer_score = wer("hello world", "hello world") | |
| assert cer_score == 0. | |
| assert wer_score == 0. | |
| # one deletion case | |
| cer_score = cer("hello world", "hello world!") | |
| wer_score = wer("hello world", "hello world!") | |
| assert cer_score == 1. / 11. | |
| assert wer_score == 1. / 2. | |
| # one substitution case | |
| cer_score = cer("hello world", "hallo world") | |
| wer_score = wer("hello world", "hallo world") | |
| assert cer_score == 1. / 11. | |
| assert wer_score == 1. / 2. | |
| # one insertion case | |
| cer_score = cer("hello world", "ello world") | |
| wer_score = wer("hello world", "ello world") | |
| assert cer_score == 1. / 11. | |
| assert wer_score == 1. / 2. |
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