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""" Character Error Ratio (CER) metric. """ |
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import jiwer |
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import jiwer.transforms as tr |
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from typing import List |
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import datasets |
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class SentencesToListOfCharacters(tr.AbstractTransform): |
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def process_string(self, s: str): |
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return list(s) |
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def process_list(self, inp: List[str]): |
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chars = [] |
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for sentence in inp: |
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chars.extend(self.process_string(sentence)) |
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return chars |
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cer_transform = tr.Compose( |
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[ |
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tr.RemoveMultipleSpaces(), |
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tr.Strip(), |
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SentencesToListOfCharacters(), |
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] |
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) |
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_CITATION = """\ |
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@inproceedings{inproceedings, |
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author = {Morris, Andrew and Maier, Viktoria and Green, Phil}, |
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year = {2004}, |
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month = {01}, |
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pages = {}, |
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title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Character error rate (CER) is a common metric of the performance of an automatic speech recognition system. |
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CER is similar to Word Error Rate (WER), but operate on character insted of 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 always a number between 0 and 1. This value indicates the percentage of characters that were incorrectly predicted. The lower the value, 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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_KWARGS_DESCRIPTION = """ |
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Computes CER score of transcribed segments against references. |
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Args: |
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references: list of references for each speech input. |
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predictions: list of transcribtions to score. |
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Returns: |
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(float): the character error rate |
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Examples: |
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>>> predictions = ["this is the prediction", "there is an other sample"] |
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>>> references = ["this is the reference", "there is another one"] |
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>>> cer = datasets.load_metric("cer") |
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>>> cer_score = cer.compute(predictions=predictions, references=references) |
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>>> print(cer_score) |
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0.34146341463414637 |
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""" |
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@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) |
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class CER(datasets.Metric): |
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def _info(self): |
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return datasets.MetricInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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inputs_description=_KWARGS_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"predictions": datasets.Value("string", id="sequence"), |
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"references": datasets.Value("string", id="sequence"), |
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} |
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), |
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codebase_urls=["https://github.com/jitsi/jiwer/"], |
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reference_urls=[ |
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"https://en.wikipedia.org/wiki/Word_error_rate", |
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"https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates", |
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], |
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) |
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def _compute(self, predictions, references): |
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return jiwer.wer(references, predictions, truth_transform=cer_transform, hypothesis_transform=cer_transform) |
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