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| # Copyright 2021 The HuggingFace Evaluate Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Character Error Ratio (CER) metric. """ | |
| from typing import List | |
| import datasets | |
| import jiwer | |
| import jiwer.transforms as tr | |
| from datasets.config import PY_VERSION | |
| from packaging import version | |
| import evaluate | |
| if PY_VERSION < version.parse("3.8"): | |
| import importlib_metadata | |
| else: | |
| import importlib.metadata as importlib_metadata | |
| SENTENCE_DELIMITER = "" | |
| if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"): | |
| class SentencesToListOfCharacters(tr.AbstractTransform): | |
| def __init__(self, sentence_delimiter: str = " "): | |
| self.sentence_delimiter = sentence_delimiter | |
| def process_string(self, s: str): | |
| return list(s) | |
| def process_list(self, inp: List[str]): | |
| chars = [] | |
| for sent_idx, sentence in enumerate(inp): | |
| chars.extend(self.process_string(sentence)) | |
| if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(inp) - 1: | |
| chars.append(self.sentence_delimiter) | |
| return chars | |
| cer_transform = tr.Compose( | |
| [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)] | |
| ) | |
| else: | |
| cer_transform = tr.Compose( | |
| [ | |
| tr.RemoveMultipleSpaces(), | |
| tr.Strip(), | |
| tr.ReduceToSingleSentence(SENTENCE_DELIMITER), | |
| tr.ReduceToListOfListOfChars(), | |
| ] | |
| ) | |
| _CITATION = """\ | |
| @inproceedings{inproceedings, | |
| author = {Morris, Andrew and Maier, Viktoria and Green, Phil}, | |
| year = {2004}, | |
| month = {01}, | |
| pages = {}, | |
| title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Character 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. | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Computes CER score of transcribed segments against references. | |
| Args: | |
| references: list of references for each speech input. | |
| predictions: list of transcribtions to score. | |
| concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result. | |
| Returns: | |
| (float): the character error rate | |
| Examples: | |
| >>> predictions = ["this is the prediction", "there is an other sample"] | |
| >>> references = ["this is the reference", "there is another one"] | |
| >>> cer = evaluate.load("cer") | |
| >>> cer_score = cer.compute(predictions=predictions, references=references) | |
| >>> print(cer_score) | |
| 0.34146341463414637 | |
| """ | |
| class CER(evaluate.Metric): | |
| def _info(self): | |
| return evaluate.MetricInfo( | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "predictions": datasets.Value("string", id="sequence"), | |
| "references": datasets.Value("string", id="sequence"), | |
| } | |
| ), | |
| codebase_urls=["https://github.com/jitsi/jiwer/"], | |
| reference_urls=[ | |
| "https://en.wikipedia.org/wiki/Word_error_rate", | |
| "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates", | |
| ], | |
| ) | |
| def _compute(self, predictions, references, concatenate_texts=False): | |
| if concatenate_texts: | |
| return jiwer.compute_measures( | |
| references, | |
| predictions, | |
| truth_transform=cer_transform, | |
| hypothesis_transform=cer_transform, | |
| )["wer"] | |
| incorrect = 0 | |
| total = 0 | |
| for prediction, reference in zip(predictions, references): | |
| measures = jiwer.compute_measures( | |
| reference, | |
| prediction, | |
| truth_transform=cer_transform, | |
| hypothesis_transform=cer_transform, | |
| ) | |
| incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"] | |
| total += measures["substitutions"] + measures["deletions"] + measures["hits"] | |
| return incorrect / total | |