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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. | |
| """ Word Error Ratio (WER) metric. """ | |
| import datasets | |
| from jiwer import compute_measures | |
| import evaluate | |
| _KWARGS_DESCRIPTION = """ | |
| Compute WER score of transcribed segments against references. | |
| Args: | |
| references: List of references for each speech input. | |
| predictions: List of transcriptions to score. | |
| concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively. | |
| Returns: | |
| (float): the word error rate | |
| Examples: | |
| >>> predictions = ["this is the prediction", "there is an other sample"] | |
| >>> references = ["this is the reference", "there is another one"] | |
| >>> wer = evaluate.load("wer") | |
| >>> wer_score = wer.compute(predictions=predictions, references=references) | |
| >>> print(wer_score) | |
| 0.5 | |
| """ | |
| class WER(evaluate.Metric): | |
| def _info(self): | |
| return evaluate.MetricInfo( | |
| 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", | |
| ], | |
| ) | |
| def _compute(self, predictions=None, references=None, concatenate_texts=False): | |
| if concatenate_texts: | |
| return compute_measures(references, predictions)["wer"] | |
| else: | |
| incorrect = 0 | |
| total = 0 | |
| for prediction, reference in zip(predictions, references): | |
| measures = compute_measures(reference, prediction) | |
| incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"] | |
| total += measures["substitutions"] + measures["deletions"] + measures["hits"] | |
| return incorrect / total | |