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Model: ctl/wav2vec2-large-xlsr-cantonese
Source: Original Platform

cer.py

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+# coding=utf-8
+# Copyright 2021 The HuggingFace Datasets 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. """
+
+import jiwer
+import jiwer.transforms as tr
+from typing import List
+
+import datasets
+
+class SentencesToListOfCharacters(tr.AbstractTransform):
+    def process_string(self, s: str):
+        return list(s)
+
+    def process_list(self, inp: List[str]):
+        chars = []
+
+        for sentence in inp:
+            chars.extend(self.process_string(sentence))
+
+        return chars
+
+
+cer_transform = tr.Compose(
+    [
+        tr.RemoveMultipleSpaces(),
+        tr.Strip(),
+        SentencesToListOfCharacters(),
+    ]
+)
+
+
+_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 operate on character insted 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 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
+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.
+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 = datasets.load_metric("cer")
+    >>> cer_score = cer.compute(predictions=predictions, references=references)
+    >>> print(cer_score)
+    0.34146341463414637
+"""
+
+
+@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class CER(datasets.Metric):
+    def _info(self):
+        return datasets.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):
+        return jiwer.wer(references, predictions, truth_transform=cer_transform, hypothesis_transform=cer_transform)