cer.py

 # coding=utf-8
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""" 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 words,
N is the number of words 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)
    0.5
"""


@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#whitespace",
            ],
        )

    def _compute(self, predictions, references):
        return jiwer.wer(references, predictions, truth_transform=cer_transform, hypothesis_transform=cer_transform)