eval_cer.py

""" Character Error Ratio (CER) metric. """
from typing import List
import datasets, evaluate , jiwer
import jiwer.transforms as tr
from datasets.config import PY_VERSION
from packaging import version


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 standard metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER) but operates on characters instead of words. Please refer to the docs of WER for further information.

The 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, particularly 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 transcriptions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for a more accurate result.
Returns:
    (float): the character error rate

Examples for the Hungarian Language:
    >>> # Colab usage 
    >>> !pip install evaluate jiwer
    >>> import evaluate 
    >>> from evaluate import load  

    >>> predictions = ["ez a jóslat", "van egy másik minta is"]
    >>> references = ["ez a hivatkozás", "van még egy"]
    >>> cer = evaluate.load("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    >>> 0.9615384615384616
"""



@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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