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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 Information Loss (WIL) metric. """
import datasets
from jiwer import process_words
import evaluate
_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.}
}y
"""
_DESCRIPTION = """\
Word Information Loss can be used to evaluate the performance of an automatic speech recognizer. It has information-theoretic backings, is symmetric between predictions and targets, and is bounded between 0 and 1.
The formula for WIL is
WIL = 1 - (C/P)(C/T)
where
C is the number of correct words,
P is the number of words in the prediction,
T is the number of words in the target.
This value measures the amount of information loss between two sentences. A score of 0 indicates that the prediction and target match perfectly.
"""
_KWARGS_DESCRIPTION = """
Compute the WIL between two sets of words.
Args:
targets: List of target words.
predictions: List of transcriptions to evaluate.
concatenate_texts (bool, default=False): Whether to concatenate the WIL of the concanated strings or the mean WIL for each pair.
Returns:
(float): the word information loss
Examples:
>>> predictions = ["this is a prediction", "there is an other sample"]
>>> targets = ["this is the target", "there is another one"]
>>> wil = evaluate.load("wil")
>>> wil_score = wil.compute(predictions=predictions, targets=targets)
>>> print(wil_score)
0.775
>>> wil_score = wil.compute(predictions=targets, targets=predictions)
>>> print(wil_score)
0.775
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class WIL(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",
],
)
def _compute(self, predictions=None, references=None, concatenate_texts=False):
if concatenate_texts:
return process_words(references, predictions).wil
else:
total = 0
for prediction, reference in zip(predictions, references):
measures = process_words(reference, prediction).wil
total += measures
return total/len(predictions)
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