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# Copyright The Lightning team.
#
# 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.
from typing import Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.text.helper import _edit_distance
def _wer_update(
preds: Union[str, list[str]],
target: Union[str, list[str]],
) -> tuple[Tensor, Tensor]:
"""Update the wer score with the current set of references and predictions.
Args:
preds: Transcription(s) to score as a string or list of strings
target: Reference(s) for each speech input as a string or list of strings
Returns:
Number of edit operations to get from the reference to the prediction, summed over all samples
Number of words overall references
"""
if isinstance(preds, str):
preds = [preds]
if isinstance(target, str):
target = [target]
errors = tensor(0, dtype=torch.float)
total = tensor(0, dtype=torch.float)
for pred, tgt in zip(preds, target):
pred_tokens = pred.split()
tgt_tokens = tgt.split()
errors += _edit_distance(pred_tokens, tgt_tokens)
total += len(tgt_tokens)
return errors, total
def _wer_compute(errors: Tensor, total: Tensor) -> Tensor:
"""Compute the word error rate.
Args:
errors: Number of edit operations to get from the reference to the prediction, summed over all samples
total: Number of words overall references
Returns:
Word error rate score
"""
return errors / total
def word_error_rate(preds: Union[str, list[str]], target: Union[str, list[str]]) -> Tensor:
"""Word error rate (WordErrorRate_) is a common metric of performance of an automatic speech recognition system.
This value indicates the percentage of words that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
Args:
preds: Transcription(s) to score as a string or list of strings
target: Reference(s) for each speech input as a string or list of strings
Returns:
Word error rate score
Examples:
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> word_error_rate(preds=preds, target=target)
tensor(0.5000)
"""
errors, total = _wer_update(preds, target)
return _wer_compute(errors, total)
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