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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 _cer_update(
preds: Union[str, list[str]],
target: Union[str, list[str]],
) -> tuple[Tensor, Tensor]:
"""Update the cer 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 character 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
tgt_tokens = tgt
errors += _edit_distance(list(pred_tokens), list(tgt_tokens))
total += len(tgt_tokens)
return errors, total
def _cer_compute(errors: Tensor, total: Tensor) -> Tensor:
"""Compute the Character error rate.
Args:
errors: Number of edit operations to get from the reference to the prediction, summed over all samples
total: Number of characters over all references
Returns:
Character error rate score
"""
return errors / total
def char_error_rate(preds: Union[str, list[str]], target: Union[str, list[str]]) -> Tensor:
"""Compute Character Error Rate used for performance of an automatic speech recognition system.
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.
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:
Character error rate score
Examples:
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> char_error_rate(preds=preds, target=target)
tensor(0.3415)
"""
errors, total = _cer_update(preds, target)
return _cer_compute(errors, total)
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