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wer.py
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wer.py
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# Copyright The PyTorch 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 Any, List, Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.text.wer import _wer_compute, _wer_update
from torchmetrics.metric import Metric
class WordErrorRate(Metric):
r"""Word error rate (WordErrorRate_) is a common metric of the 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.
Word error rate can then be computed as:
.. math::
WER = \frac{S + D + I}{N} = \frac{S + D + I}{S + D + C}
where:
- :math:`S` is the number of substitutions,
- :math:`D` is the number of deletions,
- :math:`I` is the number of insertions,
- :math:`C` is the number of correct words,
- :math:`N` is the number of words in the reference (:math:`N=S+D+C`).
Compute WER score of transcribed segments against references.
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
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"]
>>> metric = WordErrorRate()
>>> metric(preds, target)
tensor(0.5000)
"""
is_differentiable: bool = False
higher_is_better: bool = False
full_state_update: bool = False
error: Tensor
total: Tensor
def __init__(
self,
**kwargs: Any,
):
super().__init__(**kwargs)
self.add_state("errors", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state("total", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) -> None: # type: ignore
"""Store references/predictions for computing Word Error Rate scores.
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
"""
errors, total = _wer_update(preds, target)
self.errors += errors
self.total += total
def compute(self) -> Tensor:
"""Calculate the word error rate.
Returns:
Word error rate score
"""
return _wer_compute(self.errors, self.total)