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mer.py
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mer.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, Callable, List, Optional, Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.text.mer import _mer_compute, _mer_update
from torchmetrics.metric import Metric
class MatchErrorRate(Metric):
r"""
Match error rate (MatchErrorRate_) is a common metric of the performance of an automatic speech recognition system.
This value indicates the percentage of words that were incorrectly predicted and inserted.
The lower the value, the better the performance of the ASR system with a MatchErrorRate of 0 being a perfect score.
Match error rate can then be computed as:
.. math::
mer = \frac{S + D + I}{N + I} = \frac{S + D + I}{S + D + C + I}
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 words,
- N is the number of words in the reference (N=S+D+C).
Args:
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called.
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather
Returns:
Match error rate score
Examples:
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> metric = MatchErrorRate()
>>> metric(preds, target)
tensor(0.4444)
"""
is_differentiable = False
higher_is_better = False
error: Tensor
total: Tensor
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
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( # type: ignore
self,
preds: Union[str, List[str]],
target: Union[str, List[str]],
) -> None:
"""Store references/predictions for computing Match 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 = _mer_update(
preds,
target,
)
self.errors += errors
self.total += total
def compute(self) -> Tensor:
"""Calculate the Match error rate.
Returns:
Match error rate
"""
return _mer_compute(self.errors, self.total)