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Co-authored-by: Hervé BREDIN <[email protected]>
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# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
|
||
|
||
from .audio.diarization_error_rate import ( | ||
DiarizationErrorRate, | ||
FalseAlarmRate, | ||
MissedDetectionRate, | ||
SpeakerConfusionRate, | ||
) | ||
|
||
__all__ = [ | ||
"DiarizationErrorRate", | ||
"FalseAlarmRate", | ||
"MissedDetectionRate", | ||
"SpeakerConfusionRate", | ||
] |
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# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
|
||
|
||
from .diarization_error_rate import ( | ||
DiarizationErrorRate, | ||
FalseAlarmRate, | ||
MissedDetectionRate, | ||
SpeakerConfusionRate, | ||
) | ||
|
||
__all__ = [ | ||
"DiarizationErrorRate", | ||
"SpeakerConfusionRate", | ||
"MissedDetectionRate", | ||
"FalseAlarmRate", | ||
] |
119 changes: 119 additions & 0 deletions
119
pyannote/audio/torchmetrics/audio/diarization_error_rate.py
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# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
|
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import torch | ||
from torchmetrics import Metric | ||
|
||
from pyannote.audio.torchmetrics.functional.audio.diarization_error_rate import ( | ||
_der_compute, | ||
_der_update, | ||
) | ||
|
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|
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class DiarizationErrorRate(Metric): | ||
"""Diarization error rate | ||
Parameters | ||
---------- | ||
threshold : float, optional | ||
Threshold used to binarize predictions. Defaults to 0.5. | ||
Notes | ||
----- | ||
While pyannote.audio conventions is to store speaker activations with | ||
(batch_size, num_frames, num_speakers)-shaped tensors, this torchmetrics metric | ||
expects them to be shaped as (batch_size, num_speakers, num_frames) tensors. | ||
""" | ||
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higher_is_better = False | ||
is_differentiable = False | ||
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def __init__(self, threshold: float = 0.5): | ||
super().__init__() | ||
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self.threshold = threshold | ||
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self.add_state("false_alarm", default=torch.tensor(0.0), dist_reduce_fx="sum") | ||
self.add_state( | ||
"missed_detection", default=torch.tensor(0.0), dist_reduce_fx="sum" | ||
) | ||
self.add_state( | ||
"speaker_confusion", default=torch.tensor(0.0), dist_reduce_fx="sum" | ||
) | ||
self.add_state("speech_total", default=torch.tensor(0.0), dist_reduce_fx="sum") | ||
|
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def update( | ||
self, | ||
preds: torch.Tensor, | ||
target: torch.Tensor, | ||
) -> None: | ||
"""Compute and accumulate components of diarization error rate | ||
Parameters | ||
---------- | ||
preds : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped continuous predictions. | ||
target : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped (0 or 1) targets. | ||
Returns | ||
------- | ||
false_alarm : torch.Tensor | ||
missed_detection : torch.Tensor | ||
speaker_confusion : torch.Tensor | ||
speech_total : torch.Tensor | ||
Diarization error rate components accumulated over the whole batch. | ||
""" | ||
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false_alarm, missed_detection, speaker_confusion, speech_total = _der_update( | ||
preds, target, threshold=self.threshold | ||
) | ||
self.false_alarm += false_alarm | ||
self.missed_detection += missed_detection | ||
self.speaker_confusion += speaker_confusion | ||
self.speech_total += speech_total | ||
|
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def compute(self): | ||
return _der_compute( | ||
self.false_alarm, | ||
self.missed_detection, | ||
self.speaker_confusion, | ||
self.speech_total, | ||
) | ||
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class SpeakerConfusionRate(DiarizationErrorRate): | ||
def compute(self): | ||
# TODO: handler corner case where speech_total == 0 | ||
return self.speaker_confusion / self.speech_total | ||
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class FalseAlarmRate(DiarizationErrorRate): | ||
def compute(self): | ||
# TODO: handler corner case where speech_total == 0 | ||
return self.false_alarm / self.speech_total | ||
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class MissedDetectionRate(DiarizationErrorRate): | ||
def compute(self): | ||
# TODO: handler corner case where speech_total == 0 | ||
return self.missed_detection / self.speech_total |
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# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. |
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@@ -0,0 +1,21 @@ | ||
# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. |
128 changes: 128 additions & 0 deletions
128
pyannote/audio/torchmetrics/functional/audio/diarization_error_rate.py
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# MIT License | ||
# | ||
# Copyright (c) 2022- CNRS | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
|
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from typing import Tuple | ||
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import torch | ||
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from pyannote.audio.utils.permutation import permutate | ||
|
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|
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def _der_update( | ||
preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5 | ||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: | ||
"""Compute components of diarization error rate | ||
Parameters | ||
---------- | ||
preds : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped continuous predictions. | ||
target : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped (0 or 1) targets. | ||
threshold : float, optional | ||
Threshold used to binarize predictions. Defaults to 0.5. | ||
Returns | ||
------- | ||
false_alarm : torch.Tensor | ||
missed_detection : torch.Tensor | ||
speaker_confusion : torch.Tensor | ||
speech_total : torch.Tensor | ||
Diarization error rate components accumulated over the whole batch. | ||
""" | ||
|
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# TODO: consider doing the permutation before the binarization | ||
# in order to improve robustness to mis-calibration. | ||
preds_bin = (preds > threshold).float() | ||
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# convert to/from "permutate" expected shapes | ||
hypothesis, _ = permutate( | ||
torch.transpose(target, 1, 2), torch.transpose(preds_bin, 1, 2) | ||
) | ||
hypothesis = torch.transpose(hypothesis, 1, 2) | ||
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detection_error = torch.sum(hypothesis, 1) - torch.sum(target, 1) | ||
false_alarm = torch.maximum(detection_error, torch.zeros_like(detection_error)) | ||
missed_detection = torch.maximum( | ||
-detection_error, torch.zeros_like(detection_error) | ||
) | ||
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speaker_confusion = torch.sum((hypothesis != target) * hypothesis, 1) - false_alarm | ||
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false_alarm = torch.sum(false_alarm) | ||
missed_detection = torch.sum(missed_detection) | ||
speaker_confusion = torch.sum(speaker_confusion) | ||
speech_total = 1.0 * torch.sum(target) | ||
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return false_alarm, missed_detection, speaker_confusion, speech_total | ||
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def _der_compute( | ||
false_alarm: torch.Tensor, | ||
missed_detection: torch.Tensor, | ||
speaker_confusion: torch.Tensor, | ||
speech_total: torch.Tensor, | ||
) -> torch.Tensor: | ||
"""Compute diarization error rate from its components | ||
Parameters | ||
---------- | ||
false_alarm : torch.Tensor | ||
missed_detection : torch.Tensor | ||
speaker_confusion : torch.Tensor | ||
speech_total : torch.Tensor | ||
Diarization error rate components, in number of frames. | ||
Returns | ||
------- | ||
der : torch.Tensor | ||
Diarization error rate. | ||
""" | ||
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# TODO: handle corner case where speech_total == 0 | ||
return (false_alarm + missed_detection + speaker_confusion) / speech_total | ||
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|
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def diarization_error_rate( | ||
preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5 | ||
) -> torch.Tensor: | ||
"""Compute diarization error rate | ||
Parameters | ||
---------- | ||
preds : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped continuous predictions. | ||
target : torch.Tensor | ||
(batch_size, num_speakers, num_frames)-shaped (0 or 1) targets. | ||
threshold : float, optional | ||
Threshold to binarize predictions. Defaults to 0.5. | ||
Returns | ||
------- | ||
der : torch.Tensor | ||
Aggregated diarization error rate | ||
""" | ||
false_alarm, missed_detection, speaker_confusion, speech_total = _der_update( | ||
preds, target, threshold=threshold | ||
) | ||
return _der_compute(false_alarm, missed_detection, speaker_confusion, speech_total) |