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multilabel_detection.py
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multilabel_detection.py
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# The MIT License (MIT)
#
# Copyright (c) 2017-2020 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 dataclasses import dataclass
from functools import reduce
from itertools import chain
from typing import Union, Optional, List, Dict, TYPE_CHECKING, Text
import numpy as np
from pyannote.core import Annotation, SlidingWindowFeature
from pyannote.metrics.base import BaseMetric
from pyannote.metrics.detection import DetectionPrecisionRecallFMeasure
from pyannote.metrics.identification import IdentificationErrorRate
from pyannote.pipeline.parameter import ParamDict, Uniform
from sortedcontainers import SortedDict
from pyannote.audio import Inference
from pyannote.audio.core.io import AudioFile
from pyannote.audio.core.pipeline import Pipeline
from .utils import PipelineModel, get_devices, get_model
from ..utils.signal import Binarize
SpeakerClass = Text
MetaClasses = Dict[SpeakerClass, List[SpeakerClass]]
if TYPE_CHECKING:
from ..tasks.segmentation.voice_type_classification import VoiceTypeClassification
@dataclass
class MultilabelDetectionSpecifications:
classes: List[SpeakerClass]
unions: MetaClasses
intersections: MetaClasses
unions_idx: Optional[SortedDict] = None
intersections_idx: Optional[SortedDict] = None
def __post_init__(self):
# for each metaclass, mapping metaclass label to vector of its
# classes's ids (used for encoding)
self.unions_idx = self.to_metaclasses_idx(self.unions,
self.classes)
self.intersections_idx = self.to_metaclasses_idx(self.intersections,
self.classes)
@property
def all_classes(self) -> List[str]:
return (self.classes
+ list(self.unions.keys())
+ list(self.intersections.keys()))
@staticmethod
def to_metaclasses_idx(metaclasses: MetaClasses, classes: List[SpeakerClass]) -> SortedDict:
return SortedDict({
intersection_label: np.array([classes.index(klass)
for klass in intersection_classes])
for intersection_label, intersection_classes
in metaclasses.items()
})
def derive_unions_encoding(self, one_hot_array: np.ndarray):
arrays: List[np.ndarray] = []
for label, idx in self.unions_idx.items():
arrays.append(one_hot_array[:, idx].max(axis=1))
return np.vstack(arrays).swapaxes(0, 1)
def derive_intersections_encoding(self, one_hot_array: np.ndarray):
arrays: List[np.ndarray] = []
for label, idx in self.intersections_idx.items():
arrays.append(one_hot_array[:, idx].min(axis=1))
return np.vstack(arrays).swapaxes(0, 1)
def derive_reference(self, annotation: Annotation) -> Annotation:
derived = annotation.subset(self.classes)
# Adding union labels
for union_label, subclasses in self.unions.items():
mapping = {k: union_label for k in subclasses}
metalabel_annot = annotation.subset(union_label).rename_labels(mapping=mapping)
derived.update(metalabel_annot.support())
# adding intersection labels
for intersect_label, subclasses in self.intersections.items():
subclasses_tl = [annotation.label_timeline(subclass) for subclass in subclasses]
overlap_tl = reduce(lambda x, y: x.crop(y), subclasses_tl)
derived.update(overlap_tl.to_annotation(intersect_label))
return derived
@classmethod
def from_parameters(
cls,
classes: List[SpeakerClass], # VTC-specific parameter
unions: Optional[MetaClasses] = None,
intersections: Optional[MetaClasses] = None, ) \
-> 'MultilabelDetectionSpecifications':
if unions is not None:
assert set(chain.from_iterable(unions.values())).issubset(set(classes))
if intersections is not None:
assert set(chain.from_iterable(intersections.values())).issubset(set(classes))
classes = sorted(list(set(classes)))
return cls(classes,
unions if unions else dict(),
intersections if intersections else dict())
class MultilabelFMeasure(BaseMetric):
"""Compute the mean Fscore over all labels
"""
def metric_components(self):
return self.mtl_specs.all_classes
@classmethod
def metric_name(cls):
return "AVG[Labels]"
def __init__(self, mtl_specs: MultilabelDetectionSpecifications, # noqa
collar=0.0, skip_overlap=False,
beta=1., parallel=False, **kwargs):
self.parallel = parallel
self.metric_name_ = self.metric_name()
self.collar = collar
self.skip_overlap = skip_overlap
self.beta = beta
self.mtl_specs = mtl_specs
self.components_ = set(self.metric_components())
self.submetrics: Dict[str, DetectionPrecisionRecallFMeasure] = {
label: DetectionPrecisionRecallFMeasure(collar=collar,
skip_overlap=skip_overlap,
beta=beta,
**kwargs)
for label in self.mtl_specs.all_classes
}
self.reset()
def reset(self):
super().reset()
for submetric in self.submetrics.values():
submetric.reset()
def compute_components(self, reference: Annotation, hypothesis: Annotation, uem=None, **kwargs):
details = self.init_components()
reference = self.mtl_specs.derive_reference(reference)
for label, submetric in self.submetrics.items():
details[label] = submetric(reference=reference.subset([label]),
hypothesis=hypothesis.subset([label]),
uem=uem,
**kwargs)
return details
def compute_metric(self, detail: Dict[str, float]):
return np.mean(list(detail.values()))
def __abs__(self):
return np.mean([abs(submetric) for submetric in self.submetrics.values()])
class MultilabelIER(IdentificationErrorRate):
def __init__(self, mtl_specs: MultilabelDetectionSpecifications,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.mtl_specs = mtl_specs
def compute_components(self, reference, hypothesis, uem=None,
collar=None, skip_overlap=None, **kwargs):
# deriving labels
reference = self.mtl_specs.derive_reference(reference)
return super().compute_components(reference, hypothesis,
uem=uem, collar=collar,
skip_overlap=skip_overlap,
**kwargs)
class MultilabelDetection(Pipeline):
""""""
def __init__(self,
segmentation: PipelineModel = "pyannote/vtc",
fscore: bool = False,
**inference_kwargs,
):
super().__init__()
self.segmentation = segmentation
self.fscore = fscore
# load model and send it to GPU (when available and not already on GPU)
model = get_model(segmentation)
if model.device.type == "cpu":
(segmentation_device,) = get_devices(needs=1)
model.to(segmentation_device)
task: 'VoiceTypeClassification' = model.task
self.mtl_specs = task.clsf_specs
self.labels = task.clsf_specs.all_classes
self.segmentation_inference_ = Inference(model, **inference_kwargs)
self.binarize_hparams = ParamDict(**{
class_name: ParamDict(
onset=Uniform(0., 1.),
offset=Uniform(0., 1.),
min_duration_on=Uniform(0., 2.),
min_duration_off=Uniform(0., 2.),
pad_onset=Uniform(-1., 1.),
pad_offset=Uniform(-1., 1.)
) for class_name in self.labels
})
def initialize(self):
"""Initialize pipeline with current set of parameters"""
self.freeze({'binarize_hparams': {
class_name: {
"pad_onset": 0.0,
"pad_offset": 0.0
} for class_name in self.labels
}})
self._binarizers = {
class_name: Binarize(
onset=self.binarize_hparams[class_name]["onset"],
offset=self.binarize_hparams[class_name]["offset"],
min_duration_on=self.binarize_hparams[class_name]["min_duration_on"],
min_duration_off=self.binarize_hparams[class_name]["min_duration_off"],
pad_onset=self.binarize_hparams[class_name]["pad_onset"],
pad_offset=self.binarize_hparams[class_name]["pad_offset"])
for class_name in self.labels
}
CACHED_ACTIVATIONS = "@multilabel_detection/activations"
def apply(self, file: AudioFile) -> Annotation:
"""Apply voice type classification
Parameters
----------
file : AudioFile
Processed file.
Returns
-------
speech : `pyannote.core.Annotation`
Annotated classification.
"""
multilabel_scores: SlidingWindowFeature
if self.training:
if self.CACHED_ACTIVATIONS not in file:
file[self.CACHED_ACTIVATIONS] = self.segmentation_inference_(file)
multilabel_scores = file[self.CACHED_ACTIVATIONS]
else:
multilabel_scores = self.segmentation_inference_(file)
# for each class name, add class-specific "VAD" pipeline
full_annot = Annotation(uri=file["uri"])
for class_idx, class_name in enumerate(self.labels):
# selecting scores for only one label
label_scores_array: np.ndarray = multilabel_scores.data[:, class_idx]
# creating a fake "num_classes" dim
label_scores_array = np.expand_dims(label_scores_array, axis=1)
# creating a new sliding window for that label
label_scores = SlidingWindowFeature(label_scores_array,
multilabel_scores.sliding_window)
binarizer: Binarize = self._binarizers[class_name]
class_annot = binarizer(label_scores)
class_annot.rename_labels({label: class_name for label in class_annot.labels()}, copy=False)
full_annot.update(class_annot)
return full_annot
def get_metric(self) -> Union[MultilabelFMeasure, IdentificationErrorRate]:
"""Return new instance of identification metric"""
if self.fscore:
return MultilabelFMeasure(mtl_specs=self.mtl_specs,
collar=0.0, skip_overlap=False)
else:
return MultilabelIER(mtl_specs=self.mtl_specs,
collar=0.0, skip_overlap=False)
def get_direction(self):
if self.fscore:
return "maximize"
else:
return "minimize"