def _turn_level(self, current_file: dict,
speech_turns: Annotation) -> Annotation:
"""Apply clustering at speech turn level
Parameters
----------
current_file : `dict`
File as provided by a pyannote.database protocol.
speech_turns : `Annotation`
Speech turns. Should only contain `str` labels.
Returns
-------
hypothesis : `pyannote.core.Annotation`
Clustering result.
"""
assert_string_labels(speech_turns, "speech_turns")
embedding = self._embedding(current_file)
labels = speech_turns.labels()
X, clustered_labels, skipped_labels = [], [], []
for l, label in enumerate(labels):
timeline = speech_turns.label_timeline(label, copy=False)
# be more and more permissive until we have
# at least one embedding for current speech turn
for mode in ["strict", "center", "loose"]:
x = embedding.crop(timeline, mode=mode)
if len(x) > 0:
break
# skip labels so small we don't have any embedding for it
if len(x) < 1:
skipped_labels.append(label)
continue
clustered_labels.append(label)
X.append(np.mean(x, axis=0))
# apply clustering of label embeddings
clusters = self.clustering(np.vstack(X))
# map each clustered label to its cluster (between 1 and N_CLUSTERS)
mapping = {label: k for label, k in zip(clustered_labels, clusters)}
# map each skipped label to its own cluster
# (between -1 and -N_SKIPPED_LABELS)
for l, label in enumerate(skipped_labels):
mapping[label] = -(l + 1)
# do the actual mapping
return speech_turns.rename_labels(mapping=mapping)
def __call__(
self, current_file: dict, speech_turns: Annotation, targets: Annotation
) -> Annotation:
"""Assign each speech turn to closest target (if close enough)
Parameters
----------
current_file : `dict`
File as provided by a pyannote.database protocol.
speech_turns : `Annotation`
Speech turns. Should only contain `int` labels.
targets : `Annotation`
Targets. Should only contain `str` labels.
Returns
-------
assigned : `Annotation`
Assigned speech turns.
"""
assert_string_labels(targets, "targets")
assert_int_labels(speech_turns, "speech_turns")
embedding = self._embedding(current_file)
# gather targets embedding
labels = targets.labels()
X_targets, targets_labels = [], []
for l, label in enumerate(labels):
timeline = targets.label_timeline(label, copy=False)
# be more and more permissive until we have
# at least one embedding for current speech turn
for mode in ["center", "loose"]:
x = embedding.crop(timeline, mode=mode)
if len(x) > 0:
break
# skip labels so small we don't have any embedding for it
if len(x) < 1:
continue
targets_labels.append(label)
X_targets.append(np.mean(x, axis=0))
# gather speech turns embedding
labels = speech_turns.labels()
X, assigned_labels, skipped_labels = [], [], []
for l, label in enumerate(labels):
timeline = speech_turns.label_timeline(label, copy=False)
# be more and more permissive until we have
# at least one embedding for current speech turn
for mode in ["center", "loose"]:
x = embedding.crop(timeline, mode=mode)
if len(x) > 0:
break
# skip labels so small we don't have any embedding for it
if len(x) < 1:
skipped_labels.append(label)
continue
assigned_labels.append(label)
X.append(np.mean(x, axis=0))
# assign speech turns to closest class
assignments = self.closest_assignment(np.vstack(X_targets), np.vstack(X))
mapping = {
label: targets_labels[k]
for label, k in zip(assigned_labels, assignments)
if not k < 0
}
return speech_turns.rename_labels(mapping=mapping)
