def _preprocess(self, reference, hypothesis):
if not isinstance(reference, Annotation):
raise TypeError('reference must be an instance of `Annotation`')
if isinstance(hypothesis, Annotation):
hypothesis = hypothesis.get_timeline()
# reference where short intra-label gaps are removed
filled = Timeline()
for label in reference.labels():
label_timeline = reference.label_timeline(label)
for gap in label_timeline.gaps():
if gap.duration < self.tolerance:
label_timeline.add(gap)
for segment in label_timeline.coverage():
filled.add(segment)
# reference coverage after filling gaps
coverage = filled.coverage()
reference_partition = self._partition(filled, coverage)
hypothesis_partition = self._partition(hypothesis, coverage)
return reference_partition, hypothesis_partition
def _get_collar(self, reference, duration):
# initialize empty timeline
collar = Timeline(uri=reference.uri)
if duration == 0.:
return collar
# iterate over all segments in reference
for segment in reference.itersegments():
# add collar centered on start time
t = segment.start
collar.add(Segment(t - .5 * duration, t + .5 * duration))
# add collar centered on end time
t = segment.end
collar.add(Segment(t - .5 * duration, t + .5 * duration))
# merge overlapping collars and return
return collar.coverage()
def run(self):
# wav file duration
wav = self.in_wav().path
with contextlib.closing(wave.open(wav, 'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / rate
extent = Segment(0., duration)
with self.in_speaker().open('r') as fp:
speaker = pyannote.core.json.load(fp)
timeline = Timeline()
for segment, _ in speaker.itertracks():
timeline.add(segment)
# fill gaps
for gap in timeline.gaps(extent):
if gap.duration < self.fill_gaps:
timeline.add(gap)
timeline = timeline.coverage()
# dump as annotation...
if self.to_annotation:
annotation = Annotation()
for s, segment in enumerate(timeline):
annotation[segment] = s
annotation = annotation.anonymize_labels(generator='string')
with self.out_put().open('w') as fp:
pyannote.core.json.dump(annotation, fp)
# ... or as timeline
else:
with self.out_put().open('w') as fp:
pyannote.core.json.dump(timeline, fp)
def apply(self, predictions, dimension=0):
"""
Parameters
----------
predictions : SlidingWindowFeature
Must be mono-dimensional
dimension : int, optional
Which dimension to process
"""
if len(predictions.data.shape) == 1:
data = predictions.data
elif predictions.data.shape[1] == 1:
data = predictions.data[:, 0]
else:
data = predictions.data[:, dimension]
n_samples = predictions.getNumber()
window = predictions.sliding_window
timestamps = [window[i].middle for i in range(n_samples)]
# initial state
start = timestamps[0]
label = data[0] > self.onset
# timeline meant to store 'active' segments
active = Timeline()
for t, y in zip(timestamps[1:], data[1:]):
# currently active
if label:
# switching from active to inactive
if y < self.offset:
segment = Segment(start - self.pad_onset,
t + self.pad_offset)
active.add(segment)
start = t
label = False
# currently inactive
else:
# switching from inactive to active
if y > self.onset:
start = t
label = True
# if active at the end, add final segment
if label:
segment = Segment(start - self.pad_onset, t + self.pad_offset)
active.add(segment)
# because of padding, some 'active' segments might be overlapping
# therefore, we merge those overlapping segments
active = active.coverage()
# remove short 'active' segments
active = Timeline(
[s for s in active if s.duration > self.min_duration[1]])
# fill short 'inactive' segments
inactive = active.gaps()
for s in inactive:
if s.duration < self.min_duration[0]:
active.add(s)
active = active.coverage()
return active