def get_labels_for_file(self, wav_path, frame_sec):
sname = get_sname(wav_path)
sample = self.labels[self.labels.Sample == sname]
incidents = sample.loc[:, 'type_voc_0':'end_voc_5']
incidents = incidents.dropna(axis=1, how='all')
incidents = incidents.values[0]
rate, audio = wav.read(wav_path)
laughts = self._interval_generator(incidents)
laughts = [
interv_to_range(x, len(audio), self.duration) for x in laughts
]
laught_along = [
1 if in_any(t, laughts) else 0 for t, _ in enumerate(audio)
]
frame_size = int(self.sample_rate * frame_sec)
is_laughter = np.array(
[self.most(la) for la in chunks(laught_along, frame_size)])
frame_step = int(frame_size / 5)
is_laughter = [
self.most(laught_along[i:i + frame_size])
for i in range(0, self.default_len - frame_size, frame_step)
]
df = pd.DataFrame({'IS_LAUGHTER': is_laughter, 'SNAME': sname})
return df
def _interval_generator(incidents):
for itype, start, end in chunks(incidents, 3):
if itype == 'laughter':
yield start, end