durations = np.diff(onset_frame_indices)
onset_times = onset_frame_indices / samples.samples_per_second
onset_durations = durations / samples.samples_per_second
annotations = []
for onset_time, duration in zip(onset_times[:-1], onset_durations):
annotations.append({
'start_seconds': onset_time,
'duration_seconds': duration,
'tags': ['onset']
})
logger.info(f'Created {len(annotations)} onsets for {sound["id"]}')
if not annotations:
return
self.client.create_annotations(sound['id'], *annotations)
if __name__ == '__main__':
main(
user_name='onset_bot',
bucket_name='onset-bot',
email='*****@*****.**',
about_me='onset_bot.md',
info_url=
'https://librosa.github.io/librosa/generated/librosa.onset.onset_detect.html',
listener_cls=OnsetListener,
logger=logger)
# pack the chroma data and create the resources
binary_data = BinaryData(chroma)
sound_id = self._sound_id_from_uri(annotation['sound'])
# push output to s3
data_url = self.s3_client.put_object(
sound_id, binary_data.packed_file_like_object(),
'application/octet-stream')
logger.info(f'pushed binary data to {data_url}')
# create annotation
self.client.create_annotations(
sound_id, {
'start_seconds': annotation['start_seconds'],
'duration_seconds': annotation['duration_seconds'],
'data_url': data_url
})
logger.info('created annotation')
if __name__ == '__main__':
main(user_name='chroma_bot',
bucket_name='chroma-bot',
email='*****@*****.**',
about_me='chroma_bot.md',
info_url='https://en.wikipedia.org/wiki/Chroma_feature',
listener_cls=ChromaListener,
logger=logger)
# processing pipeline to compute spectrograms
samples = zounds.AudioSamples.from_file(raw_audio)
binary_data = self._process_samples(samples)
# push output to s3
data_url = self.s3_client.put_object(
sound['id'], binary_data.packed_file_like_object(),
'application/octet-stream')
logger.info(f'pushed binary data to {data_url}')
# create annotation
self.client.create_annotations(
sound['id'], {
'start_seconds': 0,
'duration_seconds': sound['duration_seconds'],
'data_url': data_url
})
logger.info('created annotation')
if __name__ == '__main__':
main(user_name='stft_bot',
bucket_name='stft-bot',
email='*****@*****.**',
about_me='stft_bot.md',
info_url='https://en.wikipedia.org/wiki/Short-time_Fourier_transform',
listener_cls=FFTListener,
logger=logger)
raw_audio = BytesIO(resp.content)
# processing pipeline to compute spectrograms
samples = zounds.AudioSamples.from_file(raw_audio).mono
binary_data = self._process_samples(samples)
# push output to s3
data_url = self.s3_client.put_object(
sound['id'], binary_data.packed_file_like_object(),
'application/octet-stream')
logger.info(f'pushed binary data to {data_url}')
# create annotation
self.client.create_annotations(
sound['id'], {
'start_seconds': 0,
'duration_seconds': sound['duration_seconds'],
'data_url': data_url
})
logger.info('created annotation')
if __name__ == '__main__':
main(user_name='spectrogram_bot',
bucket_name='spectrogram-bot',
email='*****@*****.**',
about_me='spectrogram_bot.md',
info_url='https://en.wikipedia.org/wiki/Spectrogram',
listener_cls=SpectrogramListener,
logger=logger)
zounds.IdentityDimension()]).astype(np.float32)
# pack the chroma data and create the resources
binary_data = BinaryData(mfcc)
sound_id = self._sound_id_from_uri(annotation['sound'])
# push output to s3
data_url = self.s3_client.put_object(
sound_id, binary_data.packed_file_like_object(),
'application/octet-stream')
logger.info(f'pushed binary data to {data_url}')
# create annotation
self.client.create_annotations(
sound_id, {
'start_seconds': annotation['start_seconds'],
'duration_seconds': annotation['duration_seconds'],
'data_url': data_url
})
logger.info('created annotation')
if __name__ == '__main__':
main(user_name='mfcc_bot',
bucket_name='mfcc-bot',
email='*****@*****.**',
about_me='mfcc_bot.md',
info_url='https://en.wikipedia.org/wiki/Mel-frequency_cepstrum',
listener_cls=MFCCListener,
logger=logger)