def _process_annotation(self, annotation):
# fetch the fft data
resp = requests.get(annotation['data_url'])
fft_feature = BinaryData.unpack(resp.content)
# compute the chroma feature
chroma = CHROMA_SCALE.apply(fft_feature, zounds.HanningWindowingFunc())
chroma = zounds.ArrayWithUnits(
chroma, [fft_feature.dimensions[0],
zounds.IdentityDimension()]).astype(self.dtype)
# 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')
def produce_fake_hash(x):
"""
Produce random, binary features, totally irrespective of the content of
x, but in the same shape as x.
"""
h = np.random.binomial(1, 0.5, (x.shape[0], 1024))
packed = np.packbits(h, axis=-1).view(np.uint64)
return zounds.ArrayWithUnits(
packed, [x.dimensions[0], zounds.IdentityDimension()])
def compute_embedding(samples, network):
# TODO: resampling can fail for some odd sampling rates
samples = zounds.soundfile.resample(samples, zounds.SR11025())
freq = samples.frequency * 8192
windowed = samples.sliding_window(
zounds.SampleRate(frequency=freq, duration=freq))
dims = windowed.dimensions
output = zounds.learn.apply_network(network, windowed, chunksize=8)
logger.info(output.shape)
output = zounds.ArrayWithUnits(
output, [dims[0], zounds.IdentityDimension()])
return output
def _process_annotation(self, annotation):
# fetch the fft data
resp = requests.get(annotation['data_url'])
fft_feature = BinaryData.unpack(resp.content)
# compute the chroma feature
mel_spectrogram = scale.apply(fft_feature,
zounds.HanningWindowingFunc())
mel_spectrogram = zounds.ArrayWithUnits(
mel_spectrogram,
[fft_feature.dimensions[0],
zounds.FrequencyDimension(scale)])
mel_spectrogram = 20 * np.log10(mel_spectrogram + 1)
mfcc = np.abs(dct(mel_spectrogram, axis=1)[:, 1:14])
mfcc = zounds.ArrayWithUnits(
mfcc, [fft_feature.dimensions[0],
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')
original = snd.resampled
slow = zounds.AudioSamples(time_stretch(original, 0.75).squeeze(), sr)
fast = zounds.AudioSamples(time_stretch(original, 1.25).squeeze(), sr)
higher = zounds.AudioSamples(pitch_shift(original, 1.0).squeeze(), sr)
lower = zounds.AudioSamples(pitch_shift(original, -1.0).squeeze(), sr)
# apply a sliding window to demonstrate time stretch and pitch shift in
# batch mode
windowing_sr = zounds.SampleRate(frequency=zounds.Seconds(5),
duration=zounds.Seconds(10))
windowed = snd.resampled.sliding_window(windowing_sr)
windowed = zounds.ArrayWithUnits(
windowed, [zounds.IdentityDimension(), windowed.dimensions[1]])
def samples(x):
return zounds.AudioSamples(x, sr)
batch_slow = map(samples, time_stretch(windowed, 0.75))
batch_fast = map(samples, time_stretch(windowed, 1.25))
batch_higher = map(samples, pitch_shift(windowed, 1.0))
batch_lower = map(samples, pitch_shift(windowed, -1.0))
app = zounds.ZoundsApp(model=Sound,
visualization_feature=Sound.fft,
audio_feature=Sound.resampled,
globals=globals(),
locals=locals(),