samplerate = zounds.SR22050()
BaseModel = zounds.stft(resample_to=samplerate, store_fft=True)
@zounds.simple_in_memory_settings
class Sound(BaseModel):
pass
if __name__ == '__main__':
url = 'https://ia802606.us.archive.org/9/items/AOC11B/onclassical_luisi_bach_partita_e-minor_bwv-830_3.ogg'
_id = Sound.process(meta=url)
snd = Sound(_id)
band = zounds.FrequencyBand(50, samplerate.nyquist)
bark_scale = zounds.BarkScale(band, 100)
mel_scale = zounds.MelScale(band, 100)
chroma_scale = zounds.ChromaScale(band)
bark_bands = bark_scale.apply(snd.fft, zounds.HanningWindowingFunc())
mel_bands = mel_scale.apply(snd.fft, zounds.HanningWindowingFunc())
chroma_bands = chroma_scale.apply(snd.fft, zounds.HanningWindowingFunc())
app = zounds.ZoundsApp(
model=Sound,
visualization_feature=Sound.fft,
audio_feature=Sound.ogg,
globals=globals(),
locals=locals())
app.start(9999)
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(),
secret=args.app_secret)
app.start(args.port)
rep = next(make_stream())
data = rep.data
data = packed_channels(data)
data = packed_fractal(data, FRACTAL_WINDOW_SIZE)
norms = data[:, :1]
indices = km.predict(data[:, 1:])
centroids = km.cluster_centers_[indices]
centroids = np.concatenate([norms, centroids], axis=1)
centroids = unpacked_fractal_recon(centroids, FRACTAL_WINDOW_SIZE)
centroids = unpacked_channels(centroids, time_dim)
recon_rep = rep.__class__(centroids, samplerate)
return rep, recon_rep
def make_stream():
return repr_stream(MelPhaseRecovery)
if __name__ == '__main__':
app = zounds.ZoundsApp(locals=locals(), globals=globals())
app.start_in_thread(8888)
rs = make_stream()
for i, km in learn_clusters(rs):
print(f'kmeans iter {i}')
input('Waiting...')
"""
Do a forward and backward pass over the audio, and return the
reconstructed audio
"""
if _id:
doc = Document(_id)
else:
doc = choice(docs)
print doc._id
recon_audio = full_pass(doc.mdct, kmeans.pipeline)
recon_audio_log_amp = full_pass(doc.mdct, kmeans_log_amplitude.pipeline)
return doc.ogg[:], recon_audio, recon_audio_log_amp
mono_orig, mono, mono_log = reconstruction(
'FlavioGaete22/TFS1_TReich11.wav')
bass_orig, bass, bass_log = reconstruction(
'FlavioGaete22/TFS1_TBass05.wav')
beat_orig, beat, beat_log = reconstruction(
'FlavioGaete22/SIKBeat02.wav')
cello_orig, cello, cello_log = reconstruction(
'FlavioGaete22/TFS2_TVla09.wav')
app = zounds.ZoundsApp(
model=Document,
audio_feature=Document.ogg,
visualization_feature=Document.mdct,
globals=globals(),
locals=locals())
app.start(8888)
