def readAudio( filename ): #returns list, [ samplingRate, values ] import os name, ext = os.path.splitext(filename) if ext == '.wav': #reading a .wav return wavfile.read(filename) else: #using numm to read a .webm values = numm.sound2np(filename) #average out stereo input #values = numpy.mean( values, axis=1 ) #we use just the left channel values = numpy.split( values, [1], axis=1 )[0] #numm defaults to 44.1 kHz sampling rate #samplingRate = 44100 #return [ samplingRate, values ] #dev machine is REALLY BAD, concessions made for test purposes toPad = numpy.zeros( values.size % 5 ) if toPad.size > 0: values = numpy.concatenate( values, toPad ) reshaped = values.reshape( ( values.size / 5, 5) ) values = numpy.mean( reshaped, axis=1 ) samplingRate = 44100 / 5 return [ samplingRate, values ]
def _get_array(self):
return numm.sound2np(self.path)
def _get_array(self):
return numm.sound2np(self.path)
elif (key == 's' and outY < 0.99): outY += 0.01 print "outX: " + str(outX) + " outY: " + str(outY) if (__name__ == "__main__"): # The three command-line arguments are the audio chunk size (in samples) # and the filenames of the two files to analyze. chunkSize = int(sys.argv[1]) fileName1 = sys.argv[2] fileName2 = sys.argv[3] # Extract the files to sound! print "Extracting " + fileName1 + " and " + fileName2 + "..." audio1 = numm.sound2np(fileName1) audio2 = numm.sound2np(fileName2) # Chunkulate the nparrays. print "Chunkulating..." chunks1 = chunkulate(audio1, chunkSize) chunks2 = chunkulate(audio2, chunkSize) print "Chunked " + fileName1 + " into " + str(chunks1.shape) + " chunks." print "Chunked " + fileName2 + " into " + str(chunks2.shape) + " chunks." # Window the chunks. print "Windowing the chunks..." windowed1 = chunks1 * np.array( [np.array( [np.hamming(chunkSize),]*2 ).T,] * chunks1.shape[0] ) windowed2 = chunks2 * np.array( [np.array( [np.hamming(chunkSize),]*2 ).T,] * chunks2.shape[0] ) # Run the thing!
#done with this buffer
buffers.pop(buf_idx)
else:
buffers[buf_idx] = buffers[buf_idx][amnt:]
# # Defer frames to avoid pitch-shifting
# if len(buffers) > 1:
# buffers[buf_idx] = np.roll(buffers[buf_idx], -amnt*len(buffers), axis=0)
if len(buffers) == 0:
return out
buf_idx = (buf_idx + 1) % len(buffers)
if __name__ == '__main__':
import numm
import sys
buffers = [numm.sound2np(X) for X in sys.argv[1:]]
nframes = sum([len(X) for X in buffers])
# freqrot = [440, 800, 1500, 300]
freqrot = [4400, 1800, 1500, 2222]
comp = []
f_idx = 0
w_len = R / 4
while nframes > 0:
amnt = min(nframes, w_len)
comp.append((freqrot[f_idx], amnt))
f_idx = (f_idx + 1) % len(freqrot)
nframes -= amnt
src_np[int(R * st):int(R * (st + dur))] for (st, dur, _idx) in segs
]
if len(segchunks) == 0:
print 'zero-length cluster', idx
continue
segchunks = np.concatenate(segchunks)
numm.np2sound(segchunks, outpattern % (int(idx)))
if __name__ == '__main__':
import sys
# keys = ["AvgTonalCentroid(6)", "AvgMFCC(13)", "AvgChroma(12)"]
keys = ["AvgMFCC(13)"]
nclusters = 36
for src in sys.argv[1:]:
src_np = None
for key in keys:
jsonfile = "%s-%s-%d.json" % (src, key, nclusters)
if os.path.exists(jsonfile):
continue
if src_np is None:
src_np = numm.sound2np(src)
clusters = cluster(src, key=key, nbins=nclusters)
wave(src_np, clusters,
"%s-%s-%d-%%06d.wav" % (src, key, nclusters))
import numm
import numpy as np
from zerocrossings import zerocrossings, next_zerocrossing
a = numm.sound2np('a.wav')
b = numm.sound2np('b.wav')
# a = np.int16(2**14 * np.sin(np.linspace(0, 2*np.pi*440, 44100)))
# b = np.int16(2**14 * np.sin(np.linspace(0, 2*np.pi*640, 44100)))
DITHER_FRAMES = 1024
next_ditherpt = 1024
dit_idx = 0
cur_snd = a
def flip():
global cur_snd, dit_idx
if cur_snd.data == a.data:
cur_snd = b
else:
cur_snd = a
def audio_out(out):
global dit_idx, next_ditherpt
nrem = len(out)
while nrem > 0:
amnt = min(next_ditherpt-dit_idx, nrem)
out_st = len(out)-nrem
import numm
import numpy as np
def mix(l, r):
"Mix two stereo tracks"
out = np.zeros((max(len(l), len(r)), 2), int)
out[:len(l),0] = l[:,0]
out[:len(l),1] = l[:,1]/2
out[:len(r),0] += r[:,0]/2
out[:len(r),1] += r[:,1]
return (out / (out.max() / float(2**15-1))).astype(np.int16)
if __name__=='__main__':
import sys
res = mix(numm.sound2np(sys.argv[1]),
numm.sound2np(sys.argv[2]))
numm.np2sound(res, 'mixed.wav')
def wave(src_np, clusters, outpattern, R=44100):
for idx, segs in clusters.items():
segchunks = [src_np[int(R * st) : int(R * (st + dur))] for (st, dur, _idx) in segs]
if len(segchunks) == 0:
print "zero-length cluster", idx
continue
segchunks = np.concatenate(segchunks)
numm.np2sound(segchunks, outpattern % (int(idx)))
if __name__ == "__main__":
import sys
# keys = ["AvgTonalCentroid(6)", "AvgMFCC(13)", "AvgChroma(12)"]
keys = ["AvgMFCC(13)"]
nclusters = 36
for src in sys.argv[1:]:
src_np = None
for key in keys:
jsonfile = "%s-%s-%d.json" % (src, key, nclusters)
if os.path.exists(jsonfile):
continue
if src_np is None:
src_np = numm.sound2np(src)
clusters = cluster(src, key=key, nbins=nclusters)
wave(src_np, clusters, "%s-%s-%d-%%06d.wav" % (src, key, nclusters))
import numm
import numpy as np
from zerocrossings import zerocrossings, next_zerocrossing
a = numm.sound2np('a.wav')
b = numm.sound2np('b.wav')
# a = np.int16(2**14 * np.sin(np.linspace(0, 2*np.pi*440, 44100)))
# b = np.int16(2**14 * np.sin(np.linspace(0, 2*np.pi*640, 44100)))
DITHER_FRAMES = 1024
next_ditherpt = 1024
dit_idx = 0
cur_snd = a
def flip():
global cur_snd, dit_idx
if cur_snd.data == a.data:
cur_snd = b
else:
cur_snd = a
def audio_out(out):
global dit_idx, next_ditherpt
nrem = len(out)
while nrem > 0:
amnt = min(next_ditherpt - dit_idx, nrem)
import cluster
import numm
import random
R = 44100
# PADDING = R / 4 # frames between segments
# SOURCE = 'snd/Dance_A.wav'
SOURCE = 'snd/Duran_A.wav'
NBINS = 50
cur_cluster = 0
cluster_idx = 0
paused = False
frame_idx = 0
audio = numm.sound2np(SOURCE)
clusters = cluster.cluster(SOURCE, NBINS)
for c in clusters.values():
random.shuffle(c)
def get_segment(cluster, idx):
idx = idx % len(clusters[cluster])
start, duration = clusters[cluster][idx]
return audio[int(R * start):int(R * (start + duration))]
def audio_out(a):
global frame_idx, cluster_idx, paused
import numm
import numpy as np
import sys
for f in sys.argv[1:]:
snd = numm.sound2np(f)
np.save(f+'.npy', snd[:,0])
import cluster
import numm
import random
R = 44100
# PADDING = R / 4 # frames between segments
# SOURCE = 'snd/Dance_A.wav'
SOURCE = 'snd/Duran_A.wav'
NBINS = 50
cur_cluster = 0
cluster_idx = 0
paused = False
frame_idx = 0
audio = numm.sound2np(SOURCE)
clusters = cluster.cluster(SOURCE, NBINS)
for c in clusters.values():
random.shuffle(c)
def get_segment(cluster, idx):
idx = idx % len(clusters[cluster])
start, duration = clusters[cluster][idx]
return audio[int(R*start):int(R*(start+duration))]
def audio_out(a):
global frame_idx, cluster_idx, paused
if paused:
paused = False
if amnt == len(buffers[buf_idx]):
#done with this buffer
buffers.pop(buf_idx)
else:
buffers[buf_idx] = buffers[buf_idx][amnt:]
# # Defer frames to avoid pitch-shifting
# if len(buffers) > 1:
# buffers[buf_idx] = np.roll(buffers[buf_idx], -amnt*len(buffers), axis=0)
if len(buffers) == 0:
return out
buf_idx = (buf_idx + 1) % len(buffers)
if __name__=='__main__':
import numm
import sys
buffers = [numm.sound2np(X) for X in sys.argv[1:]]
nframes = sum([len(X) for X in buffers])
# freqrot = [440, 800, 1500, 300]
freqrot = [4400, 1800, 1500, 2222]
comp = []
f_idx = 0
w_len = R/4
while nframes > 0:
amnt = min(nframes, w_len)
comp.append((freqrot[f_idx], amnt))
f_idx = (f_idx + 1) % len(freqrot)
nframes -= amnt
