Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
 Community
text
stringlengths
1
93.6k
types = ('*.wav', )
filesList = []
for files in types:
filesList.extend(glob.glob(os.path.join(dirName, files)))
filesList = sorted(filesList)
filesListIrr = []
filesListIrr = sorted(filesListIrr)
stWin = 0.020
stStep = 0.015
for f in filesList:
[Fs, x] = audioBasicIO.readAudioFile(f)
x = audioBasicIO.stereo2mono(x)
createSpectrogramFile(x, Fs, f.replace(".wav",".png"), stWin, stStep)
else:
dirName = argv[1]
dirNameIrrelevant = argv[2]
types = ('*.wav', )
filesList = []
for files in types:
filesList.extend(glob.glob(os.path.join(dirName, files)))
filesList = sorted(filesList)
filesListIrr = []
for files in types:
filesListIrr.extend(glob.glob(os.path.join(dirNameIrrelevant, files)))
filesListIrr = sorted(filesListIrr)
print filesListIrr
WIDTH_SEC = 1.5
stWin = 0.040
stStep = 0.005
WIDTH = WIDTH_SEC / stStep
for f in filesList:
print f
[Fs, x] = audioBasicIO.readAudioFile(f)
x = audioBasicIO.stereo2mono(x)
x = x.astype(float) / x.max()
for i in range(3):
if x.shape[0] > WIDTH_SEC * Fs + 200:
randStartSignal = random.randrange(0, int(x.shape[0] - WIDTH_SEC * Fs - 200) )
x2 = x[randStartSignal : randStartSignal + int ( (WIDTH_SEC + stStep) * Fs) ]
createSpectrogramFile(x2, Fs, f.replace(".wav",".png"), stWin, stStep) # ORIGINAL
if len(dirNameIrrelevant) > 0:
# AUGMENTED
randIrrelevant = random.randrange(0, len(filesListIrr))
[Fs, xnoise] = audioBasicIO.readAudioFile(filesListIrr[randIrrelevant])
xnoise = xnoise.astype(float) / xnoise.max()
randStartNoise = random.randrange(0, xnoise.shape[0] - WIDTH_SEC * Fs - 200)
R = 5; xN = (R * x2.astype(float) + xnoise[randStartNoise : randStartNoise + x2.shape[0]].astype(float)) / float(R+1)
wavfile.write(f.replace(".wav","_rnoise{0:d}1.wav".format(i)), Fs, (16000 * xN).astype('int16'))
createSpectrogramFile(xN, Fs, f.replace(".wav","_rnoise{0:d}1.png".format(i)), stWin, stStep)
randStartNoise = random.randrange(0, xnoise.shape[0] - WIDTH_SEC * Fs - 200)
R = 4; xN = (R * x2.astype(float) + xnoise[randStartNoise : randStartNoise + x2.shape[0]].astype(float)) / float(R+1)
wavfile.write(f.replace(".wav","_rnoise{0:d}2.wav".format(i)), Fs, (16000 * xN).astype('int16'))
createSpectrogramFile(xN, Fs, f.replace(".wav","_rnoise{0:d}2.png".format(i)), stWin, stStep)
randStartNoise = random.randrange(0, xnoise.shape[0] - WIDTH_SEC * Fs - 200)
R = 3; xN = (R * x2.astype(float) + xnoise[randStartNoise : randStartNoise + x2.shape[0]].astype(float)) / float(R+1)
wavfile.write(f.replace(".wav","_rnoise{0:d}3.wav".format(i)), Fs, (16000 * xN).astype('int16'))
createSpectrogramFile(xN, Fs, f.replace(".wav","_rnoise{0:d}3.png".format(i)), stWin, stStep)
#specgramOr, TimeAxis, FreqAxis = aF.stSpectogram(x2, Fs, round(Fs * stWin), round(Fs * stStep), False)
#im2 = Image.fromarray(numpy.uint8(matplotlib.cm.jet(specgram)*255))
#plt.subplot(2,1,1)
#plt.imshow(im1)
#plt.subplot(2,1,2)
#plt.imshow(im2)
#plt.show()
'''
if int(specgramOr.shape[0]/2) - WIDTH/2 - int((0.2) / stStep) > 0:
specgram = specgramOr[int(specgramOr.shape[0]/2) - WIDTH/2 - int((0.2) / stStep):int(specgramOr.shape[0]/2) + WIDTH/2 - int((0.2) / stStep), :]
specgram = scipy.misc.imresize(specgram, float(227.0) / float(specgram.shape[0]), interp='bilinear')
im = Image.fromarray(numpy.uint8(matplotlib.cm.jet(specgram)*255))
print specgram.shape
scipy.misc.imsave(f.replace(".wav","_02A.png"), im)
specgram = specgramOr[int(specgramOr.shape[0]/2) - WIDTH/2 + int((0.2) / stStep):int(specgramOr.shape[0]/2) + WIDTH/2 + int((0.2) / stStep), :]
specgram = scipy.misc.imresize(specgram, float(227.0) / float(specgram.shape[0]), interp='bilinear')
print specgram.shape
im = Image.fromarray(numpy.uint8(matplotlib.cm.jet(specgram)*255))
scipy.misc.imsave(f.replace(".wav","_02B.png"), im)
# ONLY FOR SPEECH (fewer samples). Must comment for music
specgram = specgramOr[int(specgramOr.shape[0]/2) - WIDTH/2 - int((0.1) / stStep):int(specgramOr.shape[0]/2) + WIDTH/2 - int((0.1) / stStep), :]
specgram = scipy.misc.imresize(specgram, float(227.0) / float(specgram.shape[0]), interp='bilinear')
im = Image.fromarray(numpy.uint8(matplotlib.cm.jet(specgram)*255))
print specgram.shape
scipy.misc.imsave(f.replace(".wav","_01A.png"), im)
specgram = specgramOr[int(specgramOr.shape[0]/2) - WIDTH/2 + int((0.1) / stStep):int(specgramOr.shape[0]/2) + WIDTH/2 + int((0.1) / stStep), :]