def __call__(self, combo):
c = np.array(combo)
chunk_size = self.chunk_size
db = self.db
tt=transformFFT(frameSize=4096, hopSize=512, sampleRate=44100, window=blackmanharris)
maxLength=0
for i in range(len(self.sources)):
instlen = int(util.getMidiLength(self.sources_midi[i]+'_g'+self.style_midi[s],os.path.join(db,f)))
if instlen>maxLength:
maxLength = instlen
if chunk_size>maxLength:
chunk_size = maxLength
for chnk in range(int(np.floor(maxLength/chunk_size))):
chunk_start = chunk_size * chnk
chunk_end = (chnk+1) * chunk_size
if not os.path.isfile(os.path.join(feature_path,f,self.style[s],f+'_'+str(c)+'_'+str(chnk)+'.data')):
try:
for i in range(len(self.sources)):
nframes = int(np.ceil(chunk_size*self.sampleRate / np.double(tt.hopSize))) + 2
size = int(chunk_size*self.sampleRate-int(np.max( c[:,0].astype(float))*self.sampleRate))
if self.sampleRate != 44100:
print 'sample rate is not consistent'
if i==0:
audio = np.zeros((size,len(self.sources)+1))
melody,melodyBegin,melodyEnd,melNotes = util.getMidi(self.sources_midi[i]+'_g'+self.style_midi[s],os.path.join(db,f),chunk_start,chunk_end,self.sampleRate,tt.hopSize,tt.frameSize,c[i,0],c[i,0],nframes,1)
#generate the audio, note by note
for m in range(len(melNotes)):
note = self.instruments[i].getNote(melNotes[m],self.allowed_dynamics[int(c[i,1])],self.allowed_styles[int(c[i,2])],int(c[i,3]))
if note is None:
raise GetOutOfLoop
else:
segment = note.getAudio(max_duration=melodyEnd[m]-melodyBegin[m])
if len(segment)>(len(audio)-int(np.floor(melodyBegin[m]*self.sampleRate))):
audio[int(np.floor(melodyBegin[m]*self.sampleRate)):int(np.floor(melodyBegin[m]*self.sampleRate)+len(segment)),i+1] = segment[:len(audio)-int(np.floor(melodyBegin[m]*self.sampleRate))]
else:
audio[int(np.floor(melodyBegin[m]*self.sampleRate)):int(np.floor(melodyBegin[m]*self.sampleRate)+len(segment)),i+1] = segment
segment = None
note = None
segment = None
audio[:,0] = np.sum(audio[:,1:len(self.sources)+1],axis=1)
tt.compute_transform(audio,os.path.join(feature_path,f,self.style[s],f+'_'+str(c).encode('base64','strict')+'_'+str(chnk)+'.data'),phase=False)
audio = None
melody= None
except GetOutOfLoop:
pass
else:
feature_path = os.path.join(db, 'transforms', 't1')
assert os.path.isdir(
db
), "Please input the directory for the iKala dataset with --db path_to_iKala"
tt = None
for f in os.listdir(os.path.join(db, "Wavfile")):
if f.endswith(".wav"):
#read the audio file
audioObj, sampleRate, bitrate = util.readAudioScipy(
os.path.join(db, "Wavfile", f))
if tt is None:
#initialize the transform object which will compute the STFT
tt = transformFFT(frameSize=1024,
hopSize=512,
sampleRate=sampleRate,
window=blackmanharris)
pitchhop = 0.032 * float(sampleRate) #seconds to frames
assert sampleRate == 44100, "Sample rate needs to be 44100"
audio = np.zeros((audioObj.shape[0], 3))
audio[:,
0] = audioObj[:,
0] + audioObj[:,
1] #create mixture voice + accompaniment
audio[:, 1] = audioObj[:, 1] #voice
audio[:, 2] = audioObj[:, 0] #accompaniment
audioObj = None
#read pitches so they can be written as separate features
if kwargs.__getattribute__('skip'):
skip = int(kwargs.__getattribute__('skip'))
else:
skip = False
path_in = []
testfile_list = []
path_in = feature_path
for f in sorted(os.listdir(db)):
if os.path.isdir(os.path.join(db, f)) and f[0].isdigit():
testfile_list.append(f)
#tt object needs to be the same as the one in compute_features
tt = transformFFT(frameSize=4096,
hopSize=512,
sampleRate=44100,
window=blackmanharris)
ld1 = LargeDataset(path_transform_in=path_in,
nsources=4,
nsamples=nsamples,
batch_size=batch_size,
batch_memory=batch_memory,
time_context=time_context,
overlap=overlap,
nprocs=nprocs,
mult_factor_in=scale_factor,
mult_factor_out=scale_factor)
logging.info(" Maximum:\t\t{:.6f}".format(ld1.getMax()))
logging.info(" Mean:\t\t{:.6f}".format(ld1.getMean()))
logging.info(" Standard dev:\t\t{:.6f}".format(ld1.getStd()))
def __call__(self, combo):
c = np.array(combo)
chunk_size = self.chunk_size
db = self.db
feature_path = self.feature_path
tt = transformFFT(frameSize=4096,
hopSize=512,
sampleRate=44100,
window=blackmanharris)
maxLength = 0
for i in range(len(self.sources)):
instlen = util.getMidiLength(
self.sources_midi[i] + '_g' + self.style_midi, db)
if instlen > maxLength:
maxLength = instlen
if chunk_size > maxLength:
chunk_size = maxLength
for chnk in range(int(np.floor(maxLength / chunk_size))):
chunk_start = float(chunk_size * chnk)
chunk_end = float((chnk + 1) * chunk_size)
if not os.path.isfile(
os.path.join(
feature_path, self.style,
str(c).encode('base64', 'strict') + '_' + str(chnk) +
'.data')):
try:
nelem_g = 1
for i in range(len(self.sources)):
ng = util.getMidiNum(
self.sources_midi[i] + '_g' + self.style_midi, db,
chunk_start, chunk_end)
nelem_g = np.maximum(ng, nelem_g)
melody_g = np.zeros((len(self.sources), int(nelem_g),
2 * self.nharmonics + 3))
melody_e = np.zeros((len(self.sources), int(nelem_g),
2 * self.nharmonics + 3))
for i in range(len(self.sources)):
nframes = int(
np.ceil(chunk_size * self.sampleRate /
np.double(tt.hopSize))) + 2
size = int(chunk_size * self.sampleRate - int(
np.max(c[:, 0].astype(float)) * self.sampleRate))
if self.sampleRate != 44100:
print 'sample rate is not consistent'
if i == 0:
audio = np.zeros((size, len(self.sources) + 1))
tmp = util.expandMidi(
self.sources_midi[i] + '_g' + self.style_midi, db,
chunk_start, chunk_end, self.interval,
self.tuning_freq, self.nharmonics, self.sampleRate,
tt.hopSize, tt.frameSize, c[i, 0], c[i,
0], nframes)
melody_g[i, :tmp.shape[0], :] = tmp
tmp = None
tmp = util.expandMidi(self.sources_midi[i] + '_g' +
self.style_midi,
db,
chunk_start,
chunk_end,
self.interval,
self.tuning_freq,
self.nharmonics,
self.sampleRate,
tt.hopSize,
tt.frameSize,
c[i, 0] + 0.2,
c[i, 0] + 0.2,
nframes,
fermata=c[i, 0] + 0.5)
melody_e[i, :tmp.shape[0], :] = tmp
tmp = None
#generate the audio, note by note
for m in range(nelem_g):
if melody_g[i, m, 2] > 0:
note = self.instruments[i].getNote(
melody_g[i, m, 2],
self.allowed_dynamics[int(c[i, 1])],
self.allowed_styles[int(c[i, 2])],
int(c[i, 3]))
if note is None:
raise GetOutOfLoop
else:
segment = note.getAudio(
max_duration=float(melody_g[i, m, 1] -
melody_g[i, m, 0]) *
tt.hopSize / self.sampleRate)
if len(segment) > (len(audio) - int(
np.floor(melody_g[i, m, 0] *
tt.hopSize))):
audio[int(
np.floor(melody_g[i, m, 0] *
tt.hopSize)
):int(
np.floor(melody_g[i, m, 0] *
tt.hopSize) +
len(segment)), i +
1] = segment[:len(audio) - int(
np.floor(melody_g[i, m, 0] *
tt.hopSize))]
else:
audio[int(
np.floor(melody_g[i, m, 0] *
tt.hopSize)
):int(
np.floor(melody_g[i, m, 0] *
tt.hopSize) +
len(segment)), i + 1] = segment
segment = None
note = None
segment = None
audio[:, 0] = np.sum(audio[:, 1:len(self.sources) + 1],
axis=1)
tt.compute_transform(
audio,
os.path.join(
feature_path, self.style,
str(c).encode('base64', 'strict') + '_' +
str(chnk) + '.data'),
phase=False)
tt.saveTensor(melody_g, '__g_')
tt.saveTensor(melody_e, '__e_')
audio = None
melody_g = None
melody_e = None
except GetOutOfLoop:
pass
nsamples = int(kwargs.__getattribute__('nsamples'))
else:
nsamples = 0
if kwargs.__getattribute__('function'):
function = kwargs.__getattribute__('function')
else:
function = 'build_ca'
funcs = {'build_ca': build_ca}
if function not in funcs:
function = 'build_ca'
path_in = [feature_path]
#tt object needs to be the same as the one in compute_features
tt = transformFFT(frameSize=1024,
hopSize=512,
sampleRate=44100,
window=hanning)
ld1 = LargeDatasetMulti(path_transform_in=path_in, nsources=4, nsamples=nsamples, batch_size=batch_size, batch_memory=batch_memory, time_context=time_context, overlap=overlap, nprocs=nprocs,mult_factor_in=scale_factor,mult_factor_out=scale_factor,\
sampleRate=tt.sampleRate,tensortype=theano.config.floatX)
logging.info(" Maximum input:\t\t{:.6f}".format(ld1.getMax()))
logging.info(" Minimum input:\t\t{:.6f}".format(ld1.getMin()))
logging.info(" Mean input:\t\t{:.6f}".format(ld1.getMean()))
logging.info(" Standard dev input:\t\t{:.6f}".format(ld1.getStd()))
logging.info(" Maximum:\t\t{:.6f}".format(ld1.getMax(inputs=False)))
logging.info(" Minimum:\t\t{:.6f}".format(ld1.getMin(inputs=False)))
logging.info(" Mean:\t\t{:.6f}".format(ld1.getMean(inputs=False)))
logging.info(" Standard dev:\t\t{:.6f}".format(ld1.getStd(inputs=False)))
if not os.path.exists(os.path.join(output, 'output', model)):
os.makedirs(os.path.join(output, 'output', model))
