def comp_feat(self, sig, rate):
'''
compute the features
Args:
sig: the audio signal as a 1-D numpy array
rate: the sampling rate
Returns:
the features as a [seq_length x feature_dim] numpy array
'''
#snip the edges
sig = snip(sig, rate, float(self.conf['winlen']),
float(self.conf['winstep']))
feat, energy = base.mfcc(sig, rate, self.conf)
if self.conf['include_energy'] == 'True':
feat = np.append(feat, energy[:, np.newaxis], 1)
if self.conf['dynamic'] == 'delta':
feat = base.delta(feat)
elif self.conf['dynamic'] == 'ddelta':
feat = base.ddelta(feat)
elif self.conf['dynamic'] != 'nodelta':
raise Exception('unknown dynamic type')
return feat
def comp_feat(self, sig, rate):
'''
compute the features
Args:
sig: the audio signal as a 1-D numpy array
rate: the sampling rate
Returns:
the features as a [seq_length x feature_dim] numpy array
'''
feat, energy = base.logfbank(sig, rate, self.conf)
if self.conf['include_energy'] == 'True':
feat = np.append(feat, energy[:, np.newaxis], 1)
if self.conf['dynamic'] == 'delta':
feat = base.delta(feat)
elif self.conf['dynamic'] == 'ddelta':
feat = base.ddelta(feat)
elif self.conf['dynamic'] != 'nodelta':
raise Exception('unknown dynamic type')
#mean and variance normalize the features
if self.conf['mvn'] == 'True':
feat = (feat - feat.mean(0)) / feat.std(0)
return feat
def comp_feat(self, sig, rate):
'''
compute the features
Args:
sig: the audio signal as a 1-D numpy array
rate: the sampling rate
Returns:
the features as a [seq_length x feature_dim] numpy array
'''
feat, energy = base.mfcc(sig, rate, self.conf)
# write the wav to temporary location and invoke external pitch extractor.
# make sure 'reaper' is in your $PATH
tempdir = os.path.join('/tmp', str(os.getpid()))
if not os.path.isdir(tempdir):
os.makedirs(tempdir)
name = 'mix'
wav.write(os.path.join(tempdir, name + '.wav'), rate, np.int16(sig))
os.system('reaper -i ' + os.path.join(tempdir, name + '.wav') +
' -f ' + os.path.join(tempdir, name + '.txt -a -e 0.01'))
pitch = np.loadtxt(os.path.join(tempdir, name + '.txt'), skiprows=7)[:,
2]
pitch = np.pad(pitch, (0, max(0, feat.shape[0] - pitch.shape[0])),
'edge')
# linear interpolation in voiceless regions
voiceless = np.where(pitch == -1)[0]
jump = np.where((voiceless[1:] - voiceless[:-1]) > 1)[0]
segments = np.split(voiceless, jump + 1)
for seg in segments:
idx1 = seg[0] - 1
idx2 = seg[-1] + 1
val1 = -1
val2 = -1
if idx1 >= 0:
val1 = pitch[idx1]
if idx2 < pitch.size:
val2 = pitch[idx2]
if val1 == -1: #segment starts at utterence start
val1 = val2
if val2 == -1: # segment ends at utterance end
val2 = val1
if val1 == -1: #segment is the whole utterance => make up a value
val1 = 150
val2 = 150
#interpolate
pitch[seg] = (val2 - val1) * (np.array(seg) -
idx1) / float(idx2 - idx1) + val1
feat = np.append(feat, pitch[:feat.shape[0], np.newaxis], 1)
if self.conf['include_energy'] == 'True':
feat = np.append(feat, energy[:, np.newaxis], 1)
if self.conf['dynamic'] == 'delta':
feat = base.delta(feat)
elif self.conf['dynamic'] == 'ddelta':
feat = base.ddelta(feat)
elif self.conf['dynamic'] != 'nodelta':
raise Exception('unknown dynamic type')
#mean and variance normalize the features
if self.conf['mvn'] == 'True':
feat = (feat - feat.mean(0)) / (
feat.std(0) + 1e-20
) # features could be constant, e.g. voiceless speech
return feat