def load_speaker_recognition_newtork(filename, create_new=False):
"""
Load or create (if you want) network for speker recognition form file
returns tuple: (network, people_names, people_number)
"""
people = voice_sample.get_names();
people_num = len(people)
network = None
try:
network = loadnet(filename)
except IOError, ex:
if create_new:
network = ffnet(mlgraph((LPC_COE_NUM, people_num + LPC_COE_NUM,
#network = ffnet(mlgraph((LPC_COE_NUM, 10,
people_num)) )
def load_speaker_recognition_newtork(filename, create_new=False):
"""
Load or create (if you want) network for speker recognition form file
returns tuple: (network, people_names, people_number)
"""
people = voice_sample.get_names()
people_num = len(people)
network = None
try:
network = loadnet(filename)
except IOError, ex:
if create_new:
network = ffnet(
mlgraph((
LPC_COE_NUM,
people_num + LPC_COE_NUM,
#network = ffnet(mlgraph((LPC_COE_NUM, 10,
people_num)))
import pylab, scipy, spectrum
import voice_sample
def frame(x, framesize, framehop):
w = scipy.hamming(framesize)
return scipy.array([w*x[i:i+framesize]
for i in range(0, len(x)-framesize, framehop)])
if __name__ == '__main__':
import scipy.io.wavfile as wf
names = voice_sample.get_names()
for num, name in zip(range(1, len(names) + 1), names):
pylab.subplot(2,3, num)
for it in range(0, 9):
(fs, sd) = wf.read(voice_sample.SAMPLES_DIR + '/%s/imie/%02d.wav' % (name, it+1) )
sd = sd - scipy.mean(sd)
sd = sd / scipy.amax(sd)
#sf = frame(sd, int(fs*0.03), int(fs*0.02))
#lpcc = scipy.array([ lpc(f, 10) for f in sf ]);
#lpcc_mean = scipy.mean(lpcc, 0)
#pylab.figure();
#pylab.plot(sd);
lpcc, e = spectrum.lpc(sd, 12)
pylab.plot(lpcc, label="Zapis {:02d}".format(it+1) );
pylab.title(name)
pylab.ylim(-5, 5)
pylab.show();
