def get_gmm(self):
from sklearn.mixture import GMM as skGMM
from gmmset import GMM as pyGMM
if GMM == skGMM:
print 'using GMM from sklearn'
return GMM(self.nr_mixture)
else:
print 'using pyGMM'
return GMM(nr_mixture=self.nr_mixture,
nr_iteration=500,
init_with_kmeans=0,
concurrency=8,
threshold=1e-15,
verbosity=2)
def get_gmm():
# from sklearn.mixture import GMM as skGMM
from sklearn.mixture import GaussianMixture as skGMM
from gmmset import GMM as pyGMM
if GMM == skGMM:
print('using GMM from sklearn')
return GMM(nr_mixture)
else:
print('using pyGMM')
return GMM(nr_mixture=nr_mixture,
nr_iteration=500,
init_with_kmeans=0,
concurrency=8,
threshold=1e-15,
verbosity=2)
def main():
nr_person = 50
fpaths = get_training_data_fpaths()
X_train, y_train, X_test, y_test = datautil.read_data(fpaths, nr_person)
ubm = GMM.load('model/ubm-32.model')
for x, y in zip(X_train, y_train):
gmm = GMM(concurrency=8, threshold=0.01, nr_iteration=100, verbosity=1)
gmm.fit(x, ubm=ubm)
gmm.dump("model/" + y + ".32.model")
print y_test[i], y_pred[i], '' if y_test[i] == y_pred[i] else 'wrong'
for imposter_audio_file in map(lambda x: 'test-{}.wav'.format(x),
range(5)):
fs, signal = wavfile.read(imposter_audio_file)
signal = monotize_signal(signal)
imposter_x = mix_feature((fs, signal))
print gmmset.predict_one_with_rejection(imposter_x)
test_ubm_var_channel()
import sys
sys.exit(0)
ubm = GMM.load('model/ubm.mixture-32.person-20.immature.model')
gmm = GMM(32, verbosity=1)
#audio_file = 'test-data/corpus.silence-removed/Style_Reading/f_001_03.wav'
fs, signal = wavfile.read(audio_file)
signal = monotize_signal(signal)
X = mix_feature((fs, signal))
ubm = GMM.load('model/ubm.mixture-32.person-20.immature.model')
gmm = GMM(32, verbosity=1)
X = X[:1000]
gmm.fit(X, ubm=ubm)
gmm.dump('xinyu.model')
# vim: foldmethod=marker