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)
Exemple #3
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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")
Exemple #4
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        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