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)
Example #3
0
def test_ubm_var_channel():
    ubm = GMM.load('model/ubm.mixture-32.person-20.immature.model')

    train_duration = 8.
    nr_test = 5
    test_duration = 3.
    audio_files = ['xinyu.vad.wav', 'wyx.wav']
    X_train, y_train, X_test, y_test = [], [], [], []
    for audio_file in audio_files:
        fs, signal = wavfile.read(audio_file)
        signal = monotize_signal(signal)

        train_len = int(fs * train_duration)
        test_len = int(fs * test_duration)

        X_train.append(mix_feature((fs, signal[:train_len])))
        y_train.append(audio_file)

        for i in range(nr_test):
            start = random.randint(train_len, len(signal) - test_len)
            X_test.append(mix_feature((fs, signal[start:start + train_len])))
            y_test.append(audio_file)

    gmmset = GMMSet(32, ubm=ubm)
    gmmset.fit(X_train, y_train)
    y_pred = gmmset.predict_with_reject(X_test)
    for i in xrange(len(y_pred)):
        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)
def test_ubm_var_channel():
    ubm = GMM.load('model/ubm.mixture-32.person-20.immature.model')

    train_duration = 8.
    nr_test = 5
    test_duration = 3.
    audio_files = ['xinyu.vad.wav', 'wyx.wav']
    X_train, y_train, X_test, y_test = [], [], [], []
    for audio_file in audio_files:
        fs, signal = wavfile.read(audio_file)
        signal = monotize_signal(signal)

        train_len = int(fs * train_duration)
        test_len = int(fs * test_duration)

        X_train.append(mix_feature((fs, signal[:train_len])))
        y_train.append(audio_file)

        for i in range(nr_test):
            start = random.randint(train_len, len(signal) - test_len)
            X_test.append(mix_feature((fs, signal[start:start+train_len])))
            y_test.append(audio_file)

    gmmset = GMMSet(32, ubm=ubm)
    gmmset.fit(X_train, y_train)
    y_pred = gmmset.predict_with_reject(X_test)
    for i in xrange(len(y_pred)):
        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)
Example #5
0
 def load(fname):
     """ load from a dumped model file"""
     with open(fname, 'r') as f:
         R = pickle.load(f)
         R.gmmset.after_pickle()
         ubm = GMM.load(ModelInterface.UBM_MODEL_FILE)
         R.gmmset.ubm = ubm
         return R
Example #6
0
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")
 def _get_gmm_set(self):
     if os.path.isfile(self.ubm_model_file):
         try:
             from gmmset import GMMSetPyGMM
             if GMMSet is GMMSetPyGMM:
                 return GMMSet(ubm=GMM.load(self.ubm_model_file))
         except Exception as e:
             print "Warning: failed to import gmmset. You may forget to compile gmm:"
             print e
             print "Try running `make -C src/gmm` to compile gmm module."
             print "But gmm from sklearn will work as well! Using it now!"
         return GMMSet()
     return GMMSet()
Example #8
0
 def _get_gmm_set(self):
     if self.UBM_MODEL_FILE and os.path.isfile(self.UBM_MODEL_FILE):
         try:
             from gmmset import GMMSetPyGMM
             if GMMSet is GMMSetPyGMM:
                 return GMMSet(ubm=GMM.load(self.UBM_MODEL_FILE))
         except Exception as e:
             print("Warning: failed to import gmmset. You may forget to compile gmm:")
             print(e)
             print("Try running `make -C src/gmm` to compile gmm module.")
             print("But gmm from sklearn will work as well! Using it now!")
         return GMMSet()
     return GMMSet()
 def _get_gmm_set(self):
     if os.path.isfile(self.UBM_MODEL_FILE):
         try:
             from gmmset import GMMSetPyGMM
             if GMMSet is GMMSetPyGMM:
                 return GMMSet(ubm=GMM.load(self.UBM_MODEL_FILE))
         except Exception as e:
             print "Warning: failed to import gmmset. You may forget to compile gmm:"
             print e
             print "Try running `make -C src/gmm` to compile gmm module."
             print "But gmm from sklearn will work as well! Using it now!"
         return GMMSet()
     return GMMSet()
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")
 def predict(self):
     """ return a label (name)"""
     try:
         '''
         mfcc = MFCC.extract(self.FS, self.signal)
         lpc = LPC.extract(self.FS, self.signal)
         features = np.concatenate((mfcc, lpc), axis=1)
         '''
         features = self.mix_feature()
     except Exception as e:
         print tb.format_exc()
         return None
     self.gmmset.ubm = GMM.load(self.ubm_model_file)
     #print 'The registered users are :', len(self.gmmset.y)
     #print self.gmmset.y
     #print [y for y in self.gmmset.y]
     return self.gmmset.predict_one_with_rejection(features)
Example #12
0
    for i in xrange(len(y_pred)):
        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')
    y_pred = gmmset.predict_with_reject(X_test)
    for i in xrange(len(y_pred)):
        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')