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()
Beispiel #2
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 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()
Beispiel #3
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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 load_gmmset(labels, nr_person):
    gmmset = GMMSet(concurrency=8)
    for fpath in sorted(glob.glob('model.new-mfcc/*')):
        fname = os.path.basename(fpath)
        base = fname[:fname.find('.')]
        if base not in labels:
            continue
        if fname.endswith("32.model"):
            print(base, fname)
            gmmset.load_gmm(base, fpath)
    return gmmset
Beispiel #5
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 def __init__(self):
     self.features = defaultdict(list)
     self.gmmset = GMMSet()
     self.vad = VAD()