def train_all_together_ubm(): global nr_mixture nr_utt_in_ubm = 300 fpaths = get_all_data_fpaths() random.shuffle(fpaths) fpaths = fpaths[:nr_utt_in_ubm] X = datautil.read_raw_data(fpaths) gmm = get_gmm() gmm.fit(X) gmm.dump('model/ubm.mixture-{}.utt-{}.model' . format( nr_mixture, nr_utt_in_ubm))
def train_all_together_ubm(path, ubmOutputPath): global nr_mixture # nr_utt_in_ubm = 300 fpaths = get_all_data_fpaths(path) # random.shuffle(fpaths) # fpaths = fpaths[:nr_utt_in_ubm] # print "The path is ",fpaths X_train = datautil.read_raw_data(fpaths) gmm = get_gmm() gmm.fit(X_train) gmm.dump(ubmOutputPath + 'ubm.mixture-{}.model'.format(nr_mixture))
def train_all_together_ubm(): global nr_mixture nr_utt_in_ubm = 300 fpaths = get_all_data_fpaths() random.shuffle(fpaths) fpaths = fpaths[:nr_utt_in_ubm] X = datautil.read_raw_data(fpaths) gmm = get_gmm() gmm.fit(X) gmm.dump('model/ubm.mixture-{}.utt-{}.model'.format( nr_mixture, nr_utt_in_ubm))
def main(): train_all_together_ubm() return global nr_mixture # file_pattern = 'test-data/mfcc-data/Style_Reading/*.mfcc' # X_train, y_train, X_test, y_test = datautil.read_data(file_pattern, 10) fpaths = map(lambda x: 'test-data/mfcc-lpc-data/Style_Reading/' + x + '.mfcc', config.ubm_set) print fpaths X = datautil.read_raw_data(fpaths) gmm = get_gmm() gmm.fit(X) gmm.dump('model/ubm-{}.model' . format(nr_mixture))
def main(): train_all_together_ubm() return global nr_mixture # file_pattern = 'test-data/mfcc-data/Style_Reading/*.mfcc' # X_train, y_train, X_test, y_test = datautil.read_data(file_pattern, 10) fpaths = map( lambda x: 'test-data/mfcc-lpc-data/Style_Reading/' + x + '.mfcc', config.ubm_set) print fpaths X = datautil.read_raw_data(fpaths) gmm = get_gmm() gmm.fit(X) gmm.dump('model/ubm-{}.model'.format(nr_mixture))