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))
