def extract_dur_features(self, orig_file, output_file):
io_funcs = BinaryIOCollection()
totalMat = io_funcs.file2matrix(orig_file)
self.label_dimension = totalMat.shape[1] - 5 # collum num
durMat = totalMat[:, -5:]
io_funcs.array_to_binary_file(durMat, output_file)
def extract_label_features(self, orig_file, output_file):
io_funcs = BinaryIOCollection()
totalMat = io_funcs.file2matrix(orig_file)
self.label_dimension = totalMat.shape[1] - 5 # collum num
labelMat = totalMat[:, :-5]
#print orig_file, totalMat.shape, labelMat.shape
io_funcs.array_to_binary_file(labelMat, output_file)
def compose_predict_label(self, orig_label_file, gen_label_file,
predict_duration_file):
io_funcs = BinaryIOCollection()
origMat = io_funcs.file2matrix(orig_label_file)
state_number = 5
duration, in_frame_number = io_funcs.load_binary_file_frame(
predict_duration_file, state_number)
assert origMat.shape[0] == in_frame_number
origMat[:, -5:] = duration
origMat = origMat.astype(int)
io_funcs.matrix2file(origMat, gen_label_file)
def extract_acousitc_label_features(self, orig_file, output_file):
io_funcs = BinaryIOCollection()
totalMat = io_funcs.file2matrix(orig_file, numpy.int)
labelMat = totalMat[:, :-5]
durMat = totalMat[:, -5:]
label_len = totalMat.shape[1] - 5
self.label_dimension = label_len + 9
phone_number = labelMat.shape[0]
label_feature_matrix = numpy.empty((100000, self.label_dimension))
state_number = 5
label_feature_index = 0
for phone_index in xrange(phone_number):
label_vector = labelMat[phone_index, :]
state_vector = durMat[phone_index, :]
phone_duration = 0
state_duration_bases = numpy.zeros((5, ), dtype=numpy.int)
for state_index in xrange(state_number):
state_duration_bases[state_index] = phone_duration
phone_duration = phone_duration + state_vector[state_index]
for state_index in xrange(state_number):
frame_number = state_vector[state_index]
current_block_binary_array = numpy.zeros(
(frame_number, self.label_dimension))
state_duration_base = state_duration_bases[state_index]
state_index_backward = state_number - state_index
for i in xrange(frame_number):
current_block_binary_array[i, 0:label_len] = label_vector
current_block_binary_array[
i, label_len] = float(i + 1) / float(
frame_number) ## fraction through state (forwards)
current_block_binary_array[
i, label_len + 1] = float(frame_number - i) / float(
frame_number
) ## fraction through state (backwards)
current_block_binary_array[i, label_len + 2] = float(
frame_number) ## length of state in frames
current_block_binary_array[i, label_len + 3] = float(
state_index) ## state index (counting forwards)
current_block_binary_array[i, label_len + 4] = float(
state_index_backward
) ## state index (counting backwards)
current_block_binary_array[i, label_len + 5] = float(
phone_duration) ## length of phone in frames
current_block_binary_array[
i, label_len + 6] = float(frame_number) / float(
phone_duration
) ## fraction of the phone made up by current state
current_block_binary_array[i, label_len + 7] = float(
phone_duration - i - state_duration_base) / float(
phone_duration
) ## fraction through phone (forwards)
current_block_binary_array[
i, label_len +
8] = float(state_duration_base + i + 1) / float(
phone_duration
) ## fraction through phone (backwards)
label_feature_matrix[
label_feature_index:label_feature_index +
frame_number, ] = current_block_binary_array
label_feature_index = label_feature_index + frame_number
label_feature_matrix = label_feature_matrix[0:label_feature_index, ]
#print label_feature_matrix.shape
io_funcs.array_to_binary_file(label_feature_matrix, output_file)
