def make_equal_frames(self, in_file_list, ref_file_list, in_dimension_dict):
logger = logging.getLogger("test")
logger.info('making equal number of lines...')
io_funcs = BinaryIOCollection()
utt_number = len(in_file_list)
for i in xrange(utt_number):
in_file_name = in_file_list[i]
in_data_stream_name = in_file_name.split('.')[-1]
in_feature_dim = in_dimension_dict[in_data_stream_name]
in_features, in_frame_number = io_funcs.load_binary_file_frame(in_file_name, in_feature_dim)
ref_file_name = ref_file_list[i]
ref_data_stream_name = ref_file_name.split('.')[-1]
ref_feature_dim = in_dimension_dict[ref_data_stream_name]
ref_features, ref_frame_number = io_funcs.load_binary_file_frame(ref_file_name, ref_feature_dim)
target_features = numpy.zeros((ref_frame_number, in_feature_dim))
if in_frame_number == ref_frame_number:
continue;
elif in_frame_number > ref_frame_number:
target_features[0:ref_frame_number, ] = in_features[0:ref_frame_number, ]
elif in_frame_number < ref_frame_number:
target_features[0:in_frame_number, ] = in_features[0:in_frame_number, ]
io_funcs.array_to_binary_file(target_features, in_file_name)
logger.info('Finished: made equal rows in data stream %s with reference to data stream %s ' %(in_data_stream_name, ref_data_stream_name))
def generate_lf0(source_f0_folder, target_lf0_folder, file_lengths):
io_funcs = BinaryIOCollection()
for file_id, file_length in file_lengths.iteritems():
source_file_path = os.path.join(source_f0_folder,
'{}.f0'.format(file_id))
target_file_path = os.path.join(target_lf0_folder,
'{}.lf0'.format(file_id))
with open(source_file_path, 'rt') as handle:
lines = handle.readlines()
f0s = [float(line) for line in lines]
if len(lines) <= file_length:
f0s += [0.] * (file_length - len(lines))
else:
f0s = f0s[:file_length]
lf0s = [-1e+10] * file_length
for i in xrange(file_length):
if f0s[i] <= 10.0:
continue
lf0s[i] = math.log(f0s[i])
print(target_file_path)
io_funcs.array_to_binary_file(lf0s, target_file_path)
print
def shift_for_one_utterance(self, utt, feat_dim, semi):
if semi == 0:
return os.path.basename(utt)
io_funcs = BinaryIOCollection()
feat, num_frame = io_funcs.load_binary_file_frame(utt, feat_dim)
for f in feat:
self.shift_pitch_feat(f[curr_start_ind: curr_end_ind + 1], semi)
self.shift_pitch_feat(f[prev_start_ind: prev_end_ind + 1], semi)
self.shift_pitch_feat(f[next_start_ind: next_end_ind + 1], semi)
if semi > 0:
filename = utt + '_u' + str(semi)
else:
filename = utt + '_d' + str(-semi)
io_funcs.array_to_binary_file(feat, filename)
return os.path.basename(filename)
class AlignFeats(object):
def __init__(self):
self.io_funcs = BinaryIOCollection()
def align_src_feats(self, src_feat_file, src_aligned_feat_file, feat_dim, dtw_path_dict):
'''
align source feats as per the dtw path (matching target length)
'''
src_features, frame_number = self.io_funcs.load_binary_file_frame(src_feat_file, feat_dim)
tgt_length = len(dtw_path_dict)
src_aligned_features = numpy.zeros((tgt_length, feat_dim))
for i in range(tgt_length):
src_aligned_features[i, ] = src_features[dtw_path_dict[i]]
self.io_funcs.array_to_binary_file(src_aligned_features, src_aligned_feat_file)
def duration_decomposition(self, in_file_list, dimension,
out_dimension_dict, file_extension_dict):
logger = logging.getLogger('param_generation')
logger.debug('duration_decomposition for %d files' % len(in_file_list))
state_number = 5 ## hard coding, try removing in future?
if len(list(out_dimension_dict.keys())) > 1:
logger.critical(
"we don't support any additional features along with duration as of now."
)
sys.exit(1)
else:
feature_name = list(out_dimension_dict.keys())[0]
io_funcs = BinaryIOCollection()
findex = 0
flen = len(in_file_list)
for file_name in in_file_list:
findex = findex + 1
dir_name = os.path.dirname(file_name)
file_id = os.path.splitext(os.path.basename(file_name))[0]
features, frame_number = io_funcs.load_binary_file_frame(
file_name, dimension)
gen_features = numpy.int32(numpy.round(features))
gen_features[gen_features < 1] = 1
if dimension > state_number:
gen_features = gen_features[:, state_number]
logger.info('processing %4d of %4d: %s' %
(findex, flen, file_name))
new_file_name = os.path.join(
dir_name, file_id + file_extension_dict[feature_name])
io_funcs.array_to_binary_file(gen_features, new_file_name)
logger.debug('wrote to file %s' % new_file_name)
class AlignFeats(object):
def __init__(self):
self.io_funcs = BinaryIOCollection()
def align_src_feats(self, src_feat_file, src_aligned_feat_file, feat_dim,
dtw_path_dict):
'''
align source feats as per the dtw path (matching target length)
'''
src_features, frame_number = self.io_funcs.load_binary_file_frame(
src_feat_file, feat_dim)
tgt_length = len(dtw_path_dict)
src_aligned_features = numpy.zeros((tgt_length, feat_dim))
for i in range(tgt_length):
src_aligned_features[i, ] = src_features[dtw_path_dict[i]]
self.io_funcs.array_to_binary_file(src_aligned_features,
src_aligned_feat_file)
def acoustic_decomposition(self, in_file_list, dimension, out_dimension_dict, file_extension_dict, var_file_dict, do_MLPG=True, cfg=None):
print ('param_generation')
print ('acoustic_decomposition for %d files' % len(in_file_list) )
self.load_covariance(var_file_dict, out_dimension_dict)
stream_start_index = {}
dimension_index = 0
recorded_vuv = True
vuv_dimension = None
for feature_name in list(out_dimension_dict.keys()):
if feature_name != 'vuv':
stream_start_index[feature_name] = dimension_index
else:
vuv_dimension = dimension_index
dimension_index += out_dimension_dict[feature_name]
io_funcs = BinaryIOCollection()
mlpg_algo = MLParameterGeneration()
findex=0
flen=len(in_file_list)
for file_name in in_file_list:
findex=findex+1
dir_name = os.path.dirname(file_name)
file_id = os.path.splitext(os.path.basename(file_name))[0]
features, frame_number = io_funcs.load_binary_file_frame(file_name, dimension)
print('processing %4d of %4d: %s' % (findex,flen,file_name) )
for feature_name in self.gen_wav_features:
print(' feature: %s' % feature_name)
current_features = features[:, stream_start_index[feature_name]:stream_start_index[feature_name]+out_dimension_dict[feature_name]]
if FAST_MLPG:
### fast version wants variance per frame, not single global one:
var = self.var[feature_name]
var = numpy.transpose(numpy.tile(var,frame_number))
else:
var = self.var[feature_name]
# print var.shape[1]
if do_MLPG == False:
gen_features = current_features
else:
gen_features = mlpg_algo.generation(current_features, var, out_dimension_dict[feature_name]//3)
print(' feature dimensions: %d by %d' %(gen_features.shape[0], gen_features.shape[1]))
if feature_name in ['lf0', 'F0']:
if 'vuv' in stream_start_index:
vuv_feature = features[:, stream_start_index['vuv']:stream_start_index['vuv']+1]
for i in range(frame_number):
if new_vuv_feature[i] < 0.5:
gen_features[i, 0] = self.inf_float
new_file_name = os.path.join(dir_name, file_id + file_extension_dict[feature_name])
if self.enforce_silence:
silence_pattern = cfg.silence_pattern
label_align_dir = cfg.in_label_align_dir
in_f = open(label_align_dir+'/'+file_id+'.lab','r')
for line in in_f.readlines():
line = line.strip()
if len(line) < 1:
continue
temp_list = re.split('\s+', line)
start_time = int(int(temp_list[0])*(10**-4)/5)
end_time = int(int(temp_list[1])*(10**-4)/5)
full_label = temp_list[2]
label_binary_flag = self.check_silence_pattern(full_label, silence_pattern)
if label_binary_flag:
if feature_name in ['lf0', 'F0', 'mag']:
gen_features[start_time:end_time, :] = self.inf_float
else:
gen_features[start_time:end_time, :] = 0.0
io_funcs.array_to_binary_file(gen_features, new_file_name)
print(' wrote to file %s' % new_file_name)
'vuv' : '.vuv'}
var_file_dict = {'mgc':'{}/mgc.var'.format(args.var_dir),
'vuv':'{}/vuv.var'.format(args.var_dir),
'lf0':'{}/lf0.var'.format(args.var_dir)}
generator = ParameterGeneration()
# out_dimension_dict is the cmp structrue of your nnet output cmp
generator.acoustic_decomposition(in_file_list, 127, out_dimension_dict, file_extension_dict, var_file_dict)
if not os.path.exists(lf0_dir):
os.mkdir(lf0_dir)
if not os.path.exists(mgc_dir):
os.mkdir(mgc_dir)
os.system('mv {}/*.lf0 {}'.format(cmp_dir, lf0_dir))
os.system('mv {}/*.mgc {}'.format(cmp_dir, mgc_dir))
io_funcs = BinaryIOCollection()
inf_float = -1.0e+10
for item in os.listdir(cmp_dir):
vuv = numpy.reshape(numpy.fromfile(os.path.join(cmp_dir, item), dtype=numpy.float32), [-1,127])[:,123]
name, ext = os.path.splitext(item)
lf0 = numpy.reshape(numpy.fromfile(os.path.join(lf0_dir, "{}.lf0".format(name)),dtype=numpy.float32),[-1,1])
mgc = numpy.reshape(numpy.fromfile(os.path.join(mgc_dir, "{}.mgc".format(name)),dtype=numpy.float32),[-1,41])
mgc = signal.convolve2d(
mgc, [[1.0 / 3], [1.0 / 3], [1.0 / 3]], mode="same", boundary="symm")
lf0[vuv < 0.5] = inf_float
io_funcs.array_to_binary_file(lf0, os.path.join(lf0_dir, "{}.lf0".format(name)))
io_funcs.array_to_binary_file(mgc, os.path.join(mgc_dir, "{}.mgc".format(name)))
def prepare_data(self, in_file_list_dict, out_file_list, in_dimension_dict,
out_dimension_dict):
logger = logging.getLogger("acoustic_comp")
stream_start_index = {}
stream_dim_index = 0
for stream_name in out_dimension_dict.keys():
if not stream_start_index.has_key(stream_name):
stream_start_index[stream_name] = stream_dim_index
stream_dim_index += out_dimension_dict[stream_name]
io_funcs = BinaryIOCollection()
for i in xrange(self.file_number):
out_file_name = out_file_list[i]
#if os.path.isfile(out_file_name):
# logger.info('processing file %4d of %4d : %s exists' % (i+1, self.file_number, out_file_name))
# continue
logger.info('processing file %4d of %4d : %s' %
(i + 1, self.file_number, out_file_name))
out_data_matrix = None
out_frame_number = 0
for k in xrange(self.data_stream_number):
data_stream_name = self.data_stream_list[k]
in_file_name = in_file_list_dict[data_stream_name][i]
in_feature_dim = in_dimension_dict[data_stream_name]
features, frame_number = io_funcs.load_binary_file_frame(
in_file_name, in_feature_dim)
if k == 0:
out_frame_number = frame_number
out_data_matrix = numpy.zeros(
(out_frame_number, self.out_dimension))
if frame_number > out_frame_number:
features = features[0:out_frame_number, ]
frame_number = out_frame_number
try:
assert out_frame_number == frame_number
except AssertionError:
logger.critical(
'the frame number of data stream %s is not consistent with others: current %d others %d'
% (data_stream_name, out_frame_number, frame_number))
raise
dim_index = stream_start_index[data_stream_name]
if data_stream_name in ['lf0', 'F0']: ## F0 added for GlottHMM
features, vuv_vector = self.interpolate_f0(features)
### if vuv information to be recorded, store it in corresponding column
if self.record_vuv:
out_data_matrix[0:out_frame_number,
stream_start_index['vuv']:
stream_start_index['vuv'] +
1] = vuv_vector
out_data_matrix[0:out_frame_number, dim_index:dim_index +
in_feature_dim] = features
dim_index = dim_index + in_feature_dim
if self.compute_dynamic[data_stream_name]:
delta_features = self.compute_dynamic_matrix(
features, self.delta_win, frame_number, in_feature_dim)
acc_features = self.compute_dynamic_matrix(
features, self.acc_win, frame_number, in_feature_dim)
out_data_matrix[0:out_frame_number, dim_index:dim_index +
in_feature_dim] = delta_features
dim_index = dim_index + in_feature_dim
out_data_matrix[0:out_frame_number, dim_index:dim_index +
in_feature_dim] = acc_features
### write data to file
io_funcs.array_to_binary_file(out_data_matrix, out_file_name)
logger.debug(' wrote %d frames of features', out_frame_number)