def interpolate_f0(self, f0_file):
io_funcs = BinaryIOCollection()
data = io_funcs.load_float_file(f0_file)
ip_data = data
frame_number = len(data)
last_value = 0.0
for i in xrange(frame_number):
if data[i] <= 0.0:
j = i + 1
for j in range(i + 1, frame_number):
if data[j] > 0.0:
break
if j < frame_number - 1:
if last_value > 0.0:
step = (data[j] - data[i - 1]) / float(j - i)
for k in range(i, j):
ip_data[k] = data[i - 1] + step * (k - i + 1)
else:
for k in range(i, j):
ip_data[k] = data[j]
else:
for k in range(i, frame_number):
ip_data[k] = last_value
else:
ip_data[i] = data[i]
last_value = data[i]
return ip_data
os.makedirs(out_f0_dir)
### Single file processing ###
SFP = False
if SFP:
# filename = 'TheHareandtheTortoise_097_24-23-1-2-1-0'
filename = 'GoldilocksandtheThreeBears_000_24-0-24-3-0-2'
print 'processing...' + filename
f0_file = os.path.join(f0_dir, filename + '.f0')
lab_file = os.path.join(lab_dir, filename + '.lab')
out_file = os.path.join(out_dir, filename + '.cmp')
### processing f0 file ###
ori_f0_data = io_funcs.load_float_file(
f0_file) # # to process float f0 file
# ori_lf0_data, frame_number = io_funcs.load_binary_file_frame(lf0_file, 1)
# ori_lf0_data = np.exp(ori_f0_data)
# interp_f0_arr = f0_processing.process(ori_f0_data)
interp_f0_arr = prosody_funcs.interpolate_f0(f0_file)
### read label file ###
[phone, ph_arr,
mean_f0_arr] = prosody_funcs.read_state_align_label_file(lab_file)
### decomposition of f0 file ###
[dct_features, stat_features
] = prosody_funcs.DCT_decomposition(phone, ph_arr, mean_f0_arr,
interp_f0_arr, decomposition_unit,
coef_size)
prosody_feats = np.concatenate((dct_features, stat_features), axis=0)
org_dur_features.append(st_arr[j][k] /
(np.power(10, 4) * 5))
org_dur_features = np.array(org_dur_features).reshape(
num_of_phones, 5)
org_dur_all_files = np.concatenate(
(org_dur_all_files,
np.sum(np.array(org_dur_features), axis=1)),
axis=0)
#print np.sum(np.array(org_dur_features), axis=1)
### load generated output ###
# gen_data = io_funcs.load_binary_file(gen_file, 1)
gen_file = os.path.join(gen_dir, filename + '.cmp')
gen_data = io_funcs.load_float_file(gen_file)
##### denormalization of data #####
print 'denormalizing the data....'
if (normalization == "MVN"):
denorm_data = data_normalization.MVN_denormalize(
gen_data, out_dim, stats_file)
#gen_data = io_funcs.load_float_file(gen_file)
#bkp_denorm_data = data_normalization.MVN_denormalize(gen_data, out_dim, stats_file)
else:
denorm_data = gen_data
denorm_data = np.array(denorm_data)
#bkp_denorm_data = np.array(bkp_denorm_data)
### post-processing of state-duration modification ###
ph_all_files.append(phone[j])
for k in range(5):
org_dur_features.append(st_arr[j][k]/(np.power(10,4)*5))
org_dur_features = np.array(org_dur_features).reshape(num_of_phones, 5)
org_dur_all_files = np.concatenate((org_dur_all_files,
np.sum(np.array(org_dur_features), axis=1)), axis=0)
#print np.sum(np.array(org_dur_features), axis=1)
### load generated output ###
# gen_data = io_funcs.load_binary_file(gen_file, 1)
gen_file = os.path.join(gen_dir, filename + '.cmp')
gen_data = io_funcs.load_float_file(gen_file)
##### denormalization of data #####
print 'denormalizing the data....'
if(normalization == "MVN"):
denorm_data = data_normalization.MVN_denormalize(gen_data, out_dim, stats_file)
#gen_data = io_funcs.load_float_file(gen_file)
#bkp_denorm_data = data_normalization.MVN_denormalize(gen_data, out_dim, stats_file)
else:
denorm_data = gen_data
denorm_data = np.array(denorm_data)
#bkp_denorm_data = np.array(bkp_denorm_data)
### post-processing of state-duration modification ###
if out_dim==8:
os.makedirs(out_f0_dir)
### Single file processing ###
SFP = False;
if SFP:
# filename = 'TheHareandtheTortoise_097_24-23-1-2-1-0'
filename = 'GoldilocksandtheThreeBears_000_24-0-24-3-0-2'
print 'processing...'+filename
f0_file = os.path.join(f0_dir, filename + '.f0')
lab_file = os.path.join(lab_dir, filename + '.lab')
out_file = os.path.join(out_dir, filename + '.cmp')
### processing f0 file ###
ori_f0_data = io_funcs.load_float_file(f0_file) # # to process float f0 file
# ori_lf0_data, frame_number = io_funcs.load_binary_file_frame(lf0_file, 1)
# ori_lf0_data = np.exp(ori_f0_data)
# interp_f0_arr = f0_processing.process(ori_f0_data)
interp_f0_arr = prosody_funcs.interpolate_f0(f0_file)
### read label file ###
[phone, ph_arr, mean_f0_arr] = prosody_funcs.read_state_align_label_file(lab_file)
### decomposition of f0 file ###
[dct_features, stat_features] = prosody_funcs.DCT_decomposition(phone, ph_arr, mean_f0_arr, interp_f0_arr, decomposition_unit, coef_size)
prosody_feats = np.concatenate((dct_features, stat_features), axis=0)
# io_funcs.array_to_binary_file(dct_features, out_file)
nrows = len(dct_features)/coef_size
X1 = np.array(dct_features).reshape(nrows, coef_size)
feat_dir_path + '/')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
### Single file processing ###
SFP = 1
if SFP:
# filename = 'TheHareandtheTortoise_097_24-23-1-2-1-0'
filename = 'GoldilocksandtheThreeBears_000_24-0-24-3-0-2'
f0_file = os.path.join(f0_dir, filename + '.f0')
lab_file = os.path.join(lab_dir, filename + '.lab')
out_file = os.path.join(out_dir, filename + '.cmp')
### processing f0 file ###
ori_f0_data = io_funcs.load_float_file(
f0_file) # # to process float f0 file
# ori_lf0_data, frame_number = io_funcs.load_binary_file_frame(lf0_file, 1)
# ori_lf0_data = np.exp(ori_f0_data)
# interp_f0_arr = f0_processing.process(ori_f0_data)
interp_f0_arr = prosody_funcs.interpolate_f0(f0_file)
### read label file ###
[phone, ph_arr,
mean_f0_arr] = prosody_funcs.read_state_align_label_file(lab_file)
### decomposition of f0 file ###
[dct_features, stat_features
] = prosody_funcs.DCT_decomposition(phone, ph_arr, mean_f0_arr,
interp_f0_arr, decomposition_unit,
coef_size)
prosody_feats = np.concatenate((dct_features, stat_features), axis=0)
