def cal_lf0(config):
base_path = config['base_path']
label_path = config['label_path']
name = config['name']
outfilepath = config['outfilepath']
var_path = config['var_path']
syllable_base_path = config['syllable_base_path']
syllable_var_path = config['syllable_var_path']
#----------Syllable level--------#
dur_list, names = PoGUtility.gen_dur_and_name_list(label_path, name)
# print dur_list
# print names
syl_mean = np.load('{}/mean.npy'.format(syllable_base_path))
# syl_mean, cccc = gen_mean_and_cov_of_dct_fake(names)
syl_cov = np.load('{}/cov.npy'.format(syllable_base_path))
print syl_cov
var = np.load('{}'.format(syllable_var_path))
vv = []
for i, v in enumerate(var):
vv.append(v[i])
syl_var = np.array(vv)
o = []
for data_dct, dur in zip(syl_mean, dur_list):
i_dct = PoGUtility.generate_inverse_DCT(data_dct, dur)
# print i_dct
o = o + i_dct
o = np.array(o)
o[o < 3] = np.nan
print o.shape
org = Utility.read_lf0_into_ascii(
'/work/w2/decha/Data/GPR_speccom_data/data_before_remove_silence/lf0/tsc/sd/j/{}.lf0'
.format(name))
org[org < 0] = np.nan
diff = len(org) - len(o)
plt.plot(np.arange(len(o)) + diff, o, label='syn')
plt.plot(range(len(org)), org, label='org')
plt.legend()
plt.savefig('./{}_dct_16_test.eps'.format(name))
sys.exit()
pass
def cal_lf0(config):
base_path = config['base_path']
label_path = config['label_path']
name = config['name']
outfilepath = config['outfilepath']
var_path = config['var_path']
syllable_base_path = config['syllable_base_path']
syllable_var_path = config['syllable_var_path']
original = config['original']
koriyama_gen = config['koriyama_gen']
figure_path = config['figure_path']
ph_in_syl_object_path = config['phone_in_syllable_object_path']
stress = config['stress']
original_vuv = config['original_vuv']
p_in_s_file = Utility.load_obj(ph_in_syl_object_path)
vuv = np.load('{}/class.npy'.format(config['vuv_path']))
# vuv = original_vuv
#--------Frame-------#
lf0_mean = np.load('{}/mean.npy'.format(base_path))
lf0_cov = np.load('{}/cov.npy'.format(base_path))
var = np.load('{}'.format(var_path))
if len(lf0_cov) > len(vuv):
for i in range(len(lf0_cov) - len(vuv)):
vuv.append(-1, axis=0)
elif len(lf0_cov) < len(vuv):
vuv = vuv[0:len(lf0_cov)]
lf0_var = np.sum(var, axis=0)
lf0_mean = np.array([lf0_mean[:, 0], lf0_mean[:, 1], lf0_mean[:, 2]])
lf0_w = PoGUtility.generate_W_for_GPR_generate_features(len(lf0_cov), vuv)
frame_B = alpha * PoGUtility.cal_sum_of_mean_part(lf0_var, lf0_w, lf0_cov,
lf0_mean)
frame_A = alpha * PoGUtility.cal_sum_of_weight_part(
lf0_var, lf0_w, lf0_cov)
L = linalg.cholesky(frame_A, lower=True)
lf0 = linalg.cho_solve((L, True), frame_B)
# lf0 = lf0_gen_with_vuv(lf0, vuv)
print lf0.shape
frame_lf0_nomask = np.copy(lf0)
# lf0 = lf0_gen_with_vuv(lf0, vuv)
lf0[lf0 < 1] = np.nan
frame_lf0 = np.copy(lf0)
#----------Syllable level--------#
dur_list, names = PoGUtility.gen_dur_and_name_list(label_path, name)
# print np.sum(dur_list)
if np.sum(dur_list) < len(original):
dur_list[0] = dur_list[0] + len(original) - np.sum(dur_list)
# print np.sum(dur_list)
syl_mean = np.load('{}/mean.npy'.format(syllable_base_path))
syl_cov = np.load('{}/cov.npy'.format(syllable_base_path))
s_mean = syl_mean
var = np.load('{}'.format(syllable_var_path))
syl_var = np.sum(var, axis=0)
temp_mean = []
for i in range(len(syl_mean[0])):
temp_mean.append(syl_mean[:, i])
syl_mean = np.array(temp_mean)
syl_w = PoGUtility.generate_DCT_W_without_consonant_on_stress(
len(lf0_cov), dur_list, num_coeff, p_in_s_file, stress)
syl_B = beta * PoGUtility.cal_sum_of_mean_part(syl_var, syl_w, syl_cov,
syl_mean)
syl_A = beta * PoGUtility.cal_sum_of_weight_part(syl_var, syl_w, syl_cov)
#----------Combine Model--------#
L = linalg.cholesky(frame_A + syl_A, lower=True)
lf0 = linalg.cho_solve((L, True), frame_B + syl_B)
# print lf0.shape
lf0[lf0 < 1] = np.nan
PlotUtility.plot([lf0, original, frame_lf0_nomask],
['Multi', 'original', 'Single'],
'{}/{}_no_mask.eps'.format(figure_path, name))
lf0 = lf0_gen_with_vuv(lf0, vuv)
lf0[lf0 < 1] = np.nan
frame_lf0 = lf0_gen_with_vuv(frame_lf0, vuv)
frame_lf0[frame_lf0 < 1] = np.nan
np.save(outfilepath, lf0)
print min(lf0)
PlotUtility.plot([lf0, original, frame_lf0],
['Multi', 'original', 'Single'],
'{}/{}_multi.eps'.format(figure_path, name))
#----------Combine Model--------#
o = []
for data_dct, dur in zip(s_mean, dur_list):
i_dct = PoGUtility.generate_inverse_DCT(data_dct, dur)
o = o + i_dct
o = np.concatenate((np.zeros(len(original) - len(o)), np.array(o)), axis=0)
o = lf0_gen_with_vuv(o, vuv)
o[o <= 1] = np.nan
# print o.shape
PlotUtility.plot([o, original, lf0, frame_lf0],
['dct', 'original', 'Multi', 'frame_lf0'],
'{}/{}_dct.eps'.format(figure_path, name))
pass
errors_tuple = []
true = np.array([])
dct_regen = np.array([])
for coeff in [3, 4, 7]:
for name in d:
data = d[name]
w = PoGUtility.generate_W_for_DCT(len(data), coeff)
data_dct = PoGUtility.generate_DCT(data, coeff)
data_dct = np.dot(w, data)
i_dct = PoGUtility.generate_inverse_DCT(data_dct, len(data))
true = np.concatenate((true, data))
dct_regen = np.concatenate((dct_regen, i_dct))
rmse = np.sqrt(sklearn.metrics.mean_squared_error(
data, i_dct)) * 1200 / np.log(2)
# print rmse
errors[name] = rmse
errors_list.append(rmse)
if (int(all_dict[name]['stress']) == 1):
syl_dct[name] = data_dct
tone = int(all_dict[name]['tone'])
tone_dct_dict[tone][name] = data_dct
def cal_lf0(config):
base_path = config['base_path']
label_path = config['label_path']
name = config['name']
outfilepath = config['outfilepath']
var_path = config['var_path']
syllable_base_path = config['syllable_base_path']
syllable_var_path = config['syllable_var_path']
original = config['original']
koriyama_gen = config['koriyama_gen']
figure_path = config['figure_path']
vuv = np.load('{}/class.npy'.format(config['vuv_path']))
#--------Frame-------#
lf0_mean = np.load('{}/mean.npy'.format(base_path))
lf0_cov = np.load('{}/cov.npy'.format(base_path))
var = np.load('{}'.format(var_path))
lf0_var = np.sum(var, axis=0)
lf0_mean = np.array( [ lf0_mean[:,0], lf0_mean[:,1], lf0_mean[:,2] ] )
lf0_w = PoGUtility.generate_W_for_GPR_generate_features(len(lf0_cov), vuv)
frame_B = alpha * PoGUtility.cal_sum_of_mean_part(lf0_var, lf0_w, lf0_cov, lf0_mean)
frame_A = alpha * PoGUtility.cal_sum_of_weight_part(lf0_var, lf0_w, lf0_cov)
L = linalg.cholesky(frame_A, lower=True)
lf0 = linalg.cho_solve((L, True) , frame_B)
lf0 = lf0_gen_with_vuv(lf0, vuv)
print lf0.shape
lf0[lf0<0] = np.nan
frame_lf0 = lf0
#----------Syllable level--------#
dur_list, names = PoGUtility.gen_dur_and_name_list(label_path, name)
# print np.sum(dur_list)
if np.sum(dur_list) < len(original):
dur_list[0] = dur_list[0] + len(original)-np.sum(dur_list)
# print np.sum(dur_list)
syl_mean = np.load('{}/mean.npy'.format(syllable_base_path))
syl_cov = np.load('{}/cov.npy'.format(syllable_base_path))
s_mean = syl_mean
var = np.load('{}'.format(syllable_var_path))
syl_var = np.sum(var, axis=0)
temp_mean = []
for i in range( len(syl_mean[0]) ):
temp_mean.append( syl_mean[:,i] )
syl_mean = np.array(temp_mean)
syl_w = PoGUtility.generate_DCT_W_with_vuv(len(lf0_cov), dur_list, num_coeff, vuv)
syl_B = beta * PoGUtility.cal_sum_of_mean_part(syl_var, syl_w, syl_cov, syl_mean)
syl_A = beta * PoGUtility.cal_sum_of_weight_part(syl_var, syl_w, syl_cov)
# print syl_B
# Utility.write_to_file_line_by_line('./syl_B.txt', syl_B)
# Utility.write_to_file_line_by_line('./syl_A.txt', syl_A)
#----------Combine Model--------#
L = linalg.cholesky(frame_A + syl_A, lower=True)
lf0 = linalg.cho_solve((L, True) , frame_B + syl_B)
# lf0 = np.dot(
# inv(frame_A + syl_A),
# frame_B + syl_B
# )
lf0 = lf0_gen_with_vuv(lf0, vuv)
# print lf0.shape
lf0[lf0<0] = np.nan
np.save(outfilepath, lf0)
PlotUtility.plot([lf0, original, frame_lf0], ['Multi', 'original', 'Single'], '{}/{}_multi.eps'.format(figure_path, name))
#----------Combine Model--------#
o = []
for data_dct, dur in zip(s_mean, dur_list):
i_dct = PoGUtility.generate_inverse_DCT(data_dct, dur)
o = o + i_dct
o = np.concatenate(
(np.zeros(len(original)-len(o)),
np.array(o)) , axis=0)
o = lf0_gen_with_vuv(o, vuv)
o[o<0] = np.nan
# print o.shape
PlotUtility.plot([o, original, lf0], ['dct', 'original', 'Multi'], '{}/{}_dct.eps'.format(figure_path, name))
pass
