def evaluate(original_file, test_file):
ori_arr = read_file_test(original_file, 13, )
test_arr = read_file_test(test_file, 13)
ori_energy = ori_arr[:,0]
test_energy = test_arr[:,0]
ori_arr = ori_arr[:,1:13]#normalise_mfcc(ori_arr) # take 12 coefficients
test_arr = test_arr[:,1:13]
#test_arr = normalise_mfcc(test_arr)
#print test_arr.max()
subtract = (ori_arr - test_arr) ** 2
rms = sqrt(subtract.sum()) #/(ori_arr.shape[0] * ori_arr.shape[1])
#print rms #, ori_arr.shape[0] * ori_arr.shape[1]
#subtract = (ori_arr - test_arr)
#print subtract.max()
[m,n] = ori_arr.shape
a = ori_arr.reshape(1,m*n)
b = test_arr.reshape(1,m*n)
#print a
#print b
#print np.corrcoef(a,b)[0,1]
return np.corrcoef(a,b)[0,1]
return rms
def get_position(filename):
arr = read_file_test(filename, num_features) #0:13, enegy+MFCC, 13:49, articulotory position
position = arr[:,13:13 + 12] # position at i
#print position.max(), position.min()
if '10ms' in artic_dir:
return interpolate_10(position)
elif '20ms' in artic_dir:
return interpolate_20(position)
else:
return interpolate_50(position)
def _testing_original_file(self, test_dir, type_data):
dnn_predict_dir = '/home/danglab/3P/' + self.hidden_layer +'SQR/' + self.artic + type_data + 'test_' + str(self.test_number) + '/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
for afile in self.missing_filename_list:
test_arr, factors = read_file_test(test_dir + afile + '_in.txt', self.n_input_f, "factors") #read a missing_feature
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
self._write_predict_2_file(dnn_predict_dir + afile + '.txt', energy, self.predict(test_arr), factors) # write result to file
def evaluate(original_file, test_file):
#print original_file
#print test_file
ori_arr, factors = read_file_test(original_file, 13, "factors")
test_arr = read_file_test(test_file, 13)
ori_arr = ori_arr[:,1:13] * factors #normalise_mfcc(ori_arr) # take 12 coefficients
#print ori_arr
test_arr = test_arr[:,1:13]
#print test_arr
subtract = (ori_arr - test_arr) ** 2
rms = sqrt(subtract.sum()) #/(ori_arr.shape[0] * ori_arr.shape[1])
[m,n] = ori_arr.shape
a = ori_arr.reshape(1,m*n)
b = test_arr.reshape(1,m*n)
return np.corrcoef(a,b)[0,1]
return rms
def _testing_aritc_interpolation(self, test_dir):
dnn_predict_dir = '/home/danglab/Results/PhonemicRestoration/' + self.hidden_layer + 'space/artic_inter/'+'test_' + str(self.test_number) + '/10ms/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
for afile in self.missing_filename_list:
test_arr, factors = read_file_test(test_dir + afile + '_10ms_in.txt', self.n_input_f, "factors") #read a missing_feature
#print factors
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
#test_arr[:,12:self.n_input_f] = 0 # remove articulatory data
self._write_predict_2_file(dnn_predict_dir + afile + '.txt', energy, self.predict(test_arr), factors) # write result to file
def _testing_DNN(self):
if self.space:
test_dir = "/home/danglab/Phong/features_3p/Space/"
else:
test_dir = "/home/danglab/Phong/features_3p/Noise/"
for type_test in sorted(os.listdir(test_dir)):
if not type_test.endswith("zip"):
type_test_dir = test_dir + type_test + "/"
print type_test_dir
dnn_predict_dir = (
"/home/danglab/3P/"
+ self.hidden_layer
+ "SQR/"
+ self.artic
+ "test_"
+ str(self.test_number)
+ "/"
+ type_test
+ "/"
)
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
print type_test
duration = type_test.split("_")[1] # 50ms, 100ms
# listtest = sorted(os.listdir(type_test_dir))
# for afile in listtest:
for prefix_file in self.missing_filename_list:
afile = prefix_file + "_" + duration + "_in.txt"
test_arr, factors = read_file_test(
type_test_dir + afile, self.n_input_f, "factors"
) # read a missing_feature
find_ = [m.start() for m in re.finditer("_", afile)]
energy = test_arr[:, 0] # ko cho energy vao DNN
test_arr = test_arr[:, 1 : self.n_input_f]
# print factors
self._write_predict_2_file(
dnn_predict_dir + afile.replace(afile[find_[4] : len(afile) - 4], ""),
energy,
self.predict(test_arr),
factors,
) # write result to file
def write_features(filename, position, vec, acc, res_file):
file_feature = open(res_file, 'w')
acoustic_feature, factors = read_file_test(filename, 37, "factor") # energy , mfcc, del,del-del
#mfcc = acoustic_feature[:,0:13]
#delta = acoustic_feature[:,13:25]
#delta_delta = acoustic_feature[:,25:37]
min_nframes = min(acoustic_feature.shape[0],position.shape[0])
#print min_nframes
#list_feature = []
file_feature.write('%s \n' %(factors))
for fa in xrange(min_nframes):
frame_feature = np.concatenate((acoustic_feature[fa,:],
position[fa,:], vec[fa,:], acc[fa,:]), axis = 0)
#print frame_feature.shape
for i in frame_feature:
file_feature.write("%s " %(i))
file_feature.write("\n")
file_feature.close()
def _testing_noise_space(self, test_dir, type_data):
for type_test in sorted(os.listdir(test_dir)):
if (not type_test.endswith('zip')) and 'output' not in type_test:
type_test_dir = test_dir + type_test + '/'
print type_test_dir
dnn_predict_dir = '/home/danglab/3P/' + self.hidden_layer +'SQR/' + self.artic + type_data + 'test_' + str(self.test_number) + '/' + type_test + '/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
print type_test
duration = type_test.split('_')[1] # 50ms, 100ms
#listtest = sorted(os.listdir(type_test_dir))
#for afile in listtest:
for prefix_file in self.missing_filename_list:
afile = prefix_file + '_' + duration + '_in.txt'
test_arr, factors = read_file_test(type_test_dir + afile, self.n_input_f, "factors") #read a missing_feature
find_ = [m.start() for m in re.finditer('_', afile)]
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
#print factors
self._write_predict_2_file(dnn_predict_dir + afile.replace(afile[find_[4]:len(afile)-4],''), energy, self.predict(test_arr), factors) # write result to file
def _testing_DNN(self):
# if self.space:
# test_dir = '/home/danglab/Phong/features_3p/Space/'
# else:
# test_dir = '/home/danglab/Phong/features_3p/Noise/'
test_dir = '/home/danglab/Phong/features_3p/origin/input/'
dnn_predict_dir = '/home/danglab/3P/' + self.hidden_layer +'SQR/' + self.artic + 'test_' + str(self.test_number) + '/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
#listtest = sorted(os.listdir(type_test_dir))
#for afile in listtest:
for afile in self.missing_filename_list:
test_arr, factors = read_file_test(test_dir + afile + '_in.txt', self.n_input_f, "factors") #read a missing_feature
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
#print factors
self._write_predict_2_file(dnn_predict_dir + afile.replace('_in',''), energy, self.predict(test_arr), factors) # write result to file
