def process_audio(self, wav_path, label_file, char_file, h5_file):
#read map file
self.char_map, self.int2phone = process_map_file(char_file)
#read the label file
label_dict = process_label_file(label_file, self.out_type,
self.char_map)
#extract spectrum
spec_dict = dict()
f = open(wav_path, 'r')
for line in f.readlines():
utt, path = line.strip().split()
spect = parse_audio(path,
audio_conf,
windows,
normalize=self.normalize)
spec_dict[utt] = spect.numpy()
f.close()
assert len(spec_dict) == len(label_dict)
self.features_label = []
#save the data as h5 file
f = h5py.File(h5_file, 'w')
f.create_dataset("phone_map_key", data=self.char_map.keys())
f.create_dataset("phone_map_value", data=self.char_map.values())
for utt in spec_dict:
grp = f.create_group(utt)
self.features_label.append(
(torch.FloatTensor(spec_dict[utt]), label_dict[utt].tolist()))
grp.create_dataset('data', data=spec_dict[utt])
grp.create_dataset('label', data=label_dict[utt])
print("Saved the %s data to h5py file" % self.data_set)
def process_audio(self, wav_path, label_file, char_file, h5_file):
#read map file
self.char_map, self.int2phone = process_map_file(char_file)
#read the label file
label_dict = process_label_file(label_file, self.out_type,
self.char_map)
#extract spectrum
spec_dict = dict()
f = open(wav_path, 'r')
for line in f.readlines():
utt, path = line.strip().split()
spect = parse_audio(path, audio_conf, windows)
#print(spect)
spec_dict[utt] = spect.numpy()
f.close()
if self.normalize:
i = 0
for utt in spec_dict:
if i == 0:
spec_all = torch.FloatTensor(spec_dict[utt])
else:
spec_all = torch.cat(
(spec_all, torch.FloatTensor(spec_dict[utt])), 0)
i += 1
mean = torch.mean(spec_all, 0, True)
std = torch.std(spec_all, 0, True)
for utt in spec_dict:
tmp = torch.add(torch.FloatTensor(spec_dict[utt]), -1, mean)
spec_dict[utt] = torch.div(tmp, std).numpy()
if len(spec_dict) != len(label_dict):
print("%s data: The num of wav and text are not the same!" %
self.data_set)
sys.exit(1)
self.features_label = []
#save the data as h5 file
f = h5py.File(h5_file, 'w')
f.create_dataset("phone_map_key", data=self.char_map.keys())
f.create_dataset("phone_map_value", data=self.char_map.values())
for utt in spec_dict:
grp = f.create_group(utt)
self.features_label.append(
(torch.FloatTensor(spec_dict[utt]), label_dict[utt].tolist()))
grp.create_dataset('data', data=spec_dict[utt])
grp.create_dataset('label', data=label_dict[utt])
print("Saved the %s data to h5py file" % self.data_set)
def __getitem__(self, idx):
return parse_audio(self.path[idx],
audio_conf,
windows,
normalize=self.normalize), self.label[idx]
def __getitem__(self, idx):
return parse_audio(self.path[idx], audio_conf, windows, normalize=self.normalize), self.label[idx]