def synthesize(self, args, text, idx):
seq = text_to_sequence(text)
dec_input = np.zeros((1, 200, mel_dim), dtype='float32')
pred = []
for i in range(1, 201):
mel_out, alignment = self.session.run(
[self.mel_output, self.alignment],
feed_dict={
self.model.enc_input: [np.asarray(seq, dtype=np.int32)],
self.model.sequence_length:
np.asarray([len(seq)], dtype=np.int32),
self.model.dec_input:
dec_input
})
if i < 200:
dec_input[:, i, :] = mel_out[5 * i - 1, :]
pred.extend(mel_out[5 * (i - 1):5 * i, :])
np.save(os.path.join(args.save_dir, 'mel-{}'.format(idx)),
pred,
allow_pickle=False)
input_seq = sequence_to_text(seq)
alignment_dir = os.path.join(args.save_dir, 'align-{}.png'.format(idx))
plot_alignment(alignment, alignment_dir, input_seq)
def test_step(text, idx):
seq = text_to_sequence(text)
enc_input = np.asarray([seq], dtype=np.int32)
sequence_length = np.asarray([len(seq)], dtype=np.int32)
dec_input = np.zeros((1, max_iter, mel_dim), dtype=np.float32)
pred = []
for i in range(1, max_iter + 1):
mel_out, alignment = model(enc_input,
sequence_length,
dec_input,
is_training=False)
if i < max_iter:
dec_input[:, i, :] = mel_out[:, reduction * i - 1, :]
pred.extend(mel_out[:, reduction * (i - 1):reduction * i, :])
pred = np.reshape(np.asarray(pred), [-1, mel_dim])
alignment = np.squeeze(alignment, axis=0)
np.save(os.path.join(save_dir, 'mel-{}'.format(idx)),
pred,
allow_pickle=False)
input_seq = sequence_to_text(seq)
alignment_dir = os.path.join(save_dir, 'align-{}.png'.format(idx))
plot_alignment(alignment, alignment_dir, input_seq)
def get_mfccs_and_phones(wav_file, mode='train'):
n_fft, hop_length, win_length = _stft_parameters()
mfccs = np.load(wav_file)
num_timesteps = mfccs.shape[0]
(rpath, temp) = os.path.split(wav_file)
(name, _) = os.path.splitext(temp)
phn_file = name + ".PHN"
phn_file = os.path.join(rpath, phn_file)
# get sentence
sentence_file = phn_file.replace(".PHN", ".TXT")
with open(sentence_file) as f:
sentence = " ".join(f.readline().strip().split(" ")[2:])
sentence = np.asarray(text_to_sequence(sentence), dtype=np.int32)
# get phones
phn2idx, idx2phn = load_vocab()
phns = np.zeros(shape=(num_timesteps, ))
bnd_list = []
for line in open(phn_file, 'r').read().splitlines():
start_point, end_point, phn = line.split()
bnd = int(start_point) // hop_length
if phn in mapping_48.keys():
phn = mapping_48[phn]
phns[bnd:] = phn2idx[phn]
bnd_list.append(bnd)
if mode == 'train':
if hparams.aug == True:
mfccs = spec_augment(mfccs)
return mfccs, sentence, phns, len(phns), len(sentence)
else:
return wav_file, mfccs, sentence, phns, len(phns), len(sentence)
def _get_next_example(self):
if self._offset >= len(self._metadata): # 배치 초기화
self._offset = 0
random.shuffle(self._metadata)
meta = self._metadata[self._offset]
self._offset += 1
text = meta[3]
input_data = np.asarray(text_to_sequence(text), dtype=np.int32)
linear_target = np.load(os.path.join(self._datadir, meta[0]))
mel_target = np.load(os.path.join(self._datadir, meta[1]))
return (input_data, mel_target, linear_target, len(linear_target))
def synthesize(self, text):
# 将中文转换为注音字符
text = Pinyin().get_pinyin(text, " ", tone_marks='numbers')
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
# 注音字符到序列
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32)}
wav = self.session.run(self.wav_output, feed_dict=feed_dict)
wav = audio.inv_preemphasis(wav)
out = io.BytesIO()
audio.save_wav(wav, out)
return out.getvalue()
def synthesize(self, text, base_path, idx):
seq = text_to_sequence(text)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32)
}
input_seq, wav, alignment = self.session.run(
[self.inputs, self.wav_output, self.alignments],
feed_dict=feed_dict)
wav = audio.inv_preemphasis(wav)
wav = wav[:audio.find_endpoint(wav)]
out = io.BytesIO()
audio.save_wav(wav, out)
input_seq = sequence_to_text(input_seq)
plot.plot_alignment(alignment, '%s-%d-align.png' % (base_path, idx),
input_seq)
return out.getvalue()
metadata = pd.read_csv(text_dir[0], dtype='object', sep='|', header=None)
wav_dir = metadata[0].values
text = metadata[3].values
out_dir = './data'
os.makedirs(out_dir, exist_ok=True)
os.makedirs(out_dir + '/text', exist_ok=True)
os.makedirs(out_dir + '/mel', exist_ok=True)
os.makedirs(out_dir + '/spec', exist_ok=True)
os.makedirs(out_dir + '/dec', exist_ok=True)
# text
print('Load Text')
for idx, s in enumerate(tqdm(text)):
sentence = re.sub(re.compile(filters), '', s)
sentence = text_to_sequence(sentence)
text_name = 'kss-text-%05d.npy' % idx
np.save(os.path.join(out_dir + '/text', text_name),
sentence,
allow_pickle=False)
print('Text Done')
# audio
print('Load Audio')
mel_len_list = []
for idx, fn in enumerate(tqdm(wav_dir)):
file_dir = './kss/' + fn
wav, _ = librosa.load(file_dir, sr=sample_rate)
wav, _ = librosa.effects.trim(wav)
wav = np.append(wav[0], wav[1:] - preemphasis * wav[:-1])
stft = librosa.stft(wav,