def postprocess(self, feats, curr_sample_rate):
if self.sample_rate != curr_sample_rate:
wav_tensor = feats.clone().detach()
wav_tensor = Resample(curr_sample_rate, self.sample_rate)(wav_tensor)
feats = wav_tensor.numpy()
if feats.dim() == 2:
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
if self.normalize:
with torch.no_grad():
feats = F.layer_norm(feats, feats.shape)
return feats
def process_utterance(in_dir, out_dir, spker, basename):
wav_path = os.path.join(in_dir, 'wav48', spker, '{}.wav'.format(basename))
tg_path = os.path.join(out_dir, 'TextGrid', spker,
'{}.TextGrid'.format(basename))
if not os.path.exists(tg_path):
return None
# Get alignments
textgrid = tgt.io.read_textgrid(tg_path)
phone, duration, start, end = get_alignment(
textgrid.get_tier_by_name('phones'))
text = '{' + '}{'.join(
phone) + '}' # '{A}{B}{$}{C}', $ represents silent phones
text = text.replace('{$}', ' ') # '{A}{B} {C}'
text = text.replace('}{', ' ') # '{A B} {C}'
if start >= end:
return None
# Read and trim wav files
sr, wav = read(wav_path)
wav = torch.tensor(wav.astype(np.float32))
if sr != hp.sampling_rate:
wav = Resample(orig_freq=sr, new_freq=hp.sampling_rate)(wav)
wav = wav[int(hp.sampling_rate * start):int(hp.sampling_rate * end)]
# Compute fundamental frequency
f0, _ = pw.dio(wav.numpy().astype(np.float64),
hp.sampling_rate,
frame_period=hp.hop_length / hp.sampling_rate * 1000)
f0 = f0[:sum(duration)]
# Compute mel-scale spectrogram and energy
mel_spectrogram, energy = Audio.tools.get_mel_from_wav(wav)
mel_spectrogram = mel_spectrogram.cpu().numpy().astype(
np.float32)[:, :sum(duration)]
energy = energy.numpy().astype(np.float32)[:sum(duration)]
if mel_spectrogram.shape[1] >= hp.max_seq_len:
return None
# if the shape is not right, you can check get_alignment function
try:
assert (f0.shape[0] == energy.shape[0] == mel_spectrogram.shape[1])
except AssertionError as e:
print("duration problem: {}".format(wav_path))
return None
# Save alignment
ali_filename = '{}-ali-{}.npy'.format(hp.dataset, basename)
np.save(os.path.join(out_dir, 'alignment', ali_filename),
duration,
allow_pickle=False)
# Save fundamental prequency
f0_filename = '{}-f0-{}.npy'.format(hp.dataset, basename)
np.save(os.path.join(out_dir, 'f0', f0_filename), f0, allow_pickle=False)
# Save energy
energy_filename = '{}-energy-{}.npy'.format(hp.dataset, basename)
np.save(os.path.join(out_dir, 'energy', energy_filename),
energy,
allow_pickle=False)
# Save spectrogram
mel_filename = '{}-mel-{}.npy'.format(hp.dataset, basename)
np.save(os.path.join(out_dir, 'mel', mel_filename),
mel_spectrogram.T,
allow_pickle=False)
try:
return '|'.join([basename, text]), max(f0), min([
f for f in f0 if f != 0
]), max(energy), min(energy), mel_spectrogram.shape[1]
except:
print(basename)
return None