def mels_to_wavs_GL(mels, taco_stft, ref_level_db=0, magnitude_power=1.5):
#print('inside mels_wavs', len(mels))
for i, mel in enumerate(mels):
stime = time.time()
mel_decompress = mel_denormalize(
torch.from_numpy(mel).cuda().unsqueeze(0), hp.max_abs_value)
#print(mel_decompress.shape)
mel_decompress = taco_stft.spectral_de_normalize(mel_decompress +
ref_level_db)**(
1 /
magnitude_power)
#print(mel_decompress.shape)
mel_decompress_ = mel_decompress.transpose(1, 2).data.cpu()
spec_from_mel_scaling = 1000
spec_from_mel = torch.mm(mel_decompress_[0], taco_stft.mel_basis)
spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
spec_from_mel = spec_from_mel * spec_from_mel_scaling
waveform = griffin_lim(torch.autograd.Variable(spec_from_mel[:, :, :]),
taco_stft.stft_fn, 60)
waveform = waveform[0].data.cpu().numpy()
dec_time = time.time() - stime
len_audio = float(len(waveform)) / float(hp.sample_rate)
str = "{}th sentence, audio length: {:.2f} sec, mel_to_wave time: {:.2f}".format(
i, len_audio, dec_time)
print(str)
#write(os.path.join(output_dir,"sentence_{}.wav".format(i)), hp.sample_rate, waveform)
return waveform
def mels_to_wavs_GL(hparams,
mels,
taco_stft,
output_dir="",
ref_level_db=0,
magnitude_power=1.5):
for i, mel in enumerate(mels):
stime = time.time()
mel_decompress = mel_denormalize(
torch.from_numpy(mel).cuda().unsqueeze(0))
mel_decompress = taco_stft.spectral_de_normalize(mel_decompress +
ref_level_db)**(
1 /
magnitude_power)
mel_decompress_ = mel_decompress.transpose(1, 2).data.cpu()
spec_from_mel_scaling = 1000
spec_from_mel = torch.mm(mel_decompress_[0], taco_stft.mel_basis)
spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
spec_from_mel = spec_from_mel * spec_from_mel_scaling
waveform = griffin_lim(torch.autograd.Variable(spec_from_mel[:, :, :]),
taco_stft.stft_fn, 60)
# waveform = signal.lfilter([1], [1, -0.97], waveform)
#waveform = torch.as_tensor(waveform[0]).data.cpu().numpy()
waveform = waveform[0].data.cpu().numpy()
dec_time = time.time() - stime
len_audio = float(len(waveform)) / float(hparams.sampling_rate)
str = "{}th sentence, audio length: {:.2f} sec, mel_to_wave time: {:.2f}".format(
i, len_audio, dec_time)
print(str)
write(os.path.join(output_dir, "sentence_{}.wav".format(i)),
hparams.sampling_rate, waveform)
def test(hparams,
mel,
output_path="test.wav",
ref_level_db=20,
magnitude_power=1.5):
taco_stft = TacotronSTFT(hparams)
stime = time.time()
mel_decompress = mel_denormalize(mel).unsqueeze(0)
mel_decompress = taco_stft.spectral_de_normalize(mel_decompress +
ref_level_db)**(
1 / magnitude_power)
mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
spec_from_mel_scaling = 1000
spec_from_mel = torch.mm(mel_decompress[0], taco_stft.mel_basis)
spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
spec_from_mel = spec_from_mel * spec_from_mel_scaling
waveform = griffin_lim(torch.autograd.Variable(spec_from_mel[:, :, :]),
taco_stft.stft_fn, 60)
waveform = waveform[0].data.cpu().numpy()
waveform = waveform / abs(waveform).max() * 0.99 * 2**15
waveform = waveform.astype(dtype=np.int16)
dec_time = time.time() - stime
len_audio = float(len(waveform)) / float(hparams.sampling_rate)
str = "audio length: {:.2f} sec, mel_to_wave time: {:.2f}".format(
len_audio, dec_time)
print(str)
write(os.path.join(output_path), hparams.sampling_rate, waveform)
def cepstrum_from_mel(self, mel, ref_level_db=20, magnitude_power=1.5):
assert (torch.min(mel.data) >= -self.max_abs_mel_value)
assert (torch.max(mel.data) <= self.max_abs_mel_value)
#print('mel: ', mel.max(), mel.min())
spec = mel_denormalize(mel, self.max_abs_mel_value)
#print('spec: ', spec.max(), spec.min())
magnitudes = self.spectral_de_normalize(spec + ref_level_db)**(
1 / magnitude_power)
#print('Magnitude: ', Magnitude.max(), Magnitude.min())
pow_spec = (magnitudes**2) / 1024 # if filter_length = 1024
#print('pow_spec: ', pow_spec.max(), pow_spec.min())
db_pow_spec = torch.log(torch.clamp(pow_spec, min=1e-5)) * 20 #db
#print('db_pow_spec: ', db_pow_spec.max(), db_pow_spec.min())
mcc = dct(db_pow_spec, 'ortho')
return mcc