def test_preemphasis():
for dtype in [np.float32, np.float64]:
np.random.seed(1234)
x = np.random.rand(16000 * 5).astype(dtype)
y = preemphasis(x, 0.97)
assert x.shape == y.shape
assert x.dtype == y.dtype
x_hat = inv_preemphasis(y, 0.97)
assert x_hat.dtype == x.dtype
assert np.allclose(x_hat, x, atol=1e-5)
x = np.random.rand(16000)
assert np.allclose(P.preemphasis(x, 0), x)
assert np.allclose(P.inv_preemphasis(P.preemphasis(x, 0), 0), x)
def inv_spectrogram(spectrogram):
'''Converts spectrogram to waveform using librosa'''
S = _db_to_amp(_denormalize(spectrogram) + hparams.ref_level_db) # Convert back to linear
processor = _lws_processor()
D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y)
def inv_preemphasis(x):
# allow disable preemphasis
if hparams.preemphasis == -1.0:
return x
else:
from nnmnkwii.preprocessing import inv_preemphasis
return inv_preemphasis(x, hparams.preemphasis)
def inv_mel_spectrogram(mel_spectrogram):
D = _denormalize(mel_spectrogram)
S = _mel_to_linear(
_db_to_amp(D + hparams.ref_level_db)) # Convert back to linear
processor = _lws_processor()
D = processor.run_lws(S.astype(np.float64).T**hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y)
def inv_spectrogram(spectrogram):
'''Converts spectrogram to waveform using librosa'''
S = _db_to_amp(_denormalize(spectrogram) +
hparams.spec_ref_level_db) # Convert back to linear
#S = librosa.db_to_amplitude(_denormalize(spectrogram) + hparams.spec_ref_level_db)
D = librosa.griffinlim(S**hparams.power,
hop_length=hparams.hop_size,
win_length=hparams.fft_wsize)
return inv_preemphasis(D)
def inv_preemphasis(x):
from nnmnkwii.preprocessing import inv_preemphasis
return inv_preemphasis(x, hparams.preemphasis)
def inv_preemphasis(x, coef=0.85):
return P.inv_preemphasis(x, coef)
def _inv_preemphasis(self, x):
from nnmnkwii.preprocessing import inv_preemphasis
return inv_preemphasis(x, self.hparams.preemphasis)
def inv_preemphasis(x):
from nnmnkwii.preprocessing import inv_preemphasis
return inv_preemphasis(x, preemphasis_factor)