def test_griffinlim(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
wav = torch.randn(2, 1, 8000)
spec = stft(wav)
tf_mask = torch.sigmoid(torch.randn_like(spec))
masked_spec = spec * tf_mask
mag = transforms.mag(masked_spec, -2)
angles = None if not feed_angle else transforms.angle(masked_spec, -2)
griffin_lim(mag, stft, angles=angles, istft_dec=istft, n_iter=3)
def apply_masks(self, tf_rep, est_masks):
if self.target == "TMS":
ang = angle(tf_rep)
return from_magphase(est_masks, ang)
elif self.target == "TCS":
return est_masks
else:
return apply_complex_mask(tf_rep, est_masks)
def test_angle_mag_recompostion(dim):
""" Test complex --> (mag, angle) --> complex conversions"""
max_tested_ndim = 4
# Random tensor shape
tensor_shape = [random.randint(1, 10) for _ in range(max_tested_ndim)]
# Make sure complex dimension has even shape
tensor_shape[dim] = 2 * tensor_shape[dim]
complex_tensor = torch.randn(tensor_shape)
phase = transforms.angle(complex_tensor, dim=dim)
mag = transforms.mag(complex_tensor, dim=dim)
tensor_back = transforms.from_magphase(mag, phase, dim=dim)
assert_allclose(complex_tensor, tensor_back)
def test_misi(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
n_src = 3
# Create mixture
wav = torch.randn(2, 1, 8000)
spec = stft(wav).unsqueeze(1)
# Create n_src masks on mixture spec and apply them
shape = list(spec.shape)
shape[1] *= n_src
tf_mask = torch.sigmoid(torch.randn(*shape))
masked_specs = spec * tf_mask
# Separate mag and angle.
mag = transforms.mag(masked_specs, -2)
angles = None if not feed_angle else transforms.angle(masked_specs, -2)
est_wavs = misi(wav, mag, stft, angles=angles, istft_dec=istft, n_iter=2)
# We actually don't know the last dim because ISTFT(STFT()) cuts the end
assert est_wavs.shape[:-1] == (2, n_src)
def apply_masks(self, tf_rep, est_masks):
ang = angle(tf_rep)
return from_magphase(est_masks, ang)
def forward_masker(self, tf_rep):
output, _, _ = self.forward_vae_mu_logvar(torch.pow(mag(tf_rep), 2))
return from_magphase(torch.sqrt(output), angle(tf_rep))