def inverse(self, magnitude, phase):
recombine_magnitude_phase = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)],
dim=1)
inverse_transform = F.conv_transpose1d(recombine_magnitude_phase,
Variable(self.inverse_basis,
requires_grad=False),
stride=self.hop_length,
padding=0)
if self.window is not None:
window_sum = window_sumsquare(self.window,
magnitude.size(-1),
hop_length=self.hop_length,
win_length=self.win_length,
n_fft=self.filter_length,
dtype=np.float32)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0])
window_sum = torch.autograd.Variable(torch.from_numpy(window_sum),
requires_grad=False).cuda()
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
approx_nonzero_indices]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[:, :,
int(self.filter_length / 2):]
inverse_transform = inverse_transform[:, :, :-int(self.filter_length /
2):]
return inverse_transform
def inverse(self, magnitude, phase):
recombine_magnitude_phase = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)],
dim=1)
if magnitude.device.type == "cuda":
inverse_transform = F.conv_transpose1d(recombine_magnitude_phase,
self.inverse_basis,
stride=self.hop_length,
padding=0)
if self.window is not None:
window_sum = window_sumsquare(self.window,
magnitude.size(-1),
hop_length=self.hop_length,
win_length=self.win_length,
n_fft=self.filter_length,
dtype=np.float32)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0])
window_sum = torch.from_numpy(window_sum).to(
inverse_transform.device)
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
approx_nonzero_indices]
# scale by hop ratio
inverse_transform *= float(
self.filter_length) / self.hop_length
inverse_transform = inverse_transform[:, :,
int(self.filter_length / 2):]
inverse_transform = inverse_transform[:, :, :-int(
self.filter_length / 2):]
inverse_transform = inverse_transform.squeeze(1)
else:
x_org = recombine_magnitude_phase.detach().numpy()
n_b, n_f, n_t = x_org.shape
x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64)
x.real = x_org[:, :n_f // 2]
x.imag = x_org[:, n_f // 2:]
inverse_transform = []
for y in x:
y_ = istft(y, self.hop_length, self.win_length, self.window)
inverse_transform.append(y_[None, :])
inverse_transform = np.concatenate(inverse_transform, 0)
inverse_transform = torch.from_numpy(inverse_transform).to(
recombine_magnitude_phase.dtype)
return inverse_transform
