class NetFeeder(object):
def __init__(self, device, win_size=320, hop_size=160):
self.eps = torch.finfo(torch.float32).eps
self.stft = STFT(win_size, hop_size).to(device)
def __call__(self, mix, sph):
real_mix, imag_mix = self.stft.stft(mix)
feat = torch.stack([real_mix, imag_mix], dim=1)
real_sph, imag_sph = self.stft.stft(sph)
lbl = torch.stack([real_sph, imag_sph], dim=1)
return feat, lbl
class NetFeeder(object):
def __init__(self, device, win_size=320, hop_size=160):
self.eps = torch.finfo(torch.float32).eps
self.stft = STFT(win_size, hop_size).to(device)
def __call__(self, mix, sph):
real_mix, imag_mix = self.stft.stft(mix)
mag_mix = torch.sqrt(real_mix**2 + imag_mix**2)
feat = mag_mix
real_sph, imag_sph = self.stft.stft(sph)
mag_sph = torch.sqrt(real_sph**2 + imag_sph**2)
lbl = mag_sph
return feat, lbl
class Resynthesizer(object):
def __init__(self, device, win_size=320, hop_size=160):
self.stft = STFT(win_size, hop_size).to(device)
def __call__(self, est, mix):
real_mix, imag_mix = self.stft.stft(mix)
pha_mix = torch.atan2(imag_mix.data, real_mix.data)
real_est = est * torch.cos(pha_mix)
imag_est = est * torch.sin(pha_mix)
sph_est = self.stft.istft(torch.stack([real_est, imag_est], dim=1))
sph_est = F.pad(sph_est, [0, mix.shape[1]-sph_est.shape[1]])
return sph_est