Function compute_istft

utils/strand_util.py:408–448

(input, fft_size, hop_size, win_length, window_type="hann_window", spatial_size=256)

#inverse of compute_stft
def compute_istft(input, fft_size, hop_size, win_length, window_type="hann_window", spatial_size=256):
    window = get_window(window_type, win_length).cuda()

    #if the input if 5 dimensional and the last dimension 2, then we view it as complex
    if len(input.shape)==5 and input.shape[-1]==2:
        input=torch.view_as_complex(input)

    if len(input.shape)==3:
        #we have a (B,T) input so we can just run stft
         x= torch.istft(
            input,
            n_fft=fft_size,
            hop_length=hop_size,
            win_length=win_length,
            window=window,
            onesided=True,
            return_complex=False,
        )
    elif len(input.shape)==4:
        #do stft for each dimension
        dim=input.shape[-1]
        x_total=[]
        for i in range(dim):
            input_axis = input[:,:,:,i]
            x_axis = torch.istft(
                input_axis,
                n_fft=fft_size,
                hop_length=hop_size,
                win_length=win_length,
                window=window,
                onesided=True,
                return_complex=False,
                length=spatial_size,
            ).unsqueeze(-1)
            x_total.append(x_axis)
        x = torch.cat(x_total,-1)
    else:
        return None
    

    return x


def compute_fft(input):