I print the window_sum in stft, line: 155， find that the value will a constant, except for the former and latter padding positions. the window function only plays the role of linear scaling. Does this result meet the windowing expectations?

My command to run:

python3 train.py --config config_v1.json --input_wavs_dir /home/yehor/iSTFTNet-pytorch/lada_wavs --input_training_file /home/yehor/iSTFTNet-pytorch/training_list.txt --input_validation_file /home/yehor/iSTFTNet-pytorch/validation_list.txt

Error:

...        (2): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
      )
    )
  )
  (conv_post): Conv1d(128, 18, kernel_size=(7,), stride=(1,), padding=(3,))
  (reflection_pad): ReflectionPad1d((1, 0))
)
checkpoints directory :  cp_hifigan
Epoch: 1
/home/yehor/.local/lib/python3.8/site-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
/home/yehor/.local/lib/python3.8/site-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
/home/yehor/.local/lib/python3.8/site-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
/home/yehor/.local/lib/python3.8/site-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
Traceback (most recent call last):
  File "train.py", line 280, in <module>
    main()
  File "train.py", line 276, in main
    train(0, a, h)
  File "train.py", line 126, in train
    y_g_hat = stft.inverse(spec, phase)
  File "/home/yehor/iSTFTNet-pytorch/stft.py", line 198, in inverse
    inverse_transform = torch.istft(
RuntimeError: istft input and window must be on the same device but got self on cuda:0 and window on cpu

Hi @rishikksh20 , thanks for your work.

I have a question. If I want to use the 16K sampling rate, how do I modify the configuration file? It should not just modify sampling_rate in json.

1. Added 2 extra hyperparameters to config_v1.json:

   • "gen_istft_n_fft": 16
   • "gen_istft_hop_size": 4

2. Replaced Seetharaman's version of STFT with built-in torch implementation for a speed boost (because function window_sumsquare invoked by STFT.inverse is implemented in plain CPU)

stft.py line 164->165: window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices], would get errors . Because, inverse_transform might in cuda1 while window_sum in cuda0. Change line 164 to window_sum = window_sum.to(inverse_transform.device()) if magnitude.is_cuda else window_sum will fix the problem.

Thanks for the implemention of ISTFT. It has better inference speed than hifigan v1.However, I found that there is a single frequency line which would cause little noise.I use 16KHZ dataset for training.And all the line is extractly at 4k which is the middle of the all frequency.I'm trying to fix this problem, do you have the same problem?

As the issue https://github.com/rishikksh20/iSTFTNet-pytorch/issues/1, the line 164 in stft.py was changed to https://github.com/rishikksh20/iSTFTNet-pytorch/blob/e928a6b604033a3857757562af36241f9225adfc/stft.py#L164

But inverse_transform.device() will raise the exception mentioned in the title. So it can be changed to inverse_transform.device to fix the problem.