nnAudio.features.griffin_lim.Griffin_Lim

class nnAudio.features.griffin_lim.Griffin_Lim(n_fft, n_iter=32, hop_length=None, win_length=None, window='hann', center=True, pad_mode='reflect', momentum=0.99, device='cpu')

Bases: torch.nn.modules.module.Module

Converting Magnitude spectrograms back to waveforms based on the “fast Griffin-Lim”[1]. This Griffin Lim is a direct clone from librosa.griffinlim.

[1] Perraudin, N., Balazs, P., & Søndergaard, P. L. “A fast Griffin-Lim algorithm,” IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), Oct. 2013.

Parameters

  • n_fft (int) – The window size. Default value is 2048.

  • n_iter=32 (int) – The number of iterations for Griffin-Lim. The default value is 32

  • hop_length (int) – The hop (or stride) size. Default value is None which is equivalent to n_fft//4. Please make sure the value is the same as the forward STFT.

  • window (str) – The windowing function for iSTFT. It uses scipy.signal.get_window, please refer to scipy documentation for possible windowing functions. The default value is ‘hann’. Please make sure the value is the same as the forward STFT.

  • center (bool) – Putting the iSTFT keneral at the center of the time-step or not. If False, the time index is the beginning of the iSTFT kernel, if True, the time index is the center of the iSTFT kernel. Default value if True. Please make sure the value is the same as the forward STFT.

  • momentum (float) – The momentum for the update rule. The default value is 0.99.

  • device (str) – Choose which device to initialize this layer. Default value is ‘cpu’

Methods

__init__

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward

Convert a batch of magnitude spectrograms to waveforms.
forward(S)

Convert a batch of magnitude spectrograms to waveforms.

Parameters

S (torch tensor) – Spectrogram of the shape (batch, n_fft//2+1, timesteps)