torchaudio.functional.griffinlim

torchaudio.functional.griffinlim(specgram: Tensor, window: Tensor, n_fft: int, hop_length: int, win_length: int, power: float, n_iter: int, momentum: float, length: Optional[int], rand_init: bool) → Tensor

Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.

Implementation ported from librosa [Brian McFee et al., 2015], A fast Griffin-Lim algorithm [Perraudin et al., 2013] and Signal estimation from modified short-time Fourier transform [Griffin and Lim, 1983].

Parameters:

• specgram (Tensor) – A magnitude-only STFT spectrogram of dimension (…, freq, frames) where freq is n_fft // 2 + 1.

• window (Tensor) – Window tensor that is applied/multiplied to each frame/window

• n_fft (int) – Size of FFT, creates n_fft // 2 + 1 bins

• hop_length (int) – Length of hop between STFT windows. ( Default: win_length // 2)

• win_length (int) – Window size. (Default: n_fft)

• power (float) – Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.

• n_iter (int) – Number of iteration for phase recovery process.

• momentum (float) – The momentum parameter for fast Griffin-Lim. Setting this to 0 recovers the original Griffin-Lim method. Values near 1 can lead to faster convergence, but above 1 may not converge.

• length (int or None) – Array length of the expected output.

• rand_init (bool) – Initializes phase randomly if True, to zero otherwise.

Returns:

waveform of (…, time), where time equals the length parameter if given.

Return type:

Tensor