def __init__(
self,
n_fft: int,
win_length: int,
hop_length: int,
n_iter: int,
window_fn=torch.hann_window,
):
super(GriffinLim, self).__init__()
self.transform = TTSSpectrogram(n_fft,
win_length,
hop_length,
return_phase=True)
basis = get_fourier_basis(n_fft)
basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :]
basis *= get_window(window_fn, n_fft, win_length)
self.register_buffer("basis", basis)
self.n_fft = n_fft
self.win_length = win_length
self.hop_length = hop_length
self.n_iter = n_iter
self.tiny = 1.1754944e-38
def extract_logmel_spectrogram(
waveform: torch.Tensor, sample_rate: int,
output_path: Optional[Path] = None, win_length: int = 1024,
hop_length: int = 256, n_fft: int = 1024,
win_fn: callable = torch.hann_window, n_mels: int = 80,
f_min: float = 0., f_max: float = 8000, eps: float = 1e-5,
overwrite: bool = False, target_length: Optional[int] = None
):
if output_path is not None and output_path.is_file() and not overwrite:
return
spectrogram_transform = TTSSpectrogram(
n_fft=n_fft, win_length=win_length, hop_length=hop_length,
window_fn=win_fn
)
mel_scale_transform = TTSMelScale(
n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
n_stft=n_fft // 2 + 1
)
spectrogram = spectrogram_transform(waveform)
mel_spec = mel_scale_transform(spectrogram)
logmel_spec = torch.clamp(mel_spec, min=eps).log()
assert len(logmel_spec.shape) == 3 and logmel_spec.shape[0] == 1
logmel_spec = logmel_spec.squeeze().t() # D x T -> T x D
if target_length is not None:
logmel_spec = trim_or_pad_to_target_length(logmel_spec, target_length)
if output_path is not None:
np.save(output_path.as_posix(), logmel_spec)
else:
return logmel_spec