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from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from librosa.util import pad_center
from scipy.signal import get_window
class STFT(torch.nn.Module):
def __init__(
self,
filter_length=1024,
hop_length=512,
win_length: Optional[int] = None,
window="hann",
use_torch_stft=True,
):
"""
This module implements an STFT using 1D convolution and 1D transpose convolutions.
This is a bit tricky so there are some cases that probably won't work as working
out the same sizes before and after in all overlap add setups is tough. Right now,
this code should work with hop lengths that are half the filter length (50% overlap
between frames).
Keyword Arguments:
filter_length {int} -- Length of filters used (default: {1024})
hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512})
win_length {[type]} -- Length of the window function applied to each frame (if not specified, it
equals the filter length). (default: {None})
window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris)
(default: {'hann'})
"""
super(STFT, self).__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.pad_amount = int(self.filter_length / 2)
self.win_length = win_length
self.hann_window = {}
self.use_torch_stft = use_torch_stft
if use_torch_stft:
return
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int((self.filter_length / 2 + 1))
fourier_basis = np.vstack(
[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
)
forward_basis = torch.FloatTensor(fourier_basis)
inverse_basis = torch.FloatTensor(np.linalg.pinv(fourier_basis))
if win_length is None or not win_length:
win_length = filter_length
assert filter_length >= win_length
# get window and zero center pad it to filter_length
fft_window = get_window(window, win_length, fftbins=True)
fft_window = pad_center(fft_window, size=filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis = (inverse_basis.T * fft_window).T
self.register_buffer("forward_basis", forward_basis.float())
self.register_buffer("inverse_basis", inverse_basis.float())
self.register_buffer("fft_window", fft_window.float())
def __call__(
self,
input_data: torch.Tensor,
keyshift: int = 0,
speed: int = 1,
center: bool = True,
) -> torch.Tensor:
return super().__call__(input_data, keyshift, speed, center)
def transform(
self,
input_data: torch.Tensor,
return_phase=False,
) -> Tuple[Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]]:
"""Take input data (audio) to STFT domain.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
"""
input_data = F.pad(
input_data,
(self.pad_amount, self.pad_amount),
mode="reflect",
)
forward_transform = input_data.unfold(
1, self.filter_length, self.hop_length
).permute(0, 2, 1)
forward_transform = torch.matmul(self.forward_basis, forward_transform)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
if return_phase:
phase = torch.atan2(imag_part.data, real_part.data)
return magnitude, phase
else:
return magnitude
def inverse(
self,
magnitude: torch.Tensor,
phase: torch.Tensor,
) -> torch.Tensor:
"""Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
by the ```transform``` function.
Arguments:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
Returns:
inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
"""
cat = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
)
fold = torch.nn.Fold(
output_size=(1, (cat.size(-1) - 1) * self.hop_length + self.filter_length),
kernel_size=(1, self.filter_length),
stride=(1, self.hop_length),
)
inverse_transform = torch.matmul(self.inverse_basis, cat)
inverse_transform: torch.Tensor = fold(inverse_transform)[
:, 0, 0, self.pad_amount : -self.pad_amount
]
window_square_sum = (
self.fft_window.pow(2).repeat(cat.size(-1), 1).T.unsqueeze(0)
)
window_square_sum = fold(window_square_sum)[
:, 0, 0, self.pad_amount : -self.pad_amount
]
inverse_transform /= window_square_sum
return inverse_transform
def forward(
self,
input_data: torch.Tensor,
keyshift: int = 0,
speed: int = 1,
center: bool = True,
) -> torch.Tensor:
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.filter_length * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
if self.use_torch_stft:
keyshift_key = str(keyshift) + "_" + str(input_data.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(
self.win_length,
).to(input_data.device)
fft = torch.stft(
input_data,
n_fft=n_fft_new,
hop_length=hop_length_new,
win_length=win_length_new,
window=self.hann_window[keyshift_key],
center=center,
return_complex=True,
)
return torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
return self.transform(input_data)
"""Take input data (audio) to STFT domain and then back to audio.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
reconstruction = self.inverse(
self.transform(input_data, return_phase=True),
)
return reconstruction
"""
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