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| """ | |
| BSD 3-Clause License | |
| Copyright (c) 2017, Prem Seetharaman | |
| All rights reserved. | |
| * Redistribution and use in source and binary forms, with or without | |
| modification, are permitted provided that the following conditions are met: | |
| * Redistributions of source code must retain the above copyright notice, | |
| this list of conditions and the following disclaimer. | |
| * Redistributions in binary form must reproduce the above copyright notice, this | |
| list of conditions and the following disclaimer in the | |
| documentation and/or other materials provided with the distribution. | |
| * Neither the name of the copyright holder nor the names of its | |
| contributors may be used to endorse or promote products derived from this | |
| software without specific prior written permission. | |
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND | |
| ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED | |
| WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | |
| DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR | |
| ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES | |
| (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | |
| LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON | |
| ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | |
| (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | |
| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
| """ | |
| import torch | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| from scipy.signal import get_window | |
| from librosa.util import pad_center, tiny | |
| from librosa import stft, istft | |
| from audio_processing import window_sumsquare | |
| class STFT(torch.nn.Module): | |
| """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" | |
| def __init__( | |
| self, filter_length=800, hop_length=200, win_length=800, window="hann" | |
| ): | |
| super(STFT, self).__init__() | |
| self.filter_length = filter_length | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.window = window | |
| self.forward_transform = None | |
| scale = self.filter_length / self.hop_length | |
| 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[:, None, :]) | |
| inverse_basis = torch.FloatTensor( | |
| np.linalg.pinv(scale * fourier_basis).T[:, None, :] | |
| ) | |
| if window is not None: | |
| 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, filter_length) | |
| fft_window = torch.from_numpy(fft_window).float() | |
| # window the bases | |
| forward_basis *= fft_window | |
| inverse_basis *= fft_window | |
| self.register_buffer("forward_basis", forward_basis.float()) | |
| self.register_buffer("inverse_basis", inverse_basis.float()) | |
| def transform(self, input_data): | |
| num_batches = input_data.size(0) | |
| num_samples = input_data.size(1) | |
| self.num_samples = num_samples | |
| if input_data.device.type == "cuda": | |
| # similar to librosa, reflect-pad the input | |
| input_data = input_data.view(num_batches, 1, num_samples) | |
| input_data = F.pad( | |
| input_data.unsqueeze(1), | |
| (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), | |
| mode="reflect", | |
| ) | |
| input_data = input_data.squeeze(1) | |
| forward_transform = F.conv1d( | |
| input_data, self.forward_basis, stride=self.hop_length, padding=0 | |
| ) | |
| cutoff = int((self.filter_length / 2) + 1) | |
| real_part = forward_transform[:, :cutoff, :] | |
| imag_part = forward_transform[:, cutoff:, :] | |
| else: | |
| x = input_data.detach().numpy() | |
| real_part = [] | |
| imag_part = [] | |
| for y in x: | |
| y_ = stft( | |
| y, self.filter_length, self.hop_length, self.win_length, self.window | |
| ) | |
| real_part.append(y_.real[None, :, :]) | |
| imag_part.append(y_.imag[None, :, :]) | |
| real_part = np.concatenate(real_part, 0) | |
| imag_part = np.concatenate(imag_part, 0) | |
| real_part = torch.from_numpy(real_part).to(input_data.dtype) | |
| imag_part = torch.from_numpy(imag_part).to(input_data.dtype) | |
| magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) | |
| phase = torch.atan2(imag_part.data, real_part.data) | |
| return magnitude, phase | |
| def inverse(self, magnitude, phase): | |
| recombine_magnitude_phase = torch.cat( | |
| [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 | |
| ) | |
| if magnitude.device.type == "cuda": | |
| inverse_transform = F.conv_transpose1d( | |
| recombine_magnitude_phase, | |
| self.inverse_basis, | |
| stride=self.hop_length, | |
| padding=0, | |
| ) | |
| if self.window is not None: | |
| window_sum = window_sumsquare( | |
| self.window, | |
| magnitude.size(-1), | |
| hop_length=self.hop_length, | |
| win_length=self.win_length, | |
| n_fft=self.filter_length, | |
| dtype=np.float32, | |
| ) | |
| # remove modulation effects | |
| approx_nonzero_indices = torch.from_numpy( | |
| np.where(window_sum > tiny(window_sum))[0] | |
| ) | |
| window_sum = torch.from_numpy(window_sum).to(inverse_transform.device) | |
| inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ | |
| approx_nonzero_indices | |
| ] | |
| # scale by hop ratio | |
| inverse_transform *= float(self.filter_length) / self.hop_length | |
| inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] | |
| inverse_transform = inverse_transform[ | |
| :, :, : -int(self.filter_length / 2) : | |
| ] | |
| inverse_transform = inverse_transform.squeeze(1) | |
| else: | |
| x_org = recombine_magnitude_phase.detach().numpy() | |
| n_b, n_f, n_t = x_org.shape | |
| x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64) | |
| x.real = x_org[:, : n_f // 2] | |
| x.imag = x_org[:, n_f // 2 :] | |
| inverse_transform = [] | |
| for y in x: | |
| y_ = istft(y, self.hop_length, self.win_length, self.window) | |
| inverse_transform.append(y_[None, :]) | |
| inverse_transform = np.concatenate(inverse_transform, 0) | |
| inverse_transform = torch.from_numpy(inverse_transform).to( | |
| recombine_magnitude_phase.dtype | |
| ) | |
| return inverse_transform | |
| def forward(self, input_data): | |
| self.magnitude, self.phase = self.transform(input_data) | |
| reconstruction = self.inverse(self.magnitude, self.phase) | |
| return reconstruction | |