""" 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