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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 from scipy.signal import get_window import librosa.util as librosa_util import torch.nn.functional as F from torch.autograd import Variable from librosa.util import pad_center, tiny def window_sumsquare( window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None, ): """ # from librosa 0.6 Compute the sum-square envelope of a window function at a given hop length. This is used to estimate modulation effects induced by windowing observations in short-time fourier transforms. Parameters ---------- window : string, tuple, number, callable, or list-like Window specification, as in `get_window` n_frames : int > 0 The number of analysis frames hop_length : int > 0 The number of samples to advance between frames win_length : [optional] The length of the window function. By default, this matches `n_fft`. n_fft : int > 0 The length of each analysis frame. dtype : np.dtype The data type of the output Returns ------- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` The sum-squared envelope of the window function """ if win_length is None: win_length = n_fft n = n_fft + hop_length * (n_frames - 1) x = np.zeros(n, dtype=dtype) # Compute the squared window at the desired length win_sq = get_window(window, win_length, fftbins=True) win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2 win_sq = librosa_util.pad_center(win_sq, size=n_fft) # Fill the envelope for i in range(n_frames): sample = i * hop_length x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))] return x def griffin_lim(magnitudes, stft_fn, n_iters=30): """ PARAMS ------ magnitudes: spectrogram magnitudes stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods """ angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) angles = angles.astype(np.float32) angles = torch.autograd.Variable(torch.from_numpy(angles)) signal = stft_fn.inverse(magnitudes, angles).squeeze(1) for i in range(n_iters): _, angles = stft_fn.transform(signal) signal = stft_fn.inverse(magnitudes, angles).squeeze(1) return signal def dynamic_range_compression(x, C=1, clip_val=1e-5): """ PARAMS ------ C: compression factor """ return torch.log(torch.clamp(x, min=clip_val) * C) def dynamic_range_decompression(x, C=1): """ PARAMS ------ C: compression factor used to compress """ return torch.exp(x) / C 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 win_length >= filter_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 *= 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 # 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, Variable(self.forward_basis, requires_grad=False), stride=self.hop_length, padding=0, ) 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) phase = torch.autograd.Variable(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 ) inverse_transform = F.conv_transpose1d( recombine_magnitude_phase, Variable(self.inverse_basis, requires_grad=False), 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.autograd.Variable( torch.from_numpy(window_sum), requires_grad=False ) window_sum = window_sum.to(magnitude.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) :] 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