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