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71 lines
No EOL
3.3 KiB
Python
71 lines
No EOL
3.3 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jean-Marc Valin */
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/*
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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
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this 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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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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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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"""
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import torch
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from torch import nn
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import torch.nn.functional as F
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# x is (batch, nb_in_channels, nb_frames*frame_size)
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# kernels is (batch, nb_out_channels, nb_in_channels, nb_frames, coeffs)
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def adaconv_kernel(x, kernels, half_window, fft_size=256):
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device=x.device
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overlap_size=half_window.size(-1)
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nb_frames=kernels.size(3)
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nb_batches=kernels.size(0)
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nb_out_channels=kernels.size(1)
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nb_in_channels=kernels.size(2)
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kernel_size = kernels.size(-1)
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x = x.reshape(nb_batches, 1, nb_in_channels, nb_frames, -1)
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frame_size = x.size(-1)
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# build window: [zeros, rising window, ones, falling window, zeros]
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window = torch.cat(
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[
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torch.zeros(frame_size, device=device),
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half_window,
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torch.ones(frame_size - overlap_size, device=device),
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1 - half_window,
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torch.zeros(fft_size - 2 * frame_size - overlap_size,device=device)
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])
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x_prev = torch.cat([torch.zeros_like(x[:, :, :, :1, :]), x[:, :, :, :-1, :]], dim=-2)
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x_next = torch.cat([x[:, :, :, 1:, :overlap_size], torch.zeros_like(x[:, :, :, -1:, :overlap_size])], dim=-2)
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x_padded = torch.cat([x_prev, x, x_next, torch.zeros(nb_batches, 1, nb_in_channels, nb_frames, fft_size - 2 * frame_size - overlap_size, device=device)], -1)
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k_padded = torch.cat([torch.flip(kernels, [-1]), torch.zeros(nb_batches, nb_out_channels, nb_in_channels, nb_frames, fft_size-kernel_size, device=device)], dim=-1)
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# compute convolution
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X = torch.fft.rfft(x_padded, dim=-1)
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K = torch.fft.rfft(k_padded, dim=-1)
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out = torch.fft.irfft(X * K, dim=-1)
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# combine in channels
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out = torch.sum(out, dim=2)
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# apply the cross-fading
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out = window.reshape(1, 1, 1, -1)*out
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crossfaded = out[:,:,:,frame_size:2*frame_size] + torch.cat([torch.zeros(nb_batches, nb_out_channels, 1, frame_size, device=device), out[:, :, :-1, 2*frame_size:3*frame_size]], dim=-2)
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return crossfaded.reshape(nb_batches, nb_out_channels, -1) |