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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| #################### Anti-aliasing #################### | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| from .filter import * | |
| # This code is adopted from BigVGAN under the MIT License | |
| # https://github.com/NVIDIA/BigVGAN | |
| class UpSample1d(nn.Module): | |
| def __init__(self, ratio=2, kernel_size=None): | |
| super().__init__() | |
| self.ratio = ratio | |
| self.kernel_size = ( | |
| int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size | |
| ) | |
| self.stride = ratio | |
| self.pad = self.kernel_size // ratio - 1 | |
| self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 | |
| self.pad_right = ( | |
| self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 | |
| ) | |
| filter = kaiser_sinc_filter1d( | |
| cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size | |
| ) | |
| self.register_buffer("filter", filter) | |
| # x: [B, C, T] | |
| def forward(self, x): | |
| _, C, _ = x.shape | |
| x = F.pad(x, (self.pad, self.pad), mode="replicate") | |
| x = self.ratio * F.conv_transpose1d( | |
| x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C | |
| ) | |
| x = x[..., self.pad_left : -self.pad_right] | |
| return x | |
| class DownSample1d(nn.Module): | |
| def __init__(self, ratio=2, kernel_size=None): | |
| super().__init__() | |
| self.ratio = ratio | |
| self.kernel_size = ( | |
| int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size | |
| ) | |
| self.lowpass = LowPassFilter1d( | |
| cutoff=0.5 / ratio, | |
| half_width=0.6 / ratio, | |
| stride=ratio, | |
| kernel_size=self.kernel_size, | |
| ) | |
| def forward(self, x): | |
| xx = self.lowpass(x) | |
| return xx | |