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5d9b308 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # SPDX-License-Identifier: Apache-2.0
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
import torch.nn as nn
from torch.nn import functional as F
from .dac_alias_free_filter import LowPassFilter1d
from .dac_alias_free_filter import kaiser_sinc_filter1d
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
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