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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.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreEmphasis(torch.nn.Module):
def __init__(self, coef: float = 0.97) -> None:
super().__init__()
self.coef = coef
# make kernel
# In pytorch, the convolution operation uses cross-correlation. So, filter is flipped.
self.register_buffer(
"flipped_filter",
torch.FloatTensor([-self.coef, 1.0]).unsqueeze(0).unsqueeze(0),
)
def forward(self, input: torch.tensor) -> torch.tensor:
assert (
len(input.size()) == 2
), "The number of dimensions of input tensor must be 2!"
# reflect padding to match lengths of in/out
input = input.unsqueeze(1)
input = F.pad(input, (1, 0), "reflect")
return F.conv1d(input, self.flipped_filter)
class AFMS(nn.Module):
"""
Alpha-Feature map scaling, added to the output of each residual block[1,2].
Reference:
[1] RawNet2 : https://www.isca-speech.org/archive/Interspeech_2020/pdfs/1011.pdf
[2] AMFS : https://www.koreascience.or.kr/article/JAKO202029757857763.page
"""
def __init__(self, nb_dim: int) -> None:
super().__init__()
self.alpha = nn.Parameter(torch.ones((nb_dim, 1)))
self.fc = nn.Linear(nb_dim, nb_dim)
self.sig = nn.Sigmoid()
def forward(self, x):
y = F.adaptive_avg_pool1d(x, 1).view(x.size(0), -1)
y = self.sig(self.fc(y)).view(x.size(0), x.size(1), -1)
x = x + self.alpha
x = x * y
return x
class Bottle2neck(nn.Module):
def __init__(
self,
inplanes,
planes,
kernel_size=None,
dilation=None,
scale=4,
pool=False,
):
super().__init__()
width = int(math.floor(planes / scale))
self.conv1 = nn.Conv1d(inplanes, width * scale, kernel_size=1)
self.bn1 = nn.BatchNorm1d(width * scale)
self.nums = scale - 1
convs = []
bns = []
num_pad = math.floor(kernel_size / 2) * dilation
for i in range(self.nums):
convs.append(
nn.Conv1d(
width,
width,
kernel_size=kernel_size,
dilation=dilation,
padding=num_pad,
)
)
bns.append(nn.BatchNorm1d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv1d(width * scale, planes, kernel_size=1)
self.bn3 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU()
self.width = width
self.mp = nn.MaxPool1d(pool) if pool else False
self.afms = AFMS(planes)
if inplanes != planes: # if change in number of filters
self.residual = nn.Sequential(
nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=False)
)
else:
self.residual = nn.Identity()
def forward(self, x):
residual = self.residual(x)
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(sp)
sp = self.bns[i](sp)
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = torch.cat((out, spx[self.nums]), 1)
out = self.conv3(out)
out = self.relu(out)
out = self.bn3(out)
out += residual
if self.mp:
out = self.mp(out)
out = self.afms(out)
return out
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