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"""
This file contains implementation of RawNet3 architecture.
The original implementation can be found here: https://github.com/Jungjee/RawNet/tree/master/python/RawNet3
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from asteroid_filterbanks import Encoder, ParamSincFB # pip install asteroid_filterbanks
class RawNet3(nn.Module):
def __init__(self, block, model_scale, context, summed, C=1024, **kwargs):
super().__init__()
nOut = kwargs["nOut"]
self.context = context
self.encoder_type = kwargs["encoder_type"]
self.log_sinc = kwargs["log_sinc"]
self.norm_sinc = kwargs["norm_sinc"]
self.out_bn = kwargs["out_bn"]
self.summed = summed
self.preprocess = nn.Sequential(
PreEmphasis(), nn.InstanceNorm1d(1, eps=1e-4, affine=True)
)
self.conv1 = Encoder(
ParamSincFB(
C // 4,
251,
stride=kwargs["sinc_stride"],
)
)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm1d(C // 4)
self.layer1 = block(
C // 4, C, kernel_size=3, dilation=2, scale=model_scale, pool=5
)
self.layer2 = block(
C, C, kernel_size=3, dilation=3, scale=model_scale, pool=3
)
self.layer3 = block(C, C, kernel_size=3, dilation=4, scale=model_scale)
self.layer4 = nn.Conv1d(3 * C, 1536, kernel_size=1)
if self.context:
attn_input = 1536 * 3
else:
attn_input = 1536
print("self.encoder_type", self.encoder_type)
if self.encoder_type == "ECA":
attn_output = 1536
elif self.encoder_type == "ASP":
attn_output = 1
else:
raise ValueError("Undefined encoder")
self.attention = nn.Sequential(
nn.Conv1d(attn_input, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Conv1d(128, attn_output, kernel_size=1),
nn.Softmax(dim=2),
)
self.bn5 = nn.BatchNorm1d(3072)
self.fc6 = nn.Linear(3072, nOut)
self.bn6 = nn.BatchNorm1d(nOut)
self.mp3 = nn.MaxPool1d(3)
def forward(self, x):
"""
:param x: input mini-batch (bs, samp)
"""
with torch.cuda.amp.autocast(enabled=False):
x = self.preprocess(x)
x = torch.abs(self.conv1(x))
if self.log_sinc:
x = torch.log(x + 1e-6)
if self.norm_sinc == "mean":
x = x - torch.mean(x, dim=-1, keepdim=True)
elif self.norm_sinc == "mean_std":
m = torch.mean(x, dim=-1, keepdim=True)
s = torch.std(x, dim=-1, keepdim=True)
s[s < 0.001] = 0.001
x = (x - m) / s
if self.summed:
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(self.mp3(x1) + x2)
else:
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x = self.layer4(torch.cat((self.mp3(x1), x2, x3), dim=1))
x = self.relu(x)
t = x.size()[-1]
if self.context:
global_x = torch.cat(
(
x,
torch.mean(x, dim=2, keepdim=True).repeat(1, 1, t),
torch.sqrt(
torch.var(x, dim=2, keepdim=True).clamp(
min=1e-4, max=1e4
)
).repeat(1, 1, t),
),
dim=1,
)
else:
global_x = x
w = self.attention(global_x)
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt(
(torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-4, max=1e4)
)
x = torch.cat((mu, sg), 1)
x = self.bn5(x)
x = self.fc6(x)
if self.out_bn:
x = self.bn6(x)
return x
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
def prepare_model():
model = RawNet3(
Bottle2neck,
model_scale=8,
context=True,
summed=True,
encoder_type="ECA",
nOut=1, # number of slices
out_bn=False,
sinc_stride=10,
log_sinc=True,
norm_sinc="mean",
grad_mult=1,
)
return model
if __name__ == "__main__":
model = RawNet3(
Bottle2neck,
model_scale=8,
context=True,
summed=True,
encoder_type="ECA",
nOut=1, # number of slices
out_bn=False,
sinc_stride=10,
log_sinc=True,
norm_sinc="mean",
grad_mult=1,
)
gpu = False
model.eval()
print("RawNet3 initialised & weights loaded!")
if torch.cuda.is_available():
print("Cuda available, conducting inference on GPU")
model = model.to("cuda")
gpu = True
audios = torch.rand(32, 64_600)
out = model(audios)
print(out.shape)
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