Spaces:
Sleeping
Sleeping
File size: 5,741 Bytes
8324298 b11593d 8324298 |
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from torchaudio.functional import resample
import math
class SEModule(nn.Module):
def __init__(self, channels, bottleneck=128):
super(SEModule, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, bottleneck, kernel_size=1, padding=0),
nn.ReLU(),
# nn.BatchNorm1d(bottleneck), # I remove this layer
nn.Conv1d(bottleneck, channels, kernel_size=1, padding=0),
nn.Sigmoid(),
)
def forward(self, input):
x = self.se(input)
return input * x
class Bottle2neck(nn.Module):
def __init__(self, inplanes, planes, kernel_size=None, dilation=None, scale = 8):
super(Bottle2neck, self).__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.se = SEModule(planes)
def forward(self, x):
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 = self.se(out)
out += residual
return out
class PreEmphasis(torch.nn.Module):
def __init__(self, coef: float = 0.97):
super().__init__()
self.coef = coef
self.register_buffer(
'flipped_filter', torch.FloatTensor([-self.coef, 1.]).unsqueeze(0).unsqueeze(0)
)
def forward(self, input: torch.tensor) -> torch.tensor:
input = input.unsqueeze(1)
input = F.pad(input, (1, 0), 'reflect')
return F.conv1d(input, self.flipped_filter).squeeze(1)
class ECAPA_gender(nn.Module):
def __init__(self, config):
super(ECAPA_gender, self).__init__()
self.config = config
C = config["C"]
self.torchfbank = torch.nn.Sequential(
PreEmphasis(),
torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=512, win_length=400, hop_length=160, \
f_min = 20, f_max = 7600, window_fn=torch.hamming_window, n_mels=80),
)
self.conv1 = nn.Conv1d(80, C, kernel_size=5, stride=1, padding=2)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm1d(C)
self.layer1 = Bottle2neck(C, C, kernel_size=3, dilation=2, scale=8)
self.layer2 = Bottle2neck(C, C, kernel_size=3, dilation=3, scale=8)
self.layer3 = Bottle2neck(C, C, kernel_size=3, dilation=4, scale=8)
# I fixed the shape of the output from MFA layer, that is close to the setting from ECAPA paper.
self.layer4 = nn.Conv1d(3*C, 1536, kernel_size=1)
self.attention = nn.Sequential(
nn.Conv1d(4608, 256, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(256),
nn.Tanh(), # I add this layer
nn.Conv1d(256, 1536, kernel_size=1),
nn.Softmax(dim=2),
)
self.bn5 = nn.BatchNorm1d(3072)
self.fc6 = nn.Linear(3072, 192)
self.bn6 = nn.BatchNorm1d(192)
self.fc7 = nn.Linear(192, 2)
self.pred2gender = {0 : 'male', 1 : 'female'}
def forward(self, x):
with torch.no_grad():
x = self.torchfbank(x)+1e-6
x = x.log()
x = x - torch.mean(x, dim=-1, keepdim=True)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x1 = self.layer1(x)
x2 = self.layer2(x+x1)
x3 = self.layer3(x+x1+x2)
x = self.layer4(torch.cat((x1,x2,x3),dim=1))
x = self.relu(x)
t = x.size()[-1]
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)).repeat(1,1,t)), dim=1)
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) )
x = torch.cat((mu,sg),1)
x = self.bn5(x)
x = self.fc6(x)
x = self.bn6(x)
x = self.relu(x)
x = self.fc7(x)
return x
def load_audio(self, path):
audio, sr = torchaudio.load(path)
if sr != 16000:
audio = resample(audio, sr, 16000)
return audio
def predict(self, audio):
audio = self.load_audio(audio)
self.eval()
with torch.no_grad():
output = self.forward(audio)
_, pred = output.max(1)
return self.pred2gender[pred.item()]
|