File size: 5,203 Bytes
cb95574 eec5d10 cb95574 eec5d10 cb95574 eec5d10 cb95574 eec5d10 9e1ee6d ed67948 cb95574 eec5d10 cb95574 eec5d10 cb95574 |
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 |
'''
This is the ECAPA-TDNN model.
This model is modified and combined based on the following three projects:
1. https://github.com/clovaai/voxceleb_trainer/issues/86
2. https://github.com/lawlict/ECAPA-TDNN/blob/master/ecapa_tdnn.py
3. https://github.com/speechbrain/speechbrain/blob/96077e9a1afff89d3f5ff47cab4bca0202770e4f/speechbrain/lobes/models/ECAPA_TDNN.py
'''
import math, torch, torchaudio
import torch.nn as nn
import torch.nn.functional as F
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 EcapaTdnnEncoder(nn.Module):
def __init__(self, C):
super(EcapaTdnnEncoder, self).__init__()
# self.conv1 = nn.Conv1d(80, C, kernel_size=5, stride=1, padding=2)
# self.conv1 = nn.Conv1d(256, C, kernel_size=5, stride=1, padding=2)
self.conv1 = nn.Conv1d(232, 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)
def forward(self, x):
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)
return x
import torch
from transformers import PreTrainedModel, PretrainedConfig
class ECAPAConfig(PretrainedConfig):
model_type = "ecapa_tdnn"
def __init__(self, C=1024, **kwargs):
super().__init__(**kwargs)
self.C = C
class SpeakerEncoder(PreTrainedModel):
config_class = ECAPAConfig
base_model_prefix = "ecapa_tdnn"
def __init__(self, config):
super().__init__(config)
self.model = EcapaTdnnEncoder(C=config.C)
def forward(self, *args, **kwargs):
return self.model(*args, **kwargs)
|