Spaces:
Running
Running
File size: 6,918 Bytes
9791162 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
import numpy as np
import torch
from torch import nn
from TTS.utils.io import load_fsspec
class SELayer(nn.Module):
def __init__(self, channel, reduction=8):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel),
nn.Sigmoid(),
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
super(SEBasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNetSpeakerEncoder(nn.Module):
"""Implementation of the model H/ASP without batch normalization in speaker embedding. This model was proposed in: https://arxiv.org/abs/2009.14153
Adapted from: https://github.com/clovaai/voxceleb_trainer
"""
# pylint: disable=W0102
def __init__(
self,
input_dim=64,
proj_dim=512,
layers=[3, 4, 6, 3],
num_filters=[32, 64, 128, 256],
encoder_type="ASP",
log_input=False,
):
super(ResNetSpeakerEncoder, self).__init__()
self.encoder_type = encoder_type
self.input_dim = input_dim
self.log_input = log_input
self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
self.bn1 = nn.BatchNorm2d(num_filters[0])
self.inplanes = num_filters[0]
self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
self.instancenorm = nn.InstanceNorm1d(input_dim)
outmap_size = int(self.input_dim / 8)
self.attention = nn.Sequential(
nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
nn.Softmax(dim=2),
)
if self.encoder_type == "SAP":
out_dim = num_filters[3] * outmap_size
elif self.encoder_type == "ASP":
out_dim = num_filters[3] * outmap_size * 2
else:
raise ValueError("Undefined encoder")
self.fc = nn.Linear(out_dim, proj_dim)
self._init_layers()
def _init_layers(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def create_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
# pylint: disable=R0201
def new_parameter(self, *size):
out = nn.Parameter(torch.FloatTensor(*size))
nn.init.xavier_normal_(out)
return out
def forward(self, x, l2_norm=False):
x = x.transpose(1, 2)
with torch.no_grad():
with torch.cuda.amp.autocast(enabled=False):
if self.log_input:
x = (x + 1e-6).log()
x = self.instancenorm(x).unsqueeze(1)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = x.reshape(x.size()[0], -1, x.size()[-1])
w = self.attention(x)
if self.encoder_type == "SAP":
x = torch.sum(x * w, dim=2)
elif self.encoder_type == "ASP":
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt((torch.sum((x ** 2) * w, dim=2) - mu ** 2).clamp(min=1e-5))
x = torch.cat((mu, sg), 1)
x = x.view(x.size()[0], -1)
x = self.fc(x)
if l2_norm:
x = torch.nn.functional.normalize(x, p=2, dim=1)
return x
@torch.no_grad()
def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True):
"""
Generate embeddings for a batch of utterances
x: 1xTxD
"""
max_len = x.shape[1]
if max_len < num_frames:
num_frames = max_len
offsets = np.linspace(0, max_len - num_frames, num=num_eval)
frames_batch = []
for offset in offsets:
offset = int(offset)
end_offset = int(offset + num_frames)
frames = x[:, offset:end_offset]
frames_batch.append(frames)
frames_batch = torch.cat(frames_batch, dim=0)
embeddings = self.forward(frames_batch, l2_norm=True)
if return_mean:
embeddings = torch.mean(embeddings, dim=0, keepdim=True)
return embeddings
def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if use_cuda:
self.cuda()
if eval:
self.eval()
assert not self.training
|