Spaces:
Sleeping
Sleeping
File size: 10,819 Bytes
1646c30 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 |
# just for speaker similarity evaluation, third-party code
# From https://github.com/microsoft/UniSpeech/blob/main/downstreams/speaker_verification/models/
# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
''' Res2Conv1d + BatchNorm1d + ReLU
'''
class Res2Conv1dReluBn(nn.Module):
'''
in_channels == out_channels == channels
'''
def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4):
super().__init__()
assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
self.scale = scale
self.width = channels // scale
self.nums = scale if scale == 1 else scale - 1
self.convs = []
self.bns = []
for i in range(self.nums):
self.convs.append(nn.Conv1d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias))
self.bns.append(nn.BatchNorm1d(self.width))
self.convs = nn.ModuleList(self.convs)
self.bns = nn.ModuleList(self.bns)
def forward(self, x):
out = []
spx = torch.split(x, self.width, 1)
for i in range(self.nums):
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
# Order: conv -> relu -> bn
sp = self.convs[i](sp)
sp = self.bns[i](F.relu(sp))
out.append(sp)
if self.scale != 1:
out.append(spx[self.nums])
out = torch.cat(out, dim=1)
return out
''' Conv1d + BatchNorm1d + ReLU
'''
class Conv1dReluBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True):
super().__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
self.bn = nn.BatchNorm1d(out_channels)
def forward(self, x):
return self.bn(F.relu(self.conv(x)))
''' The SE connection of 1D case.
'''
class SE_Connect(nn.Module):
def __init__(self, channels, se_bottleneck_dim=128):
super().__init__()
self.linear1 = nn.Linear(channels, se_bottleneck_dim)
self.linear2 = nn.Linear(se_bottleneck_dim, channels)
def forward(self, x):
out = x.mean(dim=2)
out = F.relu(self.linear1(out))
out = torch.sigmoid(self.linear2(out))
out = x * out.unsqueeze(2)
return out
''' SE-Res2Block of the ECAPA-TDNN architecture.
'''
# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
# return nn.Sequential(
# Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
# Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
# Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
# SE_Connect(channels)
# )
class SE_Res2Block(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim):
super().__init__()
self.Conv1dReluBn1 = Conv1dReluBn(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.Res2Conv1dReluBn = Res2Conv1dReluBn(out_channels, kernel_size, stride, padding, dilation, scale=scale)
self.Conv1dReluBn2 = Conv1dReluBn(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
self.shortcut = None
if in_channels != out_channels:
self.shortcut = nn.Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
)
def forward(self, x):
residual = x
if self.shortcut:
residual = self.shortcut(x)
x = self.Conv1dReluBn1(x)
x = self.Res2Conv1dReluBn(x)
x = self.Conv1dReluBn2(x)
x = self.SE_Connect(x)
return x + residual
''' Attentive weighted mean and standard deviation pooling.
'''
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, attention_channels=128, global_context_att=False):
super().__init__()
self.global_context_att = global_context_att
# Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
if global_context_att:
self.linear1 = nn.Conv1d(in_dim * 3, attention_channels, kernel_size=1) # equals W and b in the paper
else:
self.linear1 = nn.Conv1d(in_dim, attention_channels, kernel_size=1) # equals W and b in the paper
self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1) # equals V and k in the paper
def forward(self, x):
if self.global_context_att:
context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
x_in = torch.cat((x, context_mean, context_std), dim=1)
else:
x_in = x
# DON'T use ReLU here! In experiments, I find ReLU hard to converge.
alpha = torch.tanh(self.linear1(x_in))
# alpha = F.relu(self.linear1(x_in))
alpha = torch.softmax(self.linear2(alpha), dim=2)
mean = torch.sum(alpha * x, dim=2)
residuals = torch.sum(alpha * (x ** 2), dim=2) - mean ** 2
std = torch.sqrt(residuals.clamp(min=1e-9))
return torch.cat([mean, std], dim=1)
class ECAPA_TDNN(nn.Module):
def __init__(self, feat_dim=80, channels=512, emb_dim=192, global_context_att=False,
feat_type='wavlm_large', sr=16000, feature_selection="hidden_states", update_extract=False, config_path=None):
super().__init__()
self.feat_type = feat_type
self.feature_selection = feature_selection
self.update_extract = update_extract
self.sr = sr
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
try:
local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main")
self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source='local', config_path=config_path)
except:
self.feature_extract = torch.hub.load('s3prl/s3prl', feat_type)
if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"):
self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False
if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"):
self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False
self.feat_num = self.get_feat_num()
self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
if feat_type != 'fbank' and feat_type != 'mfcc':
freeze_list = ['final_proj', 'label_embs_concat', 'mask_emb', 'project_q', 'quantizer']
for name, param in self.feature_extract.named_parameters():
for freeze_val in freeze_list:
if freeze_val in name:
param.requires_grad = False
break
if not self.update_extract:
for param in self.feature_extract.parameters():
param.requires_grad = False
self.instance_norm = nn.InstanceNorm1d(feat_dim)
# self.channels = [channels] * 4 + [channels * 3]
self.channels = [channels] * 4 + [1536]
self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
self.layer2 = SE_Res2Block(self.channels[0], self.channels[1], kernel_size=3, stride=1, padding=2, dilation=2, scale=8, se_bottleneck_dim=128)
self.layer3 = SE_Res2Block(self.channels[1], self.channels[2], kernel_size=3, stride=1, padding=3, dilation=3, scale=8, se_bottleneck_dim=128)
self.layer4 = SE_Res2Block(self.channels[2], self.channels[3], kernel_size=3, stride=1, padding=4, dilation=4, scale=8, se_bottleneck_dim=128)
# self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
cat_channels = channels * 3
self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
self.pooling = AttentiveStatsPool(self.channels[-1], attention_channels=128, global_context_att=global_context_att)
self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
def get_feat_num(self):
self.feature_extract.eval()
wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
with torch.no_grad():
features = self.feature_extract(wav)
select_feature = features[self.feature_selection]
if isinstance(select_feature, (list, tuple)):
return len(select_feature)
else:
return 1
def get_feat(self, x):
if self.update_extract:
x = self.feature_extract([sample for sample in x])
else:
with torch.no_grad():
if self.feat_type == 'fbank' or self.feat_type == 'mfcc':
x = self.feature_extract(x) + 1e-6 # B x feat_dim x time_len
else:
x = self.feature_extract([sample for sample in x])
if self.feat_type == 'fbank':
x = x.log()
if self.feat_type != "fbank" and self.feat_type != "mfcc":
x = x[self.feature_selection]
if isinstance(x, (list, tuple)):
x = torch.stack(x, dim=0)
else:
x = x.unsqueeze(0)
norm_weights = F.softmax(self.feature_weight, dim=-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
x = (norm_weights * x).sum(dim=0)
x = torch.transpose(x, 1, 2) + 1e-6
x = self.instance_norm(x)
return x
def forward(self, x):
x = self.get_feat(x)
out1 = self.layer1(x)
out2 = self.layer2(out1)
out3 = self.layer3(out2)
out4 = self.layer4(out3)
out = torch.cat([out2, out3, out4], dim=1)
out = F.relu(self.conv(out))
out = self.bn(self.pooling(out))
out = self.linear(out)
return out
def ECAPA_TDNN_SMALL(feat_dim, emb_dim=256, feat_type='wavlm_large', sr=16000, feature_selection="hidden_states", update_extract=False, config_path=None):
return ECAPA_TDNN(feat_dim=feat_dim, channels=512, emb_dim=emb_dim,
feat_type=feat_type, sr=sr, feature_selection=feature_selection, update_extract=update_extract, config_path=config_path)
|