467 lines
11 KiB
Python
467 lines
11 KiB
Python
# borrowed with love from "https://github.com/keonlee9420/evaluate-zero-shot-tts/blob/master/src/evaluate_zero_shot_tts/utils/speaker_verification/models/ecapa_tdnn.py"
|
|
# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchaudio.transforms as trans
|
|
|
|
#from .utils import UpstreamExpert
|
|
|
|
""" 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="fbank",
|
|
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
|
|
|
|
if feat_type == "fbank" or feat_type == "mfcc":
|
|
self.update_extract = False
|
|
|
|
win_len = int(sr * 0.025)
|
|
hop_len = int(sr * 0.01)
|
|
|
|
if feat_type == "fbank":
|
|
self.feature_extract = trans.MelSpectrogram(
|
|
sample_rate=sr,
|
|
n_fft=512,
|
|
win_length=win_len,
|
|
hop_length=hop_len,
|
|
f_min=0.0,
|
|
f_max=sr // 2,
|
|
pad=0,
|
|
n_mels=feat_dim,
|
|
)
|
|
elif feat_type == "mfcc":
|
|
melkwargs = {
|
|
"n_fft": 512,
|
|
"win_length": win_len,
|
|
"hop_length": hop_len,
|
|
"f_min": 0.0,
|
|
"f_max": sr // 2,
|
|
"pad": 0,
|
|
}
|
|
self.feature_extract = trans.MFCC(
|
|
sample_rate=sr,
|
|
n_mfcc=feat_dim,
|
|
log_mels=False,
|
|
melkwargs=melkwargs,
|
|
)
|
|
else:
|
|
"""
|
|
if config_path is None:
|
|
self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
|
|
else:
|
|
self.feature_extract = UpstreamExpert(config_path)
|
|
"""
|
|
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="fbank",
|
|
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,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
x = torch.zeros(2, 32000)
|
|
model = ECAPA_TDNN_SMALL(
|
|
feat_dim=768,
|
|
emb_dim=256,
|
|
feat_type="hubert_base",
|
|
feature_selection="hidden_states",
|
|
update_extract=False,
|
|
)
|
|
|
|
out = model(x)
|
|
# print(model)
|
|
print(out.shape) |