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import fairseq
import torch
import torch.nn as nn
___author__ = "Tianchi Liu"
__email__ = "tianchi_liu@u.nus.edu"
# modified from the model script from Hemlata Tak
class SSLModel(nn.Module):
def __init__(self, device):
super(SSLModel, self).__init__()
cp_path = (
"/app/weights/xlsr2_300m.pt" # Change the pre-trained XLSR model path.
)
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[cp_path]
)
self.model = model[0]
self.device = device
self.out_dim = 1024
return
def extract_feat(self, input_data):
# put the model to GPU if it not there
if (
next(self.model.parameters()).device != input_data.device
or next(self.model.parameters()).dtype != input_data.dtype
):
self.model.to(input_data.device, dtype=input_data.dtype)
self.model.train()
if True:
# input should be in shape (batch, length)
if input_data.ndim == 3:
input_tmp = input_data[:, :, 0]
else:
input_tmp = input_data
# [batch, length, dim]
emb = self.model(input_tmp, mask=False, features_only=True)["x"]
return emb
class SEModule(nn.Module):
def __init__(self, channels, SE_ratio=8):
super(SEModule, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, channels // SE_ratio, kernel_size=1, padding=0),
nn.ReLU(),
nn.Conv1d(channels // SE_ratio, 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, SE_ratio=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 = []
weighted_sum = []
num_pad = math.floor(kernel_size / 2) * dilation
for i in range(self.nums):
convs.append(
nn.Conv2d(
width,
width,
kernel_size=(kernel_size, 1),
dilation=(dilation, 1),
padding=(num_pad, 0),
)
)
bns.append(nn.BatchNorm2d(width))
initial_value = torch.ones(1, 1, 1, i + 2) * (1 / (i + 2))
weighted_sum.append(nn.Parameter(initial_value, requires_grad=True))
self.weighted_sum = nn.ParameterList(weighted_sum)
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, SE_ratio)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out).unsqueeze(-1) # bz c T 1
spx = torch.split(out, self.width, 1)
sp = spx[self.nums]
for i in range(self.nums):
sp = torch.cat((sp, spx[i]), -1)
sp = self.bns[i](self.relu(self.convs[i](sp)))
sp_s = sp * self.weighted_sum[i]
sp_s = torch.sum(sp_s, dim=-1, keepdim=False)
if i == 0:
out = sp_s
else:
out = torch.cat((out, sp_s), 1)
out = torch.cat((out, spx[self.nums].squeeze(-1)), 1)
out = self.conv3(out)
out = self.relu(out)
out = self.bn3(out)
out = self.se(out)
out += residual
return out
class ASTP(nn.Module):
"""Attentive statistics pooling: Channel- and context-dependent
statistics pooling, first used in ECAPA_TDNN.
"""
def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False):
super(ASTP, self).__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, bottleneck_dim, kernel_size=1
) # equals W and b in the paper
else:
self.linear1 = nn.Conv1d(
in_dim, bottleneck_dim, kernel_size=1
) # equals W and b in the paper
self.linear2 = nn.Conv1d(
bottleneck_dim, in_dim, kernel_size=1
) # equals V and k in the paper
def forward(self, x):
"""
x: a 3-dimensional tensor in tdnn-based architecture (B,F,T)
or a 4-dimensional tensor in resnet architecture (B,C,F,T)
0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
"""
if len(x.shape) == 4:
x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3])
assert len(x.shape) == 3
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! ReLU may be 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)
var = torch.sum(alpha * (x**2), dim=2) - mean**2
std = torch.sqrt(var.clamp(min=1e-10))
return torch.cat([mean, std], dim=1)
class Nested_Res2Net_TDNN(nn.Module):
def __init__(
self,
Nes_ratio=[8, 8],
input_channel=1024,
n_output_logits=2,
dilation=2,
pool_func="mean",
SE_ratio=[8],
):
super(Nested_Res2Net_TDNN, self).__init__()
self.Nes_ratio = Nes_ratio[0]
assert input_channel % Nes_ratio[0] == 0
C = input_channel // Nes_ratio[0]
self.C = C
Build_in_Res2Nets = []
bns = []
for i in range(Nes_ratio[0] - 1):
Build_in_Res2Nets.append(
Bottle2neck(
C,
C,
kernel_size=3,
dilation=dilation,
scale=Nes_ratio[1],
SE_ratio=SE_ratio[0],
)
)
bns.append(nn.BatchNorm1d(C))
self.Build_in_Res2Nets = nn.ModuleList(Build_in_Res2Nets)
self.bns = nn.ModuleList(bns)
self.bn = nn.BatchNorm1d(1024)
self.relu = nn.ReLU()
self.pool_func = pool_func
if pool_func == "mean":
self.fc = nn.Linear(1024, n_output_logits)
elif pool_func == "ASTP":
self.pooling = ASTP(
in_dim=input_channel, bottleneck_dim=128, global_context_att=False
)
self.fc = nn.Linear(2048, n_output_logits)
def forward(self, x):
spx = torch.split(x, self.C, 1)
for i in range(self.Nes_ratio - 1):
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.Build_in_Res2Nets[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[-1]), 1)
out = self.bn(out)
out = self.relu(out)
if self.pool_func == "mean":
out = torch.mean(out, dim=-1)
elif self.pool_func == "ASTP":
out = self.pooling(out)
out = self.fc(out)
return out
class wav2vec2_Nes2Net_no_Res_w_allT(nn.Module):
def __init__(self, args, device):
super().__init__()
self.device = device
self.n_output_logits = args.n_output_logits
####
# create network wav2vec 2.0
####
self.ssl_model = SSLModel(self.device)
self.Nested_Res2Net_TDNN = Nested_Res2Net_TDNN(
Nes_ratio=args.Nes_ratio,
input_channel=1024,
n_output_logits=self.n_output_logits,
dilation=args.dilation,
pool_func=args.pool_func,
SE_ratio=args.SE_ratio,
)
def forward(self, x):
# -------pre-trained Wav2vec model fine tunning ------------------------##
x_ssl_feat = self.ssl_model.extract_feat(x.squeeze(-1))
x_ssl_feat = x_ssl_feat.permute(0, 2, 1)
output = self.Nested_Res2Net_TDNN(x_ssl_feat)
return output
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--n_output_logits", type=int, default=2)
parser.add_argument("--dilation", type=int, default=2) # not important
parser.add_argument(
"--pool_func",
type=str,
default="mean",
choices=["mean", "ASTP"],
help="pooling function, choose from mean and ASTP",
)
parser.add_argument(
"--Nes_ratio",
type=int,
nargs="+",
default=[8, 8],
help="Nes_ratio, from outer to inner",
)
parser.add_argument(
"--SE_ratio",
type=int,
nargs="+",
default=[1],
help="SE downsampling ratio in the bottleneck",
)
args = parser.parse_args()
model = wav2vec2_Nes2Net_no_Res_w_allT(args=args, device="cpu")
x = torch.rand((4, 32000)).to("cpu")
model = model.to("cpu")
y = model(x)
print(y)
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("all:", trainable_params)
trainable_params = sum(
p.numel() for p in model.ssl_model.parameters() if p.requires_grad
)
print("SSL:", trainable_params)
trainable_params = sum(
p.numel() for p in model.Nested_Res2Net_TDNN.parameters() if p.requires_grad
)
print("Backend:", trainable_params)
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