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models.py
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#!/bin/python3.6
"""
Date Created: Feb 10 2020
This file contains the model descriptions, including original x-vector
architecture. The first two models are in active developement. All others
are provided below
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
class simpleTDNN(nn.Module):
def __init__(self, numSpkrs, p_dropout):
super(simpleTDNN, self).__init__()
self.tdnn1 = nn.Conv1d(in_channels=30, out_channels=128, kernel_size=5, dilation=1)
self.bn_tdnn1 = nn.BatchNorm1d(128, momentum=0.1, affine=False)
self.dropout_tdnn1 = nn.Dropout(p=p_dropout)
self.tdnn2 = nn.Conv1d(in_channels=128, out_channels=128, kernel_size=5, dilation=2)
self.bn_tdnn2 = nn.BatchNorm1d(128, momentum=0.1, affine=False)
self.dropout_tdnn2 = nn.Dropout(p=p_dropout)
self.tdnn3 = nn.Conv1d(in_channels=128, out_channels=128, kernel_size=1, dilation=1)
self.bn_tdnn3 = nn.BatchNorm1d(128, momentum=0.1, affine=False)
self.dropout_tdnn3 = nn.Dropout(p=p_dropout)
self.fc1 = nn.Linear(2*128,128)
self.bn_fc1 = nn.BatchNorm1d(128, momentum=0.1, affine=False)
self.dropout_fc1 = nn.Dropout(p=p_dropout)
self.fc2 = nn.Linear(128,64)
self.bn_fc2 = nn.BatchNorm1d(64, momentum=0.1, affine=False)
self.dropout_fc2 = nn.Dropout(p=p_dropout)
self.fc3 = nn.Linear(64,numSpkrs)
def forward(self, x, eps):
# Note: x must be (batch_size, feat_dim, chunk_len)
x = self.dropout_tdnn1(self.bn_tdnn1(F.relu(self.tdnn1(x))))
x = self.dropout_tdnn2(self.bn_tdnn2(F.relu(self.tdnn2(x))))
x = self.dropout_tdnn3(self.bn_tdnn3(F.relu(self.tdnn3(x))))
if self.training:
x = x + torch.randn(x.size()).cuda()*eps
stats = torch.cat((x.mean(dim=2), x.std(dim=2)), dim=1)
x = self.dropout_fc1(self.bn_fc1(F.relu(self.fc1(stats))))
x = self.dropout_fc2(self.bn_fc2(F.relu(self.fc2(x))))
x = self.fc3(x)
return x
class xvecTDNN(nn.Module):
def __init__(self, numSpkrs, p_dropout):
super(xvecTDNN, self).__init__()
self.tdnn1 = nn.Conv1d(in_channels=30, out_channels=512, kernel_size=5, dilation=1)
self.bn_tdnn1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn1 = nn.Dropout(p=p_dropout)
self.tdnn2 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=5, dilation=2)
self.bn_tdnn2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn2 = nn.Dropout(p=p_dropout)
self.tdnn3 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=7, dilation=3)
self.bn_tdnn3 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn3 = nn.Dropout(p=p_dropout)
self.tdnn4 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=1, dilation=1)
self.bn_tdnn4 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn4 = nn.Dropout(p=p_dropout)
self.tdnn5 = nn.Conv1d(in_channels=512, out_channels=1500, kernel_size=1, dilation=1)
self.bn_tdnn5 = nn.BatchNorm1d(1500, momentum=0.1, affine=False)
self.dropout_tdnn5 = nn.Dropout(p=p_dropout)
self.fc1 = nn.Linear(3000,512)
self.bn_fc1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc1 = nn.Dropout(p=p_dropout)
self.fc2 = nn.Linear(512,512)
self.bn_fc2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc2 = nn.Dropout(p=p_dropout)
self.fc3 = nn.Linear(512,numSpkrs)
def forward(self, x, eps):
# Note: x must be (batch_size, feat_dim, chunk_len)
x = self.dropout_tdnn1(self.bn_tdnn1(F.relu(self.tdnn1(x))))
x = self.dropout_tdnn2(self.bn_tdnn2(F.relu(self.tdnn2(x))))
x = self.dropout_tdnn3(self.bn_tdnn3(F.relu(self.tdnn3(x))))
x = self.dropout_tdnn4(self.bn_tdnn4(F.relu(self.tdnn4(x))))
x = self.dropout_tdnn5(self.bn_tdnn5(F.relu(self.tdnn5(x))))
if self.training:
shape = x.size()
noise = torch.cuda.FloatTensor(shape)
torch.randn(shape, out=noise)
x += noise*eps
stats = torch.cat((x.mean(dim=2), x.std(dim=2)), dim=1)
x = self.dropout_fc1(self.bn_fc1(F.relu(self.fc1(stats))))
x = self.dropout_fc2(self.bn_fc2(F.relu(self.fc2(x))))
x = self.fc3(x)
return x
class proto_xvecTDNN(nn.Module):
def __init__(self, numSpkrs, p_dropout):
super(proto_xvecTDNN, self).__init__()
self.tdnn1 = nn.Conv1d(in_channels=30, out_channels=512, kernel_size=5, dilation=1)
self.bn_tdnn1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn1 = nn.Dropout(p=p_dropout)
self.tdnn2 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=5, dilation=2)
self.bn_tdnn2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn2 = nn.Dropout(p=p_dropout)
self.tdnn3 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=7, dilation=3)
self.bn_tdnn3 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn3 = nn.Dropout(p=p_dropout)
self.tdnn4 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=1, dilation=1)
self.bn_tdnn4 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn4 = nn.Dropout(p=p_dropout)
self.tdnn5 = nn.Conv1d(in_channels=512, out_channels=1500, kernel_size=1, dilation=1)
self.bn_tdnn5 = nn.BatchNorm1d(1500, momentum=0.1, affine=False)
self.dropout_tdnn5 = nn.Dropout(p=p_dropout)
self.fc1 = nn.Linear(3000,512)
self.bn_fc1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc1 = nn.Dropout(p=p_dropout)
self.fc2 = nn.Linear(512,512)
self.bn_fc2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc2 = nn.Dropout(p=p_dropout)
self.fc3 = nn.Linear(512,512)
self.bn_fc3 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc3 = nn.Dropout(p=p_dropout)
self.fc4 = nn.Linear(512,512)
def forward(self, x, eps):
# Note: x must be (batch_size, feat_dim, chunk_len)
x = self.dropout_tdnn1(self.bn_tdnn1(F.relu(self.tdnn1(x))))
x = self.dropout_tdnn2(self.bn_tdnn2(F.relu(self.tdnn2(x))))
x = self.dropout_tdnn3(self.bn_tdnn3(F.relu(self.tdnn3(x))))
x = self.dropout_tdnn4(self.bn_tdnn4(F.relu(self.tdnn4(x))))
x = self.dropout_tdnn5(self.bn_tdnn5(F.relu(self.tdnn5(x))))
if self.training:
shape = x.size()
noise = torch.cuda.FloatTensor(shape)
torch.randn(shape, out=noise)
x += noise*eps
stats = torch.cat((x.mean(dim=2), x.std(dim=2)), dim=1)
x = self.dropout_fc1(self.bn_fc1(F.relu(self.fc1(stats))))
x = self.dropout_fc2(self.bn_fc2(F.relu(self.fc2(x))))
x = self.dropout_fc3(self.bn_fc3(F.relu(self.fc3(x))))
x = self.fc4(x)
return x
class relation_encoder_xvecTDNN(nn.Module):
def __init__(self, numSpkrs, p_dropout):
super(relation_encoder_xvecTDNN, self).__init__()
self.tdnn1 = nn.Conv1d(in_channels=30, out_channels=512, kernel_size=5, dilation=1)
self.bn_tdnn1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn1 = nn.Dropout(p=p_dropout)
self.tdnn2 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=5, dilation=2)
self.bn_tdnn2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn2 = nn.Dropout(p=p_dropout)
self.tdnn3 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=7, dilation=3)
self.bn_tdnn3 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn3 = nn.Dropout(p=p_dropout)
self.tdnn4 = nn.Conv1d(in_channels=512, out_channels=512, kernel_size=1, dilation=1)
self.bn_tdnn4 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_tdnn4 = nn.Dropout(p=p_dropout)
self.tdnn5 = nn.Conv1d(in_channels=512, out_channels=1500, kernel_size=1, dilation=1)
self.bn_tdnn5 = nn.BatchNorm1d(1500, momentum=0.1, affine=False)
self.dropout_tdnn5 = nn.Dropout(p=p_dropout)
self.fc1 = nn.Linear(3000,512)
self.bn_fc1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc1 = nn.Dropout(p=p_dropout)
self.fc2 = nn.Linear(512,512)
self.bn_fc2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.dropout_fc2 = nn.Dropout(p=p_dropout)
self.fc3 = nn.Linear(512,512)
def forward(self, x, eps):
# Note: x must be (batch_size, feat_dim, chunk_len)
x = self.dropout_tdnn1(self.bn_tdnn1(F.relu(self.tdnn1(x))))
x = self.dropout_tdnn2(self.bn_tdnn2(F.relu(self.tdnn2(x))))
x = self.dropout_tdnn3(self.bn_tdnn3(F.relu(self.tdnn3(x))))
x = self.dropout_tdnn4(self.bn_tdnn4(F.relu(self.tdnn4(x))))
x = self.dropout_tdnn5(self.bn_tdnn5(F.relu(self.tdnn5(x))))
if self.training:
shape = x.size()
noise = torch.cuda.FloatTensor(shape)
torch.randn(shape, out=noise)
x += noise*eps
stats = torch.cat((x.mean(dim=2), x.std(dim=2)), dim=1)
x = self.dropout_fc1(self.bn_fc1(F.relu(self.fc1(stats))))
x = self.dropout_fc2(self.bn_fc2(F.relu(self.fc2(x))))
x = self.fc3(x)
return x
class relation_relation_xvecTDNN(nn.Module):
def __init__(self):
super(relation_relation_xvecTDNN, self).__init__()
self.fc1 = nn.Linear(1024,512)
self.bn_fc1 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.fc2 = nn.Linear(512,512)
self.bn_fc2 = nn.BatchNorm1d(512, momentum=0.1, affine=False)
self.fc3 = nn.Linear(512,1)
def forward(self, x):
x = self.bn_fc1(F.relu(self.fc1(x)))
x = self.bn_fc2(F.relu(self.fc2(x)))
x = self.fc3(x)
return x
"""============================ OLD MODELS ==============================="""
class simpleCNN(nn.Module):
def __init__(self):
super(simpleCNN, self).__init__()
# 1 input image channel, 6 output channels, 3x3 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 16, 5)
self.conv2 = nn.Conv2d(16, 16, 3)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 21 * 1, 64) # 6*6 from image dimension
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 460)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (5, 5))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.conv2(x)), 3)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
class simpleLSTM(nn.Module):
def __init__(self):
super(simpleLSTM, self).__init__()
self.lstm1 = nn.LSTM(input_size=30, hidden_size=128, num_layers=1, batch_first=True)
self.fc1 = nn.Linear(128,64)
self.fc2 = nn.Linear(64,64)
self.fc3 = nn.Linear(64,460)
def forward(self, x):
# x's shape must be (batch, seq_len, input_size)
_,(h,_) = self.lstm1(x)
x = F.relu(self.fc1(h.view(h.shape[1], h.shape[2])))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x