import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
import hyperparams as hp
use_cuda = torch.cuda.is_available()
class SeqLinear(nn.Module):
"""
Linear layer for sequences
"""
def __init__(self, input_size, output_size, time_dim=2):
"""
:param input_size: dimension of input
:param output_size: dimension of output
:param time_dim: index of time dimension
"""
super(SeqLinear, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.time_dim = time_dim
self.linear = nn.Linear(input_size, output_size)
def forward(self, input_):
"""
:param input_: sequences
:return: outputs
"""
batch_size = input_.size()[0]
if self.time_dim == 2:
input_ = input_.transpose(1, 2).contiguous()
input_ = input_.view(-1, self.input_size)
out = self.linear(input_).view(batch_size, -1, self.output_size)
if self.time_dim == 2:
out = out.contiguous().transpose(1, 2)
return out
class Prenet(nn.Module):
"""
Prenet before passing through the network
"""
def __init__(self, input_size, hidden_size, output_size):
"""
:param input_size: dimension of input
:param hidden_size: dimension of hidden unit
:param output_size: dimension of output
"""
super(Prenet, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.layer = nn.Sequential(OrderedDict([
('fc1', SeqLinear(self.input_size, self.hidden_size)),
('relu1', nn.ReLU()),
('dropout1', nn.Dropout(0.5)),
('fc2', SeqLinear(self.hidden_size, self.output_size)),
('relu2', nn.ReLU()),
('dropout2', nn.Dropout(0.5)),
]))
def forward(self, input_):
out = self.layer(input_)
return out
class CBHG(nn.Module):
"""
CBHG Module
"""
def __init__(self, hidden_size, K=16, projection_size = 128, num_gru_layers=2, max_pool_kernel_size=2, is_post=False):
"""
:param hidden_size: dimension of hidden unit
:param K: # of convolution banks
:param projection_size: dimension of projection unit
:param num_gru_layers: # of layers of GRUcell
:param max_pool_kernel_size: max pooling kernel size
:param is_post: whether post processing or not
"""
super(CBHG, self).__init__()
self.hidden_size = hidden_size
self.num_gru_layers = num_gru_layers
self.projection_size = projection_size
self.convbank_list = nn.ModuleList()
self.convbank_list.append(nn.Conv1d(in_channels=projection_size,
out_channels=hidden_size,
kernel_size=1,
padding=int(np.floor(1/2))))
for i in range(2, K+1):
self.convbank_list.append(nn.Conv1d(in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=i,
padding=int(np.floor(i/2))))
self.batchnorm_list = nn.ModuleList()
for i in range(1, K+1):
self.batchnorm_list.append(nn.BatchNorm1d(hidden_size))
convbank_outdim = hidden_size * K
if is_post:
self.conv_projection_1 = nn.Conv1d(in_channels=convbank_outdim,
out_channels=hidden_size * 2,
kernel_size=3,
padding=int(np.floor(3/2)))
self.conv_projection_2 = nn.Conv1d(in_channels=hidden_size * 2,
out_channels=projection_size,
kernel_size=3,
padding=int(np.floor(3/2)))
self.batchnorm_proj_1 = nn.BatchNorm1d(hidden_size * 2)
else:
self.conv_projection_1 = nn.Conv1d(in_channels=convbank_outdim,
out_channels=hidden_size,
kernel_size=3,
padding=int(np.floor(3 / 2)))
self.conv_projection_2 = nn.Conv1d(in_channels=hidden_size,
out_channels=projection_size,
kernel_size=3,
padding=int(np.floor(3 / 2)))
self.batchnorm_proj_1 = nn.BatchNorm1d(hidden_size)
self.batchnorm_proj_2 = nn.BatchNorm1d(projection_size)
self.max_pool = nn.MaxPool1d(max_pool_kernel_size, stride=1, padding=1)
self.highway = Highwaynet(self.projection_size)
self.gru = nn.GRU(self.projection_size, self.hidden_size, num_layers=2,
batch_first=True,
bidirectional=True)
def _conv_fit_dim(self, x, kernel_size=3):
if kernel_size % 2 == 0:
return x[:,:,:-1]
else:
return x
def forward(self, input_):
input_ = input_.contiguous()
batch_size = input_.size()[0]
convbank_list = list()
convbank_input = input_
# Convolution bank filters
for k, (conv, batchnorm) in enumerate(zip(self.convbank_list, self.batchnorm_list)):
convbank_input = F.relu(batchnorm(self._conv_fit_dim(conv(convbank_input), k+1).contiguous()))
convbank_list.append(convbank_input)
# Concatenate all features
conv_cat = torch.cat(convbank_list, dim=1)
# Max pooling
conv_cat = self.max_pool(conv_cat)[:,:,:-1]
# Projection
conv_projection = F.relu(self.batchnorm_proj_1(self._conv_fit_dim(self.conv_projection_1(conv_cat))))
conv_projection = self.batchnorm_proj_2(self._conv_fit_dim(self.conv_projection_2(conv_projection))) + input_
# Highway networks
highway = self.highway.forward(conv_projection)
highway = torch.transpose(highway, 1,2)
# Bidirectional GRU
if use_cuda:
init_gru = Variable(torch.zeros(2 * self.num_gru_layers, batch_size, self.hidden_size)).cuda()
else:
init_gru = Variable(torch.zeros(2 * self.num_gru_layers, batch_size, self.hidden_size))
self.gru.flatten_parameters()
out, _ = self.gru(highway, init_gru)
return out
class Highwaynet(nn.Module):
"""
Highway network
"""
def __init__(self, num_units, num_layers=4):
"""
:param num_units: dimension of hidden unit
:param num_layers: # of highway layers
"""
super(Highwaynet, self).__init__()
self.num_units = num_units
self.num_layers = num_layers
self.gates = nn.ModuleList()
self.linears = nn.ModuleList()
for _ in range(self.num_layers):
self.linears.append(SeqLinear(num_units, num_units))
self.gates.append(SeqLinear(num_units, num_units))
def forward(self, input_):
out = input_
# highway gated function
for fc1, fc2 in zip(self.linears, self.gates):
h = F.relu(fc1.forward(out))
t = F.sigmoid(fc2.forward(out))
c = 1. - t
out = h * t + out * c
return out
class AttentionDecoder(nn.Module):
"""
Decoder with attention mechanism (Vinyals et al.)
"""
def __init__(self, num_units):
"""
:param num_units: dimension of hidden units
"""
super(AttentionDecoder, self).__init__()
self.num_units = num_units
self.v = nn.Linear(num_units, 1, bias=False)
self.W1 = nn.Linear(num_units, num_units, bias=False)
self.W2 = nn.Linear(num_units, num_units, bias=False)
self.attn_grucell = nn.GRUCell(num_units // 2, num_units)
self.gru1 = nn.GRUCell(num_units, num_units)
self.gru2 = nn.GRUCell(num_units, num_units)
self.attn_projection = nn.Linear(num_units * 2, num_units)
self.out = nn.Linear(num_units, hp.num_mels * hp.outputs_per_step)
def forward(self, decoder_input, memory, attn_hidden, gru1_hidden, gru2_hidden):
memory_len = memory.size()[1]
batch_size = memory.size()[0]
# Get keys
keys = self.W1(memory.contiguous().view(-1, self.num_units))
keys = keys.view(-1, memory_len, self.num_units)
# Get hidden state (query) passed through GRUcell
d_t = self.attn_grucell(decoder_input, attn_hidden)
# Duplicate query with same dimension of keys for matrix operation (Speed up)
d_t_duplicate = self.W2(d_t).unsqueeze(1).expand_as(memory)
# Calculate attention score and get attention weights
attn_weights = self.v(F.tanh(keys + d_t_duplicate).view(-1, self.num_units)).view(-1, memory_len, 1)
attn_weights = attn_weights.squeeze(2)
attn_weights = F.softmax(attn_weights)
# Concatenate with original query
d_t_prime = torch.bmm(attn_weights.view([batch_size,1,-1]), memory).squeeze(1)
# Residual GRU
gru1_input = self.attn_projection(torch.cat([d_t, d_t_prime], 1))
gru1_hidden = self.gru1(gru1_input, gru1_hidden)
gru2_input = gru1_input + gru1_hidden
gru2_hidden = self.gru2(gru2_input, gru2_hidden)
bf_out = gru2_input + gru2_hidden
# Output
output = self.out(bf_out).view(-1, hp.num_mels, hp.outputs_per_step)
return output, d_t, gru1_hidden, gru2_hidden
def inithidden(self, batch_size):
if use_cuda:
attn_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False).cuda()
gru1_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False).cuda()
gru2_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False).cuda()
else:
attn_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False)
gru1_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False)
gru2_hidden = Variable(torch.zeros(batch_size, self.num_units), requires_grad=False)
return attn_hidden, gru1_hidden, gru2_hidden
