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import torch
from torch import nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = F.dropout(x, self.p, training=self.training)
x = self.fc2(x)
x = F.relu(x)
x = F.dropout(x, self.p, training=self.training)
return x
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.)
def forward(self, x):
x1 = self.W1(x)
x2 = self.W2(x)
g = torch.sigmoid(x2)
y = g * F.relu(x1) + (1. - g) * x
return y
class BatchNormConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel, relu=True):
super().__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False)
self.bnorm = nn.BatchNorm1d(out_channels)
self.relu = relu
def forward(self, x):
x = self.conv(x)
x = F.relu(x) if self.relu is True else x
return self.bnorm(x)
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert (kernel_size % 2 == 1)
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
class CBHG(nn.Module):
def __init__(self, K, in_channels, channels, proj_channels, num_highways):
super().__init__()
# List of all rnns to call `flatten_parameters()` on
self._to_flatten = []
self.bank_kernels = [i for i in range(1, K + 1)]
self.conv1d_bank = nn.ModuleList()
for k in self.bank_kernels:
conv = BatchNormConv(in_channels, channels, k)
self.conv1d_bank.append(conv)
self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)
self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3)
self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False)
# Fix the highway input if necessary
if proj_channels[-1] != channels:
self.highway_mismatch = True
self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False)
else:
self.highway_mismatch = False
self.highways = nn.ModuleList()
for i in range(num_highways):
hn = HighwayNetwork(channels)
self.highways.append(hn)
self.rnn = nn.GRU(channels, channels, batch_first=True, bidirectional=True)
self._to_flatten.append(self.rnn)
# Avoid fragmentation of RNN parameters and associated warning
self._flatten_parameters()
def forward(self, x):
# Although we `_flatten_parameters()` on init, when using DataParallel
# the model gets replicated, making it no longer guaranteed that the
# weights are contiguous in GPU memory. Hence, we must call it again
self._flatten_parameters()
# Save these for later
residual = x
seq_len = x.size(-1)
conv_bank = []
# Convolution Bank
for conv in self.conv1d_bank:
c = conv(x) # Convolution
conv_bank.append(c[:, :, :seq_len])
# Stack along the channel axis
conv_bank = torch.cat(conv_bank, dim=1)
# dump the last padding to fit residual
x = self.maxpool(conv_bank)[:, :, :seq_len]
# Conv1d projections
x = self.conv_project1(x)
x = self.conv_project2(x)
# Residual Connect
x = x + residual
# Through the highways
x = x.transpose(1, 2)
if self.highway_mismatch is True:
x = self.pre_highway(x)
for h in self.highways:
x = h(x)
# And then the RNN
x, _ = self.rnn(x)
return x
def _flatten_parameters(self):
"""Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used
to improve efficiency and avoid PyTorch yelling at us."""
[m.flatten_parameters() for m in self._to_flatten]
class TacotronEncoder(nn.Module):
def __init__(self, embed_dims, num_chars, cbhg_channels, K, num_highways, dropout):
super().__init__()
self.embedding = nn.Embedding(num_chars, embed_dims)
self.pre_net = PreNet(embed_dims, embed_dims, embed_dims, dropout=dropout)
self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels,
proj_channels=[cbhg_channels, cbhg_channels],
num_highways=num_highways)
self.proj_out = nn.Linear(cbhg_channels * 2, cbhg_channels)
def forward(self, x):
x = self.embedding(x)
x = self.pre_net(x)
x.transpose_(1, 2)
x = self.cbhg(x)
x = self.proj_out(x)
return x
class RNNEncoder(nn.Module):
def __init__(self, num_chars, embedding_dim, n_convolutions=3, kernel_size=5):
super(RNNEncoder, self).__init__()
self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=0)
convolutions = []
for _ in range(n_convolutions):
conv_layer = nn.Sequential(
ConvNorm(embedding_dim,
embedding_dim,
kernel_size=kernel_size, stride=1,
padding=int((kernel_size - 1) / 2),
dilation=1, w_init_gain='relu'),
nn.BatchNorm1d(embedding_dim))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
self.lstm = nn.LSTM(embedding_dim, int(embedding_dim / 2), 1,
batch_first=True, bidirectional=True)
def forward(self, x):
input_lengths = (x > 0).sum(-1)
input_lengths = input_lengths.cpu().numpy()
x = self.embedding(x)
x = x.transpose(1, 2) # [B, H, T]
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training) + x
x = x.transpose(1, 2) # [B, T, H]
# pytorch tensor are not reversible, hence the conversion
x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
return outputs
class DecoderRNN(torch.nn.Module):
def __init__(self, hidden_size, decoder_rnn_dim, dropout):
super(DecoderRNN, self).__init__()
self.in_conv1d = nn.Sequential(
torch.nn.Conv1d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=9, padding=4,
),
torch.nn.ReLU(),
torch.nn.Conv1d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=9, padding=4,
),
)
self.ln = nn.LayerNorm(hidden_size)
if decoder_rnn_dim == 0:
decoder_rnn_dim = hidden_size * 2
self.rnn = torch.nn.LSTM(
input_size=hidden_size,
hidden_size=decoder_rnn_dim,
num_layers=1,
batch_first=True,
bidirectional=True,
dropout=dropout
)
self.rnn.flatten_parameters()
self.conv1d = torch.nn.Conv1d(
in_channels=decoder_rnn_dim * 2,
out_channels=hidden_size,
kernel_size=3,
padding=1,
)
def forward(self, x):
input_masks = x.abs().sum(-1).ne(0).data[:, :, None]
input_lengths = input_masks.sum([-1, -2])
input_lengths = input_lengths.cpu().numpy()
x = self.in_conv1d(x.transpose(1, 2)).transpose(1, 2)
x = self.ln(x)
x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
self.rnn.flatten_parameters()
x, _ = self.rnn(x) # [B, T, C]
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
x = x * input_masks
pre_mel = self.conv1d(x.transpose(1, 2)).transpose(1, 2) # [B, T, C]
pre_mel = pre_mel * input_masks
return pre_mel
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