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# coding: utf-8
from __future__ import with_statement, print_function, absolute_import
import torch
from torch.autograd import Variable
from torch import nn
from .attention import BahdanauAttention, AttentionWrapper
from .attention import get_mask_from_lengths
class Prenet(nn.Module):
def __init__(self, in_dim, sizes=[256, 128]):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList(
[nn.Linear(in_size, out_size)
for (in_size, out_size) in zip(in_sizes, sizes)])
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.5)
def forward(self, inputs):
for linear in self.layers:
inputs = self.dropout(self.relu(linear(inputs)))
return inputs
class BatchNormConv1d(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, stride, padding,
activation=None):
super(BatchNormConv1d, self).__init__()
self.conv1d = nn.Conv1d(in_dim, out_dim,
kernel_size=kernel_size,
stride=stride, padding=padding, bias=False)
self.bn = nn.BatchNorm1d(out_dim)
self.activation = activation
def forward(self, x):
x = self.conv1d(x)
if self.activation is not None:
x = self.activation(x)
return self.bn(x)
class Highway(nn.Module):
def __init__(self, in_size, out_size):
super(Highway, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, inputs):
H = self.relu(self.H(inputs))
T = self.sigmoid(self.T(inputs))
return H * T + inputs * (1.0 - T)
class CBHG(nn.Module):
"""CBHG module: a recurrent neural network composed of:
- 1-d convolution banks
- Highway networks + residual connections
- Bidirectional gated recurrent units
"""
def __init__(self, in_dim, K=16, projections=[128, 128]):
super(CBHG, self).__init__()
self.in_dim = in_dim
self.relu = nn.ReLU()
self.conv1d_banks = nn.ModuleList(
[BatchNormConv1d(in_dim, in_dim, kernel_size=k, stride=1,
padding=k // 2, activation=self.relu)
for k in range(1, K + 1)])
self.max_pool1d = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)
in_sizes = [K * in_dim] + projections[:-1]
activations = [self.relu] * (len(projections) - 1) + [None]
self.conv1d_projections = nn.ModuleList(
[BatchNormConv1d(in_size, out_size, kernel_size=3, stride=1,
padding=1, activation=ac)
for (in_size, out_size, ac) in zip(
in_sizes, projections, activations)])
self.pre_highway = nn.Linear(projections[-1], in_dim, bias=False)
self.highways = nn.ModuleList(
[Highway(in_dim, in_dim) for _ in range(4)])
self.gru = nn.GRU(
in_dim, in_dim, 1, batch_first=True, bidirectional=True)
def forward(self, inputs, input_lengths=None):
# (B, T_in, in_dim)
x = inputs
# Needed to perform conv1d on time-axis
# (B, in_dim, T_in)
if x.size(-1) == self.in_dim:
x = x.transpose(1, 2)
T = x.size(-1)
# (B, in_dim*K, T_in)
# Concat conv1d bank outputs
x = torch.cat([conv1d(x)[:, :, :T] for conv1d in self.conv1d_banks], dim=1)
assert x.size(1) == self.in_dim * len(self.conv1d_banks)
x = self.max_pool1d(x)[:, :, :T]
for conv1d in self.conv1d_projections:
x = conv1d(x)
# (B, T_in, in_dim)
# Back to the original shape
x = x.transpose(1, 2)
if x.size(-1) != self.in_dim:
x = self.pre_highway(x)
# Residual connection
x += inputs
for highway in self.highways:
x = highway(x)
if input_lengths is not None:
x = nn.utils.rnn.pack_padded_sequence(
x, input_lengths, batch_first=True)
# (B, T_in, in_dim*2)
outputs, _ = self.gru(x)
if input_lengths is not None:
outputs, _ = nn.utils.rnn.pad_packed_sequence(
outputs, batch_first=True)
return outputs
class Encoder(nn.Module):
def __init__(self, in_dim):
super(Encoder, self).__init__()
self.prenet = Prenet(in_dim, sizes=[256, 128])
self.cbhg = CBHG(128, K=16, projections=[128, 128])
def forward(self, inputs, input_lengths=None):
inputs = self.prenet(inputs)
return self.cbhg(inputs, input_lengths)
class Decoder(nn.Module):
def __init__(self, in_dim, r):
super(Decoder, self).__init__()
self.in_dim = in_dim
self.r = r
self.prenet = Prenet(in_dim * r, sizes=[256, 128])
# (prenet_out + attention context) -> output
self.attention_rnn = AttentionWrapper(
nn.GRUCell(256 + 128, 256),
BahdanauAttention(256)
)
self.memory_layer = nn.Linear(256, 256, bias=False)
self.project_to_decoder_in = nn.Linear(512, 256)
self.decoder_rnns = nn.ModuleList(
[nn.GRUCell(256, 256) for _ in range(2)])
self.proj_to_mel = nn.Linear(256, in_dim * r)
self.max_decoder_steps = 200
def forward(self, encoder_outputs, inputs=None, memory_lengths=None):
"""
Decoder forward step.
If decoder inputs are not given (e.g., at testing time), as noted in
Tacotron paper, greedy decoding is adapted.
Args:
encoder_outputs: Encoder outputs. (B, T_encoder, dim)
inputs: Decoder inputs. i.e., mel-spectrogram. If None (at eval-time),
decoder outputs are used as decoder inputs.
memory_lengths: Encoder output (memory) lengths. If not None, used for
attention masking.
"""
B = encoder_outputs.size(0)
processed_memory = self.memory_layer(encoder_outputs)
if memory_lengths is not None:
mask = get_mask_from_lengths(processed_memory, memory_lengths)
else:
mask = None
# Run greedy decoding if inputs is None
greedy = inputs is None
if inputs is not None:
# Grouping multiple frames if necessary
if inputs.size(-1) == self.in_dim:
inputs = inputs.view(B, inputs.size(1) // self.r, -1)
assert inputs.size(-1) == self.in_dim * self.r
T_decoder = inputs.size(1)
# go frames
initial_input = Variable(
encoder_outputs.data.new(B, self.in_dim * self.r).zero_())
# Init decoder states
attention_rnn_hidden = Variable(
encoder_outputs.data.new(B, 256).zero_())
decoder_rnn_hiddens = [Variable(
encoder_outputs.data.new(B, 256).zero_())
for _ in range(len(self.decoder_rnns))]
current_attention = Variable(
encoder_outputs.data.new(B, 256).zero_())
# Time first (T_decoder, B, in_dim)
if inputs is not None:
inputs = inputs.transpose(0, 1)
outputs = []
alignments = []
t = 0
current_input = initial_input
while True:
if t > 0:
current_input = outputs[-1] if greedy else inputs[t - 1]
# Prenet
current_input = self.prenet(current_input)
# Attention RNN
attention_rnn_hidden, current_attention, alignment = self.attention_rnn(
current_input, current_attention, attention_rnn_hidden,
encoder_outputs, processed_memory=processed_memory, mask=mask)
# Concat RNN output and attention context vector
decoder_input = self.project_to_decoder_in(
torch.cat((attention_rnn_hidden, current_attention), -1))
# Pass through the decoder RNNs
for idx in range(len(self.decoder_rnns)):
decoder_rnn_hiddens[idx] = self.decoder_rnns[idx](
decoder_input, decoder_rnn_hiddens[idx])
# Residual connectinon
decoder_input = decoder_rnn_hiddens[idx] + decoder_input
output = decoder_input
output = self.proj_to_mel(output)
outputs += [output]
alignments += [alignment]
t += 1
if greedy:
if t > 1 and is_end_of_frames(output):
break
elif t > self.max_decoder_steps:
print("Warning! doesn't seems to be converged")
break
else:
if t >= T_decoder:
break
assert greedy or len(outputs) == T_decoder
# Back to batch first
alignments = torch.stack(alignments).transpose(0, 1)
outputs = torch.stack(outputs).transpose(0, 1).contiguous()
return outputs, alignments
def is_end_of_frames(output, eps=0.2):
return (output.data <= eps).all()
class Tacotron(nn.Module):
def __init__(self, n_vocab, embedding_dim=256, mel_dim=80, linear_dim=1025,
r=5, padding_idx=None, use_memory_mask=False):
super(Tacotron, self).__init__()
self.mel_dim = mel_dim
self.linear_dim = linear_dim
self.use_memory_mask = use_memory_mask
self.embedding = nn.Embedding(n_vocab, embedding_dim,
padding_idx=padding_idx)
# Trying smaller std
self.embedding.weight.data.normal_(0, 0.3)
self.encoder = Encoder(embedding_dim)
self.decoder = Decoder(mel_dim, r)
self.postnet = CBHG(mel_dim, K=8, projections=[256, mel_dim])
self.last_linear = nn.Linear(mel_dim * 2, linear_dim)
def forward(self, inputs, targets=None, input_lengths=None):
B = inputs.size(0)
inputs = self.embedding(inputs)
# (B, T', in_dim)
encoder_outputs = self.encoder(inputs, input_lengths)
if self.use_memory_mask:
memory_lengths = input_lengths
else:
memory_lengths = None
# (B, T', mel_dim*r)
mel_outputs, alignments = self.decoder(
encoder_outputs, targets, memory_lengths=memory_lengths)
# Post net processing below
# Reshape
# (B, T, mel_dim)
mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
linear_outputs = self.postnet(mel_outputs)
linear_outputs = self.last_linear(linear_outputs)
return mel_outputs, linear_outputs, alignments
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