from math import sqrt
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
class Linear(nn.Module):
def __init__(self,
in_features,
out_features,
bias=True,
init_gain='linear'):
super(Linear, self).__init__()
self.linear_layer = torch.nn.Linear(
in_features, out_features, bias=bias)
self._init_w(init_gain)
def _init_w(self, init_gain):
torch.nn.init.xavier_uniform_(
self.linear_layer.weight,
gain=torch.nn.init.calculate_gain(init_gain))
def forward(self, x):
return self.linear_layer(x)
class LinearBN(nn.Module):
def __init__(self,
in_features,
out_features,
bias=True,
init_gain='linear'):
super(LinearBN, self).__init__()
self.linear_layer = torch.nn.Linear(
in_features, out_features, bias=bias)
self.bn = nn.BatchNorm1d(out_features)
self._init_w(init_gain)
def _init_w(self, init_gain):
torch.nn.init.xavier_uniform_(
self.linear_layer.weight,
gain=torch.nn.init.calculate_gain(init_gain))
def forward(self, x):
out = self.linear_layer(x)
if len(out.shape)==3:
out = out.permute(1, 2, 0)
out = self.bn(out)
if len(out.shape) == 3:
out = out.permute(2, 0, 1)
return out
class Prenet(nn.Module):
def __init__(self, in_features, prenet_type, out_features=[256, 256]):
super(Prenet, self).__init__()
self.prenet_type = prenet_type
in_features = [in_features] + out_features[:-1]
if prenet_type == "bn":
self.layers = nn.ModuleList([
LinearBN(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_features, out_features)
])
elif prenet_type == "original":
self.layers = nn.ModuleList(
[Linear(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_features, out_features)
])
def forward(self, x):
for linear in self.layers:
if self.prenet_type == "original":
x = F.dropout(F.relu(linear(x)), p=0.5, training=self.training)
elif self.prenet_type == "bn":
x = F.relu(linear(x))
return x
class ConvBNBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, nonlinear=None):
super(ConvBNBlock, self).__init__()
assert (kernel_size - 1) % 2 == 0
padding = (kernel_size - 1) // 2
conv1d = nn.Conv1d(
in_channels, out_channels, kernel_size, padding=padding)
norm = nn.BatchNorm1d(out_channels)
dropout = nn.Dropout(p=0.5)
if nonlinear == 'relu':
self.net = nn.Sequential(conv1d, norm, nn.ReLU(), dropout)
elif nonlinear == 'tanh':
self.net = nn.Sequential(conv1d, norm, nn.Tanh(), dropout)
else:
self.net = nn.Sequential(conv1d, norm, dropout)
def forward(self, x):
output = self.net(x)
return output
class LocationLayer(nn.Module):
def __init__(self, attention_n_filters, attention_kernel_size,
attention_dim):
super(LocationLayer, self).__init__()
self.location_conv = nn.Conv1d(
in_channels=2,
out_channels=attention_n_filters,
kernel_size=31,
stride=1,
padding=(31 - 1) // 2,
bias=False)
self.location_dense = Linear(
attention_n_filters, attention_dim, bias=False, init_gain='tanh')
def forward(self, attention_cat):
processed_attention = self.location_conv(attention_cat)
processed_attention = self.location_dense(
processed_attention.transpose(1, 2))
return processed_attention
class Attention(nn.Module):
def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
attention_location_n_filters, attention_location_kernel_size,
windowing, norm, forward_attn, trans_agent):
super(Attention, self).__init__()
self.query_layer = Linear(
attention_rnn_dim, attention_dim, bias=False, init_gain='tanh')
self.inputs_layer = Linear(
embedding_dim, attention_dim, bias=False, init_gain='tanh')
self.v = Linear(attention_dim, 1, bias=True)
if trans_agent:
self.ta = nn.Linear(attention_dim + embedding_dim, 1, bias=True)
self.location_layer = LocationLayer(attention_location_n_filters,
attention_location_kernel_size,
attention_dim)
self._mask_value = -float("inf")
self.windowing = windowing
self.win_idx = None
self.norm = norm
self.forward_attn = forward_attn
self.trans_agent = trans_agent
def init_win_idx(self):
self.win_idx = -1
self.win_back = 2
self.win_front = 6
def init_forward_attn_state(self, inputs):
"""
Init forward attention states
"""
B = inputs.shape[0]
T = inputs.shape[1]
self.alpha = torch.cat([torch.ones([B, 1]), torch.zeros([B, T])[:, :-1] + 1e-7 ], dim=1).to(inputs.device)
self.u = (0.5 * torch.ones([B, 1])).to(inputs.device)
def get_attention(self, query, processed_inputs, attention_cat):
processed_query = self.query_layer(query.unsqueeze(1))
processed_attention_weights = self.location_layer(attention_cat)
energies = self.v(
torch.tanh(processed_query + processed_attention_weights +
processed_inputs))
energies = energies.squeeze(-1)
return energies, processed_query
def apply_windowing(self, attention, inputs):
back_win = self.win_idx - self.win_back
front_win = self.win_idx + self.win_front
if back_win > 0:
attention[:, :back_win] = -float("inf")
if front_win < inputs.shape[1]:
attention[:, front_win:] = -float("inf")
# this is a trick to solve a special problem.
# but it does not hurt.
if self.win_idx == -1:
attention[:, 0] = attention.max()
# Update the window
self.win_idx = torch.argmax(attention, 1).long()[0].item()
return attention
def apply_forward_attention(self, inputs, alignment, processed_query):
# forward attention
prev_alpha = F.pad(self.alpha[:, :-1].clone(), (1, 0, 0, 0)).to(inputs.device)
alpha = (((1-self.u) * self.alpha.clone().to(inputs.device) + self.u * prev_alpha) + 1e-8) * alignment
self.alpha = alpha / alpha.sum(dim=1).unsqueeze(1)
# compute context
context = torch.bmm(self.alpha.unsqueeze(1), inputs)
context = context.squeeze(1)
# compute transition agent
if self.trans_agent:
ta_input = torch.cat([context, processed_query.squeeze(1)], dim=-1)
self.u = torch.sigmoid(self.ta(ta_input))
return context, self.alpha, alignment
def forward(self, attention_hidden_state, inputs, processed_inputs,
attention_cat, mask):
attention, processed_query = self.get_attention(
attention_hidden_state, processed_inputs, attention_cat)
# apply masking
if mask is not None:
attention.data.masked_fill_(1 - mask, self._mask_value)
# apply windowing - only in eval mode
if not self.training and self.windowing:
attention = self.apply_windowing(attention, inputs)
# normalize attention values
if self.norm == "softmax":
alignment = torch.softmax(attention, dim=-1)
elif self.norm == "sigmoid":
alignment = torch.sigmoid(attention) / torch.sigmoid(
attention).sum(dim=1).unsqueeze(1)
else:
raise RuntimeError("Unknown value for attention norm type")
# apply forward attention if enabled
if self.forward_attn:
return self.apply_forward_attention(inputs, alignment, processed_query)
else:
context = torch.bmm(alignment.unsqueeze(1), inputs)
context = context.squeeze(1)
return context, alignment, alignment
class Postnet(nn.Module):
def __init__(self, mel_dim, num_convs=5):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
ConvBNBlock(mel_dim, 512, kernel_size=5, nonlinear='tanh'))
for i in range(1, num_convs - 1):
self.convolutions.append(
ConvBNBlock(512, 512, kernel_size=5, nonlinear='tanh'))
self.convolutions.append(
ConvBNBlock(512, mel_dim, kernel_size=5, nonlinear=None))
def forward(self, x):
for layer in self.convolutions:
x = layer(x)
return x
class Encoder(nn.Module):
def __init__(self, in_features=512):
super(Encoder, self).__init__()
convolutions = []
for _ in range(3):
convolutions.append(
ConvBNBlock(in_features, in_features, 5, 'relu'))
self.convolutions = nn.Sequential(*convolutions)
self.lstm = nn.LSTM(
in_features,
int(in_features / 2),
num_layers=1,
batch_first=True,
bidirectional=True)
self.rnn_state = None
def forward(self, x, input_lengths):
x = self.convolutions(x)
x = x.transpose(1, 2)
input_lengths = input_lengths.cpu().numpy()
x = nn.utils.rnn.pack_padded_sequence(
x, input_lengths, batch_first=True)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
outputs, _ = nn.utils.rnn.pad_packed_sequence(
outputs,
batch_first=True,
)
return outputs
def inference(self, x):
x = self.convolutions(x)
x = x.transpose(1, 2)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
return outputs
def inference_truncated(self, x):
"""
Preserve encoder state for continuous inference
"""
x = self.convolutions(x)
x = x.transpose(1, 2)
self.lstm.flatten_parameters()
outputs, self.rnn_state = self.lstm(x, self.rnn_state)
return outputs
# adapted from https://github.com/NVIDIA/tacotron2/
class Decoder(nn.Module):
def __init__(self, in_features, inputs_dim, r, attn_win, attn_norm, prenet_type, forward_attn, trans_agent):
super(Decoder, self).__init__()
self.mel_channels = inputs_dim
self.r = r
self.encoder_embedding_dim = in_features
self.attention_rnn_dim = 1024
self.decoder_rnn_dim = 1024
self.prenet_dim = 256
self.max_decoder_steps = 1000
self.gate_threshold = 0.5
self.p_attention_dropout = 0.1
self.p_decoder_dropout = 0.1
self.prenet = Prenet(self.mel_channels * r, prenet_type,
[self.prenet_dim, self.prenet_dim])
self.attention_rnn = nn.LSTMCell(self.prenet_dim + in_features,
self.attention_rnn_dim)
self.attention_layer = Attention(self.attention_rnn_dim, in_features,
128, 32, 31, attn_win, attn_norm, forward_attn, trans_agent)
self.decoder_rnn = nn.LSTMCell(self.attention_rnn_dim + in_features,
self.decoder_rnn_dim, 1)
self.linear_projection = Linear(self.decoder_rnn_dim + in_features,
self.mel_channels * r)
self.stopnet = nn.Sequential(
nn.Dropout(0.1),
Linear(self.decoder_rnn_dim + self.mel_channels * r,
1,
bias=True,
init_gain='sigmoid'))
self.attention_rnn_init = nn.Embedding(1, self.attention_rnn_dim)
self.go_frame_init = nn.Embedding(1, self.mel_channels * r)
self.decoder_rnn_inits = nn.Embedding(1, self.decoder_rnn_dim)
self.memory_truncated = None
def get_go_frame(self, inputs):
B = inputs.size(0)
memory = self.go_frame_init(inputs.data.new_zeros(B).long())
return memory
def _init_states(self, inputs, mask, keep_states=False):
B = inputs.size(0)
T = inputs.size(1)
if not keep_states:
self.attention_hidden = self.attention_rnn_init(
inputs.data.new_zeros(B).long())
self.attention_cell = Variable(
inputs.data.new(B, self.attention_rnn_dim).zero_())
self.decoder_hidden = self.decoder_rnn_inits(
inputs.data.new_zeros(B).long())
self.decoder_cell = Variable(
inputs.data.new(B, self.decoder_rnn_dim).zero_())
self.context = Variable(
inputs.data.new(B, self.encoder_embedding_dim).zero_())
self.attention_weights = Variable(inputs.data.new(B, T).zero_())
self.attention_weights_cum = Variable(inputs.data.new(B, T).zero_())
self.inputs = inputs
self.processed_inputs = self.attention_layer.inputs_layer(inputs)
self.mask = mask
def _reshape_memory(self, memories):
memories = memories.view(
memories.size(0), int(memories.size(1) / self.r), -1)
memories = memories.transpose(0, 1)
return memories
def _parse_outputs(self, outputs, stop_tokens, alignments):
alignments = torch.stack(alignments).transpose(0, 1)
stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
stop_tokens = stop_tokens.contiguous()
outputs = torch.stack(outputs).transpose(0, 1).contiguous()
outputs = outputs.view(
outputs.size(0), -1, self.mel_channels)
outputs = outputs.transpose(1, 2)
return outputs, stop_tokens, alignments
def decode(self, memory):
cell_input = torch.cat((memory, self.context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(
self.attention_hidden, self.p_attention_dropout, self.training)
self.attention_cell = F.dropout(
self.attention_cell, self.p_attention_dropout, self.training)
attention_cat = torch.cat((self.attention_weights.unsqueeze(1),
self.attention_weights_cum.unsqueeze(1)),
dim=1)
self.context, self.attention_weights, alignments = self.attention_layer(
self.attention_hidden, self.inputs, self.processed_inputs,
attention_cat, self.mask)
self.attention_weights_cum += alignments
memory = torch.cat(
(self.attention_hidden, self.context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
memory, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(self.decoder_hidden,
self.p_decoder_dropout, self.training)
self.decoder_cell = F.dropout(self.decoder_cell,
self.p_decoder_dropout, self.training)
decoder_hidden_context = torch.cat(
(self.decoder_hidden, self.context), dim=1)
decoder_output = self.linear_projection(
decoder_hidden_context)
stopnet_input = torch.cat((self.decoder_hidden, decoder_output), dim=1)
gate_prediction = self.stopnet(stopnet_input)
return decoder_output, gate_prediction, self.attention_weights
def forward(self, inputs, memories, mask):
memory = self.get_go_frame(inputs).unsqueeze(0)
memories = self._reshape_memory(memories)
memories = torch.cat((memory, memories), dim=0)
memories = self.prenet(memories)
self._init_states(inputs, mask=mask)
if self.attention_layer.forward_attn:
self.attention_layer.init_forward_attn_state(inputs)
outputs, stop_tokens, alignments = [], [], []
while len(outputs) < memories.size(0) - 1:
memory = memories[len(outputs)]
mel_output, stop_token, attention_weights = self.decode(
memory)
outputs += [mel_output.squeeze(1)]
stop_tokens += [stop_token.squeeze(1)]
alignments += [attention_weights]
outputs, stop_tokens, alignments = self._parse_outputs(
outputs, stop_tokens, alignments)
return outputs, stop_tokens, alignments
def inference(self, inputs):
memory = self.get_go_frame(inputs)
self._init_states(inputs, mask=None)
self.attention_layer.init_win_idx()
if self.attention_layer.forward_attn:
self.attention_layer.init_forward_attn_state(inputs)
outputs, stop_tokens, alignments, t = [], [], [], 0
stop_flags = [False, False, False]
stop_count = 0
while True:
memory = self.prenet(memory)
mel_output, stop_token, alignment = self.decode(memory)
stop_token = torch.sigmoid(stop_token.data)
outputs += [mel_output.squeeze(1)]
stop_tokens += [stop_token]
alignments += [alignment]
stop_flags[0] = stop_flags[0] or stop_token > 0.5
stop_flags[1] = stop_flags[1] or (alignment[0, -2:].sum() > 0.8 and t > inputs.shape[1])
stop_flags[2] = t > inputs.shape[1] * 2
if all(stop_flags):
stop_count += 1
if stop_count > 2:
break
elif len(outputs) == self.max_decoder_steps:
print(" | > Decoder stopped with 'max_decoder_steps")
break
memory = mel_output
t += 1
outputs, stop_tokens, alignments = self._parse_outputs(
outputs, stop_tokens, alignments)
return outputs, stop_tokens, alignments
def inference_truncated(self, inputs):
"""
Preserve decoder states for continuous inference
"""
if self.memory_truncated is None:
self.memory_truncated = self.get_go_frame(inputs)
self._init_states(inputs, mask=None, keep_states=False)
else:
self._init_states(inputs, mask=None, keep_states=True)
self.attention_layer.init_win_idx()
if self.attention_layer.forward_attn:
self.attention_layer.init_forward_attn_state(inputs)
outputs, stop_tokens, alignments, t = [], [], [], 0
stop_flags = [False, False, False]
stop_count = 0
while True:
memory = self.prenet(self.memory_truncated)
mel_output, stop_token, alignment = self.decode(memory)
stop_token = torch.sigmoid(stop_token.data)
outputs += [mel_output.squeeze(1)]
stop_tokens += [stop_token]
alignments += [alignment]
stop_flags[0] = stop_flags[0] or stop_token > 0.5
stop_flags[1] = stop_flags[1] or (alignment[0, -2:].sum() > 0.5 and t > inputs.shape[1])
stop_flags[2] = t > inputs.shape[1] * 2
if all(stop_flags):
stop_count += 1
if stop_count > 2:
break
elif len(outputs) == self.max_decoder_steps:
print(" | > Decoder stopped with 'max_decoder_steps")
break
self.memory_truncated = mel_output
t += 1
outputs, stop_tokens, alignments = self._parse_outputs(
outputs, stop_tokens, alignments)
return outputs, stop_tokens, alignments
def inference_step(self, inputs, t, memory=None):
"""
For debug purposes
"""
if t == 0:
memory = self.get_go_frame(inputs)
self._init_states(inputs, mask=None)
memory = self.prenet(memory)
mel_output, stop_token, alignment = self.decode(memory)
stop_token = torch.sigmoid(stop_token.data)
memory = mel_output
return mel_output, stop_token, alignment