def forward(self, padded_input, input_lengths):
"""
Args:
padded_input: N x T x D
input_lengths: N
Returns:
enc_output: N x T x H
"""
# Prepare masks
non_pad_mask = sequence_mask(input_lengths).unsqueeze(-1)
length = padded_input.size(1)
slf_attn_mask = get_attn_pad_mask(input_lengths, length)
# Forward
enc_output = self.dropout(
self.layer_norm_in(self.token_emb(padded_input)) +
self.positional_encoding(padded_input))
for enc_layer in self.layer_stack:
enc_output = enc_layer(
enc_output,
non_pad_mask=non_pad_mask,
slf_attn_mask=slf_attn_mask)
return enc_output
def forward(self,
encoder_padded_outputs,
encoder_input_lengths,
masking=True):
"""
Args:
padded_input: N x To
encoder_padded_outputs: N x Ti x H
Returns:
"""
# Get Deocder Input and Output
# Prepare masks
mask = sequence_mask(encoder_input_lengths) # B x T
logits = self.tgt_word_prj(encoder_padded_outputs)
if masking:
# B x T x V
mask = mask.view(mask.shape[0], mask.shape[1],
1).repeat(1, 1, self.dim_output)
logits *= mask
len_logits = encoder_input_lengths
return logits, len_logits
def forward(self, x, target, length):
target.requires_grad = False
mask = sequence_mask(sequence_length=length,
maxlen=target.size(1)).unsqueeze(2).float()
mask = mask.expand_as(x)
loss = F.l1_loss(x * mask, target * mask, reduction='sum')
loss = loss / mask.sum()
return loss
def forward(self, characters, text_lengths, mel_specs):
B = characters.size(0)
mask = sequence_mask(text_lengths).to(characters.device)
inputs = self.embedding(characters)
encoder_outputs = self.encoder(inputs)
mel_outputs, alignments, stop_tokens = self.decoder(
encoder_outputs, mel_specs, mask)
# 複数フレームがまとまっているので元に戻す
mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
# メルスペクトログラムを線形スペクトログラムに変換する
linear_outputs = self.postnet(mel_outputs)
linear_outputs = self.last_linear(linear_outputs)
return mel_outputs, linear_outputs, alignments, stop_tokens
def forward(self, padded_input, input_lengths):
"""
Args:
padded_input: N x T x D
input_lengths: N
Returns:
enc_output: N x T x H
"""
x, input_lengths = self.conv(padded_input, input_lengths)
x = self.dropout(x)
alphas = self.linear(x).squeeze(-1)
alphas = torch.sigmoid(alphas)
pad_mask = sequence_mask(input_lengths)
return alphas * pad_mask
def forward(self, encoder_padded_outputs, encoder_input_lengths):
"""
Args:
padded_input: N x To
encoder_padded_outputs: N x Ti x H
Returns:
"""
# Get Deocder Input and Output
# Prepare masks
mask = sequence_mask(encoder_input_lengths) # B x T
# B x T x V
mask = mask.view(mask.shape[0], mask.shape[1],
1).repeat(1, 1, self.dim_output)
# before softmax
logits = self.tgt_word_prj(encoder_padded_outputs) * mask
return logits