class Tacotron3(nn.Module):
def __init__(self, activate_encoder):
super(Tacotron3, self).__init__()
if activate_encoder:
self.encoder = Encoder()
else:
self.encoder = SimpleEncoder()
self.decoder = Decoder(activate_encoder)
self.postnet = Postnet()
def forward(self, inputs):
mel_source, mel_lengths, embedding, mel_target = inputs
mel_lengths = mel_lengths.data
encoder_outputs = self.encoder(mel_source, embedding)
mel_outputs, alignments = self.decoder(encoder_outputs, mel_target,
mel_lengths)
end_padding_ind = get_reverse_mask(mel_lengths)
mel_outputs_postnet = self.postnet(mel_outputs)
# FIX This sollution is ugleh - is there a better way?
mel_outputs = mel_outputs.permute(0, 2, 1)
mel_outputs[end_padding_ind, :] = 0
mel_outputs = mel_outputs.permute(0, 2, 1)
mel_outputs_postnet = mel_outputs_postnet.permute(0, 2, 1)
mel_outputs_postnet[end_padding_ind, :] = 0
mel_outputs_postnet = mel_outputs_postnet.permute(0, 2, 1)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return mel_outputs, mel_outputs_postnet, alignments
def inference(self, inputs):
mel_source, mel_lengths, embedding, _ = inputs
encoder_outputs = self.encoder(mel_source, embedding)
mel_outputs, _ = self.decoder.inference(encoder_outputs)
end_padding_ind = get_reverse_mask(mel_lengths)
mel_outputs_postnet = self.postnet(mel_outputs)
# FIX This sollution is ugleh - is there a better way?
mel_outputs = mel_outputs.permute(0, 2, 1)
mel_outputs[end_padding_ind, :] = 0
mel_outputs = mel_outputs.permute(0, 2, 1)
mel_outputs_postnet = mel_outputs_postnet.permute(0, 2, 1)
mel_outputs_postnet[end_padding_ind, :] = 0
mel_outputs_postnet = mel_outputs_postnet.permute(0, 2, 1)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return mel_outputs_postnet
class Tacotron2(nn.Module):
def __init__(self):
super(Tacotron2, self).__init__()
self.embedding = nn.Embedding(hps.n_symbols,
hps.character_embedding_dim)
std = sqrt(2.0 / (hps.n_symbols + hps.character_embedding_dim))
val = sqrt(3.0) * std
self.embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder()
self.decoder = Decoder()
self.postnet = PostNet()
def parse_outputs(self, mel_outputs, mel_outputs_postnet, gate_outputs,
output_lengths):
mask = ~get_mask_from_lengths(output_lengths, pad=True)
mask = mask.expand(80, mask.size(0), mask.size(1))
mask = mask.permute(1, 0, 2)
# mask : (B, 80, Frames)
mel_outputs.data.masked_fill_(mask, 0.0)
mel_outputs_postnet.data.masked_fill_(mask, 0.0)
# gate outputs : (B, Frames // 3)
slice_mask = torch.arange(0, mask.size(2), 1)
gate_outputs.data.masked_fill_(mask[:, 0, slice_mask], 1e3)
return mel_outputs, mel_outputs_postnet, gate_outputs
def forward(self, inputs):
text_inputs, input_lengths, mel_targets, output_lengths = inputs
# print('input text size : ', text_inputs.size())
character_embedding = self.embedding(text_inputs)
# (B, Seq_len, 512)
# (B, 512, seq_len)
# print('character embedding size : ', character_embedding.size())
character_embedding = character_embedding.transpose(1, 2)
# print('character embedding size : ', character_embedding.size())
encoder_outputs = self.encoder(character_embedding, input_lengths)
# print('encoder output size : ', encoder_outputs.size())
mel_outputs, alignments, gate_outputs = self.decoder(
encoder_outputs, mel_targets, input_lengths)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
mel_outputs, mel_outputs_postnet, gate_outputs = self.parse_outputs(
mel_outputs, mel_outputs_postnet, gate_outputs, output_lengths)
return mel_outputs, mel_outputs_postnet, gate_outputs, alignments
def inference(self, inputs):
embedded_inputs = self.embedding(inputs).transpose(1, 2)
encoder_outputs = self.encoder.inference(embedded_inputs)
mel_outputs, alignments, gate_outputs = self.decoder.inference(
encoder_outputs)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return mel_outputs, mel_outputs_postnet, alignments
