class Tacotron(nn.Module):
def __init__(self,
num_chars,
r=5,
linear_dim=1025,
mel_dim=80,
memory_size=5,
attn_win=False,
attn_norm="sigmoid",
prenet_type="original",
prenet_dropout=True,
forward_attn=False,
trans_agent=False,
location_attn=True,
separate_stopnet=True):
super(Tacotron, self).__init__()
self.r = r
self.mel_dim = mel_dim
self.linear_dim = linear_dim
self.embedding = nn.Embedding(num_chars, 256)
self.embedding.weight.data.normal_(0, 0.3)
self.encoder = Encoder(256)
self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, location_attn,
separate_stopnet)
self.postnet = PostCBHG(mel_dim)
self.last_linear = nn.Sequential(
nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),
nn.Sigmoid())
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)
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, stop_tokens
def inference(self, characters):
B = characters.size(0)
inputs = self.embedding(characters)
encoder_outputs = self.encoder(inputs)
mel_outputs, alignments, stop_tokens = self.decoder.inference(
encoder_outputs)
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, stop_tokens
class TacotronGST(nn.Module):
def __init__(self,
num_chars,
num_speakers,
r=5,
linear_dim=1025,
mel_dim=80,
memory_size=5,
attn_win=False,
attn_norm="sigmoid",
prenet_type="original",
prenet_dropout=True,
forward_attn=False,
trans_agent=False,
forward_attn_mask=False,
location_attn=True,
separate_stopnet=True):
super(TacotronGST, self).__init__()
self.r = r
self.mel_dim = mel_dim
self.linear_dim = linear_dim
self.embedding = nn.Embedding(num_chars, 256)
self.embedding.weight.data.normal_(0, 0.3)
if num_speakers > 1:
self.speaker_embedding = nn.Embedding(num_speakers, 256)
self.speaker_embedding.weight.data.normal_(0, 0.3)
self.encoder = Encoder(256)
self.gst = GST(num_mel=80,
num_heads=4,
num_style_tokens=10,
embedding_dim=256)
self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, separate_stopnet)
self.postnet = PostCBHG(mel_dim)
self.last_linear = nn.Sequential(
nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),
nn.Sigmoid())
def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
B = characters.size(0)
mask = sequence_mask(text_lengths).to(characters.device)
inputs = self.embedding(characters)
encoder_outputs = self.encoder(inputs)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
gst_outputs = self.gst(mel_specs)
gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1)
encoder_outputs = encoder_outputs + gst_outputs
mel_outputs, alignments, stop_tokens = self.decoder(
encoder_outputs, mel_specs, mask)
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, stop_tokens
def inference(self, characters, speaker_ids=None, style_mel=None):
B = characters.size(0)
inputs = self.embedding(characters)
encoder_outputs = self.encoder(inputs)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
if style_mel is not None:
gst_outputs = self.gst(style_mel)
gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1)
encoder_outputs = encoder_outputs + gst_outputs
mel_outputs, alignments, stop_tokens = self.decoder.inference(
encoder_outputs)
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, stop_tokens
def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
speaker_embeddings = self.speaker_embedding(speaker_ids)
speaker_embeddings.unsqueeze_(1)
speaker_embeddings = speaker_embeddings.expand(
encoder_outputs.size(0), encoder_outputs.size(1), -1)
encoder_outputs = encoder_outputs + speaker_embeddings
return encoder_outputs