# coding: utf-8 import torch from torch import nn from TTS.layers.gst_layers import GST from TTS.layers.tacotron import Decoder, Encoder, PostCBHG from TTS.models.tacotron_abstract import TacotronAbstract class Tacotron(TacotronAbstract): def __init__(self, num_chars, num_speakers, r=5, postnet_output_dim=1025, decoder_output_dim=80, attn_type='original', 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, attn_K=5, separate_stopnet=True, bidirectional_decoder=False, double_decoder_consistency=False, ddc_r=None, gst=False, memory_size=5): super(Tacotron, self).__init__(num_chars, num_speakers, r, postnet_output_dim, decoder_output_dim, attn_type, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, forward_attn_mask, location_attn, attn_K, separate_stopnet, bidirectional_decoder, double_decoder_consistency, ddc_r, gst) decoder_in_features = 512 if num_speakers > 1 else 256 encoder_in_features = 512 if num_speakers > 1 else 256 speaker_embedding_dim = 256 proj_speaker_dim = 80 if num_speakers > 1 else 0 # base model layers self.embedding = nn.Embedding(num_chars, 256, padding_idx=0) self.embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(encoder_in_features) self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, forward_attn_mask, location_attn, attn_K, separate_stopnet, proj_speaker_dim) self.postnet = PostCBHG(decoder_output_dim) self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, postnet_output_dim) # speaker embedding layers if num_speakers > 1: self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim) self.speaker_embedding.weight.data.normal_(0, 0.3) self.speaker_project_mel = nn.Sequential( nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh()) self.speaker_embeddings = None self.speaker_embeddings_projected = None # global style token layers if self.gst: gst_embedding_dim = 256 self.gst_layer = GST(num_mel=80, num_heads=4, num_style_tokens=10, embedding_dim=gst_embedding_dim) # backward pass decoder if self.bidirectional_decoder: self._init_backward_decoder() # setup DDC if self.double_decoder_consistency: self._init_coarse_decoder() def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None): """ Shapes: - characters: B x T_in - text_lengths: B - mel_specs: B x T_out x D - speaker_ids: B x 1 """ self._init_states() input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths) # B x T_in x embed_dim inputs = self.embedding(characters) # B x speaker_embed_dim if speaker_ids is not None: self.compute_speaker_embedding(speaker_ids) if self.num_speakers > 1: # B x T_in x embed_dim + speaker_embed_dim inputs = self._concat_speaker_embedding(inputs, self.speaker_embeddings) # B x T_in x encoder_in_features encoder_outputs = self.encoder(inputs) # sequence masking encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs) # global style token if self.gst: # B x gst_dim encoder_outputs = self.compute_gst(encoder_outputs, mel_specs) if self.num_speakers > 1: encoder_outputs = self._concat_speaker_embedding( encoder_outputs, self.speaker_embeddings) # decoder_outputs: B x decoder_in_features x T_out # alignments: B x T_in x encoder_in_features # stop_tokens: B x T_in decoder_outputs, alignments, stop_tokens = self.decoder( encoder_outputs, mel_specs, input_mask, self.speaker_embeddings_projected) # sequence masking if output_mask is not None: decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs) # B x T_out x decoder_in_features postnet_outputs = self.postnet(decoder_outputs) # sequence masking if output_mask is not None: postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs) # B x T_out x posnet_dim postnet_outputs = self.last_linear(postnet_outputs) # B x T_out x decoder_in_features decoder_outputs = decoder_outputs.transpose(1, 2).contiguous() if self.bidirectional_decoder: decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask) return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward if self.double_decoder_consistency: decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask) return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward return decoder_outputs, postnet_outputs, alignments, stop_tokens @torch.no_grad() def inference(self, characters, speaker_ids=None, style_mel=None): inputs = self.embedding(characters) self._init_states() if speaker_ids is not None: self.compute_speaker_embedding(speaker_ids) if self.num_speakers > 1: inputs = self._concat_speaker_embedding(inputs, self.speaker_embeddings) encoder_outputs = self.encoder(inputs) if self.gst and style_mel is not None: encoder_outputs = self.compute_gst(encoder_outputs, style_mel) if self.num_speakers > 1: encoder_outputs = self._concat_speaker_embedding( encoder_outputs, self.speaker_embeddings) decoder_outputs, alignments, stop_tokens = self.decoder.inference( encoder_outputs, self.speaker_embeddings_projected) postnet_outputs = self.postnet(decoder_outputs) postnet_outputs = self.last_linear(postnet_outputs) decoder_outputs = decoder_outputs.transpose(1, 2) return decoder_outputs, postnet_outputs, alignments, stop_tokens