# coding: utf-8 import torch import copy from torch import nn from TTS.layers.tacotron import Encoder, Decoder, PostCBHG from TTS.utils.generic_utils import sequence_mask from TTS.layers.gst_layers import GST class Tacotron(nn.Module): def __init__(self, num_chars, num_speakers, r=5, postnet_output_dim=1025, decoder_output_dim=80, memory_size=5, attn_type='original', attn_win=False, gst=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): super(Tacotron, self).__init__() self.r = r self.decoder_output_dim = decoder_output_dim self.postnet_output_dim = postnet_output_dim self.gst = gst self.num_speakers = num_speakers self.bidirectional_decoder = bidirectional_decoder decoder_dim = 512 if num_speakers > 1 else 256 encoder_dim = 512 if num_speakers > 1 else 256 proj_speaker_dim = 80 if num_speakers > 1 else 0 # embedding layer self.embedding = nn.Embedding(num_chars, 256) self.embedding.weight.data.normal_(0, 0.3) # boilerplate model self.encoder = Encoder(encoder_dim) self.decoder = Decoder(decoder_dim, 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) if self.bidirectional_decoder: self.decoder_backward = copy.deepcopy(self.decoder) 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, 256) self.speaker_embedding.weight.data.normal_(0, 0.3) self.speaker_project_mel = nn.Sequential( nn.Linear(256, 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) def _init_states(self): self.speaker_embeddings = None self.speaker_embeddings_projected = None def compute_speaker_embedding(self, speaker_ids): if hasattr(self, "speaker_embedding") and speaker_ids is None: raise RuntimeError( " [!] Model has speaker embedding layer but speaker_id is not provided" ) if hasattr(self, "speaker_embedding") and speaker_ids is not None: self.speaker_embeddings = self._compute_speaker_embedding( speaker_ids) self.speaker_embeddings_projected = self.speaker_project_mel( self.speaker_embeddings).squeeze(1) def compute_gst(self, inputs, mel_specs): gst_outputs = self.gst_layer(mel_specs) inputs = self._add_speaker_embedding(inputs, gst_outputs) return inputs def forward(self, characters, text_lengths, mel_specs, 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() mask = sequence_mask(text_lengths).to(characters.device) # B x T_in x embed_dim inputs = self.embedding(characters) # B x speaker_embed_dim 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_dim encoder_outputs = self.encoder(inputs) 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_dim x T_out # alignments: B x T_in x encoder_dim # stop_tokens: B x T_in decoder_outputs, alignments, stop_tokens = self.decoder( encoder_outputs, mel_specs, mask, self.speaker_embeddings_projected) # B x T_out x decoder_dim postnet_outputs = self.postnet(decoder_outputs) # B x T_out x posnet_dim postnet_outputs = self.last_linear(postnet_outputs) # B x T_out x decoder_dim decoder_outputs = decoder_outputs.transpose(1, 2).contiguous() if self.bidirectional_decoder: decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, 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() 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 def _backward_inference(self, mel_specs, encoder_outputs, mask): decoder_outputs_b, alignments_b, _ = self.decoder_backward( encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask, self.speaker_embeddings_projected) decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous() return decoder_outputs_b, alignments_b def _compute_speaker_embedding(self, speaker_ids): speaker_embeddings = self.speaker_embedding(speaker_ids) return speaker_embeddings.unsqueeze_(1) @staticmethod def _add_speaker_embedding(outputs, speaker_embeddings): speaker_embeddings_ = speaker_embeddings.expand( outputs.size(0), outputs.size(1), -1) outputs = outputs + speaker_embeddings_ return outputs @staticmethod def _concat_speaker_embedding(outputs, speaker_embeddings): speaker_embeddings_ = speaker_embeddings.expand( outputs.size(0), outputs.size(1), -1) outputs = torch.cat([outputs, speaker_embeddings_], dim=-1) return outputs