# coding: utf-8 import torch from torch import nn from TTS.tts.layers.gst_layers import GST from TTS.tts.layers.tacotron import Decoder, Encoder, PostCBHG from TTS.tts.models.tacotron_abstract import TacotronAbstract class Tacotron(TacotronAbstract): """Tacotron as in https://arxiv.org/abs/1703.10135 It's an autoregressive encoder-attention-decoder-postnet architecture. Args: num_chars (int): number of input characters to define the size of embedding layer. num_speakers (int): number of speakers in the dataset. >1 enables multi-speaker training and model learns speaker embeddings. r (int): initial model reduction rate. postnet_output_dim (int, optional): postnet output channels. Defaults to 80. decoder_output_dim (int, optional): decoder output channels. Defaults to 80. attn_type (str, optional): attention type. Check ```TTS.tts.layers.attentions.init_attn```. Defaults to 'original'. attn_win (bool, optional): enable/disable attention windowing. It especially useful at inference to keep attention alignment diagonal. Defaults to False. attn_norm (str, optional): Attention normalization method. "sigmoid" or "softmax". Defaults to "softmax". prenet_type (str, optional): prenet type for the decoder. Defaults to "original". prenet_dropout (bool, optional): prenet dropout rate. Defaults to True. forward_attn (bool, optional): enable/disable forward attention. It is only valid if ```attn_type``` is ```original```. Defaults to False. trans_agent (bool, optional): enable/disable transition agent in forward attention. Defaults to False. forward_attn_mask (bool, optional): enable/disable extra masking over forward attention. Defaults to False. location_attn (bool, optional): enable/disable location sensitive attention. It is only valid if ```attn_type``` is ```original```. Defaults to True. attn_K (int, optional): Number of attention heads for GMM attention. Defaults to 5. separate_stopnet (bool, optional): enable/disable separate stopnet training without only gradient flow from stopnet to the rest of the model. Defaults to True. bidirectional_decoder (bool, optional): enable/disable bidirectional decoding. Defaults to False. double_decoder_consistency (bool, optional): enable/disable double decoder consistency. Defaults to False. ddc_r (int, optional): reduction rate for the coarse decoder of double decoder consistency. Defaults to None. encoder_in_features (int, optional): input channels for the encoder. Defaults to 512. decoder_in_features (int, optional): input channels for the decoder. Defaults to 512. speaker_embedding_dim (int, optional): external speaker conditioning vector channels. Defaults to None. gst (bool, optional): enable/disable global style token learning. Defaults to False. gst_embedding_dim (int, optional): size of channels for GST vectors. Defaults to 512. gst_num_heads (int, optional): number of attention heads for GST. Defaults to 4. gst_style_tokens (int, optional): number of GST tokens. Defaults to 10. gst_use_speaker_embedding (bool, optional): enable/disable inputing speaker embedding to GST. Defaults to False. memory_size (int, optional): size of the history queue fed to the prenet. Model feeds the last ```memory_size``` output frames to the prenet. """ 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, encoder_in_features=256, decoder_in_features=256, speaker_embedding_dim=None, gst=False, gst_embedding_dim=256, gst_num_heads=4, gst_style_tokens=10, memory_size=5, gst_use_speaker_embedding=False): 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, encoder_in_features, decoder_in_features, speaker_embedding_dim, gst, gst_embedding_dim, gst_num_heads, gst_style_tokens, gst_use_speaker_embedding) # speaker embedding layers if self.num_speakers > 1: if not self.embeddings_per_sample: speaker_embedding_dim = 256 self.speaker_embedding = nn.Embedding(self.num_speakers, speaker_embedding_dim) self.speaker_embedding.weight.data.normal_(0, 0.3) # speaker and gst embeddings is concat in decoder input if self.num_speakers > 1: self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim # embedding layer self.embedding = nn.Embedding(num_chars, 256, padding_idx=0) self.embedding.weight.data.normal_(0, 0.3) # base model layers self.encoder = Encoder(self.encoder_in_features) self.decoder = Decoder(self.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) self.postnet = PostCBHG(decoder_output_dim) self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, postnet_output_dim) # global style token layers if self.gst: self.gst_layer = GST(num_mel=80, num_heads=gst_num_heads, num_style_tokens=gst_style_tokens, gst_embedding_dim=self.gst_embedding_dim, speaker_embedding_dim=speaker_embedding_dim if self.embeddings_per_sample and self.gst_use_speaker_embedding else None) # backward pass decoder if self.bidirectional_decoder: self._init_backward_decoder() # setup DDC if self.double_decoder_consistency: self.coarse_decoder = Decoder( self.decoder_in_features, decoder_output_dim, ddc_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) def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None, speaker_embeddings=None): """ Shapes: characters: [B, T_in] text_lengths: [B] mel_specs: [B, T_out, C] mel_lengths: [B] speaker_ids: [B, 1] speaker_embeddings: [B, C] """ input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths) # B x T_in x embed_dim inputs = self.embedding(characters) # 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, speaker_embeddings if self.gst_use_speaker_embedding else None) # speaker embedding if self.num_speakers > 1: if not self.embeddings_per_sample: # B x 1 x speaker_embed_dim speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None] else: # B x 1 x speaker_embed_dim speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1) encoder_outputs = self._concat_speaker_embedding(encoder_outputs, 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) # 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, speaker_embeddings=None): inputs = self.embedding(characters) encoder_outputs = self.encoder(inputs) if self.gst: # B x gst_dim encoder_outputs = self.compute_gst(encoder_outputs, style_mel, speaker_embeddings if self.gst_use_speaker_embedding else None) if self.num_speakers > 1: if not self.embeddings_per_sample: # B x 1 x speaker_embed_dim speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None] else: # B x 1 x speaker_embed_dim speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1) encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings) decoder_outputs, alignments, stop_tokens = self.decoder.inference( encoder_outputs) 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