# coding: utf-8 import torch from torch import nn from TTS.tts.utils.measures import alignment_diagonal_score from TTS.tts.utils.visual import plot_alignment, plot_spectrogram from TTS.tts.layers.tacotron.gst_layers import GST from TTS.tts.layers.tacotron.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. prenet_dropout_at_inference (bool, optional): use dropout at inference time. This leads to a better quality for some models. Defaults to False. 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. use_gst (bool, optional): enable/disable Global style token module. gst (Coqpit, optional): Coqpit to initialize the GST module. If `None`, GST is disabled. Defaults to None. memory_size (int, optional): size of the history queue fed to the prenet. Model feeds the last ```memory_size``` output frames to the prenet. gradual_trainin (List): Gradual training schedule. If None or `[]`, no gradual training is used. Defaults to `[]`. """ 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, prenet_dropout_at_inference=False, 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, use_gst=False, gst=None, memory_size=5, gradual_training=[] ): super().__init__( num_chars, num_speakers, r, postnet_output_dim, decoder_output_dim, attn_type, attn_win, attn_norm, prenet_type, prenet_dropout, prenet_dropout_at_inference, 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, use_gst, gst, gradual_training ) # 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) # setup prenet dropout self.decoder.prenet.dropout_at_inference = prenet_dropout_at_inference # global style token layers if self.gst and self.use_gst: self.gst_layer = GST( num_mel=decoder_output_dim, speaker_embedding_dim=speaker_embedding_dim, num_heads=gst.gst_num_heads, num_style_tokens=gst.gst_num_style_tokens, gst_embedding_dim=gst.gst_embedding_dim, ) # 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, text, text_lengths, mel_specs=None, mel_lengths=None, cond_input=None): """ Shapes: text: [B, T_in] text_lengths: [B] mel_specs: [B, T_out, C] mel_lengths: [B] cond_input: 'speaker_ids': [B, 1] and 'x_vectors':[B, C] """ outputs = { 'alignments_backward': None, 'decoder_outputs_backward': None } input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths) # B x T_in x embed_dim inputs = self.embedding(text) # 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 and self.use_gst: # B x gst_dim encoder_outputs = self.compute_gst(encoder_outputs, mel_specs, cond_input['x_vectors']) # 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(cond_input['speaker_ids'])[:, None] else: # B x 1 x speaker_embed_dim speaker_embeddings = torch.unsqueeze(cond_input['x_vectors'], 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) outputs['alignments_backward'] = alignments_backward outputs['decoder_outputs_backward'] = decoder_outputs_backward if self.double_decoder_consistency: decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass( mel_specs, encoder_outputs, alignments, input_mask) outputs['alignments_backward'] = alignments_backward outputs['decoder_outputs_backward'] = decoder_outputs_backward outputs.update({ 'model_outputs': postnet_outputs, 'decoder_outputs': decoder_outputs, 'alignments': alignments, 'stop_tokens': stop_tokens }) return outputs @torch.no_grad() def inference(self, text_input, cond_input=None): inputs = self.embedding(text_input) encoder_outputs = self.encoder(inputs) if self.gst and self.use_gst: # B x gst_dim encoder_outputs = self.compute_gst(encoder_outputs, cond_input['style_mel'], cond_input['x_vectors']) if self.num_speakers > 1: if not self.embeddings_per_sample: # B x 1 x speaker_embed_dim speaker_embeddings = self.speaker_embedding(cond_input['speaker_ids'])[:, None] else: # B x 1 x speaker_embed_dim speaker_embeddings = torch.unsqueeze(cond_input['x_vectors'], 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) outputs = { 'model_outputs': postnet_outputs, 'decoder_outputs': decoder_outputs, 'alignments': alignments, 'stop_tokens': stop_tokens } return outputs def train_step(self, batch, criterion): """Perform a single training step by fetching the right set if samples from the batch. Args: batch ([type]): [description] criterion ([type]): [description] """ text_input = batch['text_input'] text_lengths = batch['text_lengths'] mel_input = batch['mel_input'] mel_lengths = batch['mel_lengths'] linear_input = batch['linear_input'] stop_targets = batch['stop_targets'] speaker_ids = batch['speaker_ids'] x_vectors = batch['x_vectors'] # forward pass model outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, cond_input={ 'speaker_ids': speaker_ids, 'x_vectors': x_vectors }) # set the [alignment] lengths wrt reduction factor for guided attention if mel_lengths.max() % self.decoder.r != 0: alignment_lengths = ( mel_lengths + (self.decoder.r - (mel_lengths.max() % self.decoder.r))) // self.decoder.r else: alignment_lengths = mel_lengths // self.decoder.r cond_input = {'speaker_ids': speaker_ids, 'x_vectors': x_vectors} outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, cond_input) # compute loss loss_dict = criterion( outputs['model_outputs'], outputs['decoder_outputs'], mel_input, linear_input, outputs['stop_tokens'], stop_targets, mel_lengths, outputs['decoder_outputs_backward'], outputs['alignments'], alignment_lengths, outputs['alignments_backward'], text_lengths, ) # compute alignment error (the lower the better ) align_error = 1 - alignment_diagonal_score(outputs['alignments']) loss_dict["align_error"] = align_error return outputs, loss_dict def train_log(self, ap, batch, outputs): postnet_outputs = outputs['model_outputs'] alignments = outputs['alignments'] alignments_backward = outputs['alignments_backward'] mel_input = batch['mel_input'] pred_spec = postnet_outputs[0].data.cpu().numpy() gt_spec = mel_input[0].data.cpu().numpy() align_img = alignments[0].data.cpu().numpy() figures = { "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), "alignment": plot_alignment(align_img, output_fig=False), } if self.bidirectional_decoder or self.double_decoder_consistency: figures["alignment_backward"] = plot_alignment( alignments_backward[0].data.cpu().numpy(), output_fig=False) # Sample audio train_audio = ap.inv_spectrogram(pred_spec.T) return figures, train_audio def eval_step(self, batch, criterion): return self.train_step(batch, criterion) def eval_log(self, ap, batch, outputs): return self.train_log(ap, batch, outputs)