# coding: utf-8 import torch from torch import nn from TTS.tts.layers.tacotron.gst_layers import GST from TTS.tts.layers.tacotron.tacotron2 import Decoder, Encoder, Postnet from TTS.tts.models.tacotron_abstract import TacotronAbstract from TTS.tts.utils.measures import alignment_diagonal_score from TTS.tts.utils.visual import plot_alignment, plot_spectrogram class Tacotron2(TacotronAbstract): """Tacotron2 as in https://arxiv.org/abs/1712.05884 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.tacotron.common_layers.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. gradual_trainin (List): Gradual training schedule. If None or `[]`, no gradual training is used. Defaults to `[]`. """ def __init__( self, num_chars, num_speakers, r, postnet_output_dim=80, decoder_output_dim=80, attn_type="original", attn_win=False, attn_norm="softmax", 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=512, decoder_in_features=512, speaker_embedding_dim=None, use_gst=False, gst=None, gradual_training=None, ): 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 layer if self.num_speakers > 1: if not self.embeddings_per_sample: speaker_embedding_dim = 512 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, 512, padding_idx=0) # base model layers self.encoder = Encoder(self.encoder_in_features) self.decoder = Decoder( self.decoder_in_features, self.decoder_output_dim, r, 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 = Postnet(self.postnet_output_dim) # setup prenet dropout self.decoder.prenet.dropout_at_inference = prenet_dropout_at_inference # global style token layers if self.gst and 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, self.decoder_output_dim, ddc_r, attn_type, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, forward_attn_mask, location_attn, attn_K, separate_stopnet, ) @staticmethod def shape_outputs(mel_outputs, mel_outputs_postnet, alignments): mel_outputs = mel_outputs.transpose(1, 2) mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2) return mel_outputs, mel_outputs_postnet, alignments 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} # compute mask for padding # B x T_in_max (boolean) input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths) # B x D_embed x T_in_max embedded_inputs = self.embedding(text).transpose(1, 2) # B x T_in_max x D_en encoder_outputs = self.encoder(embedded_inputs, text_lengths) if self.gst and self.use_gst: # B x gst_dim encoder_outputs = self.compute_gst(encoder_outputs, mel_specs, 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) encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs) # B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r decoder_outputs, alignments, stop_tokens = self.decoder(encoder_outputs, mel_specs, input_mask) # sequence masking if mel_lengths is not None: decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs) # B x mel_dim x T_out postnet_outputs = self.postnet(decoder_outputs) postnet_outputs = decoder_outputs + postnet_outputs # sequence masking if output_mask is not None: postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs) # B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in decoder_outputs, postnet_outputs, alignments = self.shape_outputs(decoder_outputs, postnet_outputs, alignments) 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, cond_input=None): embedded_inputs = self.embedding(text).transpose(1, 2) encoder_outputs = self.encoder.inference(embedded_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: x_vector = self.speaker_embedding(cond_input['speaker_ids'])[:, None] x_vector = torch.unsqueeze(x_vector, 0).transpose(1, 2) else: x_vector = cond_input["x_vectors"] encoder_outputs = self._concat_speaker_embedding(encoder_outputs, x_vector) decoder_outputs, alignments, stop_tokens = self.decoder.inference(encoder_outputs) postnet_outputs = self.postnet(decoder_outputs) postnet_outputs = decoder_outputs + postnet_outputs decoder_outputs, postnet_outputs, alignments = self.shape_outputs(decoder_outputs, postnet_outputs, alignments) 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_melspectrogram(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)