Source code for TTS.tts.models.tacotron

# coding: utf-8

from typing import Dict, List, Union

import torch
from torch import nn
from torch.cuda.amp.autocast_mode import autocast

from TTS.tts.layers.tacotron.gst_layers import GST
from TTS.tts.layers.tacotron.tacotron import Decoder, Encoder, PostCBHG
from TTS.tts.models.base_tacotron import BaseTacotron
from TTS.tts.utils.measures import alignment_diagonal_score
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram


[docs]class Tacotron(BaseTacotron): """Tacotron as in https://arxiv.org/abs/1703.10135 It's an autoregressive encoder-attention-decoder-postnet architecture. Check `TacotronConfig` for the arguments. Args: config (TacotronConfig): Configuration for the Tacotron model. speaker_manager (SpeakerManager): Speaker manager to handle multi-speaker settings. Only use if the model is a multi-speaker model. Defaults to None. """ def __init__( self, config: "TacotronConfig", ap: "AudioProcessor" = None, tokenizer: "TTSTokenizer" = None, speaker_manager: SpeakerManager = None, ): super().__init__(config, ap, tokenizer, speaker_manager) # pass all config fields to `self` # for fewer code change for key in config: setattr(self, key, config[key]) # set speaker embedding channel size for determining `in_channels` for the connected layers. # `init_multispeaker` needs to be called once more in training to initialize the speaker embedding layer based # on the number of speakers infered from the dataset. if self.use_speaker_embedding or self.use_d_vector_file: self.init_multispeaker(config) self.decoder_in_features += self.embedded_speaker_dim # add speaker embedding dim if self.use_gst: self.decoder_in_features += self.gst.gst_embedding_dim # embedding layer self.embedding = nn.Embedding(self.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, self.decoder_output_dim, self.r, self.memory_size, self.attention_type, self.windowing, self.attention_norm, self.prenet_type, self.prenet_dropout, self.use_forward_attn, self.transition_agent, self.forward_attn_mask, self.location_attn, self.attention_heads, self.separate_stopnet, self.max_decoder_steps, ) self.postnet = PostCBHG(self.decoder_output_dim) self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, self.out_channels) # setup prenet dropout self.decoder.prenet.dropout_at_inference = self.prenet_dropout_at_inference # global style token layers if self.gst and self.use_gst: self.gst_layer = GST( num_mel=self.decoder_output_dim, num_heads=self.gst.gst_num_heads, num_style_tokens=self.gst.gst_num_style_tokens, gst_embedding_dim=self.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, self.ddc_r, self.memory_size, self.attention_type, self.windowing, self.attention_norm, self.prenet_type, self.prenet_dropout, self.use_forward_attn, self.transition_agent, self.forward_attn_mask, self.location_attn, self.attention_heads, self.separate_stopnet, self.max_decoder_steps, )
[docs] def forward( # pylint: disable=dangerous-default-value self, text, text_lengths, mel_specs=None, mel_lengths=None, aux_input={"speaker_ids": None, "d_vectors": None} ): """ Shapes: text: [B, T_in] text_lengths: [B] mel_specs: [B, T_out, C] mel_lengths: [B] aux_input: 'speaker_ids': [B, 1] and 'd_vectors':[B, C] """ aux_input = self._format_aux_input(aux_input) outputs = {"alignments_backward": None, "decoder_outputs_backward": None} inputs = self.embedding(text) input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths) # 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) # speaker embedding if self.use_speaker_embedding or self.use_d_vector_file: if not self.use_d_vector_file: # B x 1 x speaker_embed_dim embedded_speakers = self.speaker_embedding(aux_input["speaker_ids"])[:, None] else: # B x 1 x speaker_embed_dim embedded_speakers = torch.unsqueeze(aux_input["d_vectors"], 1) encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers) # 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, aux_input=None): aux_input = self._format_aux_input(aux_input) 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, aux_input["style_mel"], aux_input["d_vectors"]) if self.num_speakers > 1: if not self.use_d_vector_file: # B x 1 x speaker_embed_dim embedded_speakers = self.speaker_embedding(aux_input["speaker_ids"]) # reshape embedded_speakers if embedded_speakers.ndim == 1: embedded_speakers = embedded_speakers[None, None, :] elif embedded_speakers.ndim == 2: embedded_speakers = embedded_speakers[None, :] else: # B x 1 x speaker_embed_dim embedded_speakers = torch.unsqueeze(aux_input["d_vectors"], 1) encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers) 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
[docs] 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"] stop_target_lengths = batch["stop_target_lengths"] speaker_ids = batch["speaker_ids"] d_vectors = batch["d_vectors"] # forward pass model outputs = self.forward( text_input, text_lengths, mel_input, mel_lengths, aux_input={"speaker_ids": speaker_ids, "d_vectors": d_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 aux_input = {"speaker_ids": speaker_ids, "d_vectors": d_vectors} outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input) # compute loss with autocast(enabled=False): # use float32 for the criterion loss_dict = criterion( outputs["model_outputs"].float(), outputs["decoder_outputs"].float(), mel_input.float(), linear_input.float(), outputs["stop_tokens"].float(), stop_targets.float(), stop_target_lengths, mel_lengths, None if outputs["decoder_outputs_backward"] is None else outputs["decoder_outputs_backward"].float(), outputs["alignments"].float(), alignment_lengths, None if outputs["alignments_backward"] is None else outputs["alignments_backward"].float(), 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 _create_logs(self, batch, outputs, ap): postnet_outputs = outputs["model_outputs"] decoder_outputs = outputs["decoder_outputs"] alignments = outputs["alignments"] alignments_backward = outputs["alignments_backward"] mel_input = batch["mel_input"] linear_input = batch["linear_input"] pred_linear_spec = postnet_outputs[0].data.cpu().numpy() pred_mel_spec = decoder_outputs[0].data.cpu().numpy() gt_linear_spec = linear_input[0].data.cpu().numpy() gt_mel_spec = mel_input[0].data.cpu().numpy() align_img = alignments[0].data.cpu().numpy() figures = { "pred_linear_spec": plot_spectrogram(pred_linear_spec, ap, output_fig=False), "real_linear_spec": plot_spectrogram(gt_linear_spec, ap, output_fig=False), "pred_mel_spec": plot_spectrogram(pred_mel_spec, ap, output_fig=False), "real_mel_spec": plot_spectrogram(gt_mel_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 audio = ap.inv_spectrogram(pred_linear_spec.T) return figures, {"audio": audio} def train_log( self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int ) -> None: # pylint: disable=no-self-use figures, audios = self._create_logs(batch, outputs, self.ap) logger.train_figures(steps, figures) logger.train_audios(steps, audios, self.ap.sample_rate) def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: figures, audios = self._create_logs(batch, outputs, self.ap) logger.eval_figures(steps, figures) logger.eval_audios(steps, audios, self.ap.sample_rate)
[docs] @staticmethod def init_from_config(config: "TacotronConfig", samples: Union[List[List], List[Dict]] = None): """Initiate model from config Args: config (TacotronConfig): Model config. samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training. Defaults to None. """ from TTS.utils.audio import AudioProcessor ap = AudioProcessor.init_from_config(config) tokenizer, new_config = TTSTokenizer.init_from_config(config) speaker_manager = SpeakerManager.init_from_config(config, samples) return Tacotron(new_config, ap, tokenizer, speaker_manager)