# Copyright 2020 Nagoya University (Tomoki Hayashi) # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) """Tacotron 2 related modules for ESPnet2.""" import logging from typing import Dict, Optional, Sequence, Tuple import torch import torch.nn.functional as F from typeguard import check_argument_types from espnet2.torch_utils.device_funcs import force_gatherable from espnet2.tts.abs_tts import AbsTTS from espnet2.tts.gst.style_encoder import StyleEncoder from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import ( GuidedAttentionLoss, Tacotron2Loss, ) from espnet.nets.pytorch_backend.nets_utils import make_pad_mask from espnet.nets.pytorch_backend.rnn.attentions import AttForward, AttForwardTA, AttLoc from espnet.nets.pytorch_backend.tacotron2.decoder import Decoder from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder class Tacotron2(AbsTTS): """Tacotron2 module for end-to-end text-to-speech. This is a module of Spectrogram prediction network in Tacotron2 described in `Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`_, which converts the sequence of characters into the sequence of Mel-filterbanks. .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`: https://arxiv.org/abs/1712.05884 """ def __init__( self, # network structure related idim: int, odim: int, embed_dim: int = 512, elayers: int = 1, eunits: int = 512, econv_layers: int = 3, econv_chans: int = 512, econv_filts: int = 5, atype: str = "location", adim: int = 512, aconv_chans: int = 32, aconv_filts: int = 15, cumulate_att_w: bool = True, dlayers: int = 2, dunits: int = 1024, prenet_layers: int = 2, prenet_units: int = 256, postnet_layers: int = 5, postnet_chans: int = 512, postnet_filts: int = 5, output_activation: str = None, use_batch_norm: bool = True, use_concate: bool = True, use_residual: bool = False, reduction_factor: int = 1, # extra embedding related spks: Optional[int] = None, langs: Optional[int] = None, spk_embed_dim: Optional[int] = None, spk_embed_integration_type: str = "concat", use_gst: bool = False, gst_tokens: int = 10, gst_heads: int = 4, gst_conv_layers: int = 6, gst_conv_chans_list: Sequence[int] = (32, 32, 64, 64, 128, 128), gst_conv_kernel_size: int = 3, gst_conv_stride: int = 2, gst_gru_layers: int = 1, gst_gru_units: int = 128, # training related dropout_rate: float = 0.5, zoneout_rate: float = 0.1, use_masking: bool = True, use_weighted_masking: bool = False, bce_pos_weight: float = 5.0, loss_type: str = "L1+L2", use_guided_attn_loss: bool = True, guided_attn_loss_sigma: float = 0.4, guided_attn_loss_lambda: float = 1.0, ): """Initialize Tacotron2 module. Args: idim (int): Dimension of the inputs. odim: (int) Dimension of the outputs. embed_dim (int): Dimension of the token embedding. elayers (int): Number of encoder blstm layers. eunits (int): Number of encoder blstm units. econv_layers (int): Number of encoder conv layers. econv_filts (int): Number of encoder conv filter size. econv_chans (int): Number of encoder conv filter channels. dlayers (int): Number of decoder lstm layers. dunits (int): Number of decoder lstm units. prenet_layers (int): Number of prenet layers. prenet_units (int): Number of prenet units. postnet_layers (int): Number of postnet layers. postnet_filts (int): Number of postnet filter size. postnet_chans (int): Number of postnet filter channels. output_activation (str): Name of activation function for outputs. adim (int): Number of dimension of mlp in attention. aconv_chans (int): Number of attention conv filter channels. aconv_filts (int): Number of attention conv filter size. cumulate_att_w (bool): Whether to cumulate previous attention weight. use_batch_norm (bool): Whether to use batch normalization. use_concate (bool): Whether to concat enc outputs w/ dec lstm outputs. reduction_factor (int): Reduction factor. spks (Optional[int]): Number of speakers. If set to > 1, assume that the sids will be provided as the input and use sid embedding layer. langs (Optional[int]): Number of languages. If set to > 1, assume that the lids will be provided as the input and use sid embedding layer. spk_embed_dim (Optional[int]): Speaker embedding dimension. If set to > 0, assume that spembs will be provided as the input. spk_embed_integration_type (str): How to integrate speaker embedding. use_gst (str): Whether to use global style token. gst_tokens (int): Number of GST embeddings. gst_heads (int): Number of heads in GST multihead attention. gst_conv_layers (int): Number of conv layers in GST. gst_conv_chans_list: (Sequence[int]): List of the number of channels of conv layers in GST. gst_conv_kernel_size (int): Kernel size of conv layers in GST. gst_conv_stride (int): Stride size of conv layers in GST. gst_gru_layers (int): Number of GRU layers in GST. gst_gru_units (int): Number of GRU units in GST. dropout_rate (float): Dropout rate. zoneout_rate (float): Zoneout rate. use_masking (bool): Whether to mask padded part in loss calculation. use_weighted_masking (bool): Whether to apply weighted masking in loss calculation. bce_pos_weight (float): Weight of positive sample of stop token (only for use_masking=True). loss_type (str): Loss function type ("L1", "L2", or "L1+L2"). use_guided_attn_loss (bool): Whether to use guided attention loss. guided_attn_loss_sigma (float): Sigma in guided attention loss. guided_attn_loss_lambda (float): Lambda in guided attention loss. """ assert check_argument_types() super().__init__() # store hyperparameters self.idim = idim self.odim = odim self.eos = idim - 1 self.cumulate_att_w = cumulate_att_w self.reduction_factor = reduction_factor self.use_gst = use_gst self.use_guided_attn_loss = use_guided_attn_loss self.loss_type = loss_type # define activation function for the final output if output_activation is None: self.output_activation_fn = None elif hasattr(F, output_activation): self.output_activation_fn = getattr(F, output_activation) else: raise ValueError( f"there is no such an activation function. " f"({output_activation})" ) # set padding idx padding_idx = 0 self.padding_idx = padding_idx # define network modules self.enc = Encoder( idim=idim, embed_dim=embed_dim, elayers=elayers, eunits=eunits, econv_layers=econv_layers, econv_chans=econv_chans, econv_filts=econv_filts, use_batch_norm=use_batch_norm, use_residual=use_residual, dropout_rate=dropout_rate, padding_idx=padding_idx, ) if self.use_gst: self.gst = StyleEncoder( idim=odim, # the input is mel-spectrogram gst_tokens=gst_tokens, gst_token_dim=eunits, gst_heads=gst_heads, conv_layers=gst_conv_layers, conv_chans_list=gst_conv_chans_list, conv_kernel_size=gst_conv_kernel_size, conv_stride=gst_conv_stride, gru_layers=gst_gru_layers, gru_units=gst_gru_units, ) self.spks = None if spks is not None and spks > 1: self.spks = spks self.sid_emb = torch.nn.Embedding(spks, eunits) self.langs = None if langs is not None and langs > 1: self.langs = langs self.lid_emb = torch.nn.Embedding(langs, eunits) self.spk_embed_dim = None if spk_embed_dim is not None and spk_embed_dim > 0: self.spk_embed_dim = spk_embed_dim self.spk_embed_integration_type = spk_embed_integration_type if self.spk_embed_dim is None: dec_idim = eunits elif self.spk_embed_integration_type == "concat": dec_idim = eunits + spk_embed_dim elif self.spk_embed_integration_type == "add": dec_idim = eunits self.projection = torch.nn.Linear(self.spk_embed_dim, eunits) else: raise ValueError(f"{spk_embed_integration_type} is not supported.") if atype == "location": att = AttLoc(dec_idim, dunits, adim, aconv_chans, aconv_filts) elif atype == "forward": att = AttForward(dec_idim, dunits, adim, aconv_chans, aconv_filts) if self.cumulate_att_w: logging.warning( "cumulation of attention weights is disabled " "in forward attention." ) self.cumulate_att_w = False elif atype == "forward_ta": att = AttForwardTA(dec_idim, dunits, adim, aconv_chans, aconv_filts, odim) if self.cumulate_att_w: logging.warning( "cumulation of attention weights is disabled " "in forward attention." ) self.cumulate_att_w = False else: raise NotImplementedError("Support only location or forward") self.dec = Decoder( idim=dec_idim, odim=odim, att=att, dlayers=dlayers, dunits=dunits, prenet_layers=prenet_layers, prenet_units=prenet_units, postnet_layers=postnet_layers, postnet_chans=postnet_chans, postnet_filts=postnet_filts, output_activation_fn=self.output_activation_fn, cumulate_att_w=self.cumulate_att_w, use_batch_norm=use_batch_norm, use_concate=use_concate, dropout_rate=dropout_rate, zoneout_rate=zoneout_rate, reduction_factor=reduction_factor, ) self.taco2_loss = Tacotron2Loss( use_masking=use_masking, use_weighted_masking=use_weighted_masking, bce_pos_weight=bce_pos_weight, ) if self.use_guided_attn_loss: self.attn_loss = GuidedAttentionLoss( sigma=guided_attn_loss_sigma, alpha=guided_attn_loss_lambda, ) def forward( self, text: torch.Tensor, text_lengths: torch.Tensor, feats: torch.Tensor, feats_lengths: torch.Tensor, spembs: Optional[torch.Tensor] = None, sids: Optional[torch.Tensor] = None, lids: Optional[torch.Tensor] = None, joint_training: bool = False, ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: """Calculate forward propagation. Args: text (LongTensor): Batch of padded character ids (B, T_text). text_lengths (LongTensor): Batch of lengths of each input batch (B,). feats (Tensor): Batch of padded target features (B, T_feats, odim). feats_lengths (LongTensor): Batch of the lengths of each target (B,). spembs (Optional[Tensor]): Batch of speaker embeddings (B, spk_embed_dim). sids (Optional[Tensor]): Batch of speaker IDs (B, 1). lids (Optional[Tensor]): Batch of language IDs (B, 1). joint_training (bool): Whether to perform joint training with vocoder. Returns: Tensor: Loss scalar value. Dict: Statistics to be monitored. Tensor: Weight value if not joint training else model outputs. """ text = text[:, : text_lengths.max()] # for data-parallel feats = feats[:, : feats_lengths.max()] # for data-parallel batch_size = text.size(0) # Add eos at the last of sequence xs = F.pad(text, [0, 1], "constant", self.padding_idx) for i, l in enumerate(text_lengths): xs[i, l] = self.eos ilens = text_lengths + 1 ys = feats olens = feats_lengths # make labels for stop prediction labels = make_pad_mask(olens - 1).to(ys.device, ys.dtype) labels = F.pad(labels, [0, 1], "constant", 1.0) # calculate tacotron2 outputs after_outs, before_outs, logits, att_ws = self._forward( xs=xs, ilens=ilens, ys=ys, olens=olens, spembs=spembs, sids=sids, lids=lids, ) # modify mod part of groundtruth if self.reduction_factor > 1: assert olens.ge( self.reduction_factor ).all(), "Output length must be greater than or equal to reduction factor." olens = olens.new([olen - olen % self.reduction_factor for olen in olens]) max_out = max(olens) ys = ys[:, :max_out] labels = labels[:, :max_out] labels = torch.scatter( labels, 1, (olens - 1).unsqueeze(1), 1.0 ) # see #3388 # calculate taco2 loss l1_loss, mse_loss, bce_loss = self.taco2_loss( after_outs, before_outs, logits, ys, labels, olens ) if self.loss_type == "L1+L2": loss = l1_loss + mse_loss + bce_loss elif self.loss_type == "L1": loss = l1_loss + bce_loss elif self.loss_type == "L2": loss = mse_loss + bce_loss else: raise ValueError(f"unknown --loss-type {self.loss_type}") stats = dict( l1_loss=l1_loss.item(), mse_loss=mse_loss.item(), bce_loss=bce_loss.item(), ) # calculate attention loss if self.use_guided_attn_loss: # NOTE(kan-bayashi): length of output for auto-regressive # input will be changed when r > 1 if self.reduction_factor > 1: olens_in = olens.new([olen // self.reduction_factor for olen in olens]) else: olens_in = olens attn_loss = self.attn_loss(att_ws, ilens, olens_in) loss = loss + attn_loss stats.update(attn_loss=attn_loss.item()) if not joint_training: stats.update(loss=loss.item()) loss, stats, weight = force_gatherable( (loss, stats, batch_size), loss.device ) return loss, stats, weight else: return loss, stats, after_outs def _forward( self, xs: torch.Tensor, ilens: torch.Tensor, ys: torch.Tensor, olens: torch.Tensor, spembs: torch.Tensor, sids: torch.Tensor, lids: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: hs, hlens = self.enc(xs, ilens) if self.use_gst: style_embs = self.gst(ys) hs = hs + style_embs.unsqueeze(1) if self.spks is not None: sid_embs = self.sid_emb(sids.view(-1)) hs = hs + sid_embs.unsqueeze(1) if self.langs is not None: lid_embs = self.lid_emb(lids.view(-1)) hs = hs + lid_embs.unsqueeze(1) if self.spk_embed_dim is not None: hs = self._integrate_with_spk_embed(hs, spembs) return self.dec(hs, hlens, ys) def inference( self, text: torch.Tensor, feats: Optional[torch.Tensor] = None, spembs: Optional[torch.Tensor] = None, sids: Optional[torch.Tensor] = None, lids: Optional[torch.Tensor] = None, threshold: float = 0.5, minlenratio: float = 0.0, maxlenratio: float = 10.0, use_att_constraint: bool = False, backward_window: int = 1, forward_window: int = 3, use_teacher_forcing: bool = False, ) -> Dict[str, torch.Tensor]: """Generate the sequence of features given the sequences of characters. Args: text (LongTensor): Input sequence of characters (T_text,). feats (Optional[Tensor]): Feature sequence to extract style (N, idim). spembs (Optional[Tensor]): Speaker embedding (spk_embed_dim,). sids (Optional[Tensor]): Speaker ID (1,). lids (Optional[Tensor]): Language ID (1,). threshold (float): Threshold in inference. minlenratio (float): Minimum length ratio in inference. maxlenratio (float): Maximum length ratio in inference. use_att_constraint (bool): Whether to apply attention constraint. backward_window (int): Backward window in attention constraint. forward_window (int): Forward window in attention constraint. use_teacher_forcing (bool): Whether to use teacher forcing. Returns: Dict[str, Tensor]: Output dict including the following items: * feat_gen (Tensor): Output sequence of features (T_feats, odim). * prob (Tensor): Output sequence of stop probabilities (T_feats,). * att_w (Tensor): Attention weights (T_feats, T). """ x = text y = feats spemb = spembs # add eos at the last of sequence x = F.pad(x, [0, 1], "constant", self.eos) # inference with teacher forcing if use_teacher_forcing: assert feats is not None, "feats must be provided with teacher forcing." xs, ys = x.unsqueeze(0), y.unsqueeze(0) spembs = None if spemb is None else spemb.unsqueeze(0) ilens = x.new_tensor([xs.size(1)]).long() olens = y.new_tensor([ys.size(1)]).long() outs, _, _, att_ws = self._forward( xs=xs, ilens=ilens, ys=ys, olens=olens, spembs=spembs, sids=sids, lids=lids, ) return dict(feat_gen=outs[0], att_w=att_ws[0]) # inference h = self.enc.inference(x) if self.use_gst: style_emb = self.gst(y.unsqueeze(0)) h = h + style_emb if self.spks is not None: sid_emb = self.sid_emb(sids.view(-1)) h = h + sid_emb if self.langs is not None: lid_emb = self.lid_emb(lids.view(-1)) h = h + lid_emb if self.spk_embed_dim is not None: hs, spembs = h.unsqueeze(0), spemb.unsqueeze(0) h = self._integrate_with_spk_embed(hs, spembs)[0] out, prob, att_w = self.dec.inference( h, threshold=threshold, minlenratio=minlenratio, maxlenratio=maxlenratio, use_att_constraint=use_att_constraint, backward_window=backward_window, forward_window=forward_window, ) return dict(feat_gen=out, prob=prob, att_w=att_w) def _integrate_with_spk_embed( self, hs: torch.Tensor, spembs: torch.Tensor ) -> torch.Tensor: """Integrate speaker embedding with hidden states. Args: hs (Tensor): Batch of hidden state sequences (B, Tmax, eunits). spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim). Returns: Tensor: Batch of integrated hidden state sequences (B, Tmax, eunits) if integration_type is "add" else (B, Tmax, eunits + spk_embed_dim). """ if self.spk_embed_integration_type == "add": # apply projection and then add to hidden states spembs = self.projection(F.normalize(spembs)) hs = hs + spembs.unsqueeze(1) elif self.spk_embed_integration_type == "concat": # concat hidden states with spk embeds spembs = F.normalize(spembs).unsqueeze(1).expand(-1, hs.size(1), -1) hs = torch.cat([hs, spembs], dim=-1) else: raise NotImplementedError("support only add or concat.") return hs