# Copyright 2020 Nagoya University (Tomoki Hayashi) # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) """Transformer-TTS related modules.""" 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.torch_utils.initialize import initialize from espnet2.tts.abs_tts import AbsTTS from espnet2.tts.gst.style_encoder import StyleEncoder from espnet.nets.pytorch_backend.e2e_tts_transformer import ( GuidedMultiHeadAttentionLoss, TransformerLoss, ) from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, make_pad_mask from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet from espnet.nets.pytorch_backend.tacotron2.decoder import Prenet as DecoderPrenet from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder as EncoderPrenet from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention from espnet.nets.pytorch_backend.transformer.decoder import Decoder from espnet.nets.pytorch_backend.transformer.embedding import ( PositionalEncoding, ScaledPositionalEncoding, ) from espnet.nets.pytorch_backend.transformer.encoder import Encoder from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask class Transformer(AbsTTS): """Transformer-TTS module. This is a module of text-to-speech Transformer described in `Neural Speech Synthesis with Transformer Network`_, which convert the sequence of tokens into the sequence of Mel-filterbanks. .. _`Neural Speech Synthesis with Transformer Network`: https://arxiv.org/pdf/1809.08895.pdf """ def __init__( self, # network structure related idim: int, odim: int, embed_dim: int = 512, eprenet_conv_layers: int = 3, eprenet_conv_chans: int = 256, eprenet_conv_filts: int = 5, dprenet_layers: int = 2, dprenet_units: int = 256, elayers: int = 6, eunits: int = 1024, adim: int = 512, aheads: int = 4, dlayers: int = 6, dunits: int = 1024, postnet_layers: int = 5, postnet_chans: int = 256, postnet_filts: int = 5, positionwise_layer_type: str = "conv1d", positionwise_conv_kernel_size: int = 1, use_scaled_pos_enc: bool = True, use_batch_norm: bool = True, encoder_normalize_before: bool = True, decoder_normalize_before: bool = True, encoder_concat_after: bool = False, decoder_concat_after: 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 = "add", 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 transformer_enc_dropout_rate: float = 0.1, transformer_enc_positional_dropout_rate: float = 0.1, transformer_enc_attn_dropout_rate: float = 0.1, transformer_dec_dropout_rate: float = 0.1, transformer_dec_positional_dropout_rate: float = 0.1, transformer_dec_attn_dropout_rate: float = 0.1, transformer_enc_dec_attn_dropout_rate: float = 0.1, eprenet_dropout_rate: float = 0.5, dprenet_dropout_rate: float = 0.5, postnet_dropout_rate: float = 0.5, init_type: str = "xavier_uniform", init_enc_alpha: float = 1.0, init_dec_alpha: float = 1.0, use_masking: bool = False, use_weighted_masking: bool = False, bce_pos_weight: float = 5.0, loss_type: str = "L1", use_guided_attn_loss: bool = True, num_heads_applied_guided_attn: int = 2, num_layers_applied_guided_attn: int = 2, modules_applied_guided_attn: Sequence[str] = ("encoder-decoder"), guided_attn_loss_sigma: float = 0.4, guided_attn_loss_lambda: float = 1.0, ): """Initialize Transformer module. Args: idim (int): Dimension of the inputs. odim (int): Dimension of the outputs. embed_dim (int): Dimension of character embedding. eprenet_conv_layers (int): Number of encoder prenet convolution layers. eprenet_conv_chans (int): Number of encoder prenet convolution channels. eprenet_conv_filts (int): Filter size of encoder prenet convolution. dprenet_layers (int): Number of decoder prenet layers. dprenet_units (int): Number of decoder prenet hidden units. elayers (int): Number of encoder layers. eunits (int): Number of encoder hidden units. adim (int): Number of attention transformation dimensions. aheads (int): Number of heads for multi head attention. dlayers (int): Number of decoder layers. dunits (int): Number of decoder hidden units. postnet_layers (int): Number of postnet layers. postnet_chans (int): Number of postnet channels. postnet_filts (int): Filter size of postnet. use_scaled_pos_enc (bool): Whether to use trainable scaled pos encoding. use_batch_norm (bool): Whether to use batch normalization in encoder prenet. encoder_normalize_before (bool): Whether to apply layernorm layer before encoder block. decoder_normalize_before (bool): Whether to apply layernorm layer before decoder block. encoder_concat_after (bool): Whether to concatenate attention layer's input and output in encoder. decoder_concat_after (bool): Whether to concatenate attention layer's input and output in decoder. positionwise_layer_type (str): Position-wise operation type. positionwise_conv_kernel_size (int): Kernel size in position wise conv 1d. 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. transformer_lr (float): Initial value of learning rate. transformer_warmup_steps (int): Optimizer warmup steps. transformer_enc_dropout_rate (float): Dropout rate in encoder except attention and positional encoding. transformer_enc_positional_dropout_rate (float): Dropout rate after encoder positional encoding. transformer_enc_attn_dropout_rate (float): Dropout rate in encoder self-attention module. transformer_dec_dropout_rate (float): Dropout rate in decoder except attention & positional encoding. transformer_dec_positional_dropout_rate (float): Dropout rate after decoder positional encoding. transformer_dec_attn_dropout_rate (float): Dropout rate in decoder self-attention module. transformer_enc_dec_attn_dropout_rate (float): Dropout rate in source attention module. init_type (str): How to initialize transformer parameters. init_enc_alpha (float): Initial value of alpha in scaled pos encoding of the encoder. init_dec_alpha (float): Initial value of alpha in scaled pos encoding of the decoder. eprenet_dropout_rate (float): Dropout rate in encoder prenet. dprenet_dropout_rate (float): Dropout rate in decoder prenet. postnet_dropout_rate (float): Dropout rate in postnet. use_masking (bool): Whether to apply masking for padded part in loss calculation. use_weighted_masking (bool): Whether to apply weighted masking in loss calculation. bce_pos_weight (float): Positive sample weight in bce calculation (only for use_masking=true). loss_type (str): How to calculate loss. use_guided_attn_loss (bool): Whether to use guided attention loss. num_heads_applied_guided_attn (int): Number of heads in each layer to apply guided attention loss. num_layers_applied_guided_attn (int): Number of layers to apply guided attention loss. modules_applied_guided_attn (Sequence[str]): List of module names to apply 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.reduction_factor = reduction_factor self.use_gst = use_gst self.use_guided_attn_loss = use_guided_attn_loss self.use_scaled_pos_enc = use_scaled_pos_enc self.loss_type = loss_type self.use_guided_attn_loss = use_guided_attn_loss if self.use_guided_attn_loss: if num_layers_applied_guided_attn == -1: self.num_layers_applied_guided_attn = elayers else: self.num_layers_applied_guided_attn = num_layers_applied_guided_attn if num_heads_applied_guided_attn == -1: self.num_heads_applied_guided_attn = aheads else: self.num_heads_applied_guided_attn = num_heads_applied_guided_attn self.modules_applied_guided_attn = modules_applied_guided_attn # use idx 0 as padding idx self.padding_idx = 0 # get positional encoding class pos_enc_class = ( ScaledPositionalEncoding if self.use_scaled_pos_enc else PositionalEncoding ) # define transformer encoder if eprenet_conv_layers != 0: # encoder prenet encoder_input_layer = torch.nn.Sequential( EncoderPrenet( idim=idim, embed_dim=embed_dim, elayers=0, econv_layers=eprenet_conv_layers, econv_chans=eprenet_conv_chans, econv_filts=eprenet_conv_filts, use_batch_norm=use_batch_norm, dropout_rate=eprenet_dropout_rate, padding_idx=self.padding_idx, ), torch.nn.Linear(eprenet_conv_chans, adim), ) else: encoder_input_layer = torch.nn.Embedding( num_embeddings=idim, embedding_dim=adim, padding_idx=self.padding_idx ) self.encoder = Encoder( idim=idim, attention_dim=adim, attention_heads=aheads, linear_units=eunits, num_blocks=elayers, input_layer=encoder_input_layer, dropout_rate=transformer_enc_dropout_rate, positional_dropout_rate=transformer_enc_positional_dropout_rate, attention_dropout_rate=transformer_enc_attn_dropout_rate, pos_enc_class=pos_enc_class, normalize_before=encoder_normalize_before, concat_after=encoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, ) # define GST if self.use_gst: self.gst = StyleEncoder( idim=odim, # the input is mel-spectrogram gst_tokens=gst_tokens, gst_token_dim=adim, 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, ) # define spk and lang embedding self.spks = None if spks is not None and spks > 1: self.spks = spks self.sid_emb = torch.nn.Embedding(spks, adim) self.langs = None if langs is not None and langs > 1: self.langs = langs self.lid_emb = torch.nn.Embedding(langs, adim) # define projection layer 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 not None: if self.spk_embed_integration_type == "add": self.projection = torch.nn.Linear(self.spk_embed_dim, adim) else: self.projection = torch.nn.Linear(adim + self.spk_embed_dim, adim) # define transformer decoder if dprenet_layers != 0: # decoder prenet decoder_input_layer = torch.nn.Sequential( DecoderPrenet( idim=odim, n_layers=dprenet_layers, n_units=dprenet_units, dropout_rate=dprenet_dropout_rate, ), torch.nn.Linear(dprenet_units, adim), ) else: decoder_input_layer = "linear" self.decoder = Decoder( odim=odim, # odim is needed when no prenet is used attention_dim=adim, attention_heads=aheads, linear_units=dunits, num_blocks=dlayers, dropout_rate=transformer_dec_dropout_rate, positional_dropout_rate=transformer_dec_positional_dropout_rate, self_attention_dropout_rate=transformer_dec_attn_dropout_rate, src_attention_dropout_rate=transformer_enc_dec_attn_dropout_rate, input_layer=decoder_input_layer, use_output_layer=False, pos_enc_class=pos_enc_class, normalize_before=decoder_normalize_before, concat_after=decoder_concat_after, ) # define final projection self.feat_out = torch.nn.Linear(adim, odim * reduction_factor) self.prob_out = torch.nn.Linear(adim, reduction_factor) # define postnet self.postnet = ( None if postnet_layers == 0 else Postnet( idim=idim, odim=odim, n_layers=postnet_layers, n_chans=postnet_chans, n_filts=postnet_filts, use_batch_norm=use_batch_norm, dropout_rate=postnet_dropout_rate, ) ) # define loss function self.criterion = TransformerLoss( use_masking=use_masking, use_weighted_masking=use_weighted_masking, bce_pos_weight=bce_pos_weight, ) if self.use_guided_attn_loss: self.attn_criterion = GuidedMultiHeadAttentionLoss( sigma=guided_attn_loss_sigma, alpha=guided_attn_loss_lambda, ) # initialize parameters self._reset_parameters( init_type=init_type, init_enc_alpha=init_enc_alpha, init_dec_alpha=init_dec_alpha, ) def _reset_parameters(self, init_type, init_enc_alpha=1.0, init_dec_alpha=1.0): # initialize parameters if init_type != "pytorch": initialize(self, init_type) # initialize alpha in scaled positional encoding if self.use_scaled_pos_enc: self.encoder.embed[-1].alpha.data = torch.tensor(init_enc_alpha) self.decoder.embed[-1].alpha.data = torch.tensor(init_dec_alpha) 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, Tmax). text_lengths (LongTensor): Batch of lengths of each input batch (B,). feats (Tensor): Batch of padded target features (B, Lmax, 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 transformer outputs after_outs, before_outs, logits = self._forward( xs=xs, ilens=ilens, ys=ys, olens=olens, spembs=spembs, sids=sids, lids=lids, ) # modifiy mod part of groundtruth olens_in = olens if self.reduction_factor > 1: assert olens.ge( self.reduction_factor ).all(), "Output length must be greater than or equal to reduction factor." olens_in = olens.new([olen // self.reduction_factor for olen in olens]) olens = olens.new([olen - olen % self.reduction_factor for olen in olens]) max_olen = max(olens) ys = ys[:, :max_olen] labels = labels[:, :max_olen] labels = torch.scatter( labels, 1, (olens - 1).unsqueeze(1), 1.0 ) # see #3388 # calculate loss values l1_loss, l2_loss, bce_loss = self.criterion( after_outs, before_outs, logits, ys, labels, olens ) if self.loss_type == "L1": loss = l1_loss + bce_loss elif self.loss_type == "L2": loss = l2_loss + bce_loss elif self.loss_type == "L1+L2": loss = l1_loss + l2_loss + bce_loss else: raise ValueError("unknown --loss-type " + self.loss_type) stats = dict( l1_loss=l1_loss.item(), l2_loss=l2_loss.item(), bce_loss=bce_loss.item(), ) # calculate guided attention loss if self.use_guided_attn_loss: # calculate for encoder if "encoder" in self.modules_applied_guided_attn: att_ws = [] for idx, layer_idx in enumerate( reversed(range(len(self.encoder.encoders))) ): att_ws += [ self.encoder.encoders[layer_idx].self_attn.attn[ :, : self.num_heads_applied_guided_attn ] ] if idx + 1 == self.num_layers_applied_guided_attn: break att_ws = torch.cat(att_ws, dim=1) # (B, H*L, T_text, T_text) enc_attn_loss = self.attn_criterion(att_ws, ilens, ilens) loss = loss + enc_attn_loss stats.update(enc_attn_loss=enc_attn_loss.item()) # calculate for decoder if "decoder" in self.modules_applied_guided_attn: att_ws = [] for idx, layer_idx in enumerate( reversed(range(len(self.decoder.decoders))) ): att_ws += [ self.decoder.decoders[layer_idx].self_attn.attn[ :, : self.num_heads_applied_guided_attn ] ] if idx + 1 == self.num_layers_applied_guided_attn: break att_ws = torch.cat(att_ws, dim=1) # (B, H*L, T_feats, T_feats) dec_attn_loss = self.attn_criterion(att_ws, olens_in, olens_in) loss = loss + dec_attn_loss stats.update(dec_attn_loss=dec_attn_loss.item()) # calculate for encoder-decoder if "encoder-decoder" in self.modules_applied_guided_attn: att_ws = [] for idx, layer_idx in enumerate( reversed(range(len(self.decoder.decoders))) ): att_ws += [ self.decoder.decoders[layer_idx].src_attn.attn[ :, : self.num_heads_applied_guided_attn ] ] if idx + 1 == self.num_layers_applied_guided_attn: break att_ws = torch.cat(att_ws, dim=1) # (B, H*L, T_feats, T_text) enc_dec_attn_loss = self.attn_criterion(att_ws, ilens, olens_in) loss = loss + enc_dec_attn_loss stats.update(enc_dec_attn_loss=enc_dec_attn_loss.item()) # report extra information if self.use_scaled_pos_enc: stats.update( encoder_alpha=self.encoder.embed[-1].alpha.data.item(), decoder_alpha=self.decoder.embed[-1].alpha.data.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]: # forward encoder x_masks = self._source_mask(ilens) hs, h_masks = self.encoder(xs, x_masks) # integrate with GST if self.use_gst: style_embs = self.gst(ys) hs = hs + style_embs.unsqueeze(1) # integrate with SID and LID embeddings 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) # integrate speaker embedding if self.spk_embed_dim is not None: hs = self._integrate_with_spk_embed(hs, spembs) # thin out frames for reduction factor # (B, T_feats, odim) -> (B, T_feats//r, odim) if self.reduction_factor > 1: ys_in = ys[:, self.reduction_factor - 1 :: self.reduction_factor] olens_in = olens.new([olen // self.reduction_factor for olen in olens]) else: ys_in, olens_in = ys, olens # add first zero frame and remove last frame for auto-regressive ys_in = self._add_first_frame_and_remove_last_frame(ys_in) # forward decoder y_masks = self._target_mask(olens_in) zs, _ = self.decoder(ys_in, y_masks, hs, h_masks) # (B, T_feats//r, odim * r) -> (B, T_feats//r * r, odim) before_outs = self.feat_out(zs).view(zs.size(0), -1, self.odim) # (B, T_feats//r, r) -> (B, T_feats//r * r) logits = self.prob_out(zs).view(zs.size(0), -1) # postnet -> (B, T_feats//r * r, odim) if self.postnet is None: after_outs = before_outs else: after_outs = before_outs + self.postnet( before_outs.transpose(1, 2) ).transpose(1, 2) return after_outs, before_outs, logits 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_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 embedding (T_feats', 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_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): Source attn weight (#layers, #heads, T_feats, T_text). """ 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." # get teacher forcing outputs 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, *_ = self._forward( xs=xs, ilens=ilens, ys=ys, olens=olens, spembs=spembs, sids=sids, lids=lids, ) # get attention weights att_ws = [] for i in range(len(self.decoder.decoders)): att_ws += [self.decoder.decoders[i].src_attn.attn] att_ws = torch.stack(att_ws, dim=1) # (B, L, H, T_feats, T_text) return dict(feat_gen=outs[0], att_w=att_ws[0]) # forward encoder xs = x.unsqueeze(0) hs, _ = self.encoder(xs, None) # integrate GST if self.use_gst: style_embs = self.gst(y.unsqueeze(0)) hs = hs + style_embs.unsqueeze(1) # integrate spk & lang embeddings 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) # integrate speaker embedding if self.spk_embed_dim is not None: spembs = spemb.unsqueeze(0) hs = self._integrate_with_spk_embed(hs, spembs) # set limits of length maxlen = int(hs.size(1) * maxlenratio / self.reduction_factor) minlen = int(hs.size(1) * minlenratio / self.reduction_factor) # initialize idx = 0 ys = hs.new_zeros(1, 1, self.odim) outs, probs = [], [] # forward decoder step-by-step z_cache = self.decoder.init_state(x) while True: # update index idx += 1 # calculate output and stop prob at idx-th step y_masks = subsequent_mask(idx).unsqueeze(0).to(x.device) z, z_cache = self.decoder.forward_one_step( ys, y_masks, hs, cache=z_cache ) # (B, adim) outs += [ self.feat_out(z).view(self.reduction_factor, self.odim) ] # [(r, odim), ...] probs += [torch.sigmoid(self.prob_out(z))[0]] # [(r), ...] # update next inputs ys = torch.cat( (ys, outs[-1][-1].view(1, 1, self.odim)), dim=1 ) # (1, idx + 1, odim) # get attention weights att_ws_ = [] for name, m in self.named_modules(): if isinstance(m, MultiHeadedAttention) and "src" in name: att_ws_ += [m.attn[0, :, -1].unsqueeze(1)] # [(#heads, 1, T),...] if idx == 1: att_ws = att_ws_ else: # [(#heads, l, T), ...] att_ws = [ torch.cat([att_w, att_w_], dim=1) for att_w, att_w_ in zip(att_ws, att_ws_) ] # check whether to finish generation if int(sum(probs[-1] >= threshold)) > 0 or idx >= maxlen: # check mininum length if idx < minlen: continue outs = ( torch.cat(outs, dim=0).unsqueeze(0).transpose(1, 2) ) # (T_feats, odim) -> (1, T_feats, odim) -> (1, odim, T_feats) if self.postnet is not None: outs = outs + self.postnet(outs) # (1, odim, T_feats) outs = outs.transpose(2, 1).squeeze(0) # (T_feats, odim) probs = torch.cat(probs, dim=0) break # concatenate attention weights -> (#layers, #heads, T_feats, T_text) att_ws = torch.stack(att_ws, dim=0) return dict(feat_gen=outs, prob=probs, att_w=att_ws) def _add_first_frame_and_remove_last_frame(self, ys: torch.Tensor) -> torch.Tensor: ys_in = torch.cat( [ys.new_zeros((ys.shape[0], 1, ys.shape[2])), ys[:, :-1]], dim=1 ) return ys_in def _source_mask(self, ilens): """Make masks for self-attention. Args: ilens (LongTensor): Batch of lengths (B,). Returns: Tensor: Mask tensor for self-attention. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: >>> ilens = [5, 3] >>> self._source_mask(ilens) tensor([[[1, 1, 1, 1, 1], [[1, 1, 1, 0, 0]]], dtype=torch.uint8) """ x_masks = make_non_pad_mask(ilens).to(next(self.parameters()).device) return x_masks.unsqueeze(-2) def _target_mask(self, olens: torch.Tensor) -> torch.Tensor: """Make masks for masked self-attention. Args: olens (LongTensor): Batch of lengths (B,). Returns: Tensor: Mask tensor for masked self-attention. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: >>> olens = [5, 3] >>> self._target_mask(olens) tensor([[[1, 0, 0, 0, 0], [1, 1, 0, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 1]], [[1, 0, 0, 0, 0], [1, 1, 0, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]]], dtype=torch.uint8) """ y_masks = make_non_pad_mask(olens).to(next(self.parameters()).device) s_masks = subsequent_mask(y_masks.size(-1), device=y_masks.device).unsqueeze(0) return y_masks.unsqueeze(-2) & s_masks 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, adim). spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim). Returns: Tensor: Batch of integrated hidden state sequences (B, Tmax, adim). """ 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 and then apply projection spembs = F.normalize(spembs).unsqueeze(1).expand(-1, hs.size(1), -1) hs = self.projection(torch.cat([hs, spembs], dim=-1)) else: raise NotImplementedError("support only add or concat.") return hs