# Copyright 2019 Tomoki Hayashi # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) """TTS-Transformer related modules.""" import logging import torch import torch.nn.functional as F from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import GuidedAttentionLoss from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import ( Tacotron2Loss as TransformerLoss, ) from espnet.nets.pytorch_backend.nets_utils import make_non_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.initializer import initialize from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask from espnet.nets.tts_interface import TTSInterface from espnet.utils.cli_utils import strtobool from espnet.utils.fill_missing_args import fill_missing_args class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss): """Guided attention loss function module for multi head attention. Args: sigma (float, optional): Standard deviation to control how close attention to a diagonal. alpha (float, optional): Scaling coefficient (lambda). reset_always (bool, optional): Whether to always reset masks. """ def forward(self, att_ws, ilens, olens): """Calculate forward propagation. Args: att_ws (Tensor): Batch of multi head attention weights (B, H, T_max_out, T_max_in). ilens (LongTensor): Batch of input lengths (B,). olens (LongTensor): Batch of output lengths (B,). Returns: Tensor: Guided attention loss value. """ if self.guided_attn_masks is None: self.guided_attn_masks = ( self._make_guided_attention_masks(ilens, olens) .to(att_ws.device) .unsqueeze(1) ) if self.masks is None: self.masks = self._make_masks(ilens, olens).to(att_ws.device).unsqueeze(1) losses = self.guided_attn_masks * att_ws loss = torch.mean(losses.masked_select(self.masks)) if self.reset_always: self._reset_masks() return self.alpha * loss try: from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport except (ImportError, TypeError): TTSPlot = None else: class TTSPlot(PlotAttentionReport): """Attention plot module for TTS-Transformer.""" def plotfn( self, data_dict, uttid_list, attn_dict, outdir, suffix="png", savefn=None ): """Plot multi head attentions. Args: data_dict (dict): Utts info from json file. uttid_list (list): List of utt_id. attn_dict (dict): Multi head attention dict. Values should be numpy.ndarray (H, L, T) outdir (str): Directory name to save figures. suffix (str): Filename suffix including image type (e.g., png). savefn (function): Function to save figures. """ import matplotlib.pyplot as plt from espnet.nets.pytorch_backend.transformer.plot import ( # noqa: H301 _plot_and_save_attention, ) for name, att_ws in attn_dict.items(): for utt_id, att_w in zip(uttid_list, att_ws): filename = "%s/%s.%s.%s" % (outdir, utt_id, name, suffix) if "fbank" in name: fig = plt.Figure() ax = fig.subplots(1, 1) ax.imshow(att_w, aspect="auto") ax.set_xlabel("frames") ax.set_ylabel("fbank coeff") fig.tight_layout() else: fig = _plot_and_save_attention(att_w, filename) savefn(fig, filename) class Transformer(TTSInterface, torch.nn.Module): """Text-to-Speech Transformer module. This is a module of text-to-speech Transformer described in `Neural Speech Synthesis with Transformer Network`_, which convert the sequence of characters or phonemes into the sequence of Mel-filterbanks. .. _`Neural Speech Synthesis with Transformer Network`: https://arxiv.org/pdf/1809.08895.pdf """ @staticmethod def add_arguments(parser): """Add model-specific arguments to the parser.""" group = parser.add_argument_group("transformer model setting") # network structure related group.add_argument( "--embed-dim", default=512, type=int, help="Dimension of character embedding in encoder prenet", ) group.add_argument( "--eprenet-conv-layers", default=3, type=int, help="Number of encoder prenet convolution layers", ) group.add_argument( "--eprenet-conv-chans", default=256, type=int, help="Number of encoder prenet convolution channels", ) group.add_argument( "--eprenet-conv-filts", default=5, type=int, help="Filter size of encoder prenet convolution", ) group.add_argument( "--dprenet-layers", default=2, type=int, help="Number of decoder prenet layers", ) group.add_argument( "--dprenet-units", default=256, type=int, help="Number of decoder prenet hidden units", ) group.add_argument( "--elayers", default=3, type=int, help="Number of encoder layers" ) group.add_argument( "--eunits", default=1536, type=int, help="Number of encoder hidden units" ) group.add_argument( "--adim", default=384, type=int, help="Number of attention transformation dimensions", ) group.add_argument( "--aheads", default=4, type=int, help="Number of heads for multi head attention", ) group.add_argument( "--dlayers", default=3, type=int, help="Number of decoder layers" ) group.add_argument( "--dunits", default=1536, type=int, help="Number of decoder hidden units" ) group.add_argument( "--positionwise-layer-type", default="linear", type=str, choices=["linear", "conv1d", "conv1d-linear"], help="Positionwise layer type.", ) group.add_argument( "--positionwise-conv-kernel-size", default=1, type=int, help="Kernel size of positionwise conv1d layer", ) group.add_argument( "--postnet-layers", default=5, type=int, help="Number of postnet layers" ) group.add_argument( "--postnet-chans", default=256, type=int, help="Number of postnet channels" ) group.add_argument( "--postnet-filts", default=5, type=int, help="Filter size of postnet" ) group.add_argument( "--use-scaled-pos-enc", default=True, type=strtobool, help="Use trainable scaled positional encoding " "instead of the fixed scale one.", ) group.add_argument( "--use-batch-norm", default=True, type=strtobool, help="Whether to use batch normalization", ) group.add_argument( "--encoder-normalize-before", default=False, type=strtobool, help="Whether to apply layer norm before encoder block", ) group.add_argument( "--decoder-normalize-before", default=False, type=strtobool, help="Whether to apply layer norm before decoder block", ) group.add_argument( "--encoder-concat-after", default=False, type=strtobool, help="Whether to concatenate attention layer's input and output in encoder", ) group.add_argument( "--decoder-concat-after", default=False, type=strtobool, help="Whether to concatenate attention layer's input and output in decoder", ) group.add_argument( "--reduction-factor", default=1, type=int, help="Reduction factor" ) group.add_argument( "--spk-embed-dim", default=None, type=int, help="Number of speaker embedding dimensions", ) group.add_argument( "--spk-embed-integration-type", type=str, default="add", choices=["add", "concat"], help="How to integrate speaker embedding", ) # training related group.add_argument( "--transformer-init", type=str, default="pytorch", choices=[ "pytorch", "xavier_uniform", "xavier_normal", "kaiming_uniform", "kaiming_normal", ], help="How to initialize transformer parameters", ) group.add_argument( "--initial-encoder-alpha", type=float, default=1.0, help="Initial alpha value in encoder's ScaledPositionalEncoding", ) group.add_argument( "--initial-decoder-alpha", type=float, default=1.0, help="Initial alpha value in decoder's ScaledPositionalEncoding", ) group.add_argument( "--transformer-lr", default=1.0, type=float, help="Initial value of learning rate", ) group.add_argument( "--transformer-warmup-steps", default=4000, type=int, help="Optimizer warmup steps", ) group.add_argument( "--transformer-enc-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer encoder except for attention", ) group.add_argument( "--transformer-enc-positional-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer encoder positional encoding", ) group.add_argument( "--transformer-enc-attn-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer encoder self-attention", ) group.add_argument( "--transformer-dec-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer decoder " "except for attention and pos encoding", ) group.add_argument( "--transformer-dec-positional-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer decoder positional encoding", ) group.add_argument( "--transformer-dec-attn-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer decoder self-attention", ) group.add_argument( "--transformer-enc-dec-attn-dropout-rate", default=0.1, type=float, help="Dropout rate for transformer encoder-decoder attention", ) group.add_argument( "--eprenet-dropout-rate", default=0.5, type=float, help="Dropout rate in encoder prenet", ) group.add_argument( "--dprenet-dropout-rate", default=0.5, type=float, help="Dropout rate in decoder prenet", ) group.add_argument( "--postnet-dropout-rate", default=0.5, type=float, help="Dropout rate in postnet", ) group.add_argument( "--pretrained-model", default=None, type=str, help="Pretrained model path" ) # loss related group.add_argument( "--use-masking", default=True, type=strtobool, help="Whether to use masking in calculation of loss", ) group.add_argument( "--use-weighted-masking", default=False, type=strtobool, help="Whether to use weighted masking in calculation of loss", ) group.add_argument( "--loss-type", default="L1", choices=["L1", "L2", "L1+L2"], help="How to calc loss", ) group.add_argument( "--bce-pos-weight", default=5.0, type=float, help="Positive sample weight in BCE calculation " "(only for use-masking=True)", ) group.add_argument( "--use-guided-attn-loss", default=False, type=strtobool, help="Whether to use guided attention loss", ) group.add_argument( "--guided-attn-loss-sigma", default=0.4, type=float, help="Sigma in guided attention loss", ) group.add_argument( "--guided-attn-loss-lambda", default=1.0, type=float, help="Lambda in guided attention loss", ) group.add_argument( "--num-heads-applied-guided-attn", default=2, type=int, help="Number of heads in each layer to be applied guided attention loss" "if set -1, all of the heads will be applied.", ) group.add_argument( "--num-layers-applied-guided-attn", default=2, type=int, help="Number of layers to be applied guided attention loss" "if set -1, all of the layers will be applied.", ) group.add_argument( "--modules-applied-guided-attn", type=str, nargs="+", default=["encoder-decoder"], help="Module name list to be applied guided attention loss", ) return parser @property def attention_plot_class(self): """Return plot class for attention weight plot.""" return TTSPlot def __init__(self, idim, odim, args=None): """Initialize TTS-Transformer module. Args: idim (int): Dimension of the inputs. odim (int): Dimension of the outputs. args (Namespace, optional): - 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 positional encoding. - use_batch_norm (bool): Whether to use batch normalization in encoder prenet. - encoder_normalize_before (bool): Whether to perform layer normalization before encoder block. - decoder_normalize_before (bool): Whether to perform layer normalization 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. - reduction_factor (int): Reduction factor. - spk_embed_dim (int): Number of speaker embedding dimenstions. - spk_embed_integration_type: How to integrate speaker embedding. - transformer_init (float): How to initialize transformer parameters. - 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 & 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 deocoder self-attention module. - transformer_enc_dec_attn_dropout_rate (float): Dropout rate in encoder-deocoder attention module. - 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 (list): 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. """ # initialize base classes TTSInterface.__init__(self) torch.nn.Module.__init__(self) # fill missing arguments args = fill_missing_args(args, self.add_arguments) # store hyperparameters self.idim = idim self.odim = odim self.spk_embed_dim = args.spk_embed_dim if self.spk_embed_dim is not None: self.spk_embed_integration_type = args.spk_embed_integration_type self.use_scaled_pos_enc = args.use_scaled_pos_enc self.reduction_factor = args.reduction_factor self.loss_type = args.loss_type self.use_guided_attn_loss = args.use_guided_attn_loss if self.use_guided_attn_loss: if args.num_layers_applied_guided_attn == -1: self.num_layers_applied_guided_attn = args.elayers else: self.num_layers_applied_guided_attn = ( args.num_layers_applied_guided_attn ) if args.num_heads_applied_guided_attn == -1: self.num_heads_applied_guided_attn = args.aheads else: self.num_heads_applied_guided_attn = args.num_heads_applied_guided_attn self.modules_applied_guided_attn = args.modules_applied_guided_attn # use idx 0 as padding idx padding_idx = 0 # get positional encoding class pos_enc_class = ( ScaledPositionalEncoding if self.use_scaled_pos_enc else PositionalEncoding ) # define transformer encoder if args.eprenet_conv_layers != 0: # encoder prenet encoder_input_layer = torch.nn.Sequential( EncoderPrenet( idim=idim, embed_dim=args.embed_dim, elayers=0, econv_layers=args.eprenet_conv_layers, econv_chans=args.eprenet_conv_chans, econv_filts=args.eprenet_conv_filts, use_batch_norm=args.use_batch_norm, dropout_rate=args.eprenet_dropout_rate, padding_idx=padding_idx, ), torch.nn.Linear(args.eprenet_conv_chans, args.adim), ) else: encoder_input_layer = torch.nn.Embedding( num_embeddings=idim, embedding_dim=args.adim, padding_idx=padding_idx ) self.encoder = Encoder( idim=idim, attention_dim=args.adim, attention_heads=args.aheads, linear_units=args.eunits, num_blocks=args.elayers, input_layer=encoder_input_layer, dropout_rate=args.transformer_enc_dropout_rate, positional_dropout_rate=args.transformer_enc_positional_dropout_rate, attention_dropout_rate=args.transformer_enc_attn_dropout_rate, pos_enc_class=pos_enc_class, normalize_before=args.encoder_normalize_before, concat_after=args.encoder_concat_after, positionwise_layer_type=args.positionwise_layer_type, positionwise_conv_kernel_size=args.positionwise_conv_kernel_size, ) # define projection layer if self.spk_embed_dim is not None: if self.spk_embed_integration_type == "add": self.projection = torch.nn.Linear(self.spk_embed_dim, args.adim) else: self.projection = torch.nn.Linear( args.adim + self.spk_embed_dim, args.adim ) # define transformer decoder if args.dprenet_layers != 0: # decoder prenet decoder_input_layer = torch.nn.Sequential( DecoderPrenet( idim=odim, n_layers=args.dprenet_layers, n_units=args.dprenet_units, dropout_rate=args.dprenet_dropout_rate, ), torch.nn.Linear(args.dprenet_units, args.adim), ) else: decoder_input_layer = "linear" self.decoder = Decoder( odim=-1, attention_dim=args.adim, attention_heads=args.aheads, linear_units=args.dunits, num_blocks=args.dlayers, dropout_rate=args.transformer_dec_dropout_rate, positional_dropout_rate=args.transformer_dec_positional_dropout_rate, self_attention_dropout_rate=args.transformer_dec_attn_dropout_rate, src_attention_dropout_rate=args.transformer_enc_dec_attn_dropout_rate, input_layer=decoder_input_layer, use_output_layer=False, pos_enc_class=pos_enc_class, normalize_before=args.decoder_normalize_before, concat_after=args.decoder_concat_after, ) # define final projection self.feat_out = torch.nn.Linear(args.adim, odim * args.reduction_factor) self.prob_out = torch.nn.Linear(args.adim, args.reduction_factor) # define postnet self.postnet = ( None if args.postnet_layers == 0 else Postnet( idim=idim, odim=odim, n_layers=args.postnet_layers, n_chans=args.postnet_chans, n_filts=args.postnet_filts, use_batch_norm=args.use_batch_norm, dropout_rate=args.postnet_dropout_rate, ) ) # define loss function self.criterion = TransformerLoss( use_masking=args.use_masking, use_weighted_masking=args.use_weighted_masking, bce_pos_weight=args.bce_pos_weight, ) if self.use_guided_attn_loss: self.attn_criterion = GuidedMultiHeadAttentionLoss( sigma=args.guided_attn_loss_sigma, alpha=args.guided_attn_loss_lambda, ) # initialize parameters self._reset_parameters( init_type=args.transformer_init, init_enc_alpha=args.initial_encoder_alpha, init_dec_alpha=args.initial_decoder_alpha, ) # load pretrained model if args.pretrained_model is not None: self.load_pretrained_model(args.pretrained_model) def _reset_parameters(self, init_type, init_enc_alpha=1.0, init_dec_alpha=1.0): # initialize parameters 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 _add_first_frame_and_remove_last_frame(self, ys): ys_in = torch.cat( [ys.new_zeros((ys.shape[0], 1, ys.shape[2])), ys[:, :-1]], dim=1 ) return ys_in def forward(self, xs, ilens, ys, labels, olens, spembs=None, *args, **kwargs): """Calculate forward propagation. Args: xs (Tensor): Batch of padded character ids (B, Tmax). ilens (LongTensor): Batch of lengths of each input batch (B,). ys (Tensor): Batch of padded target features (B, Lmax, odim). olens (LongTensor): Batch of the lengths of each target (B,). spembs (Tensor, optional): Batch of speaker embedding vectors (B, spk_embed_dim). Returns: Tensor: Loss value. """ # remove unnecessary padded part (for multi-gpus) max_ilen = max(ilens) max_olen = max(olens) if max_ilen != xs.shape[1]: xs = xs[:, :max_ilen] if max_olen != ys.shape[1]: ys = ys[:, :max_olen] labels = labels[:, :max_olen] # forward encoder x_masks = self._source_mask(ilens).to(xs.device) hs, h_masks = self.encoder(xs, x_masks) # 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, Lmax, odim) -> (B, Lmax//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).to(xs.device) zs, _ = self.decoder(ys_in, y_masks, hs, h_masks) # (B, Lmax//r, odim * r) -> (B, Lmax//r * r, odim) before_outs = self.feat_out(zs).view(zs.size(0), -1, self.odim) # (B, Lmax//r, r) -> (B, Lmax//r * r) logits = self.prob_out(zs).view(zs.size(0), -1) # postnet -> (B, Lmax//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) # modifiy 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_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) report_keys = [ {"l1_loss": l1_loss.item()}, {"l2_loss": l2_loss.item()}, {"bce_loss": bce_loss.item()}, {"loss": 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_in, T_in) enc_attn_loss = self.attn_criterion(att_ws, ilens, ilens) loss = loss + enc_attn_loss report_keys += [{"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_out, T_out) dec_attn_loss = self.attn_criterion(att_ws, olens_in, olens_in) loss = loss + dec_attn_loss report_keys += [{"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_out, T_in) enc_dec_attn_loss = self.attn_criterion(att_ws, ilens, olens_in) loss = loss + enc_dec_attn_loss report_keys += [{"enc_dec_attn_loss": enc_dec_attn_loss.item()}] # report extra information if self.use_scaled_pos_enc: report_keys += [ {"encoder_alpha": self.encoder.embed[-1].alpha.data.item()}, {"decoder_alpha": self.decoder.embed[-1].alpha.data.item()}, ] self.reporter.report(report_keys) return loss def inference(self, x, inference_args, spemb=None, *args, **kwargs): """Generate the sequence of features given the sequences of characters. Args: x (Tensor): Input sequence of characters (T,). inference_args (Namespace): - threshold (float): Threshold in inference. - minlenratio (float): Minimum length ratio in inference. - maxlenratio (float): Maximum length ratio in inference. spemb (Tensor, optional): Speaker embedding vector (spk_embed_dim). Returns: Tensor: Output sequence of features (L, odim). Tensor: Output sequence of stop probabilities (L,). Tensor: Encoder-decoder (source) attention weights (#layers, #heads, L, T). """ # get options threshold = inference_args.threshold minlenratio = inference_args.minlenratio maxlenratio = inference_args.maxlenratio use_att_constraint = getattr( inference_args, "use_att_constraint", False ) # keep compatibility if use_att_constraint: logging.warning( "Attention constraint is not yet supported in Transformer. Not enabled." ) # forward encoder xs = x.unsqueeze(0) hs, _ = self.encoder(xs, None) # 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) ) # (L, odim) -> (1, L, odim) -> (1, odim, L) if self.postnet is not None: outs = outs + self.postnet(outs) # (1, odim, L) outs = outs.transpose(2, 1).squeeze(0) # (L, odim) probs = torch.cat(probs, dim=0) break # concatenate attention weights -> (#layers, #heads, L, T) att_ws = torch.stack(att_ws, dim=0) return outs, probs, att_ws def calculate_all_attentions( self, xs, ilens, ys, olens, spembs=None, skip_output=False, keep_tensor=False, *args, **kwargs ): """Calculate all of the attention weights. Args: xs (Tensor): Batch of padded character ids (B, Tmax). ilens (LongTensor): Batch of lengths of each input batch (B,). ys (Tensor): Batch of padded target features (B, Lmax, odim). olens (LongTensor): Batch of the lengths of each target (B,). spembs (Tensor, optional): Batch of speaker embedding vectors (B, spk_embed_dim). skip_output (bool, optional): Whether to skip calculate the final output. keep_tensor (bool, optional): Whether to keep original tensor. Returns: dict: Dict of attention weights and outputs. """ self.eval() with torch.no_grad(): # forward encoder x_masks = self._source_mask(ilens).to(xs.device) hs, h_masks = self.encoder(xs, x_masks) # 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, Lmax, odim) -> (B, Lmax//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).to(xs.device) zs, _ = self.decoder(ys_in, y_masks, hs, h_masks) # calculate final outputs if not skip_output: before_outs = self.feat_out(zs).view(zs.size(0), -1, self.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) # modifiy mod part of output lengths due to reduction factor > 1 if self.reduction_factor > 1: olens = olens.new([olen - olen % self.reduction_factor for olen in olens]) # store into dict att_ws_dict = dict() if keep_tensor: for name, m in self.named_modules(): if isinstance(m, MultiHeadedAttention): att_ws_dict[name] = m.attn if not skip_output: att_ws_dict["before_postnet_fbank"] = before_outs att_ws_dict["after_postnet_fbank"] = after_outs else: for name, m in self.named_modules(): if isinstance(m, MultiHeadedAttention): attn = m.attn.cpu().numpy() if "encoder" in name: attn = [a[:, :l, :l] for a, l in zip(attn, ilens.tolist())] elif "decoder" in name: if "src" in name: attn = [ a[:, :ol, :il] for a, il, ol in zip( attn, ilens.tolist(), olens_in.tolist() ) ] elif "self" in name: attn = [ a[:, :l, :l] for a, l in zip(attn, olens_in.tolist()) ] else: logging.warning("unknown attention module: " + name) else: logging.warning("unknown attention module: " + name) att_ws_dict[name] = attn if not skip_output: before_outs = before_outs.cpu().numpy() after_outs = after_outs.cpu().numpy() att_ws_dict["before_postnet_fbank"] = [ m[:l].T for m, l in zip(before_outs, olens.tolist()) ] att_ws_dict["after_postnet_fbank"] = [ m[:l].T for m, l in zip(after_outs, olens.tolist()) ] self.train() return att_ws_dict def _integrate_with_spk_embed(self, hs, spembs): """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 def _source_mask(self, ilens): """Make masks for self-attention. Args: ilens (LongTensor or List): 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) return x_masks.unsqueeze(-2) def _target_mask(self, olens): """Make masks for masked self-attention. Args: olens (LongTensor or List): 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) s_masks = subsequent_mask(y_masks.size(-1), device=y_masks.device).unsqueeze(0) return y_masks.unsqueeze(-2) & s_masks @property def base_plot_keys(self): """Return base key names to plot during training. keys should match what `chainer.reporter` reports. If you add the key `loss`, the reporter will report `main/loss` and `validation/main/loss` values. also `loss.png` will be created as a figure visulizing `main/loss` and `validation/main/loss` values. Returns: list: List of strings which are base keys to plot during training. """ plot_keys = ["loss", "l1_loss", "l2_loss", "bce_loss"] if self.use_scaled_pos_enc: plot_keys += ["encoder_alpha", "decoder_alpha"] if self.use_guided_attn_loss: if "encoder" in self.modules_applied_guided_attn: plot_keys += ["enc_attn_loss"] if "decoder" in self.modules_applied_guided_attn: plot_keys += ["dec_attn_loss"] if "encoder-decoder" in self.modules_applied_guided_attn: plot_keys += ["enc_dec_attn_loss"] return plot_keys