# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Modified from espnet(https://github.com/espnet/espnet) """Fastspeech2 related modules for paddle""" from typing import Dict from typing import Sequence from typing import Tuple import paddle import paddle.nn.functional as F from paddle import nn from typeguard import check_argument_types from paddlespeech.t2s.modules.fastspeech2_predictor.duration_predictor import DurationPredictor from paddlespeech.t2s.modules.fastspeech2_predictor.duration_predictor import DurationPredictorLoss from paddlespeech.t2s.modules.fastspeech2_predictor.length_regulator import LengthRegulator from paddlespeech.t2s.modules.fastspeech2_predictor.variance_predictor import VariancePredictor from paddlespeech.t2s.modules.fastspeech2_transformer.embedding import PositionalEncoding from paddlespeech.t2s.modules.fastspeech2_transformer.embedding import ScaledPositionalEncoding from paddlespeech.t2s.modules.fastspeech2_transformer.encoder import Encoder as TransformerEncoder from paddlespeech.t2s.modules.nets_utils import initialize from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask from paddlespeech.t2s.modules.nets_utils import make_pad_mask from paddlespeech.t2s.modules.tacotron2.decoder import Postnet class FastSpeech2(nn.Layer): """FastSpeech2 module. This is a module of FastSpeech2 described in `FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`_. Instead of quantized pitch and energy, we use token-averaged value introduced in `FastPitch: Parallel Text-to-speech with Pitch Prediction`_. .. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`: https://arxiv.org/abs/2006.04558 .. _`FastPitch: Parallel Text-to-speech with Pitch Prediction`: https://arxiv.org/abs/2006.06873 """ def __init__( self, # network structure related idim: int, odim: int, adim: int=384, aheads: int=4, elayers: int=6, eunits: int=1536, dlayers: int=6, dunits: int=1536, postnet_layers: int=5, postnet_chans: int=512, 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, encoder_type: str="transformer", decoder_type: str="transformer", # duration predictor duration_predictor_layers: int=2, duration_predictor_chans: int=384, duration_predictor_kernel_size: int=3, # energy predictor energy_predictor_layers: int=2, energy_predictor_chans: int=384, energy_predictor_kernel_size: int=3, energy_predictor_dropout: float=0.5, energy_embed_kernel_size: int=9, energy_embed_dropout: float=0.5, stop_gradient_from_energy_predictor: bool=False, # pitch predictor pitch_predictor_layers: int=2, pitch_predictor_chans: int=384, pitch_predictor_kernel_size: int=3, pitch_predictor_dropout: float=0.5, pitch_embed_kernel_size: int=9, pitch_embed_dropout: float=0.5, stop_gradient_from_pitch_predictor: bool=False, # spk emb num_speakers: int=None, spk_embed_dim: int=None, spk_embed_integration_type: str="add", # tone emb num_tones: int=None, tone_embed_dim: int=None, tone_embed_integration_type: str="add", # 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, duration_predictor_dropout_rate: float=0.1, 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, ): """Initialize FastSpeech2 module.""" assert check_argument_types() super().__init__() # store hyperparameters self.idim = idim self.odim = odim self.eos = idim - 1 self.reduction_factor = reduction_factor self.encoder_type = encoder_type self.decoder_type = decoder_type self.stop_gradient_from_pitch_predictor = stop_gradient_from_pitch_predictor self.stop_gradient_from_energy_predictor = stop_gradient_from_energy_predictor self.use_scaled_pos_enc = use_scaled_pos_enc self.spk_embed_dim = spk_embed_dim if self.spk_embed_dim is not None: self.spk_embed_integration_type = spk_embed_integration_type self.tone_embed_dim = tone_embed_dim if self.tone_embed_dim is not None: self.tone_embed_integration_type = tone_embed_integration_type # use idx 0 as padding idx self.padding_idx = 0 # initialize parameters initialize(self, init_type) if self.spk_embed_dim is not None: self.spk_embedding_table = nn.Embedding( num_embeddings=num_speakers, embedding_dim=self.spk_embed_dim, padding_idx=self.padding_idx) if self.tone_embed_dim is not None: self.tone_embedding_table = nn.Embedding( num_embeddings=num_tones, embedding_dim=self.tone_embed_dim, padding_idx=self.padding_idx) # get positional encoding class pos_enc_class = (ScaledPositionalEncoding if self.use_scaled_pos_enc else PositionalEncoding) # define encoder encoder_input_layer = nn.Embedding( num_embeddings=idim, embedding_dim=adim, padding_idx=self.padding_idx) if encoder_type == "transformer": self.encoder = TransformerEncoder( 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, ) else: raise ValueError(f"{encoder_type} is not supported.") # define additional projection for speaker embedding if self.spk_embed_dim is not None: if self.spk_embed_integration_type == "add": self.spk_projection = nn.Linear(self.spk_embed_dim, adim) else: self.spk_projection = nn.Linear(adim + self.spk_embed_dim, adim) # define additional projection for tone embedding if self.tone_embed_dim is not None: if self.tone_embed_integration_type == "add": self.tone_projection = nn.Linear(self.tone_embed_dim, adim) else: self.tone_projection = nn.Linear(adim + self.tone_embed_dim, adim) # define duration predictor self.duration_predictor = DurationPredictor( idim=adim, n_layers=duration_predictor_layers, n_chans=duration_predictor_chans, kernel_size=duration_predictor_kernel_size, dropout_rate=duration_predictor_dropout_rate, ) # define pitch predictor self.pitch_predictor = VariancePredictor( idim=adim, n_layers=pitch_predictor_layers, n_chans=pitch_predictor_chans, kernel_size=pitch_predictor_kernel_size, dropout_rate=pitch_predictor_dropout, ) # We use continuous pitch + FastPitch style avg self.pitch_embed = nn.Sequential( nn.Conv1D( in_channels=1, out_channels=adim, kernel_size=pitch_embed_kernel_size, padding=(pitch_embed_kernel_size - 1) // 2, ), nn.Dropout(pitch_embed_dropout), ) # define energy predictor self.energy_predictor = VariancePredictor( idim=adim, n_layers=energy_predictor_layers, n_chans=energy_predictor_chans, kernel_size=energy_predictor_kernel_size, dropout_rate=energy_predictor_dropout, ) # We use continuous enegy + FastPitch style avg self.energy_embed = nn.Sequential( nn.Conv1D( in_channels=1, out_channels=adim, kernel_size=energy_embed_kernel_size, padding=(energy_embed_kernel_size - 1) // 2, ), nn.Dropout(energy_embed_dropout), ) # define length regulator self.length_regulator = LengthRegulator() # define decoder # NOTE: we use encoder as decoder # because fastspeech's decoder is the same as encoder if decoder_type == "transformer": self.decoder = TransformerEncoder( idim=0, attention_dim=adim, attention_heads=aheads, linear_units=dunits, num_blocks=dlayers, # in decoder, don't need layer before pos_enc_class (we use embedding here in encoder) input_layer=None, dropout_rate=transformer_dec_dropout_rate, positional_dropout_rate=transformer_dec_positional_dropout_rate, attention_dropout_rate=transformer_dec_attn_dropout_rate, pos_enc_class=pos_enc_class, normalize_before=decoder_normalize_before, concat_after=decoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, ) else: raise ValueError(f"{decoder_type} is not supported.") # define final projection self.feat_out = nn.Linear(adim, odim * 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, )) nn.initializer.set_global_initializer(None) self._reset_parameters( init_enc_alpha=init_enc_alpha, init_dec_alpha=init_dec_alpha, ) def forward( self, text: paddle.Tensor, text_lengths: paddle.Tensor, speech: paddle.Tensor, speech_lengths: paddle.Tensor, durations: paddle.Tensor, pitch: paddle.Tensor, energy: paddle.Tensor, tone_id: paddle.Tensor=None, spembs: paddle.Tensor=None, spk_id: paddle.Tensor=None ) -> Tuple[paddle.Tensor, Dict[str, paddle.Tensor], paddle.Tensor]: """Calculate forward propagation. Parameters ---------- text : Tensor(int64) Batch of padded token ids (B, Tmax). text_lengths : Tensor(int64) Batch of lengths of each input (B,). speech : Tensor Batch of padded target features (B, Lmax, odim). speech_lengths : Tensor(int64) Batch of the lengths of each target (B,). durations : Tensor(int64) Batch of padded durations (B, Tmax). pitch : Tensor Batch of padded token-averaged pitch (B, Tmax, 1). energy : Tensor Batch of padded token-averaged energy (B, Tmax, 1). tone_id : Tensor, optional(int64) Batch of padded tone ids (B, Tmax). spembs : Tensor, optional Batch of speaker embeddings (B, spk_embed_dim). spk_id : Tnesor, optional(int64) Batch of speaker ids (B,) Returns ---------- Tensor mel outs before postnet Tensor mel outs after postnet Tensor duration predictor's output Tensor pitch predictor's output Tensor energy predictor's output Tensor speech Tensor speech_lengths, modified if reduction_factor > 1 """ # input of embedding must be int64 xs = paddle.cast(text, 'int64') ilens = paddle.cast(text_lengths, 'int64') ds = paddle.cast(durations, 'int64') olens = paddle.cast(speech_lengths, 'int64') ys = speech ps = pitch es = energy if spk_id is not None: spk_id = paddle.cast(spk_id, 'int64') if tone_id is not None: tone_id = paddle.cast(tone_id, 'int64') # forward propagation before_outs, after_outs, d_outs, p_outs, e_outs = self._forward( xs, ilens, olens, ds, ps, es, is_inference=False, spembs=spembs, spk_id=spk_id, tone_id=tone_id) # modify mod part of groundtruth if self.reduction_factor > 1: olens = paddle.to_tensor( [olen - olen % self.reduction_factor for olen in olens.numpy()]) max_olen = max(olens) ys = ys[:, :max_olen] return before_outs, after_outs, d_outs, p_outs, e_outs, ys, olens def _forward(self, xs: paddle.Tensor, ilens: paddle.Tensor, olens: paddle.Tensor=None, ds: paddle.Tensor=None, ps: paddle.Tensor=None, es: paddle.Tensor=None, is_inference: bool=False, alpha: float=1.0, spembs=None, spk_id=None, tone_id=None) -> Sequence[paddle.Tensor]: # forward encoder x_masks = self._source_mask(ilens) # (B, Tmax, adim) hs, _ = self.encoder(xs, x_masks) # integrate speaker embedding if self.spk_embed_dim is not None: if spembs is not None: hs = self._integrate_with_spk_embed(hs, spembs) elif spk_id is not None: spembs = self.spk_embedding_table(spk_id) hs = self._integrate_with_spk_embed(hs, spembs) # integrate tone embedding if self.tone_embed_dim is not None: if tone_id is not None: tone_embs = self.tone_embedding_table(tone_id) hs = self._integrate_with_tone_embed(hs, tone_embs) # forward duration predictor and variance predictors d_masks = make_pad_mask(ilens) if self.stop_gradient_from_pitch_predictor: p_outs = self.pitch_predictor(hs.detach(), d_masks.unsqueeze(-1)) else: p_outs = self.pitch_predictor(hs, d_masks.unsqueeze(-1)) if self.stop_gradient_from_energy_predictor: e_outs = self.energy_predictor(hs.detach(), d_masks.unsqueeze(-1)) else: e_outs = self.energy_predictor(hs, d_masks.unsqueeze(-1)) if is_inference: # (B, Tmax) if ds is not None: d_outs = ds else: d_outs = self.duration_predictor.inference(hs, d_masks) if ps is not None: p_outs = ps if es is not None: e_outs = es # use prediction in inference # (B, Tmax, 1) p_embs = self.pitch_embed(p_outs.transpose((0, 2, 1))).transpose( (0, 2, 1)) e_embs = self.energy_embed(e_outs.transpose((0, 2, 1))).transpose( (0, 2, 1)) hs = hs + e_embs + p_embs # (B, Lmax, adim) hs = self.length_regulator(hs, d_outs, alpha) else: d_outs = self.duration_predictor(hs, d_masks) # use groundtruth in training p_embs = self.pitch_embed(ps.transpose((0, 2, 1))).transpose( (0, 2, 1)) e_embs = self.energy_embed(es.transpose((0, 2, 1))).transpose( (0, 2, 1)) hs = hs + e_embs + p_embs # (B, Lmax, adim) hs = self.length_regulator(hs, ds) # forward decoder if olens is not None and not is_inference: if self.reduction_factor > 1: olens_in = paddle.to_tensor( [olen // self.reduction_factor for olen in olens.numpy()]) else: olens_in = olens h_masks = self._source_mask(olens_in) else: h_masks = None # (B, Lmax, adim) zs, _ = self.decoder(hs, h_masks) # (B, Lmax, odim) before_outs = self.feat_out(zs).reshape( (paddle.shape(zs)[0], -1, self.odim)) # 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((0, 2, 1))).transpose((0, 2, 1)) return before_outs, after_outs, d_outs, p_outs, e_outs def inference( self, text: paddle.Tensor, speech: paddle.Tensor=None, durations: paddle.Tensor=None, pitch: paddle.Tensor=None, energy: paddle.Tensor=None, alpha: float=1.0, use_teacher_forcing: bool=False, spembs=None, spk_id=None, tone_id=None, ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Generate the sequence of features given the sequences of characters. Parameters ---------- text : Tensor(int64) Input sequence of characters (T,). speech : Tensor, optional Feature sequence to extract style (N, idim). durations : Tensor, optional (int64) Groundtruth of duration (T,). pitch : Tensor, optional Groundtruth of token-averaged pitch (T, 1). energy : Tensor, optional Groundtruth of token-averaged energy (T, 1). alpha : float, optional Alpha to control the speed. use_teacher_forcing : bool, optional Whether to use teacher forcing. If true, groundtruth of duration, pitch and energy will be used. spembs : Tensor, optional peaker embedding vector (spk_embed_dim,). spk_id : Tensor, optional(int64) Speaker embedding vector (spk_embed_dim). tone_id : Tensor, optional(int64) Batch of padded tone ids (B, Tmax). Returns ---------- Tensor Output sequence of features (L, odim). """ # input of embedding must be int64 x = paddle.cast(text, 'int64') y = speech spemb = spembs if durations is not None: d = paddle.cast(durations, 'int64') p, e = pitch, energy # setup batch axis ilens = paddle.shape(x)[0] xs, ys = x.unsqueeze(0), None if y is not None: ys = y.unsqueeze(0) if spemb is not None: spembs = spemb.unsqueeze(0) else: spembs = None if use_teacher_forcing: # use groundtruth of duration, pitch, and energy ds = d.unsqueeze(0) if d is not None else None ps = p.unsqueeze(0) if p is not None else None es = e.unsqueeze(0) if e is not None else None # ds, ps, es = , p.unsqueeze(0), e.unsqueeze(0) # (1, L, odim) _, outs, d_outs, *_ = self._forward( xs, ilens, ys, ds=ds, ps=ps, es=es, spembs=spembs, spk_id=spk_id, tone_id=tone_id, is_inference=True) else: # (1, L, odim) _, outs, d_outs, *_ = self._forward( xs, ilens, ys, is_inference=True, alpha=alpha, spembs=spembs, spk_id=spk_id, tone_id=tone_id) return outs[0] def _integrate_with_spk_embed(self, hs, spembs): """Integrate speaker embedding with hidden states. Parameters ---------- 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.spk_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( shape=[-1, hs.shape[1], -1]) hs = self.spk_projection(paddle.concat([hs, spembs], axis=-1)) else: raise NotImplementedError("support only add or concat.") return hs def _integrate_with_tone_embed(self, hs, tone_embs): """Integrate speaker embedding with hidden states. Parameters ---------- hs : Tensor Batch of hidden state sequences (B, Tmax, adim). tone_embs : Tensor Batch of speaker embeddings (B, Tmax, tone_embed_dim). Returns ---------- Tensor Batch of integrated hidden state sequences (B, Tmax, adim) """ if self.tone_embed_integration_type == "add": # apply projection and then add to hidden states tone_embs = self.tone_projection(F.normalize(tone_embs)) hs = hs + tone_embs elif self.tone_embed_integration_type == "concat": # concat hidden states with tone embeds and then apply projection tone_embs = F.normalize(tone_embs).expand( shape=[-1, hs.shape[1], -1]) hs = self.tone_projection(paddle.concat([hs, tone_embs], axis=-1)) else: raise NotImplementedError("support only add or concat.") return hs def _source_mask(self, ilens: paddle.Tensor) -> paddle.Tensor: """Make masks for self-attention. Parameters ---------- ilens : Tensor Batch of lengths (B,). Returns ------- Tensor Mask tensor for self-attention. dtype=paddle.bool Examples ------- >>> ilens = [5, 3] >>> self._source_mask(ilens) tensor([[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0]]]) bool """ x_masks = make_non_pad_mask(ilens) return x_masks.unsqueeze(-2) def _reset_parameters(self, init_enc_alpha: float, init_dec_alpha: float): # initialize alpha in scaled positional encoding if self.encoder_type == "transformer" and self.use_scaled_pos_enc: init_enc_alpha = paddle.to_tensor(init_enc_alpha) self.encoder.embed[-1].alpha = paddle.create_parameter( shape=init_enc_alpha.shape, dtype=str(init_enc_alpha.numpy().dtype), default_initializer=paddle.nn.initializer.Assign( init_enc_alpha)) if self.decoder_type == "transformer" and self.use_scaled_pos_enc: init_dec_alpha = paddle.to_tensor(init_dec_alpha) self.decoder.embed[-1].alpha = paddle.create_parameter( shape=init_dec_alpha.shape, dtype=str(init_dec_alpha.numpy().dtype), default_initializer=paddle.nn.initializer.Assign( init_dec_alpha)) class FastSpeech2Inference(nn.Layer): def __init__(self, normalizer, model): super().__init__() self.normalizer = normalizer self.acoustic_model = model def forward(self, text, spk_id=None): normalized_mel = self.acoustic_model.inference(text, spk_id=spk_id) logmel = self.normalizer.inverse(normalized_mel) return logmel class FastSpeech2Loss(nn.Layer): """Loss function module for FastSpeech2.""" def __init__(self, use_masking: bool=True, use_weighted_masking: bool=False): """Initialize feed-forward Transformer loss module. Parameters ---------- use_masking : bool Whether to apply masking for padded part in loss calculation. use_weighted_masking : bool Whether to weighted masking in loss calculation. """ assert check_argument_types() super().__init__() assert (use_masking != use_weighted_masking) or not use_masking self.use_masking = use_masking self.use_weighted_masking = use_weighted_masking # define criterions reduction = "none" if self.use_weighted_masking else "mean" self.l1_criterion = nn.L1Loss(reduction=reduction) self.mse_criterion = nn.MSELoss(reduction=reduction) self.duration_criterion = DurationPredictorLoss(reduction=reduction) def forward( self, after_outs: paddle.Tensor, before_outs: paddle.Tensor, d_outs: paddle.Tensor, p_outs: paddle.Tensor, e_outs: paddle.Tensor, ys: paddle.Tensor, ds: paddle.Tensor, ps: paddle.Tensor, es: paddle.Tensor, ilens: paddle.Tensor, olens: paddle.Tensor, ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Calculate forward propagation. Parameters ---------- after_outs : Tensor Batch of outputs after postnets (B, Lmax, odim). before_outs : Tensor Batch of outputs before postnets (B, Lmax, odim). d_outs : Tensor Batch of outputs of duration predictor (B, Tmax). p_outs : Tensor Batch of outputs of pitch predictor (B, Tmax, 1). e_outs : Tensor Batch of outputs of energy predictor (B, Tmax, 1). ys : Tensor Batch of target features (B, Lmax, odim). ds : Tensor Batch of durations (B, Tmax). ps : Tensor Batch of target token-averaged pitch (B, Tmax, 1). es : Tensor Batch of target token-averaged energy (B, Tmax, 1). ilens : Tensor Batch of the lengths of each input (B,). olens : Tensor Batch of the lengths of each target (B,). Returns ---------- Tensor L1 loss value. Tensor Duration predictor loss value. Tensor Pitch predictor loss value. Tensor Energy predictor loss value. """ # apply mask to remove padded part if self.use_masking: out_masks = make_non_pad_mask(olens).unsqueeze(-1) before_outs = before_outs.masked_select( out_masks.broadcast_to(before_outs.shape)) if after_outs is not None: after_outs = after_outs.masked_select( out_masks.broadcast_to(after_outs.shape)) ys = ys.masked_select(out_masks.broadcast_to(ys.shape)) duration_masks = make_non_pad_mask(ilens) d_outs = d_outs.masked_select( duration_masks.broadcast_to(d_outs.shape)) ds = ds.masked_select(duration_masks.broadcast_to(ds.shape)) pitch_masks = make_non_pad_mask(ilens).unsqueeze(-1) p_outs = p_outs.masked_select( pitch_masks.broadcast_to(p_outs.shape)) e_outs = e_outs.masked_select( pitch_masks.broadcast_to(e_outs.shape)) ps = ps.masked_select(pitch_masks.broadcast_to(ps.shape)) es = es.masked_select(pitch_masks.broadcast_to(es.shape)) # calculate loss l1_loss = self.l1_criterion(before_outs, ys) if after_outs is not None: l1_loss += self.l1_criterion(after_outs, ys) duration_loss = self.duration_criterion(d_outs, ds) pitch_loss = self.mse_criterion(p_outs, ps) energy_loss = self.mse_criterion(e_outs, es) # make weighted mask and apply it if self.use_weighted_masking: out_masks = make_non_pad_mask(olens).unsqueeze(-1) out_weights = out_masks.cast(dtype=paddle.float32) / out_masks.cast( dtype=paddle.float32).sum( axis=1, keepdim=True) out_weights /= ys.shape[0] * ys.shape[2] duration_masks = make_non_pad_mask(ilens) duration_weights = (duration_masks.cast(dtype=paddle.float32) / duration_masks.cast(dtype=paddle.float32).sum( axis=1, keepdim=True)) duration_weights /= ds.shape[0] # apply weight l1_loss = l1_loss.multiply(out_weights) l1_loss = l1_loss.masked_select( out_masks.broadcast_to(l1_loss.shape)).sum() duration_loss = (duration_loss.multiply(duration_weights) .masked_select(duration_masks).sum()) pitch_masks = duration_masks.unsqueeze(-1) pitch_weights = duration_weights.unsqueeze(-1) pitch_loss = pitch_loss.multiply(pitch_weights) pitch_loss = pitch_loss.masked_select( pitch_masks.broadcast_to(pitch_loss.shape)).sum() energy_loss = energy_loss.multiply(pitch_weights) energy_loss = energy_loss.masked_select( pitch_masks.broadcast_to(energy_loss.shape)).sum() return l1_loss, duration_loss, pitch_loss, energy_loss