# Copyright (c) 2022 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. """Generator module in JETS. This code is based on https://github.com/imdanboy/jets. """ import logging import math from typing import Any from typing import Dict from typing import List from typing import Optional from typing import Sequence from typing import Tuple import numpy as np import paddle from paddle import nn from typeguard import check_argument_types from paddlespeech.t2s.models.hifigan import HiFiGANGenerator from paddlespeech.t2s.models.jets.alignments import AlignmentModule from paddlespeech.t2s.models.jets.alignments import average_by_duration from paddlespeech.t2s.models.jets.alignments import viterbi_decode from paddlespeech.t2s.models.jets.length_regulator import GaussianUpsampling from paddlespeech.t2s.modules.nets_utils import get_random_segments 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.predictor.duration_predictor import DurationPredictor from paddlespeech.t2s.modules.predictor.length_regulator import LengthRegulator from paddlespeech.t2s.modules.predictor.variance_predictor import VariancePredictor from paddlespeech.t2s.modules.style_encoder import StyleEncoder from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding from paddlespeech.t2s.modules.transformer.encoder import ConformerEncoder from paddlespeech.t2s.modules.transformer.encoder import TransformerEncoder class JETSGenerator(nn.Layer): """Generator module in JETS. """ def __init__( self, idim: int, odim: int, adim: int=256, aheads: int=2, elayers: int=4, eunits: int=1024, dlayers: int=4, dunits: int=1024, 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", 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_activation_type: str="relu", # only for conformer conformer_rel_pos_type: str="legacy", conformer_pos_enc_layer_type: str="rel_pos", conformer_self_attn_layer_type: str="rel_selfattn", conformer_activation_type: str="swish", use_macaron_style_in_conformer: bool=True, use_cnn_in_conformer: bool=True, zero_triu: bool=False, conformer_enc_kernel_size: int=7, conformer_dec_kernel_size: int=31, # duration predictor duration_predictor_layers: int=2, duration_predictor_chans: int=384, duration_predictor_kernel_size: int=3, duration_predictor_dropout_rate: float=0.1, # 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, # 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 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, segment_size: int=64, # hifigan generator generator_out_channels: int=1, generator_channels: int=512, generator_global_channels: int=-1, generator_kernel_size: int=7, generator_upsample_scales: List[int]=[8, 8, 2, 2], generator_upsample_kernel_sizes: List[int]=[16, 16, 4, 4], generator_resblock_kernel_sizes: List[int]=[3, 7, 11], generator_resblock_dilations: List[List[int]]=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], generator_use_additional_convs: bool=True, generator_bias: bool=True, generator_nonlinear_activation: str="LeakyReLU", generator_nonlinear_activation_params: Dict[ str, Any]={"negative_slope": 0.1}, generator_use_weight_norm: bool=True, ): """Initialize JETS generator module. Args: idim (int): Dimension of the inputs. odim (int): Dimension of the outputs. adim (int): Attention dimension. aheads (int): Number of attention heads. elayers (int): Number of encoder layers. eunits (int): Number of encoder hidden units. dlayers (int): Number of decoder layers. dunits (int): Number of decoder hidden units. 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. reduction_factor (int): Reduction factor. encoder_type (str): Encoder type ("transformer" or "conformer"). decoder_type (str): Decoder type ("transformer" or "conformer"). 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. conformer_rel_pos_type (str): Relative pos encoding type in conformer. conformer_pos_enc_layer_type (str): Pos encoding layer type in conformer. conformer_self_attn_layer_type (str): Self-attention layer type in conformer conformer_activation_type (str): Activation function type in conformer. use_macaron_style_in_conformer: Whether to use macaron style FFN. use_cnn_in_conformer: Whether to use CNN in conformer. zero_triu: Whether to use zero triu in relative self-attention module. conformer_enc_kernel_size: Kernel size of encoder conformer. conformer_dec_kernel_size: Kernel size of decoder conformer. duration_predictor_layers (int): Number of duration predictor layers. duration_predictor_chans (int): Number of duration predictor channels. duration_predictor_kernel_size (int): Kernel size of duration predictor. duration_predictor_dropout_rate (float): Dropout rate in duration predictor. pitch_predictor_layers (int): Number of pitch predictor layers. pitch_predictor_chans (int): Number of pitch predictor channels. pitch_predictor_kernel_size (int): Kernel size of pitch predictor. pitch_predictor_dropout_rate (float): Dropout rate in pitch predictor. pitch_embed_kernel_size (float): Kernel size of pitch embedding. pitch_embed_dropout_rate (float): Dropout rate for pitch embedding. stop_gradient_from_pitch_predictor: Whether to stop gradient from pitch predictor to encoder. energy_predictor_layers (int): Number of energy predictor layers. energy_predictor_chans (int): Number of energy predictor channels. energy_predictor_kernel_size (int): Kernel size of energy predictor. energy_predictor_dropout_rate (float): Dropout rate in energy predictor. energy_embed_kernel_size (float): Kernel size of energy embedding. energy_embed_dropout_rate (float): Dropout rate for energy embedding. stop_gradient_from_energy_predictor: Whether to stop gradient from energy predictor to encoder. 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: How to integrate speaker embedding. use_gst (str): Whether to use global style token. gst_tokens (int): The number of GST embeddings. gst_heads (int): The number of heads in GST multihead attention. gst_conv_layers (int): The 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): The number of GRU layers in GST. gst_gru_units (int): The number of GRU units in GST. 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. 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. segment_size (int): Segment size for random windowed discriminator generator_out_channels (int): Number of output channels. generator_channels (int): Number of hidden representation channels. generator_global_channels (int): Number of global conditioning channels. generator_kernel_size (int): Kernel size of initial and final conv layer. generator_upsample_scales (List[int]): List of upsampling scales. generator_upsample_kernel_sizes (List[int]): List of kernel sizes for upsample layers. generator_resblock_kernel_sizes (List[int]): List of kernel sizes for residual blocks. generator_resblock_dilations (List[List[int]]): List of list of dilations for residual blocks. generator_use_additional_convs (bool): Whether to use additional conv layers in residual blocks. generator_bias (bool): Whether to add bias parameter in convolution layers. generator_nonlinear_activation (str): Activation function module name. generator_nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation function. generator_use_weight_norm (bool): Whether to use weight norm. If set to true, it will be applied to all of the conv layers. """ super().__init__() self.segment_size = segment_size self.upsample_factor = int(np.prod(generator_upsample_scales)) self.idim = idim self.odim = odim 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.use_gst = use_gst # use idx 0 as padding idx self.padding_idx = 0 # get positional encoding layer type transformer_pos_enc_layer_type = "scaled_abs_pos" if self.use_scaled_pos_enc else "abs_pos" # check relative positional encoding compatibility if "conformer" in [encoder_type, decoder_type]: if conformer_rel_pos_type == "legacy": if conformer_pos_enc_layer_type == "rel_pos": conformer_pos_enc_layer_type = "legacy_rel_pos" logging.warning( "Fallback to conformer_pos_enc_layer_type = 'legacy_rel_pos' " "due to the compatibility. If you want to use the new one, " "please use conformer_pos_enc_layer_type = 'latest'.") if conformer_self_attn_layer_type == "rel_selfattn": conformer_self_attn_layer_type = "legacy_rel_selfattn" logging.warning( "Fallback to " "conformer_self_attn_layer_type = 'legacy_rel_selfattn' " "due to the compatibility. If you want to use the new one, " "please use conformer_pos_enc_layer_type = 'latest'.") elif conformer_rel_pos_type == "latest": assert conformer_pos_enc_layer_type != "legacy_rel_pos" assert conformer_self_attn_layer_type != "legacy_rel_selfattn" else: raise ValueError( f"Unknown rel_pos_type: {conformer_rel_pos_type}") # 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_layer_type=transformer_pos_enc_layer_type, normalize_before=encoder_normalize_before, concat_after=encoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, activation_type=transformer_activation_type) elif encoder_type == "conformer": self.encoder = ConformerEncoder( 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, normalize_before=encoder_normalize_before, concat_after=encoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, macaron_style=use_macaron_style_in_conformer, pos_enc_layer_type=conformer_pos_enc_layer_type, selfattention_layer_type=conformer_self_attn_layer_type, activation_type=conformer_activation_type, use_cnn_module=use_cnn_in_conformer, cnn_module_kernel=conformer_enc_kernel_size, zero_triu=zero_triu, ) else: raise ValueError(f"{encoder_type} is not supported.") # 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 = nn.Embedding(spks, adim) self.langs = None if langs is not None and langs > 1: self.langs = langs self.lid_emb = nn.Embedding(langs, adim) # define additional projection for speaker embedding 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 = nn.Linear(self.spk_embed_dim, adim) else: self.projection = nn.Linear(adim + self.spk_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, ) # NOTE(kan-bayashi): 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, ) # NOTE(kan-bayashi): 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 = GaussianUpsampling() # 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_layer_type=transformer_pos_enc_layer_type, normalize_before=decoder_normalize_before, concat_after=decoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, activation_type=conformer_activation_type, ) elif decoder_type == "conformer": self.decoder = ConformerEncoder( idim=0, attention_dim=adim, attention_heads=aheads, linear_units=dunits, num_blocks=dlayers, 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, normalize_before=decoder_normalize_before, concat_after=decoder_concat_after, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, macaron_style=use_macaron_style_in_conformer, pos_enc_layer_type=conformer_pos_enc_layer_type, selfattention_layer_type=conformer_self_attn_layer_type, activation_type=conformer_activation_type, use_cnn_module=use_cnn_in_conformer, cnn_module_kernel=conformer_dec_kernel_size, ) else: raise ValueError(f"{decoder_type} is not supported.") self.generator = HiFiGANGenerator( in_channels=adim, out_channels=generator_out_channels, channels=generator_channels, global_channels=generator_global_channels, kernel_size=generator_kernel_size, upsample_scales=generator_upsample_scales, upsample_kernel_sizes=generator_upsample_kernel_sizes, resblock_kernel_sizes=generator_resblock_kernel_sizes, resblock_dilations=generator_resblock_dilations, use_additional_convs=generator_use_additional_convs, bias=generator_bias, nonlinear_activation=generator_nonlinear_activation, nonlinear_activation_params=generator_nonlinear_activation_params, use_weight_norm=generator_use_weight_norm, ) self.alignment_module = AlignmentModule(adim, odim) # initialize parameters self._reset_parameters( init_type=init_type, init_enc_alpha=init_enc_alpha, init_dec_alpha=init_dec_alpha, ) def forward( self, text: paddle.Tensor, text_lengths: paddle.Tensor, feats: paddle.Tensor, feats_lengths: paddle.Tensor, durations: paddle.Tensor, durations_lengths: paddle.Tensor, pitch: paddle.Tensor, energy: paddle.Tensor, sids: Optional[paddle.Tensor]=None, spembs: Optional[paddle.Tensor]=None, lids: Optional[paddle.Tensor]=None, use_alignment_module: bool=False, ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, ], ]: """Calculate forward propagation. Args: text (Tensor): Text index tensor (B, T_text). text_lengths (Tensor): Text length tensor (B,). feats (Tensor): Feature tensor (B, aux_channels, T_feats). feats_lengths (Tensor): Feature length tensor (B,). pitch (Tensor): Batch of padded token-averaged pitch (B, T_text, 1). energy (Tensor): Batch of padded token-averaged energy (B, T_text, 1). sids (Optional[Tensor]): Speaker index tensor (B,) or (B, 1). spembs (Optional[Tensor]): Speaker embedding tensor (B, spk_embed_dim). lids (Optional[Tensor]): Language index tensor (B,) or (B, 1). use_alignment_module (bool): Whether to use alignment module. Returns: Tensor: Waveform tensor (B, 1, segment_size * upsample_factor). Tensor: binarization loss () Tensor: log probability attention matrix (B,T_feats,T_text) Tensor: Segments start index tensor (B,). Tensor: predicted duration (B,T_text) Tensor: ground-truth duration obtained from an alignment module (B,T_text) Tensor: predicted pitch (B,T_text,1) Tensor: ground-truth averaged pitch (B,T_text,1) Tensor: predicted energy (B,T_text,1) Tensor: ground-truth averaged energy (B,T_text,1) """ if use_alignment_module: text = text[:, :text_lengths.max()] # for data-parallel feats = feats[:, :feats_lengths.max()] # for data-parallel pitch = pitch[:, :durations_lengths.max()] # for data-parallel energy = energy[:, :durations_lengths.max()] # for data-parallel else: text = text[:, :text_lengths.max()] # for data-parallel feats = feats[:, :feats_lengths.max()] # for data-parallel pitch = pitch[:, :feats_lengths.max()] # for data-parallel energy = energy[:, :feats_lengths.max()] # for data-parallel # forward encoder x_masks = self._source_mask(text_lengths) hs, _ = self.encoder(text, x_masks) # (B, T_text, adim) # 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.reshape([-1])) hs = hs + sid_embs.unsqueeze(1) if self.langs is not None: lid_embs = self.lid_emb(lids.reshape([-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) # forward alignment module and obtain duration, averaged pitch, energy h_masks = make_pad_mask(text_lengths) if use_alignment_module: log_p_attn = self.alignment_module(hs, feats, h_masks) ds, bin_loss = viterbi_decode(log_p_attn, text_lengths, feats_lengths) ps = average_by_duration(ds, pitch.squeeze(-1), text_lengths, feats_lengths).unsqueeze(-1) es = average_by_duration(ds, energy.squeeze(-1), text_lengths, feats_lengths).unsqueeze(-1) else: ds = durations ps = pitch es = energy log_p_attn = attn = bin_loss = None # forward duration predictor and variance predictors if self.stop_gradient_from_pitch_predictor: p_outs = self.pitch_predictor(hs.detach(), h_masks.unsqueeze(-1)) else: p_outs = self.pitch_predictor(hs, h_masks.unsqueeze(-1)) if self.stop_gradient_from_energy_predictor: e_outs = self.energy_predictor(hs.detach(), h_masks.unsqueeze(-1)) else: e_outs = self.energy_predictor(hs, h_masks.unsqueeze(-1)) d_outs = self.duration_predictor(hs, h_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 # upsampling h_masks = make_non_pad_mask(feats_lengths) # d_masks = make_non_pad_mask(text_lengths).to(ds.device) d_masks = make_non_pad_mask(text_lengths) hs = self.length_regulator(hs, ds, h_masks, d_masks) # (B, T_feats, adim) # forward decoder h_masks = self._source_mask(feats_lengths) zs, _ = self.decoder(hs, h_masks) # (B, T_feats, adim) # get random segments z_segments, z_start_idxs = get_random_segments( zs.transpose([0, 2, 1]), feats_lengths, self.segment_size, ) # forward generator wav = self.generator(z_segments) if use_alignment_module: return wav, bin_loss, log_p_attn, z_start_idxs, d_outs, ds, p_outs, ps, e_outs, es else: return wav, None, None, z_start_idxs, d_outs, ds, p_outs, ps, e_outs, es def inference( self, text: paddle.Tensor, text_lengths: paddle.Tensor, feats: Optional[paddle.Tensor]=None, feats_lengths: Optional[paddle.Tensor]=None, pitch: Optional[paddle.Tensor]=None, energy: Optional[paddle.Tensor]=None, sids: Optional[paddle.Tensor]=None, spembs: Optional[paddle.Tensor]=None, lids: Optional[paddle.Tensor]=None, use_alignment_module: bool=False, ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Run inference. Args: text (Tensor): Input text index tensor (B, T_text,). text_lengths (Tensor): Text length tensor (B,). feats (Tensor): Feature tensor (B, T_feats, aux_channels). feats_lengths (Tensor): Feature length tensor (B,). pitch (Tensor): Pitch tensor (B, T_feats, 1) energy (Tensor): Energy tensor (B, T_feats, 1) sids (Optional[Tensor]): Speaker index tensor (B,) or (B, 1). spembs (Optional[Tensor]): Speaker embedding tensor (B, spk_embed_dim). lids (Optional[Tensor]): Language index tensor (B,) or (B, 1). use_alignment_module (bool): Whether to use alignment module. Returns: Tensor: Generated waveform tensor (B, T_wav). Tensor: Duration tensor (B, T_text). """ # forward encoder x_masks = self._source_mask(text_lengths) hs, _ = self.encoder(text, x_masks) # (B, T_text, adim) # 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) h_masks = make_pad_mask(text_lengths) if use_alignment_module: # forward alignment module and obtain duration, averaged pitch, energy log_p_attn, attn = self.alignment_module(hs, feats, h_masks) d_outs, _ = viterbi_decode(log_p_attn, text_lengths, feats_lengths) p_outs = average_by_duration(d_outs, pitch.squeeze(-1), text_lengths, feats_lengths).unsqueeze(-1) e_outs = average_by_duration(d_outs, energy.squeeze(-1), text_lengths, feats_lengths).unsqueeze(-1) else: # forward duration predictor and variance predictors p_outs = self.pitch_predictor(hs, h_masks.unsqueeze(-1)) e_outs = self.energy_predictor(hs, h_masks.unsqueeze(-1)) d_outs = self.duration_predictor.inference(hs, h_masks) 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 # upsampling if feats_lengths is not None: h_masks = make_non_pad_mask(feats_lengths) else: h_masks = None d_masks = make_non_pad_mask(text_lengths) hs = self.length_regulator(hs, d_outs, h_masks, d_masks) # (B, T_feats, adim) # forward decoder if feats_lengths is not None: h_masks = self._source_mask(feats_lengths) else: h_masks = None zs, _ = self.decoder(hs, h_masks) # (B, T_feats, adim) # forward generator wav = self.generator(zs.transpose([0, 2, 1])) return wav.squeeze(1), d_outs def _integrate_with_spk_embed(self, hs: paddle.Tensor, spembs: paddle.Tensor) -> paddle.Tensor: """Integrate speaker embedding with hidden states. Args: hs (Tensor): Batch of hidden state sequences (B, T_text, adim). spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim). Returns: Tensor: Batch of integrated hidden state sequences (B, T_text, 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.shape[1], -1) hs = self.projection(paddle.concat([hs, spembs], axis=-1)) else: raise NotImplementedError("support only add or concat.") return hs def _generate_path(self, dur: paddle.Tensor, mask: paddle.Tensor) -> paddle.Tensor: """Generate path a.k.a. monotonic attention. Args: dur (Tensor): Duration tensor (B, 1, T_text). mask (Tensor): Attention mask tensor (B, 1, T_feats, T_text). Returns: Tensor: Path tensor (B, 1, T_feats, T_text). """ b, _, t_y, t_x = paddle.shape(mask) cum_dur = paddle.cumsum(dur, -1) cum_dur_flat = paddle.reshape(cum_dur, [b * t_x]) path = paddle.arange(t_y, dtype=dur.dtype) path = path.unsqueeze(0) < cum_dur_flat.unsqueeze(1) path = paddle.reshape(path, [b, t_x, t_y]) ''' path will be like (t_x = 3, t_y = 5): [[[1., 1., 0., 0., 0.], [[[1., 1., 0., 0., 0.], [1., 1., 1., 1., 0.], --> [0., 0., 1., 1., 0.], [1., 1., 1., 1., 1.]]] [0., 0., 0., 0., 1.]]] ''' path = paddle.cast(path, dtype='float32') pad_tmp = self.pad1d(path)[:, :-1] path = path - pad_tmp return path.unsqueeze(1).transpose([0, 1, 3, 2]) * mask def _source_mask(self, ilens: paddle.Tensor) -> paddle.Tensor: """Make masks for self-attention. Args: ilens (LongTensor): Batch of lengths (B,). Returns: Tensor: Mask tensor for self-attention. dtype=paddle.uint8 Examples: >>> ilens = [5, 3] >>> self._source_mask(ilens) tensor([[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0]]], dtype=torch.uint8) """ x_masks = paddle.to_tensor(make_non_pad_mask(ilens)) return x_masks.unsqueeze(-2) def _reset_parameters(self, init_type: str, init_enc_alpha: float, init_dec_alpha: float): # initialize parameters initialize(self, init_type) # initialize alpha in scaled positional encoding if self.encoder_type == "transformer" and self.use_scaled_pos_enc: self.encoder.embed[-1].alpha.data = paddle.to_tensor(init_enc_alpha) if self.decoder_type == "transformer" and self.use_scaled_pos_enc: self.decoder.embed[-1].alpha.data = paddle.to_tensor(init_dec_alpha)