# Copyright 2020 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Fastspeech2 related modules for ESPnet2."""
import logging
from typing import Dict, Optional, Sequence, Tuple
import torch
import torch.nn.functional as F
from typeguard import typechecked
from espnet2.torch_utils.device_funcs import force_gatherable
from espnet2.torch_utils.initialize import initialize
from espnet2.tts2.abs_tts2 import AbsTTS2
from espnet2.tts2.fastspeech2.loss import FastSpeech2LossDiscrete
from espnet2.tts.fastspeech2.variance_predictor import VariancePredictor
from espnet.nets.pytorch_backend.conformer.encoder import Encoder as ConformerEncoder
from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictor
from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
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.transformer.embedding import (
PositionalEncoding,
ScaledPositionalEncoding,
)
from espnet.nets.pytorch_backend.transformer.encoder import (
Encoder as TransformerEncoder,
)
[docs]class FastSpeech2Discrete(AbsTTS2):
"""FastSpeech2 module with discrete output.
This is a module of discrete-output Fastspeech2: it uses the same
Fastspeech2 architecture as tts1, but with discrete token as output.
"""
@typechecked
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,
postnet_dropout_rate: float = 0.5,
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",
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,
# 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",
# 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,
ignore_id: int = 0, # adjust this in collate_fn (default -1)
):
"""Initialize FastSpeech2 module.
Args:
idim (int): Dimension of the inputs.
odim (int): Dimension of the outputs.
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.
postnet_layers (int): Number of postnet layers.
postnet_chans (int): Number of postnet channels.
postnet_filts (int): Kernel size of postnet.
postnet_dropout_rate (float): Dropout rate in 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.
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.
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.
"""
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
# 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
)
# 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 = torch.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,
)
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 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 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 = torch.nn.Linear(self.spk_embed_dim, adim)
else:
self.projection = torch.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 = torch.nn.Sequential(
torch.nn.Conv1d(
in_channels=1,
out_channels=adim,
kernel_size=pitch_embed_kernel_size,
padding=(pitch_embed_kernel_size - 1) // 2,
),
torch.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 = torch.nn.Sequential(
torch.nn.Conv1d(
in_channels=1,
out_channels=adim,
kernel_size=energy_embed_kernel_size,
padding=(energy_embed_kernel_size - 1) // 2,
),
torch.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,
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,
)
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.")
# define final projection
self.feat_out = torch.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,
)
)
# initialize parameters
self._reset_parameters(
init_type=init_type,
init_enc_alpha=init_enc_alpha,
init_dec_alpha=init_dec_alpha,
)
# define criterions
self.criterion = FastSpeech2LossDiscrete(
use_masking=use_masking,
use_weighted_masking=use_weighted_masking,
ignore_id=ignore_id,
)
[docs] def forward(
self,
text: torch.Tensor,
text_lengths: torch.Tensor,
discrete_feats: torch.Tensor,
discrete_feats_lengths: torch.Tensor,
durations: torch.Tensor,
durations_lengths: torch.Tensor,
pitch: torch.Tensor,
pitch_lengths: torch.Tensor,
energy: torch.Tensor,
energy_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 token ids (B, T_text).
text_lengths (LongTensor): Batch of lengths of each input (B,).
discrete_feats (Tensor): Discrete speech tensor (B, T_token).
discrete_feats_lengths (LongTensor): Discrete speech length tensor (B,).
durations (LongTensor): Batch of padded durations (B, T_text + 1).
durations_lengths (LongTensor): Batch of duration lengths (B, T_text + 1).
pitch (Tensor): Batch of padded token-averaged pitch (B, T_text + 1, 1).
pitch_lengths (LongTensor): Batch of pitch lengths (B, T_text + 1).
energy (Tensor): Batch of padded token-averaged energy (B, T_text + 1, 1).
energy_lengths (LongTensor): Batch of energy lengths (B, T_text + 1).
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
discrete_feats = discrete_feats[
:, : discrete_feats_lengths.max()
] # for data-parallel
durations = durations[:, : durations_lengths.max()] # for data-parallel
pitch = pitch[:, : pitch_lengths.max()] # for data-parallel
energy = energy[:, : energy_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, ds, ps, es = discrete_feats, durations, pitch, energy
olens = discrete_feats_lengths
# forward propagation
before_outs, after_outs, d_outs, p_outs, e_outs = self._forward(
xs,
ilens,
ys,
olens,
ds,
ps,
es,
spembs=spembs,
sids=sids,
lids=lids,
is_inference=False,
)
# modify mod part of groundtruth
if self.reduction_factor > 1:
olens = olens.new([olen - olen % self.reduction_factor for olen in olens])
max_olen = max(olens)
ys = ys[:, :max_olen]
# calculate loss
if self.postnet is None:
after_outs = None
# calculate loss
# (Jinchuan): sometimes the duration and discrete target can have
# small mismatch. Cut it.
(
ce_loss,
duration_loss,
pitch_loss,
energy_loss,
before_acc,
after_acc,
) = self.criterion(
after_outs=after_outs,
before_outs=before_outs,
d_outs=d_outs,
p_outs=p_outs,
e_outs=e_outs,
ys=ys,
ds=ds,
ps=ps,
es=es,
ilens=ilens,
olens=olens,
)
loss = ce_loss + duration_loss + pitch_loss + energy_loss
stats = dict(
ce_loss=ce_loss.item(),
duration_loss=duration_loss.item(),
pitch_loss=pitch_loss.item(),
energy_loss=energy_loss.item(),
before_acc=before_acc.item(),
)
if after_acc is not None:
stats.update(after_acc=after_acc)
# report extra information
if self.encoder_type == "transformer" and self.use_scaled_pos_enc:
stats.update(
encoder_alpha=self.encoder.embed[-1].alpha.data.item(),
)
if self.decoder_type == "transformer" and self.use_scaled_pos_enc:
stats.update(
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 if after_outs is not None else before_outs
def _forward(
self,
xs: torch.Tensor,
ilens: torch.Tensor,
ys: Optional[torch.Tensor] = None,
olens: Optional[torch.Tensor] = None,
ds: Optional[torch.Tensor] = None,
ps: Optional[torch.Tensor] = None,
es: Optional[torch.Tensor] = None,
spembs: Optional[torch.Tensor] = None,
sids: Optional[torch.Tensor] = None,
lids: Optional[torch.Tensor] = None,
is_inference: bool = False,
alpha: float = 1.0,
) -> Sequence[torch.Tensor]:
# forward encoder
x_masks = self._source_mask(ilens)
hs, _ = self.encoder(xs, x_masks) # (B, T_text, adim)
# 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
# (Jinchuan): add or concat; with linear -> same feature dim
if self.spk_embed_dim is not None:
hs = self._integrate_with_spk_embed(hs, spembs)
# forward duration predictor and variance predictors
d_masks = make_pad_mask(ilens).to(xs.device)
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:
d_outs = self.duration_predictor.inference(hs, d_masks) # (B, T_text)
# use prediction in inference
p_embs = self.pitch_embed(p_outs.transpose(1, 2)).transpose(1, 2)
e_embs = self.energy_embed(e_outs.transpose(1, 2)).transpose(1, 2)
hs = hs + e_embs + p_embs
hs = self.length_regulator(hs, d_outs, alpha) # (B, T_feats, adim)
else:
d_outs = self.duration_predictor(hs, d_masks)
# use groundtruth in training
p_embs = self.pitch_embed(ps.transpose(1, 2)).transpose(1, 2)
e_embs = self.energy_embed(es.transpose(1, 2)).transpose(1, 2)
hs = hs + e_embs + p_embs
hs = self.length_regulator(hs, ds) # (B, T_feats, adim)
# Length mismatch between duration and discrete target
if ys is not None:
minlen = min(ys.size(1), hs.size(1))
hs, ys = hs[:, :minlen], ys[:, :minlen]
# forward decoder
if olens is not None and not is_inference:
if self.reduction_factor > 1:
olens_in = olens.new(
[
torch.div(olen, self.reduction_factor, rounding_mode="trunc")
for olen in olens
]
)
else:
olens_in = olens
h_masks = self._source_mask(olens_in)
else:
h_masks = None
zs, _ = self.decoder(hs, h_masks) # (B, T_feats, adim)
before_outs = self.feat_out(zs).view(
zs.size(0), -1, self.odim
) # (B, T_feats, odim)
# 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 before_outs, after_outs, d_outs, p_outs, e_outs
[docs] def inference(
self,
text: torch.Tensor,
durations: Optional[torch.Tensor] = None,
spembs: torch.Tensor = None,
sids: Optional[torch.Tensor] = None,
lids: Optional[torch.Tensor] = None,
pitch: Optional[torch.Tensor] = None,
energy: Optional[torch.Tensor] = None,
alpha: float = 1.0,
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,).
durations (Optional[Tensor): Groundtruth of duration (T_text + 1,).
spembs (Optional[Tensor): Speaker embedding vector (spk_embed_dim,).
sids (Optional[Tensor]): Speaker ID (1,).
lids (Optional[Tensor]): Language ID (1,).
pitch (Optional[Tensor]): Groundtruth of token-avg pitch (T_text + 1, 1).
energy (Optional[Tensor]): Groundtruth of token-avg energy (T_text + 1, 1).
alpha (float): Alpha to control the speed.
use_teacher_forcing (bool): Whether to use teacher forcing.
If true, groundtruth of duration, pitch and energy will be used.
Returns:
Dict[str, Tensor]: Output dict including the following items:
* feat_gen (Tensor): Output sequence of features (T_feats, odim).
* duration (Tensor): Duration sequence (T_text + 1,).
* pitch (Tensor): Pitch sequence (T_text + 1,).
* energy (Tensor): Energy sequence (T_text + 1,).
"""
spemb, d, p, e = spembs, durations, pitch, energy
# add eos at the last of sequence
x = F.pad(text, [0, 1], "constant", self.eos)
# setup batch axis
ilens = torch.tensor([x.shape[0]], dtype=torch.long, device=x.device)
xs, ys = x.unsqueeze(0), None
if spemb is not None:
spembs = spemb.unsqueeze(0)
if use_teacher_forcing:
# use groundtruth of duration, pitch, and energy
ds, ps, es = d.unsqueeze(0), p.unsqueeze(0), e.unsqueeze(0)
_, outs, d_outs, p_outs, e_outs = self._forward(
xs,
ilens,
ys,
ds=ds,
ps=ps,
es=es,
spembs=spembs,
sids=sids,
lids=lids,
) # (1, T_feats, odim)
else:
_, outs, d_outs, p_outs, e_outs = self._forward(
xs,
ilens,
ys,
spembs=spembs,
sids=sids,
lids=lids,
is_inference=True,
alpha=alpha,
) # (1, T_feats, odim)
return dict(
feat_gen=outs[0],
duration=d_outs[0],
pitch=p_outs[0],
energy=e_outs[0],
)
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, 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.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: torch.Tensor) -> torch.Tensor:
"""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 _reset_parameters(
self, init_type: str, init_enc_alpha: float, init_dec_alpha: float
):
# initialize parameters
if init_type != "pytorch":
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 = torch.tensor(init_enc_alpha)
if self.decoder_type == "transformer" and self.use_scaled_pos_enc:
self.decoder.embed[-1].alpha.data = torch.tensor(init_dec_alpha)