# Copyright 2020 Nagoya University (Tomoki Hayashi)
# Copyright 2023 Renmin University of China (Yuning Wu)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""XiaoiceSing2 related loss module for ESPnet2."""
from typing import Tuple
import torch
from typeguard import check_argument_types
from espnet.nets.pytorch_backend.fastspeech.duration_predictor import ( # noqa: H301
DurationPredictorLoss,
)
from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
[docs]class XiaoiceSing2Loss(torch.nn.Module):
"""Loss function module for FastSpeech2."""
def __init__(self, use_masking: bool = True, use_weighted_masking: bool = False):
"""Initialize feed-forward Transformer loss module.
Args:
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 = torch.nn.L1Loss(reduction=reduction)
self.mse_criterion = torch.nn.MSELoss(reduction=reduction)
self.bce_criterion = torch.nn.BCEWithLogitsLoss(reduction=reduction)
self.duration_criterion = DurationPredictorLoss(reduction=reduction)
[docs] def forward(
self,
after_outs: torch.Tensor,
before_outs: torch.Tensor,
d_outs: torch.Tensor,
p_outs: torch.Tensor,
v_outs: torch.Tensor,
ys: torch.Tensor,
ds: torch.Tensor,
ps: torch.Tensor,
vs: torch.Tensor,
ilens: torch.Tensor,
olens: torch.Tensor,
loss_type: str = "L2",
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Calculate forward propagation.
Args:
after_outs (Tensor): Batch of outputs after postnets (B, T_feats, odim).
before_outs (Tensor): Batch of outputs before postnets (B, T_feats, odim).
d_outs (LongTensor): Batch of outputs of duration predictor (B, T_text).
p_outs (Tensor): Batch of outputs of log_f0 (B, T_text, 1).
v_outs (Tensor): Batch of outputs of VUV (B, T_text, 1).
ys (Tensor): Batch of target features (B, T_feats, odim).
ds (LongTensor): Batch of durations (B, T_text).
ps (Tensor): Batch of target log_f0 (B, T_text, 1).
vs (Tensor): Batch of target VUV (B, T_text, 1).
ilens (LongTensor): Batch of the lengths of each input (B,).
olens (LongTensor): Batch of the lengths of each target (B,).
Returns:
Tensor: Mel loss value.
Tensor: Duration predictor loss value.
Tensor: Pitch predictor loss value.
Tensor: VUV predictor loss value.
"""
# apply mask to remove padded part
if self.use_masking:
out_masks = make_non_pad_mask(olens).unsqueeze(-1).to(ys.device)
before_outs = before_outs.masked_select(out_masks)
if after_outs is not None:
after_outs = after_outs.masked_select(out_masks)
ys = ys.masked_select(out_masks)
duration_masks = make_non_pad_mask(ilens).to(ys.device)
d_outs = d_outs.masked_select(duration_masks)
ds = ds.masked_select(duration_masks)
p_outs = p_outs.masked_select(out_masks)
v_outs = v_outs.masked_select(out_masks)
ps = ps.masked_select(out_masks)
vs = vs.masked_select(out_masks)
# calculate loss
if loss_type == "L1":
mel_loss = self.l1_criterion(before_outs, ys)
if after_outs is not None:
mel_loss += self.l1_criterion(after_outs, ys)
else:
mel_loss = self.mse_criterion(before_outs, ys)
if after_outs is not None:
mel_loss += self.mse_criterion(after_outs, ys)
duration_loss = self.duration_criterion(d_outs, ds)
pitch_loss = self.mse_criterion(p_outs, ps)
vuv_loss = self.bce_criterion(v_outs, vs.float())
# make weighted mask and apply it
if self.use_weighted_masking:
out_masks = make_non_pad_mask(olens).unsqueeze(-1).to(ys.device)
out_weights = out_masks.float() / out_masks.sum(dim=1, keepdim=True).float()
out_weights /= ys.size(0) * ys.size(2)
duration_masks = make_non_pad_mask(ilens).to(ys.device)
duration_weights = (
duration_masks.float() / duration_masks.sum(dim=1, keepdim=True).float()
)
duration_weights /= ds.size(0)
# apply weight
mel_loss = mel_loss.mul(out_weights).masked_select(out_masks).sum()
duration_loss = (
duration_loss.mul(duration_weights).masked_select(duration_masks).sum()
)
pitch_loss = pitch_loss.mul(pitch_weights).masked_select(out_masks).sum()
vuv_loss = vuv_loss.mul(pitch_weights).masked_select(pitch_masks).sum()
return mel_loss, duration_loss, pitch_loss, vuv_loss