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from typing import List, Tuple
from auraloss.time import ESRLoss, SDSDRLoss, SISDRLoss, SNRLoss
import torch
from torch import Tensor
from torch.nn import Module
from models.config import VocoderBasicConfig, VocoderModelConfig
from .multi_resolution_stft_loss import MultiResolutionSTFTLoss
class UnivnetLoss(Module):
r"""UnivnetLoss is a PyTorch Module that calculates the generator and discriminator losses for Univnet."""
def __init__(self):
r"""Initializes the UnivnetLoss module."""
super().__init__()
train_config = VocoderBasicConfig()
self.stft_lamb = train_config.stft_lamb
self.model_config = VocoderModelConfig()
self.stft_criterion = MultiResolutionSTFTLoss(self.model_config.mrd.resolutions)
self.esr_loss = ESRLoss()
self.sisdr_loss = SISDRLoss()
self.snr_loss = SNRLoss()
self.sdsdr_loss = SDSDRLoss()
def forward(
self,
audio: Tensor,
fake_audio: Tensor,
res_fake: List[Tuple[Tensor, Tensor]],
period_fake: List[Tuple[Tensor, Tensor]],
res_real: List[Tuple[Tensor, Tensor]],
period_real: List[Tuple[Tensor, Tensor]],
) -> Tuple[
Tensor,
Tensor,
Tensor,
Tensor,
Tensor,
Tensor,
]:
r"""Calculate the losses for the generator and discriminator.
Args:
audio (torch.Tensor): The real audio samples.
fake_audio (torch.Tensor): The generated audio samples.
res_fake (List[Tuple[Tensor, Tensor]]): The discriminator's output for the fake audio.
period_fake (List[Tuple[Tensor, Tensor]]): The discriminator's output for the fake audio in the period.
res_real (List[Tuple[Tensor, Tensor]]): The discriminator's output for the real audio.
period_real (List[Tuple[Tensor, Tensor]]): The discriminator's output for the real audio in the period.
Returns:
tuple: A tuple containing the univnet loss, discriminator loss, STFT loss, score loss, ESR, SISDR, SNR and SDSDR losses.
"""
# Calculate the STFT loss
sc_loss, mag_loss = self.stft_criterion(fake_audio.squeeze(1), audio.squeeze(1))
stft_loss = (sc_loss + mag_loss) * self.stft_lamb
# Pad the fake audio to match the length of the real audio
padding = audio.shape[2] - fake_audio.shape[2]
fake_audio_padded = torch.nn.functional.pad(fake_audio, (0, padding))
esr_loss = self.esr_loss.forward(fake_audio_padded, audio)
snr_loss = self.snr_loss.forward(fake_audio_padded, audio)
# Calculate the score loss
score_loss = torch.tensor(0.0, device=audio.device)
for _, score_fake in res_fake + period_fake:
score_loss += torch.mean(torch.pow(score_fake - 1.0, 2))
score_loss = score_loss / len(res_fake + period_fake)
# Calculate the total generator loss
total_loss_gen = score_loss + stft_loss + esr_loss + snr_loss
# Calculate the discriminator loss
total_loss_disc = torch.tensor(0.0, device=audio.device)
for (_, score_fake), (_, score_real) in zip(
res_fake + period_fake, res_real + period_real
):
total_loss_disc += torch.mean(torch.pow(score_real - 1.0, 2)) + torch.mean(
torch.pow(score_fake, 2)
)
total_loss_disc = total_loss_disc / len(res_fake + period_fake)
return (
total_loss_gen,
total_loss_disc,
stft_loss,
score_loss,
esr_loss,
snr_loss,
)
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