wehub-resource-sync
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
|
|
# Copyright 2019 Tomoki Hayashi
|
|
# MIT License (https://opensource.org/licenses/MIT)
|
|
|
|
"""STFT-based Loss modules."""
|
|
import librosa
|
|
import torch
|
|
|
|
from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft
|
|
|
|
|
|
class STFTLoss(torch.nn.Module):
|
|
"""STFT loss module."""
|
|
|
|
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window",
|
|
use_mel_loss=False):
|
|
"""Initialize STFT loss module."""
|
|
super(STFTLoss, self).__init__()
|
|
self.fft_size = fft_size
|
|
self.shift_size = shift_size
|
|
self.win_length = win_length
|
|
self.window = getattr(torch, window)(win_length)
|
|
self.spectral_convergenge_loss = SpectralConvergengeLoss()
|
|
self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
|
|
self.use_mel_loss = use_mel_loss
|
|
self.mel_basis = None
|
|
|
|
def forward(self, x, y):
|
|
"""Calculate forward propagation.
|
|
|
|
Args:
|
|
x (Tensor): Predicted signal (B, T).
|
|
y (Tensor): Groundtruth signal (B, T).
|
|
|
|
Returns:
|
|
Tensor: Spectral convergence loss value.
|
|
Tensor: Log STFT magnitude loss value.
|
|
|
|
"""
|
|
x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
|
|
y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
|
|
if self.use_mel_loss:
|
|
if self.mel_basis is None:
|
|
self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T
|
|
x_mag = x_mag @ self.mel_basis
|
|
y_mag = y_mag @ self.mel_basis
|
|
|
|
sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
|
|
mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
|
|
|
|
return sc_loss, mag_loss
|
|
|
|
|
|
class MultiResolutionSTFTLoss(torch.nn.Module):
|
|
"""Multi resolution STFT loss module."""
|
|
|
|
def __init__(self,
|
|
fft_sizes=[1024, 2048, 512],
|
|
hop_sizes=[120, 240, 50],
|
|
win_lengths=[600, 1200, 240],
|
|
window="hann_window",
|
|
use_mel_loss=False):
|
|
"""Initialize Multi resolution STFT loss module.
|
|
|
|
Args:
|
|
fft_sizes (list): List of FFT sizes.
|
|
hop_sizes (list): List of hop sizes.
|
|
win_lengths (list): List of window lengths.
|
|
window (str): Window function type.
|
|
|
|
"""
|
|
super(MultiResolutionSTFTLoss, self).__init__()
|
|
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
|
|
self.stft_losses = torch.nn.ModuleList()
|
|
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
|
|
self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)]
|
|
|
|
def forward(self, x, y):
|
|
"""Calculate forward propagation.
|
|
|
|
Args:
|
|
x (Tensor): Predicted signal (B, T).
|
|
y (Tensor): Groundtruth signal (B, T).
|
|
|
|
Returns:
|
|
Tensor: Multi resolution spectral convergence loss value.
|
|
Tensor: Multi resolution log STFT magnitude loss value.
|
|
|
|
"""
|
|
sc_loss = 0.0
|
|
mag_loss = 0.0
|
|
for f in self.stft_losses:
|
|
sc_l, mag_l = f(x, y)
|
|
sc_loss += sc_l
|
|
mag_loss += mag_l
|
|
sc_loss /= len(self.stft_losses)
|
|
mag_loss /= len(self.stft_losses)
|
|
|
|
return sc_loss, mag_loss
|