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# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""STFT-based Loss modules."""
import librosa
import torch
from text_to_speech.modules.vocoder.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.
"""
if self.window.device != x.device:
self.window = self.window.to(x.device)
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
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