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136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2019 Tomoki Hayashi
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# MIT License (https://opensource.org/licenses/MIT)
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"""STFT-based Loss modules."""
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import torch
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import torch.nn.functional as F
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def stft(x, fft_size, hop_size, win_length, window):
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"""Perform STFT and convert to magnitude spectrogram.
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Args:
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x (Tensor): Input signal tensor (B, T).
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fft_size (int): FFT size.
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hop_size (int): Hop size.
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win_length (int): Window length.
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window (str): Window function type.
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Returns:
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Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
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"""
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x_stft = torch.stft(x, fft_size, hop_size, win_length, window)
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real = x_stft[..., 0]
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imag = x_stft[..., 1]
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# NOTE(kan-bayashi): clamp is needed to avoid nan or inf
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return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
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class SpectralConvergengeLoss(torch.nn.Module):
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"""Spectral convergence loss module."""
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def __init__(self):
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"""Initilize spectral convergence loss module."""
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super(SpectralConvergengeLoss, self).__init__()
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Args:
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x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns:
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Tensor: Spectral convergence loss value.
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"""
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return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
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class LogSTFTMagnitudeLoss(torch.nn.Module):
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"""Log STFT magnitude loss module."""
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def __init__(self):
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"""Initilize los STFT magnitude loss module."""
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super(LogSTFTMagnitudeLoss, self).__init__()
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Args:
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x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns:
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Tensor: Log STFT magnitude loss value.
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"""
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return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
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class STFTLoss(torch.nn.Module):
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"""STFT loss module."""
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def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
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"""Initialize STFT loss module."""
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super(STFTLoss, self).__init__()
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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self.window = getattr(torch, window)(win_length)
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self.spectral_convergenge_loss = SpectralConvergengeLoss()
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self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
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def forward(self, x, y):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Predicted signal (B, T).
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y (Tensor): Groundtruth signal (B, T).
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Returns:
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Tensor: Spectral convergence loss value.
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Tensor: Log STFT magnitude loss value.
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"""
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x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window.to(x.get_device()))
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y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(x.get_device()))
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sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
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mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
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return sc_loss, mag_loss
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class MultiResolutionSTFTLoss(torch.nn.Module):
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"""Multi resolution STFT loss module."""
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def __init__(self,
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fft_sizes=[1024, 2048, 512],
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hop_sizes=[120, 240, 50],
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win_lengths=[600, 1200, 240],
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window="hann_window"):
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"""Initialize Multi resolution STFT loss module.
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Args:
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fft_sizes (list): List of FFT sizes.
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hop_sizes (list): List of hop sizes.
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win_lengths (list): List of window lengths.
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window (str): Window function type.
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"""
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super(MultiResolutionSTFTLoss, self).__init__()
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assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
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self.stft_losses = torch.nn.ModuleList()
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for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
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self.stft_losses += [STFTLoss(fs, ss, wl, window)]
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def forward(self, x, y):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Predicted signal (B, T).
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y (Tensor): Groundtruth signal (B, T).
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Returns:
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Tensor: Multi resolution spectral convergence loss value.
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Tensor: Multi resolution log STFT magnitude loss value.
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"""
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sc_loss = 0.0
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mag_loss = 0.0
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for f in self.stft_losses:
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sc_l, mag_l = f(x, y)
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sc_loss += sc_l
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mag_loss += mag_l
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sc_loss /= len(self.stft_losses)
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mag_loss /= len(self.stft_losses)
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return sc_loss, mag_loss |