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220 lines
6.7 KiB
220 lines
6.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from scipy import signal
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def stft(x,
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fft_size,
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hop_length=None,
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win_length=None,
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window='hann',
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center=True,
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pad_mode='reflect'):
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"""Perform STFT and convert to magnitude spectrogram.
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Parameters
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----------
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x : Tensor
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Input signal tensor (B, T).
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fft_size : int
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FFT size.
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hop_size : int
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Hop size.
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win_length : int
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window : str, optional
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window : str
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Name of window function, see `scipy.signal.get_window` for more
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details. Defaults to "hann".
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center : bool, optional
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center (bool, optional): Whether to pad `x` to make that the
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:math:`t \times hop\_length` at the center of :math:`t`-th frame. Default: `True`.
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pad_mode : str, optional
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Choose padding pattern when `center` is `True`.
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Returns
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----------
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Tensor:
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Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
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"""
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# calculate window
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window = signal.get_window(window, win_length, fftbins=True)
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window = paddle.to_tensor(window)
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x_stft = paddle.signal.stft(
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x,
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fft_size,
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hop_length,
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win_length,
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window=window,
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center=center,
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pad_mode=pad_mode)
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real = x_stft.real()
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imag = x_stft.imag()
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return paddle.sqrt(paddle.clip(real**2 + imag**2, min=1e-7)).transpose(
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[0, 2, 1])
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class SpectralConvergenceLoss(nn.Layer):
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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().__init__()
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Parameters
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----------
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x_mag : Tensor
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Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag : Tensor)
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Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns
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----------
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Tensor
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Spectral convergence loss value.
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"""
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return paddle.norm(
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y_mag - x_mag, p="fro") / paddle.clip(
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paddle.norm(y_mag, p="fro"), min=1e-10)
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class LogSTFTMagnitudeLoss(nn.Layer):
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"""Log STFT magnitude loss module."""
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def __init__(self, epsilon=1e-7):
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"""Initilize los STFT magnitude loss module."""
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super().__init__()
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self.epsilon = epsilon
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Parameters
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----------
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x_mag : Tensor
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Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag : Tensor
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Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns
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----------
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Tensor
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Log STFT magnitude loss value.
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"""
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return F.l1_loss(
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paddle.log(paddle.clip(y_mag, min=self.epsilon)),
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paddle.log(paddle.clip(x_mag, min=self.epsilon)))
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class STFTLoss(nn.Layer):
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"""STFT loss module."""
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def __init__(self,
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fft_size=1024,
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shift_size=120,
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win_length=600,
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window="hann"):
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"""Initialize STFT loss module."""
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super().__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 = window
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self.spectral_convergence_loss = SpectralConvergenceLoss()
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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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Parameters
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----------
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x : Tensor
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Predicted signal (B, T).
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y : Tensor
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Groundtruth signal (B, T).
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Returns
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----------
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Tensor
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Spectral convergence loss value.
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Tensor
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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,
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self.window)
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y_mag = stft(y, self.fft_size, self.shift_size, self.win_length,
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self.window)
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sc_loss = self.spectral_convergence_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(nn.Layer):
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"""Multi resolution STFT loss module."""
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def __init__(
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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", ):
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"""Initialize Multi resolution STFT loss module.
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Parameters
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----------
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fft_sizes : list
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List of FFT sizes.
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hop_sizes : list
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List of hop sizes.
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win_lengths : list
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List of window lengths.
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window : str
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Window function type.
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"""
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super().__init__()
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assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
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self.stft_losses = nn.LayerList()
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for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
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self.stft_losses.append(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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Parameters
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----------
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x : Tensor
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Predicted signal (B, T) or (B, #subband, T).
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y : Tensor
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Groundtruth signal (B, T) or (B, #subband, T).
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Returns
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----------
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Tensor
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Multi resolution spectral convergence loss value.
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Tensor
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Multi resolution log STFT magnitude loss value.
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"""
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if len(x.shape) == 3:
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# (B, C, T) -> (B x C, T)
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x = x.reshape([-1, x.shape[2]])
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# (B, C, T) -> (B x C, T)
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y = y.reshape([-1, y.shape[2]])
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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
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