139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
# 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 parakeet.modules.audio import STFT
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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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Args:
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x_mag (Tensor): Magnitude spectrogram of predicted signal (B, C, T).
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y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, C, T).
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Returns:
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Tensor: 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.norm(
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y_mag, p="fro")
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class LogSTFTMagnitudeLoss(nn.Layer):
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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().__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(paddle.log(y_mag), paddle.log(x_mag))
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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.stft = STFT(
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n_fft=fft_size,
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hop_length=shift_size,
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win_length=win_length,
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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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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 = self.stft.magnitude(x)
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y_mag = self.stft.magnitude(y)
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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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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().__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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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
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