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""" | |
BSD 3-Clause License | |
Copyright (c) 2018, NVIDIA Corporation | |
All rights reserved. | |
Redistribution and use in source and binary forms, with or without | |
modification, are permitted provided that the following conditions are met: | |
* Redistributions of source code must retain the above copyright notice, this | |
list of conditions and the following disclaimer. | |
* Redistributions in binary form must reproduce the above copyright notice, | |
this list of conditions and the following disclaimer in the documentation | |
and/or other materials provided with the distribution. | |
* Neither the name of the copyright holder nor the names of its | |
contributors may be used to endorse or promote products derived from | |
this software without specific prior written permission. | |
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | |
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | |
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | |
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | |
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | |
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | |
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | |
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
""" | |
from torch import nn | |
class Tacotron2Loss(nn.Module): | |
def __init__(self): | |
super(Tacotron2Loss, self).__init__() | |
def forward(self, model_output, targets): | |
mel_target, gate_target = targets[0], targets[1] | |
mel_target.requires_grad = False | |
gate_target.requires_grad = False | |
gate_target = gate_target.view(-1, 1) | |
mel_out, mel_out_postnet, gate_out, _ = model_output | |
gate_out = gate_out.view(-1, 1) | |
mel_loss = nn.MSELoss()(mel_out, mel_target) + nn.MSELoss()(mel_out_postnet, mel_target) | |
gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target) | |
return mel_loss + gate_loss | |