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- # *****************************************************************************
- # 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 NVIDIA CORPORATION 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 NVIDIA CORPORATION 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
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