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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 | |
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| # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | |
| # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
| # | |
| # ***************************************************************************** | |
| import torch | |
| class WaveGlowLoss(torch.nn.Module): | |
| def __init__(self, sigma=1.0): | |
| super(WaveGlowLoss, self).__init__() | |
| self.sigma = sigma | |
| def forward(self, model_output, clean_audio): | |
| # clean_audio is unused; | |
| z, log_s_list, log_det_W_list = model_output | |
| for i, log_s in enumerate(log_s_list): | |
| if i == 0: | |
| log_s_total = torch.sum(log_s) | |
| log_det_W_total = log_det_W_list[i] | |
| else: | |
| log_s_total = log_s_total + torch.sum(log_s) | |
| log_det_W_total += log_det_W_list[i] | |
| loss = torch.sum( | |
| z * z) / (2 * self.sigma * self.sigma) - log_s_total - log_det_W_total # noqa: E501 | |
| meta = {} | |
| return loss / (z.size(0) * z.size(1) * z.size(2)), meta | |