83 lines
4 KiB
Python
83 lines
4 KiB
Python
# *****************************************************************************
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# *****************************************************************************
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import torch
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import torch.nn.functional as F
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from torch import nn
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from common.utils import mask_from_lens
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class FastPitchLoss(nn.Module):
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def __init__(self, dur_predictor_loss_scale=1.0,
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pitch_predictor_loss_scale=1.0):
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super(FastPitchLoss, self).__init__()
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self.dur_predictor_loss_scale = dur_predictor_loss_scale
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self.pitch_predictor_loss_scale = pitch_predictor_loss_scale
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def forward(self, model_out, targets, is_training=True, meta_agg='mean'):
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mel_out, dec_mask, dur_pred, log_dur_pred, pitch_pred = model_out
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mel_tgt, dur_tgt, dur_lens, pitch_tgt = targets
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mel_tgt.requires_grad = False
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# (B,H,T) => (B,T,H)
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mel_tgt = mel_tgt.transpose(1, 2)
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dur_mask = mask_from_lens(dur_lens, max_len=dur_tgt.size(1))
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log_dur_tgt = torch.log(dur_tgt.float() + 1)
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loss_fn = F.mse_loss
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dur_pred_loss = loss_fn(log_dur_pred, log_dur_tgt, reduction='none')
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dur_pred_loss = (dur_pred_loss * dur_mask).sum() / dur_mask.sum()
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ldiff = mel_tgt.size(1) - mel_out.size(1)
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mel_out = F.pad(mel_out, (0, 0, 0, ldiff, 0, 0), value=0.0)
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mel_mask = mel_tgt.ne(0).float()
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loss_fn = F.mse_loss
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mel_loss = loss_fn(mel_out, mel_tgt, reduction='none')
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mel_loss = (mel_loss * mel_mask).sum() / mel_mask.sum()
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ldiff = pitch_tgt.size(1) - pitch_pred.size(1)
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pitch_pred = F.pad(pitch_pred, (0, ldiff, 0, 0), value=0.0)
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pitch_loss = F.mse_loss(pitch_tgt, pitch_pred, reduction='none')
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pitch_loss = (pitch_loss * dur_mask).sum() / dur_mask.sum()
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loss = mel_loss
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loss = (mel_loss + pitch_loss * self.pitch_predictor_loss_scale
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+ dur_pred_loss * self.dur_predictor_loss_scale)
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meta = {
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'loss': loss.clone().detach(),
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'mel_loss': mel_loss.clone().detach(),
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'duration_predictor_loss': dur_pred_loss.clone().detach(),
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'pitch_loss': pitch_loss.clone().detach(),
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'dur_error': (torch.abs(dur_pred - dur_tgt).sum()
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/ dur_mask.sum()).detach(),
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}
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assert meta_agg in ('sum', 'mean')
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if meta_agg == 'sum':
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bsz = mel_out.size(0)
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meta = {k: v * bsz for k,v in meta.items()}
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return loss, meta
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