mirror of https://github.com/coqui-ai/TTS.git
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
import torch
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from torch.nn import functional
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from torch.autograd import Variable
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from torch import nn
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# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
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def _sequence_mask(sequence_length, max_len=None):
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if max_len is None:
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max_len = sequence_length.data.max()
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batch_size = sequence_length.size(0)
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seq_range = torch.arange(0, max_len).long()
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seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
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seq_range_expand = Variable(seq_range_expand)
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if sequence_length.is_cuda:
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seq_range_expand = seq_range_expand.cuda()
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seq_length_expand = (sequence_length.unsqueeze(1)
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.expand_as(seq_range_expand))
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return seq_range_expand < seq_length_expand
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class L1LossMasked(nn.Module):
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def __init__(self):
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super(L1LossMasked, self).__init__()
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def forward(self, input, target, length):
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"""
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Args:
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input: A Variable containing a FloatTensor of size
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(batch, max_len, dim) which contains the
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unnormalized probability for each class.
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target: A Variable containing a LongTensor of size
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(batch, max_len, dim) which contains the index of the true
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class for each corresponding step.
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length: A Variable containing a LongTensor of size (batch,)
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which contains the length of each data in a batch.
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Returns:
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loss: An average loss value masked by the length.
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"""
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input = input.contiguous()
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target = target.contiguous()
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# logits_flat: (batch * max_len, dim)
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input = input.view(-1, input.shape[-1])
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# target_flat: (batch * max_len, dim)
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target_flat = target.view(-1, target.shape[-1])
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# losses_flat: (batch * max_len, dim)
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losses_flat = functional.l1_loss(input, target, size_average=False,
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reduce=False)
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# losses: (batch, max_len, dim)
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losses = losses_flat.view(*target.size())
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# mask: (batch, max_len, 1)
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mask = _sequence_mask(sequence_length=length,
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max_len=target.size(1)).unsqueeze(2)
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losses = losses * mask.float()
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loss = losses.sum() / (length.float().sum() * float(target.shape[2]))
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return loss
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