mirror of https://github.com/coqui-ai/TTS.git
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
import torch
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from torch.autograd import Variable
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from torch import nn
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from torch.nn import functional as F
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class BahdanauAttention(nn.Module):
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def __init__(self, dim):
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super(BahdanauAttention, self).__init__()
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self.query_layer = nn.Linear(dim, dim, bias=False)
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self.tanh = nn.Tanh()
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self.v = nn.Linear(dim, 1, bias=False)
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def forward(self, query, processed_inputs):
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"""
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Args:
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query: (batch, 1, dim) or (batch, dim)
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processed_inputs: (batch, max_time, dim)
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"""
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if query.dim() == 2:
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# insert time-axis for broadcasting
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query = query.unsqueeze(1)
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# (batch, 1, dim)
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processed_query = self.query_layer(query)
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# (batch, max_time, 1)
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alignment = self.v(self.tanh(processed_query + processed_inputs))
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# (batch, max_time)
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return alignment.squeeze(-1)
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def get_mask_from_lengths(memory, memory_lengths):
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"""Get mask tensor from list of length
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Args:
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memory: (batch, max_time, dim)
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memory_lengths: array like
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"""
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mask = memory.data.new(memory.size(0), memory.size(1)).byte().zero_()
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for idx, l in enumerate(memory_lengths):
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mask[idx][:l] = 1
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return ~mask
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class AttentionWrapper(nn.Module):
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def __init__(self, rnn_cell, alignment_model,
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score_mask_value=-float("inf")):
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super(AttentionWrapper, self).__init__()
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self.rnn_cell = rnn_cell
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self.alignment_model = alignment_model
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self.score_mask_value = score_mask_value
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def forward(self, query, context_vec, cell_state, memory,
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processed_inputs=None, mask=None, memory_lengths=None):
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if processed_inputs is None:
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processed_inputs = memory
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if memory_lengths is not None and mask is None:
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mask = get_mask_from_lengths(memory, memory_lengths)
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# Alignment
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# (batch, max_time)
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# e_{ij} = a(s_{i-1}, h_j)
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alignment = self.alignment_model(cell_state, processed_inputs)
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if mask is not None:
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mask = mask.view(query.size(0), -1)
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alignment.data.masked_fill_(mask, self.score_mask_value)
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# Normalize context_vec weight
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alignment = F.softmax(alignment, dim=-1)
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# Attention context vector
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# (batch, 1, dim)
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# c_i = \sum_{j=1}^{T_x} \alpha_{ij} h_j
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context_vec = torch.bmm(alignment.unsqueeze(1), memory)
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context_vec = context_vec.squeeze(1)
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# Concat input query and previous context_vec context
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cell_input = torch.cat((query, context_vec), -1)
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cell_input = cell_input.unsqueeze(1)
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# Feed it to RNN
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# s_i = f(y_{i-1}, c_{i}, s_{i-1})
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cell_output = self.rnn_cell(cell_input, cell_state)
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return cell_output, context_vec, alignment
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