espnet.nets.pytorch_backend.rnn.attentions.AttForwardTA

source

class espnet.nets.pytorch_backend.rnn.attentions.AttForwardTA(eunits, dunits, att_dim, aconv_chans, aconv_filts, odim)

Bases: Module

Forward attention with transition agent module.

Reference: Forward attention in sequence-to-sequence acoustic modeling for speech synthesis

(https://arxiv.org/pdf/1807.06736.pdf)

• Parameters:
  • eunits (int) – # units of encoder
  • dunits (int) – # units of decoder
  • att_dim (int) – attention dimension
  • aconv_chans (int) – # channels of attention convolution
  • aconv_filts (int) – filter size of attention convolution
  • odim (int) – output dimension

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(enc_hs_pad, enc_hs_len, dec_z, att_prev, out_prev, scaling=1.0, last_attended_idx=None, backward_window=1, forward_window=3)

Calculate AttForwardTA forward propagation.

• Parameters:
  • enc_hs_pad (torch.Tensor) – padded encoder hidden state (B, Tmax, eunits)
  • enc_hs_len (list) – padded encoder hidden state length (B)
  • dec_z (torch.Tensor) – decoder hidden state (B, dunits)
  • att_prev (torch.Tensor) – attention weights of previous step
  • out_prev (torch.Tensor) – decoder outputs of previous step (B, odim)
  • scaling (float) – scaling parameter before applying softmax
  • last_attended_idx (int) – index of the inputs of the last attended
  • backward_window (int) – backward window size in attention constraint
  • forward_window (int) – forward window size in attetion constraint
• Returns: attention weighted encoder state (B, dunits)
• Return type: torch.Tensor
• Returns: previous attention weights (B, Tmax)
• Return type: torch.Tensor

reset()