espnet.nets.pytorch_backend.rnn.attentions.AttLocRec

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class espnet.nets.pytorch_backend.rnn.attentions.AttLocRec(eprojs, dunits, att_dim, aconv_chans, aconv_filts, han_mode=False)

Bases: Module

location-aware recurrent attention

This attention is an extended version of location aware attention. With the use of RNN, it take the effect of the history of attention weights into account.

• Parameters:
  • eprojs (int) – # projection-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
  • han_mode (bool) – flag to swith on mode of hierarchical attention and not store pre_compute_enc_h

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

forward(enc_hs_pad, enc_hs_len, dec_z, att_prev_states, scaling=2.0)

AttLocRec forward

• Parameters:
  • enc_hs_pad (torch.Tensor) – padded encoder hidden state (B x T_max x D_enc)
  • enc_hs_len (list) – padded encoder hidden state length (B)
  • dec_z (torch.Tensor) – decoder hidden state (B x D_dec)
  • att_prev_states (tuple) – previous attention weight and lstm states ((B, T_max), ((B, att_dim), (B, att_dim)))
  • scaling (float) – scaling parameter before applying softmax
• Returns: attention weighted encoder state (B, D_enc)
• Return type: torch.Tensor
• Returns: previous attention weights and lstm states (w, (hx, cx)) ((B, T_max), ((B, att_dim), (B, att_dim)))
• Return type: tuple

reset()

reset states