Cache attention annot vectors for the whole sequence.

layers/attention.py

@@ -56,6 +56,7 @@ class LocationSensitiveAttention(nn.Module):
         self.query_layer = nn.Linear(query_dim, attn_dim, bias=True)
         self.annot_layer = nn.Linear(annot_dim, attn_dim, bias=True)
         self.v = nn.Linear(attn_dim, 1, bias=False)
+        self.processed_annots = None
         # self.init_layers()
 
     def init_layers(self):
@@ -72,6 +73,9 @@ class LocationSensitiveAttention(nn.Module):
             self.v.weight,
             gain=torch.nn.init.calculate_gain('linear'))
 
+    def reset(self):
+        self.processed_annots = None
+
     def forward(self, annot, query, loc):
         """
         Shapes:
@@ -86,9 +90,11 @@ class LocationSensitiveAttention(nn.Module):
         loc_conv = loc_conv.transpose(1, 2)
         processed_loc = self.loc_linear(loc_conv)
         processed_query = self.query_layer(query)
-        processed_annots = self.annot_layer(annot)
+        # cache annots
+        if self.processed_annots is None:
+            self.processed_annots = self.annot_layer(annot)
         alignment = self.v(
-            torch.tanh(processed_query + processed_annots + processed_loc))
+            torch.tanh(processed_query + self.processed_annots + processed_loc))
         # (batch, max_time)
         return alignment.squeeze(-1)
 
@@ -120,7 +126,7 @@ class AttentionRNNCell(nn.Module):
                 'b' (Bahdanau) or 'ls' (Location Sensitive).".format(
                 align_model))
 
-    def forward(self, memory, context, rnn_state, annots, atten, mask):
+    def forward(self, memory, context, rnn_state, annots, atten, mask, t):
         """
         Shapes:
             - memory: (batch, 1, dim) or (batch, dim)
@@ -130,6 +136,8 @@ class AttentionRNNCell(nn.Module):
             - atten: (batch, 2, max_time)
             - mask: (batch,)
         """
+        if t == 0:
+            self.alignment_model.reset()
         # Concat input query and previous context context
         rnn_input = torch.cat((memory, context), -1)
         # Feed it to RNN
@@ -147,7 +155,8 @@ class AttentionRNNCell(nn.Module):
             alignment.masked_fill_(1 - mask, -float("inf"))
         # Normalize context weight
         # alignment = F.softmax(alignment, dim=-1)
-        alignment  = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
+        # alignment = 5 * alignment
+        alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
         # Attention context vector
         # (batch, 1, dim)
         # c_i = \sum_{j=1}^{T_x} \alpha_{ij} h_j

layers/tacotron.py

@@ -402,7 +402,7 @@ class Decoder(nn.Module):
                 (attention.unsqueeze(1), attention_cum.unsqueeze(1)), dim=1)
             attention_rnn_hidden, current_context_vec, attention = self.attention_rnn(
                 processed_memory, current_context_vec, attention_rnn_hidden,
-                inputs, attention_cat, mask)
+                inputs, attention_cat, mask, t)
             attention_cum += attention
             # Concat RNN output and attention context vector
             decoder_input = self.project_to_decoder_in(