Only use one frame of pre-loading

Author: jmvalin

dnn/torch/fargan/fargan.py

@@ -235,18 +235,21 @@ def forward(self, features, period, nb_frames, pre=None, states=None):
         exc_mem = torch.zeros(batch_size, 256, device=device)
         nb_pre_frames = pre.size(1)//self.frame_size if pre is not None else 0
 
-        if states is None:
-            states = (
-                torch.zeros(batch_size, self.cond_size, device=device),
-                torch.zeros(batch_size, self.cond_size, device=device),
-                torch.zeros(batch_size, self.cond_size, device=device),
-                torch.zeros(batch_size, self.passthrough_size, device=device)
-            )
+        states = (
+            torch.zeros(batch_size, self.cond_size, device=device),
+            torch.zeros(batch_size, self.cond_size, device=device),
+            torch.zeros(batch_size, self.cond_size, device=device),
+            torch.zeros(batch_size, self.passthrough_size, device=device)
+        )
 
         sig = torch.zeros((batch_size, 0), device=device)
         cond = self.cond_net(features, period)
         passthrough = torch.zeros(batch_size, self.passthrough_size, device=device)
-        for n in range(nb_frames+nb_pre_frames):
+        if pre is not None:
+            prev[:,:] = pre[:, self.frame_size-self.subframe_size : self.frame_size]
+            exc_mem[:,-self.frame_size:] = pre[:, :self.frame_size]
+        start = 1 if nb_pre_frames>0 else 0
+        for n in range(start, nb_frames+nb_pre_frames):
             for k in range(self.nb_subframes):
                 pos = n*self.frame_size + k*self.subframe_size
                 preal = phase_real[:, pos:pos+self.subframe_size]