diff --git a/config.json b/config.json
index c645ab2b..46ea7865 100644
--- a/config.json
+++ b/config.json
@@ -1,6 +1,6 @@
 {
     "run_name": "bos",
-    "run_description": "bos character added to get away with the first char miss",
+    "run_description": "finetune entropy model due to some spelling mistakes.",
 
     "audio":{
         // Audio processing parameters
@@ -41,7 +41,7 @@
     "memory_size": 5,       // TO BE IMPLEMENTED -- memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5. 
     "attention_norm": "softmax",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
 
-    "batch_size": 16,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
+    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
     "eval_batch_size":16,   
     "r": 1,                 // Number of frames to predict for step.
     "wd": 0.000001,         // Weight decay weight.
diff --git a/layers/tacotron2.py b/layers/tacotron2.py
index 1af72f34..b9ee1c5c 100644
--- a/layers/tacotron2.py
+++ b/layers/tacotron2.py
@@ -125,8 +125,8 @@ class Attention(nn.Module):
         self._mask_value = -float("inf")
         self.windowing = windowing
         if self.windowing:
-            self.win_back = 3
-            self.win_front = 6
+            self.win_back = 1
+            self.win_front = 3
             self.win_idx = None
         self.norm = norm
 
@@ -394,7 +394,8 @@ class Decoder(nn.Module):
 
         self.attention_layer.init_win_idx()
         outputs, stop_tokens, alignments, t = [], [], [], 0
-        stop_flags = [False, False, False]
+        stop_flags = [True, False, False]
+        stop_count = 0
         while True:
             memory = self.prenet(memory)
             mel_output, stop_token, alignment = self.decode(memory)
@@ -404,10 +405,12 @@ class Decoder(nn.Module):
             alignments += [alignment]
 
             stop_flags[0] = stop_flags[0] or stop_token > 0.5
-            stop_flags[1] = stop_flags[1] or (alignment[0, -2:].sum() > 0.5 and t > inputs.shape[1])
-            stop_flags[2] = t > inputs.shape[1]
+            stop_flags[1] = stop_flags[1] or (alignment[0, -2:].sum() > 0.8 and t > inputs.shape[1])
+            stop_flags[2] = t > inputs.shape[1] * 2
             if all(stop_flags):
-                break
+                stop_count += 1
+                if stop_count > 10:
+                    break
             elif len(outputs) == self.max_decoder_steps:
                 print("   | > Decoder stopped with 'max_decoder_steps")
                 break