diff --git a/whisper/model.py b/whisper/model.py
index dae750a..f20c4e0 100644
--- a/whisper/model.py
+++ b/whisper/model.py
@@ -309,43 +309,39 @@ class Whisper(nn.Module):
 
         return attn_maps
     
-    def plot_attention_on_padded(self, seq_length: int = 100):
-        """Plots attention weights focusing on padded regions."""
+    def plot_attention_distribution(self, seq_length: int = 100):
+        """Plots attention distribution over sequence length."""
         attn_maps = self.get_attention_weights()
 
         if not attn_maps:
             print("No attention weights found!")
             return
 
-        # Convert list to NumPy array
+        # Convert to numpy array and print shape
         attn_maps = np.array(attn_maps)
+        print(f"Attention Maps Shape: {attn_maps.shape}")
 
-        # Print debug info
-        print(f"Attention Maps Shape (Before Averaging): {attn_maps.shape}")
+        # Average over layers and heads to get per-token attention
+        avg_attn = np.mean(attn_maps, axis=(0, 2))  # Shape: (batch, seq_len, seq_len)
 
-        # Average over layers and heads
-        avg_attn = np.mean(attn_maps, axis=(0, 2))  # Shape: (batch, ?, seq_len)
+        print(f"Averaged Attention Shape: {avg_attn.shape}")
 
-        print(f"Averaged Attention Shape (Before Squeeze): {avg_attn.shape}")
+        # Remove batch dim if present
+        avg_attn = np.squeeze(avg_attn)
 
-        # Squeeze to remove any extra singleton dimensions
-        avg_attn = np.squeeze(avg_attn)  # Removes batch dim
+        # Extract the attention scores to the first token in each position
+        token_attention = np.mean(avg_attn, axis=0)  # Shape: (seq_len,)
 
-        print(f"Averaged Attention Shape (After Squeeze): {avg_attn.shape}")
+        # Ensure we only plot up to `seq_length`
+        token_attention = token_attention[:seq_length]
+        x_positions = np.arange(len(token_attention))
 
-        # Ensure correct shape
-        if avg_attn.ndim == 1:  # If still incorrect (seq_len,)
-            avg_attn = avg_attn.reshape((1, -1))  # Force into 2D shape for heatmap
-
-        # Ensure shape matches seq_length
-        avg_attn = avg_attn[:seq_length, :seq_length]  # Truncate to fit expected size
-
-        # Plot heatmap
-        plt.figure(figsize=(8, 6))
-        sns.heatmap(avg_attn, cmap="Blues", annot=False)
-        plt.xlabel("Input Positions")
-        plt.ylabel("Output Positions")
-        plt.title("Attention Weights on Padded Regions")
+        # Plot attention distribution as spikes
+        plt.figure(figsize=(12, 4))
+        plt.bar(x_positions, token_attention, width=1.5, alpha=0.7)
+        plt.xlabel("Token Position")
+        plt.ylabel("Attention Score")
+        plt.title("Attention Distribution Over Sequence")
         plt.show()