diff --git a/Backend/server.py b/Backend/server.py
index 92d0f1f..e8ed1ae 100644
--- a/Backend/server.py
+++ b/Backend/server.py
@@ -52,24 +52,31 @@ manager = ConnectionManager()
 # Helper function to convert audio data
 async def decode_audio_data(audio_data: str) -> torch.Tensor:
     """Decode base64 audio data to a torch tensor"""
-    # Decode base64 audio data
-    binary_data = base64.b64decode(audio_data.split(',')[1] if ',' in audio_data else audio_data)
-    
-    # Load audio from binary data
-    buf = BytesIO(binary_data)
-    audio_tensor, sample_rate = torchaudio.load(buf)
-    
-    # Resample if needed
-    if sample_rate != generator.sample_rate:
-        audio_tensor = torchaudio.functional.resample(
-            audio_tensor.squeeze(0), 
-            orig_freq=sample_rate, 
-            new_freq=generator.sample_rate
-        )
-    else:
-        audio_tensor = audio_tensor.squeeze(0)
+    try:
+        # Decode base64 audio data
+        binary_data = base64.b64decode(audio_data.split(',')[1] if ',' in audio_data else audio_data)
         
-    return audio_tensor
+        # Save to a temporary WAV file first
+        temp_file = BytesIO(binary_data)
+        
+        # Load audio from binary data, explicitly specifying the format
+        audio_tensor, sample_rate = torchaudio.load(temp_file, format="wav")
+        
+        # Resample if needed
+        if sample_rate != generator.sample_rate:
+            audio_tensor = torchaudio.functional.resample(
+                audio_tensor.squeeze(0), 
+                orig_freq=sample_rate, 
+                new_freq=generator.sample_rate
+            )
+        else:
+            audio_tensor = audio_tensor.squeeze(0)
+            
+        return audio_tensor
+    except Exception as e:
+        print(f"Error decoding audio: {str(e)}")
+        # Return a small silent audio segment as fallback
+        return torch.zeros(generator.sample_rate // 2)  # 0.5 seconds of silence
 
 
 async def encode_audio_data(audio_tensor: torch.Tensor) -> str:
@@ -95,43 +102,57 @@ async def websocket_endpoint(websocket: WebSocket):
             action = request.get("action")
             
             if action == "generate":
-                text = request.get("text", "")
-                speaker_id = request.get("speaker", 0)
-                
-                # Generate audio response
-                print(f"Generating audio for: '{text}' with speaker {speaker_id}")
-                audio_tensor = generator.generate(
-                    text=text,
-                    speaker=speaker_id,
-                    context=context_segments,
-                    max_audio_length_ms=10_000,
-                )
-                
-                # Add to conversation context
-                context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
-                
-                # Convert audio to base64 and send back to client
-                audio_base64 = await encode_audio_data(audio_tensor)
-                await websocket.send_json({
-                    "type": "audio_response",
-                    "audio": audio_base64
-                })
+                try:
+                    text = request.get("text", "")
+                    speaker_id = request.get("speaker", 0)
+                    
+                    # Generate audio response
+                    print(f"Generating audio for: '{text}' with speaker {speaker_id}")
+                    audio_tensor = generator.generate(
+                        text=text,
+                        speaker=speaker_id,
+                        context=context_segments,
+                        max_audio_length_ms=10_000,
+                    )
+                    
+                    # Add to conversation context
+                    context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
+                    
+                    # Convert audio to base64 and send back to client
+                    audio_base64 = await encode_audio_data(audio_tensor)
+                    await websocket.send_json({
+                        "type": "audio_response",
+                        "audio": audio_base64
+                    })
+                except Exception as e:
+                    print(f"Error generating audio: {str(e)}")
+                    await websocket.send_json({
+                        "type": "error",
+                        "message": f"Error generating audio: {str(e)}"
+                    })
                 
             elif action == "add_to_context":
-                text = request.get("text", "")
-                speaker_id = request.get("speaker", 0)
-                audio_data = request.get("audio", "")
-                
-                # Convert received audio to tensor
-                audio_tensor = await decode_audio_data(audio_data)
-                
-                # Add to conversation context
-                context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
-                
-                await websocket.send_json({
-                    "type": "context_updated",
-                    "message": "Audio added to context"
-                })
+                try:
+                    text = request.get("text", "")
+                    speaker_id = request.get("speaker", 0)
+                    audio_data = request.get("audio", "")
+                    
+                    # Convert received audio to tensor
+                    audio_tensor = await decode_audio_data(audio_data)
+                    
+                    # Add to conversation context
+                    context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
+                    
+                    await websocket.send_json({
+                        "type": "context_updated",
+                        "message": "Audio added to context"
+                    })
+                except Exception as e:
+                    print(f"Error adding to context: {str(e)}")
+                    await websocket.send_json({
+                        "type": "error",
+                        "message": f"Error processing audio: {str(e)}"
+                    })
                 
             elif action == "clear_context":
                 context_segments = []