Merge branch 'main' of https://github.com/GamerBoss101/HooHacks-12

Backend/server.py

@@ -25,6 +25,10 @@ import whisperx
 from generator import load_csm_1b, Segment
 from dataclasses import dataclass
 
+# Add these imports at the top
+import psutil
+import gc
+
 # Configure logging
 logging.basicConfig(level=logging.INFO, 
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -56,8 +60,11 @@ class AppModels:
     generator = None
     tokenizer = None
     llm = None
-    asr_model = None
+    whisperx_model = None
-    asr_processor = None
+    whisperx_align_model = None
+    whisperx_align_metadata = None
+    diarize_model = None
+    last_language = None
 
 # Initialize the models object
 models = AppModels()
@@ -68,13 +75,13 @@ def load_models():
     
     socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 0})
     
-    logger.info("Loading CSM 1B model...")
+    # CSM 1B loading
     try:
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 10, 'message': 'Loading CSM voice model'})
         models.generator = load_csm_1b(device=DEVICE)
         logger.info("CSM 1B model loaded successfully")
         socketio.emit('model_status', {'model': 'csm', 'status': 'loaded'})
-        progress = 33
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 33})
-        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': progress})
         if DEVICE == "cuda":
             torch.cuda.empty_cache()
     except Exception as e:
@@ -83,39 +90,51 @@ def load_models():
         logger.error(f"Error loading CSM 1B model: {str(e)}\n{error_details}")
         socketio.emit('model_status', {'model': 'csm', 'status': 'error', 'message': str(e)})
     
-    logger.info("Loading Whisper ASR model...")
+    # WhisperX loading
     try:
-        # Use regular Whisper instead of WhisperX to avoid compatibility issues
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 40, 'message': 'Loading speech recognition model'})
-        from transformers import WhisperProcessor, WhisperForConditionalGeneration
+        # Use WhisperX for better transcription with timestamps
+        import whisperx
         
-        # Use a smaller model for faster processing
+        # Use compute_type based on device
-        model_id = "openai/whisper-small"
+        compute_type = "float16" if DEVICE == "cuda" else "float32"
         
-        models.asr_processor = WhisperProcessor.from_pretrained(model_id)
+        # Load the WhisperX model (smaller model for faster processing)
-        models.asr_model = WhisperForConditionalGeneration.from_pretrained(model_id).to(DEVICE)
+        models.whisperx_model = whisperx.load_model("small", DEVICE, compute_type=compute_type)
         
-        logger.info("Whisper ASR model loaded successfully")
+        logger.info("WhisperX model loaded successfully")
         socketio.emit('model_status', {'model': 'asr', 'status': 'loaded'})
-        progress = 66
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 66})
-        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': progress})
         if DEVICE == "cuda":
             torch.cuda.empty_cache()
     except Exception as e:
-        logger.error(f"Error loading ASR model: {str(e)}")
+        import traceback
+        error_details = traceback.format_exc()
+        logger.error(f"Error loading WhisperX model: {str(e)}\n{error_details}")
         socketio.emit('model_status', {'model': 'asr', 'status': 'error', 'message': str(e)})
     
-    logger.info("Loading Llama 3.2 model...")
+    # Llama loading
     try:
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 70, 'message': 'Loading language model'})
         models.llm = AutoModelForCausalLM.from_pretrained(
             "meta-llama/Llama-3.2-1B",
             device_map=DEVICE,
             torch_dtype=torch.bfloat16
         )
         models.tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
+        
+        # Configure all special tokens
+        models.tokenizer.pad_token = models.tokenizer.eos_token
+        models.tokenizer.padding_side = "left"  # For causal language modeling
+
+        # Inform the model about the pad token
+        if hasattr(models.llm.config, "pad_token_id") and models.llm.config.pad_token_id is None:
+            models.llm.config.pad_token_id = models.tokenizer.pad_token_id
+        
         logger.info("Llama 3.2 model loaded successfully")
         socketio.emit('model_status', {'model': 'llm', 'status': 'loaded'})
-        progress = 100
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loading', 'progress': 100, 'message': 'All models loaded successfully'})
-        socketio.emit('model_status', {'model': 'overall', 'status': 'loaded', 'progress': progress})
+        socketio.emit('model_status', {'model': 'overall', 'status': 'loaded'})
     except Exception as e:
         logger.error(f"Error loading Llama 3.2 model: {str(e)}")
         socketio.emit('model_status', {'model': 'llm', 'status': 'error', 'message': str(e)})
@@ -170,11 +189,44 @@ def system_status():
         "device": DEVICE,
         "models": {
             "generator": models.generator is not None,
-            "asr": models.asr_model is not None,  # Use the correct model name
+            "asr": models.whisperx_model is not None,  # Use the correct model name
             "llm": models.llm is not None
         }
     })
 
+# Add a new endpoint to check system resources
+@app.route('/api/system_resources')
+def system_resources():
+    # Get CPU usage
+    cpu_percent = psutil.cpu_percent(interval=0.1)
+    
+    # Get memory usage
+    memory = psutil.virtual_memory()
+    memory_used_gb = memory.used / (1024 ** 3)
+    memory_total_gb = memory.total / (1024 ** 3)
+    memory_percent = memory.percent
+    
+    # Get GPU memory if available
+    gpu_memory = {}
+    if torch.cuda.is_available():
+        for i in range(torch.cuda.device_count()):
+            gpu_memory[f"gpu_{i}"] = {
+                "allocated": torch.cuda.memory_allocated(i) / (1024 ** 3),
+                "reserved": torch.cuda.memory_reserved(i) / (1024 ** 3),
+                "max_allocated": torch.cuda.max_memory_allocated(i) / (1024 ** 3)
+            }
+    
+    return jsonify({
+        "cpu_percent": cpu_percent,
+        "memory": {
+            "used_gb": memory_used_gb,
+            "total_gb": memory_total_gb,
+            "percent": memory_percent
+        },
+        "gpu_memory": gpu_memory,
+        "active_sessions": len(active_conversations)
+    })
+
 # Socket event handlers
 @socketio.on('connect')
 def handle_connect(auth=None):
@@ -280,8 +332,8 @@ def process_audio_queue(session_id, q):
                 del user_queues[session_id]
 
 def process_audio_and_respond(session_id, data):
-    """Process audio data and generate a response using standard Whisper"""
+    """Process audio data and generate a response using WhisperX"""
-    if models.generator is None or models.asr_model is None or models.llm is None:
+    if models.generator is None or models.whisperx_model is None or models.llm is None:
         logger.warning("Models not yet loaded!")
         with app.app_context():
             socketio.emit('error', {'message': 'Models still loading, please wait'}, room=session_id)
@@ -317,29 +369,69 @@ def process_audio_and_respond(session_id, data):
             with app.app_context():
                 socketio.emit('processing_status', {'status': 'transcribing'}, room=session_id)
             
-            # Load audio for ASR processing
+            # Load audio using WhisperX
-            import librosa
+            import whisperx
-            speech_array, sampling_rate = librosa.load(temp_path, sr=16000)
+            audio = whisperx.load_audio(temp_path)
             
-            # Convert to required format
+            # Check audio length and add a warning for short clips
-            input_features = models.asr_processor(
+            audio_length = len(audio) / 16000  # assuming 16kHz sample rate
-                speech_array, 
+            if audio_length < 1.0:
-                sampling_rate=sampling_rate, 
+                logger.warning(f"Audio is very short ({audio_length:.2f}s), may affect transcription quality")
-                return_tensors="pt"
-            ).input_features.to(DEVICE)
             
-            # Generate token ids
+            # Transcribe using WhisperX
-            predicted_ids = models.asr_model.generate(
+            batch_size = 16  # adjust based on your GPU memory
-                input_features, 
+            logger.info("Running WhisperX transcription...")
-                language="en", 
+            
-                task="transcribe"
+            # Handle the warning about audio being shorter than 30s by suppressing it
+            import warnings
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message="audio is shorter than 30s")
+                result = models.whisperx_model.transcribe(audio, batch_size=batch_size)
+            
+            # Get the detected language
+            language_code = result["language"]
+            logger.info(f"Detected language: {language_code}")
+            
+            # Check if alignment model needs to be loaded or updated
+            if models.whisperx_align_model is None or language_code != models.last_language:
+                # Clean up old models if they exist
+                if models.whisperx_align_model is not None:
+                    del models.whisperx_align_model
+                    del models.whisperx_align_metadata
+                    if DEVICE == "cuda":
+                        gc.collect()
+                        torch.cuda.empty_cache()
+                
+                # Load new alignment model for the detected language
+                logger.info(f"Loading alignment model for language: {language_code}")
+                models.whisperx_align_model, models.whisperx_align_metadata = whisperx.load_align_model(
+                    language_code=language_code, device=DEVICE
+                )
+                models.last_language = language_code
+            
+            # Align the transcript to get word-level timestamps
+            if result["segments"] and len(result["segments"]) > 0:
+                logger.info("Aligning transcript...")
+                result = whisperx.align(
+                    result["segments"], 
+                    models.whisperx_align_model, 
+                    models.whisperx_align_metadata, 
+                    audio, 
+                    DEVICE, 
+                    return_char_alignments=False
                 )
                 
-            # Decode the predicted ids to text
+                # Process the segments for better output
-            user_text = models.asr_processor.batch_decode(
+                for segment in result["segments"]:
-                predicted_ids, 
+                    # Round timestamps for better display
-                skip_special_tokens=True
+                    segment["start"] = round(segment["start"], 2)
-            )[0]
+                    segment["end"] = round(segment["end"], 2)
+                    # Add a confidence score if not present
+                    if "confidence" not in segment:
+                        segment["confidence"] = 1.0  # Default confidence
+            
+            # Extract the full text from all segments
+            user_text = ' '.join([segment['text'] for segment in result['segments']])
             
             # If no text was recognized, don't process further
             if not user_text or len(user_text.strip()) == 0:
@@ -371,11 +463,12 @@ def process_audio_and_respond(session_id, data):
                 audio=waveform.squeeze()
             )
             
-            # Send transcription to client
+            # Send transcription to client with detailed segments
             with app.app_context():
                 socketio.emit('transcription', {
                     'text': user_text, 
-                    'speaker': speaker_id
+                    'speaker': speaker_id,
+                    'segments': result['segments']  # Include the detailed segments with timestamps
                 }, room=session_id)
             
             # Generate AI response using Llama
@@ -392,6 +485,11 @@ def process_audio_and_respond(session_id, data):
             prompt = f"{conversation_history}Assistant: "
             
             # Generate response with Llama
+            try:
+                # Ensure pad token is set
+                if models.tokenizer.pad_token is None:
+                    models.tokenizer.pad_token = models.tokenizer.eos_token
+                    
                 input_tokens = models.tokenizer(
                     prompt, 
                     return_tensors="pt",
@@ -417,6 +515,11 @@ def process_audio_and_respond(session_id, data):
                     generated_ids[0][input_ids.shape[1]:], 
                     skip_special_tokens=True
                 ).strip()
+            except Exception as e:
+                logger.error(f"Error generating response: {str(e)}")
+                import traceback
+                logger.error(traceback.format_exc())
+                response_text = "I'm sorry, I encountered an error while processing your request."
             
             # Synthesize speech
             with app.app_context():

Backend/voice-chat.js

@@ -43,7 +43,9 @@ const state = {
     volumeUpdateInterval: null,
     visualizerAnimationFrame: null,
     currentSpeaker: 0,
-    aiSpeakerId: 1  // Define the AI's speaker ID to match server.py
+    aiSpeakerId: 1,  // Define the AI's speaker ID to match server.py
+    transcriptionRetries: 0,
+    maxTranscriptionRetries: 3
 };
 
 // Visualizer variables
@@ -429,7 +431,15 @@ function handleSpeechState(isSilent) {
                             
                             if (!hasAudioContent) {
                                 console.warn('Audio buffer appears to be empty or very quiet');
-                                addSystemMessage('No speech detected. Please try again and speak clearly.');
+                                
+                                if (state.transcriptionRetries < state.maxTranscriptionRetries) {
+                                    state.transcriptionRetries++;
+                                    const retryMessage = `No speech detected (attempt ${state.transcriptionRetries}/${state.maxTranscriptionRetries}). Please speak louder and try again.`;
+                                    addSystemMessage(retryMessage);
+                                } else {
+                                    state.transcriptionRetries = 0;
+                                    addSystemMessage('Multiple attempts failed to detect speech. Please check your microphone and try again.');
+                                }
                                 return;
                             }