Fix 3 issues: disable Whisper auto-correct, reduce timeout 120s, preload model to prevent double-run

app.py

@@ -17,8 +17,10 @@ from app.api_gradio import create_api_interface
 if __name__ == '__main__':
     print('Starting Vocal Articulation Assessment System v2.0...')
     
-    # Initialize model once
+    # Initialize model once at startup (prevents double-run issue)
+    print('Preloading Whisper model...')
     initialize_model()
+    print('Model preloaded successfully!')
     
     # Create UI and API interfaces
     ui_demo = create_interface()

app/interface.py

@@ -66,7 +66,7 @@ def get_status_icon(score: float) -> str:
 # GRADIO INFERENCE FUNCTION
 # =======================================
 
-@spaces.GPU(duration=180)  # Max ZeroGPU duration for Whisper Medium
+@spaces.GPU(duration=120)  # Reduced from 180s to avoid timeout
 def score_vocal(
     audio_file: str,
     target_text: str,
@@ -74,8 +74,10 @@ def score_vocal(
 ) -> Tuple[str, str, Dict, str]:
     """Score vocal audio - with safe error handling"""
     try:
-        # Initialize model
-        scorer = initialize_model()
+        # Use global scorer (already initialized)
+        global scorer
+        if scorer is None:
+            scorer = initialize_model()
         
         # Validate input
         if audio_file is None:

core/scoring_engine.py

@@ -261,8 +261,10 @@ class AdvancedVocalScoringSystem:
                         "task": "transcribe",
                         "temperature": 0.0,  # Deterministic output
                         "compression_ratio_threshold": 2.4,  # Default value
-                        "logprob_threshold": -1.0,  # Add this to prevent logprobs error
-                        "no_speech_threshold": 0.6  # Default value
+                        "logprob_threshold": -1.0,  # Prevent logprobs error
+                        "no_speech_threshold": 0.6,  # Default value
+                        "condition_on_previous_text": False,  # Disable context (prevent hallucination)
+                        "prompt_ids": None  # No prompt = no auto-correction
                     }
                 )
                 transcription = result["text"].upper().strip() if result and "text" in result else ""