Update app.py

app.py

@@ -26,16 +26,23 @@ def load_models():
         models['hifigan'] = HifiGanModel.restore_from("models/hifigan_en.nemo").to(device)
         models['hifigan'].eval()
         
-        # Load the Spectrogram Generators
+        # Load the English Spectrogram Generator
         models['en'] = FastPitchModel.restore_from("models/fastpitch_en.nemo").to(device)
         models['en'].eval()
         
-        models['bikol'] = FastPitchModel.restore_from("models/fastpitch_bikol_repacked.nemo").to(device)
+        # Load the Bikol Spectrogram Generator with strict=False
+        # This allows loading even if there are size mismatches in embedding layers
+        models['bikol'] = FastPitchModel.restore_from(
+            "models/fastpitch_bikol_repacked.nemo",
+            strict=False  # Critical: allows loading with different vocabulary size
+        ).to(device)
         models['bikol'].eval()
 
         print("All models loaded successfully.")
     except Exception as e:
         print(f"FATAL: Could not load models. Error: {e}")
+        import traceback
+        traceback.print_exc()
         # In a real app, you might want the app to fail fast if models can't load.
 
 # --- 3. Define API Request and Response Models ---
@@ -89,9 +96,11 @@ def synthesize_speech(request: TTSRequest):
 
     except Exception as e:
         print(f"Error during synthesis: {e}")
+        import traceback
+        traceback.print_exc()
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail="An error occurred during audio synthesis."
+            detail=f"An error occurred during audio synthesis: {str(e)}"
         )
 
 # --- 5. Add a Root Endpoint for Health Check ---