Release v1.0.4: The Compatibility Update

- Added robust CPU Fallback for AMD/Non-CUDA GPUs.
- Implemented Lazy Load for AI Engine to prevent startup crashes.
- Added explicit DLL injection for Cublas/Cudnn on Windows.
- Added Corrupt Model Auto-Repair logic.
- Includes pre-compiled v1.0.4 executable.

src/core/transcriber.py

@@ -21,7 +21,7 @@ except ImportError:
     torch = None
 
 # Import directly - valid since we are now running in the full environment
-from faster_whisper import WhisperModel
+
 
 class WhisperTranscriber:
     """
@@ -62,13 +62,32 @@ class WhisperTranscriber:
             # Force offline if path exists to avoid HF errors
             local_only = new_path.exists()
 
-            self.model = WhisperModel(
-                model_input, 
-                device=device, 
-                compute_type=compute,
-                download_root=str(get_models_path()),
-                local_files_only=local_only
-            )
+            try:
+                from faster_whisper import WhisperModel
+                self.model = WhisperModel(
+                    model_input, 
+                    device=device, 
+                    compute_type=compute,
+                    download_root=str(get_models_path()),
+                    local_files_only=local_only
+                )
+            except Exception as load_err:
+                # CRITICAL FALLBACK: If CUDA/cublas fails (AMD/Intel users), fallback to CPU
+                err_str = str(load_err).lower()
+                if "cublas" in err_str or "cudnn" in err_str or "library" in err_str or "device" in err_str:
+                    logging.warning(f"CUDA Init Failed ({load_err}). Falling back to CPU...")
+                    self.config.set("compute_device", "cpu") # Update config for persistence/UI
+                    self.current_compute_device = "cpu"
+                    
+                    self.model = WhisperModel(
+                        model_input, 
+                        device="cpu", 
+                        compute_type="int8", # CPU usually handles int8 well with newer extensions, or standard
+                        download_root=str(get_models_path()),
+                        local_files_only=local_only
+                    )
+                else:
+                    raise load_err
             
             self.current_model_size = size
             self.current_compute_device = device
@@ -78,6 +97,32 @@ class WhisperTranscriber:
         except Exception as e:
             logging.error(f"Failed to load model: {e}")
             self.model = None
+            
+            # Auto-Repair: Detect vocabulary/corrupt errors
+            err_str = str(e).lower()
+            if "vocabulary" in err_str or "tokenizer" in err_str or "config.json" in err_str:
+                # ... existing auto-repair logic ...
+                logging.warning("Corrupt model detected on load. Attempting to delete and reset...")
+                try:
+                    import shutil
+                    # Differentiate between simple path and HF path
+                    new_path = get_models_path() / f"faster-whisper-{size}"
+                    if new_path.exists():
+                        shutil.rmtree(new_path)
+                        logging.info(f"Deleted corrupt model at {new_path}")
+                    else:
+                        # Try legacy HF path
+                        hf_path = get_models_path() / f"models--Systran--faster-whisper-{size}"
+                        if hf_path.exists():
+                            shutil.rmtree(hf_path)
+                            logging.info(f"Deleted corrupt HF model at {hf_path}")
+                            
+                    # Notify UI to refresh state (will show 'Download' button now)
+                    # We can't reach bridge easily here without passing it in, 
+                    # but the UI polls or listens to logs. 
+                    # The user will simply see "Model Missing" in settings after this.
+                except Exception as del_err:
+                    logging.error(f"Failed to delete corrupt model: {del_err}")
 
     def transcribe(self, audio_data, is_file: bool = False, task: Optional[str] = None) -> str:
         """
@@ -89,7 +134,7 @@ class WhisperTranscriber:
         if not self.model:
             self.load_model()
             if not self.model:
-                return "Error: Model failed to load."
+                return "Error: Model failed to load. Please check Settings -> Model Info."
 
         try:
             # Config
@@ -174,8 +219,11 @@ class WhisperTranscriber:
     def model_exists(self, size: str) -> bool:
         """Checks if a model size is already downloaded."""
         new_path = get_models_path() / f"faster-whisper-{size}"
-        if (new_path / "config.json").exists():
-            return True
+        if new_path.exists():
+            # Strict check
+            required = ["config.json", "model.bin", "vocabulary.json"]
+            if all((new_path / f).exists() for f in required):
+                return True
             
         # Legacy HF cache check
         folder_name = f"models--Systran--faster-whisper-{size}"

src/ui/bridge.py

@@ -381,25 +381,24 @@ class UIBridge(QObject):
 
             # Check new simple format used by DownloadWorker
             path_simple = get_models_path() / f"faster-whisper-{size}"
-            if path_simple.exists() and any(path_simple.iterdir()):
-                return True
+            if path_simple.exists():
+                # Strict check: Ensure all critical files exist
+                required = ["config.json", "model.bin", "vocabulary.json"]
+                if all((path_simple / f).exists() for f in required):
+                    return True
             
             # Check HF Cache format (legacy/default)
             folder_name = f"models--Systran--faster-whisper-{size}"
             path_hf = get_models_path() / folder_name
             snapshots = path_hf / "snapshots"
             if snapshots.exists() and any(snapshots.iterdir()):
-                return True
-                
-            # Check direct folder (simple)
-            path_direct = get_models_path() / size
-            if (path_direct / "config.json").exists():
-                return True
+                return True # Legacy cache structure is complex, assume valid if present
                 
+            return False
+            
         except Exception as e:
             logging.error(f"Error checking model status: {e}")
-            
-        return False
+            return False
 
     @Slot(str)
     def downloadModel(self, size):