fix pre-commit checks and added proper type safety

docs/examples/minimal_asr_pipeline.py

@@ -43,7 +43,7 @@ def get_asr_converter():
     implementation for your hardware:
     - MLX Whisper Turbo for Apple Silicon (M1/M2/M3) with mlx-whisper installed
     - Native Whisper Turbo as fallback
-    
+
     You can swap in another model spec from `docling.datamodel.asr_model_specs`
     to experiment with different model sizes.
     """

docs/examples/mlx_whisper_example.py

@@ -12,31 +12,31 @@ from pathlib import Path
 # Add the repository root to the path so we can import docling
 sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
+from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
 from docling.datamodel.asr_model_specs import (
-    WHISPER_TINY,
     WHISPER_BASE,
-    WHISPER_SMALL,
-    WHISPER_MEDIUM,
     WHISPER_LARGE,
+    WHISPER_MEDIUM,
+    WHISPER_SMALL,
+    WHISPER_TINY,
     WHISPER_TURBO,
 )
-from docling.datamodel.accelerator_options import AcceleratorOptions, AcceleratorDevice
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.pipeline_options import AsrPipelineOptions
+from docling.document_converter import AudioFormatOption, DocumentConverter
 from docling.pipeline.asr_pipeline import AsrPipeline
-from docling.document_converter import DocumentConverter, AudioFormatOption
 
 
 def transcribe_audio_with_mlx_whisper(audio_file_path: str, model_size: str = "base"):
     """
     Transcribe audio using Whisper models with automatic MLX optimization for Apple Silicon.
-    
+
     Args:
         audio_file_path: Path to the audio file to transcribe
         model_size: Size of the Whisper model to use
                   ("tiny", "base", "small", "medium", "large", "turbo")
                   Note: MLX optimization is automatically used on Apple Silicon when available
-    
+
     Returns:
         The transcribed text
     """
@@ -49,21 +49,23 @@ def transcribe_audio_with_mlx_whisper(audio_file_path: str, model_size: str = "b
         "large": WHISPER_LARGE,
         "turbo": WHISPER_TURBO,
     }
-    
+
     if model_size not in model_map:
-        raise ValueError(f"Invalid model size: {model_size}. Choose from: {list(model_map.keys())}")
-    
+        raise ValueError(
+            f"Invalid model size: {model_size}. Choose from: {list(model_map.keys())}"
+        )
+
     asr_options = model_map[model_size]
-    
+
     # Configure accelerator options for Apple Silicon
     accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
-    
+
     # Create pipeline options
     pipeline_options = AsrPipelineOptions(
         asr_options=asr_options,
         accelerator_options=accelerator_options,
     )
-    
+
     # Create document converter with MLX Whisper configuration
     converter = DocumentConverter(
         format_options={
@@ -73,16 +75,16 @@ def transcribe_audio_with_mlx_whisper(audio_file_path: str, model_size: str = "b
             )
         }
     )
-    
+
     # Run transcription
     result = converter.convert(Path(audio_file_path))
-    
+
     if result.status.value == "success":
         # Extract text from the document
         text_content = []
         for item in result.document.texts:
             text_content.append(item.text)
-        
+
         return "\n".join(text_content)
     else:
         raise RuntimeError(f"Transcription failed: {result.status}")
@@ -95,26 +97,30 @@ def main():
         print("Model sizes: tiny, base, small, medium, large, turbo")
         print("Example: python mlx_whisper_example.py audio.wav base")
         sys.exit(1)
-    
+
     audio_file_path = sys.argv[1]
     model_size = sys.argv[2] if len(sys.argv) > 2 else "base"
-    
+
     if not Path(audio_file_path).exists():
         print(f"Error: Audio file '{audio_file_path}' not found.")
         sys.exit(1)
-    
+
     try:
         print(f"Transcribing '{audio_file_path}' using Whisper {model_size} model...")
-        print("Note: MLX optimization is automatically used on Apple Silicon when available.")
+        print(
+            "Note: MLX optimization is automatically used on Apple Silicon when available."
+        )
         print()
-        
-        transcribed_text = transcribe_audio_with_mlx_whisper(audio_file_path, model_size)
-        
+
+        transcribed_text = transcribe_audio_with_mlx_whisper(
+            audio_file_path, model_size
+        )
+
         print("Transcription Result:")
         print("=" * 50)
         print(transcribed_text)
         print("=" * 50)
-        
+
     except ImportError as e:
         print(f"Error: {e}")
         print("Please install mlx-whisper: pip install mlx-whisper")