added mlx-whisper example and test. update docling cli to use MLX automatically if present.

docling/cli/main.py

@@ -611,6 +611,17 @@ def convert(  # noqa: C901
             ocr_options.psm = psm
 
         accelerator_options = AcceleratorOptions(num_threads=num_threads, device=device)
+        
+        # Auto-detect pipeline based on input file formats
+        if pipeline == ProcessingPipeline.STANDARD:
+            # Check if any input files are audio files by extension
+            audio_extensions = {'.mp3', '.wav', '.m4a', '.aac', '.ogg', '.flac', '.mp4', '.avi', '.mov'}
+            for path in input_doc_paths:
+                if path.suffix.lower() in audio_extensions:
+                    pipeline = ProcessingPipeline.ASR
+                    _log.info(f"Auto-detected ASR pipeline for audio file: {path}")
+                    break
+        
         # pipeline_options: PaginatedPipelineOptions
         pipeline_options: PipelineOptions
 
@@ -749,6 +760,10 @@ def convert(  # noqa: C901
 
         elif pipeline == ProcessingPipeline.ASR:
             pipeline_options = AsrPipelineOptions(
+                accelerator_options=AcceleratorOptions(
+                    device=device,
+                    num_threads=num_threads,
+                ),
                 # enable_remote_services=enable_remote_services,
                 # artifacts_path = artifacts_path
             )

docling/datamodel/asr_model_specs.py

@@ -17,16 +17,54 @@ from docling.datamodel.pipeline_options_asr_model import (
 
 _log = logging.getLogger(__name__)
 
-WHISPER_TINY = InlineAsrNativeWhisperOptions(
-    repo_id="tiny",
-    inference_framework=InferenceAsrFramework.WHISPER,
-    verbose=True,
-    timestamps=True,
-    word_timestamps=True,
-    temperature=0.0,
-    max_new_tokens=256,
-    max_time_chunk=30.0,
-)
+def _get_whisper_tiny_model():
+    """
+    Get the best Whisper Tiny model for the current hardware.
+    
+    Automatically selects MLX Whisper Tiny for Apple Silicon (MPS) if available,
+    otherwise falls back to native Whisper Tiny.
+    """
+    # Check if MPS is available (Apple Silicon)
+    try:
+        import torch
+        has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
+    except ImportError:
+        has_mps = False
+    
+    # Check if mlx-whisper is available
+    try:
+        import mlx_whisper  # type: ignore
+        has_mlx_whisper = True
+    except ImportError:
+        has_mlx_whisper = False
+    
+    # Use MLX Whisper if both MPS and mlx-whisper are available
+    if has_mps and has_mlx_whisper:
+        return InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+    else:
+        return InlineAsrNativeWhisperOptions(
+            repo_id="tiny",
+            inference_framework=InferenceAsrFramework.WHISPER,
+            verbose=True,
+            timestamps=True,
+            word_timestamps=True,
+            temperature=0.0,
+            max_new_tokens=256,
+            max_time_chunk=30.0,
+        )
+
+
+# Create the model instance
+WHISPER_TINY = _get_whisper_tiny_model()
 
 def _get_whisper_small_model():
     """
@@ -77,16 +115,54 @@ def _get_whisper_small_model():
 # Create the model instance
 WHISPER_SMALL = _get_whisper_small_model()
 
-WHISPER_MEDIUM = InlineAsrNativeWhisperOptions(
-    repo_id="medium",
-    inference_framework=InferenceAsrFramework.WHISPER,
-    verbose=True,
-    timestamps=True,
-    word_timestamps=True,
-    temperature=0.0,
-    max_new_tokens=256,
-    max_time_chunk=30.0,
-)
+def _get_whisper_medium_model():
+    """
+    Get the best Whisper Medium model for the current hardware.
+    
+    Automatically selects MLX Whisper Medium for Apple Silicon (MPS) if available,
+    otherwise falls back to native Whisper Medium.
+    """
+    # Check if MPS is available (Apple Silicon)
+    try:
+        import torch
+        has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
+    except ImportError:
+        has_mps = False
+    
+    # Check if mlx-whisper is available
+    try:
+        import mlx_whisper  # type: ignore
+        has_mlx_whisper = True
+    except ImportError:
+        has_mlx_whisper = False
+    
+    # Use MLX Whisper if both MPS and mlx-whisper are available
+    if has_mps and has_mlx_whisper:
+        return InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-medium-mlx-8bit",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+    else:
+        return InlineAsrNativeWhisperOptions(
+            repo_id="medium",
+            inference_framework=InferenceAsrFramework.WHISPER,
+            verbose=True,
+            timestamps=True,
+            word_timestamps=True,
+            temperature=0.0,
+            max_new_tokens=256,
+            max_time_chunk=30.0,
+        )
+
+
+# Create the model instance
+WHISPER_MEDIUM = _get_whisper_medium_model()
 
 def _get_whisper_base_model():
     """
@@ -137,16 +213,54 @@ def _get_whisper_base_model():
 # Create the model instance
 WHISPER_BASE = _get_whisper_base_model()
 
-WHISPER_LARGE = InlineAsrNativeWhisperOptions(
-    repo_id="large",
-    inference_framework=InferenceAsrFramework.WHISPER,
-    verbose=True,
-    timestamps=True,
-    word_timestamps=True,
-    temperature=0.0,
-    max_new_tokens=256,
-    max_time_chunk=30.0,
-)
+def _get_whisper_large_model():
+    """
+    Get the best Whisper Large model for the current hardware.
+    
+    Automatically selects MLX Whisper Large for Apple Silicon (MPS) if available,
+    otherwise falls back to native Whisper Large.
+    """
+    # Check if MPS is available (Apple Silicon)
+    try:
+        import torch
+        has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
+    except ImportError:
+        has_mps = False
+    
+    # Check if mlx-whisper is available
+    try:
+        import mlx_whisper  # type: ignore
+        has_mlx_whisper = True
+    except ImportError:
+        has_mlx_whisper = False
+    
+    # Use MLX Whisper if both MPS and mlx-whisper are available
+    if has_mps and has_mlx_whisper:
+        return InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-large-mlx-8bit",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+    else:
+        return InlineAsrNativeWhisperOptions(
+            repo_id="large",
+            inference_framework=InferenceAsrFramework.WHISPER,
+            verbose=True,
+            timestamps=True,
+            word_timestamps=True,
+            temperature=0.0,
+            max_new_tokens=256,
+            max_time_chunk=30.0,
+        )
+
+
+# Create the model instance
+WHISPER_LARGE = _get_whisper_large_model()
 
 def _get_whisper_turbo_model():
     """

docs/examples/mlx_whisper_example.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""
+Example script demonstrating MLX Whisper integration for Apple Silicon.
+
+This script shows how to use the MLX Whisper models for speech recognition
+on Apple Silicon devices with optimized performance.
+"""
+
+import sys
+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.asr_model_specs import (
+    WHISPER_TINY,
+    WHISPER_BASE,
+    WHISPER_SMALL,
+    WHISPER_MEDIUM,
+    WHISPER_LARGE,
+    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.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
+    """
+    # Select the appropriate Whisper model (automatically uses MLX on Apple Silicon)
+    model_map = {
+        "tiny": WHISPER_TINY,
+        "base": WHISPER_BASE,
+        "small": WHISPER_SMALL,
+        "medium": WHISPER_MEDIUM,
+        "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())}")
+    
+    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={
+            InputFormat.AUDIO: AudioFormatOption(
+                pipeline_cls=AsrPipeline,
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+    
+    # 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}")
+
+
+def main():
+    """Main function to demonstrate MLX Whisper usage."""
+    if len(sys.argv) < 2:
+        print("Usage: python mlx_whisper_example.py <audio_file_path> [model_size]")
+        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()
+        
+        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")
+        print("Or install with uv: uv sync --extra asr")
+        sys.exit(1)
+    except Exception as e:
+        print(f"Error during transcription: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tests/test_asr_mlx_whisper.py

@@ -0,0 +1,206 @@
+"""
+Test MLX Whisper integration for Apple Silicon ASR pipeline.
+"""
+import pytest
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+from docling.datamodel.asr_model_specs import (
+    WHISPER_TINY,
+    WHISPER_BASE,
+    WHISPER_SMALL,
+    WHISPER_MEDIUM,
+    WHISPER_LARGE,
+    WHISPER_TURBO,
+)
+from docling.datamodel.pipeline_options_asr_model import (
+    InferenceAsrFramework,
+    InlineAsrMlxWhisperOptions,
+)
+from docling.datamodel.accelerator_options import AcceleratorOptions, AcceleratorDevice
+from docling.pipeline.asr_pipeline import AsrPipeline, _MlxWhisperModel
+from docling.datamodel.pipeline_options import AsrPipelineOptions
+
+
+class TestMlxWhisperIntegration:
+    """Test MLX Whisper model integration."""
+
+    def test_mlx_whisper_options_creation(self):
+        """Test that MLX Whisper options are created correctly."""
+        options = InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            language="en",
+            task="transcribe",
+        )
+        
+        assert options.inference_framework == InferenceAsrFramework.MLX
+        assert options.repo_id == "mlx-community/whisper-tiny-mlx"
+        assert options.language == "en"
+        assert options.task == "transcribe"
+        assert options.word_timestamps is True
+        assert AcceleratorDevice.MPS in options.supported_devices
+
+    def test_whisper_models_auto_select_mlx(self):
+        """Test that Whisper models automatically select MLX when MPS and mlx-whisper are available."""
+        # This test verifies that the models are correctly configured
+        # In a real Apple Silicon environment with mlx-whisper installed,
+        # these models would automatically use MLX
+        
+        # Check that the models exist and have the correct structure
+        assert hasattr(WHISPER_TURBO, 'inference_framework')
+        assert hasattr(WHISPER_TURBO, 'repo_id')
+        
+        assert hasattr(WHISPER_BASE, 'inference_framework')
+        assert hasattr(WHISPER_BASE, 'repo_id')
+        
+        assert hasattr(WHISPER_SMALL, 'inference_framework')
+        assert hasattr(WHISPER_SMALL, 'repo_id')
+
+    @patch('builtins.__import__')
+    def test_mlx_whisper_model_initialization(self, mock_import):
+        """Test MLX Whisper model initialization."""
+        # Mock the mlx_whisper import
+        mock_mlx_whisper = Mock()
+        mock_import.return_value = mock_mlx_whisper
+        
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+        asr_options = InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+        
+        model = _MlxWhisperModel(
+            enabled=True,
+            artifacts_path=None,
+            accelerator_options=accelerator_options,
+            asr_options=asr_options,
+        )
+        
+        assert model.enabled is True
+        assert model.model_path == "mlx-community/whisper-tiny-mlx"
+        assert model.language == "en"
+        assert model.task == "transcribe"
+        assert model.word_timestamps is True
+
+    def test_mlx_whisper_model_import_error(self):
+        """Test that ImportError is raised when mlx-whisper is not available."""
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+        asr_options = InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+        
+        with patch('builtins.__import__', side_effect=ImportError("No module named 'mlx_whisper'")):
+            with pytest.raises(ImportError, match="mlx-whisper is not installed"):
+                _MlxWhisperModel(
+                    enabled=True,
+                    artifacts_path=None,
+                    accelerator_options=accelerator_options,
+                    asr_options=asr_options,
+                )
+
+    @patch('builtins.__import__')
+    def test_mlx_whisper_transcribe(self, mock_import):
+        """Test MLX Whisper transcription method."""
+        # Mock the mlx_whisper module and its transcribe function
+        mock_mlx_whisper = Mock()
+        mock_import.return_value = mock_mlx_whisper
+        
+        # Mock the transcribe result
+        mock_result = {
+            "segments": [
+                {
+                    "start": 0.0,
+                    "end": 2.5,
+                    "text": "Hello world",
+                    "words": [
+                        {"start": 0.0, "end": 0.5, "word": "Hello"},
+                        {"start": 0.5, "end": 1.0, "word": "world"},
+                    ]
+                }
+            ]
+        }
+        mock_mlx_whisper.transcribe.return_value = mock_result
+        
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+        asr_options = InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+        
+        model = _MlxWhisperModel(
+            enabled=True,
+            artifacts_path=None,
+            accelerator_options=accelerator_options,
+            asr_options=asr_options,
+        )
+        
+        # Test transcription
+        audio_path = Path("test_audio.wav")
+        result = model.transcribe(audio_path)
+        
+        # Verify the result
+        assert len(result) == 1
+        assert result[0].start_time == 0.0
+        assert result[0].end_time == 2.5
+        assert result[0].text == "Hello world"
+        assert len(result[0].words) == 2
+        assert result[0].words[0].text == "Hello"
+        assert result[0].words[1].text == "world"
+        
+        # Verify mlx_whisper.transcribe was called with correct parameters
+        mock_mlx_whisper.transcribe.assert_called_once_with(
+            str(audio_path),
+            path_or_hf_repo="mlx-community/whisper-tiny-mlx",
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+
+    @patch('builtins.__import__')
+    def test_asr_pipeline_with_mlx_whisper(self, mock_import):
+        """Test that AsrPipeline can be initialized with MLX Whisper options."""
+        # Mock the mlx_whisper import
+        mock_mlx_whisper = Mock()
+        mock_import.return_value = mock_mlx_whisper
+        
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+        asr_options = InlineAsrMlxWhisperOptions(
+            repo_id="mlx-community/whisper-tiny-mlx",
+            inference_framework=InferenceAsrFramework.MLX,
+            language="en",
+            task="transcribe",
+            word_timestamps=True,
+            no_speech_threshold=0.6,
+            logprob_threshold=-1.0,
+            compression_ratio_threshold=2.4,
+        )
+        pipeline_options = AsrPipelineOptions(
+            asr_options=asr_options,
+            accelerator_options=accelerator_options,
+        )
+        
+        pipeline = AsrPipeline(pipeline_options)
+        assert isinstance(pipeline._model, _MlxWhisperModel)
+        assert pipeline._model.model_path == "mlx-community/whisper-tiny-mlx"