media-engine 0.1.1

AI-powered video extraction API for metadata, transcripts, faces, scenes, objects, and more

Media Engine

AI-powered video metadata extraction API for small TV stations and content creators. Provides a "file in → JSON out" API that extracts metadata, transcripts, faces, scenes, objects, CLIP embeddings, OCR text, and camera motion from video files.

Installation

# Apple Silicon Mac
pip install media-engine[mlx]

# NVIDIA GPU
pip install media-engine[cuda]

# CPU only
pip install media-engine[cpu]

# Start the server
meng-server

Requirements: Python 3.12+, ffmpeg

Features

• Metadata extraction - Duration, resolution, codec, GPS, device info (modular per-manufacturer)
• Transcription - Whisper with speaker diarization
• Face detection - DeepFace with embedding clustering
• Scene detection - PySceneDetect content-aware boundaries
• Object detection - YOLO or Qwen VLM
• CLIP embeddings - Per-scene similarity search
• OCR - PaddleOCR text extraction
• Motion analysis - Camera pan/tilt/zoom detection
• Shot type - Aerial, interview, b-roll classification

Quick Start

Docker (Recommended)

# Clone and run
git clone https://github.com/thetrainroom/media-engine.git
cd media-engine

# Start the server
docker compose up -d

# Test
curl http://localhost:8001/health

Mount your media folder:

MEDIA_PATH=/path/to/videos docker compose up -d

For NVIDIA GPU support (uses Dockerfile.cuda):

docker compose --profile gpu up -d

Or build manually:

docker build -f Dockerfile.cuda -t media-engine-gpu .
docker run -p 8001:8001 --gpus all -v /path/to/media:/media media-engine-gpu

Apple Silicon (Recommended: Native)

Docker on macOS runs in a Linux VM without Metal/MPS access. For GPU acceleration on Apple Silicon, run natively:

pip install media-engine[mlx]
meng-server

A Dockerfile.mlx is provided for consistency, but will use CPU in Docker:

docker compose --profile mlx up -d

Development Installation

# Mac Apple Silicon
pip install -e ".[mlx]"

# NVIDIA GPU
pip install -e ".[cuda]"

# CPU only
pip install -e ".[cpu]"

# Run server with hot reload
uvicorn media_engine.main:app --reload --port 8001

API Usage

Extract metadata from a video

curl -X POST http://localhost:8001/extract \
  -H "Content-Type: application/json" \
  -d '{
    "file": "/media/video.mp4",
    "enable_metadata": true,
    "enable_transcript": true,
    "enable_faces": false,
    "enable_scenes": true,
    "enable_objects": false,
    "enable_clip": false,
    "enable_ocr": false,
    "enable_motion": false
  }'

Endpoints

Endpoint	Method	Description
/health	GET	Health check
/extract	POST	Extract features from video
/extractors	GET	List available extractors

Supported Devices

The metadata extractor automatically detects camera/device type:

Manufacturer	Models	Features
DJI	Mavic, Air, Mini, Pocket, Osmo, Action	GPS from SRT, color profiles
Sony	PXW, FX, Alpha, ZV series	XML sidecar, S-Log, GPS
Canon	Cinema EOS, EOS R	XML sidecar
Apple	iPhone, iPad	QuickTime metadata, GPS
Blackmagic	Pocket, URSA, BRAW	ProApps metadata, BRAW detection
RED	DSMC2, V-RAPTOR, KOMODO	R3D native support
ARRI	ALEXA, ALEXA Mini, AMIRA	ARRIRAW detection
Insta360	X3, X4, ONE RS, GO 3	360 video detection
FFmpeg	OBS, Handbrake, etc.	Encoder detection

Adding new manufacturers is easy - create a module in media_engine/extractors/metadata/.

Architecture

media_engine/
├── main.py              # FastAPI app
├── config.py            # Settings and platform detection
├── schemas.py           # Pydantic models
└── extractors/
    ├── metadata/        # Modular per-manufacturer
    │   ├── dji.py
    │   ├── sony.py
    │   ├── apple.py
    │   └── ...
    ├── transcribe.py    # Whisper (MLX/CUDA/CPU)
    ├── faces.py         # DeepFace + embeddings
    ├── scenes.py        # PySceneDetect
    ├── objects.py       # YOLO
    ├── objects_qwen.py  # Qwen VLM
    ├── clip.py          # CLIP embeddings
    ├── ocr.py           # PaddleOCR
    └── motion.py        # Optical flow analysis

Configuration

Settings are stored in ~/.config/polybos/config.json:

{
  "whisper_model": "large-v3",
  "fallback_language": "en",
  "hf_token": null,
  "face_sample_fps": 1.0,
  "object_sample_fps": 2.0,
  "ocr_languages": ["en", "no", "de", "fr", "es"]
}

Set hf_token to enable speaker diarization (requires accepting license at pyannote).

Development

# Install dev dependencies
pip install -e ".[dev]"

# Lint
ruff check media_engine/

# Type check
pyright media_engine/

# Test
export TEST_VIDEO_PATH=/path/to/test.mp4
pytest tests/

Contributing

Contributions welcome! To add support for a new camera manufacturer:

1. Create media_engine/extractors/metadata/yourmanufacturer.py
2. Implement detect() and extract() methods
3. Register with register_extractor("name", YourExtractor())
4. Import in metadata/__init__.py

See existing modules for examples.

License

MIT