BabitMF/bmf

Cross-platform, customizable multimedia/video processing framework. With strong GPU acceleration, heterogeneous design, multi-language support, easy to use, multi-framework compatible and high performance, the framework is ideal for transcoding, AI inference, algorithm integration, live video streaming, and more.

What it solves

BMF (Babit Multimedia Framework) is designed to handle complex multimedia processing tasks—such as transcoding, editing, and AI-driven video enhancement—across different platforms and languages. It addresses the need for a high-performance, customizable framework that can seamlessly integrate GPU acceleration and AI inference into video pipelines.

How it works

BMF uses a decoupled architecture that allows developers to build processing pipelines (Graphs) by combining independent modules. It supports multi-language development (Python, Go, and C++) and provides a powerful scheduler for heterogeneous acceleration, meaning it can manage data flow between CPU and GPU. It also includes built-in support for seamless data format conversion between popular frameworks like FFmpeg, PyTorch, OpenCV, and TensorRT.

Who it’s for

Developers and engineers building video streaming services, live transcoding, cloud editing tools, and mobile pre/post processing applications who require high throughput and hardware acceleration.

Highlights

  • Multi-Language Support: APIs available in Python, Go, and C++.
  • GPU Acceleration: Highly optimized GPU pipelines for transcoding and AI inference, developed in cooperation with NVIDIA.
  • AI Integration: Built-in capabilities for LLM video preprocessing, super-resolution (Real-ESRGAN), colorization (DeOldify), and face detection (TensorRT).
  • Cross-Platform: Native compatibility with Linux, Windows, and macOS, and optimization for x86 and ARM CPUs.
  • FFmpeg Compatibility: Fully compatible with FFmpeg options for transcoding and filtering.

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