Integrating ONNX Models into Elixir with Ortex

The article starts by introducing Bumblebee, an Elixir library offering pre-trained machine learning models. For models unsupported by Bumblebee, the author suggests using Ortex. Ortex uses Rust NIFs to provide ONNX runtime bindings in Elixir, making it straightforward to load and run ONNX models within Elixir. ONNX, the Open Neural Network Exchange, is a popular open-source model serialization format in the Python ecosystem that enables interoperability across various hardware and machine learning frameworks. Ortex simplifies the integration of ONNX models into Elixir applications without conversion hassles, offering a seamless experience for developers. The article provides a practical example of using Ortex for voice activity detection (VAD), which is crucial for developing conversational AI applications. It showcases how to set up and run Silero VAD, a lightweight, enterprise-grade VAD model, with Ortex in an Elixir environment. The process is further enhanced by combining Ortex and Bumblebee, resulting in a capable solution for building conversational assistants. Through this example, the author demonstrates the simplicity and ease of importing and using machine learning models in Elixir with the help of Ortex.