This is Nanowakeword, an open-source Python framework designed to solve a common problem in voice AI: the complex and time-consuming process of training custom, high-performance wake word models.
The core of the project is an Intelligent Configuration Engine. Instead of requiring manual hyperparameter tuning (learning rates, model architecture, etc.), the engine analyzes the user's dataset and automatically generates an optimal, data-driven training configuration. The goal is to abstract away the complexity and replace hours of manual trial-and-error with a single `--auto-config` flag.
It works by analyzing the statistical properties of the provided audio data (duration, noise, balance) and then designs a suitable model architecture and training plan. The training itself uses a modern pipeline with techniques like Cyclical Learning Rates, and outputs optimized models in ONNX and TFLite formats, ready for edge devices.
The entire project is packaged for a simple `pip install "nanowakeword[train]"` and can be run with a clean command-line tool (`nanowakeword-train`). It's fully open-source under the Apache 2.0 license.
The project is still in its early stages but the core engine is robust. It's developed under the Arcosoph initiative, with the vision of creating powerful and accessible open-source AI tools. We are actively seeking feedback, suggestions, and criticisms from the community.
Tech Stack: PyTorch, ONNX, TensorFlow (for TFLite conversion).