Nick Bild

1 day ago • AI & Machine Learning

Voice interfaces have become commonplace, but they depend on either powerful processors or cloud services to do the heavy lifting. That has kept them largely out of reach for the tiny, inexpensive microcontrollers that power countless embedded devices. But now, a toolkit called Moonshine Micro has been developed to bring voice interfaces to microcontrollers. The new open source framework is efficient enough to run on a tiny Raspberry Pi RP2350 chip, yet it is capable of running voice activity detection, speech recognition, and text-to-speech pipelines.

Moonshine Micro is a lightweight version of the broader Moonshine Voice framework, targeting embedded processors and digital signal processors while retaining the core building blocks needed for real-world voice interfaces. The entire system fits within the RP2350's 520 KB of RAM, provisioning approximately 468 KB total through clever sharing of memory between each stage of the processing pipeline.

The framework consists of three independent neural network-powered libraries. The first performs voice activity detection (VAD), allowing the system to determine when someone is actually speaking. Once speech has been detected, the speech-to-text engine processes the audio using a compact neural network called SpellingCNN. Finally, a neural text-to-speech engine generates spoken responses. Since each library is modular, developers can use them individually or combine them into complete voice-enabled applications depending on the needs of their project.

The current release focuses on command recognition rather than unrestricted conversation. It can recognize approximately 50 custom words or phrases, and developers can retrain the models to recognize commands specific to their own applications. One demonstration shows the system configuring a Wi-Fi connection entirely through voice commands on the RP2350 microcontroller.

Despite its small footprint, the framework is intended for commercial as well as hobbyist projects. It is released under the permissive MIT License and relies on TensorFlow Lite Micro for the underlying neural network computations. For more information, or to try Moonshine Micro out for yourself, have a look at the project’s GitHub repository.

voice recognition

voice control

microcontroller

Nick Bild

R&D, creativity, and building the next big thing you never knew you wanted are my specialties.