Moonshine AI open-sourced Moonshine Micro, a voice toolkit designed for embedded processors that delivers speech recognition and text-to-speech in under 500KB of memory.

The toolkit runs on microcontrollers like the Raspberry Pi RP2350, which costs just 80 cents retail. It includes voice activity detection, command recognition, and neural speech synthesis within a 470KB RAM footprint.

The system breaks down into three core components. Voice activity detection uses 89KB of flash memory and 36KB of SRAM, consuming 0.8 million multiply-accumulate operations per frame. Speech-to-text via SpellingCNN requires 1.3MB flash and 346KB SRAM at 36 million operations per second. Neural text-to-speech needs 1.8MB for the voice pack and 340KB SRAM, running at 37 million operations for typical responses.

The complete demo pipeline totals 3.6MB flash storage and provisions 468KB SRAM on the RP2350's 520KB available memory. The three components run sequentially and share a single 384KB TensorFlow Lite Micro arena, keeping total RAM usage below the half-megabyte threshold.

Why ultra-compact voice matters

The toolkit targets resource-constrained embedded systems where traditional cloud-based voice processing isn't viable. Each component can operate independently, allowing developers to integrate only needed functionality.

Moonshine Micro includes a complete WiFi connection example showing voice setup on the RP2350 microcontroller. The neural models — SpellingCNN and TinyVadCNN — are released under MIT License alongside the main codebase.

The company provides detailed memory budgets and compute requirements for each component. Classification and speech generation typically complete within 0.7 to 1.0 seconds on the reference hardware.

Moonshine AI designed the system for commercial applications, releasing all code under the permissive MIT License. The toolkit represents a significant compression of voice processing capabilities into microcontroller-class hardware.