GoKawiilMoonshine Micro — Voice Interfaces for Microcontrollers
Moonshine Voice is an open source AI toolkit for developers building real-time voice agents and applications. Moonshine Micro is a version designed specifically for embedded system processors like microcontrollers and DSPs, and uses the Raspberry Pi RP2350, which retails for just 80 cents, as its reference platform. It includes voice-activity detection, command recognition, and neural speech synthesis and can run in as little as 470 KB of RAM.
You can see a full walkthrough in the video below:
The memory and compute requirements are designed to fit resource-constrained systems. Figures below are for the RP2350 demo; the detailed memory budget breaks each one down:
Component Flash SRAM (arena peak) Compute VAD (Voice Activity Detection) ~89 KiB ~36 KiB ~0.8 MMAC/frame (~25 MMAC/s) STT (SpellingCNN Speech-to-Text) ~1.3 MiB ~346 KiB ~36 MMAC/s TTS (neural diphone synth @ 16 kHz) ~1.8 MiB voice pack ~340 KiB ~37 MMAC typical reply (~65 MMAC/s out) TOTAL (Demo pipeline) ~3.6 MiB ~468 KiB provisioned* classify + speak ~0.7–1.0 s
Notes:
Flash is .text + .rodata measured with arm-none-eabi-size on the default moonshine_micro_echo firmware (includes the embedded neural voice pack); SRAM is .bss + heap + stacks.
+ measured with on the default firmware (includes the embedded neural voice pack); SRAM is + heap + stacks. *VAD, STT, and neural TTS run sequentially and time-share one ~384 KiB TFLM arena, so SRAM is not additive — ~468 KiB is the total RAM provisioned on the 520 KiB RP2350 ( wifi_hardware ~491 KiB).
~491 KiB). A MAC is one multiply-accumulate; MMAC/s = millions per second during the active (non-idle) stage.
The code is released under the permissive MIT License, suitable for commercial applications.
... continue reading