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rp2350
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rp2350
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README.md
Outline
Moonshine 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.
*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).
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.
There’s a
complete end-to-end example
showing how to set up a wifi connection on a microcontroller using voice on an RP2350 MCU.
The VAD, STT, and TTS libraries can be used independently of each other, relying on the included
TensorFlow Lite Micro
library for the neural computations.
Documentation
Voice Activity Detection
Speech to Text
Custom Word Recognition
Neural Text to Speech
Wifi Setup Example
License
This code, apart from the source in
third-party/
, is licensed under the MIT
License — see
LICENSE
in this directory (also at the repository root).
The SpellingCNN and TinyVadCNN models in
models/
are released under
the MIT License.
The code in
third-party/
is licensed according to the terms of the open
source projects it originates from, with details in a LICENSE file in each
subfolder.
