# Speech Recognition and TTS in less than 500kb

> Source: <https://github.com/moonshine-ai/moonshine/tree/main/micro>
> Published: 2026-07-14 19:25:10+00:00

[Moonshine Voice](https://github.com/moonshine-ai/moonshine) 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](/moonshine-ai/moonshine/blob/main/micro/vad/README.md), [command recognition](/moonshine-ai/moonshine/blob/main/micro/stt/README.md), and [neural speech synthesis](/moonshine-ai/moonshine/blob/main/micro/neural-tts/README.md) 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](/moonshine-ai/moonshine/blob/main/micro/examples/rp2350/README.md); the
detailed [memory budget](/moonshine-ai/moonshine/blob/main/micro/examples/rp2350/README.md#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](#license), suitable for commercial applications.

There's a [complete end-to-end example](/moonshine-ai/moonshine/blob/main/micro/examples/rp2350/README.md) 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](https://github.com/tensorflow/tflite-micro) library for the neural computations.

[Voice Activity Detection](/moonshine-ai/moonshine/blob/main/micro/vad/README.md)[Speech to Text](/moonshine-ai/moonshine/blob/main/micro/stt/README.md)[Custom Word Recognition](/moonshine-ai/moonshine/blob/main/micro/stt-training/README.md)[Neural Text to Speech](/moonshine-ai/moonshine/blob/main/micro/neural-tts/README.md)[Wifi Setup Example](/moonshine-ai/moonshine/blob/main/micro/examples/rp2350/README.md)

This code, apart from the source in `third-party/`

, is licensed under the MIT
License — see [LICENSE](/moonshine-ai/moonshine/blob/main/micro/LICENSE) in this directory (also at the repository root).

The SpellingCNN and TinyVadCNN models in [ models/](/moonshine-ai/moonshine/blob/main/micro/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.