Today | News | Books | Recipes moonshine/micro at main · moonshine-ai/moonshine · GitHub moonshine/micro at main · moonshine-ai/moonshine · GitHub GitHub Copilot appDirect agents from issue to merge MCP RegistryNewIntegrate external tools DEVELOPER WORKFLOWS ActionsAutomate any workflow CodespacesInstant dev environments IssuesPlan and track work Code ReviewManage code changes APPLICATION SECURITY GitHub Advanced SecurityFind and fix vulnerabilities Code securitySecure your code as you build Secret protectionStop leaks before they start Solutions BY COMPANY SIZEEnterprises Small and medium teams BY USE CASEApp Modernization BY INDUSTRYHealthcare Resources EXPLORE BY TOPICAI Software Development EXPLORE BY TYPECustomer stories SUPPORT & SERVICESDocumentation Open Source COMMUNITY GitHub SponsorsFund open source developers PROGRAMSSecurity Lab Maintainer Community Enterprise ENTERPRISE SOLUTIONS Enterprise platformAI-powered developer platform AVAILABLE ADD-ONS GitHub Advanced SecurityEnterprise-grade security features Copilot for BusinessEnterprise-grade AI features Premium SupportEnterprise-grade 24/7 support Search or jump to... Use saved searches to filter your results more quickly There was an error while loading. Please reload this page. Files Expand file tree NameNameLast commit message pico_sdk_import.cmake pico_sdk_import.cmake 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 Component VAD (Voice Activity Detection) STT (SpellingCNN Speech-to-Text) TTS (neural diphone synth @ 16 kHz) TOTAL (Demo pipeline) 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. Voice Activity Detection Custom Word Recognition Neural Text to Speech This code, apart from the source in third-party/, is licensed under the MIT Links
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