The latest articles on DEV Community by Rocky Gao (@rocky_gao_66d703529b0f7c6). https://dev.to/rocky_gao_66d703529b0f7c6 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4209738%2Fdd79157d-0266-49a1-be2b-e9934c73937c.png https://dev.to/rocky_gao_66d703529b0f7c6 en Rocky Gao Tue, 27 Oct 2026 03:08:10 +0000 https://dev.to/rocky_gao_66d703529b0f7c6/running-local-asr-and-tts-on-rk3588-for-offline-voice-control-5f36 https://dev.to/rocky_gao_66d703529b0f7c6/running-local-asr-and-tts-on-rk3588-for-offline-voice-control-5f36 <p>Edge AI deployment without cloud dependency is highly valuable for industrial and embedded IoT projects. The Rockchip RK3588 platform features a built-in NPU that delivers stable inference performance for lightweight speech AI models.</p> <p>In this guide, we explore how to deploy local ASR and TTS pipelines on the [<a href="https://ieeker.com/products/rk3588/" rel="noopener noreferrer">RK3588 SBC</a>], building an offline voice interaction system that does not require internet access.</p> <h2> Hardware Preparation </h2> <p>We only need a few simple components for this build:</p> <ul> <li>RK3588 single-board computer</li> <li>5V stable power supply</li> <li>Microphone and speaker module</li> <li>Heatsink for thermal management</li> </ul> <h2> Software Workflow </h2> <ol> <li>Flash the official Rockchip embedded Linux image to eMMC or SD card</li> <li>Set up the RKNN Toolchain for model quantization and conversion</li> <li>Import optimized ASR and TTS models adapted for RK3588 NPU</li> <li>Write simple test scripts to connect audio capture, inference, and audio output</li> </ol> <h2> Testing the Voice Pipeline </h2> <p>Once all dependencies are installed, we test the full voice flow locally:<br> The microphone captures voice input, ASR converts speech to text on the NPU, then TTS generates audio feedback. All computing runs on the board locally.</p> <p>This offline voice solution reduces network latency and protects user data privacy. It fits well for on-site industrial controllers, smart equipment, and privacy-focused embedded devices.</p> <h2> Wrap-up </h2> <p>For hardware engineers looking to implement on-device voice AI, the RK3588 is a practical choice thanks to its mature Linux support and powerful NPU.</p> rk3588 embeddedsystems edgeai linux