{"id":93175,"date":"2024-03-08T05:09:58","date_gmt":"2024-03-08T05:09:58","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=93175"},"modified":"2025-08-14T10:07:41","modified_gmt":"2025-08-14T10:07:41","slug":"control-home-assistant-with-your-voice-integrate-riva-llama2-to-a-smart-home-hub-powered-by-nvidia-jetson","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2024\/03\/08\/control-home-assistant-with-your-voice-integrate-riva-llama2-to-a-smart-home-hub-powered-by-nvidia-jetson\/","title":{"rendered":"Control Home Assistant with Your Voice: Integrate RIVA, Llama2 to a Smart Home Hub Powered by NVIDIA Jetson\u00a0"},"content":{"rendered":"\n

Home Assistant has declared 2023 as “the year of voice”, setting a bold vision to empower users to interact with Home Assistant and their appliances using their natural language. This initiative aims to transcend the traditional methods of smart home management\u2014moving beyond mere terminal device usage or simple voice commands. The main challenge lies in designing a system which can figures out the user’s own intents through voice language requests to achieve real smart home automation.<\/p>\n\n\n\n

Use Home Assistant to build smart home, use LLM to make home smarter <\/h2>\n\n\n\n

Following the vision of “controlling smart home using our own language”, we built this system, regarded as Local Jarvis, integrating tinyML and LLM(large language model) into Home Assistant<\/a> as a voice control option. Local Jarvis emerges as an answer to the growing desire for smarter, more private smart home management.<\/p>\n\n\n\n

With the Home Assistant MQTT Integration, Home Assistant can connect to AWS IoT Core and a local Greengrass MQTT broker. This means it can work with the cloud, other local Greengrass parts, and devices on the same network. So, Home Assistant can use AWS’s Greengrass<\/a>, custom ones, and those made by the community, along with AWS services, to make home automation even better and do more things.<\/p>\n\n\n\n

Combining Home Assistant with LLMs allows for sophisticated natural language processing without the need for cloud reliance. This approach ensures privacy and reduces latency in command processing. Utilizing advanced language models enables the system to learn from user interactions, making smart home automation not just responsive but anticipatory.<\/p>\n\n\n\n

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The architecture of combining tinyML and LLM is designed with simplicity and scalability in mind. Starting with voice capture through ReSpeaker<\/a>, to processing with NVIDIA’s Riva <\/a>and Jetson AGX Orin<\/a>, and finally executing commands via Home Assistant Green Gateway, each component plays a critical role in transforming voice inputs into intelligent actions. This streamlined workflow not only ensures privacy and efficiency but also opens up new possibilities for developers to customize and extend their smart home solutions. The system is built with:<\/p>\n\n\n\n

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  1. Capturing voice via ReSpeaker, then converting speech to text using Riva.<\/li>\n\n\n\n
  2. Utilizing the NVIDIA Jetson AGX Orin to run LLAMA2<\/a>, analyzing the text to understand user commands and generate appropriate responses.<\/li>\n<\/ol>\n\n\n\n
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