🏠 GPT Home 🤖💬

Inspiration

The inspiration for GPT Home came from my interest in automation, combined with a Raspberry Pi 4B I had laying around. The course I took on IoT further motivated me to implement this project, though I probably would have explored this idea regardless.

What it does

GPT Home transforms a standard Raspberry Pi into a smart home hub similar to a Google Nest Hub or Amazon Alexa. It leverages the OpenAI API to provide an interactive voice assistant capable of understanding and generating human-like responses. Users can ask general questions, control home devices, or get updates about the weather, all through voice commands.

How I built it

The project was built using a Raspberry Pi 4B, equipped with Ubuntu Server as the OS, Python for programming, and various hardware components like a mini auxiliary speaker that simply plugs to the headphone jack, an OLED display, a USB microphone for voice input and audio output, and a battery pack to make it portable (something not possible with the Amazon Alexa or Google Nest Hub). The integration with the OpenAI API allows it to perform sophisticated natural language processing tasks. For a detailed guide on assembling the hardware and configuring the software, see the README.

Challenges I ran into

One of the main challenges was ensuring seamless integration of various components like the OLED display and the USB microphone with the Raspberry Pi. Configuring the audio input and output on the Ubuntu Server also required meticulous adjustments to avoid latency and feedback issues. Implementing asynchronous operations was particularly tricky, especially when trying to manage concurrent tasks like speaking, updating the OLED display, and handling queries simultaneously. Additionally, setting up Spotify's OAuth for music streaming involved navigating complex authentication flows, which proved to be quite challenging.

Accomplishments that I'm proud of

I'm particularly proud of how seamlessly the components work together to create a responsive and interactive user experience. The ability to convert text to speech and speech to text efficiently, despite the hardware limitations of the Raspberry Pi, I think stands out as a significant achievement. The project has gained the attention of RaspberryPi.com!

What I learned

This project deepened my understanding of integrating hardware with software for IoT applications. I gained practical experience in working with the OpenAI API and improved my skills in troubleshooting hardware compatibility issues on Linux-based systems. I've also greatly enhanced my understanding of Docker during this process.