https://github.com/judahpaul16/gpt-home
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!
https://www.raspberrypi.com/news/make-a-homemade-ai-home-assistant/
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.
Step 1: Plug in Microphone and Speaker Assuming you already have an operating system loaded and onto your device and you have a connection to the internet, all you need to do is plug in your speaker and microphone. You can use any speaker and microphone be they USB or auxillary as long as they are recognized devices in ALSA. After plugging in you can verify they are available by using the aplay -l and arecord -l commands. You should see an output similar to this:
# arecord -l
**** List of CAPTURE Hardware Devices ****
card 3: Device [USB PnP Sound Device], device 0: USB Audio [USB Audio]
Subdevices: 0/1
Subdevice #0: subdevice #0
# aplay -l
**** List of PLAYBACK Hardware Devices ****
card 0: vc4hdmi0 [vc4-hdmi-0], device 0: MAI PCM i2s-hifi-0 [MAI PCM i2s-hifi-0]
Subdevices: 1/1
Subdevice #0: subdevice #0
card 1: vc4hdmi1 [vc4-hdmi-1], device 0: MAI PCM i2s-hifi-0 [MAI PCM i2s-hifi-0]
Subdevices: 1/1
Subdevice #0: subdevice #0
card 2: Headphones [bcm2835 Headphones], device 0: bcm2835 Headphones [bcm2835 Headphones]
Subdevices: 8/8
Subdevice #0: subdevice #0
Subdevice #1: subdevice #1
Subdevice #2: subdevice #2
Subdevice #3: subdevice #3
Subdevice #4: subdevice #4
Subdevice #5: subdevice #5
Subdevice #6: subdevice #6
Subdevice #7: subdevice #7
card 4: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
Subdevices: 1/1
Subdevice #0: subdevice #0
Step 2: Install the Docker Container It only takes two* commands to get the container up and running on your Raspberry Pi. *Three commands if you want to use one of the models provided by LiteLLM.
Required for Semantic Routing: Make sure to export your OpenAI API Key to an environment variable.
echo "export OPENAI_API_KEY='your_api_key_here'" >> ~/.bashrc && source ~/.bashrc
Optional: If you want to use a model not provided by OpenAI, make sure your API key for the provider you want to use is exported to an environment variable called LITELLM_API_KEY. See the LiteLLM docs for a list of all supported providers.
echo "export LITELLM_API_KEY='your_api_key_here'" >> ~/.bashrc && source ~/.bashrc
Run the setup script with the --no-build flag to pull the latest image from DockerHub:
curl -s | bash -s -- --no-buildhttps://raw.githubusercontent.com/judahpaul16/gpt-home/main/contrib/setup.sh
Step 3: Configure Settings in the Web Interface There are a number of things you can customize from the web interface from choosing the LLM you want to respond to to you, to changing the keyword (default keyword is 'computer'), max tokens, languages (coming soon), to connecting your favorite services like Spotify, Philips Hue, OpenWeatherMap, and more to come!
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