BasedHardware/omi: Quick Start for AI Wearable Development

BasedHardware/omi

It's particularly helpful for building real-world applications that require a hands-free, voice-activated interface, such as a smart assistant for field workers, a personal memory aid for recording and transcribing conversations, or an instant translation tool for travelers.

OMI provides a streamlined platform that handles the complexities of hardware and low-level communication, allowing you to focus on the application logic. Here's a breakdown of its utility

Rapid Prototyping
You can quickly build and test ideas for wearable AI applications without having to design and assemble the hardware from scratch. This significantly shortens the development cycle.

Unified API
The project offers a consistent API across different languages (Python, C), which makes it easy to integrate with various backends and services, like a cloud-based AI model or a local database.

AI Integration
Since it's built with AI wearables in mind, it provides a solid foundation for integrating with pre-trained models for tasks like natural language processing (NLP) or sentiment analysis. The device handles the audio capture and transcription, and you can feed the transcribed text to your AI model for further processing.

The project's structure suggests a multi-layered approach
a hardware layer, a low-level C library, and a high-level Python library.

First, you'll need to set up the necessary environment. For the Python part, you'd typically install the OMI library via pip.

pip install basedhardware-omi

If you're working with the C library for a more performance-critical application, you'll need to clone the repository and compile it.

git clone https://github.com/BasedHardware/omi.git
cd omi/c
make

You'll need the OMI hardware device itself. Connect it to your development machine (e.g., via USB or Bluetooth) according to the device's documentation. The C library or Python wrapper will handle the communication with the device.

Before you can start, you'll likely need to configure the device and your application, such as setting up API keys for a transcription service or defining the desired audio quality. This is usually done through a configuration file or environment variables.

Let's imagine we want to build a simple application that listens for a command, transcribes it, and then prints the result. Here's how that might look using a hypothetical Python library from BasedHardware/omi.

import omi
import time

# Initialize the OMI device connection
print("Connecting to OMI device...")
try:
    device = omi.Device()
    device.connect()
    print("Device connected! Starting transcription service...")
except Exception as e:
    print(f"Failed to connect to device: {e}")
    exit()

# Create a callback function for transcribed text
def on_transcription(transcribed_text):
    """
    This function is called every time a new piece of text is transcribed.
    """
    print(f" New transcription received: '{transcribed_text}'")
    
    # You can add your AI logic here!
    if "turn on the lights" in transcribed_text.lower():
        print(" Command recognized: Turning on the lights...")
        # Add your logic to control smart lights here

# Register the callback and start listening
device.on_transcription = on_transcription
device.start_listening()

print("Listening for voice commands. Speak into the wearable device...")

try:
    # Keep the script running
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("Stopping transcription service...")
    device.stop_listening()
    device.disconnect()
    print("Disconnected. Goodbye!")

This example demonstrates a basic event-driven approach. The on_transcription function acts as a callback, which is a common pattern in asynchronous programming. When the OMI device completes a transcription, it triggers this function with the transcribed text, allowing you to react to the user's speech in real-time.

For a more performance-sensitive or embedded application, you would use the C library. The process is similar, but you're working with lower-level functions and pointers.

#include <stdio.h>
#include <omi.h>

// Callback function for transcribed text
void on_transcription_callback(const char* text) {
    printf(" New transcription received: '%s'\n", text);
    // Your logic here...
    if (strstr(text, "what's the weather") != NULL) {
        printf(" Command recognized: Checking the weather...\n");
    }
}

int main() {
    // Initialize the library
    omi_init();

    // Connect to the device
    OmiDevice* device = omi_connect();
    if (device == NULL) {
        fprintf(stderr, "Failed to connect to OMI device.\n");
        return 1;
    }

    printf("Device connected! Starting listening...\n");

    // Register the callback
    omi_set_transcription_callback(device, on_transcription_callback);

    // Start the transcription loop (this will block)
    omi_start_transcription_loop(device);

    // Cleanup (this part is usually unreachable in the transcription loop example)
    omi_disconnect(device);
    omi_destroy();

    return 0;
}

This C example shows how the callback function on_transcription_callback is registered and then called by the library whenever a new transcription is available. This low-level control is great for applications where memory and performance are critical.