Build a ChatGPT-4o Voice Assistant with Groq, Llama3, OpenAI-TTS & Faster-Whisper

Introduction

Voice assistants have become a significant trend in AI technology. OpenAI has just released their new GPT-4o multimodal voice assistant preview, creating a competitive frenzy in the tech landscape. In this article, we will guide you on how to build your own low-latency multimodal AI voice assistant using innovative technologies such as Groq, Llama3, OpenAI-TTS, and Faster-Whisper.

Introduction to the Voice Assistant

Meet your new AI voice assistant, Jarvis. Jarvis is equipped to manage various inputs, including voice prompts, image recognition, and text processing. Before diving into the coding process, let’s set up the scene.

Setting Up Your Voice Assistant

1. Creating the Project Structure:

   • Start by creating a folder named VoiceAssistant.
   • Open this folder in a code editor, like VS Code.
   • Inside your project folder, create a requirements.txt file containing all necessary dependencies.

2. Installing Python:

   • Ensure you have Python 3.11 installed.
   • Open a terminal in your project folder and run the command to install all libraries listed in requirements.txt.
   • If you encounter errors related to PiAudio on Windows, download the appropriate wheel file from PyPI and install it manually.

3. API Keys:

   • Obtain your API keys from Groq, OpenAI, and Google Generative AI. Store them securely.

Implementing Core Functionalities

1. Generating Responses Using Groq:

• Import the Groq library and initialize the Groq client with your API key.
• Define a function, grock_prompt, which processes input prompts through Groq's API, utilizing the Llama3 model.

from groq import Groq

def grock_prompt(prompt):
    convo = [("role": "user", "content": prompt)]
    response = groq_client.chat.create(convo, model="llama-3")
    return response["choices"][0]["content"]

2. Taking Screenshots and Photos:

• Implement screenshot functionality using the ImageGrab class from PIL.
• For webcam functionality, use OpenCV to capture photos.

from PIL import ImageGrab

def take_screenshot():
    screenshot = ImageGrab.grab()
    screenshot.save("screenshot.jpg", "JPEG", quality=15)

import cv2

def capture_webcam():
    cam = cv2.VideoCapture(0)
    if not cam.isOpened():
        print("Camera not accessible.")
        return
    ret, frame = cam.read()
    cv2.imwrite("webcam_photo.jpg", frame)

3. Clipboard Functionality:

• Use pyperclip to get clipboard text and return it.

import pyperclip

def get_clipboard_content():
    return pyperclip.paste()

4. Integrating Gemini for Image Processing:

• Connect to Google's Gemini AI model and define a function for processing images with the model.

import google.[generative as gen_ai](https://www.topview.ai/blog/detail/what-is-generative-ai)

def process_image_with_gemini(image_path, prompt):
    img = open(image_path, "rb")
    response = gen_ai(image=img, text=prompt)
    return response["text"]

Making the Assistant Conversational

Wrap all functionalities in a loop that handles input and responses. Integrate the speak function for converting text to speech using OpenAI's API.

from openai import OpenAI

def speak(text):
    openai_client = OpenAI(api_key="your_api_key")
    response = openai_client.Audio.create(input=text)
    return Response["audio_url"]

Enhancing Recognition with Faster-Whisper

Implement speech recognition using the Faster Whisper library to listen for wake words and respond accordingly.

import speech_recognition as sr

def start_listening():
    recognizer = sr.Recognizer()
    with sr.Microphone() as source:
        recognizer.adjust_for_ambient_noise(source)
        print("Say 'Jarvis' followed by your command.")
        audio = recognizer.listen(source)
        process_audio(audio)

Conclusion

With all components in place, you can execute your multimodal voice assistant that utilizes low-latency responses efficiently. This assistant is primed for interactive tasks, ready to serve as your AI companion.

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Keywords

• Voice Assistant
• Groq
• Llama3
• OpenAI-TTS
• Faster-Whisper
• Multimodal AI
• Python
• API Integration
• Speech Recognition
• Computer Vision

FAQ

Q: What is Groq? A: Groq is a company that specializes in hardware designed for high-speed inferencing of language models.

Q: How can I get the necessary libraries for this project? A: You can create a requirements.txt file and install the libraries using pip in your terminal.

Q: Is a high-speed connection necessary for this assistant? A: A stable internet connection is preferable but not strictly necessary as some functionalities can operate offline.

Q: Can I use local libraries for text-to-speech? A: Yes, using libraries like pyttsx3 can provide offline text-to-speech capabilities.

Q: How do I troubleshoot coding errors? A: Copy the error message and consult with chatbots like ChatGPT for quick explanations and fixes.

By following this guide, you'll create a powerful, multimodal voice assistant capable of performing a variety of tasks in real time.