How to build your own Jarvis style ChatGPT-4o AI voice assistant with memory

If you fancy building your very own Jarvis style AI assistant like the one created by Tony Stark in the Avengers and Iron Man movies you might be interested in a new tutorial kindly created by Prompt Engineering. Taking you through the process of creating your very own modular AI assistant complete with memory, voice and powered by the latest OpenAI ChatGPT-4o Omni large language model.

In the tutorial below Prompt Engineering takes you through the step-by-step process on how to build a sophisticated AI voice assistant named “Aiden” using GPT-4o. Combining cutting-edge technologies to deliver intelligent and context-aware interactions. Focusing on key components such as audio capture, transcription, query processing, text-to-speech conversion, and chat history management. Here is an  overview of the system architecture that forms the foundation of Aiden:

• Audio Capture: Utilizing a high-quality microphone is crucial for accurate audio input. Ensure that the captured audio is clear and free from background noise to facilitate precise processing.
• Transcription: Implement the Whisper model via an API to convert the captured speech into text. Whisper’s reliable transcription capabilities are essential for accurate query processing and understanding user input.
• Query Processing: Harness the power of GPT-4o to process the transcribed text queries. GPT-4o’s advanced language understanding and generation capabilities enable Aiden to provide intelligent and contextually relevant responses.
• Text-to-Speech Conversion: Transform the generated text responses into speech using OpenAI’s voice engine. This step ensures that Aiden can communicate its responses audibly, enhancing the user experience.
• Chat History Management: Maintain a chat history to retain context across interactions. By keeping track of previous conversations, Aiden can provide more personalized and coherent responses, making the interaction feel more natural and engaging.

Building a GPT-4o AI Voice Assistant

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Modular AI Framework

To create Aiden, adopt a modular code structure where each function handles a specific task. This approach promotes code reusability, maintainability, and extensibility. The key functions include:

• Recording Audio: Utilize Python packages like speech_recognition to capture audio input from the microphone. This function will serve as the entry point for user queries.
• Transcribing Audio to Text: Integrate the Whisper model to transcribe the captured audio into text. This step converts the user’s spoken query into a format that can be processed by GPT-4o.
• Generating Responses: Feed the transcribed text into GPT-4o to generate appropriate and contextually relevant responses. GPT-4o’s language generation capabilities will enable Aiden to provide intelligent and engaging answers.
• Converting Text to Speech: Employ OpenAI’s voice engine to convert the generated text responses into speech. This function will transform Aiden’s textual output into an audible format.
• Playing the Audio Response: Use libraries like Pygame to play the generated audio response back to the user. This step completes the interaction loop, allowing Aiden to communicate its responses effectively.

Chat History Management

To enable Aiden to retain context across interactions, initialize the chat history with a system role. As the user interacts with Aiden, append their inputs and the corresponding model responses to this chat history. By maintaining a record of previous conversations, Aiden can reference past interactions and provide more coherent and personalized responses.

Enhancing Your AI Assistant

While the current implementation of Aiden leverages external APIs for transcription and text-to-speech conversion, there are several planned enhancements to further improve its capabilities:

• Grok Whisper: Explore the integration of Grok Whisper, an optimized version of the Whisper model, for faster and more efficient transcription. This enhancement will reduce latency and improve the overall responsiveness of Aiden.
• Eleven Labs: Consider leveraging Eleven Labs for advanced text-to-speech voices. Their high-quality voice synthesis technology can enhance the naturalness and expressiveness of Aiden’s responses, making the interaction more engaging.
• Local GPT Integration: Integrate a local GPT model to enable Aiden to handle more complex tasks, such as document interaction and analysis. This enhancement will expand Aiden’s capabilities beyond simple conversational interactions.
• Function Calling: Implement function calling to allow Aiden to retrieve web information and perform other operations. By integrating external APIs and services, Aiden can provide more comprehensive and useful responses to user queries.

By following this guide and leveraging the power of GPT-4o, you can create your own sophisticated AI voice assistant with memory capabilities. Embrace the modular approach, implement the core components, and explore future enhancements to unlock Aiden’s full potential. Start building today and embark on an exciting journey in the world of conversational AI!

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