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Building a Local Voice AI Assistant with Llama 3.2 & OpenAI Whisper Turbo 3

Education

Introduction

In this article, we will explore the creation of a local AI Voice Assistant using Llama 3.2 and OpenAI Whisper Turbo 3. This assistant will be able to perform various tasks using voice commands, such as controlling files, managing tasks, and providing answers based on personal notes.

Requirements for the AI Voice Assistant

The following requirements will guide our development:

  1. Voice Control: We will implement voice control using a transcription model.
  2. Voice Interaction: The assistant should understand natural language and perform commands.
  3. Action Execution: The assistant should be able to perform tasks such as:
    • Creating, editing, reading, and deleting local files.
    • Sending emails.
    • Managing a task backlog.
  4. Local Setup: The transcription model and the language model must run entirely on local hardware.
  5. Knowledge Retrieval: The assistant should be able to use retrieve-augmented generation (RAG) techniques to answer questions based on larger documents.

Setting Up the Environment

Initially, we download and set up the Whisper model from Hugging Face. For this, you will want to create a virtual environment using Anaconda or Miniconda to manage dependencies. Following that, install required packages like Transformers and Datasets.

After preparing the environment, set up the audio transcription model using whisper large V3 turbo. The model will take audio input and generate a transcript, which can be processed further.

Integrating Llama 3.2 for Natural Language Processing

Next, we shift our focus to Llama 3.2, a powerful language model by Meta. We'll use an easy installation method with olama, a tool to download local models seamlessly.

We create a function to send prompts to the Llama model and receive responses. This interaction will include commands and complex requests that the model should execute based on the provided instructions.

Connecting Whisper and Llama

To integrate Whisper and Llama into a cohesive assistant, we create functions to:

  1. Record Audio: Utilize packages like PyAudio and wave to capture voice input and store it as audio files.
  2. Transcribe Audio: Use the Whisper model to transcribe the recorded audio into text.
  3. Generate Responses: Send the transcribed text to the Llama model and capture the returned response.

Task Management

We create functions that allow the assistant to manage tasks in a simple task database stored in CSV format. This includes:

  • Adding a task.
  • Updating task statuses.
  • Deleting tasks from the database.

These task management functions will communicate with the language model through the assistant.

File Operations

The assistant will execute file operations such as creating, reading, editing, and deleting files on the local machine. Each of these functionalities should be abstracted in distinct functions with a clear understanding by the Llama model on how to call the correct function based on user prompts.

Creating a Gradio Interface

To make interacting with our Voice AI Assistant user-friendly, we will build a Gradio app. This will enable recording through the microphone or uploading audio files. The app will display the results of transcription and other responses visually.

Running the App

With everything set up as described above, we can run the Gradio app and see how it performs. If any errors occur during the process, they can be mitigated by adjusting prompts or examining function definitions to ensure they align correctly.

The assistant should ideally be able to respond to queries, create tasks, manage files, and execute commands entirely via voice.

Conclusion

By incorporating these components, we can build a fully functional local voice AI assistant capable of handling daily tasks and queries. This setup promotes a hands-free experience by leveraging the power of voice controls and local models.

Keywords

FAQ

What is the purpose of this Local Voice AI Assistant?

The assistant is designed to perform various tasks using voice commands, including managing files and responding to queries.

What technologies are being used to build this assistant?

We are using Llama 3.2 for natural language processing and OpenAI Whisper Turbo 3 for audio transcription.

How does the assistant handle tasks?

It maintains a task database in CSV format, allowing it to create, read, edit, and delete tasks based on user commands.

Can the assistant send emails?

The current setup does not include email functionality but can be integrated in the future.

Is the assistant fully local?

Yes, both the transcription and the language model run locally, ensuring data privacy and faster response times.