Create Your Own Voice AI Agent with Free Open-Source Tools!

How to Build a Voice AI Agent Using Open-Source Tools

Voice technology is rapidly emerging as the next frontier in conversational AI, representing the most natural way for individuals to engage with intelligent systems. Over the past year, leading AI organizations such as OpenAI, xAI, Anthropic, Meta, and Google have introduced real-time voice services, pushing the boundaries of what voice AI can accomplish. However, the challenges surrounding latency, privacy, and customization make it clear that a one-size-fits-all solution is not viable for voice applications.

This article aims to guide readers through the process of building a voice AI agent using open-source tools. By leveraging a custom knowledge base, unique voice styles, and fine-tuned AI models, developers can create tailored voice solutions that run on personal computers. We will delve into the prerequisites, the architecture of the voice AI system, and the necessary configurations to make it all work seamlessly.

Prerequisites

Before embarking on this journey, there are several prerequisites that participants should be aware of to ensure a smooth experience:

• Access to a Linux-like system (Mac or Windows with WSL is acceptable).
• Comfort with command-line interface (CLI) tools.
• Ability to run server applications on the Linux system.
• Free API keys from Groq and ElevenLabs.
• Optional: Familiarity with compiling and building Rust source code.
• Optional: An EchoKit device or the capability to assemble one.

What it Looks Like

The cornerstone of this project is the echokit_server, an open-source agent orchestrator designed for voice AI applications. This server coordinates a variety of services, including Large Language Models (LLMs), Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Voice Activity Detection (VAD), and Multi-Channel Processing (MCP). Its goal is to generate intelligent voice responses based on user prompts.

The EchoKit server provides a WebSocket interface that allows compatible clients to send and receive voice data. Additionally, the echokit_box project offers ESP32-based firmware that serves as a client, enabling audio collection from users and playback of TTS-generated voice responses from the EchoKit server. Demos of this functionality can be found on the project's GitHub page.

Two Voice AI Approaches

There are primarily two approaches to developing a voice AI agent: traditional cloud-based solutions and localized, open-source frameworks. While cloud-based solutions provide ease of use and robust processing capabilities, they often come with concerns about data privacy and latency. In contrast, open-source solutions, such as the ones discussed here, offer greater customization and control, allowing developers to tailor their agents to specific needs and preferences.

The Voice AI Orchestrator

To successfully build a voice AI agent, several components need to be configured:

• Configure an ASR: Set up an Automatic Speech Recognition system to convert spoken language into text.
• Run and configure a VAD: Implement Voice Activity Detection to identify when a user is speaking.
• Configure a LLM: Integrate a Large Language Model that can understand and process user queries.
• Configure a TTS: Set up a Text-to-Speech system to convert text responses back into voice.
• Configure MCP and actions: Manage Multi-Channel Processing for handling multiple audio inputs and outputs efficiently.

Local AI with LlamaEdge

For those who wish to utilize a more localized approach, LlamaEdge provides an intriguing option. This framework allows developers to run AI models directly on edge devices, reducing reliance on cloud services and enhancing performance. This can significantly improve the responsiveness and personalization of voice interactions.

Impact and Implications

The development of voice AI agents using open-source tools is not just a technical exercise; it signifies a broader trend towards democratizing AI technology. By empowering developers to create customized voice solutions, we are fostering innovation and paving the way for more personalized user experiences. Furthermore, as the demand for voice interfaces continues to grow, the ability to develop tailored applications could lead to advancements in various fields, from customer service to healthcare.

In conclusion, building a voice AI agent using open-source tools is an achievable goal for developers willing to invest time and effort. As voice technology continues to evolve, those who harness its potential will be at the forefront of the next wave of conversational AI.