Build a Local Speech-to-Speech AI

8/10/2025

Talk is Cheap, But Your AI Voice Agent Doesn't Have to Be: A Guide to Local Speech-to-Speech AI

So, you've seen the demos. The slick, real-time conversations with AI assistants that sound... well, human. It's impressive stuff, but then you see the price tag, the cloud-based APIs, & the data privacy policies that make you think twice. What if I told you that you could build your own, completely local speech-to-speech AI voice agent using open-source models? It's not only possible, but it's getting easier every day.

Honestly, setting up a local voice AI is a game-changer. It’s all about privacy, customization, & cost savings. When you’re not shipping your audio data off to a third-party server, you have complete control. For businesses, this is HUGE. Think about customer service interactions or internal meetings – you want that data to stay in-house. Plus, you can forget about monthly API fees that can really add up.

I’ve been down the rabbit hole with this stuff, & I'm here to give you the inside scoop on how to get started. We're going to break down the entire process, from the essential components to the nitty-gritty of setting it all up.

The Three Pillars of a Local Voice AI

At its core, a speech-to-speech system is a pipeline of three key components:

Speech-to-Text (STT): This is where the magic begins. An STT model, also known as Automatic Speech Recognition (ASR), listens to your voice & transcribes it into text.
Large Language Model (LLM): The "brains" of the operation. The LLM takes the transcribed text, understands the intent, & generates a response.
Text-to-Speech (TTS): The final piece of the puzzle. The TTS model takes the LLM's text response & converts it into natural-sounding speech.

The goal is to get this entire pipeline running on your own machine, with minimal latency, so you can have a smooth, real-time conversation.

Diving Deep into the Tech: The Open-Source Toolkit

Now, let's get into the fun part: the open-source models that make this all possible.

Speech-to-Text: The Reign of Whisper

When it comes to open-source STT, one name dominates the conversation: Whisper. Developed by OpenAI, Whisper is incredibly accurate & robust, capable of handling different accents & background noise. It's trained on a massive dataset of 680,000 hours of multilingual audio, which is why it's so good.

But here's the thing about Whisper – the original version can be a bit slow for real-time applications. That's where the open-source community comes in. We now have several faster, more efficient versions of Whisper that are perfect for a local setup:

Fast Whisper: A popular choice that significantly speeds up transcription.
Insanely Fast Whisper: As the name suggests, this one is all about speed.
Distil-Whisper: A distilled version of Whisper that's smaller & faster.

For most local setups, one of these faster Whisper variants will be your go-to choice for the STT component.

The Brains of the Operation: Local LLMs with Ollama

Once you have your transcribed text, you need an LLM to process it. In the past, this meant relying on expensive, cloud-based APIs. Not anymore. Thanks to tools like Ollama, you can run powerful LLMs right on your own machine.

Ollama is a fantastic tool that makes it incredibly easy to download & run a wide range of open-source LLMs. You can think of it as a local hub for all your language models. Some popular choices that work great with Ollama for conversational AI include:

Llama 3: Facebook's latest and greatest, known for its strong performance.
Mistral: A powerful and popular model with a great balance of size and performance.
Phi-3: A smaller model from Microsoft that's surprisingly capable.

Setting up Ollama is a breeze. You just download the application, & then from your terminal, you can pull any model you want with a simple command like

ollama pull llama3

. Once the model is downloaded, Ollama exposes it as a local API, which makes it easy to integrate with the other components of your voice AI pipeline.

Text-to-Speech: Finding Your Voice

The final step is to give your AI a voice. This is where TTS models come in, & you have a lot of great open-source options to choose from. The best choice for you will depend on your specific needs, like whether you want to clone your own voice or if you need a model that's particularly good at conveying emotion. Here are some of the top contenders:

MeloTTS: A popular choice for its speed and quality. It's a great all-rounder for real-time applications.
StyleTTS 2: This model is known for its ability to generate expressive and natural-sounding speech.
XTTS: One of the most popular voice generation models out there. Its latest version, XTTS-v2, can clone voices from a short audio sample.
ChatTTS: As the name suggests, this model is specifically designed for conversational AI. It can even add human-like conversational fillers like "uh" and "um".
Tortoise TTS: Another excellent model that's known for its high-quality voice generation.

It's worth experimenting with a few different TTS models to find the one that you like the best. Some are faster, some sound more natural, & some offer more options for customization.

Putting It All Together: Building Your Voice Agent

Now that we've covered the individual components, let's talk about how to connect them all together to create a functional speech-to-speech pipeline. There are a few open-source projects that have already done the heavy lifting for you, providing a framework to get everything up and running.

One such project is Verbi, a local voice assistant that uses Fast Whisper for STT, Ollama for the LLM, & MeloTTS for the TTS. Another great example is Persona Engine, which also bundles together the necessary components for a local speech-to-speech loop.

The basic workflow is this:

Your microphone captures your voice.
The STT model (e.g., Fast Whisper) transcribes your speech into text.
The text is sent to your local LLM (e.g., Llama 3 running on Ollama).
The LLM generates a text response.
The text response is sent to the TTS model (e.g., MeloTTS).
The TTS model converts the text into speech, which you hear through your speakers.

All of this happens in a continuous loop, allowing for a back-and-forth conversation.

The Elephant in the Room: Challenges & Hardware

Building a local speech-to-speech AI is an amazing project, but it's not without its challenges. Here are a few things to keep in mind:

Latency: This is the big one. For a natural conversation, you need the AI's response to be almost instant. High latency can make the conversation feel clunky and unnatural. Using faster models (like the Whisper variants) & a powerful computer can help to minimize latency.
Naturalness: Getting the AI to sound truly human is a challenge. This includes things like intonation, emotion, & the ability to handle interruptions gracefully. The quality of your TTS model will play a big role here.
Hardware Requirements: Let's be honest, you're going to need a decent computer to run all of this locally. A powerful CPU is a must, & a good GPU is even better, especially for running the LLM & TTS models. For a smooth experience, you'll probably want at least a modern multi-core processor & 16GB of RAM.

The Business Case: From Hobby Project to Powerful Tool

While building a local voice AI can be a fun side project, it also has some serious business applications. Think about it – you could create a custom voice assistant for your website, a private internal tool for your team, or even a voice-powered interface for your product.

This is where a platform like Arsturn can be a game-changer. While we've been talking about building everything from scratch, Arsturn helps businesses create custom AI chatbots trained on their own data. This is PERFECT for businesses that want to provide instant customer support, answer questions, & engage with website visitors 24/7. Arsturn is a no-code platform that lets you build these chatbots without needing a team of developers. So, you can get all the benefits of a custom AI assistant without having to manage the entire infrastructure yourself. It’s a great way to build meaningful connections with your audience through personalized chatbots.

Final Thoughts: The Future is Local

The world of open-source AI is moving at an incredible pace. Just a few years ago, the idea of running a powerful voice AI on your own computer seemed like science fiction. Today, it's a reality.

There's still a lot of work to be done, especially when it comes to reducing latency and improving the naturalness of the conversation. But the progress is undeniable. The ability to have private, customizable, and cost-effective voice conversations with AI is a game-changer, and I'm excited to see what the community builds next.

I hope this was helpful! Let me know what you think, & if you decide to build your own local voice AI, I'd love to hear about your experience.