Give Your Ollama a Voice: A Guide to Local ASR & TTS
Z
Zack Saadioui
8/12/2025
Your Ollama Setup is Awesome, But it's Missing Something: A Voice
So you've got Ollama running locally. That's pretty cool, right? You're running powerful language models on your own machine, free from the cloud, completely offline. You can chat with models like Llama 3, Mistral, or whatever your hardware can handle. It's a fantastic setup for developers, researchers, or anyone who just wants to tinker with AI without an internet connection.
But let's be honest. Typing your prompts & getting text back is... well, it's a bit 2022. We're living in an age of voice assistants, smart speakers, & AI you can actually talk to. Your silent Ollama setup, as powerful as it is, is missing that natural, conversational element. It's like having a supercar but only driving it in a school zone.
Here’s the thing: you can ABSOLUTELY add voice to your Ollama setup. I'm talking about full-on Automatic Speech Recognition (ASR) to convert your speech to text, & Text-to-Speech (TTS) to have the AI talk back to you. And the best part? You can do it all locally, keeping your entire voice-enabled AI assistant offline & private.
This isn't some crazy, complicated process reserved for AI researchers with a server farm in their basement. Turns out, with a few clever tools & a bit of Python scripting, you can bolt on ASR & TTS capabilities to your Ollama instance & create your very own, fully functional, local voice assistant.
In this guide, I'm going to walk you through exactly how to do it. We'll look at the key components you need, how they fit together, & some practical examples to get you started.
The Three Musketeers of Local Voice AI: ASR, LLM, & TTS
To build our voice-interactive system, we need three core components that work together in a sequence. Think of it as a digital assembly line for conversation:
Automatic Speech Recognition (ASR): This is the "ears" of our system. Its job is to listen to your voice through a microphone & transcribe it into text. The undisputed champion for local, high-quality ASR right now is OpenAI's Whisper. Even though it's from OpenAI, it can be run completely locally on your own machine.
Large Language Model (LLM): This is the "brain." It's your Ollama instance! It takes the transcribed text from the ASR, processes it, understands the intent, & generates a coherent, text-based response.
Text-to-Speech (TTS): This is the "mouth." It takes the text response from your LLM & converts it into audible speech, which is then played through your speakers. There are several great offline options for this, like pyttsx3, OpenVoice, & espeak.
The workflow is simple: You speak -> ASR converts to text -> Ollama generates a text response -> TTS converts that response to speech -> You hear the answer. It's a beautiful, seamless loop that transforms your command-line-based Ollama into a conversational partner.
Getting Your Hands Dirty: The Tools of the Trade
Before we dive into the code, let's talk about what you'll need to install. The exact setup can vary, but a popular & effective combination involves a few key libraries. One GitHub project by
1
maudoin/ollama-voice
provides a great, simple example of combining these tools. It uses Whisper for speech recognition, Ollama for the language model, & pyttsx3 for the text-to-speech output, all working in offline mode.
Here's a typical shopping list of what you'll need to get this running:
Ollama: Well, duh. You should already have this installed & running with a model of your choice (like
1
mistral
or
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llama3
).
Python: Most modern systems have it. If not, get it.
Whisper: You'll need to install the Python library for it.
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pip install openai-whisper
should do the trick.
A TTS Engine:
pyttsx3: A super simple, cross-platform, offline TTS library. It's a great starting point.
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pip install pyttsx3
.
OpenVoice: A more advanced, high-quality voice cloning & TTS system. A GitHub Gist by
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sundy-li
shows how to integrate this with Ollama. It's a bit more involved to set up but the results are impressive.
Vosk: Another option for speech recognition, as highlighted in a YouTube tutorial by "AI Assistant". It's an offline, open-source speech recognition toolkit.
Audio Handling Libraries: You'll likely need libraries to manage microphone input & speaker output, like
1
pyaudio
. You might need to install system dependencies for this, like
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portaudio
(
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brew install portaudio
on a Mac).
The Blueprint: A Simple Python Script to Tie It All Together
Let's look at how you'd structure a Python script to make this magic happen. The core idea is to create a loop: listen, process, speak, repeat. A YouTube tutorial breaks down a great example of how to structure this.
Here's a conceptual breakdown of the code's logic, inspired by various community projects:
Step 1: Initialization
First, you import all your necessary libraries & initialize the components.