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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A talking LLM that runs on your own computer without needing the internet.
| Date | Stars |
|---|---|
| 2026-07-24 | 881 |
| 2026-07-25 | 881 |
| 2026-07-28 | 881 |
| 2026-07-30 | 881 |
| 2026-07-31 | 883 |
| 2026-08-06 | 883 |
Today
— stars today
This week
+2 stars this week
This month
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Momentum
0.0
growth rate 0.23%/day
## Build your own voice assistant and run it locally: Whisper + Ollama + ChatterBox
> Original article: https://blog.duy-huynh.com/build-your-own-voice-assistant-and-run-it-locally/
>
> **Updated May 2025**: Now using [Chatterbox TTS](https://github.com/resemble-ai/chatterbox), a state-of-the-art open-source TTS model from Resemble AI!
>
> The original implementation using Bark has been preserved in the `archive-2025-05-29` branch for reference.
[](https://www.buymeacoffee.com/vndee)
After my latest post about how to build your own RAG and run it locally. Today, we're taking it a step further by not only implementing the conversational abilities of large language models but also adding listening and speaking capabilities. The idea is straightforward: we are going to create a voice assistant reminiscent of Jarvis or Friday from the iconic Iron Man movies, which can operate offline on your computer.
**New Features with ChatterBox:**
- 🎯 **Voice Cloning**: Clone any voice with just a short audio sample
- 🎭 **Emotion Control**: Adjust emotional expressiveness of responses
- 🚀 **Better Performance**: 0.5B parameter model with faster inference
- 💧 **Watermarked Audio**: Built-in neural watermarking for authenticity
### Techstack
First, you should set up a virtual Python environment. You have several options for this, including pyenv, virtualenv, poetry, and others that serve a similar purpose. Personally, I'll use Poetry for this tutorial due to my personal preferences. Here are several crucial libraries you'll need to install:
- **rich**: For a visually appealing console output.
- **openai-whisper**: A robust tool for speech-to-text conversion.
- **chatterbox-tts**: State-of-the-art text-to-speech synthesis with voice cloning and emotion control.
- **langchain**: A straightforward library for interfacing with Large Language Models (LLMs).
- **langchain-openai**: For connecting to OpenAI-compatible cloud LLM providers like [MiniMax](https://www.minimaxi.com).
- **sounddevice**, **pyaudio**, and **speechrecognition**: Essential for audio recording and playback.
For a detailed list of dependencies, refer to the link here.
The most critical component here is the Large Language Model (LLM) backend. By default, we use **Ollama** for running LLMs locally. Alternatively, you can use **MiniMax** as a cloud LLM provider for higher-quality responses without local GPU requirements. If Ollama is new to you, I recommend checking out my previous article on offline RAG: "Build Your Own RAG and Run It Locally: Langchain + Ollama + Streamlit". Basically, you just need to download the Ollama application, pull your preferred model, and run it.
### Architecture
Okay, if everything has been set up, let's proceed to the next step. Below is the overall architecture of our application, which fundamentally comprises 3 main components:
- **Speech Recognition**: Utilizing OpenAI's Whisper, we convert spoken language into text. Whisper's training on diverse datasets ensures its proficiency across various languages and dialects.
- **Conversational Chain**: For the conversational capabilities, we'll employ the Langchain interface with a pluggable LLM backend — either a local model via Ollama (e.g., Gemma3, Llama-4) or a cloud model via [MiniMax](https://www.minimaxi.com) (MiniMax-M2.7). This setup promises a seamless and engaging conversational flow.
- **Speech Synthesizer**: The transformation of text to speech is achieved through Chatterbox TTS, a state-of-the-art model from Resemble AI, renowned for its lifelike speech production and voice cloning capabilities.
The workflow is straightforward: record speech, transcribe to text, generate a response using an LLM, and vocalize the response using ChatterBox.
```mermaid
flowchart TD
A[🎤 User Speech Input] --> B[Speech Recognition<br/>OpenAI Whisper]
B --> C[📝 Text Transcription]
C --> D[CoExcerpt of 13,406 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bc26699296a1ab66, topic:speech-recognition, topic:speech-synthesis, readme:speech recognition