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Audio & Voice AI

Local Talking LLM

Local Talking LLM is a Python voice-assistant project that combines Whisper speech recognition, a LangChain conversational chain, an LLM backend and Chatterbox text-to-speech. Its default Ollama backend supports local LLM inference, while a documented MiniMax option sends the LLM stage to the cloud.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Local Talking LLM — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to assemble a local Python voice-assistant workflow and can manage its model, audio and environment setup. Choose the MiniMax option when using a cloud LLM is acceptable and local GPU resources are a concern.

Who it is for

  • Developers building a voice-controlled assistant around locally hosted language models.
  • People who want spoken input and audio responses in a Python project.
  • Developers who want to experiment with voice cloning or adjust speech emotion and pacing.

Documented uses

  • Transcribe recorded speech into text with Whisper.
  • Send the transcription to an Ollama-hosted model or the MiniMax cloud backend.
  • Convert the generated response into speech with Chatterbox TTS.
  • Provide a short voice sample for the documented voice-cloning option.
Before you choose

Requirements & limits

The project is a public GitHub repository under the MIT license. Its README documents local Ollama use and a MiniMax cloud-provider option.

  • The default Ollama configuration keeps the LLM backend local; MiniMax is a cloud alternative.
  • Speech recognition and synthesis are described as local even when MiniMax handles the LLM stage.
  • The current README uses Chatterbox TTS; the earlier Bark implementation is kept in an archive branch.
  • The setup involves a Python environment, audio libraries and model dependencies; the README recommends uv over requirements.txt.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the project with uv and prepare Ollama

Set up the Python environment and download an Ollama model before running the assistant.

Before you start

  • A computer with Python and the project's audio/model dependencies.
  • uv for the recommended dependency-management route.
  • Ollama installed and started, with a model to download.

Steps

  1. Clone the repository and change into its directory.

  2. Run the command below to install dependencies, then activate the virtual environment.

    Documented command
    uv sync

  3. Download NLTK's punkt_tab data with the command below.

    Documented command
    python -c "import nltk; nltk.download('punkt_tab')"

  4. Install and start Ollama, then run ollama pull gemma3 or pull another preferred model.

Expected outcome

The project dependencies and a local Ollama model are prepared for the documented assistant workflow.

Guide limitations

  • This setup guide does not establish that the project will run successfully on every system.
  • The README warns that requirements.txt may not install correctly across different systems.
Documentation 1 ↗
Source-based practical guide

Configure MiniMax as the LLM backend

Prepare the documented cloud LLM alternative while retaining local speech recognition and synthesis.

Before you start

  • The project environment and dependencies.
  • A MiniMax account and API key.

Steps

  1. Sign up at the MiniMax Platform and obtain an API key.

  2. Set the MINIMAX_API_KEY environment variable.

  3. Run the assistant with the command below.

    Documented command
    python app.py --provider minimax

Expected outcome

The conversation stage uses MiniMax, while the README says speech-to-text and text-to-speech still run locally.

Guide limitations

  • This configuration sends LLM work to a cloud provider, so it is not an entirely local workflow.
  • The project documentation does not establish performance or compatibility for a particular computer.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
vndee/local-talking-llm · Official homepage title · retained in its source language
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