LibreAI / Audio / xiaozhi-esp32-server
// audio · xinnan-tech/xiaozhi-esp32-server

xiaozhi-esp32-server

xiaozhi-esp32-server est un projet de audio open source, créé par xinnan-tech (xinnan-tech/xiaozhi-esp32-server sur GitHub). Il compte 10 756 étoiles sur GitHub et 3 670 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.

Self-hosted backend that gives ESP32 'Xiaozhi' devices streaming voice chat, memory and tool control.

★ 10,8 k étoiles⑂ 3,7 k forksMITmis à jour oct. 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
10 756
Forks
3 670
Licence
MIT
Dernière mise à jour sur GitHub
8 octobre 2026
Catégorie
Audio
Type de fiche
Guide d'installation complet
Site officiel
Official site
// fonctionnement

Comment ça marche

  1. Get your ESP32 device talking first

    Flash or buy a Xiaozhi ESP32 device and confirm it works against a public test backend, so you know the hardware side is fine before self-hosting.

  2. Choose an install tier

    Pick the minimal install (single agent, config-file storage, no database) or the full-module install (multi-user, multi-agent, web control console, database) - each is offered as Docker or source deployment in the docs.

  3. Configure the AI pipeline

    Select ASR, LLM, vision and TTS providers in the config; the documented all-free starter setup uses local FunASR, EdgeTTS and free-tier cloud models.

  4. Point devices at your server

    Set your firmware to your server's OTA and WebSocket endpoints, then watch it answer, recognize speakers and run tools through the control console.

// fonctionnalités

Pourquoi on l'adore

Streaming voice pipeline

Streaming ASR, streaming TTS and voice-activity detection give low-latency, interruptible conversations in multiple languages.

Voiceprint recognition

Multiple users can register voiceprints; the server identifies who is speaking in parallel with recognition and passes that to the LLM for personalized replies.

Many model providers

LLMs connect via OpenAI-compatible APIs, Ollama, Dify, FastGPT, Coze, Xinference or Home Assistant; ASR, TTS and vision models include local options like FunASR, FishSpeech and GPT-SoVITS as well as major cloud providers.

MCP and IoT tool calling

Supports client IoT protocols, client and server MCP, and MCP access points, and can push MCP commands from the console down to ESP32 devices over MQTT.

Memory and knowledge base

Choose local short-term memory, mem0ai or PowerMem for conversation memory, and plug in a RAGFlow knowledge base the model can consult before answering.

Web management console

The full-module install adds a web admin for users, system configuration and devices (in Simplified/Traditional Chinese and English), plus performance and audio test tools.

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// lancez-le

Lancez-le vous-même

  1. Clone the repository

    Get the server source; deployment itself is covered by the project's step-by-step deployment docs.

    git clone https://github.com/xinnan-tech/xiaozhi-esp32-server.git
    cd xiaozhi-esp32-server
  2. Pick a deployment route

    Follow the deployment document for your tier - Docker or source, minimal or full-module - linked from the README's deployment section.

    # see the deployment docs linked from the README for the exact commands
  3. Connect a device and test

    Point your ESP32 firmware at the server's OTA/WebSocket endpoints and use the bundled test tools to check ASR, LLM and TTS latency.

    # verify with the performance tester and audio test tool described in the README
// cas d'usage

Ce que vous pouvez créer

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// faq

Questions fréquentes

Who maintains xiaozhi-esp32-server?

The xinnan-tech team maintains it; the project is spearheaded by Professor Siyuan Liu's team at South China University of Technology and a large contributor community.

Is it free to use?

Yes. The server is MIT-licensed, and the README documents an all-free configuration (local FunASR recognition, EdgeTTS, free-tier models) for home use.

Does it need a paid API key?

Not necessarily. You can run every module with local or free-tier components; paid cloud ASR, LLM or TTS providers are optional upgrades for speed and quality.

How does it compare with using the public Xiaozhi backend?

The public service is zero-setup but shared and third-party-run; self-hosting gives you your own data, your own model choices, multi-user management and no dependence on someone else's server.

How do I get started?

Clone the repo, follow the deployment doc for the minimal Docker install, then point one ESP32 device at your server's WebSocket endpoint.

// continuez l'exploration

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