espressif/esp-sr

Speech recognition

ESP-SR ─ Speech framework for ESP32 chips
├── Wake Word Engine (WakeNet) ─ always-on keyword detection
│   └─ models: wn9, wn9s, wn10 … "Hi ESP", "Alexa", multilingual
├── Speech Command Recognition (MultiNet) ─ up to 300 offline commands
│   └─ Chinese + English, no retraining to add commands
├── Audio Front-End (AFE) ─ AEC, VAD, BSS, NS, deep noise suppression
├── VADNet ─ voice-activity detection model (replaces WebRTC VAD)
└── Speech Synthesis (TTS) ─ Pipeline V3, multilingual wake-word training

Related

  • Project
    espressif/esp-skainet

    ESP‑Skainet is Espressif’s on‑device voice‑assistant SDK for ESP32 chips. It bundles a tiny wake‑word engine (WakeNet), an offline command recognizer (MultiNet) supporting up to 200 languages, and a full audio front‑end (AEC, VAD, NS). The stack runs on ESP32‑S3 (or other ESP32 boards) with minimal RAM/flash, enabling privacy‑preserving voice control for smart‑home, wearables, or robotics. The repo includes ready‑to‑flash examples, detailed docs, and support for custom wake words and commands.

  • Project
    espressif/esp-tflite-micro

    A component for the ESP-IDF platform that enables running TensorFlow Lite Micro on Espressif chipsets with hardware-optimized kernels for faster inference.

  • Project
    xinnan-tech/xiaozhi-esp32-server

    A backend server for the xiaozhi-esp32 project that enables ESP32 devices to become AI assistants with voice, vision, and smart home control capabilities.

  • Project
    espressif/esp-dl

    A lightweight neural network inference framework for Espressif ESP series chips that enables efficient on-device AI deployment through optimized quantization and memory management.

  • Project
    akdeb/ElatoAI

    A framework for deploying real-time voice AI on ESP32 devices, connecting low-power hardware to frontier speech-to-speech models via edge functions.