AI Features - XiaoZhi Firmware AI Technology Integration Guide | XiaoZhi.Dev

🤖 AI Features

Learn how to implement voice interaction, AI model integration and smart control functions on the ESP32-S3 platform.

🎯 Core AI Architecture

🧠 Hybrid AI Inference Architecture

  graph TB
    A[Voice Input] --> B[Local Voice Wake-up]
    B --> C[Multilingual ASR Recognition]
    C --> D{Inference Strategy Selection}
    D -->|Simple Commands| E[Edge AI Processing]
    D -->|Complex Dialogue| F[Cloud LLM Inference]
    E --> G[Device Control Execution]
    F --> H[Intelligent Response Generation]
    H --> I[TTS Voice Output]
    G --> I

🔥 Core AI Features Deep Dive

1️⃣ Offline Voice Wake-up AI

Technical Principle: Based on Espressif’s official Wake Word Engine

• 🎙️ Default Wake Word: “你好小智” (Customizable with 26+ official wake words)
• ⚡ Response Speed: <200ms ultra-low latency wake-up
• 🔋 Power Optimization: Wake-up standby power consumption <5mA
• 🌐 Offline Operation: Completely local, no network connection required

# ESP-IDF environment wake word configuration
idf.py menuconfig
# Navigate to: Component config > ESP Speech Recognition
# Select: Wake Word Model Selection
# Available words: "Hi ESP", "Alexa", "小智" and 26 other official vocabularies

2️⃣ Multilingual Intelligent Speech Recognition (ASR)

Technology Stack: Integrates industry-leading ASR engines

• 🗣️ Supported Languages: Chinese (Mandarin/Cantonese) | English | Japanese | Korean | Russian
• 🎯 Recognition Accuracy: Chinese recognition rate >95%, English recognition rate >93%
• 🔊 Audio Format: 16kHz sampling rate, 16-bit PCM encoding
• 🌍 Offline/Online: Hybrid mode, keywords offline, complex sentences online

3️⃣ Large Language Model Integration & Inference

🚀 Supported Mainstream AI Large Models

🧩 Edge AI Inference Capabilities

Lightweight Model Support (Planned Features):

• 📱 TensorFlow Lite: Support for quantized lightweight models
• 🔧 Model Size: Support for 1-10MB edge inference models
• ⚡ Inference Speed: Simple commands <100ms response
• 🎯 Use Cases: Device control, status queries, simple Q&A

// Edge AI inference example code
class EdgeAIEngine {
    TfLiteInterpreter* interpreter;
    
    bool processSimpleCommand(const char* text) {
        // Text preprocessing
        auto tokens = tokenize(text);
        
        // Model inference
        interpreter->SetInputTensorData(0, tokens.data());
        interpreter->Invoke();
        
        // Result parsing
        return parseCommandResult();
    }
};

4️⃣ Intelligent Text-to-Speech (TTS)

Multi-engine Support Strategy:

• 🎵 Cloud TTS: High-quality human voice synthesis (supports emotional speech)
• 🔧 Local TTS: ESP32-S3 onboard simple voice synthesis
• 🎭 Multiple Voices: Support for male/female/child voice selections

# TTS engine configuration
tts_config:
  primary_engine: "cloud"  # cloud/local
  voice_style: "female_warm"  # Voice selection
  speech_rate: 1.0  # Speech rate (0.5-2.0)
  pitch: 0  # Pitch (-500 to 500)
  language: "en-us"  # Output language
  
cloud_tts:
  provider: "azure"  # azure/google/baidu
  api_key: "${TTS_API_KEY}"
  region: "eastasia"

🛠️ AI Development & Integration

💻 Zero-Code AI Integration

XiaoZhi AI platform provides a graphical configuration interface, enabling non-technical users to quickly configure AI capabilities:

  graph LR
    A[Web Configuration Interface] --> B[Select AI Model]
    B --> C[Configure API Keys]
    C --> D[Test Connection]
    D --> E[One-Click Deploy]
    E --> F[AI Capabilities Activated]

🔧 Developer API Interface

// XiaoZhi AI SDK core interface
class XiaoZhiAI {
public:
    // Initialize AI engine
    bool initAI(const AIConfig& config);
    
    // Voice wake-up callback
    void onWakeWordDetected(WakeWordCallback callback);
    
    // Speech recognition
    std::string recognizeSpeech(const AudioData& audio);
    
    // Large model dialogue
    std::string chatWithLLM(const std::string& message);
    
    // Speech synthesis
    AudioData synthesizeSpeech(const std::string& text);
    
    // Device control
    bool executeCommand(const DeviceCommand& cmd);
};

📈 AI Performance Metrics

⚡ Real-time Performance

• Voice Wake-up Latency: <200ms
• ASR Recognition Latency: <500ms (local) / <1s (cloud)
• LLM Inference Response: <2s (DeepSeek) / <3s (GPT-4)
• TTS Synthesis Latency: <800ms
• End-to-end Dialogue Latency: <5s (complete dialogue flow)

🎯 Accuracy Metrics

• Wake Word Accuracy: >99% (quiet environment) / >95% (noisy environment)
• Chinese ASR Accuracy: >95% (standard Mandarin)
• English ASR Accuracy: >93% (American/British accent)
• Command Execution Success Rate: >98% (clear commands)

💾 Resource Usage

• Flash Storage: 4MB (basic AI functions)
• RAM Usage: 512KB (runtime peak)
• CPU Usage: <30% (ESP32-S3 dual-core 240MHz)
• Power Consumption: 150mA (active dialogue) / 5mA (standby wake-up)

🔮 AI Technology Roadmap

📅 2025 Q1-Q2 Roadmap

🗓️ January 2025 - Edge AI Inference In Development

• Integrate TensorFlow Lite Micro
• Support 1-5MB quantized models
• Local device control command recognition

🗓️ February 2025 - Multimodal AI Planned

• ESP32-CAM vision integration
• Image recognition + voice interaction
• Visual Question Answering (VQA) capabilities

🗓️ March 2025 - Federated Learning Research

• ESP-NOW inter-device collaborative learning
• Privacy-preserving distributed AI
• Smart home collaborative decision-making

🎯 Future AI Features

• 🧬 Personalized AI: Model fine-tuning based on user usage patterns
• 🌐 Edge AI Clusters: Multi-device collaborative distributed intelligence
• 🔐 Privacy AI: Completely localized private domain AI assistant
• 🎮 Interactive AI: AR/VR augmented reality interaction capabilities

🚀 Getting Started with AI Features

Quick Start AI Capabilities

1. Hardware Preparation: ESP32-S3 development board + XiaoZhi AI expansion board
2. Firmware Flashing: Download pre-compiled AI firmware
3. Network Configuration: Connect Wi-Fi, configure AI service APIs
4. Wake-up Test: Say “你好小智” to verify wake-up function
5. Dialogue Experience: Start natural conversation with AI assistant