AI Features - XiaoZhi Firmware AI Technology Integration Guide

🤖 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

🌐 Multilingual Localization Technical Architecture

Voice Resource Package Structure:

main/assets/locales/
├── zh-CN/           # Simplified Chinese
│   ├── 0.ogg ~ 9.ogg        # Number audio
│   ├── welcome.ogg          # Welcome prompt
│   ├── activation.ogg       # Activation success
│   ├── wificonfig.ogg       # Wi-Fi configuration
│   ├── upgrade.ogg          # Firmware upgrade
│   ├── err_pin.ogg          # PIN error
│   ├── err_reg.ogg          # Registration error
│   └── language.json        # Language metadata
├── en-US/           # English (same structure)
├── ja-JP/           # Japanese
├── bg-BG/           # Bulgarian (v2.0 new)
├── ca-ES/           # Catalan (v2.0 new)
├── da-DK/           # Danish (v2.0 new)
├── el-GR/           # Greek (v2.0 new)
├── fa-IR/           # Persian (v2.0 new)
├── fil-PH/          # Filipino (v2.0 new)
├── he-IL/           # Hebrew (v2.0 new)
├── hr-HR/           # Croatian (v2.0 new)
├── hu-HU/           # Hungarian (v2.0 new)
├── ms-MY/           # Malay (v2.0 new)
├── nb-NO/           # Norwegian (v2.0 new)
├── nl-NL/           # Dutch (v2.0 new)
├── sk-SK/           # Slovak (v2.0 new)
├── sl-SI/           # Slovenian (v2.0 new)
├── sr-RS/           # Serbian (v2.0 new)
└── sv-SE/           # Swedish (v2.0 new)

Technical Implementation:

• Audio Format: OGG Vorbis, 16kHz sampling rate
• Storage Optimization: ~150KB per language, compile on demand
• Runtime Loading: Load corresponding language package based on configuration at startup
• Memory Usage: ~50KB RAM for current language resources

Language Switching Configuration:

// sdkconfig configuration
CONFIG_XIAOZHI_LANGUAGE="zh-CN"  // Set default language

// menuconfig path
// Xiaozhi Assistant -> Language Settings -> Select Language

Supported Language Tiers:

• Tier 1 (Priority Optimized): Chinese, English, Japanese, Russian
• Tier 2 (Full Support): Spanish, French, German, Korean
• Tier 3 (Basic Support): Other 17 newly added languages

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

4️⃣ AI Vision & Image Processing (v2.0 New)

📷 Camera System Refactoring

Technical Improvements:

• Code Refactoring: esp32_camera.cc expanded from basic version to 935 lines
• Interface Standardization: Unified camera API interface, supports multiple models
• Resolution Expansion: Support for more resolution options (QVGA to 1080p)
• Performance Optimization: Frame rate increased 30%, latency reduced 50%
• Memory Management: Optimized PSRAM usage, supports higher resolutions

Supported Camera Modules:

• OV2640: 2MP, cost-effective choice
• OV3660: 3MP, better image quality
• OV5640: 5MP, high-end solution
• GC032A: Low-cost option

AI Vision Application Scenarios:

// Camera initialization example (based on v2.0 new interface)
camera_config_t config = {
    .pin_pwdn = -1,
    .pin_reset = -1,
    .pin_xclk = 15,
    .pin_sscb_sda = 4,
    .pin_sscb_scl = 5,
    .pin_d7 = 16,
    .pin_d6 = 17,
    .pin_d5 = 18,
    .pin_d4 = 12,
    .pin_d3 = 10,
    .pin_d2 = 8,
    .pin_d1 = 9,
    .pin_d0 = 11,
    .pin_vsync = 6,
    .pin_href = 7,
    .pin_pclk = 13,
    .xclk_freq_hz = 20000000,
    .pixel_format = PIXFORMAT_JPEG,
    .frame_size = FRAMESIZE_VGA,  // 640x480
    .jpeg_quality = 12,
    .fb_count = 2,  // Double buffering for better performance
    .grab_mode = CAMERA_GRAB_LATEST  // v2.0 new mode
};

esp_err_t err = esp_camera_init(&config);

🖼️ JPEG Encoding/Decoding Optimization

v2.0 Major Updates:

• Removed Old Engine: Removed 722-line old jpeg_encoder (performance bottleneck)
• New Decoder: jpeg_to_image.c (264 lines), dedicated JPEG decoding
• Encoder Refactoring: image_to_jpeg refactored with 575 lines, 3x performance boost
• Hardware Acceleration: Optimized using ESP32-S3’s DMA and vector instructions

Performance Comparison:

Technical Details:

// JPEG encoding optimization (based on v2.0)
typedef struct {
    uint8_t* buf;          // Output buffer
    size_t len;            // Data length
    size_t buf_size;       // Buffer size
    int quality;           // Compression quality (1-100)
    bool use_dma;          // Use DMA acceleration
    bool use_vector;       // Use vector instructions
} jpeg_encode_config_t;

// Encoding API
esp_err_t jpeg_encode_rgb888(
    const uint8_t* rgb_data,
    int width,
    int height,
    jpeg_encode_config_t* config,
    uint8_t** out_buf,
    size_t* out_len
);

Application Scenarios:

• Real-time Streaming: JPEG encoding for real-time transmission via WebSocket
• AI Recognition: Fast encoding/decoding for image recognition preprocessing
• Storage Optimization: JPEG compression saves storage space

🎯 SenseCAP Watcher AI Vision

Integration Solution (v2.0 Enhanced):

• SSCMA Camera: sscma_camera.cc expanded with 383 lines
• AI Models: Support for TinyML lightweight models
• Recognition Capabilities: Face detection, object recognition, gesture recognition
• Processing Speed: 640x480@15fps real-time processing

Technical Architecture:

┌─────────────────────────────────┐
│ ESP32-Camera (Hardware)         │
│  └─ OV2640/OV3660 camera module │
├─────────────────────────────────┤
│ JPEG Encode/Decode (v2.0)      │
│  ├─ Hardware DMA acceleration   │
│  └─ Vector instruction optimize │
├─────────────────────────────────┤
│ AI Inference Engine             │
│  ├─ TensorFlow Lite Micro       │
│  ├─ SSCMA Models                │
│  └─ Cloud AI Services           │
├─────────────────────────────────┤
│ Application Layer               │
│  ├─ Face Recognition            │
│  ├─ Object Detection            │
│  └─ Gesture Control             │
└─────────────────────────────────┘

Practical Use Cases:

1. Smart Door Lock: Face recognition + voice confirmation
2. Gesture Control: Gesture recognition for smart home control
3. Object Recognition: Identify objects and voice announcement

• 🎯 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