Integrating Large Models in Embedded Systems: A Social Service Assistant Based on STM32 & ESP32

1. Hardware Platform Selection and Modules

1. STM32 Solution

• Processor: STM32F407 / STM32H7 (high frequency, good performance)

• Peripheral Modules:

• WiFi Module: ESP8266 / ESP32-WROOM as the networking module

• Microphone Module: I2S MEMS microphone (e.g., INMP441)

• Speaker: I2S DAC + small speaker

• Display: TFT LCD (SPI/Parallel)

• Storage: SD card / external Flash (for caching voice data)

Applicable Scenarios: Medical devices, industrial control, information query terminals (high real-time requirements, but small data volume)

2. ESP32 Solution

• Processor: ESP32-S3 (with AI acceleration & USB OTG), more suitable for AIoT

• Peripheral Modules:

• Built-in WiFi / BLE (directly connect to cloud large models)

• Microphone: I2S MEMS (e.g., SPH0645)

• Speaker: I2S DAC + small speaker

• Screen: OLED (I2C) / TFT LCD

• Camera (optional): ESP32-CAM module (multimodal interaction)

Applicable Scenarios: Community social worker portable assistant, elderly voice assistant, policy Q&A machine

2. System Architecture Flow

┌───────────────┐
│ User Voice Input │  (Microphone, I2S)
└─────┬─────────┘
      ↓
┌───────────────┐
│ Voice Capture & Cache │  (PCM/WAV format)
└─────┬─────────┘
      ↓
┌───────────────┐
│ Voice Recognition (Whisper API or local small model)│
└─────┬─────────┘
      ↓
┌───────────────┐
│ Text Input Large Model │  (Cloud API / Local LLaMA.cpp port)
└─────┬─────────┘
      ↓
┌───────────────┐
│ Return Text → TTS │  (ESP32 TTS, PicoTTS)
└─────┬─────────┘
      ↓
┌───────────────┐
│ Voice Playback / Display │ (Speaker / LCD)
└───────────────┘

3. Pseudocode Example

1. ESP32 Solution (Cloud Calling Large Model API)

// Initialize WiFi
wifi_connect("SSID", "PASSWORD");
// Initialize I2S Microphone
i2s_init(MIC_CHANNEL);
// Initialize Speaker
i2s_init(SPK_CHANNEL);
// Main Loop
while(1) {
    // Step 1: Record
    audio_data = mic_record(3_sec);
    // Step 2: Call Cloud Whisper API → Convert to Text
    text_input = whisper_api(audio_data);
    // Step 3: Call Large Model API (e.g., OpenAI / Wenxin Yiyan)
    response = llm_api(text_input);
    // Step 4: Convert Result to Speech via TTS
    audio_out = tts_api(response);
    // Step 5: Play Speech
    spk_play(audio_out);
    // Step 6: Display Result on LCD
    lcd_print(response);
}

2. STM32 + ESP8266 Solution (Local Lightweight Model + Cloud Hybrid)

// Initialize Hardware
stm32_init();
wifi_init(esp8266);
lcd_init();
mic_init();
spk_init();
// Main Task Loop
while(1) {
    // Step 1: Record
    buffer = mic_record(5_sec);
    // Step 2: Check Network Availability
    if (wifi_status() == CONNECTED) {
        // Cloud Inference Mode
        text_input = whisper_cloud(buffer);
        answer = gpt_cloud(text_input);
    } else {
        // Offline Mode → Local Lightweight Model
        text_input = offline_stt(buffer);  // Local Speech Recognition
        answer = llama_local(text_input);  // Local Inference (Simplified)
    }
    // Step 3: TTS
    tts_result = pico_tts(answer);
    // Step 4: Output Result
    spk_play(tts_result);
    lcd_print(answer);
}

4. Project Implementation Cases (Social Work Scenarios)

Case 1: Smart Elderly Q&A Machine (ESP32-S3)

• Function: Elderly can directly ask about medical insurance policies and subsidy policies using voice.

• Hardware: ESP32-S3 + I2S Microphone + Speaker + LCD

• Software: Cloud Whisper Recognition + Large Model API → Local TTS Playback

• Application: Nursing homes, home care, reducing the difficulty for the elderly to obtain information.

Case 2: Social Worker Portable Assistant (STM32 + ESP8266)

• Function: Social workers carry a small terminal to answer common policy questions from residents in the community.

• Hardware: STM32F407 + ESP8266 + OLED + Microphone + Small Speaker

• Software: Supports cloud mode (calls large model when connected), also supports local lightweight inference (can still be used without network).

• Application: Community visits, consulting services for migrant workers.

Case 3: Learning Assistance Robot (ESP32-CAM + Jetson Nano Collaboration)

• Function: Elementary/middle school students ask questions → AI answers (Python/Math/English).

• Hardware: ESP32-CAM (captures voice + video) + Jetson Nano (local LLaMA deployment)

• Application: After-school tutoring, educational equity in remote areas.

5. Conclusion

By combining STM32 and ESP32, we can achieve a full-link solution from low-power entry-level applications (policy Q&A machine) to high-performance multimodal applications (learning assistance robot).

For social workers:

• ESP32 is more suitable for portable, networked, low-cost service terminals;

• STM32 is more suitable for industrial, medical, high-stability applications in offline mode.

In the future, with the development of lightweight models (such as TinyLlama, Phi-2) and domestic AI chips, more social service scenarios (elderly care, education, community governance) can truly be implemented.