The following optimizations are recommended when deploying TEN Agent on edge devices such as ESP32:
- Selective loading of modules: Reduce memory footprint by retaining only the core voice interaction and necessary extensions (ESP32-S3 requires a minimum of 4MB Flash)
- Use of lightweight models: Preference for optimized edge computing-friendly models such as DeepSeek R1
- Offline Mode Configuration: For latency-sensitive scenarios, preload commonly used voice packets to the device's local storage.
- network optimization: Configure Wi-Fi low-power mode and set compression parameters for voice data transmission (e.g., Opus encoding)
Specific implementation steps include: 1) Clone the esp32-client branch code 2) Enable the -Os optimization option when compiling with the ESP-IDF toolchain 3) Disable non-essential features in menuconfig. After deployment, we can test the real-time performance through the scenarios such as "voice-controlled home appliances", and the typical response latency can be controlled within 800ms.
This answer comes from the articleTEN: An open source tool for building real-time multimodal speech AI intelligencesThe
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