To help you scale smoothly with end-of-turn (EOU) models running locally, here are some friendly tips to keep in mind: • Resource Efficiency: The model is thoughtfully optimized for CPUs, using less than 500MB of RAM, and it completes inference quickly—about ~10ms for English or ~25ms for multilingual models. • Shared Inference Server: It supports multiple sessions at once, so you can make the most of your computational resources without a hitch. • Concurrency Management: The

AudioRecognition

class is designed to handle interactions smoothly by canceling any pending tasks when new speech is detected, keeping everything responsive. • CPU Optimization: It smartly uses half of your available CPU cores, striking a nice balance between performance and resource use. • Sliding Context Window: By considering up to the last four conversation turns for predictions, it keeps memory usage nice and low. • Integration: It’s flexible and works with any STT system, so you can tailor it to your setup easily. And a little pro tip for production: pre-download your model files ahead of time using

python my_agent.py download-files

. This way, you’ll avoid any delays when traffic picks up. If you have any questions or need a hand, I’m here to help! Sources: livekit-agents/livekit/agents/voice/audio_recognition.py | agents/src/voice/audio_recognition.ts | Improving voice AI's turn detection with transformers