Your error logs indicate two main issues: inference is slower than realtime, and high memory usage warnings, leading to audio output failures. Based on best practices for LiveKit and local RAG LLM integration, here are focused troubleshooting steps: • Audio Configuration & Frame Validation: Confirm that your audio input matches the expected sample rate/channel requirements (Google expects input at 16kHz/mono, output at 24kHz/mono). Validate that frame data for audio is in the correct

bytes

format and the frame length matches the configured output audio frame structure . • Audio Buffer Size: For local/continuous streaming, buffer underruns may cause capture errors. Increase

queue_size_ms

(try 1000-2000ms) in your

rtc.AudioSource

configuration to stabilize buffering, which helps prevent dropped or delayed audio frames . • Memory Usage: High memory can come from unclosed asyncio channels and stale generation objects. Ensure all message and audio channels are cleaned up after each response/generation, and monitor process memory with

psutil

to identify leaks . • Performance Monitoring: Track detailed timing metrics (duration, tokens/sec, time to first token) via

RealtimeModelMetrics

for bottleneck analysis, and assess if session resumptions or connection failures coincide with output errors . • Compute and Network: If running everything locally, ensure your system has adequate CPU/memory. High compute utilization can cause both slow inference and audio interruptions. Monitor your logs for issues when audio frames are generated or channels are closed, and verify audio frame type/size. Adjust buffer sizes and perform regular resource cleanup after each interaction. Let me know your findings or if you have additional log details for targeted diagnostics. Sources: livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/realtime... | Speech & Audio Processing | livekit/livekit_composite | DeepWiki | AI Agents Framework | livekit/livekit_composite | DeepWiki | Voice Assistants | livekit/livekit_composite | DeepWiki