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5-08-18 06:22:53,781 - WARNING asyncio - socket.se...
# ask-ai
a
2025-08-18 062253,781 - WARNING asyncio - socket.send() raised exception. 2025-08-18 062253,781 - WARNING asyncio - socket.send() raised exception. 2025-08-18 062253,782 - ERROR livekit.agents - Error predicting end of turn Traceback (most recent call last): File "/home/harshita/Outbound_dir/Backend/.venv/lib/python3.10/site-packages/livekit/agents/voice/audio_recognition.py", line 362, in _bounce_eou_task end_of_turn_probability = await turn_detector.predict_end_of_turn( File "/home/harshita/Outbound_dir/Backend/.venv/lib/python3.10/site-packages/livekit/plugins/turn_detector/multilingual.py", line 65, in predict_end_of_turn return await super().predict_end_of_turn(chat_ctx, timeout=timeout) File "/home/harshita/Outbound_dir/Backend/.venv/lib/python3.10/site-packages/livekit/plugins/turn_detector/base.py", line 231, in predict_end_of_turn assert result is not None, "end_of_utterance prediction should always returns a result" AssertionError: end_of_utterance prediction should always returns a result {"pid": 2337, "job_id": "AJ_Pjyj7JLWQ5Xn"} Im getting this error for end_of_utterance below are my configurations session = AgentSession( vad=ctx.proc.userdata["vad"], stt=stt_model, llm=llm_model, tts=tts_model, min_endpointing_delay=0.1, max_endpointing_delay=2, turn_detection=MultilingualModel(), max_tool_steps = 5, agent_false_interruption_timeout=2, user_away_timeout= user_away_timeout )
t
Thanks for reaching out, Harshita. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
The error 'end_of_utterance prediction should always return a result' can pop up if there are hiccups with model initialization or inference. No worries though, here are some friendly steps to help you sort it out: 1. Make Sure the Model Is Initialized: Double-check that your model files downloaded properly by running: 
python3 your_agent.py download-files
2. Check Your Model Setup: Confirm the model is initialized with the right parameters, like 
model = EnglishModel(unlikely_threshold=0.15)
. 3. Verify the Inference Executor: Make sure the inference executor is correctly set up in your job context, for example: 
executor = get_job_context().inference_executor
. 4. Look at the Chat Context: Ensure the chat context is valid and has the necessary messages before calling 
predict_end_of_turn
. 5. Peek at the Logs: Check the logs for any errors related to model initialization or inference. 6. Tweak Endpointing Delays: If needed, adjust delays like 
min_endpointing_delay=0.5
and 
max_endpointing_delay=6.0
. 7. Confirm Turn Detection Mode: Make sure the mode is set up correctly, for example: 
turn_detection_mode="vad"
. If you’re still stuck after trying these, just let me know—I’m here to help!
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