what's the difference between Silero VAD and MultilingualModel? are they the same or different components? i have a Realtime agent and want to try a pipeline approach (so we need some way to have turn detection)

Thanks for reaching out, Sebastián. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

we use the builtin Realtime and we're looking to make a replicate agent but with an STT -> LLM -> TTS pipeline

Thanks — that’s helpful context. You’re using the built-in Realtime behavior now and want to replicate it with a custom STT → LLM → TTS pipeline. Below is a focused recommendation on turn detection options and what to consider when building the pipeline. Quick summary • Silero VAD: audio-level voice activity detection. Fast, language-agnostic, uses silence thresholds to mark start/end of speech. Good for low-latency, lightweight detection. • MultilingualModel (turn detector): uses transcripts and linguistic context to decide if a pause is a real end-of-turn vs a hesitation. Better conversational behavior but requires STT input (and incurs STT cost/latency). Practical options for your pipeline • Option 1 — Start with Silero VAD only ◦ When to choose: you want minimal added latency and lower cost; acceptable if occasional false cutoffs are tolerable. ◦ How it fits: run Silero VAD on incoming audio to produce voice/silence events; trigger your STT only after VAD signals end-of-speech (or use streaming STT but rely on VAD to decide turn boundaries). ◦ Trade-offs: simpler but less conversationally “aware.” • Option 2 — Use MultilingualModel (turn detector) with STT ◦ When to choose: you need natural turn-taking and fewer interruptions; you already plan to run STT anyway. ◦ How it fits: feed live transcripts into the turn detector so it can evaluate linguistic context and predict end-of-turn. Use its decision to trigger LLM processing and TTS. ◦ Trade-offs: higher cost and latency because you must run STT in realtime and supply transcripts to the model, but usually better UX. • Option 3 — Hybrid approach ◦ Use Silero VAD for initial, low-latency segmentation and run STT continuously/streaming. When VAD signals a possible end, pass the latest transcript chunk to the MultilingualModel to confirm end-of-turn before sending to LLM. ◦ Benefits: balances latency and naturalness; avoids sending every short pause to the LLM while reducing false cutoffs. Questions to narrow recommendation 1. Do you plan to run streaming STT (transcribe continuously) or only transcribe after VAD signals end-of-speech? 2. Which languages will your agent need to support? 3. Is low latency (fast responses) more important than minimizing interruptions, or vice versa? 4. Do you have cost constraints that would make continuous STT undesirable? If you tell me the answers, I’ll recommend a concrete flow (including which component triggers which event) and suggest parameter guidelines (for example, sensible

min_silence_duration

values or how to batch transcripts for the MultilingualModel).

Thanks — I see you hit this error while loading the Silero VAD model: RuntimeError: process initialization failed: [ONNXRuntimeError] : 7 : INVALID_PROTOBUF : Load model from ... silero_vad.onnx failed: Protobuf parsing failed I’m sorry that blocked your progress. That error means the ONNX runtime could not parse the model file. Common causes are a corrupted or incomplete model file, an incompatible ONNX Runtime build for your Python version, or a mismatch between GPU/CPU runtimes. Here are focused steps to diagnose and fix it. Immediate checks (quickest) 1. Verify the model file exists and looks correct: ◦ Run: ls -lh .venv/lib/python3.13/site-packages/livekit/plugins/silero/resources/silero_vad.onnx • Expected size: about ~5.5 MB (5,512,072 bytes). If the file is missing or very small, it’s likely corrupted. 1. Re-download the model: ◦ From your agent project root run: python agent.py download-files • Then re-check the file size. If you installed via a different helper, rerun that download step. Check ONNX Runtime and Python compatibility 1. Confirm onnxruntime is installed: ◦ pip show onnxruntime ◦ If missing, install it (the silero extra should add it): pip install "livekit-agents[silero]~=1.3" or pip install onnxruntime 1. Python version warning ◦ You’re using Python 3.13. ONNX Runtime may not have stable wheels for Python 3.13; this often causes protobuf parse/load failures. ◦ Recommended: use Python 3.10–3.12. Create a new venv with one of those versions, reinstall dependencies, re-download the model, and try again. Force CPU (avoid GPU runtime mismatches) 1. Ensure the model loads on CPU to avoid CUDA/provider mismatches: ◦ When loading the plugin, pass force_cpu=True: from livekit.plugins import silero vad = silero.VAD.load(force_cpu=True) Test model load in isolation 1. Minimal test script:

from livekit.plugins import silero
   vad = silero.VAD.load(force_cpu=True)
   print("Model loaded successfully")

• Run this inside the venv you installed into. If re-download + switching to Python 3.11/3.12 + forcing CPU still fails 1. Verify onnxruntime version ◦ Try installing a stable onnxruntime release (avoid nightlies). Example: pip install onnxruntime==1.16.0 • Re-run the minimal test. 1. If you still see INVALID_PROTOBUF: ◦ The file may still be corrupted during download. Delete the onnx file, re-run download, and confirm checksum/size. ◦ Enable verbose ONNX logs or check stdout for provider/load messages to get more details. If you want, tell me: • Which OS you’re on (Linux/macOS/Windows) • Exact Python version (python --version) • onnxruntime version (pip show onnxruntime) • The file size shown by ls -lh for silero_vad.onnx With those details I’ll suggest the next step (exact commands to recreate venv on a supported Python, or a specific onnxruntime version to try). Sources: Silero VAD plugin | LiveKit Documentation | examples/survey | livekit/livekit_composite | DeepWiki

i had to manually download to fix it

Great — glad you were able to fix it by manually downloading the model. Thanks for letting me know. If you’d like, I can help with any of the following next steps: • Verify your pipeline is using Silero VAD correctly (example code to load it with

force_cpu=True

). • Recommend a hybrid flow (VAD + streaming STT + MultilingualModel) and concrete event triggers for STT → LLM → TTS. • Suggest sensible parameters (e.g.,

min_silence_duration

) for fewer false cutoffs. • Help confirm your environment so the issue won’t recur (Python version, onnxruntime), or give commands to recreate a compatible venv. Tell me which of these you want help with (or paste any error/output you still see) and I’ll provide the exact steps.

every time the LLM runs, it transitions to thinking state now, is it standard behaviour?

Yes — that’s expected behavior. When your agent runs the LLM it will transition into a “thinking” state each time it begins processing input and generating a response. This is part of the AgentSession lifecycle: the agent moves into a running/processing phase while the LLM produces output, and that state change is surfaced via the

agent_state_changed

events. What this means for your pipeline • Seeing “thinking” simply means the agent/LLM is actively generating a response (it’s not an error). • You’ll typically see this whenever you: ◦ Send a new turn to the LLM, or ◦ Re-run the agent to handle an updated transcript or context. Practical notes you might find useful • If you want different UX (e.g., avoid visibly showing “thinking” to end users), you can hide or debounce the state change in your client UI and only show a visible indicator when generation latency exceeds a threshold. • If the thinking state lasts unusually long, that points to latency from the LLM, STT, or network — not from the state transition itself. In that case we can investigate where time is spent. • For a pipeline STT → LLM → TTS flow, you can treat the “thinking” state as the signal to: a. stop accepting further user audio (if you want strict turn-taking), or b. continue streaming/storing user audio to append to the next context if you allow interruptions. If you want, tell me: • Is the thinking state causing UX issues (too slow, visible flicker, etc.)? • Do you prefer a continuous conversation model or strict turn-taking? I can suggest UI handling patterns or optimizations (e.g., debounce thresholds, batching transcripts, or where to insert VAD/turn-detector checks) based on your preference. Sources: Turns overview | LiveKit Documentation | Agent session | LiveKit Documentation