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The LiveKit Turn Detector uses a language model to predict turn completion from transcribed text combined with conversation history. It understands that incomplete sentences, addresses, and phone numbers are not finished turns — even when the caller pauses. Provider identifier: livekit_eos

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Source Location

api/assistant-api/internal/end_of_speech/internal/livekit/
├── livekit_end_of_speech.go   # Main implementation
├── turn_detector.go           # ONNX inference + tokenizer
├── chat_template.go           # Conversation history formatting
├── models/
│   ├── model_q8.onnx                  # English model (~66 MB, downloaded at build)
│   ├── model_q8_multilingual.onnx     # Multilingual model (~378 MB, downloaded at build)
│   └── tokenizer.json                 # Shared tokenizer

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How It Works

  1. Final SpeechToTextPacket transcripts are accumulated into the current user turn
  2. The model builds a chat template from conversation history (user + assistant turns) + current text
  3. The tokenizer encodes the text and the ONNX model predicts an end-of-utterance probability
  4. If probability >= threshold → timer set to quick_timeout
  5. If probability < threshold → timer set to silence_timeout
  6. LLMResponseDonePacket events record assistant turns in conversation history for context-aware predictions
  7. Interim transcripts reset the timer to fallback_timeout
History: [user: "I need to book a flight", assistant: "Sure, what's your destination?"]
Current: "I'd like to go to"
Model: P(complete) = 0.005 → still speaking → timer = 1500ms

Current: "I'd like to go to London please"
Model: P(complete) = 0.042 → done → timer = 250ms (quick)

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Parameters

Option KeyDefaultRangeDescription
microphone.eos.modelenen, multilingualModel variant
microphone.eos.threshold0.02890.001–0.1Turn completion probability threshold
microphone.eos.quick_timeout250 ms50–500 msSilence buffer when model says “done”
microphone.eos.silence_timeout3000 ms500–5000 msMax silence when model says “still speaking”
microphone.eos.timeout500 ms300–2000 msFallback for interim transcripts and inference failures
microphone.eos.max_history_turns61–20Conversation history turns used for context
The threshold range (0.001–0.1) is very different from Pipecat’s (0.1–0.9). These are different models with different probability distributions. Do not copy threshold values between providers.

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Model Variants

VariantIdentifierSizeLanguages
Englishen66 MBEnglish (optimized)
Multilingualmultilingual378 MBzh, de, nl, en, pt, es, fr, it, ja, ko, ru, tr, id, hi

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Model Setup

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Docker

All models are downloaded from Hugging Face and patched during the Docker build. No manual action required. 
# Set automatically in the Dockerfile
LIVEKIT_TURN_MODEL_PATH=./models/livekit_turn/model_q8.onnx
LIVEKIT_TURN_MULTI_MODEL_PATH=./models/livekit_turn/model_q8_multilingual.onnx
LIVEKIT_TURN_TOKENIZER_PATH=./models/livekit_turn/tokenizer.json

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From Source

Download the models manually: 
mkdir -p api/assistant-api/internal/end_of_speech/internal/livekit/models

# English model (required)
curl -fsSL -o api/assistant-api/internal/end_of_speech/internal/livekit/models/model_q8.onnx \
    "https://huggingface.co/livekit/turn-detector/resolve/v1.2.2-en/onnx/model_q8.onnx"

# Tokenizer (required)
curl -fsSL -o api/assistant-api/internal/end_of_speech/internal/livekit/models/tokenizer.json \
    "https://huggingface.co/livekit/turn-detector/resolve/v1.2.2-en/tokenizer.json"

# Multilingual model (optional — only if using multilingual mode)
curl -fsSL -o api/assistant-api/internal/end_of_speech/internal/livekit/models/model_q8_multilingual.onnx \
    "https://huggingface.co/livekit/turn-detector/resolve/v0.4.1-intl/onnx/model_q8.onnx"
If you encounter ONNX opset errors, patch the models: 
pip install onnx
python3 -c "
import onnx
for path in [
    'api/assistant-api/internal/end_of_speech/internal/livekit/models/model_q8.onnx',
    'api/assistant-api/internal/end_of_speech/internal/livekit/models/model_q8_multilingual.onnx',
]:
    try:
        m = onnx.load(path)
        used = {n.domain for n in m.graph.node}
        keep = [o for o in m.opset_import if o.domain == '' or o.domain in used]
        del m.opset_import[:]
        m.opset_import.extend(keep)
        onnx.save(m, path)
        print(f'Patched {path}')
    except FileNotFoundError:
        pass
"
To override paths: 
export LIVEKIT_TURN_MODEL_PATH=/path/to/model_q8.onnx
export LIVEKIT_TURN_MULTI_MODEL_PATH=/path/to/model_q8_multilingual.onnx
export LIVEKIT_TURN_TOKENIZER_PATH=/path/to/tokenizer.json
Requires ONNX Runtime (libonnxruntime) — same dependency as Silero/FireRed VAD.