LiveKit Turn Detector EOS

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

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

How It Works

Final SpeechToTextPacket transcripts are accumulated into the current user turn
The model builds a chat template from conversation history (user + assistant turns) + current text
The tokenizer encodes the text and the ONNX model predicts an end-of-utterance probability
If probability >= threshold → timer set to quick_timeout
If probability < threshold → timer set to silence_timeout
LLMResponseDonePacket events record assistant turns in conversation history for context-aware predictions
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)

Parameters

Option Key	Default	Range	Description
`microphone.eos.model`	`en`	`en`, `multilingual`	Model variant
`microphone.eos.threshold`	`0.0289`	0.001–0.1	Turn completion probability threshold
`microphone.eos.quick_timeout`	`250 ms`	50–500 ms	Silence buffer when model says “done”
`microphone.eos.silence_timeout`	`3000 ms`	500–5000 ms	Max silence when model says “still speaking”
`microphone.eos.timeout`	`500 ms`	300–2000 ms	Fallback for interim transcripts and inference failures
`microphone.eos.max_history_turns`	`6`	1–20	Conversation 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.

Model Variants

Variant	Identifier	Size	Languages
English	`en`	66 MB	English (optimized)
Multilingual	`multilingual`	378 MB	zh, de, nl, en, pt, es, fr, it, ja, ko, ru, tr, id, hi

Model Setup

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

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.

Getting Started

Dashboard (UI)

Web API

Assistant API

Integration API

Endpoint API

Document API

Source Location

How It Works

Parameters

Model Variants

Model Setup

Docker

From Source

​Source Location

​How It Works

​Parameters

​Model Variants

​Model Setup

​Docker

​From Source

Source Location

How It Works

Parameters

Model Variants

Model Setup

Docker

From Source