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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-### Recommendations
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
 ### Training Data
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
+language:
+  - en
+  - es
+  - fr
+  - de
+  - it
+  - pt
+  - nl
+  - zh
+  - ja
+  - ko
+  - id
+  - tr
+  - ru
+  - hi
+license: other
+license_name: livekit-model-license
+license_link: LICENSE
 library_name: transformers
+pipeline_tag: text-classification
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - voice-ai
+  - turn-detection
+  - end-of-utterance
+  - end-of-turn
+  - conversational-ai
+  - livekit
+  - onnx
+  - quantized
+  - knowledge-distillation
 ---
+# LiveKit Turn Detector
+An open-weights language model for contextually-aware end-of-utterance (EOU) detection in voice AI applications. The model predicts whether a user has finished speaking based on the semantic content of their transcribed speech, providing a critical complement to voice activity detection (VAD) systems.
+> **📖 For installation, usage examples, and integration guides, see the [LiveKit documentation](https://docs.livekit.io/agents/logic/turns/turn-detector/).**
+## Table of Contents
+- [Overview](#overview)
+- [Model Variants](#model-variants)
+- [How It Works](#how-it-works)
+- [Architecture and Training](#architecture-and-training)
+- [Supported Languages](#supported-languages)
+- [Benchmarks](#benchmarks)
+- [Usage](#usage)
+- [Deployment Requirements](#deployment-requirements)
+- [Limitations](#limitations)
+- [License](#license)
+- [Resources](#resources)
+## Overview
+Traditional voice agents rely on voice activity detection (VAD) to determine when a user has finished speaking. VAD works by detecting the presence or absence of speech in an audio signal and applying a silence timer. While effective for detecting pauses, VAD lacks language understanding and frequently causes false positives. For example, a user who says *"I need to think about that for a moment..."* and then pauses will be interrupted by a VAD-only system, even though they clearly intend to continue.
+This model adds semantic understanding to the turn detection process. It analyzes the transcribed text of a conversation in real time and predicts the probability that the user has completed their turn. When integrated into a voice pipeline alongside VAD, it substantially reduces unwanted interruptions while maintaining responsiveness.
+The model is particularly effective in scenarios involving structured data input — such as dictating addresses, phone numbers, email addresses, and credit card numbers — where natural pauses between segments do not indicate completion.
+## Model Variants
+| Variant | Revision | Base Model | Size on Disk | Inference Latency | RAM |
+|---|---|---|---|---|---|
+| **Multilingual** (recommended) | `v0.4.1-intl` | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) | ~396 MB | ~50–160 ms | <500 MB |
+| **English-only** (deprecated) | `v1.2.2-en` | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) | ~200 MB | ~10 ms | <200 MB |
+Both variants are distributed as INT8 quantized ONNX models (`model_q8.onnx`) optimized for CPU inference.
+> **⚠️ The English-only model (`EnglishModel`) is deprecated.** Use the **multilingual model (`MultilingualModel`)** for all new projects, including English-only applications. The multilingual model provides better accuracy across all languages — including English — thanks to knowledge distillation from a larger teacher model and an expanded training dataset. The English-only variant will not receive further updates.
+## How It Works
+The model operates on transcribed text from a speech-to-text (STT) system, not raw audio.
+1. **Input**: The recent conversation history (up to **6 turns**, truncated to **128 tokens**) is formatted using the [Qwen chat template](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) with `<|im_start|>` / `<|im_end|>` delimiters. The final user message is left *without* the closing `<|im_end|>` token.
+2. **Prediction**: The model predicts the probability of the `<|im_end|>` token appearing next. A **high probability** indicates the user has likely finished their utterance. A **low probability** indicates they are likely to continue.
+3. **Thresholding**: Per-language thresholds (stored in `languages.json`) convert the raw probability into a binary decision. These thresholds are tuned to balance responsiveness and accuracy for each supported language.
+4. **Integration with VAD**: In the LiveKit Agents framework, the model works alongside the [Silero VAD](https://docs.livekit.io/agents/logic/turns/vad/) plugin. VAD handles speech presence detection and interruption triggering, while this model provides the semantic signal for when to commit a turn.
+### Text Preprocessing
+The **multilingual** variant applies the following normalization before inference:
+- NFKC unicode normalization
+- Lowercasing
+- Punctuation removal (preserving apostrophes and hyphens)
+- Whitespace collapsing
+The **English-only** variant passes raw transcribed text without normalization.
+## Architecture and Training
+### Base Model
+Both variants are fine-tuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), selected for its strong performance on this task while enabling low-latency CPU inference.
+### Knowledge Distillation
+A **Qwen2.5-7B-Instruct** model was first fine-tuned as a teacher on end-of-turn prediction. Its knowledge was then distilled into the 0.5B student model. The distilled model approaches teacher-level accuracy while maintaining the efficiency of the smaller architecture, converging after approximately 1,500 training steps.
 ### Training Data
+The training dataset is a mix of:
+- **Real call center transcripts** covering diverse conversational patterns
+- **Synthetic dialogues** emphasizing structured data input — addresses, email addresses, phone numbers, and credit card numbers
+- **Multi-format STT outputs** to handle provider variation (e.g., "forty two" vs. "42"), ensuring consistent predictions across different STT engines without runtime overhead
+Although structured data enhancements were added only to the English training set, performance improvements generalized across languages due to the multilingual knowledge encoded in the Qwen2.5 base model.
+### Quantization
+The trained model is exported to ONNX format and quantized to INT8 (`model_q8.onnx`), enabling efficient CPU-only inference with ONNX Runtime.
+## Supported Languages
+The multilingual model supports 14 languages. The model relies on the STT provider to report the detected language, which is then used to select the appropriate per-language threshold.
+English, Spanish, French, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Indonesian, Turkish, Russian, Hindi
+## Benchmarks
+### Detection Accuracy (Multilingual Variant)
+- **True positive** — the model correctly identifies the user has finished speaking.
+- **True negative** — the model correctly identifies the user will continue speaking.
+| Language | True Positive Rate | True Negative Rate |
+|---|---|---|
+| Hindi | 99.4% | 96.3% |
+| Korean | 99.3% | 94.5% |
+| French | 99.3% | 88.9% |
+| Indonesian | 99.3% | 89.4% |
+| Japanese | 99.3% | 88.8% |
+| Dutch | 99.3% | 88.1% |
+| Russian | 99.3% | 88.0% |
+| German | 99.3% | 87.8% |
+| Portuguese | 99.4% | 87.4% |
+| Turkish | 99.3% | 87.3% |
+| English | 99.3% | 87.0% |
+| Chinese | 99.3% | 86.6% |
+| Spanish | 99.3% | 86.0% |
+| Italian | 99.3% | 85.1% |
+### Improvement Over Prior Version
+The multilingual v0.4.1 release achieved a **39.23% relative improvement** in handling structured inputs (emails, addresses, phone numbers, credit card numbers) compared to the prior version, reducing premature interruptions during data collection scenarios.
+## Usage
+The model is designed for use as a turn detection plugin within the [LiveKit Agents](https://github.com/livekit/agents) framework.
+For complete installation instructions, code examples (Python and Node.js), and configuration options, see the **[LiveKit turn detector plugin documentation](https://docs.livekit.io/agents/logic/turns/turn-detector/)**.
+For broader context on how turn detection fits into the voice pipeline — including VAD configuration, interruption handling, and manual turn control — see the **[Turns overview](https://docs.livekit.io/agents/logic/turns/)**.
+## Deployment Requirements
+- **Runtime**: CPU-only (no GPU required). Uses [ONNX Runtime](https://onnxruntime.ai/) with the `CPUExecutionProvider`.
+- **RAM**: <500 MB for the multilingual model.
+- **Instance type**: Use compute-optimized instances (e.g., AWS c6i, c7i). Avoid burstable instances (e.g., AWS t3, t4g) to prevent inference timeouts from CPU credit exhaustion.
+- **LiveKit Cloud**: The model is deployed globally on LiveKit Cloud. Agents running there automatically use the optimized remote inference service with no local resource requirements.
+## Limitations
+- **Text-only input**: The model operates on STT-transcribed text and cannot incorporate prosodic cues such as pauses, intonation, or emphasis. Future versions may integrate multimodal audio features.
+- **STT dependency**: Prediction quality depends on the accuracy and output format of the upstream STT provider. Mismatches between training and deployment STT formats may degrade performance.
+- **Context window**: Limited to 128 tokens across a maximum of 6 conversation turns.
+- **Language coverage**: Currently supports 14 languages. Performance on unsupported languages is undefined.
+- **Realtime model compatibility**: Cannot be used with audio-native realtime models (e.g., OpenAI Realtime API) without adding a separate STT service, which incurs additional cost and latency.
+## License
+This model is released under the [LiveKit Model License](LICENSE).
+## Resources
+- **[Documentation](https://docs.livekit.io/agents/logic/turns/turn-detector/)**: Full plugin documentation, installation, and integration guide.
+- **[Turns Overview](https://docs.livekit.io/agents/logic/turns/)**: How turn detection fits into the LiveKit Agents voice pipeline.
+- **[Blog: Improved End-of-Turn Model](https://blog.livekit.io/improved-end-of-turn-model-cuts-voice-ai-interruptions-39/)**: Technical deep dive on the multilingual distillation approach and benchmarks.
+- **[Blog: Using a Transformer for Turn Detection](https://blog.livekit.io/using-a-transformer-to-improve-end-of-turn-detection/)**: Original blog post introducing the concept and architecture.
+- **[Video: LiveKit Turn Detector](https://youtu.be/OZG0oZKctgw)**: Overview video demonstrating the plugin.
+- **[GitHub: Plugin Source](https://github.com/livekit/agents/tree/main/livekit-plugins/livekit-plugins-turn-detector)**: Source code for the `livekit-plugins-turn-detector` package.
+- **[PyPI](https://pypi.org/project/livekit-plugins-turn-detector/)** | **[npm](https://www.npmjs.com/package/@livekit/agents-plugin-livekit)**: Package registries.