186 lines
10 KiB
Markdown
186 lines
10 KiB
Markdown
---
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language:
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- en
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- es
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- fr
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- de
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- it
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- pt
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- nl
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- zh
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- ja
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- ko
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- id
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- tr
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- ru
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- hi
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license: other
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license_name: livekit-model-license
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license_link: LICENSE
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library_name: transformers
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pipeline_tag: text-classification
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- voice-ai
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- turn-detection
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- end-of-utterance
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- end-of-turn
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- conversational-ai
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- livekit
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- onnx
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- quantized
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- knowledge-distillation
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---
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# LiveKit Turn Detector
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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.
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> **📖 For installation, usage examples, and integration guides, see the [LiveKit documentation](https://docs.livekit.io/agents/logic/turns/turn-detector/).**
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## Table of Contents
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- [Overview](#overview)
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- [Model Variants](#model-variants)
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- [How It Works](#how-it-works)
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- [Architecture and Training](#architecture-and-training)
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- [Supported Languages](#supported-languages)
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- [Benchmarks](#benchmarks)
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- [Usage](#usage)
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- [Deployment Requirements](#deployment-requirements)
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- [Limitations](#limitations)
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- [License](LICENSE)
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- [Resources](#resources)
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## Overview
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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.
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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.
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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.
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## Model Variants
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**Multilingual** (recommended) and **English-only** (deprecated) are distributed as INT8 quantized ONNX models (`model_q8.onnx`) optimized for CPU inference.
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> **⚠️ 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.
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## How It Works
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The model operates on transcribed text from a speech-to-text (STT) system, not raw audio.
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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.
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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.
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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.
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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.
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### Text Preprocessing
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The **multilingual** variant applies the following normalization before inference:
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- NFKC unicode normalization
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- Lowercasing
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- Punctuation removal (preserving apostrophes and hyphens)
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- Whitespace collapsing
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The **English-only** variant passes raw transcribed text without normalization.
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## Architecture and Training
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### Base Model
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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.
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### Knowledge Distillation
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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.
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### Training Data
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The training dataset is a mix of:
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- **Real call center transcripts** covering diverse conversational patterns
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- **Synthetic dialogues** emphasizing structured data input — addresses, email addresses, phone numbers, and credit card numbers
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- **Multi-format STT outputs** to handle provider variation (e.g., "forty two" vs. "42"), ensuring consistent predictions across different STT engines without runtime overhead
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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.
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### Quantization
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The trained model is exported to ONNX format and quantized to INT8 (`model_q8.onnx`), enabling efficient CPU-only inference with ONNX Runtime.
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## Supported Languages
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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.
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English, Spanish, French, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Indonesian, Turkish, Russian, Hindi
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## Benchmarks
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### Detection Accuracy (Multilingual Variant)
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- **True positive** — the model correctly identifies the user has finished speaking.
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- **True negative** — the model correctly identifies the user will continue speaking.
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| Language | True Positive Rate | True Negative Rate |
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|---|---|---|
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| Hindi | 99.4% | 96.3% |
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| Korean | 99.3% | 94.5% |
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| French | 99.3% | 88.9% |
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| Indonesian | 99.3% | 89.4% |
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| Japanese | 99.3% | 88.8% |
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| Dutch | 99.3% | 88.1% |
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| Russian | 99.3% | 88.0% |
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| German | 99.3% | 87.8% |
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| Portuguese | 99.4% | 87.4% |
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| Turkish | 99.3% | 87.3% |
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| English | 99.3% | 87.0% |
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| Chinese | 99.3% | 86.6% |
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| Spanish | 99.3% | 86.0% |
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| Italian | 99.3% | 85.1% |
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### Improvement Over Prior Version
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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.
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## Usage
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The model is designed for use as a turn detection plugin within the [LiveKit Agents](https://github.com/livekit/agents) framework.
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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/)**.
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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/)**.
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## Deployment Requirements
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- **Runtime**: CPU-only (no GPU required). Uses [ONNX Runtime](https://onnxruntime.ai/) with the `CPUExecutionProvider`.
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- **RAM**: <500 MB for the multilingual model.
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- **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.
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- **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.
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## Limitations
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- **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.
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- **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.
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- **Context window**: Limited to 128 tokens across a maximum of 6 conversation turns.
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- **Language coverage**: Currently supports 14 languages. Performance on unsupported languages is undefined.
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- **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.
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## License
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This model is released under the [LiveKit Model License](./LICENSE).
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## Resources
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- **[Documentation](https://docs.livekit.io/agents/logic/turns/turn-detector/)**: Full plugin documentation, installation, and integration guide.
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- **[Turns Overview](https://docs.livekit.io/agents/logic/turns/)**: How turn detection fits into the LiveKit Agents voice pipeline.
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- **[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.
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- **[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.
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- **[Video: LiveKit Turn Detector](https://youtu.be/OZG0oZKctgw)**: Overview video demonstrating the plugin.
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- **[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.
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- **[PyPI](https://pypi.org/project/livekit-plugins-turn-detector/)** | **[npm](https://www.npmjs.com/package/@livekit/agents-plugin-livekit)**: Package registries.
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