TEN Turn Detection is an advanced model for real-time, full-duplex dialogue systems, designed to improve human-AI communication by accurately identifying user utterance states. It targets developers of conversational AI agents, offering more natural turn-taking and interruption handling.

How It Works

The system leverages a transformer-based language model (Qwen2.5-7B) for deep semantic analysis of conversational context. It classifies user input into three states: 'finished' (complete thought, expects response), 'wait' (explicitly requests AI silence), and 'unfinished' (interrupted but intends to continue). This multi-layered approach enables context-aware turn management, facilitating natural dialogue flow and reducing awkward interruptions.

Quick Start & Requirements

• Install via pip: pip install "transformers>=4.45.0" "torch>=2.0.0"
• Model weights are automatically downloaded from Hugging Face upon first inference.
• Official Hugging Face Space demo available: https://huggingface.co/spaces/TEN-framework/TEN_Turn_Detection

Highlighted Details

• Achieves superior accuracy compared to other open-source solutions on a bilingual (English/Chinese) test dataset.
• Specifically supports 'wait' state detection, which is absent in comparable models.
• Offers multilingual support for both English and Chinese.
• Part of a larger TEN ecosystem for building multimodal conversational AI agents.

Maintenance & Community

• Active community channels include Discord, X, and LinkedIn.
• The project is part of the broader TEN Framework initiative.

Licensing & Compatibility

• Licensed under Apache 2.0.
• No explicit restrictions mentioned for commercial use or closed-source linking.

Limitations & Caveats

The provided performance benchmarks are based on the project's own test dataset, and real-world performance may vary. The "WAIT ACCURACY" is listed as N/A for some competitor models, suggesting potential limitations in their evaluation methodology or feature set.