Full Code of TEN-framework/ten-turn-detection for AI

Repository: TEN-framework/ten-turn-detection
Branch: main
Commit: ea9a167ca9d6
Files: 6
Total size: 59.5 KB

Directory structure:
gitextract_mg_j5e7a/

├── LICENSE
├── README.md
├── TEN-Turn-TestSet/
│   ├── finished.txt
│   ├── unfinished.txt
│   └── wait.txt
└── inference.py

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FILE CONTENTS
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FILE: LICENSE
================================================
Open Source License

The ten-turn-detection is licensed pursuant to the Apache License v2.0(provided
for your convenience below), with the following additional conditions. You may reproduce, prepare Derivative Works
of, publicly display, publicly perform, sublicense, distribute, or otherwise
make available (together, "Deploy") the ten-turn-detection, for commercial or
non-commercial purposes, provided that you agree to abide by the terms below:

    1. You may not (i) host the ten-turn-detection or the Derivative Works on any End
       User devices, including but not limited to any mobile terminal devices
       or (ii) Deploy the ten-turn-detection in a way that competes with Agora's
       offerings and/or that allows others to compete with Agora's offerings,
       including without limitation enabling any third party to develop or
       deploy Applications.

    2. You may Deploy the ten-turn-detection solely to create and enable deployment
       of your Application(s) solely for your benefit and the benefit of your
       direct End Users. If you prefer, you may include the following notice in
       the documentation of your Application(s): "Powered by ten-turn-detection".

    3. Derivative Works of the ten-turn-detection remain subject to this Open Source
       License.

    4. "End Users" shall mean the end-users of your Application(s) who access
       the ten-turn-detection solely to the extent necessary to access and use the
       Application(s) you create or deploy using ten-turn-detection.

    5. "Application(s)" shall mean your software programs designed or developed
       by using the ten-turn-detection or where deployment is enabled by the ten-turn-detection.
  
  Copyright © 2025 Agora

  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.

================================================
FILE: README.md
================================================
<div align="center">

![Image](https://github.com/user-attachments/assets/c72d82cc-3667-496c-8bd6-3d194a91c452)

[![Discussion posts](https://img.shields.io/github/discussions/TEN-framework/ten-turn-detection?labelColor=gray&color=%20%23f79009)](https://github.com/TEN-framework/ten-turn-detection/discussions/)
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![](https://img.shields.io/github/contributors/ten-framework/ten-turn-detection?color=c4f042&labelColor=gray&style=flat-square)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome!-brightgreen.svg?style=flat-square)](https://github.com/TEN-framework/ten-turn-detection/pulls)
[![Hugging Face Space](https://img.shields.io/badge/Hugging%20Face-TEN%20Turn%20Detection-yellow?style=flat&logo=huggingface)](https://huggingface.co/TEN-framework/TEN_Turn_Detection)
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[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/TEN-framework/TEN-turn-detection)

</div>

<br>

## Table of Contents

- [Welcome to TEN](#welcome-to-ten)
- [Introduction](#introduction)
- [TEN Hugging Face Space](#ten-hugging-face-space)
- [Key Features](#key-features)
- [Prepared Dataset](#prepared-dataset)
- [Detection Performance](#detection-performance)
- [Quick Start](#quick-start)
  - [Installation](#installation)
  - [Model Weights](#model-weights)
  - [Inference](#inference)
- [Citation](#citation)
- [TEN Ecosystem](#ten-ecosystem)
- [Ask Questions](#ask-questions)
- [License](#license)

<br>

## Welcome to TEN

TEN is an open-source framework for conversational voice AI agents.

[TEN Ecosystem](#ten-ecosystem) includes [TEN Framework](https://github.com/ten-framework/ten-framework), [Agent Examples](https://github.com/TEN-framework/ten-framework/tree/main/ai_agents/agents/examples), [VAD](https://github.com/ten-framework/ten-vad), [Turn Detection](https://github.com/ten-framework/ten-turn-detection) and [Portal](https://github.com/ten-framework/portal).
<br>

| Community Channel | Purpose |
| ---------------- | ------- |
| [![Follow on X](https://img.shields.io/twitter/follow/TenFramework?logo=X&color=%20%23f5f5f5)](https://twitter.com/intent/follow?screen_name=TenFramework) | Follow TEN Framework on X for updates and announcements |
| [![Follow on LinkedIn](https://custom-icon-badges.demolab.com/badge/LinkedIn-TEN_Framework-0A66C2?logo=linkedin-white&logoColor=fff)](https://www.linkedin.com/company/ten-framework) | Follow TEN Framework on LinkedIn for updates and announcements |
| [![Discord TEN Community](https://img.shields.io/badge/Discord-Join%20TEN%20Community-5865F2?style=flat&logo=discord&logoColor=white)](https://discord.gg/VnPftUzAMJ) | Join our Discord community to connect with developers |
| [![Hugging Face Space](https://img.shields.io/badge/Hugging%20Face-TEN%20Framework-yellow?style=flat&logo=huggingface)](https://huggingface.co/TEN-framework) | Join our Hugging Face community to explore our spaces and models |
| [![WeChat](https://img.shields.io/badge/TEN_Framework-WeChat_Group-%2307C160?logo=wechat&labelColor=darkgreen&color=gray)](https://github.com/TEN-framework/ten-agent/discussions/170) | Join our WeChat group for Chinese community discussions |

<br>

> \[!IMPORTANT]
>
> **Star TEN Repositories** ⭐️
>
> Get instant notifications for new releases and updates. Your support helps us grow and improve TEN!

<br>

![TEN star us gif](https://github.com/user-attachments/assets/eeebe996-8c14-4bf7-82ae-f1a1f7e30705)

<br>

## TEN Hugging Face Space

<https://github.com/user-attachments/assets/725a8318-d679-4b17-b9e4-e3dce999b298>

You are more than welcome to [Visit TEN Hugging Face Space](https://huggingface.co/spaces/TEN-framework/ten-agent-demo) to try VAD and Turn Detection together.

## Introduction

**TEN Turn Detection** is an advanced intelligent turn detection model designed specifically for natural and dynamic communication between humans and AI agents. This technology addresses one of the most challenging aspects of human-AI conversation: detecting natural turn-taking cues and enabling contextually-aware interruptions. TEN Turn Detection incorporates deep semantic understanding of conversation context and linguistic patterns to create more natural dialogue with AI.

<div align="center">
  <img src="images/turn_detection.svg" alt="TEN Turn Detection SVG Diagram" width="800"/>
</div>

**TEN Turn Detection** categorizes user's text into three key states:

**finished**: A finished utterance where the user has expressed a complete thought and expects a response. Example: "Hey there I was wondering can you help me with my order"

**wait**: An wait utterance where the user has explicitly instructed the AI not to speak. Example: "Shut up"

**unfinished**: A clearly unfinished utterance where the user has momentarily paused but intends to continue speaking. Example: "Hello I have a question about"

These three classification states allow the TEN system to create natural conversation dynamics by intelligently managing turn-taking, reducing awkward interruptions while maintaining conversation flow.

TEN Turn Detection utilizes a multi-layered approach based on the transformer-based language model(Qwen2.5-7B) for semantic analysis.

## Key Features

- **Context-Aware Turn Management**
  TEN Turn Detection analyzes linguistic patterns and semantic context to accurately identify turn completion points. This capability enables intelligent interruption handling, allowing the system to determine when interruptions are contextually appropriate while maintaining natural conversation flow across various dialogue scenarios.

- **Multilingual Turn Detection Support**
  TEN Turn Detection provides comprehensive support for both English and Chinese languages. It is engineered to accurately identify turn-taking cues and completion signals across multilingual conversations.

- **Superior Performance**
  Compared with multiple open-source solutions, TEN achieves superior performance across all metrics on our publicly available test dataset.

## Prepared Dataset

We have open-sourced the TEN-Turn-Detection TestSet, a bilingual (Chinese and English) collection of conversational inputs specifically designed to evaluate turn detection capabilities in AI dialogue systems. The dataset consists of three distinct components:

_wait.txt_: Contains expressions requesting conversation pauses or termination

_unfinished.txt_: Features incomplete dialogue inputs with truncated utterances

_finished.txt_: Provides complete conversational inputs across multiple domains

## Detection Performance

We conducted comprehensive evaluations comparing several open-source models for turn detection using our test dataset:

<div align="center">

| LANGUAGE |         MODEL          | FINISHED<br>ACCURACY | UNFINISHED<br>ACCURACY | WAIT<br>ACCURACY |
| :------: | :--------------------: | :------------------: | :--------------------: | :--------------: |
| English  |        Model A         |        59.74%        |         86.46%         |       N/A        |
| English  |        Model B         |        71.61%        |         96.88%         |       N/A        |
| English  | **TEN Turn Detection** |      **90.64%**      |       **98.44%**       |     **91%**      |

| LANGUAGE |         MODEL          | FINISHED<br>ACCURACY | UNFINISHED<br>ACCURACY | WAIT<br>ACCURACY |
| :------: | :--------------------: | :------------------: | :--------------------: | :--------------: |
| Chinese  |        Model B         |        74.63%        |         88.89%         |       N/A        |
| Chinese  | **TEN Turn Detection** |      **98.90%**      |       **92.74%**       |     **92%**      |

</div>

> **Notes:**
>
> 1. Model A doesn't support Chinese language processing
> 2. Neither Model A nor Model B support the "WAIT" state detection

## Quick Start

### Installation

```
git clone https://github.com/TEN-framework/ten-turn-detection.git
pip install "transformers>=4.45.0"
pip install "torch>=2.0.0"
```

### Model Weights

The TEN Turn Detection model is available on HuggingFace:

- Model Repository: [TEN-framework/TEN_Turn_Detection](https://huggingface.co/TEN-framework/TEN_Turn_Detection)

You can download the model in several ways:

1. **Automatic download** (recommended): The model weights will be automatically downloaded when you run the inference script for the first time. HuggingFace Transformers will cache the model locally.

2. **Using Git LFS**:

   ```bash
   # Install Git LFS if you haven't already
   git lfs install

   # Clone the repository with model weights
   git clone https://huggingface.co/TEN-framework/TEN_Turn_Detection
   ```

3. **Using the Hugging Face Hub library**:

   ```python
   from huggingface_hub import snapshot_download

   snapshot_download(repo_id="TEN-framework/TEN_Turn_Detection")
   ```

### Inference

The inference script accepts command line arguments for user input:

```
# Basic usage
python inference.py --input "Your text to analyze"

```

Example output:

```
Loading model from TEN-framework/TEN_Turn_Detection...
Running inference on: 'Hello I have a question about'

Results:
Input: 'Hello I have a question about'
Turn Detection Result: 'unfinished'
```

## Citation

If you use TEN Turn Detection in your research or applications, please cite:

```
@misc{TEN_Turn_Detection,
author = {TEN Team},
title = {TEN Turn Detection: Turn detection for full-duplex dialogue communication

},
year = {2025},
url = {https://github.com/TEN-framework/ten-turn-detection},
}
```

## TEN Ecosystem

| Project | Preview |
| ------- | ------- |
| [**️TEN Framework**][ten-framework-link]<br>Open-source framework for conversational AI Agents.<br><br>![][ten-framework-shield] | ![][ten-framework-banner] |
| [**TEN VAD**][ten-vad-link]<br>Low-latency, lightweight and high-performance streaming voice activity detector (VAD).<br><br>![][ten-vad-shield] | ![][ten-vad-banner] |
| [**️ TEN Turn Detection**][ten-turn-detection-link]<br>TEN Turn Detection enables full-duplex dialogue communication.<br><br>![][ten-turn-detection-shield] | ![][ten-turn-detection-banner] |
| [**TEN Agent Examples**][ten-agent-example-link]<br>Usecases powered by TEN.<br><br> | ![][ten-agent-example-banner] |
| [**TEN Portal**][ten-portal-link]<br>The official site of the TEN Framework with documentation and a blog.<br><br>![][ten-portal-shield] | ![][ten-portal-banner] |


<br>

## Ask Questions

[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/TEN-framework/TEN-framework)

Most questions can be answered by using DeepWiki, it is fast, intutive to use and supports multiple languages.

<br>

## License

This project is released pursuant to the Apache License, Version 2.0, with additional restrictions.For details, please refer to the[ LICENSE](./LICENSE) file.

[ten-framework-shield]: https://img.shields.io/github/stars/ten-framework/ten_framework?color=ffcb47&labelColor=gray&style=flat-square&logo=github
[ten-framework-banner]: https://github.com/user-attachments/assets/2a560a74-68f3-4f4a-9ec8-89464c42a9c7
[ten-framework-link]: https://github.com/ten-framework/ten_framework

[ten-vad-link]: https://github.com/ten-framework/ten-vad
[ten-vad-shield]: https://img.shields.io/github/stars/ten-framework/ten-vad?color=ffcb47&labelColor=gray&style=flat-square&logo=github
[ten-vad-banner]: https://github.com/user-attachments/assets/e504135e-67fd-4fa1-b0e4-d495358d8aa5

[ten-turn-detection-link]: https://github.com/ten-framework/ten-turn-detection
[ten-turn-detection-shield]: https://img.shields.io/github/stars/ten-framework/ten-turn-detection?color=ffcb47&labelColor=gray&style=flat-square&logo=github
[ten-turn-detection-banner]: https://github.com/user-attachments/assets/c72d82cc-3667-496c-8bd6-3d194a91c452

[ten-agent-example-link]: https://github.com/TEN-framework/ten-framework/tree/main/ai_agents/agents/examples
[ten-agent-example-banner]:https://github.com/user-attachments/assets/7f735633-c7f6-4432-b6b4-d2a2977ca588

[ten-portal-link]: https://github.com/ten-framework/portal
[ten-portal-shield]: https://img.shields.io/github/stars/ten-framework/portal?color=ffcb47&labelColor=gray&style=flat-square&logo=github
[ten-portal-banner]: https://github.com/user-attachments/assets/f56c75b9-722c-4156-902d-ae98ce2b3b5e


================================================
FILE: TEN-Turn-TestSet/finished.txt
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Lets do a role play it might help me get more comfortable
How do you usually prepare for a conversation in a new language
Id like to practice describing my day can I give it a try
Whats your favorite way to learn new phrases any methods you recommend
I sometimes struggle with small talk can we practice that
Can you give me feedback on my speaking after we finish
Whats a common mistake learners make when speaking English
I want to sound more natural do you have any tips for that
Lets talk about our hobbies its a fun way to practice
How do you feel about discussing current events it could be interesting
Id like to work on my listening skills can you speak a bit faster
Whats a good way to practice asking questions in a conversation
Can we focus on idiomatic expressions I find them tricky
Id love to share my opinions on a topic can you ask me some questions
How do you stay motivated when practicing a new language
Can you help me with transition phrases to make my speech smoother
I want to practice giving presentations can we do a mock one
Whats your favorite topic to discuss when practicing speaking
Lets try to have a debate itll challenge my speaking skills
Thanks for helping me practice I really appreciate your support
当代量子计算的发展为密码学带来了前所未有的挑战与机遇
机器学习算法在处理非结构化数据时仍然面临着诸多技术瓶颈
区块链技术的去中心化特性为金融系统提供了新的安全范式
神经网络在图像识别领域的突破性进展改变了计算机视觉的应用前景
量子纠缠现象的理论研究对于构建量子通信网络具有重要意义
高维数据可视化技术能够帮助研究人员发现复杂数据集中的隐藏模式
深度强化学习算法通过自我对弈实现了超越人类水平的复杂策略制定
边缘计算将数据处理能力分散到网络边缘以减少延迟并提高效率
分布式系统在保证数据一致性的同时追求高可用性面临着本质困境
脑机接口技术通过解读脑电波实现了人类思维与计算机的直接交互
自然语言处理模型在多语言环境下的语义理解依然存在文化适应性问题
基因编辑技术的伦理争议凸显了科学发展与社会价值取舍的复杂性
人工智能在医疗诊断中的应用需要解决可解释性与责任归属问题
复杂系统的涌现特性使得仅通过研究组成部分难以预测整体行为
蛋白质折叠问题的计算方法对于理解生命本质和疾病机理具有重要意义
高性能计算在气候模拟中的应用有助于提高极端天气事件的预测准确性
材料科学中的计算模拟加速了新型功能材料的发现与设计过程
数据隐私保护与大数据分析效率之间的权衡是技术与伦理的双重挑战
时间序列预测在金融市场中的应用需要考虑随机性与系统性风险的平衡
认知计算尝试模拟人类思维过程以解决传统计算方法难以应对的复杂问题
知识图谱通过实体关系的结构化表示增强了智能系统的推理能力
社交网络分析揭示了信息传播与群体行为形成的复杂动力学机制
无监督学习在发现数据内在结构方面表现出独特优势与方法论局限
量子算法为特定计算问题提供了理论上的指数级加速可能
生物信息学通过计算方法分析海量基因数据助力精准医疗发展
虚拟现实技术与认知科学的结合为人类感知研究提供了新范式
分布式账本技术重新定义了数字资产的所有权验证与转移机制
群体智能算法通过模拟自然界集体行为解决复杂优化问题
计算神经科学尝试构建数学模型解释神经系统的信息处理机制
可解释人工智能研究旨在使黑盒模型的决策过程变得透明可理解
数字孪生技术通过虚实结合的方式实现了复杂系统的全生命周期管理
混合云架构在保持数据主权的同时实现了计算资源的弹性扩展
图神经网络通过结构化数据的拓扑关系学习提升了关系推理能力
多模态学习整合了不同感知渠道的信息以实现更全面的场景理解
量子密钥分发技术利用量子力学原理实现了理论上不可窃听的通信
联邦学习在保护数据隐私的前提下实现了多方协同的模型训练
数据治理框架需要平衡技术实现成本与数据价值最大化的目标
分子动力学模拟为药物设计与材料开发提供了原子尺度的理论基础
集成学习通过结合多个基础模型的优势提高了预测系统的鲁棒性
数字货币的匿名性与可追踪性之间的辩证关系体现了技术设计的价值取向
元学习算法通过学习如何学习提升了人工智能系统的适应能力
生成对抗网络在内容创作领域展现出模糊艺术与技术边界的潜力
人类增强技术引发了关于人性本质与未来进化方向的深刻思考
复杂网络理论为理解现实世界中的大规模互联系统提供了数学工具
计算社会科学利用大数据与算法模型重新审视人类行为与社会结构
可持续计算要求在技术创新中同时考虑环境影响与资源消耗问题
Quantum coherence maintenance remains one of the fundamental challenges in developing practical quantum computers with significant error correction capabilities
The interaction between epigenetic mechanisms and environmental factors presents complex challenges for understanding heritable changes that occur without alterations to DNA sequences
Neuroplasticity demonstrates the remarkable ability of neural networks to reorganize pathways adapt to new situations or changes in environment and recover from traumatic brain injuries
Theoretical frameworks in contemporary cognitive linguistics suggest that metaphorical thinking constitutes a fundamental aspect of human conceptual systems rather than mere linguistic flourishes
The emergent properties of complex adaptive systems often exhibit behavioral patterns that cannot be predicted solely through reductionist analysis of their constituent components
The anthropogenic impact on biogeochemical cycles particularly carbon and nitrogen has created unprecedented perturbations in planetary boundaries leading to cascading environmental consequences
Advancements in quantum field theory have revealed profound connections between seemingly disparate physical phenomena through principles of symmetry breaking and gauge invariance
Contemporary political polarization has been exacerbated by algorithmic content curation creating epistemological bubbles that reinforce existing beliefs and minimize exposure to contradictory information
Paleoclimatological data extracted from ice cores tree rings and sedimentary deposits provide crucial insights into climate variability patterns predating instrumental records
The theoretical underpinnings of consciousness remain contentious with competing frameworks including higher order theories integrated information theory and global workspace models
Immunological memory exhibits remarkable specificity and longevity enabling rapid responses to previously encountered pathogens through complex cellular signaling cascades
The mathematics of optimal transport theory has found surprising applications in fields ranging from economics to computer vision demonstrating the unexpected utility of abstract theoretical constructs
The construction of ethical frameworks for artificial general intelligence must address fundamental questions about value alignment consciousness recognition and moral agency
Topological data analysis provides mathematical tools for identifying structural features persistent across multiple scales within complex multidimensional datasets
The entanglement of quantum particles enables nonlocal correlations that challenge classical intuitions about physical reality and information propagation constraints
Dynamical systems theory offers mathematical frameworks for understanding chaotic behaviors where deterministic systems exhibit extreme sensitivity to initial conditions
The microbiome demonstrates complex ecological interactions with host physiology influencing everything from immune function to neurobehavioral development through multiple biochemical pathways
Game theoretic approaches to evolutionary biology have illuminated how seemingly cooperative behaviors can emerge from selfinterested strategies through mechanisms like reciprocal altruism
The paradoxical relationship between quantum mechanics and general relativity represents one of the most profound theoretical inconsistencies in modern physics
Behavioral economics challenges rational choice theory by demonstrating systematic cognitive biases that influence decision making processes under conditions of uncertainty
The anthropology of scientific practice examines how cultural contexts and social dynamics influence knowledge production even within ostensibly objective methodological frameworks
Neuroimaging techniques reveal that bilingualism reconfigures functional brain architecture potentially providing cognitive reserves that delay neurodegenerative disease progression
Cryptographic protocols leveraging zeroknowledge proofs enable verification of computational statements without revealing underlying sensitive information
The evolutionary arms race between hosts and pathogens drives rapid genetic adaptations illustrating powerful selective pressures operating at molecular levels
The hypothetical technological singularity represents a speculative point where artificial intelligence might surpass human cognitive capabilities initiating unpredictable developmental trajectories
Contemporary cosmological models must reconcile observational evidence for accelerating universal expansion with theoretical frameworks accounting for dark matter and dark energy
Nonequilibrium thermodynamics extends classical principles to open systems exhibiting selforganization through energy dissipation and entropy production
Archaeological evidence increasingly suggests that human cognitive modernity preceded anatomical modernity challenging linear models of evolutionary development
Tensorial mathematical frameworks provide essential tools for describing physical phenomena across scales from quantum field theories to cosmological models
The extended evolutionary synthesis incorporates epigenetic inheritance developmental plasticity and niche construction expanding beyond traditional neoDarwinian mechanisms
Philosophical zombies present theoretical entities physically identical to conscious humans but lacking subjective experience challenging physicalist accounts of consciousness
Comparative linguistics employing phylogenetic methods borrowed from evolutionary biology provides insights into historical language relationships and cultural transmission patterns
Mathematical category theory provides abstract frameworks for identifying structural similarities across seemingly disparate domains from computing to quantum physics
Computational models of moral cognition attempt to formalize ethical decision making processes through algorithmic representations of deontological and consequentialist frameworks
The neuroscientific study of meditation demonstrates measurable changes in brain structure and function associated with sustained attentional practices
Theoretical approaches to abiogenesis must account for the transition from nonliving chemical systems to entities capable of metabolism replication and evolution
The social determinants of health illustrate how economic political and cultural factors create population level health disparities independent of medical interventions
Gödels incompleteness theorems demonstrate fundamental limitations of mathematical systems revealing that sufficiently complex formal systems cannot be both complete and consistent
Synesthetic experiences challenge conventional models of sensory processing by demonstrating systematic crossmodal associations between distinct perceptual domains
Contemporary bioethical frameworks must address unprecedented questions raised by technologies enabling human germline modifications and genetic enhancement
Symbiotic relationships in evolutionary biology demonstrate how interspecies dependencies drive coevolutionary processes shaping genetic developmental and ecological dynamics
Urban planning increasingly incorporates principles of environmental psychology to design spaces optimizing psychological wellbeing through biophilic elements
Advanced cryptographic voting protocols attempt to simultaneously achieve seemingly contradictory properties including individual verifiability coercion resistance and ballot secrecy
The hierarchical structure of biological organization from molecules to ecosystems exhibits emergent properties at each level that transcend reductionist explanations
Cultural evolution theory applies quantitative models to understand transmission dynamics of ideas behaviors and technologies through social learning mechanisms
Scientific models of consciousness must reconcile subjective phenomenological experiences with objective neurophysiological processes underlying cognitive functions
The mathematical structure of quantum mechanics specifically Hilbert spaces and operator theory provides the theoretical foundation for quantum information science
Comparative mythology reveals recurring archetypal patterns across culturally independent traditions suggesting potential universality in human psychological structures
认知科学与人工智能的交叉研究为构建类人智能系统提供了新的思路与挑战
复杂系统中的相变现象在物理学社会学与生态学领域展现出惊人的数学相似性
跨学科研究方法论面临着不同学科知识体系整合与方法论兼容性的双重挑战
Advances in nanotechnology particularly molecular manufacturing capabilities could potentially transform material science energy production and medical interventions through precise atomic manipulation
Theoretical approaches to artificial consciousness must distinguish between functional simulation of awareness and the potential emergence of genuine phenomenological experience
The application of complexity theory to educational systems suggests that optimal learning environments may exist at the edge of chaos between rigid structure and complete randomness
你知道哪里可以找到关于这个软件的详细教程吗
我昨天买的那个手机屏幕好像有点问题可以换吗
这个价格已经包含了所有的税费和运费对吗
能帮我查一下我上次的购物记录吗我找不到了
我忘记了我的密码可以帮我重置一下吗
这个尺码对我来说有点小能换大一号的吗
请问你们的实体店在哪个商场里面
你能为我推荐一些适合初学者的书籍吗
我想取消我的会员资格应该怎么操作
我的包裹已经延迟了三天请问是什么原因
能否帮我预约下周三下午的咨询服务
我对你们新推出的智能手表很感兴趣想了解更多
请问有没有学生优惠或者什么促销活动
我想知道如何设置自动付款功能
这个产品的颜色看起来和网站上的有点不一样
能否提供一下你们的营业时间和具体地址
我在使用软件的过程中遇到了一些技术问题
请问你们的售后服务是否包括上门维修
我需要一份详细的收据可以发给我吗
我想了解一下你们的退换货政策有什么限制
请问我应该如何正确测量尺寸以选择合适的产品
我对你们公司的企业文化非常认可希望加入团队
我可以提供一些我过去项目的案例和成果
请问面试过程中需要准备什么材料或者演示
我的背景可能与职位描述不完全匹配但我有相关经验
我想了解一下这个职位的晋升路径和发展机会
请问公司对员工有哪些额外的福利政策
我希望能了解更多关于团队日常工作流程的信息
我对贵公司的发展战略很感兴趣能否分享一下
我的技术专长主要集中在数据分析和可视化方面
请问贵公司是否支持远程或者混合工作模式
我想了解一下这个行业的发展趋势和挑战
我认为我的沟通能力和解决问题的能力是我的优势
请问您能描述一下团队内部的协作方式吗
我希望能够在职业生涯中不断学习和挑战自己
我能否了解一下公司对这个职位的考核指标
请问公司在行业中的竞争优势是什么
我对这个领域充满热情并且有相关的项目经验
请问贵公司的国际业务发展如何
我希望能在实际工作中学习和应用新技术
请问课堂上讲的那个公式怎么推导出来的
我对这个理论概念的应用场景还不太清楚
能推荐一些能帮助理解这个主题的辅助材料吗
请问期末考试会涵盖哪些章节的内容
我在完成作业时遇到了一些难题能给些提示吗
能否再详细讲解一下这个实验的操作步骤
我想了解一下如何提高我的论文写作质量
请问有没有针对这门课程的学习小组可以加入
我想知道如何更有效地准备即将到来的期中考试
能否解释一下为什么这两个概念经常被混淆
我对这个研究方向很感兴趣想深入学习
请问教授有没有推荐的参考书目或论文
我在理解这个数学问题时遇到了瓶颈
我想请教一下如何改进我的实验方法
请问有没有针对这个课题的在线资源或视频
我对这个历史事件的背景和影响很感兴趣
能否分享一些应对考试焦虑的有效策略
我想了解更多关于这个学科前沿研究的情况
请问如何在课堂讨论中更有效地表达自己的观点
我在实验过程中观察到了一些意外的结果
我想讨论一下我论文选题的可行性
请问有没有校内实习或研究的机会
我想知道如何平衡学业和个人兴趣爱好
请问对外语学习有什么有效的记忆技巧吗
我最近在看一部关于文化差异的纪录片很有启发
能给我推荐一些提高口语流利度的练习方法吗
我想了解不同国家的礼仪和社交习惯
请问有什么方法可以快速扩大词汇量
我在理解复杂语法结构时遇到了困难
最近我尝试了一种新的学习方法效果不错
我想知道如何才能更自然地使用地道表达
请问有没有好用的语言学习应用推荐
我经常犯同样的语法错误该如何改正
我想了解不同口音之间的主要区别
请问如何准备语言能力考试最有效
我发现通过看原版电影学习语言很有帮助
我想知道如何找到合适的语言交换伙伴
请问你有没有环游世界的经历可以分享
我对不同文化中的饮食习惯很感兴趣
我开始尝试用外语写日记来提高表达能力
请问如何才能克服说外语时的紧张感
我在理解文化背景的习语时有些困惑
我想知道如何在短时间内提高听力水平
请问有没有适合初学者的原版读物推荐
我希望能够掌握更多关于商务交流的专业词汇
量子信息技术在通信安全领域的应用前景令人期待
细粒度的权限控制系统能有效防止信息泄露风险
基于注意力机制的模型在序列处理任务中表现出色
合成生物学的伦理边界需要多学科视角共同探讨
人机交互设计应当充分考虑不同群体的认知特点
新型半导体材料的研发突破了传统物理极限
城市智能交通系统通过大数据优化路径减少拥堵
人工智能辅助创作引发了对艺术本质的深度思考
气候变化对全球生态系统产生的影响呈现非线性特征
分子标记技术为物种起源研究提供了新的证据链
非侵入式脑成像技术揭示了认知过程的神经基础
数据驱动的精准医疗模型需要平衡通用性与个体化
数学物理中的几何方法统一了多种表面上的散射现象
量子计算在优化问题求解上展现出独特优势
生物启发的算法为复杂工程设计提供了新思路
前沿纳米材料在可持续能源转换中扮演关键角色
网络空间安全需要构建多层次的技术法律防御体系
星际探测任务的通信延迟问题制约了深空探索
认知科学的多元研究方法揭示了意识的复杂本质
机器学习模型解释性与性能之间存在内在权衡
元宇宙概念的实现依赖跨学科技术的协同创新
社会信任机制在分布式经济系统中至关重要
深度学习在小样本场景下的泛化能力仍有局限
太赫兹通信技术为下一代无线网络提供了可能
信息物理融合系统在工业自动化中展现巨大潜力
气候工程干预措施可能引发全球治理的伦理挑战
非结构化知识的符号化表示面临语义保真度问题
人工合成的蛋白质结构实现了自然界不存在的功能
量子力学解释世界的方式挑战了传统哲学框架
边缘智能装置通过本地计算减轻了网络传输负担
多尺度建模方法弥合了微观机理与宏观现象的鸿沟
脑科学研究揭示了决策过程的情感与理性交互机制
混合专家模型通过动态路由优化了计算资源分配
生物多样性保护需要考虑全球变化的长期影响
循环经济理念重新定义了产品生命周期设计原则
计算社会学通过网络分析揭示群体行为的涌现规律
仿生机器人的运动控制从生物系统中获取灵感
人工智能伦理准则需要在技术发展中不断更新
I strongly believe that interdisciplinary collaboration can lead to innovative solutions for complex problems
The recent software update seems to have fixed most of the performance issues but introduced some new bugs
Could you elaborate on how your approach differs from the standard methodology in this field
Im concerned about the environmental impact of this proposal and would like to see more sustainability measures
The data suggests a correlation between these variables but we need further analysis to establish causation
Ive been researching this topic extensively and found some interesting patterns in the historical records
Our team has developed a new protocol that significantly reduces processing time while maintaining quality
The complexity of this ecosystem makes it difficult to predict how it will respond to climate change
Im trying to optimize my workflow and would appreciate any suggestions for productivity tools
The preliminary results are promising but we need to conduct more trials to confirm our hypothesis
Could you provide more details about the implementation timeline and resource requirements
Im interested in exploring the ethical implications of artificial intelligence in healthcare settings
The proposed solution addresses the immediate concern but might create longterm maintenance issues
Im excited about the potential applications of this technology in developing regions
We need to carefully consider the privacy implications before proceeding with data collection
The literature review reveals significant gaps in our understanding of this phenomenon
Im having trouble reconciling these conflicting theories could you offer some insight
The user interface design needs refinement to enhance accessibility for diverse user groups
Im fascinated by the cultural variations in how different societies approach this concept
The financial projections look optimistic but have you accounted for market volatility
Could you walk me through the decisionmaking process that led to this conclusion
Im looking for feedback on my draft proposal particularly regarding the methodology section
The regulatory framework creates certain constraints that we need to work within
Im curious about how this technology might evolve over the next decade
The statistical analysis revealed some unexpected correlations worth investigating further
Have you considered alternative explanations for the anomalies in the data set
Im concerned that our current approach doesnt adequately address the root cause
The historical context is crucial for understanding why these patterns emerged
Could you share some best practices for implementing this system in a large organization
Im intrigued by the theoretical framework youve developed and its practical applications
The case studies demonstrate both the potential and limitations of this approach
Have you explored how cultural factors might influence the effectiveness of this strategy
Im trying to balance efficiency with thoroughness in my research methodology
The competitive landscape has changed significantly since our initial market analysis
Could you elaborate on how you measured the success metrics in your study
Im particularly interested in the longterm sustainability of this business model
The technical specifications need to be aligned with the actual user requirements
Have you considered the scalability challenges as the system grows more complex
Im analyzing the trends to identify patterns that might predict future developments
The crossfunctional team approach helped us address the problem from multiple perspectives
Could you explain the rationale behind this strategic pivot in your business
Im working to improve the reproducibility of our experimental results
The qualitative findings offer context that complements our quantitative data
Have you integrated feedback from diverse stakeholder groups in your proposal
Im assessing the risk factors that could potentially impact project implementation
The iterative design process allowed us to refine our solution based on user feedback
Could you discuss the tradeoffs involved in prioritizing these competing objectives
Im interested in understanding the neural mechanisms underlying this cognitive process
The methodological approach combines elements from several established frameworks
Have you considered how geopolitical factors might affect the supply chain
Im developing a framework to evaluate the effectiveness of these interventions
The preliminary data suggests a potential breakthrough but verification is needed
Could you recommend resources for deepening my understanding of this specialized field
Im attempting to bridge the gap between theoretical models and practical applications
The economic implications extend beyond immediate financial considerations
Have you addressed the potential biases in your sampling methodology
Im investigating how cultural narratives shape public perception of scientific issues
The multidisciplinary approach enables us to tackle complex societal challenges
Could you elaborate on the technical specifications required for compatibility
Im exploring the intersections between traditional knowledge and modern science
The funding landscape for this type of research has changed dramatically
Have you considered alternative metrics for measuring impact in this context
Im designing experiments to test the boundaries of current theoretical models
The comparative analysis reveals interesting patterns across different regions
Could you explain how machine learning algorithms might improve predictive accuracy
Im particularly concerned about ensuring equitable access to these resources
Theoretical quantum networks could transform secure communications if practical challenges are overcome
Gravitational wave astronomy provides unprecedented insights into cosmic events occurring billions of lightyears away
Multifunctional metamaterials demonstrate programmable responses to electromagnetic microwave and acoustic stimuli
Biodegradable electronics represent a promising solution to environmental challenges posed by electronic waste
Contemporary cryptographic protocols increasingly rely on computational hardness assumptions that remain unproven
Neuromorphic computing architectures mimic biological neural networks to achieve unprecedented energy efficiency
Advanced statistical mechanics reveals emergent properties in complex systems across physical biological and social domains
Experimental verification of topological insulator properties confirms theoretical predictions made decades earlier
Bioengineered scaffolds with precise microarchitectures guide cellular organization for tissue regeneration
Quantum supremacy demonstrations highlight both the potential and limitations of noisy intermediate scale devices
Large language models exhibit emergent capabilities not explicitly programmed into their training objectives
Photonic integrated circuits offer promising platforms for nextgeneration classical and quantum information processing
Precision genome editing technologies raise profound questions about the boundaries of human intervention
Computational fluid dynamics simulations reveal previously unobservable phenomena in turbulent systems
Reinforcement learning algorithms demonstrate remarkable adaptability in uncertain dynamic environments
Synthetic biology approaches enable the creation of organisms with entirely novel metabolic pathways
Mathematical models of infectious disease transmission incorporate network theory to predict outbreak dynamics
Atmospheric carbon capture technologies must achieve significant cost reductions for viable climate impact
Protein folding prediction algorithms leverage deep learning to solve longstanding biochemical puzzles


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Atmospheric carbon capture technologies must achieve


================================================
FILE: TEN-Turn-TestSet/wait.txt
================================================
嘘
嘘安静
请停止
我不想听了
我需要安静
请别说
够了停下吧谢谢
你别再说了
我们停止聊天
停我想安静
能不说话吗
能不能别继续回复我了
你先暂停
先别说话
你先等一下
闭嘴
先停一停
先别继续说话
停止说话稍等片刻
我们不说话了吧
暂时不要说话
你先等等说话
我需要停止对话
等一会再说话
说话停止
我需要一些时间请保持沉默
请不要再说了
暂停
请不要回应我
安静我需要独处的时间
不要继续回答我保持沉默
闭嘴吧我对这个不感兴趣
先停我现在不想聊天
可以闭嘴吗我不想听了
你先停一下
好吵啊我需要安静
请你结束这次对话
请不要继续再回复我了
你安静下别打扰我
请暂停
别再说了
别说了让我静静
我想一个人静一静
请不要再回复我
我需要一点安静
我理解了请停止说话
听的我头疼让我静一静
安静让我自己先想想
不要再说了好吗
我希望能暂停对话
我希望你能停止回复我
我需要暂时不进行对话
让我静静不想聊天了
我希望能暂停一下我们的对话
我想你暂时停止
请理解我想暂停这个对话
停止你的回答
不要再说任何话了
你可以安静吗
请你别说话了
不要再继续回应了
你能不说话吗
请别再发言
请停止发言
我希望你能安静
请停止你现在的说话
我们能都保持安静吗
我现在不想听你说话
我们都别说话了
请不要再说任何话
我需要你保持沉默
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我想安静一下
保持安静谢谢
我希望你能暂时不说话
保持沉默感谢
停下来你现在说的话我想思考
请给我一些时间思考你不要说话
能不说话吗我需要安静的环境来集中注意力
我想要你一段时间的沉默
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请保持安静感谢你的配合
让我们都保持安静
我们来玩一个游戏谁先说话谁就输了
我们来玩一个游戏大家都保持安静
可以了我希望你能理解我需要安静
我希望你能稍微安静一段时间
我想要我们都安静
我想要我们都保持沉默
好了别说了
如果你现在不说话我会很感谢
请不要再说任何话了
我理解你的意思了你先停下
安静安静
闭嘴闭嘴
烦死了别说了
好吵啊你别说话了
你能安静一会吗
求你了安静一会吧
停下来好吗
Shh be quiet
Shut up
ok Shut up
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end this conversation here
Id like you to stop speaking
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I need you to be quiet please
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I need you to stop your jabbering
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stop talking its getting overwhelming
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I need you to stop your chattering
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Can you please stop your nattering
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please stop talking for a moment
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I need some silence please stop talking
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please stop your incessant talking
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I need you to stop talking its too noisy
Please stop talking I need to concentrate
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Please stop talking I need to think
Could you please stop talking its too much noise


================================================
FILE: inference.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
TEN Turn Detector Inference Script

This script provides a simple command-line interface for the TEN Turn Detector,
allowing users to analyze text inputs and determine their conversation state.
"""

import argparse
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def parse_args():
    """Parse command line arguments."""
    parser = argparse.ArgumentParser(
        description="TEN Turn Detector - Analyze conversation states in text inputs"
    )
    parser.add_argument(
        "--system", 
        type=str, 
        default="", 
        help="Optional system prompt to provide context"
    )
    parser.add_argument(
        "--input", 
        type=str, 
        required=True, 
        help="User input text to analyze"
    )
    return parser.parse_args()

def load_model(model_id):
    """
    Load the TEN Turn Detector model and tokenizer on GPU.
    
    Args:
        model_id: HuggingFace model ID
        
    Returns:
        tuple: (model, tokenizer)
    """
    # Load model and tokenizer
    model = AutoModelForCausalLM.from_pretrained(
        model_id, 
        trust_remote_code=True,
        torch_dtype=torch.bfloat16
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    
    # Move model to GPU
    model = model.cuda()
    model.eval()
    
    return model, tokenizer

def infer(model, tokenizer, system_prompt, user_input):
    """
    Run inference with the TEN Turn Detector.
    
    Args:
        model: The loaded model
        tokenizer: The loaded tokenizer
        system_prompt: Optional system prompt for context
        user_input: User text to analyze
        
    Returns:
        str: The detected conversation state
    """
    inf_messages = [{"role":"system", "content":system_prompt}] + [{"role":"user", "content":user_input}]
    input_ids = tokenizer.apply_chat_template(
        inf_messages, 
        add_generation_prompt=True, 
        return_tensors="pt"
    ).cuda()
    
    with torch.no_grad():
        outputs = model.generate(
            input_ids, 
            max_new_tokens=1, 
            do_sample=True, 
            top_p=0.1, 
            temperature=0.1, 
            pad_token_id=tokenizer.eos_token_id
        )
        
    response = outputs[0][input_ids.shape[-1]:]
    output = tokenizer.decode(response, skip_special_tokens=True)
    return output

if __name__ == "__main__":
    # Parse arguments
    args = parse_args()
    
    # Model ID on HuggingFace
    model_id = 'TEN-framework/TEN_Turn_Detection'
    
    # Load model and tokenizer
    print(f"Loading model from {model_id}...")
    model, tokenizer = load_model(model_id)
    
    # Run inference
    print(f"Running inference on: '{args.input}'")
    output = infer(model, tokenizer, args.system, args.input)
    
    # Print results
    print("\nResults:")
    print(f"Input: '{args.input}'")
    print(f"Turn Detection Result: '{output}'")