InterPilot
内测用户反映它可以作为不被检测到的AI面试助手—InterPilot captures audio from Windows input and output devices, transcribes the audio into text, and then calls an LLM API to generate responses. Some beta users have reported that InterPilot can help/assist with interviews and even cheat.
Stars: 88
InterPilot is an AI-based assistant tool that captures audio from Windows input/output devices, transcribes it into text, and then calls the Large Language Model (LLM) API to provide answers. The project includes recording, transcription, and AI response modules, aiming to provide support for personal legitimate learning, work, and research. It may assist in scenarios like interviews, meetings, and learning, but it is strictly for learning and communication purposes only. The tool can hide its interface using third-party tools to prevent screen recording or screen sharing, but it does not have this feature built-in. Users bear the risk of using third-party tools independently.
README:
本项目是一个基于 AI 的助手工具,能够从windows的输入输出设备中捕获音频,将音频转为文字后,再调用 LLM(大语言模型) API 给出回答。项目主要包括录音、转写和 AI 回答三个模块,旨在为个人的正当学习、工作、科研提供辅助支持。
部分内测用户反映,本工具可能可以在面试、会议、学习等场景中提供一定的帮助,比如在在线会议软件中作为AI面试工具辅助面试:获取面试官的音频然后得到回答,但是请注意:本工具仅供学习交流使用,不得用于任何不正当用途。
经测试,本工具能够借助第三方工具隐藏界面以防止被录屏软件、屏幕共享等功能录制到,但工具本身不具备隐藏界面的功能。是否使用第三方工具与作者无关,风险由用户自行承担。
如果对你有所帮助,可以通过微信扫码打赏,感谢你的支持!

-
音频捕获
使用 LoopbackRecorder 从系统录制音频(支持 loopback 设备),并保存为 WAV 文件。 -
语音转写
基于 Whisper 模型在本地进行音频转写,支持多种模型规格(默认使用base模型)。 -
AI 辅助回答
通过调用 LLM API(配置在config.ini中)对转写后的文本进行分析,生成回答。支持流式返回并实时更新界面。 -
图形用户界面
基于 PyQt5 构建的简洁 GUI,支持录音、转写、发送文本至 LLM 等操作,并对 LLM 回复支持 Markdown 渲染。
C:.
│ config.ini
│ logo.png
│ main.py
| main_cmd.py
| README.md
│ requirements.txt
│
├── output
└── src
│ audio_capture.py
│ llm_client.py
│ transcriber.py
│ __init__.py
│
└── utils
│ config_loader.py
│ __init__.py
-
config.ini
配置文件,包含 API 接口地址、API key、使用的模型、设备索引、默认提示词等参数。 -
logo.png
应用程序图标(用于 GUI 窗口)。 -
main.py/main_cmd.py
程序入口,负责启动图形界面和整体工作流程。 -
output/
存放录音文件。 -
requirements.txt
列出项目依赖的 Python 包(例如 PyQt5、markdown2、whisper、openai 等)。 -
src/
存放核心模块:-
audio_capture.py:音频录制模块。 -
transcriber.py:语音转写模块。 -
llm_client.py:调用 LLM API 的客户端。 -
utils/:包含一些工具类和配置加载模块。
-
-
FFmpeg
本项目依赖 FFmpeg 进行部分音频处理,请确保已正确安装并配置环境变量。-
安装方法示例:
- Windows 用户:
- Mac 用户可使用 Homebrew 安装:
brew install ffmpeg
- whisper项目提到
You may need rust installed as well,所以需要可能安装rust(但不安装好像没事儿,建议先不装,如果transcriber.py不能正常运行再参考Whisper )
-
安装方法示例:
建议使用miniconda或者anaconda创建虚拟环境(建议安装 Python 3.10版本):
conda create -n interview python=3.10
conda activate interview然后使用以下命令安装项目所需依赖:
pip install -r requirements.txt请根据实际情况修改根目录下的 config.ini 文件,其中包括:
- API_URL:LLM API 的地址。
- API_KEY:访问 API 的密钥。
-
MODEL:调用的模型名称(例如
deepseek-ai/DeepSeek-R1-Distill-Qwen-7B,其他模型名称可以访问硅基流动(官网链接)-模型广场查看。 - SPEAKER_DEVICE_INDEX 与 MIC_DEVICE_INDEX:录音设备的索引,视具体系统配置而定。建议阅读录音设备索引和注意事项部分。
- OUTPUT_DIR:存储录音文件的目录。
-
WHISPER_MODEL_SIZW:whisper模型的大小,可选项为tiny
base、small、medium、large、turbo。 - DEFAULT_PROMPT:是拼接在发送给 LLM 的文本最前端的默认提示词,可根据使用场景调整,例如“你是一个XX方面的专家,你马上获取到的文本来自于XX,请你据此给出合理简洁的回答:”
- 建议注册硅基流动(官网链接)获取
API_KEY,新用户受邀可获取14元额度(邀请码TzKmtDJH),足够用一段时间了 - 官网左侧菜单栏-API秘钥-新建API秘钥-获取一段形如
sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx的长字符串替换config.ini里的API_KEY即可 -
使用其他支持OpenAI API的服务也可以,只需替换
API_URL和API_KEY即可(还是建议使用siliconflow,工具默认使用的deepseek-ai/DeepSeek-R1-Distill-Qwen-7B模型完全免费,白嫖万岁!)
- 默认
SPEAKER_DEVICE_INDEX置为了-1,这会自动寻找可用的默认wasapi_loopback设备,一般录制的就是你的目前的扬声器(耳机)听到的声音,但如果出现问题,建议手动运行audio_capture.py查看全部可用设备后,手动指定正确的设备。你也可以通过修改这个参数使得录制的是麦克风输入的声音。
python src/audio_capture.py项目各核心模块(录音、转写、LLM 客户端)均包含简单的测试代码。你可以分别运行下列文件,检查各功能模块是否正常运行:
-
src/audio_capture.py—— 用于实现音频录制功能(能够打印出系统中的音频设备列表)。 -
src/transcriber.py—— 用于实现音频转写功能(首次运行会自动下载模型)。 -
src/llm_client.py—— 用于实现 LLM 客户端功能(调用 LLM API 并返回回答)。
运行 main.py 启动完整的面试助手 GUI:
python main.py在 GUI 中你可以依次进行以下操作:
- 开始录音:点击“开始录音”按钮,程序将自动生成唯一的录音文件名并开始录制音频。
-
结束录音:点击“结束录音”按钮结束录音,录音文件保存在
output目录中。 - 转写文字:录音结束后(或手动点击),调用转写模块,将录音转为文字并显示在界面上。
- 发送给 LLM:转写完成后,可以将文字发送至 LLM,生成 AI 回答,并在界面上显示支持 Markdown 格式的回复。
- 修改转写文字并发送给 LLM
如果你想在终端中运行,可以使用 main_cmd.py:
python main_cmd.py-
录音设备:根据设备不同,可能需要调整
config.ini中的SPEAKER_DEVICE_INDEX和MIC_DEVICE_INDEX参数。 默认设置下,因为录制的是扬声器(你听到)的声音,所以在没有声音播放的时候,是不会录制的,所以必须播放一些音频或者视频,才能获取到音频。测试的时候可以放个视频。 - 环境变量:确保 FFmpeg 已安装并已添加到环境变量 PATH 中,否则可能会影响音频处理。
- 测试验证:建议先单独测试各模块,确认音频录制、转写和 LLM 回答均正常后再启动 GUI 整体运行。
使用shalzuth/WindowSharingHider隐藏UI界面————太棒的工具了!又方便又好用!
任务栏中图表的隐藏:
- 直接使用windows自带的任务栏隐藏功能,或者干脆把任务栏移到第二个显示器
- 使用一些隐藏工具(可以自己找一下)
使用turbotop可以使得窗口始终置顶————也是很好用的工具
-
注意一下使用顺序不然可能会出现问题:
- 先使用turbotop使得窗口置顶
- 再使用WindowSharingHider隐藏UI界面
- 如果不太就行就换一下顺序多试几下
- [ ] 在README中增加详细的使用案例或截图(GUI 操作示例、终端输出示例等)。
- [ ] 增加voice_generate功能(TTS)——已经测试好,待集成
- [ ] 增加麦克风扬声器音频共同识别功能
- [ ] 增加截图、上传LLM功能
- [ ] 任务栏中的图标隐藏功能
欢迎社区开发者提交 issue 或 pull request,一起完善这个 工具。如果有任何建议或改进意见,请随时联系。
本项目仅供技术学习与研究交流之用,严禁用于以下用途:
- 任何形式的求职面试作弊行为
- 侵犯他人隐私或商业秘密
- 违反当地法律法规的行为
使用者应对自身行为负全部法律责任,作者不承担任何因滥用本项目导致的直接或间接后果。使用即表示您已阅读并同意本声明。
本项目采用 Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) 许可证进行开源。
这意味着您可以自由地共享和修改本项目的内容,但仅限于非商业用途。
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LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.
Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.
minio
MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
mage-ai
Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.


