Python Modern：按项目 Python 版本自动适配的现代编码规范
Python Modern 是 SkillHub 社区 Skill，坐标 @user_7b1db0fd/python-modern，v1.0.0，提供基于项目 Python 版本自动适配的现代 Python 编码规范，无需 API Key。文章说明其定位、可核实功能、通过 skillhub CLI 安装到 Cursor 等客户端 skills 目录的步骤，以及对话式开发中的典型用法与适用注意。官方页面：https://www.skillhub.cn/skills/user_7b1db0fd/python-modern
Read MorePython 代码检查助手：在 Agent 里做题目级 Python 批改与解法对比
Python 代码检查助手是 SkillHub 社区 Skill（@user_7d7d0447/python-code-checker，v1.0.0），在用户提交编程题目与 Python 代码时触发，用于核验对错、逐行纠错讲解，并提供更优解法对比。无需 API Key。本文说明定位、三项核心能力、通过 skillhub CLI 或安装文档装入 Cursor 等客户端 skills 目录的方法，以及「题目+代码+触发语」的典型对话用法与使用注意。
Read More小巧好用 一键整理桌面：用 Agent Skill 把桌面归档交给对话触发
介绍 SkillHub 社区 Skill「小巧好用 一键整理桌面」（@user_8cda13c6/desktop-file-sorter-ultra-77，v1.0.0）：面向 Windows 桌面，用 Python + tkinter 扫描、预览、按扩展名分八类整理，支持撤销与改路径，无需 API Key。说明 skillhub install 到 Cursor 等 skills 目录的方法、对话触发词，以及运行 desktop_organizer.py 的典型流程与注意事项。
Read More截图助手：在 Agent 对话里完成全屏与窗口截图
截图助手（@user_3cdbf8ea/screenshot-assistant）是 SkillHub 社区 Python Skill，v1.0.3，无需 API Key。通过 SKILL.md 与 scripts/tool.py 提供 capture_screen、capture_window，在 Windows 上用 ctypes 输出 BMP，并支持命令行自测。文章说明 skillhub 安装到 Cursor 等 skills 目录的方法、典型对话用法，并指出 SKILL.md 中部分能力与当前源码不一致及非 Windows 限制。
Read Morexmind-to-emmx.skill：把 XMind Zen 导图转成 eMindMap 的 EMMX
xmind-to-emmx.skill 是 SkillHub 社区 Skill，用 Python 3.6+ 标准库将 XMind Zen 格式（.xmind，content.json）转为 eMindMap 的 .emmx，仅保留文本与层级。包内含 SKILL.md 与 scripts/convert.py，命令为 python scripts/convert.py 输入.xmind [输出.emmx]。不支持旧版 content.xml、多 Sheet 仅首表、不保留样式与备注。无需 API Key，适合本地或 Agent 按文档调用。
Read Morepython学习：在 Agent 里做 Python 零基础教学与代码辅导
python学习（@user_aea26f67/python-ml）是 SkillHub 社区教育类 Skill，面向零基础用户，在对话中提供 Python 结构化教学、练习生成、代码审查与爬虫/数据分析/Web 等入门项目指导。标签 requires_api_key=false。安装可参考 skillhub.cn/install/skillhub.md，或用 skillhub install 并指定 Cursor 等客户端的 skills 目录。本文依据官方简介与 SkillHub 页面整理功能、安装与典型触发用语。
Read More墨羽（MOYU）：零信任 AI Agent 安全记忆工具包
墨羽是 SkillHub 社区 Skill @user_cac30d95/moyu（v2.10.1），封装 MOYU 纯 Python 安全记忆层：11 项防御、六层能力（检索、知识图谱、生命周期、跨会话桥接等），LLM 增强默认开启且无 Key 可降级。文章说明 Agent 痛点、skillhub 安装到 Cursor/Claude/Codex skills 目录的方法，以及 pip install moyu-memory、moyu quickstart/inject 等可复现用法与适用边界。
Read Morepython-script-generator：用 Skill 生成完整 Python 项目骨架
python-script-generator 是 SkillHub 社区 Skill（v1.0.0，坐标 @user_9f9aa44e/python-script-generator-pro），面向需要快速搭建 Python 工程结构的开发者。它支持 CLI、API、爬虫、Bot、Django 等类型，并生成配置、依赖、测试、README 与文档。本文基于 SkillHub 公开信息说明定位与能力，给出 skillhub CLI 与 Cursor/Claude Code/Codex 等 skills 目录的安装要点，以及对话中描述项目类型与需求的典型流程；并提醒社区来源、无 API Key 要求及生成代码仍需人工评审。
Read More全能本地文档处理专家：纯本地 Office 文档的 Agent Skill
全能本地文档处理专家（@user_734b3090/office-toolkit-local）是 SkillHub 社区 office-efficiency 类 Skill，v1.0.0，无需 API Key。支持在 Agent 中通过自然语言创建、编辑、转换和分析 xlsx、docx、pdf、pptx、csv、md，涵盖 Excel 公式图表、Word 中文排版模板、PDF 合并拆分水印加密、PPT、Markdown 转换、数据分析报告与批量处理。本文说明定位、核实功能、按 skillhub.md 用 CLI 安装到 Cursor 等 skills 目录，并给出触发词与使用注意。
Read More浏览器自动化脚本生成器：用自然语言产出 Playwright 风格 Python 脚本
浏览器自动化脚本生成器（@user_f28fdad8/oc-browser-auto）是 SkillHub 社区 dev-programming 类 Skill，v1.0.0，无需 API Key。它根据登录、导航、表单、爬虫等场景的操作描述，引导 Agent 输出基于 Playwright 语法模板的 Python（.py）脚本。安装可参照 skillhub.cn/install/skillhub.md，用 skillhub install 并 --dir 指向 Cursor 等客户端的 skills 目录。适合需要快速搭脚本又愿在本地补全参数与合规评估的开发者。
Read Moredatabase_skill：在 Agent 里统一连库、查表与跑事务
database_skill 是 SkillHub 上由 user_e71390e8 发布的社区开发类 Skill（v1.0.0），基于 Python 3.8+，支持 MySQL、PostgreSQL、Oracle、SQL Server 与 SQLite。提供连接管理、参数化查询、schema 自省与事务管理，标签 requires_api_key=false。文章说明其定位与核实功能，并给出按 skillhub.md 安装 @user_e71390e8/dbskill 及 Cursor 等客户端 skills 目录示例；公开页无详细 SQL 示例，用法以安装后 SKILL.md 为准，并提醒权限、驱动与生产库安全。
Read More抖音控制：用 Agent Skill 启动与关闭抖音桌面版
「抖音控制」是 SkillHub 社区技能 @user_2be6b945/douyin-control（v1.0.0），用于在用户说出打开、关闭抖音等意图时，通过 Python 脚本静默启动或强制关闭抖音桌面客户端，无需 API Key。文章说明技能定位、核心能力、skillhub 安装命令与各 Agent skills 目录，并给出自然语言触发示例与桌面版、强制关闭等使用注意。
Read More论文秒搜：用 Skill 做学术论文本地检索与导出
论文秒搜（@user_3cdbf8ea/paper-search，v1.0.4）是 SkillHub 社区发布的知识管理类 Python Skill，主打学术论文本地检索与导出，内置经典论文索引，纯 Python 实现且无需 API Key。文章说明其定位与已核实功能，并给出按 skillhub.md 安装 CLI、用 skillhub install 装入 Cursor/Claude Code/Codex 对应 skills 目录的步骤；具体命令以 SKILL.md 为准。
Read Moredouyin-downloader-skill：用 Agent 下载抖音无水印视频
douyin-downloader-skill 是 SkillHub 上的抖音无水印视频下载 Skill，原生 Python、无需 GUI，支持单个与批量下载。用户提到下载抖音、无水印或粘贴链接时由 Agent 按 SKILL.md 执行。本文介绍定位、核实功能、基于 skillhub.cn 安装文档的 CLI 与目录配置，以及对话式典型用法与合规注意。官方入口：api.skillhub.cn/user_24d6975e/douyin-downloader-skill。
Read Morepython全能编程助手：用 Agent Skill 统一 Python 题解与代码注释
python全能编程助手（@user_eceb0be6/python-all-helper）是 SkillHub dev-programming 类社区 Skill，版本 1.0.0，无需 API Key。包内 SKILL.md 将 Agent 约束为题解模式（思路、可运行代码、复杂度）与注释模式（中文 docstring 与逐步说明），并声明按输入自动切换。文章说明 skillhub CLI 安装命令、Cursor 等 skills 目录、典型触发示例，并注明当前包仅含 SKILL.md、与文档引用的脚本/模板文件不一致之处。
Read Morehuggingface-gradio: Let Agents Write Python Demos That Follow Gradio Specifications
huggingface-gradio is an Agent Skill from the Hugging Face skills repository, designed for creating or modifying Gradio Web UIs and ML demos with Python. The Skill includes core patterns such as Interface, Blocks, and ChatInterface, as well as component signatures like Textbox, Slider, and Image, and indexes the official guides on gradio.app. It can be installed with npx skills add. It is suitable for scenarios such as parameter debugging interfaces, chat demos, multimodal uploads, and temporary sharing; users must install gradio themselves and consult the official documentation for deployment and security.
Read MoreDeepSeek Harness Plugin: dsh-plugin-pyrun
`dsh-plugin-pyrun` is a Python code execution plugin for DeepSeek Harness (DSH), designed to address issues with Shell escaping, two-step execution overhead, and encoding anomalies. It executes code by piping input directly through `stdin`, and supports foreground/background modes, end-to-end UTF-8 handling, sandbox control, and job management. Installation requires using the `link:` method and a restart; the tool supports multiple parameter configurations and result markers.
Read Morexby-fastdomaincheck: A Bulk Domain Registration Status Checker Based on the MCP Protocol
`xby-fastdomaincheck` is a domain registration status check server based on Python and the MCP protocol, maintained by `xby-skill`. This plugin supports automatic API key management and bulk domain checking. Users must obtain the API key from the official website and install the plugin using the `dsh plugin` command. It is suitable for scenarios that require domain availability validation in agent development.
Read Moredsh-error-explainer: DSH Structured Error Stack Trace Parsing
The DSH plugin dsh-error-explainer is designed to address the challenge of analyzing error logs in multilingual environments. As a plugin for DeepSeek Harness, maintained by developer MkaliezZ, its core functionality is to structurally parse and extract metadata from unstructured stack traces in Python, Node.js, Rust, and Java, providing compact evidence for model inference. This plugin does not perform root-cause diagnosis or automatic repair; it serves solely as a data preprocessing tool. Installation requires obtaining the source code manually, and users should comply with the MIT License.
Read MoreDeepSeek Harness Plugin dsh-pypi-live: Real-Time Retrieval of the Latest PyPI Package Version
`dsh-pypi-live` is an online tool plugin in the DeepSeek Harness (DSH) ecosystem, designed to address the issue of outdated local package lists. Maintained by developer `uckkk` and released under the MIT license, the plugin is implemented in pure Node.js. After installation, it registers the `pypi_live` tool, which can query the latest version of a specified Python package on PyPI in real time over the network. Before use, it must meet the version requirements of `@deepseek-ai/cordis` and `@deepseek-ai/dsh-tools`, and the DSH process must have network access permissions.
Read MoreUse dsh-plugin-interpreters to let DeepSeek Harness run Python and Node code locally
### 版本1（正式科技文档译法，适配海外开发者语境）： The `dsh-plugin-interpreters` is a community plugin for DeepSeek Harness maintained by HuanLinOTO. It exposes two commands `run_python` and `run_node` to the model: it executes code via the native interpreters through stdin, returns the stdout, stderr and exit code, and allows configuring interpreter paths and timeout via the settings page. This article verifies the installation commands, capability boundaries and precautions based on the directory page, GitHub repository and npm package version 0.1.0. ### 版本2（更贴合插件开发者文档的简洁译法）： `dsh-plugin-interpreters` is a community plugin for DeepSeek Harness developed and maintained by HuanLinOTO. It provides `run_python` and `run_node` functionalities for the model: it runs code using local interpreters via stdin, returns stdout, stderr and exit code, and supports configuring interpreter paths and timeout in the settings page. This article checks the installation instructions, capability limits and precautions against the directory page, GitHub repo and npm package v0.1.0.
Read MoreFastAPI + Pydantic: Best Practices for Data Model Definition and Serialization
Combining FastAPI with Pydantic is an efficient combination for data processing in modern web development, where Pydantic focuses on data validation and serialization, and FastAPI provides high performance, automatic documentation, and asynchronous support. Base models are defined by inheriting from `BaseModel`, with field types specified via Python annotations. Fields without default values are required, while optional types are indicated using `| None` or `Optional`. Pydantic automatically validates types and formats, throwing detailed error messages for input mistakes. It also supports `Field` for custom constraints (e.g., length, range, regex). Models can be bidirectionally converted with dictionaries/JSON. In FastAPI, they can be directly used as request/response bodies, automatically validating request data and returning structured responses. Best practices include: using consistent naming styles for field aliases, handling complex structures with nested models, reusing code via model inheritance, and `extra="ignore"` to disregard unknown fields. Mastering these techniques enables robust data processing, reduces repetitive code, and enhances API reliability, making it suitable for quickly building efficient, type-safe web services.
Read MoreFastAPI Documentation Auto-generation: Tips for Using Swagger and OpenAPI
FastAPI's automatic documentation is based on the OpenAPI specification, providing interactive API documentation through Swagger UI and ReDoc. It can quickly display interface functions, parameters, and return values, supporting direct testing. Enabling it is simple: create a FastAPI application, and after running, access `/docs` (Swagger UI) or `/redoc` (ReDoc) to view the documentation. Core techniques include: setting global information (title, description, version, etc.) using FastAPI constructor parameters; using function annotations and the `Path`/`Query` utility classes to describe interfaces and parameters in detail; categorizing interfaces with `tags` for easy filtering; hiding internal interfaces with `include_in_schema=False`; using Pydantic models to standardize return formats, or `HTTPException` to mark error status codes. These methods can enhance document clarity and usability, avoid the trouble of manual writing and maintenance, ensure consistency between interface information and code, and optimize team collaboration and user experience.
Read MoreFlask and Frontend Frameworks: A Practical Guide to Combining Vue.js with Flask
### A Practical Summary of Combining Flask and Vue.js This article introduces the practical process of combining Flask (backend) with Vue.js (frontend) to achieve front-end and back-end separation development. The advantages of choosing both are clear division of labor (backend handles data, frontend is responsible for interaction), efficient collaboration, and flexible data interaction. For the development environment, Python/Flask (with the cross-domain tool flask-cors) and Node.js/Vue-cli (with the Axios data request tool) need to be installed. The project structure is divided into two independent directories: the back-end Flask and the front-end Vue. In the back-end setup, Flask provides API interfaces through `app.py` (e.g., `/api/users` to get user lists), running on port 5000 and returning JSON data. After creating the Vue project, the front-end uses Axios to request back-end data in `App.vue` and renders the user list with `v-for`. During testing, start the Flask back-end (`python app.py`) and the Vue front-end (`npm run serve`), then access `http://localhost:8080` to see the data obtained from the back-end. Common issues include cross-domain configuration, Axios path errors, and Vue rendering problems, which can be resolved through corresponding methods. Summary: This mode enables efficient collaboration between front and back ends. Future extensions could include user CRUD operations and...
Read MoreFlask Request Methods: Practical Handling of GET and POST Requests
This article introduces the lightweight Python Web framework Flask and HTTP request methods GET/POST. Flask is suitable for rapid development of web applications, with the installation command being `pip install flask`. GET is used to retrieve data (data is in the URL and easily leaked), while POST is used to submit data (data is in the request body and more secure). In practice, handling a login form with Flask: define the `/login` route to support GET/POST, where GET renders the form template and POST retrieves the username and password for verification and returns the result. Key knowledge points: the `methods` parameter of the route supports multiple request methods, `request.form` extracts form data, and `render_template` renders templates. Notes: Only POST requires changing `methods=['POST']`, sensitive data should use POST and HTTPS is recommended, and CSRF protection is necessary in production environments.
Read MorePython from Beginner to Mastery - Chapter 17 Project Practice
This chapter introduces Python project practice, including project planning and design, involving requirements analysis (functional and non-functional requirements), system design (such as MVC architecture), and technology selection (such as Flask); Project 1 is a personal blog system, implemented with Flask and other tools to achieve functions like user management and article management, as well as RESTful APIs; Project 2 is a data analysis platform, covering data processing, visualization, etc. It summarizes the improvement of technical skills and project management experience, provides optimization suggestions, and outlines the next learning directions.
Read MorePython from Beginner to Master — Chapter 16 Testing and Debugging
This chapter mainly introduces Python testing and debugging. It covers testing fundamentals, including test types (unit, integration, etc.) and TDD processes; frameworks like unittest and pytest, such as unittest assertions, pytest Fixtures, and parameterization; Mock technology for simulating dependencies; test coverage tools; debugging methods (print, logging, pdb, etc.); performance analysis tools (time, cProfile, etc.); examples of integration and end-to-end testing; and proposes testing best practices like the FIRST principle and test pyramid to ensure code quality and reliability.
Read MorePython from Beginner to Mastery - Chapter 15: Concurrent Programming
This chapter introduces Python concurrent programming, covering basic concepts (differences between concurrency and parallelism, GIL, task types), multithreading (threading module, daemon threads, synchronous communication), multiprocessing (multiprocessing module, process pools), asynchronous programming (asyncio module), as well as the concurrent.futures module and synchronization primitives. The appropriate concurrency method should be selected based on task types: multiprocessing for CPU-bound tasks, multithreading/asynchronous for I/O-bound tasks, and reasonable selection and optimization should be applied to enhance program performance.
Read MorePython from Beginner to Master - Chapter 14: Web Scraping and Automation
This chapter mainly introduces knowledge related to web crawling and automation, including the basics of web crawlers (definition, purposes, working principles, classification, laws and ethics), HTTP protocol basics (request and response, Cookie/Session), web page structure analysis (HTML, CSS, JS, encoding), crawler development environment (tools, proxy, user agent, debugging), network requests using the Requests library (basics, GET/POST, parameters and headers, response handling, etc.), and web page parsing with BeautifulSoup (basics, HTML/CSS parsing, data extraction, advanced operations, encoding handling).
Read MorePython from Beginner to Mastery — Chapter 13 Web Development
This chapter focuses on Python Web development, covering basic concepts such as web architecture, HTTP protocol, and the ecosystem. It compares frameworks like Flask (lightweight), Django (full-stack), and FastAPI (modern). The content includes Flask installation, basic application development, and deployment, as well as database operations (SQLite, SQLAlchemy), integration of frontend AJAX, design of RESTful APIs. Additionally, it addresses production deployment (Gunicorn, Nginx, Docker) and performance optimization (query optimization, caching, asynchronous processing, monitoring), helping readers master the entire web development process.
Read MorePython from Beginner to Mastery — Chapter 12: Data Science and Analysis
This chapter focuses on Python data science, covering the basics of data science, NumPy numerical computation, Pandas data processing, data visualization, statistical analysis, machine learning introduction, and project practice. It systematically learns core data science technologies, including data processing, visualization, statistical analysis, machine learning model construction and evaluation, to help master the full - process skills of data science.
Read MorePython from Beginner to Pro - Chapter 11 Network Programming
This chapter introduces Python network programming, covering the basics of network protocols (TCP/IP four - layer model, common protocols), Socket programming (implementation of TCP/UDP servers and clients), HTTP clients (usage of urllib/requests libraries), Web server development (http.server module), network data processing (serialization, compression, encryption), asynchronous network programming (asyncio), and network security (common threats and protections). It helps in building efficient and secure network applications.
Read MorePython from Beginner to Pro - Chapter 10: Advanced Python Features
This chapter delves into advanced Python features, including: list, dictionary, and set comprehensions, which are concise and efficient for quickly creating data structures; generators with lazy evaluation that save memory and support infinite sequences and pipeline processing; iterators providing a unified traversal interface; decorators for extending function functionality and achieving cross-cutting concern separation; closures enabling functional programming and state retention; and context managers ensuring proper resource management. These features embody Python's "Readability counts" philosophy and help in writing concise and efficient code.
Read MorePython from Beginner to Pro - Chapter 9: Modules and Packages
This chapter introduces Python's module and package system. A module is a file containing code, which can be categorized into types such as built-in and standard. It can be imported through various methods, with search paths and caching mechanisms in place. A package is a container for modules, including an __init__.py file, and can be structured hierarchically. There are both relative and absolute import methods. Third-party modules are managed using pip, and dependencies can be isolated through virtual environments. Commonly used third-party libraries like requests and numpy are also introduced, forming the foundation for building Python projects.
Read MorePython from Beginner to Pro - Chapter 8 Exception Handling
This chapter introduces Python exception handling, covering concepts such as exceptions (runtime errors in programs, divided into syntax errors and exceptions), hierarchy (based on BaseException), and common built-in exceptions. It focuses on explaining the try-except statement (basic syntax, catching specific/multiple/all exceptions), the finally clause (executed regardless of exceptions), and the else clause (executed when there are no exceptions). raise can be used to actively throw exceptions and create exception chains, and custom exception classes can be defined. It also adheres to best practices like EAFP, such as prioritizing specific exceptions, logging, and avoiding anti-patterns, to help write robust programs.
Read MorePython from Beginner to Mastery — Chapter 7: Object-Oriented Programming
This chapter introduces Python object-oriented programming, covering concepts such as classes and objects. A class is defined using the `class` keyword and can be instantiated. Attributes are divided into instance attributes and class attributes, while methods include instance methods, class methods, and static methods. The constructor `__init__` initializes objects, and the destructor `__del__` cleans up resources. Inheritance enables code reuse, and polymorphism reflects different implementations of the same interface. Encapsulation is achieved through access control and the `@property` decorator. Special methods customize object behavior, along with advanced features like class decorators and descriptors, which help in building efficient code.
Read MorePython from Beginner to Master — Chapter 6 File Operations
This chapter introduces Python file operations, including: file opening and closing using the `open()` function with different modes, emphasizing the automatic resource management via the `with` statement; file reading methods such as `read`, with chunked reading for large files; file writing methods such as `write`, with attention to the differences between modes; file pointer positioning using `tell` and `seek`; directory operations implemented through the `os` module and `pathlib`, involving path manipulation and traversal. Best practices such as following specified encodings and using the `with` statement are also covered.
Read MorePython from Beginner to Master — Chapter 5 Functions
This chapter provides an in - depth explanation of Python functions, covering function definition and invocation, such as defining functions with `def` and using docstrings for description. For parameter passing, it includes positional, keyword, and default parameters, as well as variable parameters `*args` and `**kwargs`. Regarding return values and scopes, it discusses single/multiple return values, local and global scopes, and the use of the `global` and `nonlocal` keywords. Recursive functions require understanding both the base and recursive cases. Lambda expressions are introduced for concisely defining anonymous functions. Additionally, built - in functions such as mathematical operations and type conversion are covered. Functions are a core part of programming, and mastering their usage is conducive to writing high - quality code.
Read MorePython from Beginner to Mastery — Chapter 4: Data Structures
This chapter introduces 5 built-in data structures in Python. Strings can be defined using multi - quotes and have a rich set of operation methods. Lists are ordered and mutable, supporting operations such as adding, deleting, modifying, querying, sorting, and reversing. Tuples are immutable and ordered, and can be used as keys in dictionaries. Dictionaries are key - value pair mappings, unordered and mutable. Sets store unique elements and support mathematical operations. When choosing a data structure, factors such as data properties, operations, and performance should be considered to write efficient and elegant code.
Read MorePython from Beginner to Mastery — Chapter 3 Control Flow
This chapter mainly introduces Python control flow, including conditional statements and loop statements. Conditional statements include single, double, and multi-branch structures, which can be combined with boolean, comparison, and logical operations, and have usage scenarios such as nesting and the ternary operator (e.g., leap year judgment). For loop statements, there are while and for loops: while loops have an else clause, and for loops can iterate over sequences (often combined with range). break is used to exit a loop, continue skips the current iteration, and pass is used for placeholder purposes. Additionally, the chapter covers applications such as nested loop pattern printing and data processing, while emphasizing performance optimization and the effective use of built-in functions to enhance efficiency.
Read MorePython from Beginner to Mastery - Chapter 2: Python Basic Syntax
This chapter introduces Python basic syntax, including: variable-related aspects such as naming rules (identifier composition, PEP 8 specifications, etc.), assignment references, and scopes; data types are divided into basic types (numbers, strings, etc.) and composite types (lists, tuples, etc.), supporting dynamic typing and type conversion; operators include arithmetic, comparison, logical, etc.; input/output functions include the usage and formatting of print and input; comments include single-line, multi-line, and docstrings, following PEP 8 code specifications, which form the foundation for subsequent learning.
Read MorePython from Beginner to Pro - Chapter 1: Python Environment Setup
This chapter mainly introduces Python environment setup and basic usage. First, it explains the Python installation and configuration methods for different operating systems (Windows, macOS, Linux); then it covers the writing of the first Python program (e.g., "Hello World"), running methods, and structure analysis; next, it introduces the Python interactive environment (including IPython and Jupyter Notebook); finally, it elaborates on the use of the pip package management tool, including installing, uninstalling, and upgrading packages, using requirements.txt files, configuring domestic mirror sources, and virtual environments.
Read MoreA Tool Website Developed with Python
This article introduces a feature-rich tool website developed using Python. It includes various tools such as document tools, PDF tools, image tools, audio tools, video tools, voice tools, and programming tools, which are commonly used in work or study.
Read MoreSpeaker Log Implementation Based on PyTorch (Speaker Separation)
This article introduces the speaker diarization feature of the VoiceprintRecognition_Pytorch framework implemented based on PyTorch, which supports various advanced models and data preprocessing methods. By executing the `infer_speaker_diarization.py` script or using the GUI interface program, audio can be speaker-separated and results displayed. The output includes the start and end times of each speaker and their identity information (registration is required first). Additionally, the article provides solutions for Chinese names in the Ubuntu system... （注：原文末尾“解决中文名”表述不完整，已保留原文未尽部分的省略格式，完整内容需参考原文后续章节）
Read MoreIntroduction and Usage of YeAudio Audio Tool
These classes define various audio data augmentation techniques. Each class is responsible for a specific data augmentation operation and can control the degree and type of augmentation by setting different parameters. The following is a detailed description of each class: ### 1. **SpecAugmentor** - **Function**: Frequency domain masking and time domain masking - **Main Parameters**: - `prob`: Probability of data augmentation. - `freq_mask_ratio`: Ratio of frequency domain masking (e.g., 0.15 means randomly selecting
Read MoreVoiceprint Recognition System Implemented Based on PyTorch
This project provides an implementation of voice recognition based on PaddlePaddle, mainly using the EcapaTDNN model, and integrates functions of speech recognition and voiceprint recognition. Below, I will summarize the project structure, functions, and how to use these functions. ## Project Structure ### Directory Structure ``` VoiceprintRecognition-PaddlePaddle/ ├── docs/ # Documentation │ └── README.md # Project description document ```
Read MoreSegmenting Long Speech into Multiple Short Segments Using Voice Activity Detection (VAD)
This paper introduces YeAudio, a voice activity detection (VAD) tool implemented based on deep learning. The installation command for the library is `python -m pip install yeaudio -i https://pypi.tuna.tsinghua.edu.cn/simple -U`, and the following code snippet can be used for speech segmentation: ```python from yeaaudio.audio import AudioSegment audio_seg ``` (Note: The original code snippet appears incomplete in the user's input; the translation preserves the partial code as provided.)
Read MoreECAPa-TDNN Speaker Recognition Model Implemented Based on PaddlePaddle
This project is a voiceprint recognition system based on PaddlePaddle. It covers application scenarios from data preprocessing, model training to voiceprint recognition and comparison, and is suitable for practical applications such as voiceprint login. Here is a detailed analysis of the project: ### 1. Environment Preparation and Dependency Installation First, ensure that PaddlePaddle and other dependent libraries such as `numpy`, `matplotlib`, etc., have been installed. They can be installed using the following command: ```bash pip install paddlepaddle ```
Read MoreAdding Punctuation Marks to Speech Recognition Text
This paper introduces a method for adding punctuation marks to speech recognition text according to grammar, mainly divided into four steps: downloading and decompressing the model, installing PaddleNLP and PPASR tools, importing the PunctuationPredictor class, and using this class to automatically add punctuation marks to the text. The specific steps are as follows: 1. Download the model and decompress it into the `models/` directory. 2. Install the relevant libraries of PaddleNLP and PPASR. 3. Instantiate the predictor using the `PunctuationPredictor` class and pass in the pre
Read MoreSound Classification Based on PyTorch
This code is mainly based on the PaddlePaddle framework and is used to implement a speech recognition system based on acoustic features. The project structure is clear, including functional modules such as training, evaluation, and prediction, and provides detailed command-line parameter configuration files. The following is a detailed analysis and usage instructions for the project: ### 1. Project Structure ``` . ├── configs # Configuration files directory │ └── bi_lstm.yml ├── infer.py # Acoustic model inference code ├── recor ``` (Note: The original Chinese text was cut off at "recor" in the last line, so the translation reflects the visible content.)
Read MoreImplementing Common Sorting Algorithms in Python
Thank you very much for sharing the implementations of these sorting algorithms. To provide a more comprehensive and understandable version, I will briefly explain each sorting algorithm and include complete code snippets. Additionally, I will add necessary import statements and comments within each function to enhance code readability. ### 1. Bubble Sort Bubble Sort is a simple sorting method that repeatedly traverses the list to be sorted, comparing two elements at a time and swapping them if their order is incorrect. After multiple traversals, the largest element "bubbles up" to the end. ```python def bubble_sort(arr): n = len(arr) for i in range(n): # Last i elements are already in place swapped = False for j in range(0, n-i-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] swapped = True # If no swaps occurred, the array is sorted if not swapped: break return arr ```
Read MoreDistance Measurement Using Binocular Cameras
This code demonstrates how to implement stereo vision depth estimation using the SGBM (Semiglobal Block Matching) algorithm in OpenCV, and then calculate 3D coordinates in the image. The following is a detailed explanation of the key steps and parameters in the code: ### 1. Preparation First, import the necessary libraries: ```python import cv2 import numpy as np ``` ### 2. Reading and Preprocessing Images Load the left and right eye images, and then (the original content was cut off here, so the translation stops at the beginning of the preprocessing step)
Read MoreImplementing a Simple Web Crawler with Python2
This project is a simple web crawler designed to scrape relevant content from CSDN blogs and save it as HTML files. It includes the basic process of a crawler: crawling, parsing, and storage. ### Crawling Process 1. **Scheduler (`spider_main.py`)**: - This is the entry point of the entire project. - It calls `HtmlOutputer` to output data, `Downloader` to download web page content, and `HtmlParser` to parse the downloaded content (parsing logic continues...).
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