transcript 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
This is a python API which allows you to get the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles and it does not require an API key nor a headless browser, like other selenium based solutions do!
Self-hosted AI audio transcription
收集户晨风的所有内容
一条命令，把口播毛片变成可二次精修的剪辑工程。
一个React组件，使自动转录音频和视频的更简易更快。由BBC新闻实验室报道。- 进行中作品
Portable AI-agent skill: turn YouTube, Instagram, TikTok, X and other video links into Obsidian-ready Markdown notes: captions or local Whisper transcripts, frames, metadata and timestamps
openSIS 是 OS4ED 推出的一款商业级、安全、可扩展且直观的学生信息系统和学校管理软件。具备在一次安装中运行单个或多个机构的所有功能。基于 Web、PHP 代码、MySQL 数据库。
Show the subtitle as long as you speak
Python library to parse, format, validate, normalize, and map sequence variants according to HGVS Nomenclature (https://hgvs-nomenclature.org/).
Export Discord channel chats to HTML transcripts with your discord.py bot or compatible fork.
将14+个外部代理聊天历史记录（Claude Code、Codex、ChatGPT、Cursor、Gemini、Reasonix、opencode、ZCode、Grok Build、OpenClaw、Pi、Hermes、Kimi CLI、DSH）导入DeepSeek收件箱作为可搜索会话-全保真、反向输出/同步、捆绑包备份。| 从 Claude Code、Codex、Reasonix 等 Agent 工具导入历史信息到 DeepSeek并继续对话。
Simple Google Meet transcripts. Private and open source. Zoom and Teams transcripts in beta.
A modern Next.js-based tool for AI-powered YouTube video summarization. Features smart chapter detection with clickable timestamps, multi-language support (EN, DE, FR, ES, IT), visual chapter timelines, and full transcript access with markdown export.
孙学，零成本安装。孙宇晨两期访谈（邵艾伦、金明）的带时间戳全文，能动手的观点每个配一篇 AI 教程，工具都有国内能用的版本
Website for viewing and exporting full transcripts of Apple Podcasts
已上架 Chrome / Edge 商店 🚀 把 B 站视频变成学习资源的浏览器扩展：字幕阅读、双语对照、AI 章节概览、划词解释和带时间戳的笔记
YouTube Transcript Plus is an advanced Node.js package designed to fetch and process YouTube video transcripts (captions).
▶️📃 Interactive transcript plugin for video.js
Reelpack — 给 AI 的视频理解引擎 / The Video-Understanding Engine for AI · License: FSL-1.1-MIT
Desktop app for recording meetings from locally running apps and transcribing and summarizing them with a local LLM