[{"data":1,"prerenderedAt":142},["ShallowReactive",2],{"page:\u002Farticles\u002Fwhisper-subtitle-notebook":3},{"id":4,"title":5,"author":6,"body":10,"cover":128,"date":129,"description":130,"extension":131,"head":132,"layout":135,"meta":136,"navigation":137,"path":138,"seo":139,"stem":140,"__hash__":141},"pages\u002Farticles\u002F42.whisper-subtitle-notebook.md","A Notebook Workflow for Whisper Subtitles",{"name":7,"avatarUrl":8,"link":9},"Likun Cai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F103620968?v=4","https:\u002F\u002Fgithub.com\u002FAlchemist-Aloha",{"type":11,"value":12,"toc":126},"minimark",[13,17,25,36,99,110,113,122],[14,15,5],"h1",{"id":16},"a-notebook-workflow-for-whisper-subtitles",[18,19,20,24],"p",{},[21,22,23],"code",{},"whisper_subtitle"," is a Jupyter Notebook workflow based on the original OpenAI Whisper Python package. It documents the earlier local transcription path in this repository collection and credits the AudioToText project it was built from.",[18,26,27,28,31,32,35],{},"The documented Windows environment uses Python 3.12 through ",[21,29,30],{},"uv",", FFmpeg on ",[21,33,34],{},"PATH",", and a hardware-appropriate PyTorch build:",[37,38,43],"pre",{"className":39,"code":40,"language":41,"meta":42,"style":42},"language-bash shiki shiki-themes github-light github-dark","uv venv --python 3.12\n.venv\\Scripts\\activate\nuv pip install -r requirements.txt\nuv pip install ipykernel\n","bash","",[21,44,45,64,70,87],{"__ignoreMap":42},[46,47,50,53,57,61],"span",{"class":48,"line":49},"line",1,[46,51,30],{"class":52},"sScJk",[46,54,56],{"class":55},"sZZnC"," venv",[46,58,60],{"class":59},"sj4cs"," --python",[46,62,63],{"class":59}," 3.12\n",[46,65,67],{"class":48,"line":66},2,[46,68,69],{"class":52},".venv\\Scripts\\activate\n",[46,71,73,75,78,81,84],{"class":48,"line":72},3,[46,74,30],{"class":52},[46,76,77],{"class":55}," pip",[46,79,80],{"class":55}," install",[46,82,83],{"class":59}," -r",[46,85,86],{"class":55}," requirements.txt\n",[46,88,90,92,94,96],{"class":48,"line":89},4,[46,91,30],{"class":52},[46,93,77],{"class":55},[46,95,80],{"class":55},[46,97,98],{"class":55}," ipykernel\n",[18,100,101,102,109],{},"The repository explicitly notes that original Whisper is slower than whisper.cpp or faster-whisper. Its own newer path is ",[103,104,108],"a",{"href":105,"rel":106},"https:\u002F\u002Fgithub.com\u002FAlchemist-Aloha\u002FExplicitUtil",[107],"nofollow","ExplicitUtil",", which uses whisper.cpp and asynchronous processing. Start there for a maintained batch CLI; use this notebook when reproducing or studying the older Python pipeline.",[18,111,112],{},"GPU installation commands are device- and driver-specific. Do not copy a CUDA-index URL blindly: select the PyTorch build that matches the local platform, then verify inference on a short audio sample before opening a large video collection.",[18,114,115,116,121],{},"Visit the ",[103,117,120],{"href":118,"rel":119},"https:\u002F\u002Fgithub.com\u002FAlchemist-Aloha\u002Fwhisper_subtitle",[107],"whisper_subtitle repository"," for the notebook, dependencies, and historical workflow.",[123,124,125],"style",{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":42,"searchDepth":66,"depth":66,"links":127},[],"\u002Farticles\u002Fsubtitle-pipeline-cover.webp","2026-08-12T00:42:00.000Z","Revisit a Jupyter-based video transcription workflow built around the original OpenAI Whisper implementation.","md",{"title":133},{"whisper_subtitle":134},"The Original Jupyter Transcription Workflow","page",{},true,"\u002Farticles\u002Fwhisper-subtitle-notebook",{"title":5,"description":130},"articles\u002F42.whisper-subtitle-notebook","bFaajmUh4xHKY7-T1wDBY5Sp3oDCT_fAMrCxt2SRnwA",1786980373676]