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Capite turns an existing video into editable, styled captions on a machine or server you control. Its documented pipeline uses faster-whisper to transcribe speech, lets you correct the word-level transcript and caption styling, then builds ASS subtitles and renders them into video with FFmpeg and libass. The project recommends Docker Compose for a quick start; it also documents a local development setup. These are project-documented capabilities, not independently tested results.
What Capite does—and what it does not
Capite is a free, MIT-licensed, self-hosted animated subtitle studio. It is designed to caption footage you have already selected or edited, including short-form videos, podcasts, and longer videos. It is not an automated clip-selection tool. The Capite repository describes the workflow and features.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe project says processing can run offline after installation and model weights have been downloaded. Setup and model acquisition therefore require network access, but the documented caption processing does not require an external API key or ongoing network connection. This is a description of the project’s design, not a security audit or independently verified privacy guarantee.
How the transcription and rendering pipeline works
Capite uses faster-whisper, a CTranslate2-based reimplementation of OpenAI Whisper, to recognize speech. Its backend turns the transcript into ASS subtitle scripts using pysubs2; FFmpeg with libass renders those captions into the video. The project says the transcript is editable and that changing transcript text or style can be followed by a new render without repeating transcription. Recognition errors may still need manual correction.
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- Transform audio playing via your speakers and headphones
- Improve sound quality by adjusting it with effects
- Take control over the sound playing through audio hardware
There is an important FFmpeg distinction: Capite uses system FFmpeg with libass for video rendering. The faster-whisper project says its audio decoding uses PyAV, which bundles FFmpeg libraries, so faster-whisper itself does not require system FFmpeg. See the faster-whisper documentation and FFmpeg documentation; the latter tracks the newest revision, so users of older releases should check documentation for their installed version.
Choose a setup route
Docker Compose: the documented quick start
The Capite README recommends Docker Compose as the quickest route. Follow the current Compose instructions in the repository, since commands, configuration, and prerequisites may change. This route packages the documented application services rather than asking you to start each development service separately.
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Local development: run the frontend and backend separately
The project’s manual setup lists Python 3.11 or later, Node.js 20 or later, npm, and system FFmpeg with libass support. The README starts the Flask backend and Next.js frontend separately. Use its current commands and configuration rather than assuming defaults from another release; confirm that the FFmpeg build available to the application includes libass.
The README describes an architecture with a frontend studio, backend API and worker, faster-whisper transcription, pysubs2 subtitle construction, and FFmpeg/libass rendering. For the library’s audio decoding, faster-whisper uses PyAV; that does not remove Capite’s separate FFmpeg rendering prerequisite.
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Turn an existing video into captions
- Open the Capite studio. Start it through the configured Docker Compose deployment or the separately launched local frontend and backend, as described in the README.
- Select footage. The project describes selecting MP4, MOV, or WEBM files. Its stated defaults are a 500 MB upload limit and a 30-minute duration limit; these are configurable instance defaults, not universal guarantees. Deployment settings, file support, and practical throughput can vary.
- Choose a caption style and position. The repository advertises 27 motion styles. This is a project-stated count, not an independent verification of every style in every version.
- Generate captions. Capite sends audio through its faster-whisper transcription pipeline and creates timed subtitle text. Wait time depends on the model, hardware, and footage; the upstream faster-whisper benchmarks are not Capite performance estimates.
- Correct the transcript. Review recognized words and punctuation, and edit mistakes in the word-level transcript before rendering. Automatic speech recognition can mishear names, accents, noisy speech, or overlapping voices.
- Adjust styling and render. Refine caption appearance and placement, then render the video. If only text or styling changed, the project documents rerendering without running transcription again.
- Download the result or subtitle files. The README lists a rendered MP4 and SRT, VTT, TXT, and ASS exports. Check the current repository and your deployment for the exact formats available.
What performance figures do—and do not—tell you
The faster-whisper README reports results for its own benchmark setups, not for Capite. For example, it reports processing 13 minutes of audio with faster-whisper v1.1.0 large-v2 in 1 minute 3 seconds at 4,525 MB VRAM on an NVIDIA RTX 3070 Ti 8 GB using CUDA 12.4. With batch size 8, that benchmark reports 17 seconds and 6,090 MB. On an Intel Core i7-12700K using eight threads, the README reports 2 minutes 37 seconds and 2,257 MB RAM for the small model in fp32. These figures are workload- and setup-specific; they do not predict Capite’s end-to-end rendering time or establish a minimum computer specification.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How Capite fits among captioning tools
Capite’s project-authored comparison frames it as a local, self-hosted captioning and styling component for footage already in hand. When choosing a tool, useful distinctions include whether processing is local or hosted, whether an account or subscription is involved, whether word timing is editable, which animation and styling controls are available, which subtitle formats can be exported, and whether the tool also selects or edits clips.
The repository names Submagic, CapCut, and OpusClip as comparison points, but their capabilities and prices change; this guide does not make a current feature or price ranking. Another Docker-based caption project using faster-whisper and FFmpeg exists, but the available documentation does not establish a complete head-to-head comparison: AI Video Captions repository.
Quick Recap
Practical checks before relying on a deployment
- Confirm the current Capite setup instructions, configuration, and instance upload and duration limits in the project README.
- For local development, verify the installed Python, Node.js, npm, and FFmpeg/libass prerequisites against the versions and instructions you plan to use.
- Test a representative video in your deployment, including a transcript correction and a rerender, before building a workflow around a format or style.
- If offline use matters, obtain software and model weights first, then verify the workflow in your own environment; the repository’s offline claim is not an independent privacy assessment.
- Judge runtime using your own footage and machine. Upstream benchmarks describe named hardware and model settings, not a Capite service-level guarantee.
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