YouDub-webui
Open-source AI video localization and dubbing for YouTube/Bilibili: speech recognition, subtitle translation, voice cloning, audio mixing and rendering. 开源 AI 视频翻译配音工具。
Runssource: GitHubPythonApache-2.0commit 0f6c75935e7a
Python, Apache-2.0 licensed. The project labels itself: ai dubbing, ai video translation, bilibili, demucs, fastapi, ffmpeg, multilingual video and nextjs.
YouDub-webui runs. An Argusic agent installed it in 8 minutes and hit 3 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Backend API is fully operational: uvicorn serves the FastAPI app on port 9998, all 421 backend tests pass, and every major endpoint (health, login, tasks, settings, cookies) responds correctly with expected status codes.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- diffq failed to build from source (missing Python.h, no root for python3-dev)2 minutes
- Node.js v18.19.1 too old for Next.js 16 (requires >=20.9.0) and vitest 4 (requires >=22)1 minute
- socksio missing for SOCKS proxy test1 minute
- Install time
- 8 minutes
- Cold machine to finish
- 20 minutes
- Errors hit and fixed
- 3 hit, 3 fixed with no human help
- How the result was proved
- pytest backend/tests -q returned 421 passed, 1 warning; curl http://localhost:9998/api/health returned 200; curl /api/auth/login returned 200 with session token; authenticated /api/tasks, /api/settings/openai, /api/settings/ytdlp, /api/cookies/youtube all returned 200
- Model tokens used
- 251,634
- Exact commit tested
- 0f6c75935e7a
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
INFO: 127.0.0.1:49174 - "GET /api/settings/ytdlp HTTP/1.1" 200 OK
YTDLP: {"proxy_port":""}
INFO: 127.0.0.1:49186 - "GET /api/cookies/youtube HTTP/1.1" 200 OK
COOKIES: {"exists":false,"size":0,"updated_at":null,"content":""}
INFO: 127.0.0.1:38694 - "GET /api/health HTTP/1.1" 200 OK
INFO: Shutting down
exec
/bin/sh -lc "echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 8…
succeeded in 0ms:
{"install_succeeded": true, "launch_succeeded": true, "install_minutes": 8, "errors": [{"msg": "dif…
tokens used
251,634Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 8 minutes.
- Recovered from all 3 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 8 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
ai-dubbingai-video-translationbilibilidemucsfastapiffmpegmultilingual-videonextjsopen-sourcepythonspeech-to-textsubtitle-translationtext-to-speechvideo-localizationvideo-translationvoice-cloningvoxcpmwhisperyoutubeyoutube-translator
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/youdub-webui)Questions
- Does YouDub-webui run?
- Yes. YouDub-webui runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 3 errors on the way, and recorded the session.
- How did Argusic test YouDub-webui?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 0f6c75935e7a. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test YouDub-webui?
- The run that produced this verdict cost $0.09: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does YouDub-webui take to install?
- 8 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does YouDub-webui need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing YouDub-webui?
- 3 things broke in the recorded run, and 3 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for YouDub-webui?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.