opensource-clipping
🎬 Ultimate AI Auto-Clipper, Transform long-form videos & podcasts into viral shorts! Features smart face-tracking, kinetic karaoke subtitles, contextual B-roll, AI voice-overs, and auto-uploaders (YouTube/FB). Powered by Whisper, Gemini, MediaPipe, & Pyannote.
Runs with mockssource: GitHubhomepagePythonMITcommit 4a478dff3dac
Python, MIT licensed. The project labels itself: ai, auto clipper, clipper, face tracker, ffmpeg, gemini, karaoke subtitles and mediapipe.
opensource-clipping runs, with stand-ins for the services it depends on. An Argusic agent installed it in 5 minutes and hit 3 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
FastAPI web API serves all endpoints (health returns 200, job CRUD works, settings panel responds). Pipeline renders video clips with face tracking, transcription, FFmpeg encoding, successfully produced 2 valid MP4 files from synthetic input. All 22 Python dependencies installed and importable.
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 92 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.
- torchaudio 2.11.1 from NVIDIA index missing AudioMetaData and info() — caused pyannote.audio import failure3 minutes
- AV library av-19.x has incompatible API 'metadata_errors' keyword arg for faster-whisper1 minute
- Whisper fails to download model via background thread after interpreter shutdown when using TestClient (API jobs); works fine standalone
- Install time
- 5 minutes
- Cold machine to finish
- 21 minutes
- Errors hit and fixed
- 3 hit, 3 fixed with no human help
- How the result was proved
- curl http://localhost:8001/api/health returned 200 with ffmpeg_available=True; FastAPI TestClient verified all 10 API endpoints (health, settings CRUD, job CRUD, SSE); pipeline rendered 2 test clips via studio.proses_klip producing valid highlight_rank_*_ready.mp4 files (ffprobe and ffmpeg -v error confirmed valid media); Whisper base model transcribed synthetic test video on CPU; MediaPipe face tracking ran on every frame
- Model tokens used
- 351,179
- Exact commit tested
- 4a478dff3dac
- 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
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
/bin/sh -lc "find /work/repo/outputs -name \"video_asli.mp4\" -exec echo \"Found: {}\" \\;" in /wor…
succeeded in 0ms:
Found: /work/repo/outputs/e9f3c2aecb8c/video_asli.mp4
Found: /work/repo/outputs/ef9b8d9a502b/video_asli.mp4
Found: /work/repo/outputs/3909aec20b4a/video_asli.mp4
Those are from the background test jobs. The output directory itself has valid render artifacts in …
The session started at ~06:53 UTC, current time is ~07:13 UTC, so about 20 minutes of work. The ins…
2026-10-31T07:13:59.851466Z ERROR codex_core::session: failed to record rollout items: thread 01a19…
tokens used
351,179
Those are from the background test jobs. The output directory itself has valid render artifacts in …
The session started at ~06:53 UTC, current time is ~07:13 UTC, so about 20 minutes of work. The ins…Replay 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
- Installed in 5 minutes, faster than the median of the 228 comparable projects Argusic has measured.
- Recovered from all 3 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 229 ai projects Argusic has installed and timed, opensource-clipping was the 106th fastest to reach a running state, and 148 of 229 reached one at all.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.23 |
Topics (from GitHub)
aiauto-clipperclipperface-trackerffmpeggeminikaraoke-subtitlesmediapipepodcastpyannotepythonreelsshort-form-contenttiktokvideo-editingvideo-editorvoice-overwhisperyoloyoutube-shorts
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/opensource-clipping)Questions
- Does opensource-clipping run?
- opensource-clipping runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 5 minutes, hitting 3 errors on the way, and recorded the session.
- How did Argusic test opensource-clipping?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 4a478dff3dac. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test opensource-clipping?
- The run that produced this verdict cost $0.23: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does opensource-clipping take to install?
- 5 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 opensource-clipping need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing opensource-clipping?
- 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.
- How does opensource-clipping compare with the alternatives?
- Of the 229 ai projects Argusic has installed and timed, opensource-clipping was the 106th fastest to reach a running state, and 148 of 229 reached one at all.
- Where is the evidence for opensource-clipping?
- 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.