omni-transcriber

Transcribe anything from the command line: a YouTube link, an Instagram reel, any yt-dlp-supported URL, a local video or audio file, or a whole folder of media. Transcription runs locally via faster-whisper — no API key, no audio leaves your machine.

$ transcriber https://youtu.be/jNQXAC9IVRw
Alright, so here we are, one of the elephants...

$ transcriber lecture.mp4 -f srt -o lecture.srt
$ transcriber ~/podcasts -r -m turbo -o transcripts/

Install

Needs Python 3.10 or newer. uv is the smoothest route, but pipx and pip work too.

$ uv tool install omni-transcriber      # or: pipx install omni-transcriber
$ uvx --from omni-transcriber ots --help   # or run it without installing

That installs two commands for the same tool: transcriber and the shorter ots. Use whichever you prefer.

From source:

$ git clone https://github.com/AyushShah2/omni-transcriber
$ cd omni-transcriber && uv sync && uv run transcriber --help

With NVIDIA GPU acceleration:

$ uv tool install "omni-transcriber[gpu]"

The [gpu] extra is never required. On a machine with an NVIDIA card but no [gpu] install, --device cuda prints a warning and falls back to the CPU rather than failing.

Optional companions

• A JavaScript runtime — YouTube increasingly needs one. Deno is yt-dlp's preferred runtime; Node.js also works. transcriber enables whichever it finds on your PATH automatically. Without one, some videos lose formats or fail outright.
• ffmpeg — not used for transcription (the bundled PyAV decoder reads audio straight out of any container). yt-dlp needs it only for a few stream types; if it is missing when that happens, a static build is fetched automatically.

Usage

transcriber INPUTS...            files, folders, and/or URLs — mix freely
ots INPUTS...                    the same command, less typing

Both names are installed and do exactly the same thing; every example below works with either. (If you also use OpenTimestamps, which ships its own ots command, one will shadow the other on your PATH — transcriber always works.)

Where output goes

• One input, no -o → the transcript goes to stdout, so it pipes: transcriber talk.mp4 | wc -w. Progress bars and warnings all go to stderr.
• -o FILE → that exact file.
• Several inputs, a folder, a playlist, or -o DIR/ → one <title>.<format> file per item.

Playlists and channels

A link that is only a playlist or a channel expands to every video in it, one transcript file each:

$ transcriber "https://www.youtube.com/playlist?list=PL..." -o transcripts/
$ transcriber "https://www.youtube.com/@someone/videos" --limit 10 -o transcripts/

A link to a single video that also carries a &list= parameter is treated as just that one video, which is almost always what you meant. Pass --playlist when you want the whole list:

--limit N caps how many entries are fetched — worth using before you point it at a 200-video channel. If one entry is private, deleted, or region-locked, the rest still run and the closing summary shows which failed.

Everything above works for any site yt-dlp supports, not just YouTube.

Existing transcripts are skipped with a notice rather than overwritten, so re-running an interrupted batch resumes it for free. Pass --overwrite to redo them. If two items in the same run would produce the same filename — two videos with identical titles, or a/interview.mp4 and b/interview.mp4 — the later ones get (2), (3) suffixes so no transcript is lost. A batch keeps going when one item fails and prints a summary at the end; the exit code is 1 if anything failed.

Models

You never download or place a model by hand. The first time you use one it is fetched automatically and cached; every run after that reuses it. There is no models folder to manage and nothing to put anywhere.

The cache lives at ~/.cache/huggingface/hub on Linux and macOS, and at C:\Users\<you>\.cache\huggingface\hub on Windows.

Add .en to tiny/base/small/medium for slightly better English-only accuracy.

Speed: on an RTX 5070 Ti, small transcribes 468 s of audio in 3.7 s; the same job on CPU takes 122 s.

Moving the cache

Large models on a small home partition is a common squeeze. Point HF_HOME somewhere roomier:

$ export HF_HOME=/mnt/data/huggingface        # bash/zsh

> $env:HF_HOME = "D:\huggingface"             # PowerShell

Pre-downloading and offline use

To fetch a model ahead of time, just run it once on any short clip — the download happens on first use, and a progress bar shows it:

$ transcriber some-short-clip.mp3 -m turbo

Once cached, transcription needs no network at all. Set HF_HUB_OFFLINE=1 to guarantee it: cached models keep working, and an uncached one fails immediately with a clear message instead of reaching out.

Using your own model

-m also takes a HuggingFace CTranslate2 repo id, or a path to a model directory you converted or downloaded yourself:

$ transcriber talk.mp4 -m Systran/faster-whisper-large-v3
$ transcriber talk.mp4 -m /models/my-finetuned-whisper

Authentication for private media

Instagram reels, members-only videos, and anything else behind a login need your browser's cookies:

$ transcriber <reel-url> --cookies-from-browser firefox -f md -t
$ transcriber <reel-url> --cookies cookies.txt

Windows note: Chrome's app-bound cookie encryption usually defeats --cookies-from-browser chrome. Use Firefox, or export a cookies.txt with a browser extension and pass --cookies.

Exit codes

Development

From a checkout:

$ uv sync                       # dev environment
$ uv run pytest                 # fast tests: no network, no models
$ uv run pytest -m slow         # real tiny-model smoke test (~75 MB, once)
$ uv run ruff check . && uv run ruff format .

CI runs the suite on Linux, macOS, and Windows across Python 3.10 and 3.12. Python 3.10 through 3.14 are verified locally on Linux.

Contributing

Source, issues, and pull requests: https://github.com/AyushShah2/omni-transcriber

License

MIT — see LICENSE.