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Bless This Mess

Cut the crap is an automatic video editing program for streamers. It can cut out uninteresting parts by detecting silences. This was inspired by jumpcutter, where this program can get better quality results by using an (optional) dedicated microphone track. This prevents cutting of quieter consonants for example. Using ffmpeg more efficiently also produces faster results and is less error prone.

Youtube has different requirements from streams then twitch does. We want to cut out boring parts. Jumpcut has solved that problem partly and this program builds on top of that idea. At the moment we use ffmpeg for silence detection, then we do some maths to figure out which segments are sounded, which is combined into the output video.

In the future we will add support for a music track which will not be chopped up.


From source

Install the nix package manager.

git clone
cd cut-the-crap
nix-build .

Bundle build (staticly linked bundled with runtime deps)

From version 2.1.1 and onwards these nix bundles will be attached to releases on the release page. These should work on any Linux distribution. Download the executable from the release page.

Under the hood we use nix-bundle for this. These are so large because everything from libc to youtube-dl are packaged within.


  • Run nix-env -iA nixos.haskellPackages.cut-the-crap or add to systemPackages.
  • simply run cut-the-crap to display usage instructions.

This only works for nixpkgs that have cut-the-crap >= 1.4.2 or =< 1.3 There were some build issues with 1.4.0 and 1.4.1 (now fixed)

Usage notes

Up to date help is available in the program itself:


Run the program:

cut-the-crap listen

It works both with youtube or twitch videos (VODS). The program simply passes the URL to youtube-dl.

We can also run it on a local file of course:

cut-the-crap listen somelocalfile.mkv

There is also a work in progress subtitle generation:

cut-the-crap subtitles

Noise gate

Make sure to record with a noise gate on your microphone. This will cut out background buzzing and allow you to use a more aggressive threshold on noise detection.

OBS tracks

Setup OBS so that you record the microphone and the desktop audio on separate tracks. In my own setup I have track 1 for combining all audio, track 2 for just the microphone and track 3 for desktop audio. Then I can use:

    cut-the-crap listen ./recordFromObs.mkv ./someOut.mkv --voiceTrack 2 --musicTrack 3

So we throw away track 1, we use track 2 for silence detection, and track 3 get's mixed in after cutting is complete. If you don't want music being mixed back into the result, for example for further editing, you can also leave that argument out. I did this for example to mix back in the music of the original file later.

Test data

It maybe a bit awkward to record yourself just for testing data. To get some easy test date we can use youtube-dl, and make it a bit shorter with ffmpeg, for example:

youtube-dl "" && ffmpeg -i "Opening Ceremony & 'Languages all the way down' by Rob Rix - ZuriHac 2020-kCpQ4aTzlis.mkv" -t 00:20:00.00 -c copy input.mkv

Use case

I'm using this program to record my stream and upload it to my Youtube channel.

The concrete result is that your audience retention percentage will go up since the videos will be shorter, and more engaging. Sometimes on stream I have intro screens for example which completely get removed, and other times I'm simply thinking. Reducing videos by 30% is not uncommon in my case, which means by default 30% more retention. You could even decide to edit after that which means you have to spend less time on cutting out silences and more time on making it look cool.

Feel free to use or modify this program however you like. Pull requests are appreciated.


Track based silence detection

It is possible to specify one audio output as speech track. This will be used to for silence detection only. The result is very precise silence detection.

Separate music track

Another track would be background and won't be modified at all. In the end it just get's cut of how far it is.

This way we get good music and interesting stream. Another idea is to remix an entirely different source of music into the video, so we can play copyrighted music on stream and Youtube friendly music on Youtube.


This project is mostly a wrapper around ffmpeg. We use Haskell for shell programming.

We first figure out what's going on with the video. For example we do silence detection or speech recogontion, maybe even motion detection etc. After the analyze phase we act in the edit phase. Where we for example cut. Finally we produce some result.

The shelly library was chosen in support of shell programming. Originally we used turtle, but that library is much more complicated to use because it assumes you want to do stream programming, creating several unexpected bugs. So we replaced it with shelly and noticabally reduced code complexity. Now it's truly a 'dumb' wrapper around ffmpeg.

Why not to extend jumpcutter directly?

I wish to build out this idea more to essentially make all streams look like human edited Youtube videos. Although I'm familiar with python, I (am or feel) more productive in haskell, therefore I chose to integrate with, and eventually replace jumpcutter. On stream we've determined most of the functionality is basically ffmpeg. Haskell also opens up the ability to do direct native ffmpeg integration, where we use ffmpeg as a library instead of calling it as a CLI program.

One glaring limitation I've encountered with jumpcutter is that it can't handle larger video files (2 hour 30 minutes +). Scipy throws an exception complaining the wav is to big. Since this program doesn't use scipy it doesn't have that issue.

It also appears like jumpcutter is unmaintained.


This idea is obviously not new, considering ffmpeg has first class support for it. These are listed in no particular order:

Auto editor seems actively maintained and packed with features. It's target audience is different, whereas I wish to host this project on and make it available to everyone, auto editor is to be a command line tool.