For the last six months I’ve been working on a screencast video editor called Komposition, and it’s now released and open source. This is an experience report, based on a talk from Lambda World Cádiz 2018, that’ll give an overview of Komposition’s design, implementation, testing, and planned future work.

Background

It all began with Haskell at Work, the series of screencasts focused on practical Haskell that I’ve been producing the last year. The screencasts are fast-paced tutorials centered around the terminal and text editor, complemented by a voice-over audio track.

My workflow for producing screencasts looks like this:

Write a very detailed script. The script usually gets uploaded to the website as-is, being used as the show notes for the screencast. Record video separately, including all mistakes and retries, in a single video file. Each little part of editing or running commands in the terminal is separated by a rest, where I don’t type anything at all for a few seconds. Record audio separately, where the voice-over audio is based on the script. A mistake or mispronunciation is corrected by taking a small break and then trying the same sentence or paragraph again. Cut and arrange all parts of the screencast using a video editor. This is the most painful and time-consuming part of the process.

I’ve found this workflow beneficial for me, partly because of being a non-native English speaker and not being keen on coding and talking simultaneously, but also because I think it helps with organizing the content into a cohesive narrative. I can write the script almost as a text-based tutorial, read it through to make sure it flows well, and then go into recording. Having to redo the recording phase is very time-consuming, so I’m putting a lot of effort into the script phase to catch mistakes early.

Video Editors

I’ve used a bunch of video editors. First, I tried free software alternatives, including Kdenlive, OpenShot, and a few more. Unfortunately, the audio effects available were a bit disappointing. I use, at a minimum, normalization and a noise gate. Having a good compressor is a plus.

More importantly, these applications are built for general video editing, like film shot with a camera, and they are optimized for that purpose. I’m using a very small subset of their feature set, which is not suited to my editing workflow. It works, but the tasks are repetitive and time-consuming.

On the commercial side, there are applications like Premiere Pro and Final Cut Pro. These are proprietary and expensive systems, but they offer very good effects and editing capabilities. I used Premiere Pro for a while and enjoyed the stability and quality of the tools, but I still suffered from the repetitive workload of cutting and organizing the video and audio clips to form my screencasts.

What does a programmer do when faced with a repetitive task? Spend way more time on automating it! Thus, I started what came to be the greatest yak shave of my life.

Building a Screencast Video Editor

I decided to build a screencast video editor tailored to my workflow; an editor with a minimal feature set, doing only screencast editing, and doing that really well.

You might think “why not extend the free software editors to cover your needs?” That is a fair question. I wanted to rethink the editing experience, starting with a blank slate, and question the design choices made in the traditional systems. Also, to be honest, I’m not so keen on using my spare time to write C++.

I decided to write it in GHC Haskell, using GTK+ for the graphical user interface. Another option would be Electron and PureScript, but the various horror stories about Electron memory usage, in combination with it running on NodeJS, made me decide against it. As I expected my application to perform some performance-critical tasks around video and audio processing, Haskell seemed like the best choice of the two. There are many other languages and frameworks that could’ve been used, but this combination fit me well.

Komposition

About five months later, after many hours of hacking, Komposition was released open-source under the Mozilla Public License 2.0. The project working name was “FastCut,” but when making it open source I renamed it to the Swedish word for “composition.” It has nothing to do with KDE.

Komposition is a modal, cross-platform, GUI application. The modality means that you’re always in exactly one mode, and that only certain actions can be taken depending on the mode. It currently runs on Linux, Windows, and macOS, if you compile and install it from source.

At the heart of the editing model lies the hierarchical timeline, which we’ll dive into shortly. Other central features of Komposition include the automatic video and audio classification tools. They automate the tedious task of working through your recorded video and audio files to cut out the interesting parts. After importing, you’ll have a collection of classified video scenes, and a collection of classified audio parts. The audio parts are usually sentences, but it depends on how you take rests when recording your voice-over audio.

Keyboard-Driven Editing

Komposition is built for keyboard-driven editing, currently with Vim-like bindings, and commands transforming the hierarchical timeline, inspired by Paredit for Emacs.

The help dialog shows the current mode’s key bindings

There are corresponding menu items for most commands, and there’s limited support for using the mouse in the timeline. If you need help with keybindings, press the question mark key, and you will be presented with a help dialog showing the bindings available in the current mode.

The Hierarchical Timeline

The hierarchical timeline is a tree structure with a fixed depth. At the leaves of the tree there are clips. Clips are placed in the video and audio tracks of a parallel. It’s named that way because the video and audio tracks play in parallel. Clips within a track play in sequence.

Video and audio tracks play in parallel

If the audio track is longer than the video track, the remaining part of the video track is padded with still frames from an adjacent clip.

Shorter video tracks are automatically padded with still frames

Explicit gaps can be added to the video and audio tracks. Video gaps are padded with still frames, and audio gaps are silent. When adding the gap, you specify its duration.

Gaps can be added in video and audio tracks

Parallels are put in sequences, and they are played in sequence. The first parallel is played until its end, then the next is played, and so on. Parallels and sequences are used to group cohesive parts of your screencast, and to synchronize the start of related video and audio clips.

Parallels are played in sequence

When editing within a sequence or parallel, for example when deleting or adding clips, you will not affect the synchronization of other sequences or parallels. If there were only audio and video tracks in Komposition, deleting an audio clip would possibly shift many other audio clips, causing them to get out of sync with their related video clips. This is why the timeline structure is built up using sequences and parallels.

Finally, the timeline is the top-level structure that contains sequences. This is merely for organizing larger parts of a screencast. You can comfortably build up your screencast with a single sequence containing parallels.

The timeline contains sequences that are played in sequence

Note that the timeline always contains at least one sequence, and that all sequences contain at least one parallel. The tracks within a parallel can be empty, though.

Documentation

The project website includes a user guide, covering the concepts of the application, along with recommendations on how to plan and record your screencasts to achieve the best results and experience using Komposition.

Komposition’s user guide

The landing page features a tutorial screencast, explaining how to import, edit, and render a screencast. It’s already a bit outdated, but I might get around to making an updated version when doing the next release. Be sure to check it out, it’ll give you a feel for how editing with Komposition works.

I can assure you, editing a screencast, that is about editing screencasts using your screencast editor, in your screencast editor, is quite the mind-bender. And I thought I had recursion down.

Implementation

I’ve striven to keep the core domain code in Komposition pure. That is, only pure function and data structures. Currently, the timeline and focus, command and event handling, key bindings, and the video classification algorithm are all pure. There are still impure parts, like audio and video import, audio classification, preview frame rendering, and the main application control flow.

Some parts are inherently effectful, so it doesn’t make sense to try writing them as pure functions, but as soon as the complexity increases, it’s worth considering what can be separated out as pure functions. The approach of “Functional core, imperative shell” describes this style very well. If you can do this for the complex parts of your program, you have a great starting point for automated testing, something I’ll cover later in this post.

GTK+

Komposition’s GUI is built with GTK+ 3 and the Haskell bindings from gi-gtk . I noticed early in the project that the conventional programming style and the APIs of GTK+ were imperative, callback-oriented, and all operating within IO , making it painful to use from Haskell, especially while trying to keep complex parts of the application free of effects.

To mitigate this issue, I started building a library (more yak shaving!) called gi-gtk-declarative , which is a declarative layer on top of gi-gtk . The previous post in this blog describes the project in detail. Using the declarative layer, rendering becomes a pure function (state -> Widget event) , where state and event varies with the mode and view that’s being rendered. Event handling is based on values and pure functions.

There are cases where custom widgets are needed, calling the imperative APIs of gi-gtk , but they are well-isolated and few in numbers.

Type-Indexed State Machines

I had a curiosity itching when starting this project that I decided to scratch. Last year I worked on porting the Idris ST library, providing a way to encode type-indexed state machines in GHC Haskell. The library is called Motor. I wanted to try it in the context of a GUI application.

Just to give some short examples, the following type signatures, used in the main application control flow, operate on the application state machine that’s parameterized by its mode.

The start function takes a name and key maps, and creates a new application state machine associated with the name, and in the state of WelcomeScreenMode :

start :: Name n -> KeyMaps -> Actions m '[ n !+ State m WelcomeScreenMode] r ()

The returnToTimeline function takes a name of an existing state machine and a TimelineModel , and transitions the application from the current mode to TimelineMode , given that the current mode instantiates the ReturnsToTimeline class:

returnToTimeline :: ReturnsToTimeline mode mode => Name n -> TimelineModel -> Actions m '[ n := State m mode !--> State m TimelineMode] r ()

The usage of Motor in Komposition is likely the most complicated aspect of the codebase, and I have been unsure if it is worth the cost. On the positive side, combining this with GADTs and the singleton pattern for mode-specific commands and events, GHC can really help out with pattern-matching and exhaustivity-checking. No nasty (error "this shouldn't happen") as the fall-through case when pattern matching!

I’m currently in the process of rewriting much of the state machine encoding in Komposition, using it more effectively for managing windows and modals in GTK+, and I think this warrants the use of Motor more clearly. Otherwise, it might be worth falling back to a less advanced encoding, like the one I described in Finite-State Machines, Part 2: Explicit Typed State Transitions.

Singleton Pattern

The singleton pattern is used in a few places in Komposition, as mentioned above. To show a concrete example, the encoding of mode-specific commands and events is based on the Mode data type.

data Mode = WelcomeScreenMode | TimelineMode | LibraryMode | ImportMode

This type is lifted to the kind level using the DataKinds language extension, and is used in the corresponding definition of the singleton SMode .

data SMode m where SWelcomeScreenMode :: SMode WelcomeScreenMode STimelineMode :: SMode TimelineMode SLibraryMode :: SMode LibraryMode SImportMode :: SMode ImportMode

The Command data type is parameterized by the mode in which the command is valid. Some commands are valid in all modes, like Cancel and Help , while others are specific to a certain mode. FocusCommand and JumpFocus are only valid in the timeline mode, as seen in their type signatures below.

data Command ( mode :: Mode ) where Cancel :: Command mode mode Help :: Command mode mode FocusCommand :: FocusCommand -> Command TimelineMode JumpFocus :: Focus SequenceFocusType -> Command TimelineMode -- ...

Finally, by passing a singleton for a mode to the keymaps function, we get back a keymap specific to that mode. This is used to do event handling and key bindings generically for the entire application.

keymaps :: SMode m -> KeyMap ( Command m) m) = keymaps \ case SWelcomeScreenMode -> KeyChar 'q' ], Mapping Cancel ) [ ([], KeyEscape ], Mapping Cancel ) , ([], KeyChar '?' ], Mapping Help ) , ([], ] -- ...

In the spirit of calling out usage of advanced GHC features, I think singletons and GADTs are one more such instance. However, I find them very useful in this context, and worth the added cognitive load. You don’t have to go full “Dependent Haskell” or bring in the singletons library to leverage some of these techniques.

Automatic Scene Classification

The automatic classification of scenes in video is implemented using the Pipes and ffmpeg-light libraries, mainly. It begins with the readVideoFile , that given a video file path will give us a Pipes.Producer of timed frames, which are basically pixel arrays tagged with their time in the original video. The producer will stream the video file, and yield timed frames as they are consumed.

readVideoFile :: MonadIO m => FilePath -> Producer ( Timed Frame ) m () ) m ()

The frames are converted from JuicyPixels frames to massiv frames. Then, the producer is passed to the classifyMovement function together with a minimum segment duration (such that segments cannot be shorter than N seconds), which returns a producer of Classified frames, tagging each frame as being either moving or still.

classifyMovement :: Monad m => Time -- ^ Minimum segment duration -> Producer ( Timed RGB8Frame ) m () ) m () -> Producer ( Classified ( Timed RGB8Frame )) m () )) m () data Classified f = Moving f | Still f deriving ( Eq , Functor , Show )

Finally, the classifyMovingScenes function, given a full duration of the original video and a producer of classified frames, returns a producer that yields ProgressUpdate values and returns a list of time spans.

:: classifyMovingScenes Monad m => Duration -- ^ Full length of video -> Producer ( Classified ( Timed RGB8Frame )) m () )) m () -> Producer ProgressUpdate m [ TimeSpan ] m [

The time spans describe which parts of the original video are considered moving scenes, and the progress update values are used to render a progress bar in the GUI as the classification makes progress.

Automatic Sentence Classification

Similar to the video classification, Komposition also classifies audio files to find sentences or paragraphs in voice-over audio. The implementation relies on the sox tool, a separate executable that’s used to:

normalize the audio, apply a noise gate, and auto-split by silence.

One problem with sox is that it, as far as I can tell, can only write the split audio files to disk. I haven’t found a way to retrieve the time spans in the original audio file, so that information is unfortunately lost. This will become more apparent when Komposition supports editing the start and end position of clips, as it can’t be supported for audio clips produced by sox .

I hope to find some way around this, by extending or parsing output from sox somehow, by using libsox through FFI bindings, or by implementing the audio classification in Haskell. I’m trying to avoid the last alternative.

Rendering

The rendering pipeline begins with a pure function that converts the hierarchical timeline to a flat timeline representation. This representation consists of a video track and an audio track, where all gaps are made explicit, and where the tracks are of equal duration.

From the flat representation, an FFmpeg command is built up. This is based on a data type representation of the FFmpeg command-line syntax, and most importantly the filter graph DSL that’s used to build up the complex FFmpeg rendering command.

Having an intermediate data type representation when building up complex command invocations, instead of going directly to Text or String , is something I highly recommend.

Preview

The preview pipeline is very similar to the rendering pipeline. In fact, it’s using the same machinery, except for the output being a streaming HTTP server instead of a file, and that it’s passed the proxy media instead of the full-resolution original video. On the other side there’s a GStreamer widget embedded in the GTK+ user interface that plays back the HTTP video stream.

Using HTTP might seem like a strange choice for IPC between FFmpeg and GStreamer. Surprisingly, it’s the option that have worked most reliably across operating systems for me, but I’d like to find another IPC mechanism, eventually.

The HTTP solution is also somewhat unreliable, as I couldn’t find a way to ensure that the server is ready to stream, so there’s a race condition between the server and the GStreamer playback, silently “solved” with an ugly threadDelay .

Testing

Let’s talk a bit about testing in Komposition. I’ve used multiple techniques, but the one that stands out as unconventional, and specific to the domain of this application, is the color-tinting video classifier.

Output of the color-tinting video classifier

It uses the same classification functions as described before, but instead of returning time spans, it creates a new video file where moving frames are tinted green and still frames are tinted red. This tool made it much easier to tweak the classifier and test it on real recordings.

Property-Based Testing

I’ve used Hedgehog to test some of the most complex parts of the codebase. This has been incredibly valuable, and has found numerous errors and bad assumptions in the implementation. The functionality tested with Hedgehog and properties includes:

Timeline commands and movement: It generates a sequence of commands, together with a consistent timeline and focus. It folds over the commands, applying each one to the current timeline and focus, and asserts that the resulting timeline and focus are still consistent. The tested property ensures that there’s no possibility of out-of-bounds movement, and that deleting or otherwise transforming the timeline doesn’t cause an inconsistent timeline and focus pair.

It generates a sequence of commands, together with a consistent timeline and focus. It folds over the commands, applying each one to the current timeline and focus, and asserts that the resulting timeline and focus are still consistent. The tested property ensures that there’s no possibility of out-of-bounds movement, and that deleting or otherwise transforming the timeline doesn’t cause an inconsistent timeline and focus pair. Video scene classification: It generates known test scenes of random duration, that are either scenes of only still frames, or scenes with moving frames. It translates the test scenes, which are just descriptions, to real frames, and runs the classifier on the frames. Finally, it checks that the classified scenes are the same as the generated test scenes.

It generates known test scenes of random duration, that are either scenes of only still frames, or scenes with moving frames. It translates the test scenes, which are just descriptions, to real frames, and runs the classifier on the frames. Finally, it checks that the classified scenes are the same as the generated test scenes. Flattening of hierarchical timeline: The flattening process converts the hierarchical timeline to a flat representation. The tested property ensures that hierarchical and flat timelines are always of the same total duration. There are other properties that could be added, e.g. that all clips in the original timeline are present in the flat timeline.

The flattening process converts the hierarchical timeline to a flat representation. The tested property ensures that hierarchical and flat timelines are always of the same total duration. There are other properties that could be added, e.g. that all clips in the original timeline are present in the flat timeline. Round-trip properties of FFmpeg format printers and parsers: This is a conventional use of property-based tests. It ensures that parsing an FFmpeg-format timestamp string, produced by the FFmpeg-format timestamp printer, gives you back the same timestamp as you started with.

There are also cases of example-based testing, but I won’t cover them in this report.

Used Packages

Komposition depends on a fairly large collection of Haskell and non-Haskell tools and libraries to work with video, audio, and GUI rendering. I want to highlight some of them.

haskell-gi

The haskell-gi family of packages are used extensively, including:

gi-gobject

gi-glib

gi-gst

gi-gtk

gi-gdk

gi-gdkpixbuf

gi-pango

They supply bindings to GTK+, GStreamer, and more, primarily generated from the GObject Introspection metadata. While GTK+ has been problematic to work with in Haskell, these bindings have been crucial to the development of Komposition.

massiv & massiv-io

The massiv package is an array library that uses function composition to accomplish a sort of fusion. It’s used to do parallel pixel comparison in the video classifier. Thank you, Alexey Kuleshevich (author and maintainer of the massiv packages) for helping me implement the first version!

Pipes

The Pipes library is used extensively in Komposition:

The streaming video reader from ffmpeg-light is wrapped in a Pipes.Producer to provide composable streaming.

to provide composable streaming. In general, effectful operations with progress notifications are producers that yield ProgressUpdate values as they perform their work.

values as they perform their work. pipes-safe is used for handling resources and processes.

pipes-parse is used in stateful transformations in the video classifier.

A big thanks to Gabriel Gonzales, the author of Pipes and the related packages!

Others

To name a few more:

I’ve used protolude as the basis for a custom prelude.

The lens library is used for working with nested data structures, positional updates in lists, and monadic transformations.

typed-process is used together with pipes-safe, in a situation where I couldn’t use the regular process package because of version constraint issues. The typed-process API turned out to be really nice, so I think it will be used more in the future.

Summary

Looking back at this project, the best part has been to first write it for my own use, and later find out that quite a lot of people are interested in how it’s built, and even in using it themselves. I’ve already received pull requests, bug reports, usability feedback, and many kind words and encouragements. It’s been great!

Also, it’s been fun to work on an application that can be considered outside of Haskell’s comfort zone, namely a multimedia and GUI application. Komposition is not the first application to explore this space — see Movie Monad and Gifcurry for other examples — but it is exciting, nonetheless.

Speaking of using Haskell, the effort to keep complex domain logic free of effects, and the use of property-based testing with Hedgehog to lure out nasty bugs, has been incredibly satisfactory and a great learning experience.

The Problematic Parts

It’s not been all fun and games, though. I’ve spent many hours struggling with FFmpeg, video and audio codecs, containers, and streaming. Executing external programs and parsing their output has been time-consuming and very hard to test. GTK+ has been very valuable, but also difficult to work with in Haskell. Finally, management of non-Haskell dependencies, in combination with trying to be cross-platform, is painful. Nix has helped with my own setup, but everyone will not install Komposition using Nix.

Next Steps

There are many features that I’d like to add in the near future.

More commands to work with the timeline, e.g. yank, paste, and join.

More Vim-like movement commands.

Previewing of any timeline part. Currently you can only preview the entire timeline, a sequence, or a parallel.

Adjustable clips, meaning that you can change the time span of a clip. This is useful if the automatic classification generated slightly incorrect clip time spans.

Content-addressed project files, to enable reuse of generated files, and to avoid collision. This includes most files involved in importing, and generated preview frames.

It would be great to set up packaging for popular operating systems, so that people can install Komposition without compiling from source. There’s already a Nix expression that can be used, but I’d like to supply Debian packages, macOS bundles, Windows installers, and possibly more.

There are some things that I’d like to explore and assess, but that won’t necessarily happen. The first is to use GStreamer in the rendering pipeline, instead of FFmpeg. I think this is possible, but I haven’t done the research yet. The second thing, an idea that evolved when talking to people at Lambda World Cádiz, would be to use voice recognition on audio clips to show text in the preview area, instead of showing a waveform.

Finally, there are some long-awaited refactorings and cleanups waiting, and optimization of the FFmpeg filter graph and the diffing in gi-gtk-declarative. Some of these I’ve already started on.

Wrap-Up

I hope you enjoyed reading this report, and that you now have got a clearer picture of Komposition, its implementation, and where it’s going. If you’re interested in using it, let me know how it works out, either by posting in the Gitter channel or by reaching out on Twitter. If you want to contribute by reporting bugs or sending pull requests, there’s the issue tracker on GitHub.

Thanks for reading!