Solving MaxQDA error 1001: Error while converting the project!

As folx might know, I’m currently undertaking a PhD at ANU’s School of Cybernetics – where I’m researching voice and speech data and datasets that are used to train machine learning models used for things like speech recognition and wake word detection. And if you’ve been following my posts on the State of my Toolchain, and my previous post exploring Taguette, you’ll know that I’ve settled on MaxQDA as my qualitative data analysis software. In general, I’ve found MaxQDA to be great software – the user interface is intuitive and the analytical features it have make qualitative data analysis faster. It’s expensive – and is a yearly subscription – but at the moment, it’s earning its price tag.

One definite bugbear I have though is how MaxQDA interacts with SharePoint. As part of my ethics protocol, I am storing my PhD data on university systems – not to external cloud tools like DropBox or Next Cloud. Instead, I save the MaxQDA files to my local (Windows – MaxQDA doesn’t have a Linux client, unfortunately) machine. This is then synced with OneDrive to the University’s SharePoint server.

This works well. Except when it doesn’t.

A couple of months ago I had an error that seemed like a once-off; an error where MaxQDA apparently couldn’t convert the project file. This error was presented when I opened the MaxQDA file (a .mx22 file):

MaxQDA error 1001: Error while converting the project!
MaxQDA Error code: 1001 “Error while converting the project!”

Like so many error messages, it violated design principles for good error messages; it wasn’t a precise description of what had gone wrong, it wasn’t human readable, and it didn’t give me any helpful advice on how to solve the problem. So, I had to figure it out myself.

I tried the obvious things first;

  • I closed MaxQDA and re-launched the software; the error persisted.
  • I restarted my computer and then re-launched the MaxQDA software; the error persisted.
  • I stopped and started the OneDrive service; the error persisted.

At this point, it was clear I’d have to dig deeper into OneDrive. In File Explorer, I could see that the file was still synchronising with OneDrive:

Windows file explorer showing MaxQDA file still synchronising in OneDrive
MaxQDA file still synchronising with OneDrive

By rights, stopping and starting OneDrive should have re-synchronised the file; but it hadn’t.

OneDrive was also showing a synchronisation error:

MaxQDA file causing a Sync issue in OneDrive. OneDrive thinks that the file is in use.
MaxQDA – file still open in OneDrive

Clearly, OneDrive thought that the MaxQDA file was still open; and was not syncing the file to the cloud for this reason. However, closing MaxQDA, OneDrive and a whole reboot had not fixed this error.

My conclusion from this investigation is that MaxQDA somehow leaves an open file handle; for example if the application closes unexpectedly. The open file handle is not cleared via MaxQDA, via OneDrive, or via the underlying Windows operating system. So how else might you clear an open file handle?

Windows isn’t my preferred operating system; and I don’t know enough about the OS internals to go digging into file handles and how to clear them. So I went rm -rf; or about as close to it as you can get on Windows …

The solution

The only thing that did fix the issue was uninstalling OneDrive, re-installing OneDrive, and then re-authenticating OneDrive and allowing it to sync to the cloud. My working hypothesis is that the uninstallation of OneDrive forces Windows to clear any open OneDrive file handles; then the re-installation returns the MaxQDA file to a known good state.

All in all, this took about an hour of investigation to identify the issue and find a workaround. And to be clear – the solution is just a workaround – it doesn’t address the underlying problem – which is that MaxQDA files that synchronise from a local machine to the cloud via OneDrive or SharePoint are prone to synchronisation failure that manifests in an open file handle; which in turn leads to an obscure error message.

This has now happened to me twice – but at least now I know how to fix it next time …

Building a Linux video production workflow for the SRA22 Social Responsibility of Algorithms conference

As you might know, most of my toolchain is Linux and opensource-based. So when it came time to produce a video for the upcoming Social Responsibility of Algorithms conference, with my doctoral supervisor, Dr. Liz T. Williams, Linux-based options were my first choice. However, the Linux video production workflow is not as smooth as it could be – and this blog post documents my trials and tribulations.

Web camera software

While web cameras on Linux these days tend to “just work”, there are no Linux counterparts to the Logitech or other vendor-specific software that the hardware usually ships with. Way in back in January when I presented at linux.conf.au, part of my tech check (big thanks, Chris and jwoithe!) involved using the qv4l2 utility to provide finer-grained control over settings such as white balance, contrast, and brightness. This handy utility also controls auto and manual zoom on cameras, and I adjusted the zoom to better fill the screen.

From memory, I didn’t have to add an apt repository for this – it’s in the Ubuntu repos by default – and can be installed with:

sudo apt install qv4l2

Recording video and setting up a virtual background

Open Broadcaster Software

The next challenge was to record the video, using a virtual background. The problem of recording video on Linux is mostly solved these days using the Open Broadcaster Software (OBS) software.

Background removal plugin

While this software comes with a “chroma key” plugin (for green screens), it doesn’t come with an automatic background removal plugin. I read up on what plugins were available and ended up choosing this one from Roy Shilkrot (big thank you, Roy!) –

https://github.com/royshil/obs-backgroundremoval

It was well documented, and very clear about the installation steps. It did require compiling from source, but the instructions were detailed. Because I already had CUDA installed on my machine (I have an RTX2060 NVIDIA GPU, don’t get me started on Nvidia and Linux drivers and how hard they are to get working, but there are signs of hope, with NVIDIA recently open sourcing their Linux drivers – albeit through moving most of the functionality to firmware), I opted to compile with GPU support. Compilation was very straightforward, however the instructions provided for sym-linking the compiled binaries were out of date. Luckily, there was already an Issue that had been raised for this, and I used the information in the Issue to fix the sym-linking. This exercise gave me a deep appreciation for the effort that goes into packaging, and the complexity that it abstracts away for the end user.

Machine vision algorithms

This plugin uses machine vision (which is why OpenCV is one of the package’s dependencies) to determine the outline of the subject from the background, and then uses the Chroma Key to fill the non-subject space with a single colour. It allows you to choose from a range of algorithms to do the subject outline, such as SINet, MODnet, mediapipe and a model for selfies. I found I got the best results using mediapipe. The plugin also allows you to configure other inference settings, such as the frame interval between which inference samples are taken. Again, there is another trade-off here; if you resample the image frequently, this takes more computational power, but if you sample less frequently then the outline may not “keep up” with the movement of the subject. I chose to inference every frame – primarily because I had the compute power to do so 😈.

All of these computer vision (CV) algorithms are very recent – for example SINet [0] was developed just over a year ago, and MODnet [1] is less than 6 months old; I continue to be surprised at how fast the ML space is moving, and how commodotised it has become. If you have ever used the “virtual background” feature in a videoconferencing application like Zoom or Teams, it will be using a similar algorithm. However, a lot of the implementation decisions – like how often to do inference, which algorithm to use, or the characteristics of the algorithm such as smoothing or contrast, are abstracted away from the end user in these applications. What we lose in complexity and control, we gain in ease of use.

Running a noise removal filter on the video using ffmpeg

Although I had managed to do a video recording, sans background, when I played back the video, the audio had a lot of background noise. This came from the sound of the fans on my laptop; when the GPU is under load, the fans increase their speed to dissipate the heat that is generated. The inference of the background removal algorithm was causing the fans to spin up – and to generate background noise that was captured by the microphone. Tradeoffs, tradeoffs and more tradeoffs!

Rather than re-record the video, or reduce the inference frequency of the background removal plugin, I decided to see if I could improve the noise quality by running the video through a noise reduction filter. Signal processing, and acoustic signal processing, is definitely not an area that I feel comfortable with, but I had a general idea of how noise reduction filters worked – by gating (cutting off) noise above or below a particular frequency (the gating floor or gating ceiling). When I worked in videoconferencing, I’d seen a similar approach used with special digital signal processing equipment in videoconferencing-equipped spaces. Again, there’s a tradeoff – if you have a quietly spoken person, and a loud, deep speaker, but use the same gating settings, you’re going to get unexpected results.

Although I had Audacity installed, I reached for the command-line tool ffmpeg. The newer versions of ffmpeg have noise reduction filters built in, and the documentation includes several command line examples. Running the video through the noise reduction filter improved the quality markedly.

Video editing, compositing and rendering in OpenShot

Next, I used OpenShot to do video editing, addition of slides, and “topping and tailing” with the conference branding. OpenShot is a breeze to use, and although it doesn’t have a lot of fancy transitions, it’s an excellent choice for this step of the workflow. I previously converted the PowerPoint I was using for slides into PDF, then used GIMP to create a 1920 x 1080 px PNG image of each PDF. The PNG images were then imported into OpenShot and overlaid over the video tracks.

One of the branding artefacts was in a format (Quicktime MOV) that OpenShot didn’t particularly work well with; so I changed the format using Handbrake.

Again, having a reasonable GPU helped with the rendering process; each conference video is around 15 minutes long, and OpenShot rendered them at 1080p 30fps resolution in around 5 minutes 😈.

Subtitles

Subtitles are helpful for many people, and in particular they increase the accessibility of video content for folx who are hard of hearing. Several video creation tools can produce automatic subtitles. However, these are usually inaccurate at best, and at worst can semantically change the meaning of the video content. Moreover, I knew that in this video we would be using some domain-specific vocabulary (the video is all about accent bias in language technologies like speech recognition) – and that the automatic subtitles would be even less accurate.

For this task, I tried out the application Subtitle Editor, but found the interface difficult to use. I’d heard good things too about Aegisub, but in the end I just manually created the .srt file by hand-editing it in Atom. The .srt file format is surprisingly basic – and hand-editing was made a lot easier because we created a script for the video beforehand. It may not work as well if you are transcribing the subtitles from the spoken audio.

Edit: I eventually figured Subtitle Editor’s interface out, when I needed to move all the subtitles back by 7 seconds after topping and tailing the video; this feature itself is worth installing the application.

The one thing that tripped me up in hand-editing subtitles was forgetting that the time format is HH:MM:SS,ms. My video was only 16 minutes in length, and I erroneously placed the “minutes” in the “hours” column. For example, I wrote 00:05:00, 400 to mean 5 seconds into the video, but this is actually 5 minutes into the video. The millisecond separator – the comma – is unusual in English, but the SRT format was developed in France; which uses the comma. This reminded me of Lawrence Busch‘s excellent book Standards: Recipes for reality, which articulates the path dependencies that standards create.

1
00:00:05,400 --> 00:00:12,300
[Kathy] Hi everyone, I’m Kathy Reid, a PhD candidate
at the School of Cybernetics
at Australian National University,
2
00:00:12,400 --> 00:00:22,300
and I’m recording today from the unceded lands
of the Wadawarrung people here in Geelong,
on the south-west coast of Victoria.

Conclusion

Video production for conferences is becoming more prevalent, particular as the COVID-19 pandemic has taken many conferences online. Tools like Zoom and Camtasia, while mature, abstract away many of the underlying algorithms, and simplify their interfaces for usability by removing fine-grained controls from the user. By exploring open source tools for video production workflows, we obtain a deeper understanding of “what’s under the hood”.

To extend this work in the future, one of the capabilities I would like to explore is automation of the workflow pipeline. This may be possible for tasks such as topping and tailing videos with conference branding, but the actual composition of video and slides is still going to require human attention.

Footnotes

[0] Fan, D. P., Ji, G. P., Sun, G., Cheng, M. M., Shen, J., & Shao, L. (2020). Camouflaged object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2777-2787).
https://github.com/DengPingFan/SINet

[1] Ke, Z., Sun, J., Li, K., Yan, Q., & Lau, R. W. (2020). MODNet: real-time trimap-free portrait matting via objective decomposition.
https://github.com/ZHKKKe/MODNet

A review of Taguette – an open source alternative for qualitative data coding

Motivation and context

As you might know, I’m currently undertaking a PhD program at Australian National University’s School of Cybernetics, looking at voice dataset documentation practices, and what we might be able to improve about them to reduce statistical and experienced bias in voice technologies like speech recognition and wake words. As part of this journey, I’ve learned an array of new research methods – surveys, interviews, ethics approaches, literature review and so on. I’m now embarking on some early qualitative data analysis.

The default tool in the qualitative data analysis space is NVIVO, made by Melbourne-based company, QSR. However, NVIVO has both a steep learning curve and a hefty price tag. I’m lucky enough that this pricing is abstracted away from me – ANU provides NVIVO for free to HDR students and staff – but reports suggest that the enterprise licensing starts at around $USD 85 per user. NVIVO operates predominantly as a desktop-based pieces of software and is only available for Mac or Windows. My preferred operating system is Linux – as that is what my academic writing toolchain based on LaTeX, Atom and Pandoc – is based on – and I wanted to see if there was a tool with equivalent functionality that aligned with this toolchain.

About Taguette

Taguette is a BSD-3 licensed qualitative coding tool, positioned as an alternative to NVIVO. It’s written by a small team of library specialists and software developers, based in New York. The developers are very clear about their motivation in creating Taguette;

Qualitative methods generate rich, detailed research materials that leave individuals’ perspectives intact as well as provide multiple contexts for understanding the phenomenon under study. Qualitative methods are used in a wide range of fields, such as anthropology, education, nursing, psychology, sociology, and marketing. Qualitative data has a similarly wide range: observations, interviews, documents, audiovisual materials, and more. However – the software options for qualitative researchers are either far too expensive, don’t allow for the seminal method of highlighting and tagging materials, or actually perform quantitative analysis, just on text. It’s not right or fair that qualitative researchers without massive research funds cannot afford the basic software to do their research. So, to bolster a fair and equitable entry into qualitative methods, we’ve made Taguette!

Taguette.org website, “About” page

This motivation spoke to me, and aligned with my own interest in free and open source software.

Running Taguette and identifying its limitations

For reproduceability, I ran Taguette version 1.1.1 on Ubuntu 20.04 LTS with Python 3.8.10

Taguette can be run in the cloud, and the website provides a demo server so that you can explore the cloud offering. However, I was more interested in the locally-hosted option, which runs on a combination of python, calibre, and I believe sqlite as the database backend, with SQLAlchemy for mappings. The install instructions recommend running Taguette in a virtual environment, and this worked well for me – presumably running the binary from the command line spawns a flask– or gunicorn– type web application, which you can then access in your browser. This locally hosted feature was super helpful for me, as my ethics protocol has restrictions on what cloud services I could use.

To try Taguette, I first created a project, then uploaded a Word document in docx format, and began highlighting. This was smooth and seamless. However, I soon ran into my first limitation. My coding approach is to use nested codes. Taguette has no functionality for nested codes, and no concomitant functionality for “rolling up” nested codes. This was a major blocker for me.

However, I was impressed that I could add tags in multiple languages, including non-Latin orthographies, such as Japanese and Arabic. Presumably, although I didn’t check this, Taguette uses Unicode under the hood – so it’s foreseeable that you could use emojis as tags as well, which might be useful for researchers of social media.

Taguette has no statistical analysis tools built in, such as word frequency distributions, clustering or other corpus-type methods. While these weren’t as important for me at this stage of my research, they are functions that I envisage using in the future.

Taguette’s CodeBook export and import functions work really well, and I was impressed with the range of formats that could be imported or exported.

What I would like Taguette to do in the future

I really need nested tags that have aggregation functionality for Taguette to be a a viable software tool for my qualitative data analysis – this is a high priority feature, followed by statistical analysis tools.

Some thoughts on the broader academic software ecosystem

Even though I won’t be adopting Taguette, I admire and respect the vision it has – to free qualitative researchers from being anchored to expensive, limiting tools. While I’m fortunate enough to be afforded an NVIVO license, many smaller, less wealthy or less research-intensive universities will struggle to provide a license seat for all qualitative researchers.

This is another manifestation of universities becoming increasingly beholden to large software manufacturers, rather than having in-house capabilities to produce and manage software that directly adds value to a university’s core capability of generating new knowledge. We’ve seen it in academic journals – with companies like EBSCO, Sage and Elsevier intermediating the publication of journals, hording copyrights to articles and collecting a tidy profit in the process – and we’re increasingly seeing it in academic software. Learning Management Systems such as Desire2Learn and Blackboard are now prohibitively expensive, while open source alternatives such as Moodle still require skilled (and therefore expensive) staff to be maintained and integrated – a challenge when universities are shedding staff in the post-COVID era.

Moreover, tools like NVIVO are imbricated in other structures which reinforce their dominance. University HDR training courses and resource guides are devoted to software tools which are in common use. Additionally, supervisors and senior academics are likely to use the dominant software, and so are in an influential position to recommend its use to their students. This support infrastructure reinforces their dominance by ascribing them a special, or reified status within the institution. At a broader level, even though open source has become a dominant business model, the advocacy behind free and open source software (FOSS) appears to be waning; open source is now the mainstream, and it no longer requires a rebel army of misfits, nerds and outliers (myself included) to be its flag-bearers. This begs the question – who advocates for FOSS within the academy? And more importantly – what influence do they have compared with a slick marketing and sales effort from a global multi-national? I’m reminded here of Eben Moglen’s wise words at linux.conf.au 2015 in Auckland in the context of opposing patent trolls through collective efforts – “freedom itself depends upon how we make use of the technologies we are creating”. That is, universities themselves have created the dependence on academic technologies which now restrict them.

There is hope, however. Platforms like ArXiv – the free distribution service and open access archive for nearly two million pre-prints in mathematics, computer science and other (primarily quant) fields – are starting to challenge the status quo. For example, the Australian Research Council recently overturned their prohibition on the citation of pre-prints in competitive grant applications.

Imagine if universities combined their resources – like they have done with ArXiv – to provide an open source qualitative coding tool, locally hosted, and accessible to everyone. In the words of Freire,

“Reading is not walking on the words; it’s grasping the soul of them.”

Paulo Freire, Pedagogy of the Oppressed

Qualitative analysis tools allow us to grasp the soul of the artefacts we create through research; and that ability should be afforded to everyone – not just those that can afford it.