Introduction

Videos are a crucial way people learn and they are pervasive in online education platforms (Hsin and Cigas 2013; Hartsell and Yuen 2006). Producing educational videos with a lecturer speaking over slides takes time, energy, and usually video editing skills. Maintaining the accuracy and relevancy of lecture videos focused on technical subjects like computing programming or data science can often require remaking an entire video, requiring extensive editing and splicing of new segments. We present , the Automated R Instructor as a tool to address these issues by creating reproducible presentations and videos that can be automatically generated from plain text files or similar artifacts. By using , we provide a tool for users to rapidly create and update video content.

In its simplest form a lecture video is comprised of visual content (e.g. slides and figures) and a spoken explanation of the visual content. Instead of a human lecturer, the package uses a text-to-speech system to synthesize spoken audio for a lecture. Modern text-to-speech systems that take advantage of recent advancements in artificial intelligence research are available from Google, Microsoft, and Amazon. Many of these synthesizers make use of deep learning methods, such as WaveNet (Van Den Oord et al. 2016) and have interfaces in R (Edmondson 2019; Muschelli 2019a; Leeper 2017). Currently in , synthesis of the the audio can be rendered using any of these services through the package (Muschelli 2019b). The default is Amazon Polly, which has text-to-speech voice generation in over 21 languages, including a total of 29 dialects, implemented in the package (Leeper 2017). In addition to multiple languages, the speech generation services provide voices with several pitches representing different genders within the same language. We present the package with reproducible use case examples and the video outputs with different voices in multiple languages.

The package relies on the package for splitting and combining audio files appropriately so that lecture narration is synced with each slide (Ligges et al. 2018). Once the audio is generated, it is synced with images to make a lecture video. Multiple open source tools for video editing and splicing exist; takes advantage of the ffmpeg (http://www.ffmpeg.org/) software, a command-line interface to the libav library. These powerful tools have been thoroughly tested with a development history spanning almost 20 years. has been built with presets for ffmpeg which allow output videos to be compatible with multiple platforms, including the YouTube and Coursera players. These presets include specifying the bitrate, audio and video codecs, and the output video format. The numerous additional video specifications for customization can be applied to command-line arguments ffmpeg through .

We have developed a workflow with as the centerpiece for automatically generating educational videos. The narration script for lecture videos is stored in a plain text format, so that it can be synthesized into audio files via text-to-speech services. By storing lecture narration in plain text it can be updated, tracked, and collaboratively or automatically updated with version control software like Git and GitHub. If the figures in the lecture slides are created in a reproducible framework, such as generated using R code, the entire video can be reproducibly created and automatically updated. Thus, is the Automated R Instructor. We will provide examples of creating videos based on the following sets of source files: a slide deck built with R Markdown, a set of images and a script, or a Google Slide deck or PowerPoint presentation.
The overview of the processes demonstrated in this paper are seen in Figure . We will also demonstrate the , which contains helper functions extensions of .

Ari is designed to fit into several existing workflows for creating lectures and presentations. Videos can be created with Ari from a series of images and a narrative script, from an R Markdown document, or from a PowerPoint presentation with speaker notes. Ari is pre-configured so that videos are ready to be uploaded to popular platforms like YouTube or Coursera.

Ari is designed to fit into several existing workflows for creating lectures and presentations. Videos can be created with Ari from a series of images and a narrative script, from an R Markdown document, or from a PowerPoint presentation with speaker notes. Ari is pre-configured so that videos are ready to be uploaded to popular platforms like YouTube or Coursera.

Making videos with ari: ari_stitch

The main workhorse of is the ari_stitch function. This function requires an ordered set of images and an ordered set of audio objects, either paths to wav files or Wave objects, that correspond to each image. The ari_stitch function sequentially “stitches” each image in the video for the duration of its corresponding audio object using ffmpeg. In order to use , one must have an ffmpeg installation to combine the audio and images. Other packages such as have a similar requirement. Moreover, on shinyapps.io, a dependency on the package will trigger an installation of ffmpeg so can be used on shinyapps.io. In the example below, 2 images (packaged with ) are overlaid withe white noise for demonstration. This example also allows users to check if the output of ffmpeg works with a desired video player.

library(tuneR)
library(ari)
result = ari_stitch(
  ari_example(c("mab1.png", "mab2.png")),
  list(noise(), noise()),
  output = "noise.mp4")
isTRUE(result)
[1] TRUE

The output is a logical indicator, but additional attributes are available, such as the path of the output file:

attributes(result)$outfile
[1] "noise.mp4"

The video for this output can be seen at https://youtu.be/3kgaYf-EV90.

Synthesizer authentication

The above example uses tuneR::noise() to generate audio and to show that any audio object can be used with . In most cases however, is most useful when combined with synthesizing audio using a text-to-speech system. Though one can generate the spoken audio in many ways, such as fitting a custom deep learning model, we will focus on using the aforementioned services (e.g. Amazon Polly) as they have straightforward public web APIs. One obstacle in using such services is that users must go through steps to provide authentication, whereas most of these APIs and the associated R packages do not allow for interactive authentication such as OAuth.

The package provides a unified interface to these 3 text-to-speech services, and we will focus on Amazon Polly and its authentication requirements. Polly is authenticated using the package (Leeper 2019). The documentation provides options and steps to create the relevant credentials; we have also provided an additional tutorial. Essentially, the user must sign up for the service and retrieve public and private API keys and put them into their R profile or other areas accessible to R. Running text2speech::tts_auth(service = "amazon") will indicate if authentication was successful (if using a different service, change the service argument). NB: The APIs are generally paid services, but many have free tiers or limits, such as Amazon Polly’s free tier for the first year (https://aws.amazon.com/polly/pricing/).

Creating Speech from Text: ari_spin

After Polly has been authenticated, videos can be created using the ari_spin function with an ordered set of images and a corresponding ordered set of text strings. This text is the “script” that is spoken over the images to create the output video. The number of elements in the text needs to be equal to the number of images. Let us take a part of Mercutio’s speech from Shakespeare’s Romeo and Juliet (Shakespeare 2003) and overlay it on two images from the Wikipedia page about Mercutio (https://en.wikipedia.org/wiki/Mercutio):

speech =  c(
  "I will now perform part of Mercutio's speech from Shakespeare's Romeo and Juliet.", 
  "O, then, I see Queen Mab hath been with you.
   She is the fairies' midwife, and she comes
   In shape no bigger than an agate-stone
   On the fore-finger of an alderman,
   Drawn with a team of little atomies
   Athwart men's noses as they lies asleep;")
mercutio_file = "death_of_mercutio.png"
mercutio_file2 = "mercutio_actor.png"
shakespeare_result = ari_spin(
  c(mercutio_file, mercutio_file2),
  speech, output = "romeo.mp4", voice = "Joanna")
isTRUE(shakespeare_result)
[1] TRUE

The speech output can be seen at https://youtu.be/SFhvM9gI0kE.
We chose the voice “Joanna” which is designated as a female sounding US-English speaker for the script. Each voice is language-dependent; we can see the available voices for English for Amazon Polly at https://docs.aws.amazon.com/polly/latest/dg/SupportedLanguage.html.

Though the voice generation is relatively clear, we chose a Shakespearean example to demonstrate the influence and production value of the variety of dialects available from these text-to-speech services. Compare the video of “Joanna” to the same video featuring “Brian” who “speaks” with a British English dialect:

gb_result = ari_spin(
  c(mercutio_file, mercutio_file2),
  speech, output = "romeo_gb.mp4", voice = "Brian")
isTRUE(gb_result)
[1] TRUE

The resulting video can be seen at https://youtu.be/fSS0JSb4VxM.

The output video format is MP4 by default, but several formats can be specified via specifying the appropriate “muxer” for ffmpeg (see the function ffmpeg_muxers). Supported codecs can be founded using the functions ffmpeg_audio_codecs and ffmpeg_video_codecs. Additional options can be passed to ffmpeg from ari_stitch and ari_spin to customize the video to the necessary specifications.

We now discuss the number of image and script inputs that is designed to work with, including text files and a series of PNG images, a Google Slide deck or a PowerPoint presentation with the script written in the speaker notes section, or an HTML slide presentation created from an R Markdown, where the script is written in the HTML comments.

Creating Videos from R Markdown Documents

Many R users have experience creating slide decks with R Markdown, for example using the or packages (Allaire et al. 2019; Xie, Allaire, and Grolemund 2018; Xie 2018). In , the HTML slides are rendered using (Chang 2018) and the script is located in HTML comments (i.e. between <!-- and -->). For example, in the file ari_comments.Rmd included in , which is an ioslides type of R Markdown slide deck, we have the last slide:

x = readLines(ari_example("ari_comments.Rmd"))
tail(x[ x != ""], 4)
[1] "## Conclusion"                                             
[2] "<!--"                                                      
[3] "Thank you for watching this video and good luck using Ari!"
[4] "-->"

so that the first words spoken on that slide are "Thank you". This setup allows for one plain text, version-controllable, integrated document that can reproducibly generate a video. We believe these features allow creators to make agile videos, that can easily be updated with new material or changed when errors or typos are found. Moreover, this framework provides an opportunity to translate videos into multiple languages, we will discuss in the future directions.

Using ari_narrate, users can create videos from R Markdown documents that create slide decks. The can be passed in, and the output will be created using the render function from (Allaire et al. 2019). If the slides are already rendered, the user can pass these slides and the original document, where the script is extracted. Passing rendered slides allows with the option for a custom rendering script. Here we create the video for ari_comments.Rmd, where the slides are rendered inside ari_narrate:

# Create a video from an R Markdown file with comments and slides
res = ari_narrate(
  script = ari_example("ari_comments.Rmd"),
  voice = "Kendra",
  capture_method = "iterative")

The output video is located at https://youtu.be/rv9fg_qsqc0. In our experience with several users we have found that some HTML slides take more or less time to render when using ; for example they may be tinted with gray because they are in the middle of a slide transition when the image of the slide is captured. Therefore we provide the delay argument in ari_narrate which is passed to . This can resolve these issues by allowing more time for the page to fully render, however this means it may take for more time to create each video. We also provide the argument capture_method to allow for finely-tuned control of webshot. When capture_method = "vectorized", is run on the entire slide deck in a faster process, however we have experienced slide rendering issues with this setting depending on the configuration of an individual’s computer. However when capture_method = "iterative", each slide is rendered individually in webshot, which solves many rendering issues, however it causes videos to be rendered more slowly.
In the future, other HTML headless rendering engines (webshot uses PhantomJS) may be used if they achieve better performance, but we have found to work well in most of our applications.

With respect to accessibility, encourages video creators to type out a script by design. This provides an effortless source of subtitles for people with hearing loss rather than relying on other services, such as YouTube, to provide speech-to-text subtitles. When using ari_spin, if the subtitles argument is TRUE, then an SRT file for subtitles will be created with the video.

One issue with synthesis of technical information is that changes to the script are required for Amazon Polly or other services to provide a correct pronunciation. For example, if you want the service to say “RStudio” or “ggplot2”, the phrases “R Studio” or “g g plot 2” must be written exactly that way in the script. These phrases will then appear in an SRT subtitle file, which may be confusing to a viewer. Thus, some post-processing of the SRT file may be needed.

Creating Videos from Other Documents

In order to create a video from a Google Slide deck or PowerPoint presentation, the slides should be converted to a set of images. We recommend using the PNG format for these images. In order to get the script for the video, we suggest putting the script for each slide in the speaker notes section of that slide. Several of the following features for video generation are in our package (https://github.com/muschellij2/ariExtra). The speaker notes of slides can be extracted using (Noorazman 2018) for Google Slides via the API or using / (Gohel 2019; Ewing 2017) to read from PowerPoint documents. Google Slides can be downloaded as a PDF and converted to PNGs using the package (Ooms 2019). The package also has a pptx_notes function for reading PowerPoint notes. Converting PowerPoint files to PDF can be done using LibreOffice and the package (Rudis and Muir 2019) which contains the necessary wrapper functions.

To demonstrate this, we use an example PowerPoint is located on Figshare (https://figshare.com/articles/Example_PowerPoint_for_ari/8865230). We can convert the PowerPoint to PDF, then to a set of PNG images, then extract the speaker notes.

pptx = "ari.pptx"
download.file(paste0("https://s3-eu-west-1.amazonaws.com/", 
                     "pfigshare-u-files/16252631/ari.pptx"),
              destfile = pptx)
pdf = docxtractr::convert_to_pdf(pptx) # >= 0.6.2 
pngs = pdftools::pdf_convert(pdf, dpi = 300)
notes = ariExtra::pptx_notes(pptx)
notes
[1] "Sometimes it’s hard for an instructor to take the time to record their lectures.
For example, I’m in a coffee shop and it may be loud."

[2] "Here is an example of a plot with really small axes.  We plot the x versus the y
-variables and a smoother between them."

The package also can combine these processes and take multiple input types (Google Slides, PDFs, PPTX) and harmonize the output. The pptx_to_ari function combines the above steps:

doc = ariExtra::pptx_to_ari(pptx)
Converting page 1 to /var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpDXudKg/filede345269ba53.png... done!
Converting page 2 to /var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpDXudKg/filede341b47c96a.png... done!
doc[c("images", "script")]
$images
[1] "/private/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T/RtmpDXudKg/filede345f85c24_files/slide_1.png"
[2] "/private/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T/RtmpDXudKg/filede345f85c24_files/slide_2.png"

$script
[1] "Sometimes it’s hard for an instructor to take the time to record their lectures.  For example, I’m in a coffee shop and it may be loud."
[2] "Here is an example of a plot with really small axes.  We plot the x versus the y-variables and a smoother between them."

which can be passed to ari_spin.

We can then render the video with the “Kimberly” voice. We use the divisible_height argument to forcibly scale the height of the images to be divisible by 2 if necessary. This is required by the x264 (default) codec which we have specified as a preset:

pptx_result = ari_spin(pngs, notes, output = "pptx.mp4", voice = "Kimberly",
    divisible_height = TRUE, subtitles = TRUE)
isTRUE(pptx_result)

You can see the output at https://youtu.be/TBb3Am6xsQw. Here we can see the first few lines of the subtitle file:

[1] "1"                                       
[2] "00:00:00,000 --> 00:00:02,025"           
[3] "Sometimes it’s hard for an instructor to"
[4] "2"                                       
[5] "00:00:02,025 --> 00:00:04,005"           
[6] "take the time to record their lectures."

For Google Slides, the slide deck can be downloaded as a PowerPoint and the previous steps can be used, however it can also be downloaded directly as a PDF. We will use the same presentation, but uploaded to Google Slides. The package has the function gs_to_ari to wrap this functionality (as long as link sharing is turned on), where we can pass the Google identifier:

gs_doc = ariExtra::gs_to_ari("14gd2DiOCVKRNpFfLrryrGG7D3S8pu9aZ")
Converting page 1 to /var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpDXudKg/filede343b24ef07.png... done!
Converting page 2 to /var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpDXudKg/filede3475c8d2e6.png... done!

Note, as Google provides a PDF version of the slides, this obviates the LibreOffice dependency.

Alternatively, the notes can be extracted using and for Google Slides via the API, but requires authentication, so we will omit it here. Thus, we should be able to create videos using R Markdown, Google Slides, or PowerPoint presentations in an automatic fashion.

Summary

The package combines multiple open-source tools and APIs to create reproducible workflows for creating videos. These videos can be created using R Markdown documents, PowerPoint presentations, Google Slide decks, or simply series of images. The audio overlaid on the images can be separate or contained within the storage of the images. These workflows can then be reproduced in the future and easily updated. As the current voice synthesis options are somewhat limited in the tenacity and inflection given, we believe that educational and informational videos are the most applicable area.

Future directions

The package is already being used to build data science curricula (Kross and Guo 2019) and we look forward to collaborating with video creators to augment according to their changing needs. In the following section we outline possible directions for the future of the project.

Since is designed for teaching technical content, we plan to provide better support for the pronunciation of technical terms like the names of popular software tools. These names are usually not pronounced correctly by text-to-speech services because they are not words contained in the training data used in the deep learning models used by these services. To address this concern we plan to compile a dictionary of commonly used technical terms and the phonetic phrasing and spelling of these terms that are required in order to achieve the correct pronunciation from text-to-speech services.

In addition to still images and synthesized voices, we would like to develop new technologies for incorporating other automatically generated videos into lectures generated by . As computer programming, statistics, and data science instructors we often rely on live coding (Chen and Guo 2019) to demonstrate software tools to our students. Live coding videos suffer from many of the same problems as other kinds of technical videos as we addressed in the introduction. Therefore we plan to build a system for automating the creation of live coding videos. These videos would also be created using plain text documents like R Markdown. They would integrate synthesized narration with code chunks that would be displayed and executed according to specialized commands that would specify when code should be executed in an IDE like RStudio. These commands could also control which panes and tabs of the IDE are visible or emphasized.

As programmatic video creation software improves, we plan to extend so it can expand its compatibility with different technologies. For example we believe the heavy reliance on an ffmpeg installation can be mitigated in the future with advances in the package. Though the package has powerful functionality and is currently porting more from libav and therefore ffmpeg, it currently does not have the capabilities required for . Although third party installation from https://ffmpeg.org/ can be burdensome to a user, package managers such as brew for OSX and choco for Windows provide an easier installation and configuration experience.

Although we rely on Amazon Polly for voice synthesis, other packages provide voice synthesis, such as for Microsoft and for Google. We created the package to harmonize these synthesis options for . Thus, switching from one voice generation service to another simply involves switching the service and voice arguments in , assuming the service is properly authenticated. This ease of switching allows researchers to compare and test which voices and services are most effective at delivering content.

We see significant potential in how could expand global learning opportunities. Video narration scripts can be automatically translated into other languages with services like the Google Translation API, where provides an interface. Amazon Polly can speak languages other than English, meaning that one can write a lecture once and generate lecture videos in multiple languages. Therefore this workflow can greatly expand the potential audience for educational videos with relatively little additional effort from lecture creators. We plan to flesh out these workflows so that video creators can manage videos in multiple languages. We hope to add functionality so that communities of learners with language expertise can easily suggest modifications to automatically translated videos, and tooling so suggestions can be incorporated quickly.

The workflow we have specified requires many interlocking pieces of software, therefore we have created a Docker environment (https://github.com/seankross/bologna) which contains the required packages to create videos using . This Docker image ensures that our workflow is completely reproducible, and it also enables deployment to multiple disk images for creating lecture videos at scale.

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