I have been working on an interesting eLearning project as of late, and I say this because of the challenges that were present right at the beginning of the programming phase.
In this project, our client has asked us to revamp online training material that was developed over a decade ago using Adobe Captivate, however, the challenge began when we were informed that the source files (.cptx files) have disappeared since the modules were initially developed. For the most part, this would be fine, since we can recreate it from scratch, but the main issue came to be when we needed to create an audio script to make modifications and record.
To put things into perspective, eLearning modules developed in older versions of Adobe Captivate (and even in the most recent versions) when published, create SWF files that are then wrapped in HTML and SCORM to be played in a LMS. The issue with this, is that SWF files are containers that encapsulate and encode different kinds of data, such as images, videos, audio, text, etc. and that becomes problematic when you want to edit or extract information, as opposed to the current HTML5 export, in which files are encoded but are still somewhat available without much revers engineering.
In any case, we needed to create a transcript from the audio in all the sections of the online training, which added to 9 sections, with an average of 20 slides each. If you want to manually transcribe all that audio, it could take you days, depending on how long this audio is. In our world, we usually say that one screen equals one minute of eLearning, so I was already prepared to transcribe approximately 180 minutes of audio (!).
Thankfully, there are tools out there that can simplify and automate our tasks, and although the results may not be 100% accurate, these tools can save you tons of time.
So, for extracting the audio files from the SWF files, I used a software called SFWRIP, which extracts and categorizes all the elements in a SWF file and then you can export these elements to a folder in your computer:
After having done this and having the audio as MP3 files, we can now create the transcript by using an AWS service called Amazon Transcribe. To do this you must, in a nutshell:
Have an S3 bucket available where you can upload your files (S3 is the storage platform in AWS)
Once you have uploaded these files to your S3 bucket, you can generate S3 links for each of the files and then create jobs in Amazon Transcribe.
The latter might be a bit tedious, because you need to create a job per file, but at the end of the day, the couple hours you spend doing this, will potentially save you days of manually transcribing every single file.