Transcribe Your Zoom Meetings For Free with AWS!
By: Paul Singman
Tags: Technology, Productivity, Data Science, Programming, AWS

Pretty good! The full text (not split by speaker) looks like:

Jack. I want to thank you for what he did. Not just for for pulling me back, but for your discretion. You're welcome. Look, I know what you must be thinking. Poor little rich girl. What does she know about misery? No, no, it's not what I was thinking. What I was thinking was what could have happened to this girl to make her think she had no way out. Yes. Well, I you It was everything. It was my whole world and all the people in it. And the inertia of my life plunging ahead in me, powerless to stop it. God, look at that. He would have gone straight to the bottom. 500 invitations have gone out. All of Philadelphia society will be there. And all the while I feel I'm standing in the middle of a crowded room screaming at the top of my lungs and no one even looks up. Do you love him? Pardon me, Love. You're being very rude. You shouldn't be asking me this. Well, it's a simple question. Do you love the guy or not? This is not a suitable conversation. Why can't you just answer the question? This is absurd. you don't know me, and I don't know you. And we are not having this conversation at all. You are rude and uncouth and presumptuous, and I am leaving now. Jack. Mr. Dawson, it's been a pleasure. I sought you out to thank you. And now I have thanked you and insulted. Well, you deserve it, right? Right. I thought you were leaving. I am. You are so annoying. Ha ha. Wait. I don't have to leave. Things is my part of the ship. You leave. Oh, well, well, well, now who's being rude? What is the stupid thing you're carrying around? So, what are you, an artist or something? He's a rather good. Yeah, very good, actually.

The text results are pleasantly accurate. Not something you could copy verbatim into an article, but it’s an amazing head start compared to transcribing a recording yourself from scratch.

Unfortunately, it did struggle a bit with identifying the different speakers correctly. After watching the clip again, I can see how maybe this wasn’t the best choice of scene since they do sound kind of similar, especially in the low-quality format produced by a converted YouTube video.

From personal experience, I can say that with Zoom recordings it’s had greater success thus far in correctly identifying each speaker, though I still haven’t tried it on a meeting with more than two people.

Final Thoughts

Overall, I’m happy with the functionality I was able to produce in a few hours of Thanksgiving weekend tinkering.

In truth, the reason I’m interested in this capability is I’d like to start supplanting my OC articles here on Medium with interviews of other data professionals. With this functionality figured out, I feel ready to reach out to people in my network and see if they are interested in sharing their thoughts and knowledge about working in data.

Oh, and if you are curious for the full transcribe.py script, I’ve included it below for your perusing pleasure!

Note: The code snippet above draws from this excellent AWS tutorial on the Amazon Transcribe service.

For more interesting content like this, follow me on Medium :)