In a rather interesting article on creative collaboration, Here Comes the Automation: How AI is Poised to Change Filmmaking, we get this quote:
“When a distinguished but elderly industry executive says that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.” — Clarke’s Law No. 1, slightly modified
It led me to think of how many of our creative tools in use every day were simply impossible a few years back. You don’t have to go back too far to be in a pre Internet era. Non-Linear Video Editing is less than 30 years old. A million dollar Da Vinci Resolve suite is now a free download from that Internet!
HD and 4K capable cameras on portable computers good enough to edit that with. (Speaking of which, check out LumaTouch for a look at what can be done on those iPhones and iPads carrying the camera.) Creative storytelling is more accessible than ever.
Our creative tools are in a constant state of evolution – a.k.a. change – and we’ve only just started realizing how “artificial intelligence” (i.e. machine learning based) tools are going to work their way into creative tools and workflows. This will likely fundamentally change the way we interact with creative tools, much the way non-linear editing of video on computers did 25 years ago.
Being open to change is essential, otherwise we risk being that “elderly industry executive” saying something was impossible, that others are doing every day!
I’ve certainly learnt to stop saying “that’s impossible” because it’s rarely true for very long.
I don’t always cross post my appearances on Larry Jordan’s Digital Production BuZZ, but I thought I did a particularly good explanation of the basics of AI and Machine Learning and how they might apply in production, that I thought I’d share this one.
Philip Hodgetts: The Basics of AI – Explained
Web APIs (Application Programming Interface) allow us to send data to a remote service and get a result back. Machine learning tools and Cognitive Services like speech-to-text and image recognition are mostly online APIs. Trained machines can be integrated into apps, but in general these services operate through an API.
The big advantage is that they keep getting better, without the local developer getting involved.
Nearly two years ago I wrote of my experience with SpeedScriber*, which was the first of the machine learning based transcription apps on the market. At the time I was impressed that I could get the results of a 16 minute interview back in less than 16 minutes, including prep and upload time. Usually the overall time was around the run time of the file.
Upload time is the downside of of web based APIs and is significantly holding back image recognition on video. That is why high quality proxy files are created for audio to be transcribed, which reduces upload time.
My most recent example sourced from a 36 minute WAV, took around one minute to convert to archival quality m4a which reduced the file size from 419 MB to 71MB. The five times faster upload – now 2’15” – compared with more than 12 minutes to upload the original, more than compensates for the small prep time for the m4a.
The result was emailed back to me 2’30.” That’s 36 minutes of speech transcribed with about 98% accuracy, in 2.5 minutes. That’s more than 14x real time. The entire time from instigating the upload to finished transcript back was 5’45” for 36 minutes of interview.
These APIs keep getting faster and can run on much “heavier iron” than my local iMac which is no doubt part of the reason they are so fast, but that’s just another reason they’re good for developers. Plus, every time the speech-to-text algorithm gets improved, every app that calls on the API gets the improvement for free.
*I have’t used SpeedScriber recently but I would expect that it has similarly benefited from improvements on the service side of the API they work with.
For a book project I recorded a 46 minute interview and had it transcribed by Speechmatics.com (as part of our testing for Lumberjack Builder). The interview was about 8600 words raw.
The good news is that it was over 99.98% accurate. I corrected 15 words out of a final 8100. The interview had good audio. I’m sure an audio perfectionist would have made it better, as would recording in a perfect environment, but this was pretty typical of most interview setups. It was recorded to a Zoom H1N as a WAV file. No compression.
Naturally, my off-mic questions and commentary was not transcribed accurately but it was never expected or intended to be. Although, to be fair, it was clear enough that a human transcriber would probably have got closer.
The less good news: my one female speaker was identified as about 15 different people! If I wanted a perfect transcript I probably would have cleaned up the punctuations as it wasn’t completely clean. But reality is that people do not speak in nice, neat sentences.
But neither the speaker identification nor the punctuation matter for the uses I’m going to make. I recognize that accurate punctuation would be needed for Closed (or open) Captioning for an output, but for production purposes perfect reproduction of the words is enough.
Multiple speakers will be handled in Builder’s Keyword Manager and reduced to one there. SpeedScriber has a feature to eliminate the speaker ID totally, which I would have used if a perfect output was my goal. For this project I simply eliminated any speaker ID.
The punctuation would also not be an issue in Builder, where we break on periods, but you can combine and break paragraphs with simple keystrokes. It’s not a problem for the book project as it will mostly be rewritten from spoken form to a more formal written style.
Most importantly for our needs, near perfect text is the perfect input for keyword, concept and emotion extraction.
On the night of the Supermeet 2011 Final Cut Pro X preview I was told that this was the “foundation for the next 10 years.” Well, as of last week, seven of the ten have elapsed. I do not, for one minute, think that Apple intended to convey a ten year limit to Final Cut Pro X’s ongoing development, but maybe it’s smart to plan obsolescence. To limit the time an app continues to be developed before its suitability for the task is re-evaluated.
Continue reading Maybe 10 Years is Enough for Final Cut Pro X
As someone who’s watched the development of machine learning, and who is in the business of providing tools for post production workflows that “take the boring out of post” you’d think I’d be full of ideas of how post can be enhanced by machine learning.
Continue reading What Do We Want Machine Learning to be Used for in Post?
Two recent announcement place IBM’s Artificial Intelligence play, Watson, right in the sports spotlight.
Watson is being used for tagging World Cup coverage, and the relationship with Wimbledon from picking highlights and enhancing user experience to, this year, designing the poster!
Continue reading IBM Watson is a Sports Guru?
Endgaget recently had an article on transferring facial movements from a person in one video, to a different person in a different video. Unlike previous approaches, this latest development requires only a few minutes of the target person’s video, and correctly handles shadows.
Combined with other research that allows us to literally “put words in people’s mouths” by typing them and having them created in a person’s voice that never said the words. Completely synthesized and indistinguishable from the person saying it.
Transferred facial movements plus created words in that person’s voice and it will be a forensic operation to determine if the results are “genuine” or created.
This is the first time I’ve taken a deep look at a TV show and worked out what I think would be the perfect metadata workflow from shoot to edit bay. I chose to look at Pie Town’s House Hunters franchise because it is so built on a (obviously winning) formulae, and I thought that might make it easier for automation or Artificial Intelligence approaches.
But first a disclaimer. I am in no way associated with Pie Town Productions. I know for certain they are not a Lumberjack System customer and am also pretty sure they – like the rest of Hollywood – build their post on Avid Media Composer (and apparently Media Central as well). This is purely a thought exercise built around a readily available example and our Lumberjack System’s capabilities.
Continue reading Modern Logging and Pre-Editing Approaches: “House Hunters” Style Reality TV
In some way I guess this is another example of Artificial Intelligence (by which we mean Machine Learning) taking work away from skilled technicians, but human recall has been replaced with facial identification at the recent Royal Wedding in the UK, where Amazon’s facial recognition technology was used to identify guests arriving sat the wedding.
Users of Sky News’ livestream were able to use a “Who’s Who Live” function:
As guests arrived at St. George’s Chapel at Windsor Castle, the function identified royals and other notable guests through on-screen captions, interesting information about each celebrity and how they are connected to Prince Harry and Meghan Markle.
The function was made possible by Amazon Rekognition, a cloud-based technology that uses AI to recognize and analyze faces, as well as objects, scenes and activities in images and video. And Sky News isn’t the first to use it: C-SPAN utilizes Rekognition to tag people speaking on camera.
Rekognition is also being used by law enforcement.
Facial recognition and identification would obviously be useful for logging in reality and documentary production.