Anyone getting into audio/video editing for the first time is almost immediately struck with the sheer enormity and complexity of it all. Even if you have the physical hardware, the proper software, and the creative spark to produce media, that doesn’t make the process of editing it all into a cohesive product any less daunting. For those of us struggling under the sisyphean weight of complicated editing workflows, a new product aims to relieve us all of this struggle. Enter Descript, an automatic transcription tool.
Descript uses machine-learning to transcribe your raw audio and video files into a dialogue script. This in itself is an incredibly valuable tool for anyone looking to transcribe podcasts, youtube videos, or whatever kind of media you produce. But this is just the beginning of what makes this app so special.
Descript is the world’s first audio word processor. Using the transcript the app creates from your audio, you can edit the text script to change the media itself. Removing the “umms” and “ahhs” from your speech — or removing whole sentences at a time — is as simple as using the backspace key on a word processor.
As a would-be podcaster, I played around with the app over the weekend, so I can tell you my initial impressions of the app. While it’s not for me (not yet, anyway), it is incredibly easy and fun and quite frankly mindblowing to use.
First things first, let’s talk about the cost.
The app works on a subscription model that pays by the minute. New users are able to upload up to 30 minutes of audio for free, but anything past that will require paying 15 cents per minute or signing up for a monthly subscription. Keep in mind these costs apply to total raw audio uploaded, not finished product audio produced. So if you’re the type (like me) to record several hours of audio per week only to trim it down to a single hour of product, this may be a bit on the wasteful side.
As for the transcription itself, the program’s machine-learning transcription transcribed my dulcet tones into the appropriate written words with nearly complete accuracy. I did have a few issues with the program understanding other speakers, but I believe that may have been a fault on my end that I’ll go into later. If the machine-learning transcription isn’t accurate enough for you, you can also choose to pay extra in order to have your audio specially transcribed by real human professionals.
The app can divide audio between different people speaking, but not automatically. If you have different audio files for each speaker, then each audio file will be labeled separately from the start. If multiple speakers are on the same audio track (like mine), then you’ll have to notate these differing speakers in the script yourself. I believe this is why the program had difficulty transcribing other speakers on the audio than myself. Being on the same audio track, the machine attuned itself to my voice (the first speaker on the recording) and was trying to interpret other people’s words as if I were the one saying them.
As for the audio editing aspect of this program, well, it really needs to be experienced to be believed. I was told what the program could do beforehand, but actually editing audio just by changing words around on a script is something else entirely. Cutting out non sequitur sentences, removing unnecessary articles, or even changing the order of words around to better suit the flow of conversation — through a literal word processor — will make you feel like an arcane grammar wizard.
Will this replace your entire audio/video workflow? Probably not. At least not yet. In addition to the cost factor which may be prohibitive to some users, there are some issues of editing that aren’t based on word choice. I found myself frustrated at my inability to change the timing of spaces between words, sometimes leaving gaps between sentences (or not enough space between words). Of course, I only had the program for a weekend, so this could very well be attributed to user error.
Whatever flaws real or imagined this program may have, it’s very important to keep in mind that Descript is the first of its kind.
It can only improve from here, not to mention potentially inspire a wave of similar programs that may very well function better. Whether or not Descript is right for you, what’s undeniable is that this program is the start of something amazing.
China no longer dependent on U.S. for smartphone components
(TECH NEWS) Trump’s trade war, more specifically, the ban on shipping phone components, to China has begun to take a toll on chip manufacturing.
Once upon a time, the U.S. and China were buddies, exporting and importing from each other with ease. However, President Trump’s recent actions regarding trade with China is certainly putting a damper on things.
It seems that Chinese companies have moved past the need to import certain products, like smartphone chips, from the U.S. – something they previously relied heavily on by working with American companies like Qorvo, Inc. in North Carolina, Skyworks, Inc. in Massachusetts, Broadcom, Inc. in California, and Cirrus Logic in Texas.
Since the ban in May, Trump specifically barred shipments from the U.S. from companies like Qualcomm and Intel Corp to companies like Chinese tech conglomerate, Huawei Technologies Co. But much like the bans that came before the Trump administration, it didn’t last long. With tensions high, the U.S. actually recently started rolling back some aspects of the ban and started making exceptions that allow American tech companies to continue to work with Chinese companies like Huawei.
Of course, China’s lack of U.S. parts hasn’t stopped them from rolling out new and improved products. As a matter of fact, in September, Huawei unveiled its newest phone, the Mate 30, which boasts highly-desired features, such as a curved screen and a wide angle camera. This makes the phone a pretty solid competitor of Apple’s newest iPhone, the iPhone 11, of which China was sent 10 million of in September and October.
After Huawei’s announcement, investment and banking firm UBS, and Japanese technology lab Fomalhaut Techno Solutions, partnered up and took to their labs to analyze the phone’s components. Their analysis was simple and straightforward. They found that there were absolutely zero American components in the phone. In fact, the chips in the Mate 30 are actually from Huawei’s in-house chip design agency, HiSilicon. They also provided Huawei with WiFi and Bluetooth chips. With HiSilicon’s 20 + years experience in the industry, 200+ chipsets, and 8000+ patents, it’s no wonder U.S. chip companies are getting nervous. Qualcomm, for example, announced a 31-40% decrease in estimated chip shipments over the next year.
Although the chip ban has made a big impact on larger U.S. companies who make and supply chips to China, there are still many other businesses that have been affected in Trump’s trade war. As it happens, U.S. Commerce Secretary Wilbur Ross recently confessed that, since May, when the ban was put in place, the U.S. has received at least 260 requests, asking that they excuse them from the ban and be allowed to work with China as they previously had.
But really, at the end of the day, with so many American companies relying on China for both import and export, it’s probable that the ban will be short-lived and that exceptions won’t need to be made. As Americans, we can be hopeful that the end-result of this trade war will be a positive one, but only time will tell.
AI cameras could cut down traffic deaths, but there may be flaws
(TECH NEWS) Traffic accidents have plagued humanity since motor vehicles were created, can AI help cut down on text and drive incidents?
What if we told you Australian officials believe they have found a way to reduce driving deaths by almost 30% in just two years? It’s a pretty appealing concept. After all, Australia alone faces an average of over 3 deaths a day due to driving accidents. And Australia’s average death rate clocks in at just half of what we face in the United States.
There’s just one problem with Australia’s proposed solution: it’s basically Big Brother.
Basically, Australia plans to use AI cameras to catch people texting and driving. There are plenty of places that have outlawed texting and driving, but that rule is very hard to enforce – it basically means catching someone in the act. With AI cameras, hands free driving can be monitored and fined.
Australia has already started rolling out some of these systems in South Wales. Because this is a new initiative, first time offenses will be let off with a warning. The following offenses can add up quickly, though, with fines anywhere from $233 to $309 USD. After a six month trial period, this program is projected to expand significantly.
But there are real concerns with this project.
Surprisingly, privacy isn’t one of these worries. Sure, “AI cameras built to monitor individuals” sounds like a plot point from 1984, but it’s not quite as dire as it seems. First, many places already have traffic cameras in order to catch things like people running red lights. More importantly, though, is the fact these machines aren’t being trained to identify faces. Instead, the machine learning for the cameras will focus on aspects of distracted driving, like hands off the wheel.
The bigger concern is what will come from placing the burden of proof on drivers. Because machine learning isn’t perfect, it will be paired with humans who will review the tagged photographs in order to eliminate false positives. The problem is, humans aren’t perfect either. There’s bound to be false positives to fall through the cracks.
Some worry that the imperfect system will slow down the judicial system as more people go to court over traffic violations they believe are unfair. Others are concerned that some indicators for texting while driving (such as hands off the wheel) might not simply apply texting. What if, for instance, someone was passing a phone to the back seat? Changing the music? There are subtleties that might not be able to be captured in a photograph or identified by an AI.
No matter what you think of the system, however, only time can tell if the project will be effective.
DeepComposer: AWS’ piano keyboard turns AI up to 11
(TECH NEWS) Amazon has been busy with machine learning, which includes a camera, a car, and now DeepComposer that’s able to add to classics on the fly
Musicians, listen up, there’s a new kid in town, its name is DeepComposer and it’s coming to take your creativity and turn it up to 11.
Artificial Intelligence has taken a leap into what has long been considered the “pinnacle of human creativity”, as Amazon revealed what is said to be the world’s first machine learning-enabled keyboard capable of creating music.
Amazon unveiled its AWS DeepComposer keyboard Monday during AWS re:Invent, a learning conference Amazon Web Services hosted for the global cloud computing community in Las Vegas.
Demonstrating DeepComposer’s abilities, Dr. Matt Wood, Amazon’s VP of Artificial Intelligence, played a snippet of Beethoven’s “Ode to Joy” and then let the keyboard riff on it with drums, synthesizer, guitar, and bass, sharing a more rockin’ version of the masterpiece.
Generative AI, is considered by scientists at MIT to be one of the most promising advances in AI in the past decade, Wood told the crowd. Generative AI allows for a machine not only to learn from example, as a human would but to take it next level and connect the dots, making the next creative step to composing something completely new.
“It [Generative AI] opens the door to an entire world of possibilities for human and computer creativity, with practical applications emerging across industries, from turning sketches into images for accelerated product development, to improving computer-aided design of complex objects, Amazon said on its AWS re:Invent website.
How does it work? The Generative AI technique pits two different neural networks against each other to produce new and original digital works based on sample inputs, according to Amazon. The generator creates, the discriminator provides feedback for tweaks and together they create “exquisite music”, Wood explained.
A user inputs a melody on the keyboard, then using the console they choose the genre, rock, classical, pop, jazz or create your own and voila, you have a new piece of music. Then, if so desired users can share their creations with the world through SoundCloud.
This is the third machine learning teaching device Amazon has made available, according to TechCrunch. It introduced the DeepLens camera in 2017 and in 2018 the DeepRacer racing cars. DeepComposer isn’t available just yet, but AWS account holders can sign up for a preview once it is.
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