The future has been here for a while. Source: over 1 year ago

That's what I did for my personal SMMRY lib. 100% outweighed by the value of my time spent on it but I enjoy not paying for APIs. Source: over 1 year ago

This is done by autotldr[1] which is powered by smmry[2]. Smmry is pretty neat actually ! [1] http://autotldr.io/ [2] https://smmry.com/. - Source: Hacker News / over 1 year ago

Thanks but that's supposed to be free to view? That's what it said as covid-fare. You might like this for your toolbar. Source: over 1 year ago

Ah, but you see, if it isn't in the headline, it should be in the top few comments. Or, if I'm really pressed, I can always use https://smmry.com/ which made u/autotldr. Source: over 1 year ago

The auto caption is from assemblyai.com, they do a pretty good job. As for manual, you can do `Add Layer` > `Text` from the short-form editor then trim each text layer. Its slow going though. Ideally we will figure out a better interface and build it. For now I recommend using the auto caption, then modifying it to your liking, if there is more than a few words it will probably be faster. Thanks for the kind words! Source: about 1 year ago

Assemblyai is a great tool for extracting transcripts from videos, I have used it for investor presentations from other sources. - Source: dev.to / almost 2 years ago

AssemblyAI is pioneering accurate and accessible speech recognition powered by cutting edge Deep Learning, Machine Learning, and AI research. Its Speech-to-Text API transcribes audio and video files and live audio streams with industry-best accuracy. In addition, the company offers Audio Intelligence APIs that secure higher ROI for users, including Sentiment Analysis, Topic Detection, Content Moderation, Auto... - Source: dev.to / over 2 years ago

Check out http://assemblyai.com/ - the API has pretty good Diarization results and is free for small volumes of data. Source: over 2 years ago

Hello r/python community. I spent a couple weeks analyzing some podcast data from Up First and The Daily over the last year, 8/21/2020 to 8/21/2021 and compared spikes in the frequency of negative news in the podcast to how the stock market performed over the last year. Specifically against the DJIA, the NASDAQ, and the price of Gold. I used Python Selenium to crawl ListenNotes to get links to the mp3 files,... Source: almost 3 years ago