Kaldi VS AssemblyAI

Yeah, whisper is the closest thing we have, but even it requires more processing power than is present in most of these edge devices in order to feel smooth. I've started a voice interface project on a Raspberry Pi 4, and it takes about 3 seconds to produce a result. That's impressive, but not fast enough for Alexa. From what I gather a Pi 5 can do it in 1.5 seconds, which is closer, so I suspect it's only a... - Source: Hacker News / 4 months ago

You can study CTC in isolation, ignoring all the HMM background. That is how CTC was also originally introduced, by mostly ignoring any of the existing HMM literature. So e.g. Look at the original CTC paper. But I think the distill.pub article (https://distill.pub/2017/ctc/) is also good. For studying HMMs, any speech recognition lecture should cover that. We teach that at RWTH Aachen University but I don't think... - Source: Hacker News / 7 months ago

I also tried Kaldi but the build process was too much for my tiny brain; I've also heard good things about vosk but didn't try that. Source: about 1 year ago

Frameworks as well as toolkits like Kaldi were at first promoted by the research study area, yet nowadays used by both scientists and also market experts, reduced the access obstacle in the advancement of automatic speech recognition systems. Nonetheless, cutting edge methods need big speech data readies to achieve a usable system. Source: over 1 year ago

If you interested in unix-like software design and not yet familiar with kaldi toolkit, you definitely need to check it https://kaldi-asr.org It extended Unix design with archives, control lists and matrices and enabled really flexible unix-like processing. For example, recognition of a dataset looks like this: extract-wav scp:list.scp ark:- | compute-mfcc-feats ark:- ark:- | lattice-decoder-faster final.mdl... - Source: Hacker News / 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: 12 months ago

Assemblyai is a great tool for extracting transcripts from videos, I have used it for investor presentations from other sources. - Source: dev.to / over 1 year 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: over 2 years ago