There comes a time when a unique opportunity presents itself to upgrade your secondary machine GPU to something a bit beefier and up-to-par with some of the more recent specs. Such an opportunity just happened to come up this week - in the midst of the crypto crash there are now magically GPUs available at MSRP again. It’s like Christmas in the middle of Summer. With Founders Edition (FE) versions being available at select retailers, I decided to invest in a NVIDIA GeForce RTX 3080 Ti that will replace my (not yet aging) EVGA GeForce RTX 2080 Super which I will now use as a backup card, or at some point - as an external pass-through GPU for a Linux gaming box.
One of the things that I am diving much deeper (pun intended) into lately is deep learning. And with deep learning, one of the things that can help you the most when it comes to having the right hardware locally is a beefy Graphics Processing Unit (GPU). As it turns out, offloading tasks that involve matrix multiplication to the GPU yield massive performance benefits compared to doing the same thing on the Central Processing Unit (CPU).
You might have many reason to do speech-to-text (STT) transformations locally - privacy, you have custom-trained models, or maybe you just don’t need the latency that comes with online services. I have a podcast, that I want to transcribe and generate captions for, and I wanted to do that blazingly fast. One of the choices for STT might be DeepSpeech - a library developed by Mozilla that does just that. More than that, it comes with a pre-trained English speech model that you can start using right away.
One of the things that I am really curious about is analysis of publicly-available data. There is a lot of useful context that can shed a lot of light on some important happenings and trends. I’ve started with one of the resources that has a lot of rich, user-created content: Reddit. I also wanted to focus on a local implementation, that does not require me to sign up for a big data service, such as BigQuery.