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fred123

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Why? On AWS you can rent a 24 TB, 500 core machine. Almost all problems are smaller than that so don’t need to scale to more than one machine.

Building applications that run on multiple machines is at least one order of magnitude more complex and thus slower (in development velocity), so needlessly building an application to work distributedly is just bad engineering.

SSDs and other storage drives have two layers (or more). The last layer is stable storage (= when you disconnect power no data is lost or corrupted). When you write to such a device your writes are first made in an earlier layer that is more like your computer’s main memory than actual storage (when you lose power your data is gone or corrupted). Only after time or when the cache is full an actual persistent write is made.

What’s your expertise and technology stack?

100€ is easily possible, I think it is kind of the default rate in my space (Python Data Engineering, cloud stuff, …).

From my experience there are almost exclusively very professional people on those platforms so no low balling.

Typical contract will be 4-5 days a week for 3-18 months

Bjoern author here.

The code looks very nice, looks like it’s inspired by bjoern but much cleaner. I’ve wanted to clean up the mess that the bjoern code is for years but never came around to actually doing it.

That being said I don’t think it provides a lot of value in practice. Same with bjoern. With a reasonably fast server implementation around 99% of time is spent in your Python application, not the server. So it doesn’t actually provide much value in practice to optimize the server. But it’s a nice project to learn about how to write a HTTP and WSGI server :)

Click 5 years ago

Reported by the browser but not always accurate. Used in browser fingerprinting btw

Yes. Noise suppression is very similar to speech separation (separating multiple speaker voices that talk at the same time). For example you can use ConvTasNet for both speech separation and denoising; in the denoising case you set target track 1 = speech, track 2 = noise, hence you get a noise-only track.

I guess you can also simply subtract the clean speech from the original mixture to get the noise-only track.