flashlight VS common-voice

I think a comparison to PyTorch, TensorFlow and/or JAX is more relevant than a comparison to CuPy/NumPy.

And then maybe also a comparison to Flashlight (https://github.com/flashlight/flashlight) or other C/C++ based ML/computing libraries?

Also, there is no mention of it, so I suppose this does not support automatic differentiation?

This Facebook ai project seems reasonably structured after looking at its CMakeLists.txt. CMake is a build generator for c++, it's how you make binaries to run your project: https://github.com/flashlight/flashlight

Continue reading | Check out the paper and github link

I built and maintain Flashlight, a C++-first library for ML/DL. We built Flashlight to be:

I’ve never used it, but Facebook’s flashlight looks interesting

Flashlight bro, not flash. Read again

I've had good results with https://github.com/flashlight/flashlight/blob/master/flashli.... Seems to work well with spoken english in a variety of accents. Biggest limitation is that the architecture they have pretrained models for doesn't really work well with clips longer than ~15 seconds, so you have to segment your input files.

Mozillas Common Voice Project (https://commonvoice.mozilla.org/) is creating an open dataset for many minority languages to make it easier to support them in STT systems. If you speak one of these languages please consider donating a few minutes of your voice.

> it was not at all obvious to me there was some way of speeding up getting a language in the first place.

Yeah, that's the biggest failing of Common Voice in my opinion. Getting a new language up to speed could be much improved by simply adding a few links to documentation, but even the existing links are broken, which I reported in March 2022... https://github.com/common-voice/common-voice/issues/3637

> I have no interest in wasting time contributing to a UI translation I actively don't want to be subjected to

Translating the UI may still help you get other people to record, even if you don't want to use it yourself.

> I'll see if I can submit some sentences at least

If you want to go faster, there's also a project to extract sentences from Wikipedia etc. in small doses Mozilla's lawyers and Wikimedia's lawyers have agreed are fair use. I think you'd only need to define how Norwegian Bokmål separates sentences. (E.g. after a period but not if it's a common abbreviation like "etc." in the preceding sentence.)

There is a lot of TTS and SST development going on (https://github.com/mozilla/TTS; https://github.com/mozilla/DeepSpeech; https://github.com/common-voice/common-voice). That is the only way they work: Contributions from the wild.

Since you're interested in STT and TTS, let me just plug in Mozilla's Common Voice, a way for everyone to contribute to an open source data set for STT. You can record yourself or verify other people's recordings!

[커먼보이스 전자우편](mailto:[email protected]) || Common Voice || Korean Language Homepage || FAQs || Speaking Aloud and Reviewing Recordings || Sentence Collector || NVidia/NeMo

How can it be used for transcription?

In their website I only see an interface for either uploading audio or submitting transcriptions:

https://commonvoice.mozilla.org/es

The Github repo they mention (https://github.com/common-voice/common-voice) seems to be just that sample collection software. I do not see where I can download the software to transcribe audio.

A practical example: in the speech community, getting good results on datasets such as TIMITwould not be revolutionary, since it's super small and very old. On the contrary, things like Common Voice (which is constantly crowd-sourced) would be much more impactful. Just my two cents :)