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The second was a little surprising (and maybe I missed it?) There was not much in the way of easily accessing transcribed output to and from shell scripts?
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Good speech recognition generally requites massive mountains of training data, both labelled and unlabelled. Massive mountains of data tends to be incompatible with opensource projects. Even Mozilla collecting user statistics is pretty controversial. Imagine someone like Mozilla trying to collect hundreds of voice clips from each of tens of millions of users!!
> Imagine someone like Mozilla trying to collect hundreds of voice clips from each of tens of millions of users!! They do, and it's working! https://commonvoice.mozilla.org/en
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> Imagine someone like Mozilla trying to collect hundreds of voice clips from each of tens of millions of users!! They do, and it's working! https://commonvoice.mozilla.org/en
Except they have 12k hours of audio, when really they could do with 12B hours of audio...
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That's fine for training your own model, but I don't think you could distribute the training set. That seems like a clear copyright violation, against one of the groups that cares most about copyright. Maybe you could convince a couple of indie creators or state-run programs to licence their audio? But I'm not sure if negotiating that is more efficient than just recording a bit more audio, or promoting the project to…
Does using copyrighted works to train a machine learning model make that model infringing?
Also I don't think there have been any major court cases about this, so there's no clear precedent in either direction.
Good FLOSS speech recognition and TTS is badly needed. Such interaction should not be left to an oligoply with bad history of not respecting users freedoms and privacy.
Speech recognition algorithms today require lots of data, lots of training computation, and a decent design. Decent designs are in published papers all over the place, so thats a solved issue. Lots of compute requires lots of $$$, which isn't opensource-friendly. Lots of data also isn't really opensource friendly. Sadly this is a niche that the opensource business model doesn't really fit.
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Speech recognition algorithms today require lots of data, lots of training computation, and a decent design. Decent designs are in published papers all over the place, so thats a solved issue. Lots of compute requires lots of $$$, which isn't opensource-friendly. Lots of data also isn't really opensource friendly. Sadly this is a niche that the opensource business model doesn't really fit.
> Lots of compute requires lots of $$$, which isn't opensource-friendly. Not really, look up BOINC.
Has anyone had any success getting the software to work? It's entirely unpackaged: https://repology.org/projects/?search=voice2json https://pkgs.org/search/?q=voice2json Docker image is broken, how'd that happen? $ voice2json --debug train-profile ImportError: numpy.core.multiarray failed to import Traceback (most recent call last): File "/usr/lib/voice2json/.venv/lib/python3.7/site-packages/deepspeech/impl.py", line…
Good FLOSS speech recognition and TTS is badly needed. Such interaction should not be left to an oligoply with bad history of not respecting users freedoms and privacy.
So companies with a lot of data, then.