101–110 of 114 posts
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.
I'd check out coqui https://coqui.ai/ It's well-documented and works basically out of box. I wish the STT models bundled were closer to the quality of Kaldi but the ease-of-use has no comparisons. And maybe with time it will surpass Kaldi in quality too.
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I'd check out coqui https://coqui.ai/ It's well-documented and works basically out of box. I wish the STT models bundled were closer to the quality of Kaldi but the ease-of-use has no comparisons. And maybe with time it will surpass Kaldi in quality too.
is this free as in speech and beer and selfhostable? and not opencore?
Really interesting use of intents and entities. I feel like some of this is reinventing the wheel, since there is already a grammar specification, but novel use of intents/entities. https://www.w3.org/TR/speech-grammar/
I would have preferred to use a standard. Perhaps this is something for a future version.
Author here. Thanks to everyone for checking out voice2json! The TLDR of this project is: a unified command-line interface to different offline speech recognition projects, with the ability to train your own grammar/intent recognizer in one step. My apologies for the broken packages; I'll get those fixed shortly. My focus lately has been on Rhasspy ( https://github.com/rhasspy/rhasspy ), which has a lot of the same i…
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The source package does have installation instructions and appears to use Autotools: https://voice2json.org/install.html#from-source . Hopefully at least building from source works.
Building the v2.0 tag (or even master) using docker does not: E: The repository 'http://security.ubuntu.com/ubuntu eoan-security Release' does not have a Release file. And just bumping the image tag to ":groovy" caused subsequent silliness, so this project is obviously only for folks who enjoy fighting with build systems (and that matches my experience of anything in the world that touches Numpy and friends)
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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
Author here. Thanks to everyone for checking out voice2json! The TLDR of this project is: a unified command-line interface to different offline speech recognition projects, with the ability to train your own grammar/intent recognizer in one step. My apologies for the broken packages; I'll get those fixed shortly. My focus lately has been on Rhasspy ( https://github.com/rhasspy/rhasspy ), which has a lot of the same i…
Is the primary use case for NLP interfaces? I'm looking for a good tool for automated transcriptions of long-form (10-60 minutes) of audio.
voice2json is better suited for limited domain speech, where each sentence is a specific voice command (think home automation).
I wonder if this would pass the Debian Deep Learning Team's Machine Learning policy, which requires public data under a libre license that is retrainable using software under a libre license, without any proprietary drivers: https://salsa.debian.org/deeplearning-team/ml-policy
My aim has been to train "good enough" models for any public/free data I can get my hands on.
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Then you need a lot of people that listen to those 12B hours of audio, and multiple listeners agree for each chunk of audio that what is spoken corresponds to the transcript.
Lots of machine learning systems can use unsupervised and semi-supervised learning. Then nobody has to listen to and annotate all that audio.
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Are you talking about phrases like "please do the needful"?
That's not one I've heard. Examples that come to mind are 'even I' (which seems closer to 'I too' than the 'you'd scarcely believe it but I' that it naturally sounds to me), 'he himself' (or similar subject emphasis), and adverb repetition. I'd say it's mostly subtler (I suppose that should be the expected distribution!) things I've noticed though, they're just harder to recall as a result. (Just want to emphasise I'…
Hindi is much more economical, to put it literally, one says things like 'than/from/compared to orange, lemon is sour', and 'orange is little/less [without comparison] sour'.
Which, I believe, is what gives rise to InE sentences like 'the salt in this is very less' (it needs more salt, there's very little).