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A 2019 Guide for Automatic Speech Recognition

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Re: A 2019 Guide for Automatic Speech Recognition

#21

Post by lunixbochs »

priansh wrote: post #9
gok wrote: post #5

I'm a little confused about the title because the first paper is from 2014. It's also too bad this doesn't mention any traditional HMM-based ASR techniques, as HMMs continue to be used on many SOTA systems, particularly those that can be reproduced publicly: https://github.com/syhw/wer_are_we

This. The article quotes DeepSpeech, wavenets, LSTMs of all sorts; essentially, all the neural networks that scale terribly. DeepSpeech for example is pretty heavy and requires a decent GPU to get anywhere near a realtime factor of 1. Meanwhile ASR through HMM's consistently hits realtime factors sub-1 and can run on small CPUs. e.g. the default models Kaldi ships with outperform DeepSpeech on a lot of modern example…

I'm hitting full-encode/decode realtime-factor in the ballpark of <0.01x on a quad core CPU with a tuned wav2letter

Re: A 2019 Guide for Automatic Speech Recognition

#22

Post by lunixbochs »

skykooler wrote: post #4

So how can one actually use one of these systems? I'm familiar with pocketsphinx, where you download it and then run procketsphinx_continuous and it prints a transcription of the microphone. Or Julius, where you write a grammar and run it with that and it prints the output. The process for DeepSpeech seems to be along the lines of "get access to a machine with ten GPUs, find a huge dataset, train it, and then run tha…

I made a realtime mac demo of the acoustic model part of wav2letter++ including several trained models:

https://github.com/facebookresearch/wav2letter/issues/327

Someone in that thread ported it to linux. This demo is just "acoustic model emissions", which are character level predictions (with no repeated characters), but I have "decoding" (turning into english sentences) working locally as well and I'll post a new demo at some point.

Re: A 2019 Guide for Automatic Speech Recognition

#23

Post by GordonS »

wes-k wrote: post #15
GordonS wrote: post #12

Are there any good quality OSS speech recognition libraries that are easy to get started with, or is it still so complex/expensive that this is a fantasy? I really love the idea of hacking something together for development so I don't need to use my arms and hands so much, or could lessen my mouse use (for accessibility reasons), but I don't know how realistic that is. Edit: I looked into cloud-based ASR, such as tha…

I dream of building a competitor to Siri and google and I’d probably use https://snips.ai/ . I think it gains recognition accuracy by having a limited skill set. Looks good though and has functionality for defining skills.

This looks interesting, as I don't need "conversation-level" ASR, I'd just need it to work with a limited grammar.

But I'm unsure of what Snips actually is - I had a look at the website, but I don't know if this is OSS, commercial software, a library or what?

Re: A 2019 Guide for Automatic Speech Recognition

#24

Post by GordonS »

harperlee wrote: post #19
GordonS wrote: post #12

Are there any good quality OSS speech recognition libraries that are easy to get started with, or is it still so complex/expensive that this is a fantasy? I really love the idea of hacking something together for development so I don't need to use my arms and hands so much, or could lessen my mouse use (for accessibility reasons), but I don't know how realistic that is. Edit: I looked into cloud-based ASR, such as tha…

(Slightly offtopic, sorry) I think the best path to “programming by conversation” is to have a programming language designed specifically for that goal, so perhaps you want to research that. I’d say a version of prolog / attempto controlled english / similar functional language (to work on small bits of mostly contained meaning), a verbal repl that is carefully designed to explore data, generate test data, manage err…

Doesn't seem particularly OT to me - it's an interesting idea!

I'm not totally convinved that the language needs to be designed specifically for voice coding though - I can see how having an IDE designed for it would be a huge bonus though... damn, another interesting side project to add to the list!

Re: A 2019 Guide for Automatic Speech Recognition

#25

Post by Nimitz14 »

lunixbochs wrote: post #21
priansh wrote: post #9

Earlier quoted context omitted.

This. The article quotes DeepSpeech, wavenets, LSTMs of all sorts; essentially, all the neural networks that scale terribly. DeepSpeech for example is pretty heavy and requires a decent GPU to get anywhere near a realtime factor of 1. Meanwhile ASR through HMM's consistently hits realtime factors sub-1 and can run on small CPUs. e.g. the default models Kaldi ships with outperform DeepSpeech on a lot of modern example…

I'm hitting full-encode/decode realtime-factor in the ballpark of <0.01x on a quad core CPU with a tuned wav2letter

Note we're talking about LVCSR, you're not going to get 0.01 RTF doing that. Still, wav2letter is quite impressive from the numbers I've seen (although you're still going to need an insane amount of compute and data to train a good model). I've been meaning to try it out, but the setup is so complicated I haven't gotten around to it (I tried it and at some point while going down the dependency tree I said "fuck this" and stopped).

Re: A 2019 Guide for Automatic Speech Recognition

#26

Post by priansh »

lunixbochs wrote: post #21
priansh wrote: post #9

Earlier quoted context omitted.

This. The article quotes DeepSpeech, wavenets, LSTMs of all sorts; essentially, all the neural networks that scale terribly. DeepSpeech for example is pretty heavy and requires a decent GPU to get anywhere near a realtime factor of 1. Meanwhile ASR through HMM's consistently hits realtime factors sub-1 and can run on small CPUs. e.g. the default models Kaldi ships with outperform DeepSpeech on a lot of modern example…

I'm hitting full-encode/decode realtime-factor in the ballpark of <0.01x on a quad core CPU with a tuned wav2letter

wav2letter is pretty fast we haven't been able to break 1.1x on a t2.medium in any of our benchmarks -- what's your setup here?

I definitely think it's a big step in the right direction; it's easily 100x faster than DeepSpeech for us.

If I could have anything I wanted for xmas, I'd ask for a speech to text system that is fast enough to work in browser thru wasm or something.

Re: A 2019 Guide for Automatic Speech Recognition

#27

Post by priansh »

GordonS wrote: post #13
priansh wrote: post #9

Earlier quoted context omitted.

This. The article quotes DeepSpeech, wavenets, LSTMs of all sorts; essentially, all the neural networks that scale terribly. DeepSpeech for example is pretty heavy and requires a decent GPU to get anywhere near a realtime factor of 1. Meanwhile ASR through HMM's consistently hits realtime factors sub-1 and can run on small CPUs. e.g. the default models Kaldi ships with outperform DeepSpeech on a lot of modern example…

Do you have any advice or links for a "normal" developer to get started with HMMs for speech recognition? I'd love to build something just for myself here.

https://github.com/jcsilva/docker-kaldi-gstreamer-server

^^ If you're getting started, just following the steps here can get you set up really fast. IIRC there's also an HTTP endpoint at /recognize you can use instead of WebSocket if you're transcribing audio files so it's pretty cool!

Re: A 2019 Guide for Automatic Speech Recognition

#28

Post by lunixbochs »

Nimitz14 wrote: post #25
lunixbochs wrote: post #21

Earlier quoted context omitted.

I'm hitting full-encode/decode realtime-factor in the ballpark of <0.01x on a quad core CPU with a tuned wav2letter

Note we're talking about LVCSR, you're not going to get 0.01 RTF doing that. Still, wav2letter is quite impressive from the numbers I've seen (although you're still going to need an insane amount of compute and data to train a good model). I've been meaning to try it out, but the setup is so complicated I haven't gotten around to it (I tried it and at some point while going down the dependency tree I said "fuck this"…

(Wikipedia doesn't make the best LM, I just wanted to test with something that knew about a lot of interesting english words)

Re: A 2019 Guide for Automatic Speech Recognition

#29

Post by Nimitz14 »

lunixbochs wrote: post #28
Nimitz14 wrote: post #25

Earlier quoted context omitted.

Note we're talking about LVCSR, you're not going to get 0.01 RTF doing that. Still, wav2letter is quite impressive from the numbers I've seen (although you're still going to need an insane amount of compute and data to train a good model). I've been meaning to try it out, but the setup is so complicated I haven't gotten around to it (I tried it and at some point while going down the dependency tree I said "fuck this"…

(Wikipedia doesn't make the best LM, I just wanted to test with something that knew about a lot of interesting english words)

Going to be honest but I don't think that is physically possible (i.e. I don't believe that, no offense). Unless you're using a very small beam and model.

Re: A 2019 Guide for Automatic Speech Recognition

#30

Post by GordonS »

priansh wrote: post #27
GordonS wrote: post #13

Earlier quoted context omitted.

Do you have any advice or links for a "normal" developer to get started with HMMs for speech recognition? I'd love to build something just for myself here.

https://github.com/jcsilva/docker-kaldi-gstreamer-server ^^ If you're getting started, just following the steps here can get you set up really fast. IIRC there's also an HTTP endpoint at /recognize you can use instead of WebSocket if you're transcribing audio files so it's pretty cool!

Nice, this doesn't look too bad to get started with - definitely going to give this a try as soon as I get a chance!
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