Kaldi Speech Recognition Toolkit VS larynx

Yeah, whisper is the closest thing we have, but even it requires more processing power than is present in most of these edge devices in order to feel smooth. I've started a voice interface project on a Raspberry Pi 4, and it takes about 3 seconds to produce a result. That's impressive, but not fast enough for Alexa.

From what I gather a Pi 5 can do it in 1.5 seconds, which is closer, so I suspect it's only a matter of time before we do have fully local STT running directly on speakers.

> Probably anathema to the space, but if the devices leaned into the ~five tasks people use them for (timers, weather, todo list?) could probably tighten up the AI models to be more accurate and/or resource efficient.

Yes, this is the approach taken by a lot of streaming STT systems, like Kaldi [0]. Rather than use a fully capable model, you train a specialized one that knows what kinds of things people are likely to say to it.

[0] http://kaldi-asr.org/

You can study CTC in isolation, ignoring all the HMM background. That is how CTC was also originally introduced, by mostly ignoring any of the existing HMM literature. So e.g. look at the original CTC paper. But I think the distill.pub article (https://distill.pub/2017/ctc/) is also good.

For studying HMMs, any speech recognition lecture should cover that. We teach that at RWTH Aachen University but I don't think there are public recordings. But probably you should find some other lectures online somewhere.

You also find a lot of tutorials for Kaldi: https://kaldi-asr.org/

Maybe check this book: https://www.microsoft.com/en-us/research/publication/automat...

The relation of CTC and HMM becomes intuitively clear once you get the concept of HMMs. Often in terms of speech recognition, it is all formulated as finite state automata (FSA) (or finite state transducer (FST), or weighted FST (WFST)), and the CTC FST just looks a bit different (simpler) than the traditional HMMs, but in all cases, you can think about having states with possible transitions.

This is all mostly about the modeling. The training is more different. For CTC, you often calculate the log prob of the full sequence over all possible alignments directly, while for HMMs, people often use a fixed alignment, and calculate framewise cross entropy.

I did some research on the relation of CTC training and HMM training: https://www-i6.informatik.rwth-aachen.de/publications/downlo...

- https://github.com/kaldi-asr/kaldi

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

Frameworks as well as toolkits like Kaldi were at first promoted by the research study area, yet nowadays used by both scientists and also market experts, reduced the access obstacle in the advancement of automatic speech recognition systems. Nonetheless, cutting edge methods need big speech data readies to achieve a usable system.

Hello. I'm trying to package the openfst library (here)[https://github.com/void-linux/void-packages/pull/39015] but a developer says 32-bit must be cross compiled from 64-bit. I see xbps-src has a nocross option, but I don't see a way to only cross compile. What do you think I should do? I have currently limited the archs to 64-bit ones. Here's my issue with the developer's response: https://github.com/kaldi-asr/kaldi/issues/4808 Thank you.

If you interested in unix-like software design and not yet familiar with kaldi toolkit, you definitely need to check it https://kaldi-asr.org

It extended Unix design with archives, control lists and matrices and enabled really flexible unix-like processing. For example, recognition of a dataset looks like this:

extract-wav scp:list.scp ark:- | compute-mfcc-feats ark:- ark:- | lattice-decoder-faster final.mdl HCLG.fst ark:- ark:- | lattice-rescore ark:- ark:'|gzip -c > lat.gzip'

Another example is gstreamer command line.

No, speaker diarization is not part of Whisper. There are open source projects - such as Kaldi [1], but it's hard to get them running if you are not an area expert.

[1] https://kaldi-asr.org/

State-of-the-art ASR, like what you get on smartphones, has unfortunately high resource requirements. Some recent smartphone models are able to run ASR on-device, but more typically, ASR is done by sending audio to a web service. Check out the (currently experimental) Web SpeechRecognition API in a Chrome browser. Here is a demo of the API in action. For something open source, check out Kaldi ASR.

The most exciting thing about Home Assistant's "Year of the Voice", for me, is that it is apparently enabling/supporting @synesthesiam's continued phenomenal contributions to the FLOSS off-line voice synthesis space.

The quality, variety & diversity of voices that synesthesiam's "Larynx" TTS project (https://github.com/rhasspy/larynx/) made available, completely transformed the Free/Open Source Text To Speech landscape.

In addition "OpenTTS" (https://github.com/synesthesiam/opentts) provided a common API for interacting with multiple FLOSS TTS projects which showed great promise for actually enabling "standing on the shoulders of" rather than re-inventing the same basic functionality every time.

The new "Piper" TTS project mentioned in the article is the apparent successor to Larynx and, along with the accompanying LibriTTS/LibriVox-based voice models, brings to FLOSS TTS something it's never had before:

* Too many voices! :)

Seriously, the current LibriTTS voice model version has 900+ voices (of varying quality levels), how do you even navigate that many?![0]

And that's not even considering the even higher quality single speaker models based on other audio recording sources.

Offline TTS while immensely valuable for individuals, doesn't seem to be attractive domain for most commercial entities due to lack of lock-in/telemetry opportunities so I was concerned that we might end up missing out on further valuable contributions from synesthesiam's specialised skills & experience due to financial realities & the human need for food. :)

I'm glad we instead get to see what happens next.

[0] See my follow-up comment about this.

Larynx!

I've had good results with https://github.com/rhasspy/larynx

Larynx is a really good text-to-speech engine

I was able to install 3.7 following this guide. https://github.com/rhasspy/larynx/issues/9

If you've not already encountered them I'd definitely encourage you to check out these Free/Open Source projects too:

* Larynx: https://github.com/rhasspy/larynx/

* OpenTTS: https://github.com/synesthesiam/opentts

* Likely Mimic3 in the near future: https://mycroft.ai/blog/mimic-3-preview/

Larynx in particular has a focus on "faster than real-time" while OpenTTS is an attempt to package & provide common REST API to all Free/Open Source Text To Speech systems so the FLOSS ecosystem can build on previous work supported by short-lived business interests, rather than start from scratch every time.

AIUI the developer of the first two projects now works for Mycroft AI & is involved in the development of Mimic3 which seems very promising given how much of an impact on quality his solo work has had in just the past couple of years or so.

I haven't used it on it's own, but Larynx has worked well for me for Rhasspy