STT VS NeMo

What has happened since then? I know Common Voice has come and gone https://en.wikipedia.org/wiki/Common_Voice https://github.com/coqui-ai/STT

And I've seen some neural approaches too

No idea where to look for comparisons though.

I basically just used coqui stt https://github.com/coqui-ai/STT

This speech-to-text works well: https://github.com/coqui-ai/STT. openai's "whisper" is probably better but I haven't tried it: https://towardsdatascience.com/transcribe-audio-files-with-openais-whisper-e973ae348aa7

I use two SST to live-translate audio that I listen to so I can look back (in paragraph form) to see things that I or the youtube has previously said: https://github.com/coqui-ai/STT https://github.com/ratwithacompiler/OBS-captions-plugin

I did have to install Coqui STT and go-asticoqui manually before i was able to run Chipper.

What do you use as STT? I have heard good things about coqui (https://github.com/coqui-ai/STT) and will use it for my Assistant-build.

If anyone is looking for a maintained version of DeepSpeech, checkout Coqui's repositories for STT and TTS. Coqui is lead by the engineers that used to work on DeepSpeech at Mozilla.

Link: https://github.com/coqui-ai/STT

I don't test WaveRNN but from the ones that I know the best that is open source is FastPitch. And it's easy to use, here is the tutorial for voice cloning.

Nowadays... bigger repos like https://github.com/NVIDIA/NeMo are all pytorch, lots of work also published by Meta and Microsoft is all torch. I check new work on GitHub all the time and I haven't seen a Tensorflow repo in years except one.

- https://github.com/NVIDIA/NeMo

Can’t you use the NeMo model and just strip the punctuation from the output again if you don’t want it? You can also fine tune the the model with capitalization only if you look at the examples https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb The capitalization and punctuation are annotated separately (U indicates that the word should be upper cased, and O - no capitalization ). The model seems to be a token level classifier not seq to seq so there should also be a way to get just the capitalization part but you would have to look into the model as it’s not shown in the examples.

I think there's been talk to do speaker diarization with whisper-asr-webservice[0] which is also written in python and should be able to make use of goodies such as pyannote-audio, py-webrtcvad, etc.

Whisper is great but at the point we get to kludging various things together it starts to make more sense to use something like Nvidia NeMo[1] which was built with all of this in mind and more

[0] - https://github.com/ahmetoner/whisper-asr-webservice

[1] - https://github.com/NVIDIA/NeMo

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The final security grain was added with speech-to-text anti-spoofing built on QuartzNet from the Nemo framework. This model provides a decent quality user experience and is suitable for real-time scenarios. To measure how close what the person says to what the system expects, requires calculation of the Levenshtein distance between them.