vosk-api VS stable-diffusion

So I don't know if my issue comes from my lack of knowledge of discord.js/voice or VOSK. so I guess the most important thing I need to see is if I am creating a proper stream for the Vosk API to capture the audio. if I can figure out how to capture an audio stream I can probably import that in to vosk and figure out how to use vosk myself. but right now I can't even get close! Thank you in advance...Sorry if this isn't the right place for this

I remember a while ago checking out the issues with Vosk speech recognition (written in C). A handful of it's issues are related to segfaults and null pointers.

first, good initiative! thanks for sharing. i think you gotta be more diligent and careful with the problem statement.

checking the weather in Sofia, Bulgaria requires cloud, current information. it's not "random speech". ESP SR capability issues don't mean that you cannot process it locally.

the comment was on "voice processing" i.e. sending speech to the cloud, not sending a call request to get the weather information.

besides, local intent detection, beyond 400 commands, there are great local STT options, working better than most cloud STTs for "random speech"

https://github.com/alphacep/vosk-api

I did a one-off text to speech tool for someone last year and had pretty good results with VOSK. One upside is that it works offline, although I imagine if you use TTS a lot you'll notice issues I didn't.

You can use vosk-api (https://github.com/alphacep/vosk-api) to listen to your audio, transform it to text, and then post the text to GPT-3, then using the vector sdk, have your responses said by vector.

The set up script wants to download https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-model-en-v0.3.45.zip, but this resource is not found. AFAICT all releases never contained a model file. Remedy: hardcode one model from https://alphacephei.com/vosk/models. I guessed and picked the one with the closest name, vosk-model-en-us-0.22.zip, just so I could continue.

Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.

an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.

It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...

Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub

I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.

stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub

Here for example --> https://github.com/CompVis/stable-diffusion