dream-factory VS whisper.cpp

Good stuff! For anyone else that uses Dream Factory, I've converted this to a .prompts file for use with it here:

BTW if you aren't already using dream factory to manage your GPUs in stable diffusion you should be. It works with automatic1111 too so you won't be losing any abilities.

Take a look at Dream Factory - it's a multi-GPU capable front-end for Auto1111. It'll let you run as many GPUs as you want in parallel, and adds a bunch of automation and remote management features as well.

I compiled these into a single Dream Factory .prompt file here: https://github.com/rbbrdckybk/dream-factory-prompt-files/tree/main/prompts

Correct and you can use Dream Factory to help manage your queue across GPUs. But keep in mind you are still only using individual cards for individual prompts meaning multiple cards can't improve the speeds of individual prompt generation nor allow for larger images.

Wow 24 instances! Just in case you weren't aware, Dream Factory is a multi-GPU frontend for Auto1111. It'll let you manage sending work to all those GPUs from one place!

Dream Factory will let you use multiple GPUs in parallel. So essentially adding a 2nd identical GPU will double your output.

No worries - Dream Factory is essentially a front-end for the popular Automatic1111 SD repo. It adds a bunch of prompt automation and remote management features, along with a multi-GPU engine (works fine with just 1 GPU too) to Auto1111.

whisper.cpp [1] has a karaoke example that uses ffmpeg's drawtext filter to display rudimentary karaoke-like captions. It also supports diarisation. Perhaps it could be a starting point to create a better script that does what you need.

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1: https://github.com/ggerganov/whisper.cpp/blob/master/README....

The ggml library is one of the first library for local LLM interference. It’s a pure C library that converts models to run on several devices, including desktops, laptops, and even mobile device - and therefore, it can also be considered as a tinkering tool, trying new optimizations, that will then be incorporated into other downstream projects. This tool is at the heart of several other projects, powering LLM interference on desktop or even mobile phones. Subprojects for running specific LLMs or LLM families exists, such as whisper.cpp.

I'm not sure if it is _fully_ openai compatible, but whispercpp has a server bundled that says it is "OAI-like": https://github.com/ggerganov/whisper.cpp/tree/master/example...

I don't have any direct experience with it... I've only played around with whisper locally, using scripts.

unless i'm misunderstanding `whisper.cpp` seems to support streaming & the repository includes a native example[0] and a WASM example[1] with a demo site[2].

[0]: https://github.com/ggerganov/whisper.cpp/tree/master/example...

Usage 1: Good to transcribe audio. An example use case could be to summarize YouTube videos or long courses. Usage 2: You talk with voice to your AI that responds with text (later with audio too). - https://github.com/ggerganov/whisper.cpp

🗣️🎙️ whisper.cpp (offline speech-to-text transcription, models trained by OpenAI, CLI based, browser based)

Has anyone played around with Whisper C++ for swift? I'm hitting a snag even on the demo. I've downloaded the github repo and everything matches up with this video [ https://youtu.be/b10OHCDHDQ4 ] but when he hits the transcribe button, it actually prints out the captioning. When I do it, it skips that part and just says "Done...". But it, does everything else - plays the audio, says it's transcribing.. just doesn't show me the transcription: and it's not in the debug window either. But the demo isn't throwing any errors, and I haven't messed with the code really so this is their example. https://github.com/ggerganov/whisper.cpp