CompreFace VS whisper

# https://github.com/exadel-inc/CompreFace/blob/master/docs/Face-services-and-plugins.md)

I looked at Double Take (UI that lets you do the training of your face recondition easily) and found CompreFace as one of models they support. It looks like what I need but there is a catch... no OpenVino (intel CPUs AI accelerator) support. I really like my low power NVR setup and would like keep it that way. Running AI on CPU without acceleration is both power inefficient and much slower. I have a spare low end GPU but if dump it in the system the current AI acceleration brakes... (I know I can prob fix it but that is a rabbit hole I would prefer to avoid).

This has good UI and also good recognition: https://github.com/exadel-inc/CompreFace

wget -q -O tmp.zip 'https://github.com/exadel-inc/CompreFace/releases/download/v1.1.0/CompreFace_1.1.0.zip' && unzip tmp.zip && rm tmp.zip

https://github.com/exadel-inc/CompreFace/ Is already a good locally-run API for face detection and facial recognition. Thankyou for your suggestion.

If you are looking for open source stuff and are able to self-host it, maybe have a look at CompreFace.

As almost always, on GitHub https://github.com/exadel-inc/CompreFace

Looking into this I found Compreface (https://exadel.com/solutions/compreface/) an open source face recognition software. There are alread some controb scripts, like contrib/photils.lua, who take some images, run them through a tool, then tag them with data coming from the tool. Converting this to use Compreface looks likea promising avenue.

Let’s not talk about local models as the hardware requirements are way beyond most of these people’s reach. I have a MacBook Air with an M2 chip and 8GB of RAM and can hardly run Whisper locally, so I use this HuggingFace space.

Last week, I launched notesGPT, a free and open source voice note app that has 35,000 visitors, 7,000 users, and over 1,000 GitHub stars so far in the last week. It allows you to record a voice note, transcribes it uses Whisper, and uses Mixtral via Together to extract action items and display them in an action items view. It’s also fully open source and comes equipped with authentication, storage, vector search, action items, and is fully responsive on mobile for ease of use.

I found a pretty good discussion in the topic here:

https://github.com/openai/whisper/discussions/1243

There is a plot of language performance on their repo: https://github.com/openai/whisper

I am not aware of a multi-lingual leaderboard for speech recognition models.

I don't know how OP does it, but here's how I'd do it:

* Generate a transcript by runing Whisper against the podcast audio file: https://github.com/openai/whisper

* Upload transcript to ChatGPT and ask it to summarize.

* Automate all the above.

Ahh, that makes sense. I've been building something like that, but only from other languages into English using Whisper

Whisper already generates subtitles[0], supporting VTT and SRT so this is just a thin wrapper around that.

[0]: https://github.com/openai/whisper/blob/e58f28804528831904c3b...

> although it does require you to wear headphones so the bot doesn't hear itself and get interrupted.

Maybe you can rely on some sort of speaker identification to sort this out?

https://github.com/openai/whisper/discussions/264