cookwherever VS Whisper

Cook Wherever: https://cookwherever.com/

Cooking is hard. I want to cook more but I am usually too hungry to focus. I am building a site to help you with all stages of cooking, not just showing you ingredients and directions.

I have also realized the knowledge I have amassed for the “why” of cooking helps me cook without needing recipes mostly. I use ML/NLP to extract entities from ingredients and directions so contextual information can be provided to someone who is curious (ex. “you preheat your oven because …”)

I really like content creators, but following videos while cooking is a no-go for my attention span. I’m working on it, but directions will work as time stamps into a video for a recipe.

[1] https://github.com/cookwherever/cookwherever

TL;DR ive been consolidating my cooking knowledge in an open source recipe site https://github.com/cookwherever/cookwherever

Hey! I am excited to see people developing in this space since this is where my head is also at. Up until two years ago I was eating canned chili and soylent before I was shown the light with The Food Lab and Salt, Fat, Acid and Heat. Ever since I have been trying to share my knowledge on cooking with others with varying levels of success.

What has become clear to me is that there is no one size fits all approach to teaching cooking since it is usually a very cultural experience for most people (as many in the comments have pointed out). That said, SFAH makes the argument that most cuisines are much closer than people think when you consider the functional properties of the ingredients that you are cooking. Pizza is just pasta with yeast *Italian grandmothers slowly turn their heads towards this atrocity*

All of this to say, I believe recipes are critical to jump start the creative process of cooking. When any type of possible failure on the path to completion is experienced (ex. missing ingredient, burnt cookies, etc.) there MUST be some way of recovering or at the very least understanding how that happened.

I have been slowing taking notes on all the cooking knowledge I have come across and have been putting it along side the recipes that inspire me. Forcing learning on someone in the kitchen, who is already probably pretty hungry if cooking is seen as a chore, is not productive. Sharing the joy you get from the art that is cooking is IMPERATIVE for any type of educational resource.

If you are interested in my progress, or want to contribute go check it out! https://github.com/cookwherever/cookwherever

completely unrelated to your post, but just wanted to say thanks for your work on the rebooting of nyt’s ingredient parser. I use it in my project here: https://github.com/cookwherever/cookwherever (site is currently down due to the server being physically moved from our house lol). If you are interested in talking more about how i’m using it I would love to share :)

I've been using WhisperDesktop ( https://github.com/Const-me/Whisper ) with great success on a 3090 for fast & accurate transcription of often poor quality euro-english hours long multispeaker audio files. If there's an easy way to compare I'm certainly going to give this a try.

Why would you want OpenCL? Pretty sure D3D11 compute shaders gonna be adequate for a Torch backend, and they even work on Linux with Wine: https://github.com/Const-me/Whisper/issues/42 Native Vulkan compute shaders would be even better.

Why would you want unified address space? At least in my experience, it’s often too slow to be useful. DMA transfers (CopyResource in D3D11, copy command queue in D3D12, transfer queue in VK) are implemented by dedicated hardware inside GPUs, and are way more efficient.

https://github.com/ggerganov/whisper.cpp

https://github.com/Const-me/Whisper

I had fun with both of these. They will both do realtime transcription. Bit you will have to download the training data sets…

Gamers don’t care about FP64 performance, and it seems nVidia is using that for market segmentation. The FP64 performance for RTX 4090 is 1.142 TFlops, for RTX 3090 Ti 0.524 TFlops. AMD doesn’t do that, FP64 performance is consistently better there, and have been this way for quite a few years. For example, the figure for 3090 Ti (a $2000 card from 2022) is similar to Radeon RX Vega 56, a $400 card from 2017 which can do 0.518 TFlops.

And another thing: nVidia forbids usage of GeForce cards in data centers, while AMD allows that. I don’t know how specifically they define datacenter, whether it’s enforceable, or whether it’s tested in courts of various jurisdictions. I just don’t want to find out answers to these questions at the legal expenses of my employer. I believe they would prefer to not cut corners like that.

I think nVidia only beats AMD due to the ecosystem: for GPGPU that’s CUDA (and especially the included first-party libraries like BLAS, FFT, DNN and others), also due to the support in popular libraries like TensorFlow. However, it’s not that hard to ignore the ecosystem, and instead write some compute shaders in HLSL. Here’s a non-trivial open-source project unrelated to CAE, where I managed to do just that with decent results: https://github.com/Const-me/Whisper That software even works on Linux, probably due to Valve’s work on DXVK 2.0 (a compatibility layer which implements D3D11 on top of Vulkan).

Currently, I use a GUI for Whisper AI (https://github.com/Const-me/Whisper) to upload MP3s of interviews to get text transcripts. However, I'm hoping to find another tool that would recognize and split out the text per speaker.

Does such a thing exist?

I just used Whisper over the weekend to transcribe 5 hours of meeting, worked nicely and it can be run on a single GPU locally. https://github.com/ggerganov/whisper.cpp

There are a few wrappers available with GUI like https://github.com/Const-me/Whisper