whisper VS ollama

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

curl -fsSL https://ollama.com/install.sh | sh

Streaming is not a problem (it's just a simple flag: https://github.com/wiktor-k/llama-chat/blob/main/index.ts#L2...) but I've never used voice input.

The examples show image input though: https://github.com/ollama/ollama/blob/main/docs/api.md#reque...

Maybe you can file an issue here: https://github.com/ollama/ollama/issues

I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.

Ollama for running LLMs locally

There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁

That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.

easiest is probably with ollama [0]. I think the ollama API is OpenAI compatible.

[0]https://ollama.com/

They ended up addressing this issue by including it on the last line of their readme as one of the "Supported backends[sic]".

https://github.com/ollama/ollama/issues/3697

LLM models are challenging to run directly via ONNX runtime on CPU. For these, we are using a GPU-accelerated Ollama server under the hood: