insanely-fast-whisper VS dstack

Hi HN! Since the launch of JigsawStack.com we've been trying to dive deeper into fully managed AI APIs built and fine tuned for specific use cases. Audio/video transcription was one of the more basic things and we wanted the best open source model and at this point it is OpenAI's whisper large v3 model based on the number languages it supports and accuracy.

The thing is, the model is huge and requires tons of GPU power for it to run efficiently at scale. Even OpenAI doesn't provide an API for their best transcription model while only providing whisper v2 at a pretty high price. I tried running the whisper large v3 model on multiple cloud providers from Modal.com, Replicate, Hugging faces dedicated interface and it takes a long time to transcribe any content about ~30mins long for 150mins of audio and this doesn't include the machine startup time for on demand GPUs. Keeping in mind at JigsawStack we aim to return any heavy computation under 25s or 2mins for async cases and any basic computation under 2s.

While exploring Replicate, I came across this project https://github.com/Vaibhavs10/insanely-fast-whisper by Vaibhav Srivastav which optimises the hell out of this whisper large v3 model with a variety of techniques like batching and using FlashAttention 2. This reduces computation time by almost 30x, check out the amazing repo for more stats! Open source wins again!!

First we tried using Replicates dedicated on-demand GPU service to run this model but that did not help, the cold startup/booting time alone of a GPU made the benefits of the optimised model pretty useless for our use case. Then tried Hugging face and modal.com and we got the same results, with a A100 80GB GPU, we were seeing around an average of ~2mins start up time to load the machine and model image. It didn't make sense for us to have a always on GPU running due to the crazy high cost. At this point I was inches away from giving up.

Next day I got an email from Fly.io: "Congrats, Yoeven D Khemlani has GPU access!" I totally forgot the Fly started providing GPUs and I'm a big fan of their infra reliability and ease to deploy. We also run a bunch of our GraphQL servers for JigsawStack on Fly's infra!

I quickly picked up some Python and Docker by referring to a bunch of other Github repos and Fly's GPU tutorials then wrote the API layer with the optimised version of whisper 3 and deployed on Fly's GPU machines.

And wow the results were pretty amazing, the start up time of the machine on average was ~20 seconds compared to the other providers at ~2mins with all the performance benefits from the optimised whisper. I've added some more stats in the Github repo. The more interesting thing to me is cost↓

Based on 10mins of audio:

There's a better parallel/batching that works on the 30s chunks resulting in 40X. From HF at https://github.com/Vaibhavs10/insanely-fast-whisper

This is again not native PyTorch so there's still room to have better RTFX numbers.

Founder of Replicate here. We open pull requests on models[0] to get them running on Replicate so people can try out a demo of the model and run them with an API. They're also packaged with Cog[1] so you can run them as a Docker image.

Somebody happened to stumble across our fork of the model and submitted it. We didn't submit it nor intend for it to be an ad. I hope the submission gets replaced with the upstream repo so the author gets full credit. :)

[0] https://github.com/Vaibhavs10/insanely-fast-whisper/pull/42

We build a similar tool except we focus on AI workloads. Also support on-prem clusters now in addition to GPU clouds. https://github.com/dstackai/dstack

Not exactly this, but something related. At https://github.com/dstackai/dstack, we build an alternative to K8S for AI infra.

You can use https://github.com/dstackai/dstack to deploy your model to the most affordable GPU clouds. It supports auto-scaling and other features.

Disclaimer: I’m the creator of dstack.

Great job on the project! It looks fantastic. Thanks to your post, I discovered Fly's GPUs. We are currently developing a platform called https://github.com/dstackai/dstack that enables users to run any model on any cloud. I am curious if it would be possible to add support for Fly.io as well. If you are interested in collaborating on this, please let me know!

I also noticed that the creator of this app, dstack, is affiliated with Tensordock, the top results for most if not all queries. If that's the case, perhaps a direct link to the cheapest machine could be provided? I haven't used Tensordock, so I don't know if this is mechanically possible.

Here's the repository with all the important links, including documentation, examples, and more: https://github.com/dstackai/dstack

Hey everyone, I'm happy to release dstack Hub, an open-source tool that helps teams manage their ML workflows more effectively without vendor lock-in.

dstack Hub extend dstack [1] with workflow scheduling capabilities and user management. Here's how it works: run dstack Hub via Docker, use its UI to configure projects and cloud credentials, then pass the URL and personal token to the dstack CLI. Now, you can run workflows through the CLI and Hub will orchestrate them in the cloud on your behalf.

This is a beta release and we plan to continuously improve it. We'd love to hear your feedback and answer any questions!

[1] https://github.com/dstackai/dstack

To help you overcome this challenge, we have written an article to guide you through the simple steps of using both diffusers and dstack to generate images from prompts, both locally and in the cloud, using a simple example.