Show HN: I Built an Open Source API with Insanely Fast Whisper and Fly GPUs

• insanely-fast-whisper-api

  1 49 6.6 Python

  An API to transcribe audio with OpenAI's Whisper Large v3!

  

• insanely-fast-whisper

  6 6,397 8.9 Jupyter Notebook

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:

• WorkOS

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• dstack

  17 1,087 9.8 Python

  dstack is an open-source orchestration engine for running AI workloads at scale in any cloud or data center. https://discord.gg/u8SmfwPpMd

  

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!

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