ANAnon84·Mar 27, 2026Submission
Insanely Fast Whisper - Blazingly fast audio transcriptions
3 points · Open discussionVaibhavs10
insanely-fast-whisper
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Vaibhavs10 / insanely-fast-whisper
insanely-fast-whisper
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| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
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What Hacker News says
13 sourcesIs this even faster? https://github.com/Vaibhavs10/insanely-fast-whisper
If so, is the quality still acceptable?
In AiOla open-sources ultra-fast ‘multi-head’ speech recognition modelShow HN: I Built an Open Source API with Insanely Fast Whisper and Fly GPUs
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 at this point it is OpenAI's whisper large v3 model based on the number of languages it supports and its 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, and 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 machi
Read the rest
ne 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 Replicate's 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 we tried Hugging face and modal.com and we got the same results, with an A100 80GB GPU, we saw around an average of ~2mins start up time to load the machine and model image. It didn't make sense for us to have an always on GPU running due to the crazy high cost. At this point, I was inches away from giving up.
The next day I got an email from Fly.io: "Congrats, Yoeven D Khemlani has GPU access!". I forgot the Fly started providing GPUs and I'm a big fan of their infra reliability and ease of deployment. We also run some 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 it 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 the cost↓
Based on 10mins of audio:
- OpenAI Whisper v2 API -> $0.06/10mins
- Insanely Fast Whisper large v3 API on Fly GPU (Cold startup) -> ~$0.029/10mins
- Insanely Fast Whisper large v3 API on Fly GPU (Warm startup) -> ~$0.011/10mins
(Note: These are rough estimates I did by taking averages after running 5 rounds each)
If you guys want to run this on any other GPU providers you can as long as they support Docker.
We'll be optimising this more over the next few days specific to Fly's infrastructure allowing for global distributed instances of whisper and will soon be providing a fully managed API on JigsawStack.com. Stay tuned!
You mean 'https://github.com/Vaibhavs10/insanely-fast-whisper' ? Did not know that until now. I'm running all of that since over ~10 months and just had it running. Happy to try that out. The GPU is fully utilized by using whisper and pytorch with cuda and all. Thanks for the link.
In Whisper: Nvidia RTX 4090 vs. M1 Pro with MLXThere'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.
In Whisper: Nvidia RTX 4090 vs. M1 Pro with MLXThe point still stands though. Popular models tend to to have massively hand optimized Nvidia implementations.
Whisper is no exception: https://github.com/Vaibhavs10/insanely-fast-whisper
SDXL is actually an interesting exception for Nvidia because most users still tend to run it in PyTorch eager mode. There are super optimized Nvidia implementations, like stable-fast but their use is less common. Apple, on the other hand, took the odd step of hand writing a Metal implementation themselves, at least for SD 1.5.
In Whisper: Nvidia RTX 4090 vs. M1 Pro with MLXUse this Whisper derivative repo instead - one hour of audio gets transcribed within a minute or less on most GPUs - https://github.com/Vaibhavs10/insanely-fast-whisper
In Whisper: Nvidia RTX 4090 vs. M1 Pro with MLXHow does this compare to insanely-fast-whisper though? https://github.com/Vaibhavs10/insanely-fast-whisper
I think that not using optimizations allows this to be a 1:1 comparison, but if the optimizations are not ported to MLX, then it would still be better to use a 4090.
Having looked at MLX recently, I think it's definitely going to get traction on Macs - and iOS when Swift bindings are released https://github.com/ml-explore/mlx/issues/15 (although there might be some C++20 compilation issue blocking right now).
In Whisper: Nvidia RTX 4090 vs. M1 Pro with MLXFounder 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
[1] https://github.com/replicate/cog
In Insanely Fast WhisperThe submission link is weird. It has far fewer stars than the repo it is forked from, and could just be an ad for replicate.com?
It is missing the most recent commits from what appears to be the real source: https://github.com/Vaibhavs10/insanely-fast-whisper
The only added commit is adding a replicate.com example, whatever that means.
In Insanely Fast WhisperI'm sort of confused - is this just a CLI wrapper around faster-whisper, transformers and distil-whisper? Will this be any faster than running those by themselves? There doesn't seem to be much code here, so this is why I'm wondering if this is actually something to get excited about if I already am aware of those projects.
Edit: Also this seems a bit suspicious - this seems like someone just forked another persons active repo (https://github.com/Vaibhavs10/insanely-fast-whisper) and posted as their own?
In Insanely Fast WhisperThe closest I could find is someone claiming to get a similar time on their RTX3090 on an issue in the repo.
https://github.com/Vaibhavs10/insanely-fast-whisper/issues/3...
In Insanely Fast Whisper