faster-whisper VS insanely-fast-whisper

Faster-whisper (https://github.com/SYSTRAN/faster-whisper)

For our real-time STT needs, we'll employ a fantastic library called faster-whisper.

Thermostats: https://www.sinopetech.com/en/products/thermostat/

I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant. Maybe someone is working on this already?

TTS: https://github.com/SYSTRAN/faster-whisper

LLM: https://github.com/Mozilla-Ocho/llamafile/releases

LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D...

It would take some tweaking to get the voice commands working correctly.

Could someone elaborate how is this accomplished and is there any quality disparity compared to original whisper?

Repos like https://github.com/SYSTRAN/faster-whisper makes immediate sense about why it's faster than the original, but this one, not so much, especially considering it's even much faster.

Cool! I had the same project idea recently. You may be interested in this for the step of speech2text: https://github.com/SYSTRAN/faster-whisper

That's the implication. If the distil models are same format as original openai models then the Distil models can be converted for faster-whisper use as per the conversion instructions on https://github.com/guillaumekln/faster-whisper/

So then we'll see whether we get the 6x model speedup on top of the stated 4x faster-whisper code speedup.

> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".

It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that.

> https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.

With all due respect to the author of the article this is "my first entry into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions.

> EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )

I don't understand what you're saying here. It (along with the other projects I linked) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights.

Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all.

[0] - https://github.com/guillaumekln/faster-whisper#benchmark

[1] - https://heywillow.io/components/willow-inference-server/#ben...

guillaumekln/faster-whisper

Neat to see a new implementation, although I'll note that for those looking for a drop-in replacement for the whisper library, I believe that both faster-whisper https://github.com/guillaumekln/faster-whisper and https://github.com/m-bain/whisperX are easier (PyTorch-based, doesn't require a web browser), and a lot faster (WhisperX is up to 70X realtime).

One caveat here is that whisper.cpp does not offer any CUDA support at all, acceleration is only available for Apple Silicon.

If you have Nvidia hardware the ctranslate2 based faster-whisper is very very fast: https://github.com/guillaumekln/faster-whisper

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