An hour of audio transcribed in about 13s on MacBook Air

Hi all. I am Shahir. This was my experiment. First of all - Thanks to whoever posted this on HN. It came as a surprise.

Just to clarify - This started as just an experiment to see if we can use the fact that text and audio share a vector space in Embedding Gemma 2 to convert audio to text. Had to hack around a few things, but, it worked. Speed was just a side benefit. Was not aiming for it.

The accuracy is good, but not in the range of cloud models, but decent enough for everyday use, and this is just a couple of days work. The WER can improve a lot with more training on varied data. Will try that next.

I will opensource the whole thing soon. Might need a few days though. Also, releasing the TTS version for this soon.

Happy to answer any questions you have.

I recently swapped to using Parakeet v3 on my old M1 Macbook Air, using MacWhisper. It was so fast I thought it had failed. As in, by the time I clicked "transcribe" and looked at my screen again a few seconds later the transcript had finished. Absolutely insanely fast.

Apple's on-device transcription model is about 3x slower than their reported results on my more powerful computer, with a similar error rate in my test (which I consider pretty good results)

I'll test theirs too if I get around to it

Here's a sample CLI wrapping the apple model and providing a simple benchmark: https://github.com/llimllib/transcribe

update: doesn't seem like they provide their model and I don't feel like trying to dig it out of their app bundle, so I'm not going to test this one

Is it open source? They mention something about the Apache License but I can't seem to find a link to GitHub or something.

Fantastic! Any plans to support other languages?

I tested a 33 minute local audio only file on a M4 Mac Mini with 24GB of Ram and it took 6.75 seconds to transcribe it using this software. Using MacWhisper/Parakeet3 it was about 7.5 secs using a stopwatch.

As someone who knows nothing on how this works, does this mean they have to pay the audio at 200x playback speed to transcribe this fast.

Or is there another approach?

Interesting... is it overkill to use a multimodal model like this on just audio? Could the model size be decreased dramatically, or does the multimodality help the accuracy and speed in some way?

I think this seems to target English only I believe. Pretty good but thinking os using some of these techniques on our multi-lingual ASR models !

Sounds great if you could use it as a server. Otherwise, why would you want this?

Pretty cool application of an embedding model

What about the quality of the transcription?

Any info on the param size and quantization of each part and in total?

interesting approach. But training ctc head on top of gemma's audio encoder will most likely produce better results and faster inference

So Marco Arment can sell some more of his Mac Minis.

It would be nice if Korean also worked. I’d like to experiment with a Korean model too.

This has come at just the right time.

Does this work for Hindi or other non English languages though?

I wanted to recently transcribe Hindi + English (mixed) Audio and I used this model[0] and created a custom transcribe command which can give text over input of mp4 files

This took me roughly around an hour or more or around this time frame for a 90 minute transcription source. I would consider myself to be slightly bored during this phase.

I would really appreciate it if something like the project could be made native for a lot more languages but I think that the approach of embedding models and using them in such a way is pretty crazy. Kudos for it and I will try it out!

I can think of many such use cases where Speech to text of even large audio files becomes near instant. As I think someone on hackernews once quoted whom I am very paraphrasing

An order of one or two magnitude of an already existing thing stops becoming a quantity upgrade but rather becomes a quality upgrade in it of itself as it provides access to workflows which previously wouldn't have been feasible.

[0]: https://huggingface.co/parismitaglobalsolutions/indicconform...

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I wonder if this approach can be used to transcribe the soounds made by dolphins and whales