WAAS VS openai-whisper-cpu

This app does it locally on newer macs for free.

https://apps.apple.com/no/app/jojo-transcribe/id1659864300?m...

And open source: https://github.com/schibsted/WAAS

Clone the repo, then run the steps in the dockerfile: https://github.com/schibsted/WAAS/blob/main/Dockerfile

What utilities related to Whisper do you wish existed? What have you had to build yourself?

On the end user application side, I wish there was something that let me pick a podcast of my choosing, get it fully transcribed, and get an embeddings search plus answer q&a on top of that podcast or set of chosen podcasts. I've seen ones for specific podcasts, but I'd like one where I can choose the podcast. (Probably won't build it)

Also on the end user side, I wish there was an Otter alternative (still paid $30/mo, but unlimited minutes per month) that had longer transcription limits. (Started building this, not much interest from users though)

Things I've seen on the dev tool side:

Gladia (API call version of Whisper)

Whisper.cpp

Whisper webservice (https://github.com/ahmetoner/whisper-asr-webservice) - via this thread

Live microphone demo (not real time, it still does it in chunks) https://github.com/mallorbc/whisper_mic

Streamlit UI https://github.com/hayabhay/whisper-ui

Whisper playground https://github.com/saharmor/whisper-playground

Real time whisper https://github.com/shirayu/whispering

Whisper as a service https://github.com/schibsted/WAAS

Improved timestamps and speaker identification https://github.com/m-bain/whisperX

MacWhisper https://goodsnooze.gumroad.com/l/macwhisper

Crossplatform desktop Whisper that supports semi-realtime https://github.com/chidiwilliams/buzz

I feel the same.

For example some stats from Whisper [0] (audio transcoding) show the following for the medium model (see other models in the link):

---

GPU medium fp32 Linear 1.7s

CPU medium fp32 nn.Linear 60.7

CPU medium qint8 (quant) nn.Linear 23.1

---

So the same model runs 35.7 times faster on GPU, and compared to an CPU-optimized model still 13.6.

I was expecting around an order or magnitude of improvement. Then again, I do not know if in the case of this article the entire model was in the GPU, or just a fraction of it (22 layers), which might explain the result.

[0] https://github.com/MiscellaneousStuff/openai-whisper-cpu

According to https://github.com/MiscellaneousStuff/openai-whisper-cpu the medium model needs 1.7 seconds to transcribe 30 seconds of audio when run on a GPU.

There is a very simple method built-in to PyTorch which can give you over 3x speed improvement for the large model, which you could also combine with the method proposed in this post. https://github.com/MiscellaneousStuff/openai-whisper-cpu

For CPU inference, model quantization is a very easy to apply method with great average speedups which is already built-in to PyTorch. For example, I applied dynamic quantization to the OpenAI Whisper model (speech recognition) across a range of model sizes (ranging from tiny which had 39M params to large which had 1.5B params). Refer to the below table for performance increases:

GitHub