Whisper is great. You can get faster results running the tiny model. I used it for podcast transcription and it is much faster and the quality is not worse than the medium model - there are some podcast episodes that the transcription is the same.
If speed is important, you're much better off using a larger model and whisper.cpp.
Re:Universal Speech Model
by pantalaimon on Thursday March 30, 2023 @06:55AM
(#35370746)
Wow thank you!
That's a nice speedup indeed, with whisper I get
33,53s user 2,05s system 443% cpu 8,023 total
with the 'tiny.en' model whereas whisper.cpp gives me 22,71s user 0,12s system 745% cpu 3,062 total
with the 'base.en' model for a 15s audio clip on an i7-3770 (8 threads).Awesome! Thanks for posting the stats.
In my workflows I've found rare but noticeable quality differences between the model sizes. So when practical I try to use the larger ones.
Re:Universal Speech Model
by malborodog on Wednesday March 29, 2023 @10:39PM
(#35367179)
why not just run whisper from the command line directly? Why put it into a docker container??
Why not keep everything tightly contained?
Re:Universal Speech Model
by malborodog on Thursday March 30, 2023 @04:05AM
(#35369534)
Hm, I'm on Mac so it takes up a bunch of ram and I'm not used to this workflow. good point though.
Unless you actually use the memory (e.g. allocate it), it won’t impact system performance, but yeah, it definitely is overhead.
some people just love making their environments needlessly complicated.
Re:Universal Speech Model
by jazzyjackson on Thursday March 30, 2023 @03:03AM
(#35369104)
complexity is in the eye of the beholder, some people just get docker enough that it's not a friction
Now installing the dependencies of every git repo I want to try on my host system, that's how an environment becoming needlessly complicated
The results from Whisper are incredible, with very few mistakes. Though it did get Nelson Mandela's first name wrong (transcribed as Nesson). What's more, Whisper finished transcribing a 60-minute audio stream in 20 minutes on commodity hardware (T1000 G8 NVIDIA GPU). Broadly, here are the steps I used:
* Download and install podman.
* Download and install git.
* Download and install curl.
* Open a command prompt.
* Run the following commands to containerize Whisper:
* Download MP3 file (e.g., filename.mp3).* Run the following command to produce a transcription: