6 ms·

Just ran it against Whisper-Large-V2 on a math lecture (my primary use case for ASR is subtitling math lectures), and it was substantially faster and only sligh

Just ran it against Whisper-Large-V2 on a math lecture (my primary use case for ASR is subtitling math lectures), and it was substantially faster and only slightly worse. Very usable for live transcription though I'll probably stick with whisper for the time being since I don't really need the subtitles to be generated in real time.

Chu4eeno 2mo ago
If it was faster but worse, maybe compare it to a smaller whisper model?
- generalizations 2mo ago
  I imagine because quality of transcription is what matters.
seviu 2mo ago
Been using it for a podcast app I have been developing for half a year lol (I hope I publish it by version 27) and I can confirm it’s real fast. Splitting the audio in multiple segments and firing it up without hitting the maximum limit of concurrent decoding streams makes it blazing fast. Fair enough you loose the cut, but it’s good enough for just podcast. In one minute it chews through one hour of audio. This on an iPhone 17 Pro.
- satvikpendem 2mo ago
  What's different about your podcast app?
  - seviu 2mo ago
    Nothing really, except that I get to play with SpeechAnalyzer APIs, foundation models, translations. It’s basically my playground where to try all things. Been listening a lot of Chinese podcasts lately, transcribed and translated by local models. Edit: all that said, the app is irrelevant. What I want to say is that live transcripts on iOS using Apples frameworks works very well. Only thing I miss is diarization support.
- rokkamokka 2mo ago
  You could perhaps run over the segment splitting points (plus a few seconds back and forward) in a second batch then merge the results in the end so you don't miss anything.
- deleted 2mo ago
  [deleted]
- dewey 2mo ago
  You probably know that already, but there's a great ATP episode about using Apple's models for podcast transcribing at scale for Overcast.app (https://appleinsider.com/articles/26/04/07/giant-mac-mini-cluster-powers-overcast-podcast-transcripts-without-the-cloud https://appleinsider.com/articles/26/04/07/giant-mac-mini-cl...)
adityas02 2mo ago
[flagged]

Chu4eeno 2mo ago

If it was faster but worse, maybe compare it to a smaller whisper model?

generalizations 2mo ago
I imagine because quality of transcription is what matters.

seviu 2mo ago

Been using it for a podcast app I have been developing for half a year lol (I hope I publish it by version 27) and I can confirm it’s real fast. Splitting the audio in multiple segments and firing it up without hitting the maximum limit of concurrent decoding streams makes it blazing fast. Fair enough you loose the cut, but it’s good enough for just podcast. In one minute it chews through one hour of audio. This on an iPhone 17 Pro.

satvikpendem 2mo ago
What's different about your podcast app?
- seviu 2mo ago
  Nothing really, except that I get to play with SpeechAnalyzer APIs, foundation models, translations. It’s basically my playground where to try all things. Been listening a lot of Chinese podcasts lately, transcribed and translated by local models. Edit: all that said, the app is irrelevant. What I want to say is that live transcripts on iOS using Apples frameworks works very well. Only thing I miss is diarization support.
rokkamokka 2mo ago
You could perhaps run over the segment splitting points (plus a few seconds back and forward) in a second batch then merge the results in the end so you don't miss anything.
deleted 2mo ago
[deleted]
dewey 2mo ago
You probably know that already, but there's a great ATP episode about using Apple's models for podcast transcribing at scale for Overcast.app (https://appleinsider.com/articles/26/04/07/giant-mac-mini-cluster-powers-overcast-podcast-transcripts-without-the-cloud https://appleinsider.com/articles/26/04/07/giant-mac-mini-cl...)

adityas02 2mo ago

[flagged]