4 ms·

Tbf LLMs are pretty incredible accessibility tools. Speech to text using whisper is almost perfect. I once worked with someone who wasn’t fluent in English, b

by dartos 2y ago

Tbf LLMs are pretty incredible accessibility tools. Speech to text using whisper is almost perfect. I once worked with someone who wasn’t fluent in English, but could read it really well. He had whisper running during meetings because he could read at the speed it translated, but couldn’t keep up with our casual speech. Honestly, we didn’t even notice he was using speech to text to keep up with us for a few weeks. We only noticed because of screen sharing. I imagine that would be a big social boon for deaf people as well. All that to say, it’s not like there was _no_ good done with all that money.

codr7 2y ago
Not a shadow of what good could have been done. Any day now you'll barely notice he isn't even there anymore; which seems to be the end goal. It's not like this mad rush into la-la-land doesn't have negative consequences for society.
thecleaner 2y ago
> Speech to text using whisper is almost perfect. This isn't true. On benchmarks whisper is not SOTA. It is said to be noise resistant but it doesn't compare well with Conformer based architectures ever on Librispeech mixed. Definitely not perfect, and it doesn't work for medical transcription.
- SR2Z 2y ago
  If anything other than a small minority of people needed medical transcripts, sure! But for the remaining 99% of use cases, a fast and easy to deploy model is what's most useful.
  - thecleaner 2y ago
    Its not fast without pre-segmentation as they do in WhisperX. It actually has terrible transcription speed. For speedup we have to use Ctranslate2 kernels. The decoding code is also a mess where its hard to plug your own custom language model. Not to mention streaming ASR requires even more tweaks. Whisper Small is very fast and quite inaccurate. If you deploy whisper on a GPU which costs around dollar per hr, you really to ensure that the cost savings are worth it. Although all of this is from a production lens. For personal use, honestly nothing is as easy to use as Whisper (even works on a laptop).

by dartos 2y ago

codr7 2y ago

Not a shadow of what good could have been done. Any day now you'll barely notice he isn't even there anymore; which seems to be the end goal. It's not like this mad rush into la-la-land doesn't have negative consequences for society.

thecleaner 2y ago

> Speech to text using whisper is almost perfect. This isn't true. On benchmarks whisper is not SOTA. It is said to be noise resistant but it doesn't compare well with Conformer based architectures ever on Librispeech mixed. Definitely not perfect, and it doesn't work for medical transcription.

SR2Z 2y ago
If anything other than a small minority of people needed medical transcripts, sure! But for the remaining 99% of use cases, a fast and easy to deploy model is what's most useful.
- thecleaner 2y ago
  Its not fast without pre-segmentation as they do in WhisperX. It actually has terrible transcription speed. For speedup we have to use Ctranslate2 kernels. The decoding code is also a mess where its hard to plug your own custom language model. Not to mention streaming ASR requires even more tweaks. Whisper Small is very fast and quite inaccurate. If you deploy whisper on a GPU which costs around dollar per hr, you really to ensure that the cost savings are worth it. Although all of this is from a production lens. For personal use, honestly nothing is as easy to use as Whisper (even works on a laptop).