[four_fifths]

so if i understand this correctly — you want the speech recognition model to identify a vocabulary of specific terms that it wasn't trained on. instead of fine-tuning with training data that includes the new vocabulary, you input the full vocabulary at test time as a list of words and the model is able to generate transcripts that include words from the vocabulary.

seems like it could be very useful but it really comes down to the specifics.

you can prompt whisper with context — how does this compare?

how large of a vocabulary can it work with? if it's a few dozen words it's only gonna help for niche use cases. if it can handle 100s-1000s with good performance that could completely replace fine-tuning for many uses

[GavCo]

I was wondering the same and found these related papers:

https://arxiv.org/pdf/2309.08561 https://arxiv.org/pdf/2406.02649

I haven't really dug in yet but from a quick skim, it looks promising. They show a big improvement over Whisper on a medical dataset (F1 increased from 80.5% to 96.58%).

The inference time for the keyword detection is about 10ms. If it scales linearly with additional keywords you could potentially scale to hundreds or thousands of keywords but it really depends on how sensitive you are to latency. For real-time with large vocabularies my guess is you might still want to fine-tune.

[agold97]

yeah — sounds about right. retraining the whole model just to add one jargon-y term isn’t super efficient. this approach lets you plug in a vocab list at runtime instead, which feels a lot more scalable.