Local-first models are not capable enough when we compare them against several different languages. Of course, a lot of times, English only would be very feasible with local models, but when we come to things like punctuation, speaker diarization, and all those kinds of models, then it becomes harder. I think the major challenges are:
- overlapping speech
- language switching
- corrections
- on-point corrections in speech
- correct speaker labeling
- diarization error rate
- word error rate
These things are important to consider. Although Google released the local-first model or local-first application for this, I believe, strictly, that competitors which have their own proprietary models or even use something like Pyannote nd whisper are certainly far better.
Local-first models are not capable enough when we compare them against several different languages. Of course, a lot of times, English only would be very feasible with local models, but when we come to things like punctuation, speaker diarization, and all those kinds of models, then it becomes harder. I think the major challenges are: - overlapping speech - language switching - corrections - on-point corrections in speech - correct speaker labeling - diarization error rate - word error rate These things are important to consider. Although Google released the local-first model or local-first application for this, I believe, strictly, that competitors which have their own proprietary models or even use something like Pyannote nd whisper are certainly far better.