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I think that's parakeet doing its job there. That is a closer reflection of what you've actually said. The trick is then throwing that output through some additional deterministic and non-deterministic steps to tidy it up however you prefer. It's exactly what I do with my free and open source dictation app (dictator.robgough.net) for Mac+iOS. And of course, everything stays entirely on-device. Gemma E4B is really wonderful for that second step, it's great at language – but takes up 6-7GB RAM.
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I use Parakeet V3 via this tool and it is actually quite reliable for me (in English): https://github.com/cjpais/Handy
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If you are using Parakeet for English only then you should be using V2. V3 is for several languages and is worse at English only.
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No, there are better open weight models: https://artificialanalysis.ai/speech-to-text/non-streaming

Apparently MOSS-Transcribe-Diarize is quite good too as it released only a few days ago.

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Agree on this point. Recent anecdotal testing I did found Whisper is still better than Parakeet.
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Parakeet is certainly faster on my machine (m3 max), but I can't stand using it over Whisper for dictating my prompts. It makes a lot more mistakes, possibly because (like you mentioned) large portions of the speech will pause / stutter as I think about what to include.

With whisper v3 turbo, I can almost always live with the few mistakes because the overall stream-of-thought word-salad I provide is still clear at a high level. The bits and pieces of context seem to help, that I might leave out if typing and focused more on traditional conciseness / clean writing. With parakeet, I needed to do frequent editing even for shorter bits of speech.

I realize some applications prioritize the latency.

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It sounds like post processing should be the job of an LLM. I would like the voice model to be faithful to what was said and then that output can be smoothed over or postprocessed as needed for the use case
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To be clear, I'm talking about high word error rate with parakeet vs whisper, not post processing and cleaning up my speech. Re: being faithful to what was said, one small example, Whisper will often put ellipses when I pause.
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