231–240 of 251 posts
I run SuperWhisper on both my Mac (where is uses Whisper) and my iPhone (where it uses SpeechAnalyzer and have found that SA does indeed run faster and anecdotally more accurately. Super exciting!
Whisper is the wrong model to benchmark against, or rather, there are better models that are state of the art now like Nemotron and Parakeet both by Nvidia, as well as Mistral's Voxtral and Cohere Transcribe. However, what's funny is, RIP to a lot of the paid apps that simply wrap Whisper, I'm sure Apple will make a native GUI such as a recorder app for macOS that obviates the need for these wrappers, which everyone…
Apple's own Voice Memos app already does automatic transcription since macOS 15 / iOS 18.
I will plug Willow for mac recording. IMO it's basically to me a "better than perfect transcription" as it cleans things up and is almost instant. I liked Superwhisper but switched to Willow as it was a big difference. Its so good that I'm not sure that it's possible to get any better. Speech to text seems like basically a solved problem, if not now then definitely in 5 years. I don't know if any of these speech to t…
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The difference using an mp3 seems to be smaller: yap seems to use about the same time but fluidaudio seems to take twice as long. Do you happen to know why?
Investigated this and it turned out to be an amusing bug: audio decoding was happening three times instead of just once lol. I've put up a PR to remove the wasteful redundant decoding: https://github.com/FluidInference/FluidAudio/pull/799 With the updated PR code, ran a test comparing transcribing (using Parakeet V3) a 1 hr stereo 44.1 kHz mp3 vs the same audio in 16 kHz mono wav format. The result was about 21.3% sl…
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The difference using an mp3 seems to be smaller: yap seems to use about the same time but fluidaudio seems to take twice as long. Do you happen to know why?
Investigated this and it turned out to be an amusing bug: audio decoding was happening three times instead of just once lol. I've put up a PR to remove the wasteful redundant decoding: https://github.com/FluidInference/FluidAudio/pull/799 With the updated PR code, ran a test comparing transcribing (using Parakeet V3) a 1 hr stereo 44.1 kHz mp3 vs the same audio in 16 kHz mono wav format. The result was about 21.3% sl…
I use fluidaudiocli and it's unfortunate that it doesn't support streaming (e.g. from a named pipe); that would have been an easy workaround to both the pipelining problem and the faster-decoder problem.
Earlier quoted context omitted.
Investigated this and it turned out to be an amusing bug: audio decoding was happening three times instead of just once lol. I've put up a PR to remove the wasteful redundant decoding: https://github.com/FluidInference/FluidAudio/pull/799 With the updated PR code, ran a test comparing transcribing (using Parakeet V3) a 1 hr stereo 44.1 kHz mp3 vs the same audio in 16 kHz mono wav format. The result was about 21.3% sl…
That app was exactly what I was looking for, something like SponsorBlock but for podcasts but I suppose using AI for finding the ads works too. Any chance it'll release on Android?
Making the ad-finding cheap enough such that I could make it free turned out to be harder than expected. The main issue you run into is dynamic, location-targeted ads. So I came up with a novel technique that uses Shazam-style audio fingerprints for accurate matching, instead of their normal use case, which is identification. This technique is what allows the ad finding to be very cheap, allowing me to make it free.
The SponsorBlock model would actually not work for podcasts, due to dynamic ads. I.e. the location and content of the ads in episodes these days varies by download location. You need the media to be static, like YouTube, for SponsorBlock model to work. Therefore, using an LLM to find the ads + the fingerprints matching in combination is an efficient technique.
Android has def been the most requested thing thus far haha. It'll be a decent undertaking due to me having written the app fully in Swift, i.e. it'll be a complete rewrite. I'll also need to replace FluidAudio with some good, fast Android equivalent.
The goal of making this app was to create something impressive so that I could get a job. Haven't gotten a job yet, but if and when I do, then I'll have time & resources to think about doing an Android version. Currently a bit stressed and occupied from the job search lol.
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Investigated this and it turned out to be an amusing bug: audio decoding was happening three times instead of just once lol. I've put up a PR to remove the wasteful redundant decoding: https://github.com/FluidInference/FluidAudio/pull/799 With the updated PR code, ran a test comparing transcribing (using Parakeet V3) a 1 hr stereo 44.1 kHz mp3 vs the same audio in 16 kHz mono wav format. The result was about 21.3% sl…
Thanks! I use fluidaudiocli and it's unfortunate that it doesn't support streaming (e.g. from a named pipe); that would have been an easy workaround to both the pipelining problem and the faster-decoder problem.
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As a Texan first, American second, I sympathize with this statement. Siri can't understand me probably 25% of the time. I use STT for iMessage while in the car, and half the time it will take 3+ times to either get it right or me give up, and hope to remember to text them by hand when I next stop.
What does this mean? When I pledged my oath to become a citizen, I had to promise to put America above all other allegiances. Is it in relation to allegiance in the general sense or some weird statement that only relates to STT?
(and if you think Texans have it bad regarding being understood, try being a Scouser [1])