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bigWav.app – web based transcription powered by Whipser and WASM
Hacker NewsbigWav.app – web based transcription powered by Whipser and WASM
Hello, I have recently released a web based transcription and annotation app. It is powered by Whipser.cpp ( https://github.com/ggerganov/whisper.cpp ) compiled to WASM. It is a PWA, so can be installed via desktop Chrome. This enables you to open video/audio/bigwav files from the file Finder/Explorer directly. More info: https://bigwav.app
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2points
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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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