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Fast Music Remover – Remove Music and Noise from Internet Media

Hacker News

Fast Music Remover – Remove Music and Noise from Internet Media

I've recently open-sourced a tool for removing music, sound effects, and noise from internet media, with an emphasis on speed and simplicity. The project processes videos at around 8% of the original media length — meaning it can handle a 10-minute video in under a minute. Repo: https://github.com/omeryusufyagci/fast-music-remover The tool is lightweight, works through Docker, and is licensed under MIT. It supports several use cases, such as improving the audio quality of lectures, field reports, and isolating speech from videos with heavy background noise. I'm currently working towards adding live stream support and would love to have contributors on board. There are interesting open issues for developers experienced in backend, frontend, Python, C++, and audio processing. Feedback, suggestions, and contributions are very welcome. Thanks for checking it out!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, open · Missing: mac, agents, macos
54%54% 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 released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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