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Nevertwenty a novel approach to chord synthesis

Hacker News

Nevertwenty a novel approach to chord synthesis

I play piano and I built this for myself recently. Type a song, hit play, and the chords come at you on the beat. It's Rock Band for real instruments: the chord you need is the one under the playhead. I've almost got the entire music database here. I dare you to find a public track that isn't in here. Catalog is 6.8M songs / 103k artists. Librosa beat detection. ONNX graph combined with my custom tuned BTC. Bass notes processed independently. Median over 4536 cold runs: 2.15s to decode, 122x realtime. Download is the slow part at 8.4s median. Moving the audio is the bottleneck. MP3 search is a fancy matching algorithm; if the song is on the internet it'll find it. It was hard to make this pipeline run fast! Should probably stream the audio instead. But I have to change the entire pipeline arch to allow it. Will continue trying. The GTA6 of RockBand play along v1 "I can sight-read along to songs I've never heard now. That isn't a thing I could do a year ago, and I didn't get there by practicing." said the computer

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
63%63% 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 · Missing: mobile apps, ios, personal
63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: computer, notes, code · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% 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
20%20% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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