Catchy melodies made with a diffusion-based neural net assistant

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I've created a diffusion-based neural net generative assistant that makes creating new melodies much easier, even for non-musicians like me. These are meant to be just the catchy "hook" parts of songs, so more work is required to make them into full songs, but this is already handled well by existing products (e.g. there are plugins that can suggest a few possible chord progressions based on the melody and there is even good singing software that I used without any tweaks to make the “voice” playlist: Synthesizer V Studio). This side project turned out to be quite challenging because of how little data there is to train on - several orders of magnitude less than DALL-E or GPT-3 had available for its training, so it required a deep dive into research of new generalization and augmentation techniques and some feature engineering. Various other instruments: Voice: https://www.youtube.com/playlist?list=PLoCzMRqh5SkE1yC8_WtJ-... Synth: https://www.youtube.com/playlist?list=PLoCzMRqh5SkFj7RNZvjr7... Bell: https://www.youtube.com/playlist?list=PLoCzMRqh5SkEYHYvHX9m9... Guitar: https://www.youtube.com/playlist?list=PLoCzMRqh5SkGKvfkP2Oex... Sax: https://www.youtube.com/playlist?list=PLoCzMRqh5SkHfsZgzzdSh... Grand Piano: https://www.youtube.com/playlist?list=PLoCzMRqh5SkFMch5x60uh... SoundCloud electric piano: https://soundcloud.com/lech-mazur-995769534/sets/ai-assistan... SoundCloud vocal: https://soundcloud.com/lech-mazur-995769534/sets/ai-assistan...

Visit product View on Hacker NewsMay 11, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, songs · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

66%66% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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