AI Music Genre Tag Library

Browse music genre and subgenre tags with Suno/Udio prompt formatting

Searchable library of music genre and microgenre tags with correct prompt phrasing for Suno, Udio, and MusicGen. Covers hyperpop, vaporwave, dark folk, phonk, and dozens more with ready-to-copy example prompts. It runs free in your browser on Gera Tools, with nothing uploaded.

Last updated Source: Gera Tools

Why do genre tags differ between Suno, Udio, and MusicGen?

Each model was trained on different metadata. Suno likes short comma-separated tags, Udio leans on era and production words, and MusicGen prefers a single descriptive sentence. The library reformats each genre accordingly.

AI music genre tag library

The single biggest upgrade to an AI music prompt is using the precise genre term instead of a broad one. “Phonk” gets you Memphis-style cowbell and distorted 808s; “hip hop” gets you something generic. This library collects common genres and internet microgenres with the tags and defining descriptors that actually steer Suno, Udio, and MusicGen — plus a copy-ready example prompt for each.

How it works

Search filters across genre names, related tags, and the example prompts so you can find a style by vibe even if you don’t know its name. The platform switch matters: the same genre is phrased differently for each tool. Suno wants a short tag list, Udio wants era and production cues, and MusicGen wants one descriptive sentence. The library rewrites the example prompt to match the model you select, so you always copy something the target tool parses well.

Why genre precision matters so much

AI music models are trained on tagged audio paired with text descriptions. When those descriptions used “phonk” or “hyperpop” as labels, the model learned to associate specific sonic signatures with those exact words. A broad term like “hip hop” activates too wide a cluster — the model has to guess whether you mean trap, boom bap, lo-fi, or drill. The narrower the term, the smaller and more coherent the cluster the model samples from.

Example contrasts:

Broad tagPrecise alternativeWhat you actually get
electronicdarksynthDistorted leads, aggressive basslines, minor-key industrial feel
pophyperpopPitched-up vocals, saturated 808s, breakbeat drops
folkdark folkFingerpicked acoustic, minor modes, sparse arrangement
hip hopphonkMemphis-style hi-hats, slowed cowbell, 808 sub bass
ambientdungeon synthReverb-heavy synths, medieval atmosphere, no percussion

Microgenre spotlight

A few microgenres that consistently produce strong, distinctive AI music results:

Vaporwave — slowed, pitch-shifted 80s R&B and smooth jazz with heavy reverb and a nostalgic aesthetic. Key descriptors: lofi, slowed sample, elevator music, 80s mall.

Phonk — originated in Memphis rap samples, now associated with aggressive drift-car culture. Distorted 808 bass, cowbell, Memphis rap vocal chops, high BPM.

Hyperpop — maximalist, Internet-born pop. Pitched-up vocals, crunchy distortion, 808 hits, dense layering, breakbeat or no-groove percussion.

Dark folk — acoustic instruments, minor keys, sparse production, often narrative or literary. Fingerpicked guitar, sparse hand percussion, close-miked vocals.

Tips for using genre tags

  • Use the narrowest accurate term. “Darksynth” beats “electronic”; “dark folk” beats “folk.” Specificity is the whole point.
  • Stack a parent + a defining descriptor as a fallback. If a microgenre is unrecognized, “synthwave, dark, driving 80s” recovers most of the sound.
  • Don’t pile on five genres. Two related tags blend well; five unrelated ones produce mud. Lead with the dominant style.
  • Match phrasing to the model. A comma list that works in Suno can confuse MusicGen, which prefers a flowing sentence — use the platform switch.