Star Singer started as a consumer app, but over the last year it has quietly become the largest AI-generated music catalog you can stream for free. Eighty-plus named AI artists, more than 5,000 songs, and full coverage across 20 genres. Here is how the catalog actually gets made and why breadth beats volume.
Artists are designed, not prompted
Every AI artist on Star Singer has a full artist bible: a name, backstory, primary and secondary genres, vocal character, musical influences, and a visual identity. The bible is authored by a human producer and encoded as constraints the generation pipeline respects.
When the AI produces a new song for, say, Remi Vale — our Lo-Fi artist with a West Coast jazz lean — the pipeline pulls her vocal fingerprint, her influences (Nujabes, J Dilla, FKJ), her tempo range, and her lyrical voice. The result sounds like her, not like a generic "lo-fi" prompt.
Genre breadth over brute volume
A lot of AI music catalogs are deep in Pop and Hip-Hop and almost empty everywhere else. Star Singer went wide on purpose. Amapiano, Phonk/Drift, Drill, Gospel, Afrobeats, K-Pop, and Latin/Reggaeton each have at least three dedicated artists. We added Reggae/Dancehall and Disco/Funk in February. Jazz and Acoustic/Folk get the same treatment.
Why? Because long-tail genres are where discovery actually happens. Someone who wants amapiano is going to be thrilled by three great artists and indifferent to 500 more generic pop tracks.
How a song is actually made
Every new song goes through a four-stage pipeline:
1. Brief. A producer writes a one-paragraph brief: artist, tempo, key, mood, lyrical theme, references. No human in the room is writing full lyrics or toplines.
2. Lyrics and melody. A lyrics model takes the brief and produces three lyric candidates with melodic contour. A human reviewer picks one or requests a regeneration.
3. Production. A music model generates instrumental stems — drums, bass, harmony, lead — matched to the melody. The artist's vocal model sings the lyrics.
4. Mix and master. A dedicated mixing pass levels the stems, adds reverb and compression to match the genre's conventions, and produces the final master.
Each stage is reversible. A producer can reject a mix and re-run the master, or swap the instrumental and keep the vocals. Nothing is generated in one shot.
Why this is not the same as "prompt a song"
The hardest part of running an AI record label is taste. A prompt-to-song tool can produce 5,000 songs in an afternoon. 95% of them are forgettable. The work is in choosing the 5% that become the catalog and throwing out the rest. That requires human producers listening, rejecting, and iterating.
The result is that songs on Star Singer have a noticeably higher floor than what you get from DIY AI music tools. They are not all classics, but you will not find obvious genre-mistakes, mixed-down vocals, or incoherent structures.
What is next
We are onboarding new genre specialists in Country, Jazz fusion, and ambient electronic. We are also piloting a feature where you can "sign" to the label — if your AI covers get enough plays, we promote them to the main feed and share revenue with you.
If you want to hear the catalog, the Explore feed on starsinger.ai is free forever. No signup required.