01
Your Music
Upload songs, demos, stems, vocals, instrumentals, MIDI and lyrics you own or control. ANYANO analyses tempo and key and can separate stems so you can train on isolated elements.
The category
Personalized AI music: your music trains your AI
Generic AI music generation starts with somebody else's model. Personalized AI music starts with your catalog. ANYANO trains a private Sound model on music you own, then uses it as the foundation for generation.
Definition
A working definition
Personalized AI music is music generation conditioned on a specific artist's own recorded material, producing a model that belongs to that artist rather than a shared general-purpose model driven only by prompts.
The distinction is architectural, not cosmetic. In a prompt-to-song product, every user queries the same weights; personalization stops at the text box. In a personalized system, the artist's material is the conditioning signal, so two artists with identical prompts get materially different output because their models were built from different music.
That has consequences beyond novelty. It changes what rights need to be cleared before training, what needs to stay private, who the output is useful to, and what the tool is actually for — not producing finished songs on demand, but exploring the space around work you have already made.
Compare
Two different architectures
Generic AI music generation
- One shared foundation model serves every user.
- The prompt is the only personal input.
- Style references usually point at other people's music.
- Output tends toward the average of the training distribution.
- Your catalog plays no role in what comes out.
Personalized AI music (ANYANO)
- A Sound model is trained per artist from uploaded material.
- Your catalog is the primary input; prompts steer within it.
- Style reference is your own work, not a named artist.
- Output stays tied to the material you provided.
- Rights and privacy are part of the product, not an afterthought.
Mechanics
How personalization actually works in ANYANO
Four stages, each visible in the app as its own step with its own status.
UploadPrepareTrainGenerateExplore
02
Preparation
Selected files are grouped into a dataset and prepared for training — normalising levels, trimming silence and computing the representations the training run consumes.
03
Training
A training run produces a versioned Sound. You choose what the run should focus on and the basic hyperparameters, and you confirm your rights before it starts.
04
Generation
A finished Sound becomes selectable in the generator alongside prompt, lyrics, mood, structure, tempo and a creativity control.
05
Directions
Any generation can be branched into variations or extended, so a promising fragment becomes a path rather than a one-off result.
06
Retraining
As your catalog grows you can prepare a new dataset and train a new version. Old versions remain listed so you can compare where your Sound has moved.
Honest limits
What a Sound learns — and what it does not
Tends to pick up
- Harmonic and melodic tendencies across your material
- Rhythmic feel and typical tempo ranges
- Timbral and production character
- Arrangement and section habits
Does not do
- Reproduce a specific recording or performance
- Clone a voice reliably or on demand
- Understand your intent, lyrics or narrative
- Replace mixing, arrangement or editorial judgement
Treating generated material as a proposal rather than a product is the whole workflow. ANYANO offers possibilities; the artist decides where the music goes.
Trust
Why ownership and privacy are part of the category
Personalization only makes sense if the artist stays in control of the material that powers it — including unreleased work.
Uploads live in a private storage bucket and database rows are constrained by row-level security policies scoped to your account, so other accounts cannot read your files, datasets, Sounds or generations. Training and generation run on connected specialist compute configured for your studio; that processing relationship is described on the privacy page rather than glossed over.
You confirm you hold the necessary rights before training, and you can delete uploaded material from your library. We do not claim more than the product and terms support — where a guarantee depends on a third-party processor, we say so.
FAQ
Frequently asked questions
Related
Keep reading
What is a Sound?The product concept: how a Sound is trained, used, managed and deleted.How training worksA plain-language explanation of what the system learns from your music.OwnershipRights you need before uploading, and rights around generated output.Compare approachesPersonalized models versus foundation models and text-to-music tools.
Your catalog isn't just your past.
It can become a creative model for what you make next.