The AI music platform design.
A practical roadmap for building audio transcription, symbolic music generation, score editing, and high-quality rendering around MIDI, MusicXML, PDF, and audio exports.
Recommended architecture
| Module | Recommended tech |
|---|---|
| Frontend web | Next.js on Vercel |
| Desktop app | Tauri 2 + Next.js, or Electron |
| Hybrid mobile | Tauri 2 mobile, Capacitor, or React Native |
| Backend API | Fastify / NestJS on Railway or Fly.io |
| Long-running jobs | Redis queue + GPU workers |
| GPU | RunPod, Modal, Replicate, Lambda Labs, Vast.ai, AWS/GCP GPU |
| Database | Neon PostgreSQL |
| Audio files | Cloudflare R2 / S3 |
| Scores | MusicXML, MIDI, PDF, GP5 |
A very accurate transcription API
For a first version, do not start by training your own model. Build an API that orchestrates audio upload, source separation, transcription by instrument, rhythmic quantization, and generation of MIDI, MusicXML, and PDF files.
- Upload audio
- Separate sources
- Transcribe each instrument
- Quantize rhythm
- Generate MIDI + MusicXML + PDF
Demucs and htdemucs are serious open-source foundations for separating vocals, instruments, and stems. Klangio API is also close to this need because it advertises audio-to-MIDI, MusicXML, PDF, GP5, instrument transcription, and source separation.
Important: complete and reliable polyphonic multi-instrument transcription in under three minutes is ambitious. It can work for simple cases or GPU-powered workflows, but dense songs with drums, bass, piano, guitar, vocals, and effects will often still need human correction.
An AI music generation agent
For the MVP, keep the scope narrow: start with one instrument. The user chooses style, key, tempo, time signature, level, length, and instrument. The agent first generates a symbolic representation such as MIDI or MusicXML, then renders audio with a virtual instrument, SoundFont, VST, or synthesis engine.
Always return audio + MIDI + MusicXML + score PDF. This is better than generating WAV directly because the main requirement is a clean score.
Desktop, mobile, and production app
Recommended option: Tauri 2 + Next.js
- Lighter than Electron
- Desktop compatible
- Can support iOS/Android depending on the case
- Good choice if you want to keep the Next.js UI
Simpler early option: Electron + Next.js
- Heavier
- More mature for desktop audio
- Stronger file-system and native integration story
- Good choice when speed matters
The production pipeline
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How to build these projects concretely
MVP transcription
- Audio upload API
- Stem separation
- MIDI/MusicXML transcription
- PDF export
MVP generation
- Monophonic or simple piano generation
- MIDI + MusicXML output
- Rendered audio
MVP desktop app
- Open projects
- Import audio
- Display scores
- Edit notes
- Export MIDI/PDF/audio
The most strategic starting point is a single instrument, such as piano or guitar. Full multi-instrument polyphonic transcription is heavy R&D; an AI music assistant that produces clean MusicXML for one instrument is much more achievable.
Demucs and Spleeter are not transcribers
Demucs and Spleeter are source-separation models only. They do not perform the musical transcription itself. A full transcription pipeline needs separation, instrument-level tracks, AI transcription, quantization, and export.
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Source separation
- Demucs
- Spleeter
- MDX-Net
- BandIt
- BS-RoFormer
Pitch detection
- Basic Pitch by Spotify
- CREPE
- SPICE
Polyphonic transcription
- MT3 by Google
- YourMT3
- ByteDance Piano Transcription
- Omnizart
How to generate the first instrument
Option 1 — Train your own model
Use a MIDI dataset, train a Transformer, generate MIDI, convert to MusicXML, create the score, then synthesize audio. This gives you the most control.
Option 2 — Fine-tune an existing model
Interesting foundations include Meta MusicGen, Magenta Music Transformer, Magenta PerformanceRNN, MuseNet, TransformerXL Music, REMI Transformer, and Pop Music Transformer.
Option 3 — Use symbolic models directly
Because the app always needs a score, prefer models that generate MIDI directly instead of pure audio.
The project I would build
I would build one specialized model per instrument: PianoGPT, GuitarGPT, ViolinGPT, BassGPT, and DrumGPT. Specialized models are simpler to train, need less data, run faster, produce cleaner notation, and can later be orchestrated into full arrangements.
1. Transcriptor
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2. Composer
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3. Renderer
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This lets the Electron or Tauri application edit MusicXML and MIDI scores, replay the piece instantly, and re-export a high-quality audio render. Over time, the platform can start with piano, then expand to guitar, bass, drums, violin, and full ensembles without rewriting the entire system.