Generate raw audio music with genre- and artist-style control
OpenAI Jukebox is a research-grade neural music model that generates multi-minute raw audio in genre and artist styles; it’s best for researchers and creators experimenting with audio synthesis rather than commercial music production, and it’s available as an open-source model and demo with no paid product tiers from OpenAI.
OpenAI Jukebox is a research neural network that generates raw audio (including singing) conditioned on genre, artist, and optional lyrics. It produces multi-kilohertz waveform outputs rather than MIDI or symbolic notation, which sets it apart in the AI music generators category. Jukebox’s value lies in stylistic audio synthesis and creative exploration rather than polished, ready-for-release masters. The project is distributed openly (model weights and code released) and accessible to technically inclined users; there is no commercial subscription product or per-track pricing from OpenAI.
OpenAI Jukebox is a research project and model released by OpenAI in 2020 that generates raw audio music conditioned on genre, artist, and lyrics. Positioned as a demonstration of large-scale autoregressive modelling for audio, Jukebox converts conditioning tokens into WaveNet-style discrete audio using hierarchical VQ-VAE quantization and multiple model stages. Its core proposition is to synthesize plausible-sounding music and singing in a wide variety of styles directly as waveforms, providing researchers and creators an unusual level of raw audio output compared with symbolic or MIDI-first systems. OpenAI published the paper, sample audio, and code/models to allow experimentation rather than offering a hosted, commercial product.
Jukebox’s feature set reflects its research roots. The model provides multi-stage generation: a VQ-VAE encoder/decoder to compress raw audio into discrete codebooks, and autoregressive transformers that predict codes at coarse and fine levels to reconstruct audio up to several minutes. Conditioning controls include genre labels, artist embeddings derived from training data, and tokenized lyrics to steer vocal content. The released code also includes scripts to sample from the models, upsample generated codes to raw audio, and playback sample files. Because Jukebox outputs waveforms, it can produce timbre, instrumentation, harmony and vocal characteristics jointly rather than assembling stems. The repository includes precomputed sample checkpoints and utilities for priming generation with short audio snippets.
OpenAI did not launch Jukebox as a paid SaaS; instead the research release is free to download under an open license with model checkpoints and inference code on OpenAI’s GitHub and blog. There are no official paid tiers, per-track fees, or hosted generation quotas provided by OpenAI for Jukebox; users run the model on their own hardware or cloud GPUs which incurs separate compute costs. Third-party services or forks may offer paid hosting or GUIs around Jukebox with their own pricing, but OpenAI’s original release remains a free research artifact — subject to the practical limits of needing substantial GPU memory and compute time to synthesize minutes of audio at high fidelity.
Typical users are researchers, audio ML engineers, and experimental musicians who need to prototype raw waveform synthesis or study style-conditioned generation. For example, a university audio-researcher might use Jukebox to reproduce and evaluate generative singing quality across genres, while an experimental electronic producer could prime the model with a short clip to generate stylistic continuations for sound design. Jukebox is less suited to mixing engineers needing release-ready masters; in that workflow you’d instead choose tools like AIVA or Soundraw for faster, export-ready stems. Compared with commercially packaged music generators, Jukebox’s differentiator is its open-source raw-audio checkpoints and multi-stage VQ-VAE+transformer architecture designed for waveform-level synthesis.
Three capabilities that set OpenAI Jukebox apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Open-source (self-hosted) | Free | No hosted quota; requires substantial GPU and storage to run models | Researchers and engineers with GPU resources |
| Cloud GPU (self-managed) | Varies (cloud compute billed separately) | Costs based on GPU hours; multi-GB memory required per run | Teams needing ad-hoc generation without local GPUs |
| Third-party hosted | Custom or subscription (varies by provider) | Provider-dependent quotas, UI, and licensing | Non-technical users wanting web UI and support |
Copy these into OpenAI Jukebox as-is. Each targets a different high-value workflow.
You are OpenAI Jukebox: generate a single, one-shot 30-second pop demo clip. Role: produce a polished example for demos. Constraints: genre 'modern pop', artist_style 'Adele-like' vocal timbre, original lyrics (no copyrighted text), stereo WAV output, duration exactly 30 seconds, accompaniment limited to piano and strings, clean mastering but not final commercial master, no profanity. Output format: attach one WAV file and a JSON metadata object: {duration_seconds, genre, artist_style, bpm, key, lyrics, seed_id}. Lyrics to use (singable, short): "Hold the night, I’m holding on, light the sky until the dawn."
You are OpenAI Jukebox: generate a 60-second ambient texture loop for sound design. Role: create a usable loopable instrumental bed. Constraints: genre 'ambient/drone', no vocals or lyrics, include evolving pads, granular percussion, and low-frequency rumble; output must be loop-friendly (end matches start within 50ms), stereo WAV, 60 seconds duration. Output format: provide one WAV file and a short JSON: {duration_seconds, genre, instruments, loopable_true, seed_id}. Example descriptor to emulate: 'slow evolving synth pad, sparse granular taps, sub bass wash.'
You are OpenAI Jukebox: produce three 45-second musical variations for benchmarking. Role: create controlled style-switched outputs. Constraints: produce Variation A (genre: indie rock, 120 bpm, key: E major), Variation B (genre: synth-pop, 100 bpm, key: C minor), Variation C (genre: jazz ballad, 80 bpm, key: Bb major). All three must use the same short lyrical phrase 'We chase the light, we never sleep' sung with appropriate timbre changes; stereo WAV outputs, 45 seconds each. Output format: a single JSON array listing three objects with {filename, genre, bpm, key, vocal_timbre, lyrics_used, seed_id, short_description}.
You are OpenAI Jukebox: given a 20–30 second seed audio clip (uploaded separately), generate a 90-second continuation that preserves the seed's timbre and melodic material. Role: extend seed into a finished demo section. Constraints: maintain key and tempo of seed, continue any existing vocal lyrics logically (if present), produce stereo WAV output with clear metadata. Output format: one WAV file (90s total including seed) and a JSON manifest {seed_filename, total_duration, resume_point_seconds, genre, bpm, key, lyrics_continued, seed_id}. If the seed contains no vocals, add a short original chorus near 60–75s.
You are OpenAI Jukebox configured for research generation. Task: produce ten 40–60 second tracks, each in a different target genre (list provided), using the same short test phrase for intelligibility benchmarking. Role: create repeatable samples for cross-genre singing analysis. Constraints: each file must use identical lyrics 'Test phrase: follow the line of melody', uniform tempo 100 bpm, maintain comparable loudness (-14 LUFS), stereo WAV outputs, include metadata. Output format: a single CSV manifest with columns: filename, genre, artist_style, duration_s, bpm, key, loudness_lufs, seed_id, brief_notes; plus ten WAV files. Example genres: pop, rock, country, opera, jazz, R&B, electronic, metal, folk, reggae.
You are OpenAI Jukebox acting as a musical director and producer. Task: generate a 3-minute arrangement with clear verse/chorus/bridge sections and stems. Role: blend two artist styles (Artist A: soulful R&B singer; Artist B: indie electronic producer) into a cohesive track. Constraints: produce separated stereo stems: vocals_stem.wav, drums_stem.wav, bass_stem.wav, pads_stem.wav, mix_stem.wav; duration 180 seconds; vocal timbre should morph between Artist A in verses and Artist B–influenced textures in chorus via processing; include provided lyrics (attach below) and a two-line chord chart. Output format: five WAV stems plus a JSON manifest {sections:[{name,start,end,bpm,key}], stems:list, lyrics_timestamps, seed_id}. Example stem naming: '01_vocals_stem.wav'. Lyrics: 'Verse 1: ...' (attach actual lyrics when running).
Choose OpenAI Jukebox over Google MusicLM if you require downloadable open-source checkpoints and local waveform sampling for research reproducibility.