# salu133445/musegan

An AI for Music Generation

Repository: https://github.com/salu133445/musegan
Canonical: https://ross.abutalabs.com/products/musegan
Homepage: https://salu133445.github.io/musegan/
Language: Python
License: MIT
License Family: permissive
Topics: music, music-generation, machine-learning, tensorflow, generative-adversarial-network, gan
Last push: 2024-06-07T10:22:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3276, "days_push": 817, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2036, forks 388 (observed 2026-08-28T04:06:08.001836+00:00)

## What it is
MuseGAN is a TensorFlow-based generative adversarial network for generating polyphonic, multitrack music (bass, drums, guitar, piano, strings), either from scratch or as accompaniment to a given track. It is a research codebase trained on the Lakh Pianoroll Dataset, accompanying an academic paper.

## Use cases
- generate multitrack music with a GAN
- accompany a user-provided track with generated instruments
- train a music generation model on pianoroll data
- reproduce MuseGAN paper experiments
- explore GAN architectures for symbolic music

## When to choose
- you want to generate polyphonic symbolic music with multiple instrument tracks
- you need a research reference implementation of music GANs
- you work with pianoroll data and TensorFlow

## When to avoid
- you need a PyTorch implementation or production-ready music generation service
- you want audio synthesis rather than symbolic/pianoroll output
- you need fine-grained per-measure or per-track latent control

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, artificial-intelligence
- platform: python
- tags: music-generation, gan, tensorflow, generative-models, pianoroll, research, audio, linux, macos

## Member repositories
- salu133445/musegan (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:08.001836+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:59:20.518630+00:00, confidence not recorded.
  - readme: https://github.com/salu133445/musegan (fetched 2026-08-28T04:06:08.001836+00:00, sha 82f6191ac7ee)
  - homepage: https://salu133445.github.io/musegan/ (fetched 2026-08-29T10:39:05.305883+00:00, sha 8b667b144c19)
- Data as of 2026-08-30T08:39:29.467469+00:00.