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Awesome AI Music Generation

repository·main·Indexed 19 days ago

https://github.com/curated-awesome-lists/awesome-ai-music-generation

A curated collection of resources, projects, and frameworks for AI music creation. Includes open-source GitHub projects like Magenta, Audiocraft, and MusPy; research papers on diffusion models and symbolic music; and tools for text-to-music generation, real-time VST plugins, and algorithmic composition.

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What's inside awesome-ai-music-generation

  1. Explore GitHub projects for AI music generation

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    The repository contains a curated list of open-source GitHub projects for various AI music generation tasks, including:

  2. Explore AI Music Generation Tools & Software

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    This repository curates various tools for AI music generation, categorized by their primary function:

    Generative Platforms & Models

    • Stable Audio (Stability AI): A latent diffusion model for generating audio conditioned on metadata and timing.
    • AudioLM Implementations: Open-source language modeling approaches to audio generation using PyTorch.
    • MuseGen: Tool for lyric writing and song generation.
    • workmusic.ai: Browser-based ambient focus music generator powered by Google Lyria.
    • webtoolz AI Music Studio: Browser-based generator for full songs with lyrics.

    Composition & Synthesis Systems

    • SuperCollider: An audio server, programming language, and IDE for sound synthesis and algorithmic composition.
    • Strasheela: A constraint-based music composition system where users define music theories via compositional rules.
    • Psycle Modular Music Creation Studio: Open-source modular music creation software.

    Commercial & User-Friendly Generators

    • Soundful: Generates royalty-free tracks instantly.
    • Song Cover Tools: Online tools for creating song covers via search, audio upload, or direct recording.

  3. Use AceTagGen to build Suno AI prompts

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    AceTagGen is a free prompt builder specifically for Suno AI. It uses structured inputs to generate prompts that fit within Suno's 200-character Style field.

    Input Parameters:

    • Mood
    • Genre
    • Instruments
    • SFX
    • Production style

    It also includes a tested example library and a quality-score validator to ensure prompts are effective.

    https://acetaggen.com

  8. Explore books on AI music generation

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    The following books provide theoretical and practical foundations for music generation using deep learning and algorithmic composition:

    • Deep Learning Techniques for Music Generation: A survey and analysis of deep learning for musical content.
    • Algorithmic Composition: Paradigms of Automated Music Generation: Focuses on practical procedures and principles of algorithmic composition.
    • Hands-On Music Generation with Magenta: A practical guide to integrating ML models (specifically Magenta) into music production tools.
    • Machine Learning and Music Generation: A comprehensive look at the intersection of ML techniques and music creation.

  9. Research papers and articles on AI music generation

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    A collection of academic papers and technical articles covering key methodologies in the field:

  10. Watch tutorials and demonstrations on AI music generation

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    Educational videos and demonstrations covering various aspects of the field:

    • Magenta & Machine Learning: Videos exploring how to use machine learning for music generation and assisted composition.
    • Genetic Algorithms: Tutorials on coding genetic algorithms for music.
    • Deep Learning Technicals: Explanations of creating deep learning models using music as input.
    • HuggingFace & Heavy Metal: Demonstrating how to use HuggingFace for specific genre generation (e.g., Heavy Metal).
    • MusicGen Explained: A breakdown of the MusicGen framework.
    • Tool Demonstrations: Videos showcasing tools like Loudly AI (for EDM) and reviews of publicly available tools (Mubert AI, AIVA, Soundraw, Beatoven AI, Boomy, and Amper Music).

  11. Research papers on music generation models

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    A collection of key research papers covering various architectures and methodologies for AI music generation:

    Text-to-Music & Diffusion Models

    • MusicGen (Simple and Controllable Music Generation): A Language Model (LM) that generates music conditioned on text or melody.
    • MeLoDy (Efficient Neural Music Generation): An LM-guided diffusion model designed for computational efficiency.
    • Noise2Music: Diffusion models trained to generate 30-second clips from text prompts.
    • JEN-1: A universal high-fidelity model for text-to-music generation using autoregressive and non-autoregressive training.
    • Mo^usai: Uses long-context latent diffusion for generating multiple minutes of stereo music from text.

    Waveform & Acoustic Modeling

    • VampNet: Uses a bidirectional transformer and masked acoustic token modeling for synthesis, inpainting, and variation.
    • Musika!: A system for fast, infinite waveform generation on consumer CPUs.

    Symbolic & Multi-track Generation

    • MuseGAN: Uses GANs for symbolic multi-track music generation, accounting for temporal dynamics.
    • Museformer: A Transformer approach using fine- and coarse-grained attention for long sequences.
    • MidiNet: Uses CNNs for generating melodies in the symbolic domain.
    • MMM: A Transformer-based system for conditional multi-track music generation.
    • Discrete Diffusion Probabilistic Models (D3PMs): Explores generating polyphonic symbolic music via discrete diffusion.

    Other Specialized Frameworks

    • Dance2Music-GAN (D2M-GAN): Generates music conditioned on dance videos using Vector Quantized (VQ) audio.
    • Symbolic Music Generation with Diffusion: Training diffusion models on sequential data for symbolic music and conditional infilling.