2024-04-19: 4:45 of Awesome

Stability Audio Finally Drops

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🎹 4:45 of Awesome

Stability Audio is finally out. (link). While the company may not survive, the products continue to push forward the open source wave.

Paper Title

What products does this enable?

  • Near Term:

    • AI Music Composition Tools: Develop user-friendly applications that allow musicians and creators to generate long-form music tracks based on text prompts or specific musical attributes.

    • Dynamic Soundtrack Generation: Create AI systems that generate adaptive soundtracks for video games or other media that respond to user actions or in-game events.

  • Long Term:

    • Personalized Music Experiences: Develop AI-powered platforms that create personalized music experiences by generating music based on user preferences, listening history, or even emotional states.

    • AI-driven Music Education: Design AI-powered tools that assist music students in learning composition, improvisation, and music theory by providing feedback and generating examples.


This research was conducted by a team of researchers at Stability AI, led by Zach Evans and including Julian D. Parker, CJ Carr, Zack Zukowski, Josiah Taylor, and Jordi Pons. Stability AI is a company known for its work in artificial intelligence research and development, particularly in the areas of generative models and deep learning.


The researchers aimed to

  • address the limitations of existing AI music generation models, which typically struggle to produce long-form music with coherent structure and musicality.

  • create a model capable of generating full-length music tracks that capture the nuances and complexities of human-composed music.


  • Model Architecture: The researchers developed a three-stage model consisting of an autoencoder, a contrastive text-audio embedding model (CLAP), and a diffusion-transformer (DiT).

  • Autoencoder: The autoencoder compresses raw audio waveforms into a lower-dimensional latent representation, reducing the computational complexity of the model while preserving important musical features.

  • CLAP Text Encoder: The CLAP model extracts text embeddings from natural language prompts, enabling the model to understand and incorporate textual descriptions of desired musical styles or attributes.

  • Diffusion-Transformer (DiT): The DiT model operates in the latent space of the autoencoder and generates music by gradually denoising a random noise signal, guided by the text embeddings and timing information.

Architecture of the diffusion-transformer (DiT). Cross-attention includes timing and text conditioning. Prepend conditioning includes timing conditioning and also the signal conditioning on the current timestep of the diffusion process.

  • Training Data: The model was trained on a massive dataset of music, sound effects, and instrument stems, paired with corresponding text metadata.

  • Variable Length Generation: The model utilizes timing conditioning to allow for the generation of music tracks of varying lengths, up to 4 minutes and 45 seconds.

What did they find?

  • State-of-the-art Results: The model achieved state-of-the-art results in both quantitative metrics (audio quality, text-prompt coherence) and qualitative evaluations (musical structure, musicality, stereo correctness).

Self-Similarity Matrices, a method of visualising structure and complexity

  • Long-form Music Generation: The model successfully generated full-length music tracks with coherent structure and musicality, surpassing the limitations of previous models that could only produce short segments.

  • Additional Capabilities: The researchers also demonstrated the model's ability to perform audio style transfer, generate vocal-like melodies, and create short-form audio-like sound effects.

What are the limitations, and what's next?

  • Bias and Ethical Considerations: The researchers acknowledge the potential for biases present in the training data to be reflected in the generated music, and they emphasize the importance of responsible development and collaboration with stakeholders.

  • Further Exploration of Creative Applications: The researchers suggest further exploration of the model's capabilities in areas like audio style transfer, personalized music generation, and music education.

Why it matters

This research significantly advances the field of AI music generation by demonstrating the ability to create long-form music with coherent structure and musicality. The model's capabilities open doors for a wide range of creative applications, empowering musicians, content creators, and educators with new tools for music composition, production, and learning.

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Birthday Cakes Made By Famous Artists - https://www.reddit.com/r/midjourney/s/Pa3agRR4Bx

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