main-database/Magenta.yaml at preview · Language-Technology-Assessment/main-database · GitHub

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---
# Thank you for contributing!
# In filling out this yaml file, please follow the criteria as described here:
# https://osai-index.eu/contribute

# You're free to build on this work and reuse the data. It is licensed under CC-BY 4.0, with the
# stipulation that attribution should come in the form of a link to https://osai-index.eu/
# and a citation to the peer-reviewed paper in which the dataset & criteria were published:

# Liesenfeld, A. and Dingemanse, M., 2024. Rethinking open source generative AI: open-washing and the EU AI Act. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1774-1787).

# Organization tags:
# - National origin: United States
# - Contributor type: Non-academic (American Big Tech)

system:
    name: Magenta
    link: https://huggingface.co/google/magenta-realtime
    type: audio
    performanceclass: full
    basemodelname:
    endmodelname: Magenta RealTime
    endmodellicense: Apache-2.0 and CC-BY-NC-4.0 and additional terms
    releasedate: 2025-06
    notes: Music generation model by Google DeepMind

org:
    name: Google DeepMind
    link: https://deepmind.google/
    notes: Major technology company, operator of Google Search.

# availability:
datasources_basemodel:
    class: closed
    link:
    notes: No details provided for training data.

datasources_endmodel:
    class: closed
    link:
    notes: No details found for training data.

weights_basemodel:
    class: open
    link: https://huggingface.co/google/magenta-realtime
    notes: Model available through HuggingFace.

weights_endmodel:
    class: open
    link: https://huggingface.co/google/magenta-realtime
    notes: Model available through HuggingFace.

trainingcode:
    class: partial
    link: https://github.com/magenta/magenta-realtime
    notes: No pre-training or instructing-tuning code made available. Some developer tools available.

# documentation:
code:
    class: closed
    link:
    notes: No pretraining or finetuning code found. Scarce code documentation except for deployment of the open weights.

hardware_architecture:
    class: partial
    link: https://huggingface.co/google/magenta-realtime
    notes: Processors used are described, for the rest scarce info is provided.

preprint:
    class: open
    link: https://arxiv.org/abs/2508.04651
    notes: Model published on arXiv.

paper:
    class: partial
    link: https://neurips.cc/virtual/2025/loc/san-diego/129287
    notes: Published as poster in NeurIPS.

modelcard:
    class: open
    link: https://huggingface.co/google/magenta-realtime
    notes: Model card provides the requisite information.

datasheet:
    class: closed
    link:
    notes: No datasheet found.

# access:
licenses:
    class: partial
    link: https://huggingface.co/google/magenta-realtime
    notes: Apache-2.0 and CC-BY-4.0, with additional terms of use