magenta-realtime

Model Card for Magenta RT

Authors: Google DeepMind

Resources:

• Blog Post
• Paper
• Colab Demo
• Repository
• HuggingFace

Terms of Use

Magenta RealTime is offered under a combination of licenses: the codebase is licensed under Apache 2.0, and the model weights under Creative Commons Attribution 4.0 International. In addition, we specify the following usage terms:

Copyright 2025 Google LLC

Use these materials responsibly and do not generate content, including outputs, that infringe or violate the rights of others, including rights in copyrighted content.

Google claims no rights in outputs you generate using Magenta RealTime. You and your users are solely responsible for outputs and their subsequent uses.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses. You are solely responsible for determining the appropriateness of using, reproducing, modifying, performing, displaying or distributing the software and materials, and any outputs, and assume any and all risks associated with your use or distribution of any of the software and materials, and any outputs, and your exercise of rights and permissions under the licenses.

Model Details

Magenta RealTime is an open music generation model from Google built from the same research and technology used to create MusicFX DJ and Lyria RealTime. Magenta RealTime enables the continuous generation of musical audio steered by a text prompt, an audio example, or a weighted combination of multiple text prompts and/or audio examples. Its relatively small size makes it possible to deploy in environments with limited resources, including live performance settings or freely available Colab TPUs.

System Components

Magenta RealTime is composed of three components: SpectroStream, MusicCoCa, and an LLM. A full technical report with more details on each component is here.

1. SpectroStream is a discrete audio codec that converts stereo 48kHz audio into tokens, building on the SoundStream RVQ codec from Zeghidour+ 21
2. MusicCoCa is a contrastive-trained model capable of embedding audio and text into a common embedding space, building on Yu+ 22 and Huang+ 22.
3. An encoder-decoder Transformer LLM generates audio tokens given context audio tokens and a tokenized MusicCoCa embedding, building on the MusicLM method from Agostinelli+ 23

Inputs and outputs

• SpectroStream RVQ codec: Tokenizes high-fidelity music audio
  • Encoder input / Decoder output: Music audio waveforms, 48kHz stereo
  • Encoder output / Decoder input: Discrete audio tokens, 25Hz frame rate, 64 RVQ depth, 10 bit codes, 16kbps
• MusicCoCa: Joint embeddings of text and music audio
  • Input: Music audio waveforms, 16kHz mono, or text representation of music style e.g. "heavy metal"
  • Output: 768 dimensional embedding, quantized to 12 RVQ depth, 10 bit codes
• Encoder-decoder Transformer LLM: Generates audio tokens given context and style
  • Encoder Input: (Context, 1000 tokens) 10s of audio context tokens w/ 4 RVQ depth, (Style, 6 tokens) Quantized MusicCoCa style embedding
  • Decoder Output: (Generated, 800 tokens) 2s of audio w/ 16 RVQ depth

Uses

Music generation models, in particular ones targeted for continuous real-time generation and control, have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

• Interactive Music Creation
  • Live Performance / Improvisation: These models can be used to generate music in a live performance setting, controlled by performers manipulating style embeddings or the audio context
  • Accessible Music-Making & Music Therapy: People with impediments to using traditional instruments (skill gaps, disabilities, etc.) can participate in communal jam sessions or solo music creation.
  • Video Games: Developers can create a custom soundtrack for users in real-time based on their actions and environment.
• Research
  • Transfer learning: Researchers can leverage representations from MusicCoCa and Magenta RT to recognize musical information.
• Personalization
  • Musicians can finetune models with their own catalog to customize the model to their style (fine tuning support coming soon).
• Education
  • Exploring Genres, Instruments, and History: Natural language prompting enables users to quickly learn about and experiment with musical concepts.

Out-of-Scope Use

See our Terms of Use above for usage we consider out of scope.

Bias, Risks, and Limitations

Magenta RT supports the real-time generation and steering of instrumental music. The purpose and intention of this capability is to foster the development of new real-time, interactive co-creation workflows that seamlessly integrate with human-centered forms of musical creativity.

Every AI music generation model, including Magenta RT, carries a risk of impacting the economic and cultural landscape of music. We aim to mitigate these risks through the following avenues:

• Prioritizing human-AI interaction as fundamental in the design of Magenta RT.
• Distributing the model under a terms of service that prohibit developers from generating outputs that infringe or violate the rights of others, including rights in copyrighted content.
• Training on primarily instrumental data. With specific prompting, this model has been observed to generate some vocal sounds and effects, though those vocal sounds and effects tend to be non-lexical.

Known limitations

Coverage of broad musical styles. Magenta RT's training data primarily consists of Western instrumental music. As a consequence, Magenta RT has incomplete coverage of both vocal performance and the broader landscape of rich musical traditions worldwide. For real-time generation with broader style coverage, we refer users to our Lyria RealTime API.

Vocals. While the model is capable of generating non-lexical vocalizations and humming, it is not conditioned on lyrics and is unlikely to generate actual words. However, there remains some risk of generating explicit or culturally-insensitive lyrical content.

Latency. Because the Magenta RT LLM operates on two second chunks, user inputs for the style prompt may take two or more seconds to influence the musical output.

Limited context. Because the Magenta RT encoder has a maximum audio context window of ten seconds, the model is unable to directly reference music that has been output earlier than that. While the context is sufficient to enable the model to create melodies, rhythms, and chord progressions, the model is not capable of automatically creating longer-term song structures.

Benefits

At the time of release, Magenta RealTime represents the only open weights model supporting real-time, continuous musical audio generation. It is designed specifically to enable live, interactive musical creation, bringing new capabilities to musical performances, art installations, video games, and many other applications.

How to Get Started with the Model

See our Colab demo and GitHub repository for usage examples.

Training Details

Training Data

Magenta RealTime was trained on ~190k hours of stock music from multiple sources, mostly instrumental.

Hardware

Magenta RealTime was trained using Tensor Processing Unit (TPU) hardware (TPUv6e / Trillium).

Software

Training was done using JAX and T5X, utilizing SeqIO for data pipelines. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models.

Evaluation

Model evaluation metrics and results will be shared in our forthcoming technical report.

Citation

Please cite our technical report:

BibTeX:

@article{gdmlyria2025live,
    title={Live Music Models},
    author={Caillon, Antoine and McWilliams, Brian and Tarakajian, Cassie and Simon, Ian and Manco, Ilaria and Engel, Jesse and Constant, Noah and Li, Pen and Denk, Timo I. and Lalama, Alberto and Agostinelli, Andrea and Huang, Anna and Manilow, Ethan and Brower, George and Erdogan, Hakan and Lei, Heidi and Rolnick, Itai and Grishchenko, Ivan and Orsini, Manu and Kastelic, Matej and Zuluaga, Mauricio and Verzetti, Mauro and Dooley, Michael and Skopek, Ondrej and Ferrer, Rafael and Borsos, Zal{\'a}n and van den Oord, {\"A}aron and Eck, Douglas and Collins, Eli and Baldridge, Jason and Hume, Tom and Donahue, Chris and Han, Kehang and Roberts, Adam},
    journal={arXiv:2508.04651},
    year={2025}
}