# MusicGen
MusicGen is a single-stage autoregressive transformer language model that generates short music clips from text descriptions, released with open weights by Meta in June 2023 and published at NeurIPS 2023.[1](https://arxiv.org/html/2306.05284v3) • [2](https://papers.neurips.cc/paper_files/paper/2023/file/94b472a1842cd7c56dcb125fb2765fbd-Paper-Conference.pdf) It predicts audio tokens produced by Meta's EnCodec neural codec rather than raw waveforms or spectrograms, and it ships in three sizes (300M, 1.5B, and 3.3B parameters) with text-to-music and melody-guided variants.[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) All benchmark numbers in the public record are vendor-reported; no independent evaluation appears in the sources retrieved for this article.
| Key fact | Value |
|---|---|
| Model class | Single-stage autoregressive transformer over EnCodec tokens[1](https://arxiv.org/html/2306.05284v3) |
| Parameter sizes | 300M (small), 1.5B (medium), 3.3B (large)[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) |
| Audio representation | 32 kHz EnCodec, 4 codebooks sampled at 50 Hz[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md) |
| Training data | 20K hours, roughly 400,000 licensed recordings[1](https://arxiv.org/html/2306.05284v3) • [5](https://ai.meta.com/blog/audiocraft-musicgen-audiogen-encodec-generative-ai-audio/) |
| Maximum clip length | 30 seconds (1503 tokens)[6](https://huggingface.co/docs/transformers/en/model_doc/musicgen) |
| Licenses | Code MIT; model weights CC-BY-NC 4.0 (non-commercial)[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) |
| Training window | April to May 2023[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) |
| Vocals | Not generated realistically; stripped from training data[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) |
## How it works
The pipeline has three parts: a frozen text encoder from T5 or Flan-T5 that embeds the prompt, an autoregressive transformer decoder that generates audio tokens, and the EnCodec decoder that turns tokens back into a waveform.[6](https://huggingface.co/docs/transformers/en/model_doc/musicgen) The tokenizer is a 32 kHz EnCodec with 4 codebooks sampled at 50 Hz, so each second of audio is 200 codebook tokens.[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md)
The delay pattern is the central trick. Unlike MusicLM, MusicGen requires no self-supervised semantic representation and generates all 4 codebooks in one pass; by introducing a small delay between codebooks, the model can predict them in parallel, so only 50 autoregressive steps are needed per second of audio.[1](https://arxiv.org/html/2306.05284v3) The peer-reviewed NeurIPS version frames this single-stage design with few autoregressive steps as a deliberate simplification.[2](https://papers.neurips.cc/paper_files/paper/2023/file/94b472a1842cd7c56dcb125fb2765fbd-Paper-Conference.pdf)
**Conditioning** comes in three forms. Text prompts are encoded by the frozen T5/Flan-T5 encoder.[6](https://huggingface.co/docs/transformers/en/model_doc/musicgen) The melody variants accept a text prompt plus a reference melody, represented as chroma features, so the generated music follows the melody's pitch contour.[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) Stereo fine-tunes generate two sets of codebooks, one for the left channel and one for the right, and each set is decoded independently before combination into the final stereo output.[6](https://huggingface.co/docs/transformers/en/model_doc/musicgen) A later MusicGen-Style checkpoint (1.5B parameters) instead takes short audio excerpts for style extraction, plus text, as inputs.[7](https://huggingface.co/facebook/musicgen-style)
## Releases and versions
The initial release in June 2023 included the small, medium, and large text-to-music models plus a melody-guided variant, with code and weights on GitHub and [Hugging Face](https://www.edgepedia.org/hugging-face).[1](https://arxiv.org/html/2306.05284v3) • [3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) In August 2023 Meta bundled MusicGen into the AudioCraft framework alongside [AudioGen](https://www.edgepedia.org/audiogen) (a model for environmental sounds trained on public sound effects) and EnCodec, releasing training code under MIT.[5](https://ai.meta.com/blog/audiocraft-musicgen-audiogen-encodec-generative-ai-audio/) The AudioCraft documentation lists ten pre-trained checkpoints: the three text-to-music sizes, melody (1.5B) and melody-large (3.3B), and stereo fine-tunes of each; the stereo models were fine-tuned for 200k updates starting from the mono models.[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md) • [3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) A MusicGen-Style checkpoint of 1.5B parameters was later released on Hugging Face.[7](https://huggingface.co/facebook/musicgen-style)
## Benchmark results: vendor-reported, no independent evaluation found
The paper's authors report that human listeners judged MusicGen better than the evaluated baselines on both audio quality and adherence to the text description.[1](https://arxiv.org/html/2306.05284v3) The model card adds objective metrics per checkpoint: Fréchet Audio Distance (FAD, lower is better) of 4.88 for small, 5.14 for medium, 5.48 for large, and 4.93 for melody; Kullback-Leibler divergence (KLD) of 1.42, 1.38, 1.37, and 1.41 respectively; text consistency of 0.27 to 0.28; and a melody chroma cosine similarity of 0.44 for the melody model.[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) All of these are vendor-reported, from the same organization that trained the models. The retrieved record contains no third-party evaluation of MusicGen's audio quality or prompt adherence, and no comparison against commercial systems such as Suno and Udio or against other open models; the sources do not settle those questions.
## By the numbers
- **Parameters**: 300M (small), 1.5B (medium), 3.3B (large), with melody variants at 1.5B and 3.3B.[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) • [4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md)
- **Training data**: 20K hours of licensed music, described by Meta as roughly 400,000 recordings with text descriptions and metadata, owned by Meta or licensed for the purpose.[1](https://arxiv.org/html/2306.05284v3) • [5](https://ai.meta.com/blog/audiocraft-musicgen-audiogen-encodec-generative-ai-audio/)
- **Audio format**: 32 kHz sample rate, 4 codebooks at 50 Hz, capped at 30 seconds (1503 tokens) by the sinusoidal positional embeddings; an audio prompt counts against the cap, so a 20-second audio prompt leaves 10 seconds of generation.[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md) • [6](https://huggingface.co/docs/transformers/en/model_doc/musicgen)
- **Hardware**: local generation requires a GPU; Meta recommends 16 GB of memory, with the small model usable on smaller GPUs.[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md)
## Licensing, availability and training-data disclosure
Code is released under MIT; model weights are released under CC-BY-NC 4.0, which restricts commercial use of the weights.[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html) The same licensing is stated on the Hugging Face mirrors.[8](https://huggingface.co/facebook/musicgen-large) Meta does not release the training datasets; the AudioCraft repository provides only a small dummy dataset for illustration.[4](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md) The disclosed composition is an internal Meta dataset of 10K high-quality music tracks plus the [Shutterstock](https://www.edgepedia.org/shutterstock) collection (25K tracks) and the Pond5 collection (365K instrument-only tracks).[1](https://arxiv.org/html/2306.05284v3) Vocals were removed from the data using corresponding tags and then the open-source Hybrid Transformer for Music Source Separation (HT-Demucs).[3](https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html)
For commercial use, the practical consequence is that the code can be used freely but the weights cannot under the non-commercial license, and the training datasets are unavailable for inspection or reuse.
## References
1. Simple and Controllable Music Generation (MusicGen paper), arXiv. https://arxiv.org/html/2306.05284v3
2. Simple and Controllable Music Generation, NeurIPS 2023. https://papers.neurips.cc/paper_files/paper/2023/file/94b472a1842cd7c56dcb125fb2765fbd-Paper-Conference.pdf
3. MusicGen Model Card (AudioCraft). https://facebookresearch.github.io/audiocraft/model_cards/MUSICGEN_MODEL_CARD.html
4. AudioCraft docs: MUSICGEN.md. https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md
5. AudioCraft: A simple one-stop shop for audio modeling, Meta AI blog. https://ai.meta.com/blog/audiocraft-musicgen-audiogen-encodec-generative-ai-audio/
6. MusicGen, Hugging Face Transformers docs. https://huggingface.co/docs/transformers/en/model_doc/musicgen
7. facebook/musicgen-style, Hugging Face. https://huggingface.co/facebook/musicgen-style
8. facebook/musicgen-large model card, Hugging Face. https://huggingface.co/facebook/musicgen-large
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*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Modern AI: foundation models, generative AI, and the AI industry › Model families and named models › Audio, music, and speech models*
*Initially written Sep 17, 2026 · Edited: Oct 11, 2026; Oct 15, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license