Papers › Simple and Controllable Music Generation

Simple and Controllable Music Generation

8 Jun 2023NeurIPS 2023 11arXiv:2306.05284archive 2025-07-28

Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez

We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, both mono and stereo, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft

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facebookresearch/audiocraft mentioned in paperpytorchMIT report
atharva20038/music4all mentioned on GitHubjax report
collabora/whisperspeech mentioned on GitHubpytorchMIT report
theodorblackbird/lina-speech mentioned on GitHubpytorch report
whisperspeech/whisperspeech mentioned on GitHubpytorch report
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get_adv_criterion facebookresearch/audiocraft/audiocraft/adversarial/losses.py named in the paper ran MIT (permissive) · b94d3dcb2a918be3 · report
get_fake_criterion facebookresearch/audiocraft/audiocraft/adversarial/losses.py named in the paper ran MIT (permissive) · 88b0022a7cb492a1 · report
get_real_criterion facebookresearch/audiocraft/audiocraft/adversarial/losses.py named in the paper ran MIT (permissive) · 31e35a10d885d5a5 · report
MultiEmbedding theodorblackbird/lina-speech/model/lina.py community (archive-listed) ran licence not identified · pointer only · d7c009ddcc0fa372 · report
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sequence_mask theodorblackbird/lina-speech/model/lina.py community (archive-listed) ran · our draft was wrong licence not identified · pointer only · 7286741cac3a2cab · report
sinusoids collabora/whisperspeech/whisperspeech/modules.py community (archive-listed) ran fingerprinted MIT (permissive) · a314b3fcba001058 · report
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Tasks

Language ModelingLanguage ModellingMusic GenerationText-to-Music Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text-to-Music Generation MusicCaps MusicGen w/o melody (1.5B) FAD 3.4 #10 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/o melody (1.5B) KL_passt 1.23 #10 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/o melody (3.3B) FAD 3.8 #13 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/o melody (3.3B) FD_openl3 197.12 #13 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/o melody (3.3B) KL_passt 1.31 #13 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/ random melody (1.5B) FAD 5.0 #16 of 21 Archive leaderboard report
Text-to-Music Generation MusicCaps MusicGen w/ random melody (1.5B) KL_passt 1.31 #16 of 21 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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