Simple and Controllable Music Generation

• URL: http://arxiv.org/abs/2306.05284v3
• Date: Tue, 30 Jan 2024 04:49:16 GMT
• Title: Simple and Controllable Music Generation
• Authors: Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre D\'efossez
• Abstract summary: MusicGen is 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.
• Score: 94.61958781346176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• MusicFlow: Cascaded Flow Matching for Text Guided Music Generation [53.63948108922333]
  MusicFlow is a cascaded text-to-music generation model based on flow matching. We leverage masked prediction as the training objective, enabling the model to generalize to other tasks such as music infilling and continuation.
  arXiv  Detail & Related papers  (2024-10-27T15:35:41Z)
• UniMuMo: Unified Text, Music and Motion Generation [57.72514622935806]
  We introduce UniMuMo, a unified multimodal model capable of taking arbitrary text, music, and motion data as input conditions to generate outputs across all three modalities. By converting music, motion, and text into token-based representation, our model bridges these modalities through a unified encoder-decoder transformer architecture.
  arXiv  Detail & Related papers  (2024-10-06T16:04:05Z)
• MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation [19.878013881045817]
  MusiConGen is a temporally-conditioned Transformer-based text-to-music model. It integrates automatically-extracted rhythm and chords as the condition signal. We show that MusiConGen can generate realistic backing track music that aligns well with the specified conditions.
  arXiv  Detail & Related papers  (2024-07-21T05:27:53Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls [6.176747724853209]
  Large Language Models (LLMs) have shown promise in generating high-quality music, but their focus on autoregressive generation limits their utility in music editing tasks. We propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. Our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement.
  arXiv  Detail & Related papers  (2024-02-14T19:00:01Z)
• Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model [32.801213106782335]
  We develop a generative music AI framework, Video2Music, that can match a provided video. In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion.
  arXiv  Detail & Related papers  (2023-11-02T03:33:00Z)
• Content-based Controls For Music Large Language Modeling [6.17674772485321]
  Coco-Mulla is a content-based control method for music large language modeling. It uses a parameter-efficient fine-tuning (PEFT) method tailored for Transformer-based audio models. Our approach achieves high-quality music generation with low-resource semi-supervised learning.
  arXiv  Detail & Related papers  (2023-10-26T05:24:38Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• Quantized GAN for Complex Music Generation from Dance Videos [48.196705493763986]
  We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates musical samples conditioned on dance videos. Our proposed framework takes dance video frames and human body motion as input, and learns to generate music samples that plausibly accompany the corresponding input.
  arXiv  Detail & Related papers  (2022-04-01T17:53:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.