Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning

• URL: http://arxiv.org/abs/2405.18386v2
• Date: Wed, 29 May 2024 17:05:32 GMT
• Title: Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
• Authors: Yixiao Zhang, Yukara Ikemiya, Woosung Choi, Naoki Murata, Marco A. Martínez-Ramírez, Liwei Lin, Gus Xia, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon,
• Abstract summary: Instruct-MusicGen is a novel approach that finetunes a pretrained MusicGen model to efficiently follow editing instructions. Remarkably, Instruct-MusicGen only introduces 8% new parameters to the original MusicGen model and only trains for 5K steps.
• Score: 24.6866990804501
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation. Previous approaches in this domain have been constrained by the necessity to train specific editing models from scratch, which is both resource-intensive and inefficient; other research uses large language models to predict edited music, resulting in imprecise audio reconstruction. To Combine the strengths and address these limitations, we introduce Instruct-MusicGen, a novel approach that finetunes a pretrained MusicGen model to efficiently follow editing instructions such as adding, removing, or separating stems. Our approach involves a modification of the original MusicGen architecture by incorporating a text fusion module and an audio fusion module, which allow the model to process instruction texts and audio inputs concurrently and yield the desired edited music. Remarkably, Instruct-MusicGen only introduces 8% new parameters to the original MusicGen model and only trains for 5K steps, yet it achieves superior performance across all tasks compared to existing baselines, and demonstrates performance comparable to the models trained for specific tasks. This advancement not only enhances the efficiency of text-to-music editing but also broadens the applicability of music language models in dynamic music production environments.

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)
• MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models [24.582948932985726]
  This paper introduces a novel approach to the editing of music generated by text-to-music models. Our method transforms text editing to textitlatent space manipulation while adding an extra constraint to enforce consistency. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations.
  arXiv  Detail & Related papers  (2024-02-09T04:34:08Z)
• InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models [42.2977676825086]
  In this paper, we develop InstructME, an Instruction guided Music Editing and remixing framework based on latent diffusion models. Our framework fortifies the U-Net with multi-scale aggregation in order to maintain consistency before and after editing. Our proposed method significantly surpasses preceding systems in music quality, text relevance and harmony.
  arXiv  Detail & Related papers  (2023-08-28T07:11:42Z)
• MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies [32.482588500419006]
  We build a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music.
  arXiv  Detail & Related papers  (2023-08-03T05:35:37Z)
• Simple and Controllable Music Generation [94.61958781346176]
  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.
  arXiv  Detail & Related papers  (2023-06-08T15:31:05Z)
• GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework [58.64512825534638]
  Symbolic music generation aims to create musical notes, which can help users compose music. We introduce a framework known as GETMusic, with GET'' standing for GEnerate music Tracks'' GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time. Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
  arXiv  Detail & Related papers  (2023-05-18T09:53:23Z)
• Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task [86.72661027591394]
  We generate complete and semantically consistent symbolic music scores from text descriptions. We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
  arXiv  Detail & Related papers  (2022-11-21T07:19:17Z)

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.