SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation

• URL: http://arxiv.org/abs/2502.13128v1
• Date: Tue, 18 Feb 2025 18:52:21 GMT
• Title: SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
• Authors: Zihan Liu, Shuangrui Ding, Zhixiong Zhang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang,
• Abstract summary: SongGen is a fully open-source, single-stage auto-regressive transformer for controllable song generation. It supports two output modes: mixed mode, which generates a mixture of vocals and accompaniment directly, and dual-track mode, which synthesizes them separately. To foster community engagement and future research, we will release our model weights, training code, annotated data, and preprocessing pipeline.
• Score: 75.86473375730392
• License:
• Abstract: Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approaches often employ multi-stage generation procedures, resulting in cumbersome training and inference pipelines. In this paper, we propose SongGen, a fully open-source, single-stage auto-regressive transformer designed for controllable song generation. The proposed model facilitates fine-grained control over diverse musical attributes, including lyrics and textual descriptions of instrumentation, genre, mood, and timbre, while also offering an optional three-second reference clip for voice cloning. Within a unified auto-regressive framework, SongGen supports two output modes: mixed mode, which generates a mixture of vocals and accompaniment directly, and dual-track mode, which synthesizes them separately for greater flexibility in downstream applications. We explore diverse token pattern strategies for each mode, leading to notable improvements and valuable insights. Furthermore, we design an automated data preprocessing pipeline with effective quality control. To foster community engagement and future research, we will release our model weights, training code, annotated data, and preprocessing pipeline. The generated samples are showcased on our project page at https://liuzh-19.github.io/SongGen/ , and the code will be available at https://github.com/LiuZH-19/SongGen .

Related papers

• SongCreator: Lyrics-based Universal Song Generation [53.248473603201916]
  SongCreator is a song-generation system designed to tackle the challenge of generating songs with both vocals and accompaniment given lyrics. The model features two novel designs: a meticulously designed dual-sequence language model (M) to capture the information of vocals and accompaniment for song generation, and a series of attention mask strategies for DSLM. Experiments demonstrate the effectiveness of SongCreator by achieving state-of-the-art or competitive performances on all eight tasks.
  arXiv  Detail & Related papers  (2024-09-09T19:37:07Z)
• 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)
• RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text [45.11868756870458]
  We introduce a challenging task for simultaneously generating 3D holistic body motions and singing vocals directly from textual lyrics inputs. We first collect the RapVerse dataset, a large dataset containing synchronous rapping vocals, lyrics, and high-quality 3D holistic body meshes. By jointly performing transformer modeling on these three modalities in a unified way, our framework ensures a seamless and realistic blend of vocals and human motions.
  arXiv  Detail & Related papers  (2024-05-30T17:59:39Z)
• Bass Accompaniment Generation via Latent Diffusion [0.0]
  We present a controllable system for generating single stems to accompany musical mixes of arbitrary length. At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations. Our controllable conditional audio generation framework represents a significant step forward in creating generative AI tools to assist musicians in music production.
  arXiv  Detail & Related papers  (2024-02-02T13:44:47Z)
• 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)
• Noise2Music: Text-conditioned Music Generation with Diffusion Models [73.74580231353684]
  We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models.
  arXiv  Detail & Related papers  (2023-02-08T07:27:27Z)
• AudioGen: Textually Guided Audio Generation [116.57006301417306]
  We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive model that generates audio samples conditioned on text inputs.
  arXiv  Detail & Related papers  (2022-09-30T10:17:05Z)
• Unsupervised Cross-Domain Singing Voice Conversion [105.1021715879586]
  We present a wav-to-wav generative model for the task of singing voice conversion from any identity. Our method utilizes both an acoustic model, trained for the task of automatic speech recognition, together with melody extracted features to drive a waveform-based generator.
  arXiv  Detail & Related papers  (2020-08-06T18:29:11Z)

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.