Introduction

We propose SongBloom, a novel framework for full-length song generation that leverages an interleaved paradigm of autoregressive sketching and diffusion-based refinement. SongBloom employs an autoregressive diffusion model that combines the high fidelity of diffusion models with the scalability of language models. Specifically, it gradually extends a musical sketch from short to long and refines the details from coarse to fine-grained. The interleaved generation paradigm effectively integrates prior semantic and acoustic context to guide the generation process. Experimental results demonstrate that SongBloom outperforms existing methods across both subjective and objective metrics and achieves performance comparable to the state-of-the-art commercial music generation platforms.

Project Page

https://cypress-yang.github.io/SongBloom_demo/

Usage

Prepare Environments

Bash

conda create -n SongBloom python==3.8.12
conda activate SongBloom

# yum install libsndfile
# pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118 # For different CUDA version
pip install -r requirements.txt

Data Preparation

A .jsonl file, where each line is a json object:

JSON

{
	"idx": "The index of each sample",
	"lyrics": "The lyrics to be generated",
	"prompt_wav": "The path of the style prompt audio",
}

One example can be refered to as: example/test.jsonl

The prompt wav should be a 10-second, 48kHz audio clip.

The details about lyric format can be found in docs/lyric_format.md.

Inference

Bash

source set_env.sh

python3 infer.py --input-jsonl example/test.jsonl

# For GPUs with low VRAM like RTX4090, you should set the dtype as bfloat16
python3 infer.py --input-jsonl example/test.jsonl --dtype bfloat16

# SongBloom also supports flash-attn (optional). To enable it, please install flash-attn (v2.6.3 is used during training) manually and set os.environ['DISABLE_FLASH_ATTN'] = "0" in infer.py:8

Models

Name	Size	Max Length	Prompt type	🤗
songbloom_full_150s	2B	2m30s	10s wav	link
...

Papers

Citation

INI

@article{yang2025songbloom,
title={SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement},
author={Yang, Chenyu and Wang, Shuai and Chen, Hangting and Tan, Wei and Yu, Jianwei and Li, Haizhou},
journal={arXiv preprint arXiv:2506.07634},
year={2025}
}

conda create -n SongBloom python==3.8.12 conda activate SongBloom # yum install libsndfile # pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118 # For different CUDA version pip install -r requirements.txt

source set_env.sh python3 infer.py --input-jsonl example/test.jsonl # For GPUs with low VRAM like RTX4090, you should set the dtype as bfloat16 python3 infer.py --input-jsonl example/test.jsonl --dtype bfloat16 # SongBloom also supports flash-attn (optional). To enable it, please install flash-attn (v2.6.3 is used during training) manually and set os.environ['DISABLE_FLASH_ATTN'] = "0" in infer.py:8

Name

Size

Max Length

Prompt type

🤗

songbloom_full_150s

2m30s

10s wav

link

...

@article{yang2025songbloom, title={SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement}, author={Yang, Chenyu and Wang, Shuai and Chen, Hangting and Tan, Wei and Yu, Jianwei and Li, Haizhou}, journal={arXiv preprint arXiv:2506.07634}, year={2025} }