DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End
  Full-Length Song Generation with Latent Diffusion
Recent advancements in music generation have garnered significant attention,
yet existing approaches face critical limitations. Some current generative
models can only synthesize either the vocal track or the accompaniment track.
While some models can generate combined vocal and accompaniment, they typically
rely on meticulously designed multi-stage cascading architectures and intricate
data pipelines, hindering scalability. Additionally, most systems are
restricted to generating short musical segments rather than full-length songs.
Furthermore, widely used language model-based methods suffer from slow
inference speeds. To address these challenges, we propose DiffRhythm, the first
latent diffusion-based song generation model capable of synthesizing complete
songs with both vocal and accompaniment for durations of up to 4m45s in only
ten seconds, maintaining high musicality and intelligibility. Despite its
remarkable capabilities, DiffRhythm is designed to be simple and elegant: it
eliminates the need for complex data preparation, employs a straightforward
model structure, and requires only lyrics and a style prompt during inference.
Additionally, its non-autoregressive structure ensures fast inference speeds.
This simplicity guarantees the scalability of DiffRhythm. Moreover, we release
the complete training code along with the pre-trained model on large-scale data
to promote reproducibility and further research.
67c6a15e21d722b4248bd9c2
Ziqian Ning
67c6a15e21d722b4248bd9c3
Huakang Chen
67c6a15e21d722b4248bd9c4
Yuepeng Jiang
67c6a15e21d722b4248bd9c5
Chunbo Hao
67c6a15e21d722b4248bd9c6
Guobin Ma
67c6a15e21d722b4248bd9c7
Shuai Wang
67c6a15e21d722b4248bd9c8
Jixun Yao
67c6a15e21d722b4248bd9c9
Lei Xie
DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End
  Full-Length Song Generation with Latent Diffusion
Recent advancements in music generation have garnered significant attention,
yet existing approaches face critical limitations. Some current generative
models can only synthesize either the vocal track or the accompaniment track.
While some models can generate combined vocal and accompaniment, they typically
rely on meticulously designed multi-stage cascading architectures and intricate
data pipelines, hindering scalability. Additionally, most systems are
restricted to generating short musical segments rather than full-length songs.
Furthermore, widely used language model-based methods suffer from slow
inference speeds. To address these challenges, we propose DiffRhythm, the first
latent diffusion-based song generation model capable of synthesizing complete
songs with both vocal and accompaniment for durations of up to 4m45s in only
ten seconds, maintaining high musicality and intelligibility. Despite its
remarkable capabilities, DiffRhythm is designed to be simple and elegant: it
eliminates the need for complex data preparation, employs a straightforward
model structure, and requires only lyrics and a style prompt during inference.
Additionally, its non-autoregressive structure ensures fast inference speeds.
This simplicity guarantees the scalability of DiffRhythm. Moreover, we release
the complete training code along with the pre-trained model on large-scale data
to promote reproducibility and further research.
DiffRhythm, a latent diffusion-based model, synthesizes full-length songs with both vocals and accompaniment efficiently, using simple and scalable methods.
latent diffusion song generation vocals accompaniment inference speed non-autoregressive structure pre-trained model large-scale data