VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition

• URL: http://arxiv.org/abs/2607.27768v1
• Date: Thu, 30 Jul 2026 07:04:46 GMT
• Title: VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition
• Abstract summary: We propose VocalRender, a score-native system that directly synthesizes singing from lyrics, pitches, symbolic note values, and tempo.<n>It uses an interleaved lyric--note representation and an autoregressive diffusion model to generate continuous acoustic latents while predicting the output length.<n>It achieves strong intelligibility, strong melody control, and high speaker similarity across both in-domain and out-of-domain benchmarks.
• Score: 45.70355425430011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with practical composition workflows. We propose VocalRender, a score-native system that directly synthesizes singing from lyrics, pitches, symbolic note values, and tempo. It uses an interleaved lyric--note representation and an autoregressive diffusion model to generate continuous acoustic latents while predicting the output length, eliminating the need for explicit duration prediction. Trained on a 2,300-hour singing dataset, VocalRender achieves strong intelligibility, strong melody control, and high speaker similarity across both in-domain and out-of-domain benchmarks. Notably, it outperforms the strongest baseline by $0.42$ points in naturalness CMOS, demonstrating the effectiveness of our proposed score-native architecture.

Related papers

• YingMusic-Singer: Zero-shot Singing Voice Synthesis and Editing with Annotation-free Melody Guidance [16.462715982402884]
  Singing Voice Synthesis (SVS) remains constrained in practical deployment due to its strong dependence on accurate phoneme-level alignment.<n>We propose a melody-driven SVS framework capable of synthesizing arbitrary lyrics following any reference melody.<n>Our method builds on a Diffusion Transformer (DiT) architecture, enhanced with a dedicated melody extraction module.
  arXiv  Detail & Related papers  (2025-12-04T13:25:33Z)
• SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture [3.7937714754535503]
  SmoothSinger is a conditional diffusion model designed to synthesize high quality and natural singing voices.<n>It refines low-quality synthesized audio directly in a unified framework, mitigating the degradation associated with two-stage pipelines.<n> Experiments on the Opencpop dataset, a large-scale Chinese singing corpus, demonstrate that SmoothSinger achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-26T17:07:45Z)
• End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding [4.604877755214193]
  Existing end-to-end piano A2S systems have been trained and evaluated with only synthetic data. We propose a sequence-to-sequence (Seq2Seq) model with a hierarchical decoder that aligns with the hierarchical structure of musical scores. We propose a two-stage training scheme, which involves pre-training the model using an expressive performance rendering system on synthetic audio, followed by fine-tuning the model using recordings of human performance.
  arXiv  Detail & Related papers  (2024-05-22T10:52:04Z)
• A Real-Time Lyrics Alignment System Using Chroma And Phonetic Features For Classical Vocal Performance [7.488651253072641]
  The goal of real-time lyrics alignment is to take live singing audio as input and pinpoint the exact position within given lyrics on the fly. The task can benefit real-world applications such as the automatic subtitling of live concerts or operas. This paper presents a real-time lyrics alignment system for classical vocal performances with two contributions.
  arXiv  Detail & Related papers  (2024-01-17T13:25:32Z)
• Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training [82.94349771571642]
  This work proposes a melody-unsupervised multi-speaker pre-training method to enhance the vocal range of the single-speaker. It is the first to introduce a differentiable duration regulator to improve the rhythm naturalness of the synthesized voice. Experimental results verify that the proposed SVS system outperforms the baseline on both sound quality and naturalness.
  arXiv  Detail & Related papers  (2023-09-01T06:40:41Z)
• Towards Improving the Expressiveness of Singing Voice Synthesis with BERT Derived Semantic Information [51.02264447897833]
  This paper presents an end-to-end high-quality singing voice synthesis (SVS) system that uses bidirectional encoder representation from Transformers (BERT) derived semantic embeddings. The proposed SVS system can produce singing voice with higher-quality outperforming VISinger.
  arXiv  Detail & Related papers  (2023-08-31T16:12:01Z)
• Make-A-Voice: Unified Voice Synthesis With Discrete Representation [77.3998611565557]
  Make-A-Voice is a unified framework for synthesizing and manipulating voice signals from discrete representations. We show that Make-A-Voice exhibits superior audio quality and style similarity compared with competitive baseline models.
  arXiv  Detail & Related papers  (2023-05-30T17:59:26Z)
• RMSSinger: Realistic-Music-Score based Singing Voice Synthesis [56.51475521778443]
  RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types. We propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input. In RMSSinger, we introduce word-level modeling to avoid the time-consuming phoneme duration annotation and the complicated phoneme-level mel-note alignment.
  arXiv  Detail & Related papers  (2023-05-18T03:57:51Z)
• DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis [53.19363127760314]
  DiffSinger is a parameterized Markov chain which iteratively converts the noise into mel-spectrogram conditioned on the music score. The evaluations conducted on the Chinese singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS work with a notable margin.
  arXiv  Detail & Related papers  (2021-05-06T05:21:42Z)

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.