VocalRender

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

• Paper
• Code and documentation
• Open-source training dataset

VocalRender is a score-native singing voice synthesis (SVS) model designed for real-world composition. It synthesizes 48 kHz singing directly from lyrics, MIDI pitches, symbolic note values, and tempo through an original combination of an interleaved lyric--note representation, continuous acoustic latents, and autoregressive diffusion modeling. It does not require phoneme-level durations, an explicit duration predictor, or a time-aligned acoustic reference.

How it works

VocalRender combines three components:

1. A score-native interleaved representation serializes BPM followed by each lyric syllable and its associated (pitch, note-value) pairs. This retains lyric-to-note alignment and supports melisma.
2. An Audio VAE represents singing as compact continuous acoustic latents.
3. An autoregressive diffusion model generates the latent sequence patch by patch and decides when to stop. The VAE decoder renders the result as a waveform.

Checkpoints

This repository contains two variants with the same architecture, parameter count, and speech-pretrained base-model initialization. Their only difference is the training recipe (training corpus and schedule):

VocalRender-Pro reduces WER from 4.44 to 3.88 and improves speaker similarity from 0.922 to 0.929 on Opencpop. On CrawlSinger-Eval, WER changes from 4.52 to 4.45 and similarity from 0.919 to 0.926. The paper relates these improvements to the larger amount of real singing and broader singer coverage. VocalRender's higher score-following score may reflect more reliable score annotations in its real-data finetuning subset.

Each directory contains the model weights, AudioVAE, model configuration, and extended SVS tokenizer required by the inference code. Both checkpoints have the same model size; each is approximately 9.5 GB to download.

Installation

git clone --recurse-submodules https://github.com/pymaster17/VocalRender.git
cd VocalRender
uv sync
source .venv/bin/activate

Download

Download either checkpoint into the repository's pretrained_models/ directory:

# VocalRender
hf download pymaster/VocalRender \
    --include "VocalRender/*" \
    --local-dir pretrained_models

# Or VocalRender-Pro
hf download pymaster/VocalRender \
    --include "VocalRender-Pro/*" \
    --local-dir pretrained_models

Quick inference

The GitHub repository includes three ready-to-use prompt/score pairs selected from OpenCpop. This command runs the first demo:

python scripts/infer_vocalrender_svs_single.py \
    --ckpt_dir pretrained_models/VocalRender \
    --json_file examples/opencpop_demo.json \
    --item_name 2003000087 \
    --prompt_audio examples/prompt_audio/2003000081.wav \
    --output demo_2003000087.wav

Additional bundled pairs are 2017000646 with prompt 2017000644.wav, and 2044001652 with prompt 2044001666.wav. The prompt clips are 4.19-6.17 seconds long and do not overlap their paired target segment. Clone the GitHub repository to obtain the scripts and examples. The excerpts remain subject to the OpenCpop terms.

Use pretrained_models/VocalRender-Pro as --ckpt_dir to run the Pro model. Full generation requires a CUDA-capable compute node. Prompt audio is required: the released checkpoints were trained with prompt audio on every sample (prompt_audio_prob=1.0), so prompt-free inference is unsupported and may substantially degrade quality. Use a clean 2-8 second singing clip, which also provides the target timbre. See the repository README for batch-inference options.

Input format

{
  "item_name": "demo",
  "word": ["我", "的", "孤", "独"],
  "pitch": [65, 64, 64, 65, 67, 65],
  "note": ["<NOTE_8>", "<NOTE_32>", "<NOTE_16>", "<NOTE_16>", "<NOTE_16>", "<NOTE_8>"],
  "pitch2word": [0, 1, 2, 2, 2, 3],
  "bpm": 64
}

pitch2word[i] identifies the lyric index associated with note i, allowing one lyric syllable to span multiple notes. word_dur and pitch_dur are optional fields used only for visualization and evaluation; they are not model inputs.

Limitations

• The released checkpoints primarily target Mandarin Chinese singing.
• Checkpoint loading and generation require substantial disk space, system memory, and GPU memory.
• Output quality depends on the musical validity and lyric-to-note alignment of the input score.

Model foundation

The released implementation initializes its ARDM backbone from the speech-pretrained VoxCPM2 weights. The score-native representation, SVS formulation, data pipeline, and singing training recipe are introduced by VocalRender.

Citation

@article{chen2026vocalrender,
  title   = {VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition},
  author  = {Chen, Yukun and Wang, Tianrui and Mu, Zhaoxi and Yang, Xinyu and Chng, EngSiong},
  journal = {arXiv preprint arXiv:2607.27768},
  year    = {2026},
  url     = {https://arxiv.org/abs/2607.27768}
}

License

Apache License 2.0. See the repository's LICENSE file for details.