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DiffSinger
What it is
DiffSinger is a free, open-source singing voice synthesis system for self-hosted use. It uses a shallow diffusion approach and the maintained version adapts synthesized audio to 44.1 kHz rather than the original 24 kHz. Improved acoustic models and faster diffusion sampling are included. Variance models and parameters let users predict and control pitch, energy, breathiness, and other vocal characteristics. The linked MakeDiffSinger project supplies dataset-building pipelines, including recording slicing and labeling tools. Its guide covers dataset preprocessing, model training, and inference on DS files with variance or acoustic models. Installation requires Python 3.10 or later, with PyTorch 2.4.0 or later recommended. ONNX is the deployment format. OpenUtau and the developing DiffScope editor are linked production projects. Users provide their own data and assets. Commercial use is allowed, and MIDI support is listed.
For and against
For
- Free Apache-2.0 licensed repository
- Controls pitch, energy, and breathiness
- Includes dataset preparation tools
- ONNX deployment format
- MIDI support and commercial use allowed
Against
- Requires Python 3.10 or later
- PyTorch 2.4.0 or later recommended
- Users must prepare data and assets
- DiffScope editor remains under development
The verdict
DiffSinger provides a self-hosted workflow for building, training, controlling, and deploying singing voice models. Its tooling and ONNX output fit production-oriented users, but setup requires technical preparation and user-supplied assets.
Who it suits
It suits singing voice synthesis developers and the production community who can self-host software, prepare their own data, and work with Python-based training and inference.
Plans and prices
DiffSinger
Apache-2.0 licensed repository · requires Python 3.10 or later · users prepare their own data and assets
Read 4 Oct 2026 · github.com
On the record
- Audio quality
- The maintained version adapts synthesized audio to a 44.1 kHz sampling rate instead of the original 24 kHz.github.com · read 4 Oct 2026
- Control
- Variance models and parameters allow prediction and control of pitch, energy, breathiness, and other aspects.github.com · read 4 Oct 2026
- Dataset tools
- The linked MakeDiffSinger project provides pipelines and tools for building DiffSinger datasets, including recording slicing and labeling tools.github.com · read 4 Oct 2026
- Deployment
- The guide states that DiffSinger uses ONNX as its deployment format.github.com · read 4 Oct 2026
- Editor integration
- The repository links OpenUtau and DiffScope as deployment or production projects; DiffScope is described as an editor powered by DiffSinger and is under development.github.com · read 4 Oct 2026
- Installation
- The getting-started guide requires Python 3.10 or later and recommends PyTorch 2.4.0 or later.github.com · read 4 Oct 2026
- Intended users
- The project says its functionality is designed for production deployment and the singing voice synthesis community.github.com · read 4 Oct 2026
- Model distribution
- The getting-started guide includes a utility to remove speaker embeddings from a checkpoint for data security when distributing models.github.com · read 4 Oct 2026
- Model improvements
- The project integrates improved acoustic models and diffusion sampling acceleration algorithms.github.com · read 4 Oct 2026
- Purpose
- DiffSinger is a singing voice synthesis system based on a shallow diffusion mechanism.github.com · read 4 Oct 2026
- Security and consent
- The repository prohibits using its functionality to generate someone's voice without that person's consent.github.com · read 4 Oct 2026
- Support
- The project points users to tutorials, GitHub issues and discussions, and QQ and Discord communication groups.github.com · read 4 Oct 2026
- Training and inference
- The guide describes preprocessing datasets, training models, and running inference on DS files using variance or acoustic models.github.com · read 4 Oct 2026
