ASLP-lab / MSVAE

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Model's Last Updated: October 12 2026

Introduction of MSVAE

Model Details of MSVAE

MSVAE: Rethinking Musical Semantics in Continuous Latent Spaces

MSVAE is a music variational autoencoder with a structured, continuous latent space. This repository contains the Stage II reconstruction checkpoint : it accepts stereo audio at 24 kHz and produces stereo audio at 48 kHz. The encoder runs at 24 kHz, while the decoder generates the 48 kHz waveform. This is an audio reconstruction model, not a standalone text-to-music generator.

The model combines a frozen music self-supervised encoder with an acoustic encoder through a gated residual posterior. During training, separate parts of the 64-dimensional latent are aligned with compositional and linguistic-vocal references. The released checkpoint is for inference; training-time alignment features are not required to reconstruct audio.

Files
File Description
model.pt Stage II MSVAE reconstruction weights. The checkpoint contains a model_state_dict ; it does not bundle the pretrained MuQ encoder.
config.yml Inference settings: 24 kHz encoder input, 48 kHz stereo output, and model dimensions.
bigvgan_v2_48khz_25hz_base_vae64.json Decoder configuration, also included in the companion code repository.

The Stage I 24 kHz-to-24 kHz checkpoint is not included here. The model.pt file is approximately 721 MiB.

Requirements

Inference requires the accompanying MSVAE code and its Python environment. The checkpoint is not directly loadable through transformers.AutoModel . The model also uses OpenMuQ/MuQ-large-msd-iter , which the inference code loads separately with MuQ.from_pretrained(...) ; MuQ weights are not redistributed in this repository. Please follow the MuQ model license for that dependency.

The code repository, installation instructions, paper, and audio demo links will be added when their public releases are available.

Reconstruction

From the root of the accompanying MSVAE code repository, after installing its runtime dependencies:

CKPT_PATH=/absolute/path/to/model.pt \
bash scripts/infer.sh /absolute/path/to/input.wav ./reconstructions 0

The third positional argument selects GPU 0 . The script resamples input audio to 24 kHz before encoding and writes a 48 kHz stereo WAV file under ./reconstructions . Mono input is duplicated to stereo. By default, it samples the VAE latent with seed 42. For deterministic decoding of the posterior mean, append --latent-mode mean :

CKPT_PATH=/absolute/path/to/model.pt \
bash scripts/infer.sh /absolute/path/to/input.wav ./reconstructions 0 --latent-mode mean

The default inference configuration in the code repository, src/config/svae/example_new.yml , has the same model settings as config.yml here. Its DAC.bigvgan_conf path is resolved relative to the code repository root, where the same decoder JSON is provided.

Intended use and limitations

This checkpoint is intended for music reconstruction research and listening comparisons. The 48 kHz output is generated from a 24 kHz input; high-frequency detail is inferred by the decoder rather than recovered from the original waveform. Performance on other audio domains has not been established here. Use only input audio you are entitled to process and publish.

License and citation

The MSVAE checkpoint license and paper citation have not yet been specified. Add the chosen license and public paper reference before publishing this model repository. The external MuQ weights have their own CC BY-NC 4.0 license .

Runs of ASLP-lab MSVAE on huggingface.co

28
Total runs
0
24-hour runs
1
3-day runs
10
7-day runs
28
30-day runs

More Information About MSVAE huggingface.co Model

MSVAE huggingface.co

MSVAE huggingface.co is an AI model on huggingface.co that provides MSVAE's model effect (), which can be used instantly with this ASLP-lab MSVAE model. huggingface.co supports a free trial of the MSVAE model, and also provides paid use of the MSVAE. Support call MSVAE model through api, including Node.js, Python, http.

ASLP-lab MSVAE online free

MSVAE huggingface.co is an online trial and call api platform, which integrates MSVAE's modeling effects, including api services, and provides a free online trial of MSVAE, you can try MSVAE online for free by clicking the link below.

ASLP-lab MSVAE online free url in huggingface.co:

https://huggingface.co/ASLP-lab/MSVAE

MSVAE install

MSVAE is an open source model from GitHub that offers a free installation service, and any user can find MSVAE on GitHub to install. At the same time, huggingface.co provides the effect of MSVAE install, users can directly use MSVAE installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

MSVAE install url in huggingface.co:

https://huggingface.co/ASLP-lab/MSVAE

Url of MSVAE

Provider of MSVAE huggingface.co

ASLP-lab
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