pymss-core

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Core model, configuration, and checkpoint package for music source separation.

pymss-core is the shared low-level package for higher-level projects such as pymss inference and pymsst training. It contains model definitions, configuration loading, and checkpoint compatibility helpers. It intentionally does not include inference DSP pipelines, chunked demixing, audio file I/O, model downloads, catalog management, CLI, HTTP server, WebUI, datasets, losses, or training loops.

Install

pip install pymss-core

For local development:

uv sync --dev

Optional MLX backend on Apple Silicon:

pip install "pymss-core[mlx]"

Public API

from pymss_core import (
    get_model_from_config,
    load_config,
    load_model_weights,
)

model, config = get_model_from_config("bs_roformer", "config.yaml")
load_model_weights(model, "model.ckpt", model_type="bs_roformer", strict=True)

model.eval()

Package Boundary

Included:

• YAML config loading with AttrDict
• PyTorch model definitions under pymss_core.modules
• Optional MLX backend implementations for supported model forward paths
• Model factory: get_model_from_config(model_type, config_path)
• Checkpoint helpers for common MSS checkpoint containers
• Small model-internal DSP math needed to construct model structures
• VR network structures and VR model parameter JSON files

Excluded:

• Audio file decoding/encoding
• Resampling, preprocessing, and full inference DSP pipelines
• Tensor-level chunked demixing runtime
• Model catalog, aliases, downloads, and cache management
• CLI, server, WebUI, and endpoint schemas
• Dataset, augmentation, loss, metrics, and trainer code
• Any default dependency on MLX, Librosa, tqdm, Lightning, FastAPI, Uvicorn, PyAV, WandB, or training extras

Repository Roles

pymss-core
  shared model/config/checkpoint layer

pymss
  user-facing inference package built on pymss-core, with audio I/O and demix

pymsst
  training package built on pymss-core, with training data/loss/runtime code