BS-RoFormer-Infer

Production-ready, inference-only toolkit for Band-Split RoPE Transformer audio source separation

BS-RoFormer-Infer provides a clean, lightweight API for running music source separation inference using Band-Split RoFormer models with automatic checkpoint management.

Features

• Inference Only: Lightweight package focused on production inference
• Auto-Download: the default model is fetched on first use and sha256-verified against recorded checksums
• CLI Tools: bs-roformer-infer and bs-roformer-download commands
• Python API: Clean programmatic interface
• Model Registry: Easy model discovery with search and category filtering

Quick Start

Installation

# Using pip
pip install bs-roformer-infer

# Using UV (recommended)
uv pip install bs-roformer-infer

CLI Inference

# First run auto-downloads the recommended BS-RoFormer-SW model (~700 MB,
# sha256-verified) into ~/.cache/bs-roformer-infer/ -- no separate download step needed
bs-roformer-infer --input_folder ./songs --store_dir ./outputs

Every WAV inside input_folder produces separated stems (vocals, drums, bass, guitar, piano, other) plus *_instrumental.wav. Explicit --config_path/--model_path arguments still work and skip auto-resolution entirely.

Python API

from ml_collections import ConfigDict
import torch
import yaml
from bs_roformer import DEFAULT_MODEL, ensure_model_assets, get_model_from_config
from bs_roformer.inference import SafeLoaderWithTuple

# Resolves local copies, or downloads (sha256-verified) on first use
ckpt_path, config_path = ensure_model_assets(DEFAULT_MODEL)

with open(config_path) as f:
    config = ConfigDict(yaml.load(f, Loader=SafeLoaderWithTuple))
model = get_model_from_config("bs_roformer", config)
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))

Model Weights

Where weights live

Downloads default to ~/.cache/bs-roformer-infer/<model-slug>/. The location is configurable, resolved in this order:

1. Explicit argument: --models_dir (inference CLI), --output-dir (download CLI), or ensure_model_assets(..., models_dir=...) (API)
2. The BS_ROFORMER_MODELS_PATH environment variable
3. The default ~/.cache/bs-roformer-infer/

A relative ./models directory (the pre-0.1.4 default) is still searched as a read fallback, so existing downloads keep working without re-fetching.

Auto-download

When bs-roformer-infer runs without --model_path/--config_path, the requested registry model (default: BS-RoFormer-SW) is looked up in the directories above and downloaded on first use. Downloads are verified against the sha256 checksums recorded in src/bs_roformer/data/checksums.json; a mismatch deletes the file and retries instead of keeping a corrupt checkpoint.

Manual download (offline / air-gapped)

The recommended BS-RoFormer-SW model needs one file (its config ships inside the package):

Place it at ~/.cache/bs-roformer-infer/roformer-model-bs-roformer-sw-by-jarredou/BS-Rofo-SW-Fixed.ckpt (or the equivalent path under your BS_ROFORMER_MODELS_PATH), and inference will pick it up without network access.

The other 9 registry models (re-hosted 2026-07-12, see CHANGELOG for full provenance) download from these mirrors:

The Chorus/Male-Female-aufr33 config (config_chorus_male_female_bs_roformer.yaml) and the three Revive checkpoints' shared config (config_bs_roformer_vocals_revive_unwa.yaml) are also fetched from Politrees/UVR_resources — see data/overrides.json for the exact URLs and data/checksums.json for their hashes.

Download CLI (manual path)

# List available models
bs-roformer-download --list-models

# Download the recommended model into the cache dir
bs-roformer-download --model roformer-model-bs-roformer-sw-by-jarredou

# Download into a custom directory
bs-roformer-download --model roformer-model-bs-roformer-sw-by-jarredou --output-dir ./models

Note on download availability (re-audited 2026-07-12): all 10 registry models now have live download sources. The 9 that fell back to the dead upstream TRvlvr repository were re-hosted to Politrees/UVR_resources (with the De-Reverb config sourced from the author's anvuew/dereverb_bs_roformer repo instead — see CHANGELOG for why). Run python tools/check_weights_liveness.py (needs network) to re-check.

Recommended Model

BS-RoFormer-SW (roformer-model-bs-roformer-sw-by-jarredou) by jarredou is the recommended default model for audio source separation. It supports 6-stem separation (vocals, drums, bass, guitar, piano, other) and provides excellent quality for production workflows.

from bs_roformer import DEFAULT_MODEL
print(DEFAULT_MODEL)  # "roformer-model-bs-roformer-sw-by-jarredou"

Available Models

Categories: multi-stem, vocals, instrumental, dereverb

As of the 2026-07-12 re-audit, all registry entries have a live download URL (see the availability note in Model Weights).

Registry Helpers

from bs_roformer import MODEL_REGISTRY

# List all categories
print(MODEL_REGISTRY.categories())

# List models by category
for model in MODEL_REGISTRY.list("vocals"):
    print(model.name, model.checkpoint)

# Search models
results = MODEL_REGISTRY.search("unwa")
for m in results:
    print(m.slug)

# Pretty-print all models
print(MODEL_REGISTRY.as_table())

Development Installation

# Clone repository
git clone https://github.com/openmirlab/bs-roformer-infer.git
cd bs-roformer-infer

# Install with UV
uv sync

# Install with pip
pip install -e ".[dev]"

Acknowledgments

This project builds upon the excellent work of several open-source projects:

• BS-RoFormer by Phil Wang (lucidrains) - Clean PyTorch implementation of the Band-Split RoPE Transformer architecture
• python-audio-separator by Andrew Beveridge (nomadkaraoke) - Pre-trained checkpoints and model configurations
• Original Research - Wei-Tsung Lu, Ju-Chiang Wang, Qiuqiang Kong, and Yun-Ning Hung for the Band-Split RoPE Transformer paper

License

MIT License - see LICENSE for details.

This project includes code and configurations adapted from:

• BS-RoFormer (MIT) - Phil Wang
• python-audio-separator (MIT) - Andrew Beveridge

Citation

If you use BS-RoFormer-Infer in your research, please cite the original paper:

@inproceedings{Lu2023MusicSS,
    title   = {Music Source Separation with Band-Split RoPE Transformer},
    author  = {Wei-Tsung Lu and Ju-Chiang Wang and Qiuqiang Kong and Yun-Ning Hung},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:261556702}
}

Support

For issues and questions:

• GitHub Issues: github.com/openmirlab/bs-roformer-infer/issues