pymss 2.0.6b1

Python package for music source separation.

pymss

Python package for music source separation.
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Install

Example of using pip to install pymss package：

pip install pymss

When installing from a source checkout, uv can use the indexes configured in pyproject.toml. On Linux and Windows, the project maps torch to the PyTorch CUDA 12.8 wheel index:

uv sync

The equivalent pip install needs the PyTorch index to be passed explicitly:

pip install . --extra-index-url https://download.pytorch.org/whl/cu128

For development tools, install the dev dependency group:

uv sync --group dev

Usage

CLI inference

Run inference by catalog model name. If the model, config, or auxiliary files are missing locally, the CLI downloads them automatically before inference.

pymss infer bs_roformer_voc_hyperacev2 \
  -i path/to/input_file_or_folder \
  -o results \
  --device auto \
  --format wav

--device auto uses CUDA first when an NVIDIA GPU is available. On Apple Silicon it uses the MLX backend by default. Use --device mlx to force MLX, or --device mps to force PyTorch MPS.

The default download source is ModelScope. You can choose another source or model directory:

pymss --model-dir /path/to/models infer bs_roformer_voc_hyperacev2 \
  --source hf-mirror \
  -i path/to/input_file_or_folder \
  -o results

When running from a source checkout without installation, use python -m pymss.cli instead of pymss.

Server and WebUI

Install the optional server dependencies to run a HTTP server with dynamic model loading, catalog browsing, model downloads, and an optional browser WebUI:

pip install "pymss[server]"
pymss serve --webui

See server CLI docs, server API docs, and server error docs for details.

Python API

Use a catalog model name directly. You do not need to pass model_type, model_path, or config_path.

from pymss import MSSeparator

separator = MSSeparator.from_model_name(
    "bs_roformer_voc_hyperacev2",
    download=True,
    device="auto",
    output_format="wav",
    store_dirs="results",
)
separator.process_folder("path/to/input_file_or_folder")

download=True downloads missing model files before loading. Omit it for strict local-only loading.

Manual model paths

Use the full constructor for custom weights that are not in the model catalog.

from pymss import MSSeparator, get_separation_logger

# init
separator = MSSeparator(
    model_type='htdemucs', 
    model_path='path/to/model',
    config_path='path/to/config',
    device='cuda',
    device_ids=[0],
    output_format='wav',
    use_tta=True,
    store_dirs={
        "vocals": "./output/vocals",
        "other": None # None or missing this stem will result in no output file for this stem. This example will output the vocal's stem in ./output/vocals and ignoring the other(instrumental) stem. Making sure the key(s) match the config file.
    },
    audio_params={"wav_bit_depth": "FLOAT", "flac_bit_depth": "PCM_24", "mp3_bit_rate": "320k", "m4a_bit_rate": "192k", "m4a_aac_at_quality": 2}, # Can be omitted
    logger=get_separation_logger(), # Can be omitted
    debug=False, # Can be omitted
    inference_params={
        "batch_size": 4,
        "overlap_size": 512,
        "chunk_size": 1024,
        "normalize": True
    } # Can be omitted
)

# process all audio files in the folder
separator.process_folder('path/to/input_folder')

Manual Constructor Parameters

• model_type: The type of model, e.g., 'htdemucs'. Must be one of ['bs_roformer', 'mel_band_roformer', 'htdemucs', 'mdx23c', 'bandit', 'bandit_v2', 'scnet', 'apollo', 'vr']
• model_path: The path to the model file.
• config_path: The path to the configuration file.
• device: The type of device, default is 'auto'. Must be one of ['auto', 'cuda', 'mps', 'cpu']
• device_ids: List of device IDs, default is [0].
• output_format: The output audio format, default is 'wav'. Must be one of ['wav', 'flac', 'mp3', 'm4a']
• use_tta: Whether to use TTA, default is False. Using TTA will triple the processing time with a little bit improvement in quality.
• store_dirs: Storage directories, can be a single folder path or a dictionary with instrument keys.
• audio_params: Audio parameters including wav_bit_depth, flac_bit_depth, mp3_bit_rate, m4a_bit_rate, and m4a_aac_at_quality. Default is {"wav_bit_depth": "FLOAT", "flac_bit_depth": "PCM_24", "mp3_bit_rate": "320k", "m4a_bit_rate": "192k", "m4a_aac_at_quality": 2}.
• logger: Logger instance. Default is pymss.get_separation_logger()
• debug: Whether to enable debug mode, default is False.
• inference_params: Inference parameters including batch_size, overlap_size, chunk_size, normalize, and cuda_attention_backend. Default is all None (means all params are depended on the config file). For model_type='vr', supported keys are batch_size, window_size, aggression, enable_tta, enable_post_process, post_process_threshold, and high_end_process.

CUDA Attention Backend

RoFormer-family models default to cuDNN attention on CUDA when the installed PyTorch build exposes it, otherwise they use PyTorch's default SDPA path. Override with inference_params={"cuda_attention_backend": "auto"} if you want fallback probing. Valid values are auto, default, flash, cudnn, efficient, math, and xformers. auto tries cuDNN attention first, then PyTorch memory-efficient SDPA, then PyTorch default SDPA. xformers is optional and only used if installed locally; it is not a required dependency.

Apple Silicon MLX Backend

Use device='mlx' to run the Apple Silicon MLX backend:

separator = MSSeparator.from_model_name(
    "bs_roformer_voc_hyperacev2",
    download=True,
    device="mlx",
    output_format="wav",
    store_dirs="results",
)

On Apple Silicon, pyproject.toml installs mlx>=0.31.0 for this backend. If MLX is missing or a non-VR backend fails, the model records _pymss_mlx_full_backend_error and falls back to Torch MPS. Advanced users can still override mps_model_backend and mps_model_compute_dtype through inference_params.

Model Compatibility

HTDemucs checkpoints whose config uses model: htdemucs and htdemucs.cac: true are supported through model_type='htdemucs'.

Legacy Demucs/TasNet .th weights can use model_type='legacy_demucs' or model_type='legacy_tasnet' without a MSST YAML config. The dependency-free legacy loader supports classic Demucs, v3 time-domain Demucs, ConvTasNet, CaC HDemucs, package-style HTDemucs, multi-frequency CaC HDemucs, and simple Demucs bag YAML files. DiffQ-quantized checkpoints and non-CaC/Wiener HDemucs still need a dedicated legacy loader.

UVR VR support is available for the supported UVR/VR series .pth weights. Use the catalog model name in the same CLI/API paths as other models. The output stems are read from the built-in VR model list, for example Vocals, Instrumental, No Echo, or Echo.

pymss infer 1_HP-UVR \
  -i path/to/input_folder \
  -o results \
  --device auto \
  --param batch_size=2 \
  --param window_size=512 \
  --param aggression=5

separator = MSSeparator.from_model_name(
    "1_HP-UVR",
    download=True,
    device="auto",
    output_format="wav",
    store_dirs="results",
    inference_params={
        "batch_size": 2,
        "window_size": 512,
        "aggression": 5,
    },
)
separator.process_folder("path/to/input_folder")

Hugging Face Configs

Some model configs downloaded from Hugging Face or MSST-WebUI use inference.num_overlap. This optimized pymss path uses inference.overlap_size instead. If the config only has num_overlap, add an explicit overlap_size or pass it through inference_params; otherwise pymss falls back to 50% overlap and inference will be much slower.

Recommended fast setting:

audio:
  chunk_size: 480000
inference:
  batch_size: 2
  overlap_size: 24000  # 5% of chunk_size

RTX 5090 Benchmark

Measured on an NVIDIA GeForce RTX 5090 with PyTorch 2.9.1+cu128, CUDA 12.8, no TTA, one warmup and three measured runs.

model	type	RTFx	1-hour audio
BS-Roformer-HyperACE_v2_voc	bs_roformer	231.83x	15.5s
model_bs_roformer_ep_368_sdr_12.9628	bs_roformer	109.06x	33.0s
logic_bs_roformer	bs_roformer	159.71x	22.5s
mel-band-roformer-deux	mel_band_roformer	169.93x	21.2s
Mel-Band-Roformer-big	mel_band_roformer	194.05x	18.6s
model_vocals_mdx23c_sdr_10.17	mdx23c	209.41x	17.2s
HTDemucs4	htdemucs	200.52x	18.0s
scnet_checkpoint_musdb18	scnet	356.85x	10.1s
model_bandit_plus_dnr_sdr_11.47	bandit	122.76x	29.3s
checkpoint-multi_state_dict	bandit_v2	112.33x	32.0s
Apollo_LQ_MP3_restoration	apollo	100.62x	35.8s

VR models were measured with batch_size=2, window_size=512, aggression=5, TTA off, post-processing off.

VR model	RTFx	1-hour audio
UVR-DeNoise-Lite	243.62x	14.8s
Harmonic_Noise_Separation_yxlllc	221.22x	16.3s
MGM_HIGHEND_v4	217.39x	16.6s
MGM_LOWEND_A_v4	133.67x	26.9s
MGM_MAIN_v4	118.56x	30.4s
11_SP-UVR-2B-32000-2	109.73x	32.8s
10_SP-UVR-2B-32000-1	109.03x	33.0s
12_SP-UVR-3B-44100	104.67x	34.4s
MGM_LOWEND_B_v4	100.64x	35.8s
15_SP-UVR-MID-44100-1	99.00x	36.4s
16_SP-UVR-MID-44100-2	98.76x	36.5s
13_SP-UVR-4B-44100-1	97.78x	36.8s
14_SP-UVR-4B-44100-2	94.97x	37.9s
5_HP-Karaoke-UVR	94.72x	38.0s
2_HP-UVR	93.94x	38.3s
UVR-De-Echo-Aggressive	90.99x	39.6s
UVR-DeNoise	90.39x	39.8s
UVR-De-Echo-Normal	87.25x	41.3s
UVR-DeReverb-aufr33-jarredou_4band_v4_ms_fullband	86.70x	41.5s
UVR-DeEcho-DeReverb	86.58x	41.6s
3_HP-Vocal-UVR	85.15x	42.3s
4_HP-Vocal-UVR	84.23x	42.7s
1_HP-UVR	84.06x	42.8s
17_HP-Wind_Inst-UVR	82.92x	43.4s
6_HP-Karaoke-UVR	81.81x	44.0s
UVR-BVE-4B_SN-44100-1	81.54x	44.2s
9_HP2-UVR	58.48x	61.6s
8_HP2-UVR	57.23x	62.9s
7_HP2-UVR	56.10x	64.2s

Contributing

Contributions are welcome!

This project uses pyproject.toml for packaging metadata and build settings. Build source and wheel distributions with:

uv build

The test suite uses pytest. The migrated integration tests live in test/ and are parameterized through test/test_all.py. They require local model weights, configs, and input audio; missing assets are skipped automatically.

uv run pytest test -q

If you prefer a standard pip environment, install the package with test dependencies first:

pip install -e . pytest --extra-index-url https://download.pytorch.org/whl/cu128
pytest test -q