pymss 2.0.7

Python package for music source separation.

pymss

Python package for music source separation.
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Install

If you want the CUDA build of PyTorch, install it first:

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

For CLI and Python API usage, install:

pip install pymss

If you need API or WebUI, install this instead:

pip install "pymss[server]"

Develop

Development requires Git, Python 3.10 or later, and uv. WebUI development also requires Node.js and npm.

Clone the Python package repository and install development dependencies:

git clone https://github.com/pymss-project/pymss
cd pymss
uv sync --group dev

If you need to develop or locally serve the WebUI, the WebUI source lives in a separate repository and must be built with Node.js:

git clone https://github.com/pymss-project/pymss-webui
cd pymss-webui
npm ci
npm run build

Copy the built WebUI assets into the Python package checkout:

cp -R dist/. ../pymss/server/webui_static/

Build source and wheel distributions from the Python package checkout:

cd ..
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

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.

CLI ensemble

pymss ensemble path/to/model_a_vocals.wav path/to/model_b_vocals.wav \
  --algorithm avg_wave \
  --weights 1 0.8 \
  -o results/ensemble_vocals.wav

Available algorithms are avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, and max_fft. Input files must use the same sample rate and channel count. Files with different lengths are truncated to the shortest input. If --weights is omitted, every input uses weight 1.

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.

MSSeparator can also be used as a context manager. Leaving the with block automatically calls separator.close(), which releases model references and clears backend caches where possible.

from pymss import MSSeparator

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

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,
        "standardize": True,
        "normalize": False
    } # Can be omitted
)

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

Manual Constructor Parameters

For a detailed explanation of every MSSeparator argument, see the MSSeparator parameter guide.

• 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, standardize, normalize, and cuda_attention_backend. standardize controls model input standardization and defaults to the model config's inference.normalize value, or False when missing. normalize controls linked output peak normalization for all returned stems. For model_type='vr', supported keys are batch_size, window_size, aggression, enable_tta, enable_post_process, post_process_threshold, high_end_process, and output normalize.

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