HT-Demucs FT — Full 4-Stem Bag, ONNX

The first complete ONNX export of HT-Demucs FT on the Hugging Face Hub. Four parity-verified ONNX models (drums, bass, other, vocals) plus a ~250-line numpy aggregator that runs the full 4-stem separation in pure onnxruntime. No PyTorch required at inference. Runs on CPU / CoreML / CUDA / DirectML.

This repo is the convenience drop — all 4 specialist sub-models of htdemucs_ft in one place, with a working bag-inference script. If you only need one stem in production, the individual stem-specialist repos below are ~75% smaller and ~4× faster per song.

TL;DR

pip install onnxruntime numpy soundfile
python bag_infer.py your-song.mp3 ./out/
# writes out/drums.wav, out/bass.wav, out/other.wav, out/vocals.wav

That's it. The 4 .onnx files (316 MB each, ~1.26 GB total) live alongside the script.

Quality

Median per-stem SDR on the MUSDB18-HQ test split (50 songs), BSS Eval v4 via museval. Identical to the official PyTorch htdemucs_ft — the bag's per-stem output IS the corresponding specialist's output (the weight matrix is one-hot per stem).

Full benchmark across every popular open-source separator: StemSplitio/stem-separation-benchmark-2026.

ONNX vs PyTorch parity: verified to < 1e-3 max abs diff on every stem during export. See the Day 1 spike report for the full engineering writeup.

Performance

Real measurements on an Apple M4 Pro:

Tooling — demucs-onnx Python package

This bag is also packaged in the open-source demucs-onnx Python package on PyPI. It auto-downloads each specialist from the matching HF repo on first use, so you don't even need to manually fetch the four .onnx files.

pip install demucs-onnx

# Full 4-stem separation (auto-downloads ~1.26 GB on first run)
demucs-onnx separate song.mp3 stems/

# From Python
python -c "from demucs_onnx import separate; stems = separate('song.mp3')"

The same package is also the canonical tool for exporting htdemucs to ONNX yourself — it bundles all four blocker fixes (complex STFT, fractions.Fraction, random.randrange, aten::_native_multi_head_attention) so vanilla torch.onnx.export works on your own demucs checkpoints.

pip install "demucs-onnx[export]"
demucs-onnx export htdemucs_ft out/   # writes 4 .onnx files

Common use cases

• Karaoke makers — out/other.wav minus out/vocals.wav gives a clean karaoke track plus an acapella in one pass.
• DAW stem export — drop the 4 .wav files into Ableton / Logic / Reaper as separate channels for remixing.
• DJ stems software — load all 4 stems as live-mixable tracks.
• AI music apps — feed each stem into downstream models (drum transcription, bassline-to-MIDI, vocal pitch correction).
• Acapella sampling — clean isolated vocals at the highest SDR available in open source.
• Mobile / on-device separation — replaces a 1+ GB PyTorch install with onnxruntime's 50 MB binary on iOS / Android.

Quick start

Python — as a library

import bag_infer

stems = bag_infer.separate_all("your-song.mp3")
# stems: dict[str, numpy.ndarray (2, samples)]
#   stems["drums"], stems["bass"], stems["other"], stems["vocals"]

Python — with execution provider control

import soundfile as sf
import bag_infer

audio, sr = sf.read("your-song.mp3", dtype="float32", always_2d=True)
stems = bag_infer.separate(
    audio.T, sr,
    providers=["CPUExecutionProvider"],  # or "CoreMLExecutionProvider", etc.
)
for name, audio in stems.items():
    sf.write(f"{name}.wav", audio.T, sr)

CLI

python bag_infer.py your-song.mp3 ./out/
python bag_infer.py your-song.mp3 ./out/ --providers cuda
python bag_infer.py your-song.mp3 ./out/ --providers coreml
python bag_infer.py your-song.mp3 ./out/ --providers dml

Web / mobile

Each specialist is a vanilla onnxruntime model; just load all 4 sessions and reuse the aggregation logic in bag_infer.py::separate. See the individual stem repos for platform-specific snippets: drums · bass · other · vocals.

How aggregation works

The htdemucs_ft bag uses a one-hot weight matrix for combining the 4 sub-models — model 0's drums output is used directly as the bag's drums stem, model 1's bass output is the bag's bass stem, and so on. No weighted-sum aggregation needed.

That means:

• The bag's drums stem == the drums specialist's drums output (bit-exact in fp32)
• Same for bass, other, vocals
• So you can ship only the specialists you need and get identical per-stem quality to the full bag at 1/4 the size

bag_infer.py simply runs all 4 specialists and picks the relevant row from each. ~30 lines of numpy.

Input / output spec per sub-model

For longer audio, the bag script handles overlap-add chunking.

Files in this repo

Total: ~1.26 GB. If that's too big, use individual stem repos.

Related work

PyTorch versions for HF Inference Endpoints: htdemucs-ft-pytorch and its 4 sibling specialist repos.

Skip the infrastructure — use the StemSplit API

Don't want to ship 1.26 GB of .onnx files in your app, manage a GPU pool, or write overlap-add chunking? Use the StemSplit API instead — same models under the hood, hosted for you, with credits and a dashboard.

• 🌐 stemsplit.io
• 📘 Developer docs
• 🔌 API reference

Or use the no-code tools that ship this same model family:

• 🎤 Vocal Remover
• 🎶 Karaoke Maker
• 🎙️ Acapella Maker
• 📺 YouTube Stem Splitter

License & attribution

MIT-licensed, matching the original HT-Demucs.

@inproceedings{rouard2023hybrid,
  title     = {Hybrid Transformers for Music Source Separation},
  author    = {Rouard, Simon and Massa, Francisco and D{\'e}fossez, Alexandre},
  booktitle = {ICASSP},
  year      = {2023}
}

• Original PyTorch model: facebookresearch/demucs
• ONNX export, parity verification, and packaging by StemSplit
• Search keywords: htdemucs onnx, demucs onnx, htdemucs bag onnx, demucs ios, demucs android, music source separation onnx, 4-stem separation onnx, stem separation mobile, onnxruntime music separation