Htdemucs Ft Drums Onnx

HT-Demucs FT — Drums Specialist, ONNX

The first working, parity-verified ONNX export of HT-Demucs FT on the Hugging Face Hub. Runs in onnxruntime on CPU out of the box — and on CoreML / CUDA / DirectML with a one-line provider change. No PyTorch required at inference.

This repo packages sub-model 0 (the drums specialist) of the htdemucs_ft 4-bag ensemble as a single .onnx file plus a 200-line numpy reference inference script.

Why this exists: every previous attempt at "demucs onnx" on GitHub stalled on one of four blockers — complex64 STFT outputs, fractions.Fraction in the segment math, random.randrange in the transformer's positional embedding, or PyTorch's fused _native_multi_head_attention C++ kernel. This export defeats all four. See How it was built below for the patches.

---

TL;DR

pip install onnxruntime numpy soundfile
python infer.py your-song.mp3 ./out/
# writes ./out/drums.wav at 44.1 kHz stereo

That's it. The repo contains a 316 MB htdemucs_ft_drums.onnx (opset 17), a self-contained infer.py, and this card.

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Quality

The drums output of this sub-model is numerically equivalent to the official PyTorch htdemucs_ft ensemble for the drums stem (the bag's weight matrix is one-hot per stem, so the bag's drums output IS this sub-model's drums output). Verified parity on synthetic and real audio: max abs diff = 1.63 × 10−4 between this ONNX model and PyTorch fp32.

Metric (MUSDB18-HQ test, 50 songs)	Value	Source
Median drums SDR	10.11 dB	StemSplitio/stem-separation-benchmark-2026
Rank among 4 popular open-source separators	#2 (mdx_extra_q is #1 at 11.49)	same
ONNX vs PyTorch max abs diff	0.000163	included verify_onnx_parity.py

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Performance

Runtime	Hardware	Per 7.8-s segment	Per 3-min song	Notes
onnxruntime CPU EP	Apple M4 Pro (CPU only)	1.59 s	~22 s	Measured. 1.31× faster than PyTorch CPU.
PyTorch CPU	Apple M4 Pro (CPU only)	2.09 s	~29 s	Baseline.
PyTorch MPS (full bag)	Apple M4 Pro	1.0 s	~12 s	But the full bag is 640 MB and runs all 4 sub-models.
onnxruntime CoreML EP	Apple M-series	TBD	TBD	First-time graph compile is slow (~5+ min on M4 Pro). Subsequent loads cached. Day 3 of the StemSplit ONNX project.
onnxruntime CUDA EP	NVIDIA L4	~0.4 s	~5 s	Extrapolated from PyTorch CUDA numbers.
onnxruntime DirectML EP	RTX 4090	~0.2 s	~2 s	Extrapolated.

Real-time factor on M4 Pro CPU: 0.20.

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Quick start

Python — minimal

import infer
drums = infer.separate_drums("your-song.mp3")
# drums: numpy array (2, samples) at 44.1 kHz

Python — full control

import soundfile as sf
import onnxruntime as ort
import infer

# Optional: pick an execution provider.
# CPU is portable and the default. On macOS swap in CoreML; on Linux/Windows
# with an NVIDIA GPU, swap in CUDA. On Windows with any DX12 GPU, DirectML.
providers = ["CPUExecutionProvider"]
# providers = ["CoreMLExecutionProvider", "CPUExecutionProvider"]
# providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
# providers = ["DmlExecutionProvider", "CPUExecutionProvider"]

audio, sr = sf.read("your-song.mp3", dtype="float32", always_2d=True)
stems = infer.separate(audio.T, sr, providers=providers)
sf.write("drums.wav", stems[0].T, sr)  # stems[0] is the drums row

CLI

python infer.py your-song.mp3 ./out/
python infer.py your-song.mp3 ./out/ --providers coreml      # macOS
python infer.py your-song.mp3 ./out/ --providers cuda        # NVIDIA
python infer.py your-song.mp3 ./out/ --providers dml         # Windows / Xbox / any DX12
python infer.py your-song.mp3 ./out/ --write-all-stems       # also writes by-products

Mobile (iOS / Swift)

import onnxruntime_objc

let modelPath = Bundle.main.path(forResource: "htdemucs_ft_drums", ofType: "onnx")!
let env = try ORTEnv(loggingLevel: .warning)
let options = try ORTSessionOptions()
try options.appendCoreMLExecutionProvider(with: ORTCoreMLExecutionProviderOptions())
let session = try ORTSession(env: env, modelPath: modelPath, sessionOptions: options)

// audio: 1 x 2 x 343980 Float32 buffer, then session.run([...]).

Mobile (Android / Kotlin)

import ai.onnxruntime.OrtEnvironment
import ai.onnxruntime.OrtSession

val env = OrtEnvironment.getEnvironment()
val opts = OrtSession.SessionOptions().apply { addNnapi() }
val session = env.createSession(modelPath, opts)
// audio: FloatBuffer of shape (1, 2, 343980), then session.run(...).

Web (onnxruntime-web)

import * as ort from "onnxruntime-web";
const session = await ort.InferenceSession.create("htdemucs_ft_drums.onnx", {
  executionProviders: ["wasm"],
  graphOptimizationLevel: "all",
});
const tensor = new ort.Tensor("float32", audioBuffer, [1, 2, 343980]);
const out = await session.run({ mix: tensor });
// out.stems.data is a Float32Array of shape (1, 4, 2, 343980); take row 0 for drums.

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Input / output spec

Tensor	Name	Shape	Dtype	Notes
Input	mix	(1, 2, 343980)	float32	Stereo audio, 44.1 kHz, 7.8 s segment. Values in [-1, 1].
Output	stems	(1, 4, 2, 343980)	float32	Stems in order [drums, bass, other, vocals]. Only stems[0, 0] is meaningfully predicted — the other 3 rows are weakly-predicted by-products of the drum specialist.

For longer audio, chunk with overlap-add — see infer.py::separate for a working 60-line implementation.

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How it was built

The export pipeline lives at github.com/musicai/musicai/tree/main/scripts/coreml-conversion (in our StemSplit monorepo). The patches that made it work:

1. torch.stft complex output → replaced with a Conv1d using sin/cos kernels that emits 2 real channels directly. Verified to 5 × 10−6 max diff against torch.stft.
2. torch.istft → mirror replacement with ConvTranspose1d plus an overlap-add window-squared envelope.
3. view_as_real / view_as_complex in _magnitude and _mask → rewritten to thread real-channel tensors throughout.
4. fractions.Fraction in model.segment → coerced to float.
5. random.randrange in CrossTransformerEncoder._get_pos_embedding → replaced the method with one that hardcodes shift=0 (mathematically identical at inference since sin_random_shift=0).
6. aten::_native_multi_head_attention (PyTorch's fused C++ MHA kernel has no ONNX symbolic) → wrote a drop-in nn.MultiheadAttention.forward replacement using plain Linear/bmm/softmax. Verified parity unchanged.

Net result: end-to-end parity vs PyTorch fp32 at 1.6 × 10−4 max abs diff across the entire output, and the model runs in onnxruntime CPU out of the box at 1.31× the speed of PyTorch CPU.

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Tooling — demucs-onnx Python package

This model can also be run (and re-exported) via the open-source demucs-onnx Python package on PyPI. It auto-downloads from this repo on first use, so you don't have to clone or wrangle file paths.

pip install demucs-onnx

# Single specialist (this repo)
demucs-onnx separate song.mp3 stems/ --stem drums

# Or via the Python API
python -c "from demucs_onnx import separate_stem; \
  drums = separate_stem('song.mp3', 'drums')"

demucs-onnx 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 checkpoints.

pip install "demucs-onnx[export]"
demucs-onnx export htdemucs_ft drums.onnx --stem drums

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Related work

The full StemSplit ONNX project:

Day	Deliverable	Status
1	htdemucs-ft-drums-onnx (this repo)	✅ live
2	htdemucs-ft-{bass,other,vocals}-onnx + htdemucs-ft-onnx bag	✅ live
3	CoreML EP profiling, INT8 quantization, mobile demos	📋 planned

Sibling ONNX repos from the same export:

Repo	Stem	Use when
htdemucs-ft-onnx	all 4	Full 4-stem separation in one repo
htdemucs-ft-drums-onnx (this)	drums	Drum extraction, beat transcription
htdemucs-ft-bass-onnx	bass	Bassline transcription, mix rebalancing
htdemucs-ft-other-onnx	other	Karaoke instrumentals, sample-flipping
htdemucs-ft-vocals-onnx	vocals	#1 open-source vocal SDR — karaoke, acapella, vocal removal

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

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

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Skip the infrastructure — use the StemSplit API

Don't want to ship a 316 MB model in your app, manage a GPU pool, or write overlap-add chunking? Use the StemSplit API instead — same model under the hood, hosted for you, with credits and a dashboard.

• 🌐 stemsplit.io
• 📘 Developer docs
• 🔌 API reference
• 📚 Guides & recipes

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

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

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Files in this repo

File	Size	Purpose
htdemucs_ft_drums.onnx	316 MB	The exported model. Opset 17. Passes onnx.checker.check_model.
infer.py	6 KB	Pure numpy + onnxruntime reference implementation. ~200 lines, no torch.
requirements.txt	<1 KB	onnxruntime, numpy, soundfile.
README.md	this file

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License & attribution

This repo is MIT-licensed, matching the original HT-Demucs.

Please cite the original authors if you use this model in research:

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

And if you use the benchmark or this ONNX packaging:

@misc{stemsplit_benchmark_2026,
  title  = {StemSplit Stem-Separation Benchmark 2026},
  author = {StemSplit},
  year   = {2026},
  url    = {https://huggingface.co/datasets/StemSplitio/stem-separation-benchmark-2026}
}

• Original PyTorch model: facebookresearch/demucs
• ONNX export, parity verification, and packaging by StemSplit
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