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Music Source Separation Toolkit 2026
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StemSplit on Hugging Face

We build StemSplit — a production stem-separation API for music apps, DJs, video editors, and ML pipelines that need fast, clean vocal / drums / bass / instrumental tracks without standing up GPUs.

This Hugging Face organization is where we open-source the measurements, models, and tooling behind the service: our benchmark suite, the converted model checkpoints we run in production, and live demos you can try in the browser.

What we ship here

Asset Type Status Description
stem-separation-benchmark-2026 Dataset ✅ live (v1.1) Reproducible SDR / ISR / SIR / SAR benchmark of every popular open-source separation model on MUSDB18-HQ. 850 rows, full eval methodology and source code.
Music Source Separation Toolkit 2026 Collection ✅ live Curated list of the open-source models a developer needs in 2026 — plus our benchmark. The fastest way to evaluate the landscape.
htdemucs-ft-pytorch Model ✅ live HT-Demucs FT (#1 open-source vocal SDR on MUSDB18-HQ) packaged with a ready-to-deploy handler.py for Hugging Face Inference Endpoints. Returns all 4 stems.
htdemucs-ft-drums-pytorch Model ✅ live Drums specialist sub-model. ~160 MB, ~2.6× faster than the full bag, identical drum SDR. For drum extraction, beat transcription, sample-pack creation.
htdemucs-ft-bass-pytorch Model ✅ live Bass specialist sub-model. Same speed/quality story as drums. For bassline transcription and mix rebalancing.
htdemucs-ft-other-pytorch Model ✅ live "Other" / instrumental specialist. Pair with the vocals model for karaoke, sample-flipping, music-bed extraction.
htdemucs-ft-onnx Model ✅ live First complete ONNX export of HT-Demucs FT on the Hub. All 4 specialists + numpy bag aggregator in one repo. Runs in onnxruntime CPU/CoreML/CUDA/DirectML, no PyTorch required.
htdemucs-ft-drums-onnx Model ✅ live Drums specialist ONNX. ~75% smaller / 4× faster than the full bag if you only need drums.
htdemucs-ft-bass-onnx Model ✅ live Bass specialist ONNX. For bassline transcription, mix rebalancing.
htdemucs-ft-other-onnx Model ✅ live Other/instrumental specialist ONNX. Pair with vocals ONNX for karaoke.
htdemucs-ft-vocals-onnx Model ✅ live #1 open-source vocal SDR (9.19 dB) as ONNX. The defensible centerpiece for iOS/Android vocal-removal apps.
CoreML EP profiling + INT8 quantization Models 🚧 in progress Mobile-quantized variants and CoreML benchmarks — Day 3 of the ONNX project.
Demo Space Space 🚧 in progress Upload a track, pick a model, compare separations in your browser.
Live leaderboard Space Space 📋 planned Community-submittable, continuously evaluated leaderboard for stem separation.

Subscribe to this org or watch our benchmark dataset to get notified when new artefacts land.

About the StemSplit API

StemSplit gives you the same SOTA open-source models you'll find in this org, wrapped in a stable REST API with credits, queueing, and a dashboard — so you can ship a separation product without operating ML infrastructure.

Minimal example (curl):

curl -X POST https://stemsplit.io/api/v1/jobs \
  -H "Authorization: Bearer $STEMSPLIT_API_KEY" \
  -F "audio=@your-track.mp3" \
  -F "model=htdemucs_ft"

The model parameter accepts any of the open-source models we benchmark here — so you can verify the quality you'll get against our public SDR numbers before shipping anything.

What we publish open-source vs what's proprietary

Open-source (here on the Hub):

  • Benchmark methodology, source code, and full results
  • Model conversions and quantisations for popular inference runtimes
  • Demo Spaces and reference implementations

Proprietary (the API):

  • Job orchestration, autoscaling, credits, and queueing
  • Customer dashboard, billing, and team features
  • Optimised production inference pipeline

We use the same open-source models you can benchmark above — see What StemSplit uses internally in the benchmark dataset card for the exact quality-tier → model mapping.

How we measure ourselves

Every model we publish or use in production is evaluated with the same museval-based BSS Eval v4 pipeline, on the standard MUSDB18-HQ test split, on hardware we disclose. The full pipeline is open source — anyone can clone it, re-run it, and challenge our numbers.

→ Read the methodology

Get in touch

  • 🐛 Found a bug in a model card or dataset? Open a discussion
  • ✉️ Business inquiries: stemsplit.io/contact
  • 🐦 Follow us: announcements land here first, then go to our blog and social

This org is maintained by the StemSplit team. All artefacts are released under permissive licenses unless noted; see individual repos for details.

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