README.md

---
license: cc-by-4.0
language:
  - en
pretty_name: "StemSplit Stem-Separation Benchmark 2026"
size_categories:
  - 1K<n<10K
task_categories:
  - audio-to-audio
tags:
  - music
  - audio
  - source-separation
  - stem-separation
  - vocal-removal
  - karaoke
  - demucs
  - spleeter
  - mdx-net
  - bs-roformer
  - benchmark
configs:
  - config_name: metrics_only
    data_files:
      - split: results
        path: metrics_only/metrics.parquet
---

# StemSplit Stem-Separation Benchmark 2026

A reproducible head-to-head comparison of every popular open-source music
source-separation model against the [StemSplit](https://stemsplit.io) production
API, evaluated on the standard MUSDB18-HQ test split using BSS Eval v4 and a
small set of CC-BY tracks for qualitative listening.

> Built and maintained by the StemSplit team. Source code:
> [`scripts/hf-benchmark`](https://github.com/yourusername/musicai/tree/main/scripts/hf-benchmark) on GitHub.

---

## Leaderboard (median SDR per stem)

| model_id         |   bass |   drums |   other |   vocals |
|:-----------------|-------:|--------:|--------:|---------:|
| htdemucs_ft      |  10.38 |   10.11 |    6.34 |     9.19 |
| mdx_extra_q      |  11.42 |   11.49 |    7.67 |     9.04 |
| htdemucs_6s      |   9.11 |    9.54 |    5.74 |     8.66 |
| htdemucs         |   9.78 |   10.01 |    6.42 |     8.53 |
| mdx_net_inst_hq3 | —      |  —      |  —      |     5.81 |

Higher is better. SDR is computed with the `museval` reference implementation
(BSS Eval v4) on 1-second windows, exactly the protocol used by SiSEC and the
SDX challenges, so these numbers are directly comparable to results reported
in the literature.

---

## Configurations

### `metrics_only` (the leaderboard)

A Parquet table — one row per `(model, track, stem)` — for the full 50-track
MUSDB18-HQ test split. **No audio is shipped here**; MUSDB18 is a research-only
corpus and we are not allowed to redistribute it.

Schema:

| column | type | meaning |
|---|---|---|
| `model_id` | string | matches `configs/models.yaml` |
| `track_id` | string | folder name in `musdb18hq/test/` |
| `stem` | string | one of `vocals`, `drums`, `bass`, `other` |
| `sdr_median` | float | median Signal-to-Distortion Ratio (dB) |
| `sdr_mean` | float | mean SDR (dB) |
| `isr_median` | float | median Image-to-Spatial Ratio (dB) |
| `sir_median` | float | median Signal-to-Interferences Ratio (dB) |
| `sar_median` | float | median Signal-to-Artifacts Ratio (dB) |
| `n_frames` | int | number of evaluation windows |
| `sample_rate` | int | typically 44100 |
| `duration_s` | float | input track length in seconds |
| `wall_time_s` | float | inference wall time |
| `rtf` | float | real-time factor (`wall_time / duration`) |
| `peak_rss_mb` | int | peak resident memory of the runner process |
| `peak_mps_mem_mb` | int | peak Metal allocation (Apple Silicon only) |
| `host_chip` | string | e.g. `Apple M4 Pro` |
| `host_unified_memory_gb` | string | unified memory size on the test machine |
| `commit_sha` | string | repo SHA the run was produced from |

```python
from datasets import load_dataset
ds = load_dataset("StemSplitio/stem-separation-benchmark-2026", "metrics_only")
df = ds["results"].to_pandas()
df.groupby(["model_id", "stem"])["sdr_median"].median().unstack()
```

### `audio_samples` (planned for v1.2)

A future config will ship a small set of CC-BY-licensed clips with reference
and predicted stems, so you can A/B them in the dataset viewer. Sourcing
genuinely commercial-friendly 4-stem multitracks in 2026 is non-trivial;
see the [v1.2 sourcing notes](https://github.com/yourusername/musicai/blob/main/scripts/hf-benchmark/configs/tracks.yaml)
for the plan.

---

## Models

### v1.1 (this release)

All five models below produced complete results on the full 50-track
MUSDB18-HQ test split.

| id | family | notes |
|---|---|---|
| `htdemucs` | Hybrid Transformer Demucs | Facebook AI's default 4-stem model |
| `htdemucs_ft` | Hybrid Transformer Demucs | Fine-tuned variant, best vocal/instrumental separation in the Demucs family |
| `htdemucs_6s` | Hybrid Transformer Demucs | 6-stem model (adds piano + guitar). Its `other`-row SDR is now computed against `piano + guitar + other` summed back together — see the [Changelog](#changelog) for details. |
| `mdx_extra_q` | Demucs MDX | MDX challenge winner, quantised, 4-model ensemble — best `bass` and `drums` of the lineup |
| `mdx_net_inst_hq3` | MDX-Net | Vocal isolator running through `audio-separator` with the CoreML ONNX provider on Apple Silicon. Only the `vocals` row is reported (it does not produce drums/bass/other). |

### Planned for v1.2

| id | family | why deferred |
|---|---|---|
| `bs_roformer` | Band-Split Roformer | Current SOTA but slow on Apple MPS due to operator fallbacks (~11 hr for the full test set). Will add once we run on CUDA. |
| `mel_band_roformer` | Mel-Band Roformer | Same reason. |
| `spleeter_4stems` | Spleeter | Legacy baseline; TensorFlow install is brittle on Apple Silicon. |

See [`configs/models.yaml`](configs/models.yaml) on GitHub for exact versions
and command lines.

---

## Changelog

### v1.1 (current)

- Added `mdx_net_inst_hq3` (vocals-only MDX-Net via `audio-separator` with the
  CoreML ONNX execution provider).
- Fixed `htdemucs_6s` `other`-stem evaluation. The 6-stem model splits piano
  and guitar out of `other`; the v1 eval pass compared its residual `other`
  output directly to MUSDB's `other` and got a misleading 0.22 dB. v1.1 sums
  the model's `piano + guitar + other` predictions back together before SDR,
  so `htdemucs_6s` is now directly comparable to its 4-stem siblings.
- New `prediction_components` column on every row records the WAV files that
  were summed to form each stem (e.g. `vocals` or `other+piano+guitar`), so
  the aggregation is fully auditable.

### v1

- Initial release: 4 Demucs-family models × 50 MUSDB18-HQ tracks = 800 rows.
- Known issues (both fixed in v1.1): missing `mdx_net_inst_hq3`,
  `htdemucs_6s` `other`-stem evaluation artefact.

---

## What StemSplit uses internally

The [StemSplit](https://stemsplit.io) hosted API runs HT-Demucs under the hood —
the same models you can benchmark above. Pick a quality tier and look up its
row in the leaderboard:

| StemSplit tier | Model row in this benchmark | When to choose it |
|---|---|---|
| `FAST` | `htdemucs` | Speed-priority previews and bulk processing |
| `BALANCED` (default) | `htdemucs_ft` | Best vocal separation per second of compute |
| `BEST` (6-stem) | `htdemucs_6s` | When you need piano + guitar separately |

In other words: **this dataset is also a benchmark of StemSplit's own
quality**. We didn't add a separate `stemsplit_api` row because it would
just duplicate those numbers.

---

## Use the StemSplit API

If you'd rather not stand up Demucs, ffmpeg, torchcodec, and a GPU yourself,
the StemSplit API ships the same models with a single HTTP call. Pricing,
quotas, and the full feature set are documented in our developer portal:

| Resource | URL |
|---|---|
| 🏠 Developer landing | [stemsplit.io/developers](https://stemsplit.io/developers) |
| 📘 Getting-started docs (auth, upload, polling) | [stemsplit.io/developers/docs](https://stemsplit.io/developers/docs) |
| 📑 API reference (every endpoint, every field) | [stemsplit.io/developers/reference](https://stemsplit.io/developers/reference) |
| 🧩 Integration guides (Zapier, n8n, Make, Pipedream, Discord, Audacity, DJ workflows, ...) | [stemsplit.io/developers/guides](https://stemsplit.io/developers/guides) |

Minimal example — submit a job, poll, download stems:

```bash
# 1. Submit
JOB=$(curl -sS -X POST https://stemsplit.io/api/v1/jobs \
  -H "Authorization: Bearer $STEMSPLIT_API_KEY" \
  -F "file=@song.wav" \
  -F "stems=4")
JOB_ID=$(echo "$JOB" | jq -r .id)

# 2. Poll until completed
while [ "$(curl -sS https://stemsplit.io/api/v1/jobs/$JOB_ID \
  -H "Authorization: Bearer $STEMSPLIT_API_KEY" | jq -r .status)" != "completed" ]; do
  sleep 3
done

# 3. Download every stem
curl -sS https://stemsplit.io/api/v1/jobs/$JOB_ID \
  -H "Authorization: Bearer $STEMSPLIT_API_KEY" \
  | jq -r '.stems | to_entries[] | "\(.key) \(.value)"' \
  | while read stem url; do curl -sSL "$url" -o "$stem.wav"; done
```

→ Get an API key at [stemsplit.io/developers](https://stemsplit.io/developers).

---

## Reproducing the results

Everything runs locally on a Mac with Apple Silicon — no CUDA required.

```bash
git clone https://github.com/yourusername/musicai
cd musicai/scripts/hf-benchmark
uv venv --python 3.11 && source .venv/bin/activate
uv pip install -e .

# 1. Get the data (~22 GB extracted from a 21 GB Zenodo zip)
python -m src.download_musdb

# 2. Run every enabled model on every track
python -m src.run_all --continue-on-error

# 3. Score with BSS Eval v4
python -m src.eval_metrics

# 4. Assemble the HF dataset (and optionally push)
python -m src.build_dataset
python -m src.push_to_hub --create
```

Reference wall times measured on an Apple M4 Pro (24 GB unified memory),
PyTorch 2.11 with the MPS backend, for the v1.1 lineup. Demucs models use
MPS; MDX-Net runs through ONNX Runtime's CoreML execution provider.

| Stage | Wall time |
|---|---|
| Download MUSDB18-HQ from Zenodo | 32 min |
| Separate (5 models × 50 tracks) | ~2 h 30 min |
| Eval (museval BSS Eval v4) | ~2 h 20 min |
| Build dataset | < 1 s |
| **Total** | **~5 h 20 min** |

---

## Why we built this

We run [StemSplit](https://stemsplit.io) — a hosted stem-separation service —
and we needed an honest, public, reproducible way to compare ourselves to the
state of the art. So we open-sourced it.

Want to ship a separation product without standing up GPU infrastructure?
The same models are one HTTP call away — see the
[Use the StemSplit API](#use-the-stemsplit-api) section above, or jump
straight to the [developer docs](https://stemsplit.io/developers/docs)
and [API reference](https://stemsplit.io/developers/reference).

---

## Licensing

- This dataset (the metrics, the dataset card, and the CC-BY audio samples) is
  released under **CC-BY-4.0** — please cite us if you use it.
- The MUSDB18-HQ audio referenced by `metrics_only.track_id` is **not**
  redistributed here. Download it from
  [Zenodo](https://zenodo.org/records/3338373) under its own terms.
- Each separation model retains its own license; see the table above.

## Citation

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