README.md

---
language: en
license: mit
library_name: demucs
pipeline_tag: audio-to-audio
tags:
  - demucs
  - stem-separation
  - source-separation
  - drums-isolation
  - music
  - htdemucs
  - audio-to-audio
  - drum-extraction
  - drum-isolation
  - beat-extraction
  - sample-extraction
datasets:
  - StemSplitio/stem-separation-benchmark-2026
inference: false
---

# HT-Demucs FT — Drums Specialist (PyTorch)

Drum isolation specialist from HT-Demucs FT, ~1/4 the size of the full ensemble.

This is sub-model 0 of the 4-bag `htdemucs_ft` ensemble by
[Défossez et al. (Meta AI)][demucs-repo], extracted as a standalone
~160 MB model. It produces the **drums** stem with the same quality as
the full ensemble (median SDR **10.11 dB** on MUSDB18-HQ — 2nd (close behind mdx_extra_q at 11.49) of all
models in our 2026 benchmark) at roughly 1/4 the compute cost.

> Want all 4 stems in one request? Use the full ensemble:
> [`StemSplitio/htdemucs-ft-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-pytorch)
>
> Want a hosted REST API with credits and a dashboard? Use the
> [**StemSplit API**](https://stemsplit.io/developers).

---

## Why this model

| Property | This model | Full `htdemucs_ft` bag |
|---|---|---|
| Disk size | **~160 MB** | ~640 MB |
| Per-3-min-song latency (M4 Pro MPS) | **~22 s** (RTF 0.12) | ~47 s (RTF 0.26) |
| Drums SDR on MUSDB18-HQ | **10.11 dB** | 10.11 dB *(identical — the bag's `drums` output IS this sub-model's output)* |
| Other stems returned | None (focused) | All 4 |

If you only need the drums stem in production, this is **strictly faster and
smaller** than the full ensemble with identical drums quality —
**~2.6× faster wall time** in our smoke tests on M4 Pro MPS.

---

## Common use cases

- **Drum sample extraction** — rip clean drum loops and one-shots from existing tracks
- **Beat transcription / MIDI** — feed the drum stem to onset/beat detectors and drum transcribers
- **Music production isolation** — rebalance or replace drum bus on existing mixes
- **Sample-pack generation** — automate drum-pack creation from a back-catalogue

---

## Quick start (Python)

```python
import base64, io, soundfile as sf
from huggingface_hub import InferenceClient

with open("your-song.mp3", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

client = InferenceClient(model="StemSplitio/htdemucs-ft-drums-pytorch")
result = client.post(json={"inputs": audio_b64})

wav, sr = sf.read(io.BytesIO(base64.b64decode(result["drums"])))
sf.write("out_drums.wav", wav, sr)
```

Or run locally without Hugging Face at all:

```python
import torch, soundfile as sf
from demucs.apply import apply_model
from demucs.audio import convert_audio
from demucs.pretrained import get_model

bag = get_model("htdemucs_ft")
model = bag.models[0].eval()  # the drums specialist
wav, sr = sf.read("your-song.mp3", dtype="float32", always_2d=True)
wav = torch.from_numpy(wav.T).contiguous()
wav = convert_audio(wav, sr, bag.samplerate, bag.audio_channels).unsqueeze(0)

with torch.no_grad():
    stems = apply_model(model, wav, device="mps" if torch.backends.mps.is_available() else "cpu")[0]

# bag.sources == ["drums", "bass", "other", "vocals"]; pick the drums row
sf.write("out_drums.wav", stems[bag.sources.index("drums")].T.numpy(), bag.samplerate)
```

---

## Deploy on Hugging Face Inference Endpoints

Click **Deploy → Inference Endpoints** above, pick a GPU instance, and HF
will spin up a container running [`handler.py`](handler.py).

| Hardware | Latency for 3-min song |
|---|---:|
| NVIDIA L4 | ~3 s |
| NVIDIA T4 small | ~7 s |
| CPU x4 (basic) | ~48 s |

(Roughly 2.6× faster than the full-bag latency, since we run only this
specialist sub-model. Cloud GPU numbers extrapolated from M4 Pro measurements.)

```bash
curl -X POST https://<your-endpoint>.endpoints.huggingface.cloud \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"inputs\": \"$(base64 < your-song.mp3)\"}"
```

---

## Try it in your browser, no code

- [StemSplit](https://stemsplit.io)
- [StemSplit API](https://stemsplit.io/developers)
- [Developer docs](https://stemsplit.io/developers/docs)
- [API reference](https://stemsplit.io/developers/reference)

---

## Related models from StemSplit

| Repo | Stem | When to use |
|---|---|---|
| [`htdemucs-ft-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-pytorch) | all 4 | When you need vocals + drums + bass + other in one request |
| [`htdemucs-ft-vocals-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-pytorch) | vocals | Best vocal SDR in our benchmark (9.19 dB) — karaoke, acapella |
| [`htdemucs-ft-drums-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-drums-pytorch) | drums | Drum extraction, beat transcription, sample-pack creation |
| [`htdemucs-ft-bass-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-bass-pytorch) | bass | Bassline transcription, mix rebalancing |
| [`htdemucs-ft-other-pytorch`](https://huggingface.co/StemSplitio/htdemucs-ft-other-pytorch) | other / instrumental | Karaoke instrumentals, sample-flipping, music-bed extraction |

Full benchmark across every popular open-source separator:
[StemSplitio/stem-separation-benchmark-2026](https://huggingface.co/datasets/StemSplitio/stem-separation-benchmark-2026).

---

## License & attribution

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

**Original authors (please cite if you use this model in research):**

```bibtex
@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 model: [`facebookresearch/demucs`][demucs-repo]
- Packaging by [StemSplit](https://stemsplit.io)
- Search keywords: drum extraction, isolate drums from song, drum stem extractor, AI drum separator

[demucs-repo]: https://github.com/facebookresearch/demucs