StemSplitio/htdemucs-ft-bass-pytorch

HT-Demucs FT — Bass Specialist (PyTorch)

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

This is sub-model 1 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 bass stem with the same quality as the full ensemble (median SDR 10.38 dB on MUSDB18-HQ — 2nd (close behind mdxextraq at 11.42) 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` Want a hosted REST API with credits and a dashboard? Use the **StemSplit API**.

Why this model

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

Common use cases

• —Bassline transcription — extract bass for tab generation, MIDI conversion, or chord detection
• —Mix rebalancing — isolate and re-equalise the bass bus on a finished mix
• —Music education — learn basslines from any record by hearing them isolated
• —Sub-bass mastering reference — compare your low-end against pro mixes

Quick start (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-bass-pytorch")
result = client.post(json={"inputs": audio_b64})

wav, sr = sf.read(io.BytesIO(base64.b64decode(result["bass"])))
sf.write("out_bass.wav", wav, sr)

Or run locally without Hugging Face at all:

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[1].eval()  # the bass 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 bass row
sf.write("out_bass.wav", stems[bag.sources.index("bass")].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`.

(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.)

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
• —StemSplit API
• —Developer docs
• —API reference

Related models from StemSplit

Full benchmark across every popular open-source separator: 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):

@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
• —Search keywords: bass extraction, isolate bass from song, bassline extractor, AI bass separator

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