README.md

metadata

license: mit
tags:
  - audio
  - audio-separation
  - stem-separation
  - demucs
  - htdemucs
  - safetensors
  - maestraea
pipeline_tag: audio-to-audio

HTDemucs Models (Safetensors)

4/6-Stem Source Separation — Vocals, Drums, Bass, Other (+Guitar, Piano)

Original Source by Facebook Research · MIT License

Converted from the original .th checkpoint format to safetensors for faster loading and safer deserialization. For use with Mæstræa AI Workstation.

Available Models

File	Stems	Size	Description
htdemucs.safetensors	4 (drums, bass, other, vocals)	84 MB	Base model
htdemucs_ft.safetensors	4 (drums, bass, other, vocals)	84 MB	Fine-tuned — best quality ⭐
htdemucs_6s.safetensors	6 (drums, bass, other, vocals, guitar, piano)	55 MB	6-stem variant

Each model has a matching *_config.json with architecture parameters (sources, sample rate, channels).

What HTDemucs Does

HTDemucs (Hybrid Transformer Demucs) separates mixed audio into individual stems:

• Vocals — Singing, spoken word
• Drums — Percussion, kick, snare, hi-hat
• Bass — Bass guitar, synth bass
• Other — Everything else (keys, synths, FX)
• Guitar — (6-stem model only)
• Piano — (6-stem model only)

Key Features

• Real-time capable on GPU
• Adjustable segment size for VRAM control
• Best-in-class separation quality (htdemucs_ft)
• ~4–6 GB VRAM

Original Checkpoint URLs

These safetensors were converted from:

Model	Original URL
htdemucs	https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/955717e8-8726e21a.th
htdemucs_ft	https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/04573f0d-f3cf25b2.th
htdemucs_6s	https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/5c90dfd2-34c22ccb.th

Usage with Mæstræa

These models are automatically downloaded by the Mæstræa AI Workstation backend. They can also be used directly with the demucs library:

from demucs.pretrained import get_model
model = get_model("htdemucs_ft")

License

MIT — same as the original Demucs release.

Credits

• Model: Facebook Research / Meta AI
• Paper: Hybrid Transformers for Music Source Separation (Rouard et al., 2023)
• Conversion & Mirror by: AEmotionStudio