A practice looper for Linux: load a song, loop a section, slow it down without changing pitch, and drill it until you can play it.
Releases · Features · Dependencies · Install · Build & develop
These work with the installed app — no ML setup.
- Sample-accurate looping — set a loop by dragging on the waveform; the seam is crossfaded. Loops are saved per song and take their names from the sections they span (
verse 2 → chorus 1). - Pitch-preserving speed — 0.25–2.0× via Rubber Band R3 (compiled in); independent pitch shift, ±12 semitones plus cents.
- Bass focus — octave-up plus low-pass to isolate basslines.
- Drill — tempo trainer that raises speed across passes, with region shaping and a recall mode that mutes playback so you play from memory.
- Practice routines — saved sequences of practice blocks; each block sets a loop span, mix, speed, lead-in, and count-in, and the app steps through them as you play.
- Overdub recording — record yourself (mic or audio interface) over the song, the selection, or the active loop. Recordings are latency-calibrated, stored in the song bundle, and play back over the mix.
- Metronome and count-in — manual BPM, or synced to the analyzed BPM once a song is analyzed.
- Tuner — chromatic tuner with note and cents and a hold-to-lock confirm. Works with no song loaded.
- Sections and notes — mark sections by hand; per-section free text with inline tablature, keyed to the section occurrence (
verse 2). - Markers — set positions in the song and jump playback to them.
- Song bundles — each song is a self-contained directory (audio +
dredge.jsonholding sections, loops, notes, analysis, recordings). Copy the folder to another machine and it loads with everything. - Wide format support — imports mp3, flac, ogg, opus, wav, and m4a, and takes the audio track from mp4/mov/webm/mkv video files (opus, webm, and mkv via
ffmpeg). - Export — render the current mix (stem balance, speed, pitch, bass focus) to WAV, or MP3 with
ffmpeg.
- MIDI foot pedal — map pedals to transport, markers, and isolation snapshots with a learn flow, hands on the instrument.
- Dock layout — the right-hand tabs (structure, loops, routines, export, …) live in resizable panels; drag tabs to reorder, merge, or split panels.
- Control socket — every command the UI uses is also available as JSON over a Unix socket; a headless daemon (
dredged) runs the same engine without the UI.
These require the optional Python tools in Dependencies.
- Detected song structure — beats, downbeats, BPM, and labelled sections detected and drawn on the waveform.
- Downbeat snapping — loop and selection edges snap to detected downbeats.
- Section click track — a click on the analyzed beats inside sections you choose, accented on downbeats.
- Stems — 6-stem separation (vocals / drums / bass / guitar / piano / other) with per-stem faders. Runs locally.
| Component | Required for | Install |
|---|---|---|
| PipeWire 1.0+ | the app to run at all | system package (pipewire) |
| Runtime libraries (webkit2gtk-4.1, gtk3, …) | the app to run | pulled in automatically by the .deb (apt) and the dredge AUR package — nothing to do |
| ffmpeg | MP3 export, opus/mkv/webm containers, stem export | sudo apt install ffmpeg · sudo pacman -S ffmpeg |
The stretch engine (Rubber Band) is compiled into dredge, so there is no rubberband package to install. The prebuilt binaries target Debian/Ubuntu library versions — on Arch, use the
dredgeAUR package.
All ML pieces require uv on PATH: sudo pacman -S uv, or on Ubuntu curl -LsSf https://astral.sh/uv/install.sh | sh. A GPU is optional throughout — CPU works, slower.
Beat / section analysis (dredge-enable-ml analyze)
- venv:
~/.local/share/dredge/analyze-venv, Python 3.12 (override path with$DREDGE_ANALYZE_VENV) - packages:
beat_this(from git),torch,soundfile,librosa,einops,rotary-embedding-torch - provides: beat / downbeat / BPM grid (beat_this) and novelty-based section boundaries
- disk: torch download, several GB
Higher-quality sections (dredge-enable-ml songformer)
- venv:
~/.local/share/dredge/songformer-venv, Python 3.11 (override with$DREDGE_SONGFORMER_VENV) - packages:
torch==2.4.0,torchaudio==2.4.0,numpy<2,transformers==4.51.1,librosa,soundfile,ema-pytorch,loguru,omegaconf,tqdm,safetensors,muq,x-transformers,msaf,einops,huggingface_hub - also downloads the
ASLP-lab/SongFormermodel snapshot from Hugging Face on first run (weights plus its own modeling code) - runs alongside the beat grid, so it also needs the analyze venv above
- VRAM at run time: ~8 GB resident, brief peak up to ~15 GB. Falls back to the novelty detector if the venv is absent or the run runs out of memory.
Stem separation (dredge-enable-ml stems)
- installed as a
uvtool:uv tool install demucs --with torchcodec - model:
htdemucs_6s, the 6-source Hybrid Transformer Demucs (Meta AI); weights download on first run - provides: 6-stem separation (vocals / drums / bass / guitar / piano / other)
- needs
ffmpeg(above) for stem export - disk: PyTorch, ~2.5 GB
Linux only. The audio engine is PipeWire-native: PipeWire 1.0+ is required, with no ALSA or PulseAudio fallback.
Arch / Arch-based
yay -S dredge # builds from source against your system librariesDebian / Ubuntu (24.04+ / Debian 13+)
Download the latest dredge_*_amd64.deb from the
releases page, then:
sudo apt install ./dredge_*_amd64.debapt pulls the runtime libraries automatically. The basic features above run with nothing else installed.
Beat/section analysis and stem separation are off by default and self-bootstrap on first use. dredge-enable-ml does that bootstrap up front, so the multi-GB downloads happen now instead of on the first analysis:
dredge-enable-ml all # analyze + songformer + stems
dredge-enable-ml analyze # beat/section analysis only
dredge-enable-ml songformer # higher-quality section labels
dredge-enable-ml stems # stem separation onlydredge-doctor reports which optional tools are installed and the exact command to add each missing one. The desktop app shows the same under Settings → capabilities.
Dredge has only been used on two machines, both running Arch Linux. Assumptions that hold there may break elsewhere — report an issue for anything you hit.
Built with Rust, Tauri 2, and Svelte 5. Building from source and hacking on it are covered in DEVELOPMENT.md.
MIT licensed; the binaries bundle the Rubber Band Library (GPL-2.0-or-later), so distributed builds are GPL-governed.