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Show HN: Musslop – turn any track into adaptive game-style music loops

Developer Siziff released musslop, an open-source tool that uses AI to turn any MP3 into adaptive, game-style music loops, with a flagship use case for tabletop RPG sessions. Musslop runs locally via a Python setup script and serves at http://localhost:8801, using SongFormer (2025) and All-In-One (2023) neural engines for structure analysis, Beat This! for beat and downbeat tracking, and Demucs for splitting tracks into drums, bass, vocals, and backing layers. First AI analysis takes about 1 minute on GPU or a few minutes on CPU, with results cached for instant reopening, and the tool works fully offline after setup.

read4 min views3 publishedSep 16, 2026
Show HN: Musslop – turn any track into adaptive game-style music loops
Image: Michielbdejong (auto-discovered)

Loop any section forever · hit «Next» · the music evolves — seamlessly, on the beat

In games, music reacts: stay in the tavern and its theme loops forever; descend into the dungeon and the score darkens with you. musslop does this with any mp3.

Drop a track → AI finds the musical structure → every section becomes a perfect loop → you drive the music live, like a game audio engine — no editing skills required.

Feature What it does
AI structure analysis two neural engines find sections and name them (Intro, Verse, Chorus, Solo):SongFormer (2025, faster, best on pop/rock/electronic) andAll-In-One (2023, steadier on orchestral); beats/downbeats viaBeat This! . Fast heuristic fallback (~2 s) works everywhere
Seamless loops sample-accurate Web Audio scheduling, bar-snapped boundaries, per-section loop-quality score (⟳%), equal-power crossfades
Intensity layers Demucs splits the track into drums / bass / vocals / backing — toggle and mix layers live: calm exploration → full combat, same track
Pro transitions post-exit tails ring out over the next section, bass-swap keeps exactly one bassline at any moment, one-shot stingers (cymbal / boom / riser) punctuate scene changes
Knows what not to loop build-ups are detected by their crescendo shape and play once, as dramatic bridges
Full editor drag boundaries with bar snapping, split/merge, loop-start markers, Ctrl+Z, zoom + scrollbar, everything auto-saved

The flagship use case is tabletop RPG: turn any track into a location theme. The party lingers in the tavern — the tavern section loops. They open the dungeon door — one keypress, and the music descends with them, on the beat, mid-session. There is even a Tavern UI theme (wood, parchment & gold) switchable in the header.

Also great for: game dev prototyping (audition adaptive behaviour before wiring FMOD/Wwise, export loop WAVs), streaming, practice looping, focus music.

Linux / macOS:

git clone https://github.com/Siziff/musslop.git && cd musslop
./setup.sh   # creates .venv, installs everything (incl. a bundled ffmpeg)
./run.sh     # → http://localhost:8801

Windows (needs Python 3.10+ with "Add to PATH" checked):

git clone https://github.com/Siziff/musslop.git
cd musslop
setup.bat
run.bat      # → http://localhost:8801

ffmpeg is downloaded automatically with the dependencies — no manual install needed.

Optional AI engines (each is one command, both auto-detected by run.sh):

Linux / macOS:

./setup-ai-songformer.sh  # SongFormer 2025 + Beat This! — newer, faster,
./setup-ai-allin1.sh      # All-In-One 2023 + Demucs — steadier on orchestral,

Windows:

setup-ai-songformer.bat   # needs git in PATH; ~15 min, ~4 GB

The All-In-One engine is not supported natively on Windows (its NATTEN dependency needs a source build); use WSL2 with the Linux script if you need it. SongFormer covers structure analysis fully, and the stem "layers" feature also works through it on Windows.

Works fully offline after setup. First AI analysis of a track: ~1 min on GPU, a few minutes on CPU; results are cached — reopening is instant.

  1. Beat & downbeat tracking — onset envelope + dynamic programming (Ellis 2007)
  2. Structure — neural (All-In-One, WASPAA 2023) or Foote novelty on beat-synced chroma+MFCC+RMS self-similarity
  3. Loop-aware refinement — boundaries move along downbeats maximizing loop closure quality (head/tail spectral similarity), phrase lengths (4/8 bars) and transition audibility
  4. Build-up detection — normalized RMS slope + trend R² + spectral-centroid rise
  5. Playback — every loop pass is an independentAudioBufferSourceNode ; transitions land on loop/phrase boundaries with micro-fades, tails and bass-swap

Built on the shoulders of: SongFormer (ASLP-lab, CC-BY-4.0) · All-In-One (Kim & Nam, MIT) · Beat This! (CPJKU, MIT) · Demucs · librosa — plus classic MIR: Ellis 2007 (beat tracking), Foote 2000 (novelty segmentation).

Endpoint Purpose
POST /api/upload ·POST /api/import_url file upload / yt-dlp import
GET /api/analyze/{id}?engine=fast|deep structure analysis
POST /api/stems/{id} ·GET /api/stems/{id}/{stem} Demucs 4-stem split
POST /api/export/{id} zip of full-quality loop WAVs
GET/POST /api/markup/{id} segment markup persistence
GET /api/tracks ·POST /api/favorite/{id} history & library
  1. curl http://localhost:8801/api/health → expect{"status":"ok", ...}
  2. Blank page → hard-refresh (Ctrl+F5 ), check browser console (F12 )
  3. Port busy → ./run.sh frees it; manually:lsof -ti tcp:8801 | xargs kill
  4. No sound → click the ♪? self-test in the volume box (bottom-right)
  5. Server errors → server.log
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