SunoJump
Audio fingerprint masking tool. Transforms audio files through a multi-pass processing pipeline to alter their acoustic fingerprint while preserving audible quality. Designed for creators who need to re-upload their own Suno-generated music as templates when detection systems produce false positives.
How It Works
SunoJump applies a 10-pass processing pipeline with non-uniform segment-based transforms — each segment of the audio gets slightly different processing parameters, breaking the constellation patterns that fingerprinting systems rely on.
Processing Pipeline
| # | Pass | What It Does |
|---|---|---|
| 1 | Metadata Strip | Removes all embedded tags, IDs, and hidden metadata |
| 2 | Spectral Perturbation | Perturbs frequency magnitudes, targets common watermark bands (sub-bass, ultrasonic) |
| 3 | Pitch Micro-Shift | Non-uniform pitch warping across random segments |
| 4 | Tempo Micro-Variation | Non-uniform time warping across random segments |
| 5 | Phase Scrambling | Randomizes phase relationships in STFT domain |
| 6 | Stereo Manipulation | Mid-side processing to alter stereo field |
| 7 | Noise Injection | Adds shaped pink noise to mask watermark energy |
| 8 | Dynamics Modification | Per-frame random gain variation to break statistical patterns |
| 9 | Humanization | Wow/flutter, dynamic breathing, micro noise floor |
| 10 | Lossy Re-encode | MP3 encode/decode cycle to degrade fine watermark detail (requires ffmpeg) |
Key Differentiator: Non-Uniform Processing
Unlike tools that apply flat transforms across the entire track, SunoJump splits audio into variable-length segments and applies different transform parameters to each segment. This breaks the relative timing and frequency relationships between spectral peaks — the exact features that constellation-based fingerprinting depends on.
Installation
Windows (recommended) -- prebuilt executable
Download SunoJump.exe from the latest release. No Python install required. Double-click to launch.
From source (any platform)
git clone https://github.com/SysAdminDoc/SunoJump.git
cd SunoJump
# Run (auto-installs dependencies)
python sunojump.py
Requirements
- Python 3.9+
- ffmpeg (optional, for Lossy Re-encode pass)
- PyQt6 Multimedia (optional, for in-app preview playback; usually bundled)
All Python dependencies install automatically on first run.
Features
- 10-pass audio processing pipeline — metadata strip, spectral perturbation, pitch/tempo micro-shift, phase scrambling, stereo manipulation, noise injection, dynamics, humanization, lossy re-encode
- Non-uniform segment processing — breaks constellation fingerprint patterns
- 4 built-in presets — Gentle, Moderate, Aggressive, Extreme + Custom
- Per-pass toggles and strength sliders — fine-grained control
- Render Preview — hear a 30-second sample with your current settings before committing to full-file processing
- Compare Presets — one click renders a 20-second sample per preset so you can A/B/C/D audition all four, then apply your favorite
- In-app A/B playback — play original and processed side-by-side without leaving the app
- Detection-signature score — logs how AI-detectable the audio looked before vs after processing (heuristic)
- Reproducible output — optional
--seedfor bit-identical runs (useful for testing and diffing) - Batch processing — drag/drop multiple files, reorder them, process in parallel
- Custom preset save/load — export your tuned settings to JSON, share, or reuse
- Chunked long-audio processing — bounded memory for songs > 1 minute
- Open Output — one-click to output folder in your file manager
- Modification strength metric — know how much you've changed before uploading
Usage
GUI Mode
python sunojump.py
- Drop audio files into the file list (or click Browse)
- Select a preset or customize individual parameters
- (Optional) Click Render Preview to process the first 30 seconds of the selected file so you can hear the result before committing; adjust settings and re-render as needed
- Click Process All to render every file in the list to the output directory with
_sjsuffix
CLI Mode
# Basic usage with preset
python sunojump.py -i song.wav -p aggressive
# Custom parameters
python sunojump.py -i song.wav --pitch 1.5 --phase 0.5 --spectral 0.4
# Batch process a directory
python sunojump.py -i ./my_songs/ -o ./output/ -p moderate -f flac
# With lossy re-encode
python sunojump.py -i song.wav -p aggressive --reencode 128
CLI Options
| Flag | Description | Default |
|---|---|---|
-i, --input |
Input file or directory | (required) |
-o, --output |
Output directory | ~/Desktop/SunoJump_Output |
-p, --preset |
gentle, moderate, aggressive, extreme | moderate |
-f, --format |
wav, flac, ogg | wav |
--preset-file |
Path to custom JSON preset (overrides -p) |
none |
--spectral |
Spectral perturbation (0.0-1.0) | preset |
--pitch |
Pitch micro-shift in semitones (0.0-5.0) | preset |
--tempo |
Tempo variation (0.0-0.15) | preset |
--phase |
Phase scrambling (0.0-1.0) | preset |
--stereo |
Stereo manipulation (0.0-0.5) | preset |
--noise |
Noise level in dB (-70 to -30) | preset |
--dynamics |
Dynamics amount (0.0-1.0) | preset |
--humanize |
Humanization amount (0.0-1.0) | preset |
--reencode |
Lossy re-encode bitrate (96-320) | disabled |
--seed |
Integer for deterministic random generator (same seed = same output) | random |
Use Save... in the GUI to export the current settings, then pass the resulting .json to --preset-file on the CLI to reproduce the same configuration across runs.
Presets
| Preset | Pitch | Spectral | Phase | Noise | Use Case |
|---|---|---|---|---|---|
| Gentle | 0.3 st | 0.10 | 0.10 | -60 dB | Minimal change, preserve quality |
| Moderate | 0.8 st | 0.30 | 0.30 | -50 dB | Good balance of masking vs quality |
| Aggressive | 1.5 st | 0.50 | 0.50 | -45 dB | Strong masking, slight quality trade-off |
| Extreme (default, recommended) | 3.0 st | 0.70 | 0.70 | -40 dB | Highest bypass success rate -- tested and confirmed |
Recommendation: Start with Extreme. Real-world testing against Suno's detection has shown it to deliver the most consistent bypass results. The lighter presets are included for cases where audio fidelity is the priority, but for re-uploading your own songs as templates, Extreme is the proven winner.
Modification Strength
After processing, SunoJump reports a modification strength percentage:
- 0-25% — Light: may not be sufficient for detection bypass
- 25-50% — Moderate: likely effective
- 50-75% — Strong: highly likely effective
- 75-100% — Extreme: verify audio quality hasn't degraded too much
Start with the default Extreme preset -- field-tested as the most reliable for bypassing Suno's detection.
Supported Formats
Input: WAV, MP3, FLAC, OGG, AIFF, Opus
Output: WAV (24-bit), FLAC, OGG Vorbis
License
MIT