---
license: mit
tags:
- audio
- stem-separation
- audio-transformation
- deep-learning
---
# 🔱 Audio Reborn
A professional GPU-accelerated audio transformation pipeline designed to bypass Content ID fingerprints using advanced AI synthesis.

## 🏗️ Project Architecture (GPU-Only)

### Stage 1: Deep Audio Analysis (`1_Analysis`)
- **Tools:** Librosa (GPU-backed via CuPy/Torch), Crepe.
- **Goal:** Extract BPM, Key, Pitch, and Spectral Centroid.

### Stage 2: AI Stem Separation (`2_Stem_Separation`)
- **Tools:** Demucs v4 (HTDemucs) - CUDA Enabled.
- **Goal:** Separate Vocals, Drums, Bass, and Other.

### Stage 3: Multi-Layer Transformation (`3_Transformation`)
- **Tools:** Pedalboard (VST3/GPU), Audiomentations (Torch-based).
- **Goal:** Phase shifting, Micro-latency, Random EQ, Reverb.

### Stage 4: AI Identity Regeneration (`4_Regeneration`)
- **Tools:** RVC v2 (CUDA), Meta AudioCraft (MusicGen/AudioGen).
- **Goal:** Complete Timbre replacement and Melody-conditioned synthesis.

### Stage 5: AI Mastering & Quality Check (`5_Mastering`)
- **Tools:** Matchering, Pyloudnorm, VISQOL.
- **Goal:** Professional loudness and artifact verification.

---
## 🛠️ Infrastructure Requirements
- **OS:** Windows (PowerShell)
- **GPU:** NVIDIA (CUDA Toolkit 11.8/12.1)
- **Python:** 3.10+ (Inside `venv`)