title	CyberLukes_Next_Gen_GenAI_SOTA_Music_Platform_for_Producers_with_Ethics_AI
app_file	app.py
sdk	gradio
sdk_version	4.44.0

🎵 Professional EDM Stem Generator

Production-grade AI music generation for EDM producers

Powered by Meta's MusicGen (Standard & Style) with professional fine-tuning capabilities.

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✨ Features

🎹 Generation Modes

1. Standard (30s): Text-to-audio with genre/BPM/instrument control + individual stem isolation (bass/drums/other)
2. Style Transfer (30s): Match production style from reference tracks with dual CFG
3. Structured (10s): Multi-modal conditioning with JASCO (chords + melody + drums - NOT stem separation)

🎓 Fine-Tuning Pipeline

• AI-Powered Dataset Preparation: Automatic genre/instrument detection via EfficientAT
• GPT-4o Metadata Generation: Production-focused descriptions from audio analysis
• Scalable Training: 100-10,000+ samples with GPU acceleration
• Cloudflare R2 Backup: Automatic checkpoint storage and resume
• H100 Optimized: Fast iteration with PyTorch 2.6+

🎨 Professional UI

• DAW-Style Interface: Music production tool aesthetic (not science/ML)
• Glass Morphism Design: Modern, premium dark theme
• Intuitive Controls: Producer-friendly terminology (no ML jargon)
• Real-Time Progress: Training time estimates and live updates

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🚀 Quick Start (H100 Deployment)

1. Clone & Setup

git clone <your-repo> RhythmForgeAI
cd RhythmForgeAI
python3 -m venv venv
source venv/bin/activate

2. Install PyTorch (H100)

# CUDA 12.6
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
  --index-url https://download.pytorch.org/whl/cu126

pip install transformers>=4.30.0

3. Install Application

pip install -r requirements_h100.txt

4. ONE-CLICK: Install EfficientAT

bash install_efficientat.sh

5. Run

python app.py

See QUICKSTART_H100.md for detailed instructions.

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📦 What's Included

Component	Purpose
app.py	Main Gradio application
training_utils.py	Fine-tuning pipeline with R2 backup
audio_analysis.py	Audio analysis + EfficientAT integration
modules/audio_tagging.py	EfficientAT wrapper for genre/instrument detection
modules/google_context.py	Web search for sample pack metadata
modules/r2_storage.py	Cloudflare R2 checkpoint management
install_efficientat.sh	One-click EfficientAT installer

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🎯 Use Cases

For Music Producers

• Generate EDM stems in your production style
• Fine-tune on your custom sound library
• Style transfer from reference tracks
• Professional-grade 30s loops

For Sample Pack Creators

• Batch process 10,000+ samples
• Auto-generate training metadata
• Train genre-specific models
• Export stems for commercial use

For Sound Designers

• Explore new timbres and textures
• Match production aesthetics
• Create variation stems
• Prototype ideas rapidly

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🔧 System Requirements

Minimum (Generation Only)

• GPU: NVIDIA GPU with 8GB VRAM
• RAM: 16GB
• Storage: 10GB (models + checkpoints)

Recommended (Fine-Tuning)

• GPU: NVIDIA H100 (80GB) or A100 (40GB)
• RAM: 32GB+
• Storage: 100GB+ (for large datasets)

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📚 Documentation

• QUICKSTART_H100.md: Complete quick start guide
• INSTALL_H100.md: Detailed installation & troubleshooting
• EFFICIENTAT_SETUP.md: EfficientAT configuration
• CLOUDFLARE_R2_SETUP.md: R2 storage setup
• ARCHITECTURE.md: Technical architecture details

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🐛 Troubleshooting

Common Issues

Error	Solution
torchvision::nms does not exist	Install matching torchvision (see INSTALL_H100.md)
T5EncoderModel not found	pip install transformers>=4.30.0
EfficientAT not available	Run bash install_efficientat.sh

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🎉 Features Added (Latest)

• ✅ MusicGen Stems integration for individual stem generation (bass/drums/other)
  • Model weights available: facebook/musicgen-stem-7cb (4.4 GB)
  • Architecture: Base transformer + 3 specialized compression models
  • 6 parallel token streams (1+1+4)
  • Awaiting full AudioCraft integration
• ✅ JASCO multi-modal conditioning (chords + melody + drums)
• ✅ MusicGen Style integration with dual CFG
• ✅ Professional DAW-style UI refactor
• ✅ One-click EfficientAT installer
• ✅ PyTorch 2.6 + H100 optimization
• ✅ Automatic GPU detection
• ✅ Cloudflare R2 checkpoint backup

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📝 License

This project uses Meta's MusicGen models under their respective licenses.

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🙏 Acknowledgments

• Meta AI: MusicGen & MusicGen Style models
• fschmid56: EfficientAT audio tagging
• Cloudflare: R2 object storage
• Gradio: Web interface framework

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Built for professional EDM production | H100 Optimized | Production Ready