LEMM - Let Everyone Make Music

Version 1.0.2 (Beta)

An advanced AI music generation system with training capabilities, built-in vocals, professional mastering, audio enhancement, and music theory intelligence. Powered by DiffRhythm2 with LoRA fine-tuning support and symbolic music understanding.

🎵 Live Demo: Try LEMM on HuggingFace Spaces
📦 LoRA Collection: Browse Trained Models
🏢 Organization: lemm-ai on GitHub

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✨ Key Features

🎵 Music Generation

• Text-to-Music: Generate music from style descriptions
• Built-in Vocals: DiffRhythm2 generates vocals directly with music (no separate TTS)
• Style Consistency: New clips inherit musical character from existing ones
• Flexible Duration: 10-120 second clips

🎓 LoRA Training

• Custom Style Training: Fine-tune on your own music datasets
• Public Datasets: GTZAN, MusicCaps, FMA support
• Continued Training: Use existing LoRAs as base models
• Automatic Upload: Trained LoRAs uploaded to HuggingFace Hub

🎚️ Professional Audio Tools

• Advanced Mastering: 32 professional presets (Pop, Rock, Electronic, etc.)
• Custom EQ: 8-band parametric equalizer
• Dynamics: Compression and limiting controls
• Audio Enhancement:
  • Stem separation (Demucs)
  • Noise reduction
  • Super resolution (upscale to 48kHz)

🎛️ DAW-Style Interface

• Horizontal Timeline: Professional multi-track layout
• Visual Waveforms: See your music as you build
• Track Management: Add, remove, rearrange clips
• Real-time Preview: Play individual clips or full timeline

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🚀 Quick Start

Option 1: HuggingFace Spaces (Recommended)

Try LEMM instantly with zero setup:

👉 Launch LEMM Space

• No installation required
• Free GPU access
• Pre-loaded models
• Immediate start

Option 2: Local Installation

Prerequisites:

• Python 3.10 or 3.11
• 16GB+ RAM recommended
• NVIDIA GPU recommended (CUDA 12.x) or CPU

Installation:

# Clone the repository
git clone https://github.com/lemm-ai/LEMM-1.0.0-ALPHA.git
cd LEMM-1.0.0-ALPHA

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# Windows:
.\.venv\Scripts\activate
# Linux/Mac:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Launch LEMM
python app.py

Access at: http://localhost:7860

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📖 Usage Guide

1️⃣ Generate Your First Track

1. Enter Music Prompt: Describe the style
   • Example: "upbeat electronic dance music with heavy bass"
2. Add Lyrics (optional): DiffRhythm2 will sing them
   • Leave empty for instrumental
3. Set Duration: 10-120 seconds (default: 30s)
4. Generate: Click "✨ Generate Music Clip"
5. Preview: Listen in the audio player

2️⃣ Build Your Composition

1. Timeline Tab: View all generated clips
2. Waveform Preview: Visual representation of each clip
3. Add More: Generate additional clips at different positions
4. Style Consistency: New clips automatically match existing style

3️⃣ Master & Export

1. Mastering Tab:
   • Choose preset (Pop, Rock, EDM, etc.)
   • Or customize: EQ, compression, limiting
2. Enhancement (optional):
   • Stem separation
   • Noise reduction
   • Audio super resolution
3. Export Tab:
   • Choose format (WAV, MP3, FLAC)
   • Download your finished track

4️⃣ Train Custom LoRAs

1. Dataset Management Tab:
   • Select public dataset (GTZAN, MusicCaps, FMA)
   • Or upload your own music
   • Download and prepare dataset
2. Training Configuration Tab:
   • Name your LoRA
   • Set training parameters
   • Choose base LoRA (optional - for continued training)
   • Start training
3. Wait for Training: Progress shown in real-time
4. Auto-Upload: LoRA uploaded to HuggingFace as model
5. Reuse: Download and use in future generations

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🏗️ Architecture

Core Technology

DiffRhythm2 (ASLP-lab)

• State-of-the-art music generation with vocals
• Continuous Flow Matching (CFM) diffusion
• MuQ-MuLan style encoding for consistency
• Native vocal generation (no separate TTS)

LoRA Fine-Tuning (PEFT)

• Low-Rank Adaptation for efficient training
• Parameter-efficient fine-tuning
• Custom style specialization
• Continued training support

System Components

LEMM/
├── app.py                      # Main Gradio interface
├── backend/
│   ├── services/
│   │   ├── diffrhythm_service.py       # DiffRhythm2 integration
│   │   ├── lora_training_service.py    # LoRA training
│   │   ├── dataset_service.py          # Dataset management
│   │   ├── mastering_service.py        # Audio mastering
│   │   ├── stem_enhancement_service.py # Audio enhancement
│   │   ├── audio_upscale_service.py    # Super resolution
│   │   ├── hf_storage_service.py       # HuggingFace uploads
│   │   └── ...
│   ├── routes/                 # API endpoints
│   ├── models/                 # Data schemas
│   └── config/                 # Configuration
├── models/
│   ├── diffrhythm2/           # Music generation model
│   ├── loras/                 # Trained LoRA adapters
│   └── ...
├── training_data/             # Prepared datasets
├── outputs/                   # Generated music
└── requirements.txt           # Dependencies

Key Dependencies

• torch: 2.4.0+ (PyTorch)
• diffusers: Diffusion models
• transformers: 4.47.1 (HuggingFace)
• peft: LoRA training
• gradio: Web interface
• pedalboard: Audio mastering
• demucs: Stem separation
• huggingface-hub: Model uploads

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🎓 Training Your Own LoRAs

Supported Datasets

Public Datasets:

• GTZAN: Music genre classification (1,000 tracks, 10 genres)
• MusicCaps: Google's music captioning dataset
• FMA (Free Music Archive): Large-scale music collection

Custom Datasets:

• Upload your own music collections
• Supports MP3, WAV, FLAC, OGG

Training Process

1. Prepare Dataset:

   • Download or upload music
   • Extract audio samples
   • Split into train/validation sets

2. Configure Training:

   • LoRA Rank: 4-64 (higher = more expressive, slower)
   • Learning Rate: 1e-4 to 1e-3
   • Batch Size: 1-8 (depends on GPU memory)
   • Epochs: 10-100 (depends on dataset size)
   • Base LoRA: Optional - continue from existing model

3. Monitor Training:

   • Real-time loss graphs
   • Validation metrics
   • Progress percentage

4. Upload & Share:

   • Automatic upload to HuggingFace Hub
   • Model ID: Gamahea/lemm-lora-{your-name}
   • Add to LEMM Collection

Example: Training on GTZAN

1. Dataset Management → Select GTZAN → Download
2. Prepare Dataset → GTZAN → Prepare (800 train, 200 val)
3. Training Configuration:
   - Name: "my_jazz_lora"
   - Dataset: gtzan
   - Epochs: 50
   - LoRA Rank: 8
   - Learning Rate: 1e-4
4. Start Training → Wait ~2-4 hours (GPU dependent)
5. ✅ Uploaded: Gamahea/lemm-lora-my-jazz-lora
6. Reuse in generation or continue training

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🎨 LoRA Management

Download from HuggingFace

1. Go to LoRA Management Tab
2. Enter model ID: Gamahea/lemm-lora-{name}
3. Click "Download from Hub"
4. Use immediately in generation

Browse Collection

👉 LEMM LoRA Collection

Discover community-trained LoRAs:

• Genre specialists (jazz, rock, electronic)
• Style adaptations
• Custom fine-tuned models

Export/Import

Export:

• Download trained LoRA as ZIP
• Share with others
• Backup your work

Import:

• Upload LoRA ZIP file
• Instantly available for use
• Continue training from checkpoint

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🔧 Advanced Configuration

GPU Acceleration

NVIDIA (Recommended):

# CUDA 12.x automatically detected
# No additional configuration needed

CPU Mode:

# Automatic fallback if no GPU detected
# Slower but fully functional

Model Paths

Models downloaded to:

• DiffRhythm2: models/diffrhythm2/
• LoRAs: models/loras/
• Training data: training_data/

Environment Variables

Create .env file:

# HuggingFace token for uploads (optional)
HF_TOKEN=hf_xxxxxxxxxxxxx

# Gradio server port (default: 7860)
GRADIO_SERVER_PORT=7860

# Enable debug logging
DEBUG=false

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📊 Technical Specifications

Generation

• Model: DiffRhythm2 (CFM-based diffusion)
• Sampling: 22050 Hz (can upscale to 48kHz)
• Duration: 10-120 seconds per clip
• Vocals: Built-in (no separate TTS)
• Style Encoding: MuQ-MuLan

Training

• Method: LoRA (Low-Rank Adaptation)
• Rank: 4-64 (configurable)
• Precision: Mixed (FP16/FP32)
• Optimizer: AdamW
• Scheduler: Cosine annealing

Audio Enhancement

• Stem Separation: Demucs 4.0.1 (4-stem)
• Noise Reduction: Spectral subtraction
• Super Resolution: AudioSR (up to 48kHz)
• Mastering: Pedalboard (Spotify LUFS-compliant)

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🤝 Contributing

We welcome contributions! Here's how:

Report Issues

• GitHub Issues
• Include: steps to reproduce, logs, system info

Share LoRAs

1. Train custom LoRA in LEMM
2. Upload to HuggingFace (automatic)
3. Add to Collection
4. Share with community

Development

# Fork the repository
# Clone your fork
git clone https://github.com/YOUR-USERNAME/LEMM-1.0.0-ALPHA.git

# Create feature branch
git checkout -b feature/your-feature

# Make changes and commit
git commit -am "Add your feature"

# Push and create PR
git push origin feature/your-feature

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📄 License

MIT License - See LICENSE file

Free to use, modify, and distribute.

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🙏 Acknowledgments

Models & Technologies

• DiffRhythm2: ASLP-lab for state-of-the-art music generation
• LoRA/PEFT: HuggingFace for parameter-efficient fine-tuning
• Gradio: For the beautiful web interface
• Demucs: Meta AI for stem separation
• Pedalboard: Spotify for professional audio processing

Datasets

• GTZAN: Music genre classification dataset
• MusicCaps: Google's music captioning dataset
• FMA: Free Music Archive community

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📞 Support & Community

• Documentation: Full Docs
• HuggingFace Space: Try Now
• LoRA Collection: Browse Models
• Issues: GitHub Issues

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🚀 What's Next

Planned Features:

• Multi-track composition tools
• Real-time style transfer
• Collaborative projects
• Mobile app
• VST plugin support

Join the Journey!

Built with ❤️ by the LEMM community

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LEMM - Let Everyone Make Music 🎵