README.md

M1: Music Generation via Diffusion Transformers 🎵🔬

M1 is a research project exploring large-scale music generation using diffusion transformers. This repository contains the implementation of our proposed architecture combining recent advances in diffusion models, transformer architectures, and music processing.

🔬 Research Overview

We propose a novel approach to music generation that combines:

• Diffusion-based generative modeling
• Multi-query attention mechanisms
• Hierarchical audio encoding
• Text-conditional generation
• Scalable training methodology

Key Hypotheses

1. Diffusion transformers can capture long-range musical structure better than traditional autoregressive models
2. Multi-query attention mechanisms can improve training efficiency without sacrificing quality
3. Hierarchical audio encoding preserves both local and global musical features
4. Text conditioning enables semantic control over generation

🏗️ Architecture

                                              ┌─────────────────┐
                                              │  Time Encoding  │
                                              └────────┬────────┘
                                                      │
┌──────────────┐                              ┌──────▼─────────┐
│ Audio Input  ├──► mel spectrogram ──────────►               │
└──────────────┘                              │   Diffusion    │
                                              │  Transformer   │ ──► Generated Audio
┌──────────────┐    ┌─────────────┐          │     Block      │
│ Text Input   ├──► │ T5 Encoder  ├──────────►               │
└──────────────┘    └─────────────┘          └───────────────┘

Implementation Details

# Key architectural dimensions
MODEL_CONFIG = {
    'dim': 512,          # Base dimension
    'depth': 12,         # Number of transformer layers
    'heads': 8,          # Attention heads
    'dim_head': 64,      # Dimension per head
    'mlp_dim': 2048,     # FFN dimension
    'dropout': 0.1       # Dropout rate
}

# Audio processing parameters
AUDIO_CONFIG = {
    'sample_rate': 16000,
    'n_mels': 80,
    'n_fft': 1024,
    'hop_length': 256
}

📊 Proposed Experiments

Phase 1: Architecture Validation

• Baseline model training on synthetic data
• Ablation studies on attention mechanisms
• Time embedding comparison study
• Audio encoding architecture experiments

Phase 2: Dataset Construction

We plan to build a research dataset from multiple sources:

1. Initial Development Dataset

   • 10k Creative Commons music samples
   • Focused on single-instrument recordings
   • Clear genre categorization

2. Scaled Dataset (Future Work)

   • Spotify API integration
   • SoundCloud API integration
   • Public domain music archives

Phase 3: Training & Evaluation

Planned training configurations:

initial_training:
  batch_size: 32
  gradient_accumulation: 4
  learning_rate: 1e-4
  warmup_steps: 1000
  max_steps: 100000
  
evaluation_metrics:
  - spectral_convergence
  - magnitude_error
  - musical_consistency
  - genre_accuracy

🛠️ Development Setup

# Clone repository
git clone https://github.com/Agora-Lab-AI/m1.git
cd m1-music

# Create environment
conda create -n m1 python=3.10
conda activate m1

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/

Example

import torch
from m1.model import ModelConfig, AudioConfig, MusicDiffusionTransformer, DiffusionScheduler, train_step, generate_audio
from loguru import logger

# Example usage
def main():
    logger.info("Setting up model configurations")
    
    # Configure logging
    logger.add("music_diffusion.log", rotation="500 MB")
    
    # Set device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info(f"Using device: {device}")
    
    # Initialize configurations
    model_config = ModelConfig(
        dim=512,
        depth=12,
        heads=8,
        dim_head=64,
        mlp_dim=2048,
        dropout=0.1
    )
    
    audio_config = AudioConfig(
        sample_rate=16000,
        n_mels=80,
        audio_length=1024,
        hop_length=256,
        win_length=1024,
        n_fft=1024
    )
    
    # Initialize model and scheduler
    model = MusicDiffusionTransformer(model_config, audio_config).to(device)
    scheduler = DiffusionScheduler(num_inference_steps=1000)
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    
    # Example forward pass
    logger.info("Preparing example forward pass")
    batch_size = 4
    example_audio = torch.randn(batch_size, audio_config.audio_length).to(device)
    example_text = {
        'input_ids': torch.randint(0, 1000, (batch_size, 50)).to(device),
        'attention_mask': torch.ones(batch_size, 50).bool().to(device)
    }
    
    # Training step
    logger.info("Executing training step")
    loss = train_step(
        model,
        scheduler,
        optimizer,
        example_audio,
        example_text,
        device
    )
    logger.info(f"Training loss: {loss:.4f}")
    generation_text = {
        'input_ids': torch.randint(0, 1000, (1, 50)).to(device),
        'attention_mask': torch.ones(1, 50).bool().to(device)
    }
    
    # Generation example
    logger.info("Generating example audio")
    generated_audio = generate_audio(
        model,
        scheduler,
        generation_text,
        device,
        audio_config.audio_length
    )
    logger.info(f"Generated audio shape: {generated_audio.shape}")

if __name__ == "__main__":
    main()

📝 Project Structure

m1/
├── configs/               # Training configurations
├── m1/
│   ├── models/           # Model architectures
│   ├── diffusion/        # Diffusion scheduling
│   ├── data/             # Data loading/processing
│   └── training/         # Training loops
├── notebooks/            # Research notebooks
├── scripts/              # Training scripts
└── tests/                # Unit tests

🧪 Current Status

This is an active research project in early stages. Current focus:

• Implementing and testing base architecture
• Setting up data processing pipeline
• Designing initial experiments
• Building evaluation framework

📚 References

Key papers informing this work:

• "Diffusion Models Beat GANs on Image Synthesis" (Dhariwal & Nichol, 2021)
• "Structured Denoising Diffusion Models" (Sohl-Dickstein et al., 2015)
• "High-Resolution Image Synthesis with Latent Diffusion Models" (Rombach et al., 2022)

🤝 Contributing

We welcome research collaborations! Areas where we're looking for contributions:

• Novel architectural improvements
• Efficient training methodologies
• Evaluation metrics
• Dataset curation tools

📬 Contact

For research collaboration inquiries:

• Submit an issue
• Start a discussion
• Email: [email protected]

⚖️ License

This research code is released under the MIT License.

🔍 Citation

If you use this code in your research, please cite:

@misc{m1music2024,
  title={M1: Experimental Music Generation via Diffusion Transformers},
  author={M1 Research Team},
  year={2024},
  publisher={GitHub},
  journal={GitHub repository},
  howpublished={\url{https://github.com/Agora-Lab-AI/m1}}
}

🚧 Disclaimer

This is experimental research code:

• Architecture and training procedures may change significantly
• Not yet optimized for production use
• Results and capabilities are being actively researched
• Breaking changes should be expected

We're sharing this code to foster collaboration and advance the field of AI music generation research.