Lakh MIDI Model with Rock Transfer Learning

Model Overview

Training Details

1. Base Training:
   • Dataset: Lakh MIDI Dataset (cleaned and filtered).
   • Training: The model was trained from scratch on this dataset to learn general musical structures and styles.
2. Transfer Learning:
   • Dataset: A subset of 500 rock MIDI compositions.
   • Epochs: 1 epoch.
   • Purpose: Fine-tuning the model to specialize in generating and understanding rock-specific musical patterns.

Files in the Repository

1. config.json:
   • Contains the configuration of the model architecture. This includes details such as the number of layers, hidden dimensions, attention heads, and other parameters used to define the model.
2. generation_config.json:
   • Contains generation-specific settings, such as maximum sequence length, temperature, top-k, and top-p sampling parameters. These configurations are crucial for controlling the behavior of the MIDI sequence generation process.
3. model.safetensors:
   • The model weights saved in the safetensors format for efficient and secure loading. This format ensures safe deserialization of model weights.
4. training_args.bin:
   • Stores the training arguments and hyperparameters used during both base training and transfer learning. This file can be useful for reproducing the training setup or understanding the specifics of the training process.

Dataset Details

Lakh MIDI Dataset

• Focus: General MIDI compositions across various genres.
• Cleaning Process: Removed duplicates, ensured proper formatting, and filtered out noisy data.

Rock MIDI Dataset (Transfer Learning)

• Focus: Rock genre-specific MIDI compositions.
• Size: 500 compositions.
• Epochs: 1 epoch.
• Purpose: Fine-tuning the model for improved rock music generation.

Usage

• General MIDI music generation.
• Specialized rock music generation tasks.
• Experimentation with transfer learning techniques in music AI.

Original Code Base

License