OpenMusic
by Hugging FaceOpenMusic is a high-quality text-to-music model based on QA-MDT (Quality-aware Masked Diffusion Transformer) technology. It utilizes advanced AI algorithms to generate music from text descriptions. The model incorporates a quality-aware training strategy that ensures the generated music is musically rich, aligns with the text description, and maintains high fidelity. OpenMusic supports various music creation functions, including audio editing, processing, and recording. It is designed to assist musicians, content creators, and educators in generating music for diverse applications such as music production, multimedia content creation, and music therapy.
What is OpenMusic?
OpenMusic is a high-quality text-to-music model based on QA-MDT (Quality-aware Masked Diffusion Transformer) technology. It uses advanced AI algorithms to generate high-quality music from text descriptions. The model incorporates a quality-aware training strategy that ensures the generated music is musically rich, aligns with the text description, and maintains high fidelity.
Main Features of OpenMusic
- Text-to-Music Generation: Generates music that matches the user-provided text description.
- Quality Control: Identifies and enhances the quality of music during generation, ensuring high-fidelity output.
- Dataset Optimization: Improves the alignment between music and text through dataset preprocessing and optimization.
- Diverse Generation: Generates music in various styles to meet different user needs.
- Complex Reasoning: Performs complex multi-hop reasoning to handle multiple contextual information.
- Audio Editing and Processing: Provides functions for audio editing, processing, and recording.
Technical Principles of OpenMusic
- Masked Diffusion Transformer (MDT): Based on the Transformer architecture, it learns the latent representation of music by masking and predicting parts of the music signal, improving the accuracy of music generation.
- Quality-Aware Training: During training, it uses a quality scoring model (e.g., pseudo-MOS score) to evaluate the quality of music samples, ensuring the model generates high-quality music.
- Text-to-Music Generation: Based on natural language processing (NLP) technology, it parses text descriptions and converts them into music features, then generates music.
- Quality Control: During the generation phase, it guides the model to generate high-quality music based on the quality information learned during training.
- Music and Text Synchronization: Uses large language models (LLMs) and CLAP models to synchronize music signals with text descriptions, enhancing the consistency between text and audio.
- Function Calling and Proxy Capabilities: The model can actively search for knowledge in external tools and perform complex reasoning and strategies.
Project Address of OpenMusic
- HuggingFace Model Library: https://huggingface.co/jadechoghari/openmusic
Application Scenarios of OpenMusic
- Music Production: Assists musicians and composers in creating new music, providing creative inspiration or as a tool in the composition process.
- Multimedia Content Creation: Generates custom background music and sound effects for advertisements, movies, TV, video games, and online videos.
- Music Education: Serves as a teaching tool to help students understand music theory and composition techniques, or for music practice and improvisation.
- Audio Content Creation: Provides original music for podcasts, audiobooks, and other audio content, enhancing the auditory experience for listeners.
- Virtual Assistants and Smart Devices: Generates personalized music and sounds for smart home devices, virtual assistants, or other smart systems, improving user experience.
- Music Therapy: Generates music in specific styles to meet the needs of music therapy, helping to alleviate stress and anxiety.
Model Capabilities
Model Type
Text-to-Music
Supported Tasks
Text-To-Music Generation
Audio Editing
Audio Processing
Music Recording
Tags
Text-to-Music
AI Music
Audio Editing
Music Generation
Multimedia
Music Therapy
Content Creation
AI Algorithms
High-Fidelity Audio
Quality-Aware Training
Usage & Integration
Pricing
free
License
Open Source
Requirements
- Python 3.8+
- torch==2.3.0+cu121
- torchvision==0.18.0+cu121
- torchaudio==2.3.0
- xformers==0.0.26.post1
- torchlibrosa==0.0.9
- librosa==0.9.2
- pytorch_lightning==2.1.3
- ftfy==6.1.1
- braceexpand
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