The fusion of AI and the arts has opened up new possibilities for exploring the essence of human creativity. Among the various forms of art, music holds a unique position due to its inherent structure and complexity. In recent years, Large Language Models (LLMs) like GPT-4, LLaMA, etc., have revolutionized text generation tasks with their impressive capabilities. However, their applications to music generation are limited. These models struggle with music and treat it as a text-limited ability. In this article, we will delve into how a new LLM ChatMusician addresses these issues with its musical capabilities, allowing it to understand and generate music.
Table of Contents
Introduction to ChatMusician
ChatMusician is an open-source Large Language Model (LLM) that aims to generate well-structured, full-length music while maintaining strong language abilities. Developed by researchers from the Multimodal Art Projection Research Community and Skywork AI PTE. LTD., ChatMusician introduces several novel techniques to imbue musical understanding and generation capabilities into LLMs.
How ChatMusician Works?
ChatMusician uses ABC notation to represent music, which offers higher compression versus MIDI and avoids issues like quantization. ABC notation intrinsically encodes repetition and structure through repeated symbols. It also retains detailed musical symbols for performance techniques. This ensures rhythmic precision in music generation.
Training Methodology of ChatMusician
ChatMusician is initialized from LLaMA and trained continuously on a custom text-music corpus called MusicPile consisting of 4.16B tokens. MusicPile includes music scores, knowledge, and summaries alongside language data. Continual pre-training and fine-tuning are done to integrate musical abilities while maintaining language skills.
Music Generation Capabilities of ChatMusician
ChatMusician can generate well-structured, coherent music conditioned on various factors like chords, melodies, motifs, forms and styles. It composes full-length musical pieces across diverse genres. It demonstrates strong control over musical elements and styles through conditional generation prompts. The dataset and model will soon be available.
Examples Generated Music by ChatMusician LLM
To check the music generation abilities of this groundbreaking LLM, please visit the project demo page. There, you can listen to a wide range of audio generated by this LLM based on the provided text input.
Performance Evaluation of ChatMusician
Extensive performance testing was conducted against popular LLMs to quantitatively evaluate ChatMusician’s abilities, which served as baselines for comparison.
1. Music Generation Abilities
Listeners in a qualitative study overwhelmingly preferred music generated by ChatMusician compared to GPT-4 and GPT-3.5 based on musicality criteria like consistency, structure and clear development. Human evaluators listened to pieces from various models in blind tests and preferred ChatMusician’s outputs in 76% of cases over GPT-4 baseline and over GPT-3.5 in 85% of cases for their musicality, coherence and structure. Additional automated metrics also supported ChatMusician’s superior generation abilities.
2. Music Understanding Abilities
To evaluate music understanding, researchers introduced the MusicTheoryBench benchmark consisting of 372 questions. In this zero-shot setting, ChatMusician outperformed GPT-3.5, GPT-4, and LLaMA2 in both the music knowledge and reasoning sections, validating the effectiveness of its training methodology.
Listeners in a qualitative study overwhelmingly preferred music generated by ChatMusician compared to GPT-4 and GPT-3.5 based on musicality criteria like consistency, structure and clear development.
3. General Language Abilities
The researchers used the Massive Multitask Language Understanding benchmark (MMLU) to measure general language skills. Under a 5-shot setting for a fair comparison, ChatMusician achieved a higher MMLU score than the baseline LLaMA2 model, demonstrating that musical fine-tuning did not negatively impact general language skills.
Conclusion
In conclusion, through its training methodology and use of ABC notation, ChatMusician is able to generate well-structured, coherent, full-length music while maintaining language abilities, surpassing popular baselines. It also demonstrates improved music understanding over baselines on the proposed benchmark. For more technical details, please visit the project arXiV paper.
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