While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language. ChatMusician can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score. Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc, surpassing GPT-4 baseline. On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs can be an excellent compressor for music, but there remains significant territory to be conquered. We release our 4B token music-language corpora MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub.

翻译：虽然大语言模型（LLMs）在文本生成方面展现出令人印象深刻的能力，但我们发现其能力尚未泛化至音乐这一人类创造性语言。我们提出ChatMusician，一个集成内在音乐能力的开源大语言模型。该模型基于文本兼容的音乐表示法ABC记谱法对LLaMA2进行持续预训练和微调，将音乐视为第二语言处理。ChatMusician无需任何外部多模态神经结构或分词器，仅凭纯文本分词器即可理解与生成音乐。有趣的是，赋予音乐能力不会损害语言能力，甚至使MMLU评分略有提升。该模型能够基于文本、和弦、旋律、动机、曲式等条件创作结构完整、全长度的音乐作品，超越GPT-4基线。在我们精心构建的大学水平音乐理解基准MusicTheoryBench上，ChatMusician在零样本设置下显著超越LLaMA2和GPT-3.5。本研究表明，大语言模型可作为优秀的音乐压缩器，但仍有广阔空间尚待探索。我们在GitHub上发布了包含4B词元的音乐-语言语料库MusicPile、整理的MusicTheoryBench基准、代码、模型及演示。