We present SongComposer, an innovative LLM designed for song composition. It
could understand and generate melodies and lyrics in symbolic song
representations, by leveraging the capability of LLM. Existing music-related
LLM treated the music as quantized audio signals, while such implicit encoding
leads to inefficient encoding and poor flexibility. In contrast, we resort to
symbolic song representation, the mature and efficient way humans designed for
music, and enable LLM to explicitly compose songs like humans. In practice, we
design a novel tuple design to format lyric and three note attributes (pitch,
duration, and rest duration) in the melody, which guarantees the correct LLM
understanding of musical symbols and realizes precise alignment between lyrics
and melody. To impart basic music understanding to LLM, we carefully collected
SongCompose-PT, a large-scale song pretraining dataset that includes lyrics,
melodies, and paired lyrics-melodies in either Chinese or English. After
adequate pre-training, 10K carefully crafted QA pairs are used to empower the
LLM with the instruction-following capability and solve diverse tasks. With
extensive experiments, SongComposer demonstrates superior performance in
lyric-to-melody generation, melody-to-lyric generation, song continuation, and
text-to-song creation, outperforming advanced LLMs like GPT-4.

SongComposer 是一种创新的基于 LLM 的歌曲作曲技术，通过利用 LLM 的能力来理解和生成具有象征性歌曲表示的旋律和歌词，通过符号化歌曲表示的方式，使 LLM 能够像人类一样明确地作曲，通过 SongCompose-PT 预训练数据集的收集和充分的预训练，SongComposer 在歌词到旋律生成、旋律到歌词生成、歌曲延续和文本到歌曲创作等领域展现出优越的性能。