Intelligent Lyric Generation for Music: Techniques, Models, and Future Directions

The talk begins by highlighting the difficulty musicians face when writing lyrics, noting that over 33% of creators find lyric writing more challenging than composing melodies, and proposes AI‑driven solutions to reduce time and cost.

It outlines the lyric‑creation workflow, emphasizing the need for efficient rhyme retrieval and semantic word association. A novel 22‑type rhyme classification that combines phonetic and character features, coupled with word‑vector nearest‑neighbor search, is introduced to improve rhyme relevance.

To generate lyric content, the article discusses two language‑model paradigms: causal language models for sequential completion and masked language models for token‑level refinement, and explains why generic pretrained models (e.g., GPT‑2, BERT) struggle with lyric‑specific constraints such as fixed syllable patterns.

The SongNet model, originally presented at ACL 2020, is described with its intra‑position embeddings and format control codes that enforce rhyme and structural constraints, while also noting its limitations in fluency and fine‑grained tokenization.

Improvement strategies include a hybrid decoding approach that mixes probabilistic sampling in the embedding stage with deterministic beam search at output, reverse‑generation for rhyme accuracy, and using part‑of‑speech boundaries to approximate fine‑grained token patterns.

Style‑controlled lyric generation is achieved by decoupling style from content: statistical analysis of style‑specific token patterns informs a pipeline that first adjusts the lyric’s metric structure before feeding it to the generation model, enabling rapid style transfer across genres such as folk, rap, and ancient‑style.

All capabilities are packaged into the "BaiZe Lyric Intelligent Assistance Platform," integrated with Tencent Music’s ecosystem, and organized into four AI‑assisted levels (L1–L4) ranging from basic rhyme assistance to fully autonomous multimodal song creation.

The presentation concludes with a forward‑looking outlook on expanding multimodal techniques to achieve near‑complete AI‑driven music production.