  Text-to-song (TTSong) is a music generation task that synthesizes accompanied
singing voices. Current TTSong methods, inherited from singing voice synthesis
(SVS), require melody-related information that can sometimes be impractical,
such as music scores or MIDI sequences. We present MelodyLM, the first TTSong
model that generates high-quality song pieces with fully text-controlled
melodies, achieving minimal user requirements and maximum control flexibility.
MelodyLM explicitly models MIDI as the intermediate melody-related feature and
sequentially generates vocal tracks in a language model manner, conditioned on
textual and vocal prompts. The accompaniment music is subsequently synthesized
by a latent diffusion model with hybrid conditioning for temporal alignment.
With minimal requirements, users only need to input lyrics and a reference
voice to synthesize a song sample. For full control, just input textual prompts
or even directly input MIDI. Experimental results indicate that MelodyLM
achieves superior performance in terms of both objective and subjective
metrics. Audio samples are available at https://melodylm666.github.io.
