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Accompanied Singing Voice Synthesis with Fully Text-controlled Melody
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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.
Forward citations
Cited by 2 Pith papers
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MeloCodec: Harnessing Melodic Priors for High-Fidelity Singing Voice Representation
MeloCodec quantizes chromagram-derived melody tokens and fuses them with acoustic tokens using a two-stage training scheme, improving pitch consistency and enabling controllable pitch shifting in a singing-voice codec.
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DiffRhythm+: Controllable and Flexible Full-Length Song Generation with Preference Optimization
DiffRhythm+ improves full-length lyric-to-song generation via balanced data scaling, MuLan-based multimodal style control, and DPO fine-tuning guided by automated aesthetic scorers.
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