Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt
원문 초록
Recent singing-voice-synthesis (SVS) methods have achieved remarkable audio quality and naturalness, yet they lack the capability to control the style attributes of the synthesized singing explicitly. We propose Prompt-Singer, the first SVS method that enables attribute controlling on singer gender, vocal range and volume with natural language. We adopt a model architecture based on a decoder-only transformer with a multi-scale hierarchy, and design a range-melody decoupled pitch representation that enables text-conditioned vocal range control while keeping melodic accuracy. Furthermore, we explore various experiment settings, including different types of text representations, text encoder fine-tuning, and introducing speech data to alleviate data scarcity, aiming to facilitate further research. Experiments show that our model achieves favorable controlling ability and audio quality. Audio samples are available at http://prompt-singer.github.io .
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
Making Language Model a Hierarchical Classifier and Generator
Attributes Shape the Embedding Space of Face Recognition Models
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
토론
이 논문과 연결된 포럼 글이 아직 없습니다.