Generation of Singing Voices

The field of Text-to-Speech (TTS) technology, while historically providing functional outcomes for a considerable time, has seen a significant leap in quality with the recent surge in AI advancements. Based on diffusion or flow matching models, speech generation has matured from a basic level to the generation of remarkably natural sounding voices, even allowing known voices to be imitated. To date, the HuggingFace model hub hosts more than 1,500 TTS models, underscoring the vast amount of work and research that has been invested into this domain. Elevating from speech to singing voice generation elevates the problem to a whole new level, as it now is not only required to yield a natural sounding voice, but it also demands the alignment with precise pitch patterns and synchronization with specific rhythms or musical accompaniments.
Navigating the landscape of available pre-trained models or model architectures, and picking the right model for a specific use case, thus require detailed knowledge about the models’ unique characteristics and benefits. However, use cases of singing voice generation go beyond the plain production of music. It holds significant potential in fields such as musical education, where it can serve as a powerful tool for teaching and learning, offering innovative ways to engage with music and improve musical skills.