Everyone-Can-Sing: Zero-Shot Singing Voice Synthesis and Conversion with Speech Reference

In the realm where music meets machine intelligence, Everyone-Can-Sing strikes a bold chord. This groundbreaking research introduces a unified framework for zero-shot singing voice synthesis (SVS) and singing voice conversion (SVC)—capable of generating expressive, high-quality singing from just a 5-second speech reference.

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Traditional SVS models stumble when asked to perform across domains or synthesize voices they’ve never “heard” before. This new approach, however, leverages disentangled representations—separating voice timbre, pronunciation, and performance style—to orchestrate realistic and musically rich vocals. It allows fine-grained control over lyrics, vocal techniques, genre, and timbre, opening the stage to unprecedented creative possibilities.

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The framework features one SVS model and two SVC models, using a diffusion-based generator trained on both speech and singing data. Even without large singing datasets, it performs with grace and accuracy, thanks to clever use of expressive performance attributes and mixed training strategies.

Empirical results demonstrate significant gains in voice similarity and musicality compared to state-of-the-art models, all while using minimal voice reference data. This isn’t just a technical leap—it’s a vision for a future where everyone, indeed, can sing.

Watch the demo here

Mastering the AI Lifecycle: A Strategic Imperative in the Age of Intelligent Transformation

In a world where Artificial Intelligence no longer whispers promises but boldly transforms industries, AI Lifecycle Management emerges as the compass guiding us through this intelligent frontier. It’s not enough to build AI—we must nurture it, govern it, and evolve it with intention.

From the spark of an idea to a model’s real-world impact, AI Lifecycle Management is the structured journey that ensures AI systems are not only effective but also ethical, adaptable, and compliant. As new regulations like the EU AI Act redefine what responsible AI looks like, organizations are called to do more than innovate—they must steward their AI with transparency, oversight, and care.

This comprehensive framework spans the entire arc of AI development: from problem scoping and data preparation to modeling, deployment, and continuous monitoring. Along the way, it empowers teams to mitigate risks, align with regulatory mandates, enhance explainability, and build trust in AI-powered systems.

But this isn’t a solitary path. Enter MLOps—the operational backbone that bridges data science, development, and IT. Through automation, version control, and real-time monitoring, MLOps ensures models are not only built fast but built right—and sustained long after they’re launched.

In embracing a structured AI lifecycle, organizations unlock a wealth of benefits: faster delivery, higher model quality, cross-functional collaboration, regulatory alignment, and long-term cost savings. It’s more than a process—it’s a strategic advantage, a moral compass, and a shield against the unknowns of tomorrow.

🌟 As we stand at the threshold of AI’s next revolution, AI Lifecycle Management invites us to lead not just with code—but with wisdom, foresight, and integrity.