Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis
This addresses a practical bottleneck for researchers and practitioners using zero-shot speech synthesis models by eliminating the need for manual hyper-parameter tuning, though it is incremental as it builds on existing VITS-based methods.
The paper tackled the problem of tuning loss trade-offs in zero-shot speech synthesis models, which previously required burdensome hyper-parameter search, by proposing a framework that automatically finds the optimal balance without search, achieving state-of-the-art performance in zero-shot TTS and VC.
Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot modifications of the VITS model have shown superior performance, while having useful properties inherited from VITS. However, the performance of VITS and VITS-based zero-shot models vary dramatically depending on how the losses are balanced. This can be problematic, as it requires a burdensome procedure of tuning loss balance hyper-parameters to find the optimal balance. In this work, we propose a novel framework that finds this optimum without search, by inducing the decoder of VITS-based models to its full reconstruction ability. With our framework, we show superior performance compared to baselines in zero-shot TTS and VC, achieving state-of-the-art performance. Furthermore, we show the robustness of our framework in various settings. We provide an explanation for the results in the discussion.