Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis
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.
Code (1)
Tasks
DecoderSpeech SynthesisSimilar Papers 제목 키워드 기반
Trade-offs in Image Generation: How Do Different Dimensions Interact?
Model performance in text-to-image (T2I) and image-to-image (I2I) generation often depends on multiple aspects, including quality, alignment, diversity, and robustness. However, models' complex trade-offs among these dim…
Image GenerationFundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models
Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs. However, it has been empirically observed that this approach to enhancing capability inevitably compromises safety, …
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong tas…
Bayesian OptimisationNeural Architecture Searchparameter-efficient fine-tuningExploring Variability in Fine-Tuned Models for Text Classification with DistilBERT
This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the inf…
regressionSST-2text-classificationText ClassificationNavigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models
Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks. However, real-world fine-tuning faces inherent conflicts between model providers' intellectual property …
Privacy Preserving