Learning Fine-Grained Controllability on Speech Generation via Efficient Fine-Tuning

In this work, we propose Voicebox Adapter, Our contributions are as follows: (1) we propose Voicebox a novel approach that integrates fine-grained conditions into a Adapter, which augments Voicebox, a pre-trained speech pre-trained Voicebox speech generation model using a crossattention generation model, with fine-grained controllability; (2) we explore module. To ensure a smooth integration of newly different efficient fine-tuning methods to bridge the gap added modules with pre-trained ones, we explore various efficient between pre-trained parameters and new fine-grained conditioning fine-tuning approaches. Our experiment shows that the modules; (3) we show that Voicebox Adapter can generalize LoRA with bias-tuning configuration yields the best performance, across various fine-grained conditions, attaining performance enhancing controllability without compromising speech comparable to that achieved by fine-tuning the entire model quality. Across three fine-grained conditional generation tasks, with significantly fewer fine-tuned parameters; (4) we conduct we demonstrate the effectiveness and resource efficiency of experiments using varying amounts of fine-tuning data and different Voicebox Adapter. Follow-up experiments further highlight the hidden dimension sizes, analyzing the performance of robustness of Voicebox Adapter across diverse data setups.