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

• URL: http://arxiv.org/abs/2406.06251v1
• Date: Mon, 10 Jun 2024 13:31:18 GMT
• Title: Learning Fine-Grained Controllability on Speech Generation via Efficient Fine-Tuning
• Authors: Chung-Ming Chien, Andros Tjandra, Apoorv Vyas, Matt Le, Bowen Shi, Wei-Ning Hsu,
• Abstract summary: Voicebox Adapter is a novel approach that integrates fine-grained conditions into a pre-trained Voicebox speech generation model. Our experiment shows that the LoRA with bias-tuning configuration yields the best performance.
• Score: 43.43337861152684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the scale of generative models continues to grow, efficient reuse and adaptation of pre-trained models have become crucial considerations. In this work, we propose Voicebox Adapter, a novel approach that integrates fine-grained conditions into a pre-trained Voicebox speech generation model using a cross-attention module. To ensure a smooth integration of newly added modules with pre-trained ones, we explore various efficient fine-tuning approaches. Our experiment shows that the LoRA with bias-tuning configuration yields the best performance, enhancing controllability without compromising speech quality. Across three fine-grained conditional generation tasks, we demonstrate the effectiveness and resource efficiency of Voicebox Adapter. Follow-up experiments further highlight the robustness of Voicebox Adapter across diverse data setups.

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