NanoVoice: Efficient Speaker-Adaptive Text-to-Speech for Multiple Speakers
<|reference_start|>NanoVoice: Efficient Speaker-Adaptive Text-to-Speech for Multiple Speakers: We present NanoVoice, a personalized text-to-speech model that efficiently constructs voice adapters for multiple speakers simultaneously. NanoVoice introduces a batch-wise speaker adaptation technique capable of fine-tuning multiple references in parallel, significantly reducing training time. Beyond building separate adapters for each speaker, we also propose a parameter sharing technique that reduces the number of parameters used for speaker adaptation. By incorporating a novel trainable scale matrix, NanoVoice mitigates potential performance degradation during parameter sharing. NanoVoice achieves performance comparable to the baselines, while training 4 times faster and using 45 percent fewer parameters for speaker adaptation with 40 reference voices. Extensive ablation studies and analysis further validate the efficiency of our model.<|reference_end|>
@article{park2024nanovoice:,
    title={NanoVoice: Efficient Speaker-Adaptive Text-to-Speech for Multiple
  Speakers},
    author={Nohil Park, Heeseung Kim, Che Hyun Lee, Jooyoung Choi, Jiheum Yeom,
  Sungroh Yoon},
    journal={arXiv preprint arXiv:2409.15760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15760},
    primaryClass={cs.SD eess.AS}
}