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arXiv · 2406.16314

DreamVoice: Text-Guided Voice Conversion

Jiarui Hai·Karan Thakkar·Helin Wang·Zengyi Qin·Mounya Elhilali
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Abstract

Generative voice technologies are rapidly evolving, offering opportunities for more personalized and inclusive experiences. Traditional one-shot voice conversion (VC) requires a target recording during inference, limiting ease of usage in generating desired voice timbres. Text-guided generation offers an intuitive solution to convert voices to desired "DreamVoices" according to the users' needs. Our paper presents two major contributions to VC technology: (1) DreamVoiceDB, a robust dataset of voice timbre annotations for 900 speakers from VCTK and LibriTTS. (2) Two text-guided VC methods: DreamVC, an end-to-end diffusion-based text-guided VC model; and DreamVG, a versatile text-to-voice generation plugin that can be combined with any one-shot VC models. The experimental results demonstrate that our proposed methods trained on the DreamVoiceDB dataset generate voice timbres accurately aligned with the text prompt and achieve high-quality VC.

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Jiarui Hai, Karan Thakkar, Helin Wang, Zengyi Qin, Mounya Elhilali. 2024-06-24. DreamVoice: Text-Guided Voice Conversion. https://arxiv.org/abs/2406.16314

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