HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models

• URL: http://arxiv.org/abs/2306.06814v1
• Date: Mon, 12 Jun 2023 01:21:41 GMT
• Title: HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models
• Authors: Ji-Sang Hwang, Sang-Hoon Lee, and Seong-Whan Lee
• Abstract summary: We propose HiddenSinger, a high-quality singing voice synthesis system using neural audio and latent diffusion models. In addition, our proposed model is extended to an unsupervised singing voice learning framework, HiddenSinger-U, to train the model. Experimental results demonstrate that our model outperforms previous models in terms of audio quality.
• Score: 25.966328901566815
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, denoising diffusion models have demonstrated remarkable performance among generative models in various domains. However, in the speech domain, the application of diffusion models for synthesizing time-varying audio faces limitations in terms of complexity and controllability, as speech synthesis requires very high-dimensional samples with long-term acoustic features. To alleviate the challenges posed by model complexity in singing voice synthesis, we propose HiddenSinger, a high-quality singing voice synthesis system using a neural audio codec and latent diffusion models. To ensure high-fidelity audio, we introduce an audio autoencoder that can encode audio into an audio codec as a compressed representation and reconstruct the high-fidelity audio from the low-dimensional compressed latent vector. Subsequently, we use the latent diffusion models to sample a latent representation from a musical score. In addition, our proposed model is extended to an unsupervised singing voice learning framework, HiddenSinger-U, to train the model using an unlabeled singing voice dataset. Experimental results demonstrate that our model outperforms previous models in terms of audio quality. Furthermore, the HiddenSinger-U can synthesize high-quality singing voices of speakers trained solely on unlabeled data.

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