高质量的文本到语音实现通过主动浅扩散机制.
Junlin Deng1, Ruihan Hou1, Yan Deng2
1Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究介绍了Cascaded MixGAN-TTS (CMG-TTS),这是一种用于快速文本转化为语音 (TTS) 合成的新型两阶段扩散模型. CMG-TTS通过单一的无声化步骤实现实时性能,优于传统的扩散模型.
科学领域:
- 语音合成 语音合成
- 深度学习 (Deep Learning) 是一种深度学习.
- 概率模型可能模型
背景情况:
- 拒绝扩散概率模型 (DDPMs) 对文本转语音 (TTS) 显示出希望.
- 由于广泛的采样要求,传统的扩散模型在实时应用中面临着挑战.
研究的目的:
- 提出一种基于扩散的新,高效和快速推断的TTS模型.
- 通过扩散模型实现实时TTS合成.
主要方法:
- 引入了级混合GAN-TTS (CMG-TTS),一种两阶段的扩散模型.
- 在分阶段培训中采用了活跃的浅层扩散机制.
- 使用混合组合机制语言编码器与音调和能量预测器.
- 整合了一个后网来优化mel-spectrogram重建.
主要成果:
- 在CMG-TTS的研究中,只用一个否定的步骤实现了令人满意的主观和客观评估指标.
- 与其他基于扩散的TTS模型相比,在实时因子 (RTF) 中表现出领先的性能.
- 废弃研究证实了CMG-TTS架构中的两个阶段的有效性.
结论:
- CMG-TTS为基于扩散的TTS提供了一种高效快速的解决方案.
- 拟议的两阶段方法显著提高了TTS的扩散模型的推断速度.
- CMG-TTS在实现实时,高质量的语音合成方面取得了重大进展.
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