风格TTS 2:通过风格扩散和使用大型语音语言模型进行对抗性训练,实现人类级别的文本到语音
Yinghao Aaron Li1, Cong Han1, Vinay S Raghavan1
1Columbia University.
Advances in neural information processing systems
|January 27, 2025
概括
StyleTTS 2通过使用大语音语言模型 (SLM) 的风格扩散和对抗训练来实现人类级别的文本到语音 (TTS) 合成. 这种新的方法产生了自然的声音,没有参考音频,超越现有的模型.
科学领域:
- 人工智能的人工智能
- 语音技术 语言技术
- 机器学习 机器学习
背景情况:
- 文本转化为语音 (TTS) 合成旨在从文本中生成类似人类语音.
- 以前的TTS模型通常需要参考语音来控制风格或缺乏自然性.
- 大型语音语言模型 (SLMs) 在提高语音合成质量方面表现有前途.
研究的目的:
- 开发一种新的文字转化为语音 (TTS) 模型,StyleTTS 2,能够进行人类级别的语音合成.
- 在TTS中提高语音自然性和风格控制,而不需要参考语音.
- 为了证明风格传播和对抗训练的有效性,在TTS中与SLM合作.
主要方法:
- StyleTTS 2使用风格扩散来模拟语音风格作为潜在变量,从而实现无需引用音频的多样化合成.
- 该模型将大型预先训练有素的SLM (例如,WavLM) 作为对抗训练的歧视者.
- 新的可差值持续时间建模用于端到端训练,增强语音自然性.
主要成果:
- 在单扬声器LJSpeech数据集上,StyleTTS 2实现了人类水平的性能,超过了人类记录.
- 该模型与多音箱VCTK数据集上的人类表现相匹配.
- 与之前的模型相比,StyleTTS 2在LibriTTS数据集上展示了优异的零射击扬声器适应性能.
结论:
- StyleTTS 2代表了TTS的重大进步,在单个和多音箱数据集上实现了第一个人类级别的合成.
- 风格传播和对抗训练与大型SLM的结合对自然和可控制的TTS非常有效.
- 该模型的音频演示和源代码的开放可用性有助于在语音合成中进一步的研究和应用.
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