With the advent of the big data and large language model era, zero-shot personalized rapid customization has emerged as a significant trend. In this report, we introduce Takin AudioLLM, a series of techniques and models, mainly including Takin TTS, Takin VC, and Takin Morphing, specifically designed for audiobook production. These models are capable of zero-shot speech production, generating high-quality speech that is nearly indistinguishable from real human speech and facilitating individuals to customize the speech content according to their own needs. Specifically, we first introduce Takin TTS, a neural codec language model that builds upon an enhanced neural speech codec and a multi-task training framework, capable of generating high-fidelity natural speech in a zero-shot way. For Takin VC, we advocate an effective content and timbre joint modeling approach to improve the speaker similarity, while advocating for a conditional flow matching based decoder to further enhance its naturalness and expressiveness. Last, we propose the Takin Morphing system with highly decoupled and advanced timbre and prosody modeling approaches, which enables individuals to customize speech production with their preferred timbre and prosody in a precise and controllable manner. Extensive experiments validate the effectiveness and robustness of our Takin AudioLLM series models. For detailed demos, please refer to https://everest-ai.github.io/takinaudiollm/.

翻译：随着大数据与大语言模型时代的到来，零样本个性化快速定制已成为一个重要趋势。本报告介绍了Takin AudioLLM，这是一系列专门为有声书制作设计的技术与模型，主要包括Takin TTS、Takin VC和Takin Morphing。这些模型能够进行零样本语音生成，产生与真实人类语音几乎无法区分的高质量语音，并支持个人根据自身需求定制语音内容。具体而言，我们首先介绍Takin TTS，这是一种基于增强型神经语音编解码器与多任务训练框架的神经编解码语言模型，能够以零样本方式生成高保真度的自然语音。对于Takin VC，我们提出了一种有效的内容与音色联合建模方法以提高说话人相似度，同时倡导采用基于条件流匹配的解码器来进一步增强其自然度与表现力。最后，我们提出了Takin Morphing系统，该系统采用高度解耦且先进的音色与韵律建模方法，使个人能够以精确可控的方式，使用其偏好的音色与韵律定制语音生成。大量实验验证了我们Takin AudioLLM系列模型的有效性与鲁棒性。详细演示请访问 https://everest-ai.github.io/takinaudiollm/。