# Zero-shot

Spark-TTS is a highly efficient text-to-speech synthesis model based on large language models, featuring single-stream decoupled speech tokens. Leveraging the power of large language models, it directly reconstructs audio predicted from code, omitting the additional acoustic feature generation model, thus improving efficiency and reducing complexity. This model supports zero-shot text-to-speech synthesis, enabling cross-lingual and code-switching scenarios, making it ideal for speech synthesis applications requiring high naturalness and accuracy. It also supports virtual voice creation; users can generate different voices by adjusting parameters such as gender, pitch, and speaking rate. The model aims to address the inefficiencies and complexities of traditional speech synthesis systems, providing a highly efficient, flexible, and powerful solution for research and production. Currently, the model is primarily intended for academic research and legitimate applications such as personalized speech synthesis, assistive technologies, and language research.

Binoculars is an advanced AI-generated text detection tool that can be used zero-configuration without any training data. Its detection approach is straightforward and clear: most因果 language models that use only the decoder have used large amounts of identical datasets for pre-training, such as Common Crawl and Pile. More information about this method and its effectiveness can be found in our paper 'Finding LLMs with Binoculars: Zero-shot Detection of Machine-generated Text'.