This study presents a comprehensive evaluation of tools available on the HuggingFace platform for two pivotal applications in artificial intelligence: image segmentation and voice conversion. The primary objective was to identify the top three tools within each category and subsequently install and configure these tools on Linux systems. We leveraged the power of pre-trained segmentation models such as SAM and DETR Model with ResNet-50 backbone for image segmentation, and the so-vits-svc-fork model for voice conversion. This paper delves into the methodologies and challenges encountered during the implementation process, and showcases the successful combination of video segmentation and voice conversion in a unified project named AutoVisual Fusion Suite.

翻译：本研究对HuggingFace平台上可用于人工智能两大关键应用——图像分割与语音转换的工具进行了全面评估。主要目标在于识别每类工具中的前三名，并在Linux系统上安装和配置这些工具。我们利用预训练分割模型（如基于ResNet-50骨干网络的SAM和DETR模型）进行图像分割，采用so-vits-svc-fork模型实现语音转换。本文深入探讨了实施过程中所采用的方法及遇到的挑战，并展示了视频分割与语音转换在一个统一项目——AutoVisual Fusion Suite中的成功结合。