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.

本研究对 HuggingFace 平台上可用的工具进行了全面评估，旨在识别人工智能中两个关键应用领域：图像分割和语音转换。通过安装和配置 Linux 系统上的顶级三个工具，我们利用了预训练的分割模型和语音转换模型，在实施过程中遇到的方法和挑战进行了详细讨论，并展示了自动视觉融合套件项目中视频分割和语音转换的成功结合。