#style transfer

The Unet-TTS project facilitates improved speaker and style transfer for one-shot voice cloning by utilizing the Unet network and AdaIN layer. This open-source project offers inferencing code and pre-trained models for generating diverse text-based audio. It addresses the complexity of out-of-domain style transfer using a neutral emotion corpus. The project automates duration statistics and supports multi-speaker TTS with a pre-trained Content Encoder, along with a detailed setup guide. Compatible with Linux and certain TensorFlow versions, implementation is accessible through both Python scripts and Colab notebooks.

Investigate the method of stylizing ImageNet to increase shape bias in CNNs for better performance. This project offers detailed tools for dataset transformation and model evaluation, supporting both PyTorch and TensorFlow frameworks. Generate in-depth reports to understand model behavior with stylized data and out-of-distribution performance. With a convenient Docker setup, this resource aids in creating Stylized-ImageNet and includes pre-trained CNNs for practical demonstration. Ideal for research focused on enhancing generalization in machine learning.

StyleShot offers a novel approach to style transfer without the need for test-time tuning, utilizing a style-aware encoder and the StyleGallery dataset. This method efficiently replicates styles like 3D, flat, and abstract, outperforming existing techniques. Available resources on platforms like HuggingFace increase its accessibility for AI researchers and developers.