Audio Pre Training

Audio pre-training leverages self-supervised learning to create robust and generalizable audio representations from massive datasets, aiming to improve downstream tasks like speech recognition, music understanding, and video-to-speech synthesis. Current research focuses on developing effective pre-training strategies, including masked prediction and utilizing transformer-based architectures, often incorporating teacher models or iterative training to refine acoustic tokenizers. These advancements significantly enhance the performance of various audio-related applications by providing high-quality, pre-trained models that can be fine-tuned for specific tasks, reducing the need for extensive task-specific training data.