Parallel Audio Generation

Parallel audio generation aims to create high-quality audio significantly faster than traditional autoregressive methods by generating multiple audio segments concurrently. Current research focuses on developing efficient model architectures, such as transformer-based models and those employing masked language modeling techniques, to improve both speed and audio fidelity. These advancements leverage large datasets and refined training strategies, leading to substantial improvements in generation speed and quality compared to autoregressive baselines, with implications for applications requiring real-time or high-throughput audio synthesis.