Text-to-Audio and Text-to-Music Innovations
Recent advancements in Text-to-Audio (TTA) and Text-to-Music (TTM) technologies have been driven by new audio models. These models outperform older methods like GANs and VAEs in creating high-quality audio. However, they struggle with long processing times, taking between 5 to 20 seconds for each operation, which limits their use in real-time applications.
Challenges and Solutions
Current methods to improve TTA and TTM mainly focus on autoregressive techniques and diffusion models. While diffusion methods excel in generating detailed audio, their slow speed is a major drawback for interactive use. Techniques like step distillation aim to speed up the process by reducing the number of steps needed. However, these methods often fall short for longer or higher-quality audio.
Introducing Presto!
Researchers from UC San Diego and Adobe have developed Presto!, a groundbreaking method that speeds up TTM generation. Presto! reduces processing time by minimizing the number of sampling steps and costs associated with each step. It features a unique score-based distribution matching distillation technique, the first of its kind for TTM, enhancing efficiency significantly.
How Presto! Works
Presto! uses a latent diffusion model to create high-quality audio. It generates mono audio at 44.1kHz, which is then converted to stereo. The model is trained on a large dataset of instrumental music and employs various techniques to improve audio quality. Performance is evaluated using metrics that measure audio quality and adherence to text prompts.
Performance Highlights
Presto! comes in two versions: Presto-S and Presto-L. Presto-L outperforms baseline models, achieving a 27% increase in speed while improving audio quality. Presto-S also excels, providing a 15 times speedup while maintaining high audio quality. Together, they achieve impressive latencies, making them significantly faster than existing solutions.
Future Directions
The researchers hope that Presto! will inspire further innovations in AI audio generation by merging different distillation techniques for better performance across various media.
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