Earlier this year, we spoke to Yuki Mitsufuji, Lead Research Scientist at Sony AI, about work concerning different aspects of image generation. Yuki and his team have since extended their work to sound generation, presenting work at ICLR 2025 entitled: SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation. We caught up with Yuki to find out more. Creating sounds for different types of multimedia, such as video games and movies, takes a lot of experimenting, as artists try to match sounds to their evolving creative ideas. New high-quality diffusion-based Text-to-Sound (T2S) generative models can help with this process, but they are often slow, which makes it harder for creators to experiment quickly.

Sound content is an indispensable element for multimedia works such as video games, music, and films. Recent high-quality diffusion-based sound generation models can serve as valuable tools for the creators. However, despite producing high-quality sounds, these models often suffer from slow inference speeds. This drawback burdens creators, who typically refine their sounds through trial and error to align them with their artistic intentions. To address this issue, we introduce Sound Consistency Trajectory Models (SoundCTM). Our model enables flexible transitioning between high-quality 1-step sound generation and superior sound quality through multi-step generation. This allows creators to initially control sounds with 1-step samples before refining them through multi-step generation. While CTM fundamentally achieves flexible 1-step and multi-step generation, its impressive performance heavily depends on an additional pretrained feature extractor and an adversarial loss, which are expensive to train and not always available in other domains. Thus, we reframe CTM's training framework and introduce a novel feature distance by utilizing the teacher's network for a distillation loss. Additionally, while distilling classifier-free guided trajectories, we train conditional and unconditional student models simultaneously and interpolate between these models during inference. We also propose training-free controllable frameworks for SoundCTM, leveraging its flexible sampling capability. SoundCTM achieves both promising 1-step and multi-step real-time sound generation without using any extra off-the-shelf networks. Furthermore, we demonstrate SoundCTM's capability of controllable sound generation in a training-free manner. Our codes, pretrained models, and audio samples are available at https://github.com/sony/soundctm.