ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation

• URL: http://arxiv.org/abs/2309.10740v3
• Date: Mon, 24 Jun 2024 06:51:55 GMT
• Title: ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
• Authors: Yatong Bai, Trung Dang, Dung Tran, Kazuhito Koishida, Somayeh Sojoudi,
• Abstract summary: Diffusion models suffer from slow inference due to an excessive number of queries to the underlying denoising network per generation. We introduce ConsistencyTTA, a framework requiring only a single non-autoregressive network query. We achieve so by proposing "CFG-aware latent consistency model," which adapts consistency generation into a latent space.
• Score: 21.335983674309475
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models are instrumental in text-to-audio (TTA) generation. Unfortunately, they suffer from slow inference due to an excessive number of queries to the underlying denoising network per generation. To address this bottleneck, we introduce ConsistencyTTA, a framework requiring only a single non-autoregressive network query, thereby accelerating TTA by hundreds of times. We achieve so by proposing "CFG-aware latent consistency model," which adapts consistency generation into a latent space and incorporates classifier-free guidance (CFG) into model training. Moreover, unlike diffusion models, ConsistencyTTA can be finetuned closed-loop with audio-space text-aware metrics, such as CLAP score, to further enhance the generations. Our objective and subjective evaluation on the AudioCaps dataset shows that compared to diffusion-based counterparts, ConsistencyTTA reduces inference computation by 400x while retaining generation quality and diversity.

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