ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling
Abstract
Text-to-audio (TTA) generation with fine-grained control signals, e.g., precise timing control or intelligible speech content, has been explored in recent works. However, constrained by data scarcity, their generation performance at scale is still compromised. In this study, we recast controllable TTA generation as a multi-task learning problem and introduce a progressive diffusion modeling approach, ControlAudio. Our method adeptly fits distributions conditioned on more fine-grained information, including text, timing, and phoneme features, through a step-by-step strategy. First, we propose a data construction method spanning both annotation and simulation, augmenting condition information in the sequence of text, timing, and phoneme. Second, at the model training stage, we pretrain a diffusion transformer (DiT) on large-scale text-audio pairs, achieving scalable TTA generation, and then incrementally integrate the timing and phoneme features with unified semantic representations, expanding controllability. Finally, at the inference stage, we propose progressively guided generation, which sequentially emphasizes more fine-grained information, aligning inherently with the coarse-to-fine sampling nature of DiT. Extensive experiments show that ControlAudio achieves state-of-the-art performance in terms of temporal accuracy and speech clarity, significantly outperforming existing methods on both objective and subjective evaluations. Demo samples are available at: https://control-audio.github.io/Control-Audio.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of ControlAudio, a progressive diffusion modeling approach that significantly enhances text-to-audio generation by integrating fine-grained control signals for timing and intelligibility, thereby setting a new standard for performance in this domain. The comprehensive analysis of the technical contributions, methodology, and significance to the field underscores the potential of this work to advance the state-of-the-art in audio generation.
Methodology
The methodology presented in ControlAudio is innovative in its approach to tackle the challenges of text-to-audio generation with fine-grained control over timing and intelligibility. The authors effectively recast the problem as a multi-task learning scenario and utilize a progressive diffusion model that integrates various control signals in a structured manner. The data construction method is particularly noteworthy, as it combines both annotation and simulation to create a rich dataset for training, which addresses the data scarcity issue prevalent in previous works. The structured prompt design for encoding text, timing, and phoneme features is a significant advancement that enhances the model's ability to generate coherent and contextually relevant audio outputs.
Results & evaluation
The experiments conducted are extensive and robust, demonstrating the effectiveness of ControlAudio across various benchmarks. The paper reports both objective and subjective evaluations, showing significant improvements in temporal accuracy and speech clarity compared to existing methods. The use of multiple datasets for evaluation strengthens the findings, and the ablation studies provide insights into the contributions of different components of the model. However, the lack of a clearly defined baseline for comparison in some cases may limit the interpretability of the results.
Reproducibility
The paper provides a detailed description of the model architecture, training procedures, and datasets used, which is essential for reproducibility. However, the absence of a publicly available code repository limits the ability of other researchers to replicate the results fully. The authors mention the use of various datasets but do not provide explicit access to all datasets used, which could hinder reproducibility efforts.
Limitations
The paper acknowledges several limitations, including the lack of explicit mechanisms to manipulate stylistic attributes such as emotion and prosody. Additionally, the model's performance is constrained by the availability of high-quality, richly annotated datasets, which are still scarce. The potential trade-off between generating high-quality general audio versus intelligible speech is another concern that may affect the model's versatility in complex scenarios.
Broader impact
The advancements made in controllable TTA generation have significant implications for various applications, including film production, gaming, and virtual reality, where high-quality audio generation is crucial. However, the potential for misuse in creating deceptive content or voice impersonations raises ethical concerns that need to be addressed through robust detection methods and responsible AI governance. The work highlights the importance of developing technologies that balance innovation with ethical considerations. The main contribution of this paper is the introduction of ControlAudio, a progressive diffusion modeling approach that significantly enhances text-to-audio generation by integrating fine-grained control signals for timing and intelligibility, thereby setting a new standard for performance in this domain. The comprehensive analysis of the technical contributions, methodology, and significance to the field underscores the potential of this work to advance the state-of-the-art in audio generation.
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