Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers
Abstract
While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren facilitates a promising pathway toward unified multi-modal generation frameworks.
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 the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
Methodology
The proposed methodology introduces a novel framework, Siren, which utilizes multiple isolated transformers with causal conditioning and anti-causal alignment. This approach effectively addresses the limitations of existing RVQ tokenizers in T2A generation by mitigating gradient conflicts and enhancing audio reconstruction fidelity. The use of reinforcement learning for alignment is innovative, although the complexity of the architecture may pose challenges for implementation and scalability.
Results & evaluation
The experiments are extensive and demonstrate that Siren outperforms both existing LM-based and diffusion-based systems, achieving state-of-the-art results. However, the paper mentions the use of a curated dataset smaller than those in prior work, which raises questions about the generalizability of the results. The evaluation metrics, particularly in terms of fidelity, are well-defined, but further comparisons with a broader range of benchmarks would strengthen the findings.
Reproducibility
The paper provides a GitHub repository link for the implementation, which is crucial for reproducibility. However, details on the training process, hyperparameters, and specific datasets used are somewhat limited, which could hinder replication efforts by other researchers.
Limitations
The authors acknowledge several limitations, including training efficiency due to the sequential training of transformer modules, the trade-off between model size and semantic richness, and the need for larger, more diverse datasets. Addressing these limitations in future work will be essential for advancing the field.
Broader impact
The work has significant implications for multi-modal generation frameworks, potentially enabling more cohesive integration of audio and text. By repositioning LMs as competitive in T2A tasks, it opens pathways for applications in content creation, gaming, and accessibility technologies. The main contribution of this paper is the introduction of the Siren framework, which effectively bridges the gap between language models and diffusion models in text-to-audio generation. The comprehensive analysis of the methodology and experimental results highlights its potential to reshape the landscape of audio synthesis, making it a notable advancement in the field.
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