Khala: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation
Abstract
A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first models high-level structure, followed by diffusion-based or neural decoding stages that reconstruct fine details. In this work, we explore an alternative view: both may be progressively modeled within a single deep acoustic-token hierarchy. To study this, we build a 64-layer residual vector quantization (RVQ) acoustic representation and propose a two-stage coarse-to-fine generation framework. A backbone model first generates coarse acoustic tokens for the full track, and a super-resolution model then completes finer tokens within the same acoustic token space. The super-resolution stage works at full-track scale and refines tokens layer by layer while running in parallel over time, leading to a fixed 62-step inference process. To jointly improve lyric alignment and fine-detail reconstruction, we further introduce hybrid-attention training: the alignment objective uses causal attention, while layer-wise refinement uses full attention. A key finding is that text--vocal alignment can emerge within pure acoustic-token language modeling, without requiring a separate semantic token stage. Moreover, initializing the super-resolution model from the trained backbone significantly improves convergence and final quality. Taken together, our results suggest that high-quality music generation can be effectively pursued without separating structure and fidelity into heterogeneous representation spaces. Instead, both can be progressively modeled within a unified acoustic-token hierarchy, pointing toward a simpler and more unified path to high-quality music generation.
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 Khala, a high-fidelity music generation system that effectively models both musical structure and acoustic fidelity within a unified acoustic-token hierarchy, demonstrating competitive performance against existing systems. This work significantly advances the field of music generation by providing a novel methodology that integrates lyric alignment and acoustic detail refinement in a single framework, showcasing the potential for future developments in this area.
Methodology
The paper presents a novel approach to music generation by using a unified acoustic-token hierarchy, which contrasts with existing methods that typically separate structure and fidelity into different stages. The introduction of a two-stage coarse-to-fine generation framework and hybrid-attention training for lyric alignment is particularly innovative. The methodology is well-structured, with clear explanations of the architecture and training processes, including the use of a 64-layer residual vector quantization (RVQ) acoustic representation. The authors effectively address the challenges of generating coherent and high-fidelity music while maintaining lyric alignment, which is a significant advancement in the field.
Results & evaluation
The experiments conducted in the paper include a large-scale human preference evaluation, which is crucial for assessing the quality of generated music. The use of both subjective (mean Overall Score and BT-derived Elo) and objective metrics provides a comprehensive evaluation framework. The results indicate that Khala performs competitively against both commercial and open-source systems, showcasing its effectiveness in real-world applications. The ablation studies further validate the importance of the proposed training strategies, particularly the backbone initialization and hybrid-attention training.
Reproducibility
The paper provides detailed implementation information, including model architectures, training strategies, and dataset descriptions. The availability of code and model checkpoints on GitHub enhances reproducibility, allowing other researchers to replicate the experiments and build upon the work. However, the paper could benefit from more explicit details on hyperparameter settings and training configurations to facilitate easier reproduction.
Limitations
One limitation noted is the reliance on a two-stage model design, which, while practical, may not fully exploit the potential of a unified model that integrates both coarse generation and fine-layer refinement. Additionally, the paper acknowledges that the current tokenizer, while effective, could be improved for even higher fidelity, suggesting that future work could focus on enhancing the acoustic representation further.
Broader impact
The work has significant implications for the field of music generation, particularly in developing systems that can produce high-quality music with coherent structure and fidelity without relying on separate semantic stages. This approach could pave the way for more integrated and efficient music generation systems, potentially impacting various applications in entertainment, education, and creative industries. The findings also suggest a promising direction for future research in audio modeling and machine learning. The main contribution of this paper is the introduction of Khala, a high-fidelity music generation system that effectively models both musical structure and acoustic fidelity within a unified acoustic-token hierarchy, demonstrating competitive performance against existing systems. This work significantly advances the field of music generation by providing a novel methodology that integrates lyric alignment and acoustic detail refinement in a single framework, showcasing the potential for future developments in this area.
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