arXiv:2606.16969v1 Announce Type: cross Abstract: Low frame rates in neural audio codecs are attractive for autoregressive speech synthesis, where the generation cost scales linearly with the sequence length. Recent work has demonstrated that codecs can operate at 12.5 Hz and below, but the mechanisms underlying low frame rate degradation remain insufficiently understood. We investigate these mechanisms through a controlled frame rate ablation. We reproduce a quality cliff at 6.25 Hz reported in previous works and evaluate candidate explanations: phonemic collisions and codebook saturation, ne
The continuous evolution of neural audio codecs necessitates a deeper understanding of their limitations at low frame rates, especially for applications like generative AI.
Improving the efficiency and quality of neural audio codecs at low frame rates is crucial for scaling generative audio applications and reducing computational demands.
This research contributes to understanding technical barriers in high-efficiency audio synthesis, potentially leading to more performant and less resource-intensive AI audio generation.
- · AI audio synthesis developers
- · Companies offering generative AI platforms
- · Researchers in audio codec design
- · Developers reliant on legacy high-bandwidth audio synthesis methods
This research provides insights into optimizing neural audio codecs for specific use cases like autoregressive speech synthesis.
Reduced computational costs for audio generation could lower barriers to entry for new AI audio applications and services.
More efficient audio codecs might enable new forms of real-time AI audio interaction in resource-constrained environments.
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Read at arXiv cs.AI