HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models
arXiv:2606.27627v1 Announce Type: new Abstract: Discrete audio representations have become increasingly popular for building multimodal text-audio systems and integrating audio capabilities into Large Language Models (LLMs). However, numerous studies report performance degradation on various downstream tasks due to information loss during discretization. To address this, we propose a novel approach combining temporally compressed discrete tokens with dimensionality-reduced continuous residuals. Our framework consists of a hybridized discrete-continuous focal modulation codec and a hybrid Trans
The increasing integration of audio into Large Language Models necessitates overcoming the information loss inherent in discrete audio representations, driving innovation in hybrid approaches.
Improving the efficiency and fidelity of speech language models directly enhances the capabilities of multimodal AI, impacting human-computer interaction and AI agent performance.
This novel 'HybridCodec' suggests a pathway to mitigate performance degradation in multimodal AI by combining discrete and continuous audio representations, leading to more robust audio integration.
- · AI developers
- · Multimodal AI platforms
- · Speech recognition companies
- · LLM providers
- · Platforms reliant solely on discrete audio processing
- · Cloud computing providers (higher efficiency leads to less compute demand for sa
Improved performance and broader adoption of AI systems with integrated audio capabilities.
Accelerated development of more natural and intuitive AI interfaces, potentially via AI agents.
Enhanced accessibility and utility of AI for a wider range of applications previously limited by audio quality or processing overhead.
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Read at arXiv cs.LG