HybridCodec: New Neural Audio Codec Combines Semantic and Acoustic Processing
Researchers have developed HybridCodec, a neural audio codec that combines separate semantic and acoustic processing streams while distilling semantic information from SSL representations. The architecture is designed to improve how speech is tokenized for use in multimodal large language models. The work demonstrates faster processing speeds and stronger semantic specialization compared to existing dual-stream models.
HybridCodec is a unified neural audio codec architecture that addresses the challenge of incorporating semantic information into codec models for speech tokenization in multimodal AI systems. The system employs two separate branches—one for semantic features and one for acoustic features—while distilling semantic information from self-supervised learning (SSL) representations into the semantic stream. This design achieves strong disentanglement between semantic and acoustic information without requiring an SSL model during inference, reducing computational overhead. According to the research, HybridCodec demonstrates superior semantic specialization in the first RVQ layer on in-domain test sets and competitive reconstruction across all RVQ layers. The model shows robustness in out-of-domain and zero-shot cross-lingual settings, achieving approximately 3x speedup compared to existing dual-stream codec models.
What different sources said
- arXiv cs.CLCenter
HybridCodec: Fast Dual-Stream, Semantically Enhanced Neural Audio Codec
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