aiqbench analysis
CARD introduces an encoder-free audio captioning architecture that distills knowledge from a teacher model directly into the projector and LLM components, eliminating inference costs associated with frozen audio encoders. By strategically routing perceptual and semantic representations across different model layers, this approach achieves significant performance gains on standard benchmarks while removing a traditional bottleneck in multimodal systems.
CARD: Cross-component Audio Representation Distillation for Encoder-Free Audio Captioning
arXiv:2607.04619v2 Announce Type: replace-cross Abstract: Modern automated audio captioning systems pair a frozen audio encoder with a large language model (LLM) via a trainable projector, incurring the encoder's inference cost and bottlenecking the model through its fixed acoustic features. We present CARD, an encoder-free audio captioning model that removes the encoder at inference: a 13.2M projector feeds a frozen LLM with merged LoRA adapters, while the teacher used to train it is discarded. CARD distills a pre-trained audio teacher (CLAP-HTSAT) into the model, but rather than injecting it into the LLM alone, it routes the teacher's representations across components: perceptual stages to the projector and semantic stages to the LLM. This placement improves CIDEr-D by +11.9 over an LLM-only distilled model on AudioCaps and by +5.0 on Clotho, reaching 55.4 against a 66.4 encoder-kept upper bound with no encoder at inference, showing that where a teacher's knowledge is placed matters as much as its presence.
Abstract:Modern automated audio captioning systems pair a frozen audio encoder with a large language model (LLM) via a trainable projector, incurring the encoder's inference cost and bottlenecking the model through its fixed acoustic features. We present CARD, an encoder-free audio captioning model that removes the encoder at inference: a 13.2M projector feeds a frozen LLM with merged LoRA adapters, while the teacher used to train it is discarded. CARD distills a pre-trained audio teacher (CLAP-HTSAT) into the model, but rather than injecting it into the LLM alone, it routes the teacher's representations across components: perceptual stages to the projector and semantic stages to the LLM. This placement improves CIDEr-D by +11.9 over an LLM-only distilled model on AudioCaps and by +5.0 on Clotho, reaching 55.4 against a 66.4 encoder-kept upper bound with no encoder at inference, showing that where a teacher's knowledge is placed matters as much as its presence.
Sound (cs.SD); Computation and Language (cs.CL)
arXiv:2607.04619 [cs.SD]
(or arXiv:2607.04619v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2607.04619 Focus to learn more arXiv-issued DOI via DataCite
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