Audio Token Compression

Audio token compression studies how to represent speech, music, and other sounds with fewer tokens or bits while retaining information needed for tasks such as speech recognition, audio question answering, and reconstruction. Approaches include learned codecs and quantizers, distillation and adaptive segmentation, and training-free token merging or pruning based on attention, temporal structure, or semantic cues; some methods also compress long-context KV caches. Evaluations span ASR sets such as LibriSpeech, audio-understanding benchmarks such as AudioCaps, Clotho, and MMAU, and codec measures of fidelity, intelligibility, and speaker similarity. Large reductions can often preserve much of a model’s performance, but the best compression depends on the task and model, and aggressive reduction can discard details such as emotion, timing, or acoustic fidelity.