TokenMapper: A Step Toward Interoperable Speech Token Translation

TokenMapper is a framework that enables direct translation between different speech tokenizers, allowing heterogeneous speech models to communicate without converting tokens to waveform audio. It handles mismatched token spaces, including single and multi-codebook representations, while maintaining a shared effective token rate. Experiments on GLM-4-Voice, MiMi, and DualCodec show that TokenMapper achieves word error rates close to native reconstructions, comparable human MOS scores, and significantly reduces latency compared to waveform bridging.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• agents
• nlp