PairAlign: New Framework for Audio Tokenization Using Self-Aligned Sequence Generation
Researchers introduced PairAlign, a framework that converts audio into discrete tokens by treating tokenization as conditional sequence generation between two content-preserving views of the same audio. The method improves upon existing audio tokenizers by directly optimizing for sequence consistency, compactness, and edit similarity rather than relying solely on quantization or clustering. This approach could improve audio retrieval systems and other applications requiring discrete symbolic representations of audio data.
PairAlign addresses a fundamental challenge in audio processing: converting continuous audio signals into discrete tokens similar to how language is tokenized into words. The framework uses an encoder-decoder architecture where an encoder maps speech to a continuous representation and an autoregressive decoder generates tokens, learning token identity, order, length, and proper termination. The key innovation is training two content-preserving views of the same audio to generate likely token sequences under each other's representations, while using unrelated examples as negative contrasts. This approach incorporates several refinements including EMA-teacher targets, cross-paired teacher forcing, prefix corruption, and likelihood contrast. Experimental results on 3-second speech samples show PairAlign produces compact, non-degenerate sequences with broad vocabulary usage and strong consistency across different views. In retrieval tasks, the method reduces archive token count by 55% while preserving edit-distance search capabilities, and demonstrates better length control and bounded edit trajectories compared to dense geometric tokenizers.
What's missing
The paper does not discuss computational costs or inference speed compared to existing tokenization methods. Additionally, evaluation is limited to 3-second speech samples; performance on longer audio sequences or other audio types (music, environmental sounds) is not addressed. The paper also does not provide details on how the method scales to very large audio datasets or compare wall-clock training time against baseline approaches.
What different sources said
- arXiv cs.LGCenter
PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization
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