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arXiv · 2406.06220

Label-Looping: Highly Efficient Decoding for Transducers

Vladimir Bataev·Hainan Xu·Daniel Galvez·Vitaly Lavrukhin·Boris Ginsburg
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Abstract

This paper introduces a highly efficient greedy decoding algorithm for Transducer-based speech recognition models. We redesign the standard nested-loop design for RNN-T decoding, swapping loops over frames and labels: the outer loop iterates over labels, while the inner loop iterates over frames searching for the next non-blank symbol. Additionally, we represent partial hypotheses in a special structure using CUDA tensors, supporting parallelized hypotheses manipulations. Experiments show that the label-looping algorithm is up to 2.0X faster than conventional batched decoding when using batch size 32. It can be further combined with other compiler or GPU call-related techniques to achieve even more speedup. Our algorithm is general-purpose and can work with both conventional Transducers and Token-and-Duration Transducers. We open-source our implementation to benefit the research community.

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BibTeXRIS

Vladimir Bataev, Hainan Xu, Daniel Galvez, Vitaly Lavrukhin, Boris Ginsburg. 2024-06-10. Label-Looping: Highly Efficient Decoding for Transducers. https://doi.org/10.1109/slt61566.2024.10832333

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