3 papers
cs.LG2024
Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU
Daniel Galvez, Vladimir Bataev, Hainan Xu +1
The vast majority of inference time for RNN Transducer (RNN-T) models today is spent on decoding. Current state-of-the-art RNN-T decoding implementations leave the GPU idle ~80% of…
eess.AS2024
Label-Looping: Highly Efficient Decoding for Transducers
Vladimir Bataev, Hainan Xu, Daniel Galvez +2
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, s…
eess.AS2023
GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech Recognition
Daniel Galvez, Tim Kaldewey
While Connectionist Temporal Classification (CTC) models deliver state-of-the-art accuracy in automated speech recognition (ASR) pipelines, their performance has been limited by CP…