Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU
<|reference_start|>Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU: 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 the time. Leveraging a new CUDA 12.4 feature, CUDA graph conditional nodes, we present an exact GPU-based implementation of greedy decoding for RNN-T models that eliminates this idle time. Our optimizations speed up a 1.1 billion parameter RNN-T model end-to-end by a factor of 2.5x. This technique can applied to the "label looping" alternative greedy decoding algorithm as well, achieving 1.7x and 1.4x end-to-end speedups when applied to 1.1 billion parameter RNN-T and Token and Duration Transducer models respectively. This work enables a 1.1 billion parameter RNN-T model to run only 16% slower than a similarly sized CTC model, contradicting the common belief that RNN-T models are not suitable for high throughput inference. The implementation is available in NVIDIA NeMo.<|reference_end|>
@article{galvez2024speed,
    title={Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models
  on GPU},
    author={Daniel Galvez, Vladimir Bataev, Hainan Xu, Tim Kaldewey},
    journal={arXiv preprint arXiv:2406.03791},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03791},
    primaryClass={cs.LG}
}