Summary of Speed Of Light Exact Greedy Decoding For Rnn-t Speech Recognition Models on Gpu, by Daniel Galvez et al.
Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU
by Daniel Galvez, Vladimir Bataev, Hainan Xu, Tim Kaldewey
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an exact GPU-based implementation of greedy decoding for Recurrent Neural Network Transducer (RNN-T) models, which significantly reduces idle time on graphics processing units (GPUs). This optimization speeds up RNN-T model inference by 2.5x for large models with over 1 billion parameters. The technique can also be applied to other transducer models, achieving notable speedups. The implementation enables a 1.1 billion parameter RNN-T model to run only slightly slower than a similarly sized Connectionist Temporal Classification (CTC) model, challenging the common belief that RNN-T models are not suitable for high-throughput inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make speech recognition models faster on computers. These models are called Recurrent Neural Network Transducers (RNN-T). They use a lot of time processing, but now they can be made faster by using new computer features. This makes it possible to process big models quickly and efficiently. |
Keywords
» Artificial intelligence » Classification » Inference » Neural network » Optimization » Rnn
