While Connectionist Temporal Classification (CTC) models deliver
state-of-the-art accuracy in automated speech recognition (ASR) pipelines,
their performance has been limited by CPU-based beam search decoding. We
introduce a GPU-accelerated Weighted Finite State Transducer (WFST) beam search
decoder compatible with current CTC models. It increases pipeline throughput
and decreases latency, supports streaming inference, and also supports advanced
features like utterance-specific word boosting via on-the-fly composition. We
provide pre-built DLPack-based python bindings for ease of use with
Python-based machine learning frameworks at
this https URL We evaluated our decoder
for offline and online scenarios, demonstrating that it is the fastest beam
search decoder for CTC models. In the offline scenario it achieves up to 7
times more throughput than the current state-of-the-art CPU decoder and in the
online streaming scenario, it achieves nearly 8 times lower latency, with same
or better word error rate.

通过引入支持当前 CTC 模型的 GPU 加速加权有限状态转录器（WFST）波束搜索解码器，我们提供了一种 GPU 加速的波束搜索解码器，可以提高管道吞吐量，减少延迟，并支持流式推理，同时支持实时组合进行话语特定的单词增强。我们针对离线和在线场景评估了我们的解码器，证明它是 CTC 模型的最快波束搜索解码器。在离线场景中，它的吞吐量比当前最先进的 CPU 解码器高出多达 7 倍，在在线流式场景中，它的延迟几乎比之前更低，而且单词错误率相同或更好。

基于 GPU 加速的 WFST 波束搜索解码器用于基于 CTC 的语音识别

GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech  Recognition

The peaky behavior of CTC models is well known experimentally. However, an
understanding about why peaky behavior occurs is missing, and whether this is a
good property. We provide a formal analysis of the peaky behavior and gradient
descent convergence properties of the CTC loss and related training criteria.
Our analysis provides a deep understanding why peaky behavior occurs and when
it is suboptimal. On a simple example which should be trivial to learn for any
model, we prove that a feed-forward neural network trained with CTC from
uniform initialization converges towards peaky behavior with a 100% error rate.
Our analysis further explains why CTC only works well together with the blank
label. We further demonstrate that peaky behavior does not occur on other
related losses including a label prior model, and that this improves
convergence.

通过分析 CTC 模型的峰形行为，得出了为什么会出现峰形行为以及何时这是不优的深刻认识，并研究了相关的训练标准和梯度下降收敛性。同时发现，CTC 只有与空白标签一起使用才能很好地工作，并证明了其它相关损失不会出现峰形行为从而提高了收敛性。