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arXiv · 2508.07315

FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities

Lilit Grigoryan·Vladimir Bataev·Nikolay Karpov·Andrei Andrusenko·Vitaly Lavrukhin·Boris Ginsburg
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Abstract

While beam search improves speech recognition quality over greedy decoding, standard implementations are slow, often sequential, and CPU-bound. To fully leverage modern hardware capabilities, we present a novel open-source FlexCTC toolkit for fully GPU-based beam decoding, designed for Connectionist Temporal Classification (CTC) models. Developed entirely in Python and PyTorch, it offers a fast, user-friendly, and extensible alternative to traditional C++, CUDA, or WFST-based decoders. The toolkit features a high-performance, fully batched GPU implementation with eliminated CPU-GPU synchronization and minimized kernel launch overhead via CUDA Graphs. It also supports advanced contextualization techniques, including GPU-powered N-gram language model fusion and phrase-level boosting. These features enable accurate and efficient decoding, making them suitable for both research and production use.

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Lilit Grigoryan, Vladimir Bataev, Nikolay Karpov, Andrei Andrusenko, Vitaly Lavrukhin, Boris Ginsburg. 2025-08-10. FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities. https://arxiv.org/abs/2508.07315

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