English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We describe initial work on an extension of the Kaldi toolkit that supportsweighted finite-state transducer (WFST) decoding on Graphics Processing Units(GPUs). We implement token recombination as an atomic GPU operation in order tofully parallelize the Viterbi beam search, and propose a dynamic load balancingstrategy for more efficient token passing scheduling among GPU threads. We alsoredesign the exact lattice generation and lattice pruning algorithms for betterutilization of the GPUs. Experiments on the Switchboard corpus show that theproposed method achieves identical 1-best results and lattice quality inrecognition and confidence measure tasks, while running 3 to 15 times fasterthan the single process Kaldi decoder. The above results are reported ondifferent GPU architectures. Additionally we obtain a 46-fold speedup withsequence parallelism and multi-process service (MPS) in GPU.

A GPU-based WFST Decoder with Exact Lattice Generation