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arXiv · 2303.10384

Powerful and Extensible WFST Framework for RNN-Transducer Losses

Aleksandr Laptev·Vladimir Bataev·Igor Gitman·Boris Ginsburg
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Abstract

This paper presents a framework based on Weighted Finite-State Transducers (WFST) to simplify the development of modifications for RNN-Transducer (RNN-T) loss. Existing implementations of RNN-T use CUDA-related code, which is hard to extend and debug. WFSTs are easy to construct and extend, and allow debugging through visualization. We introduce two WFST-powered RNN-T implementations: (1) "Compose-Transducer", based on a composition of the WFST graphs from acoustic and textual schema -- computationally competitive and easy to modify; (2) "Grid-Transducer", which constructs the lattice directly for further computations -- most compact, and computationally efficient. We illustrate the ease of extensibility through introduction of a new W-Transducer loss -- the adaptation of the Connectionist Temporal Classification with Wild Cards. W-Transducer (W-RNNT) consistently outperforms the standard RNN-T in a weakly-supervised data setup with missing parts of transcriptions at the beginning and end of utterances. All RNN-T losses are implemented with the k2 framework and are available in the NeMo toolkit.

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BibTeXRIS

Aleksandr Laptev, Vladimir Bataev, Igor Gitman, Boris Ginsburg. 2023-03-18. Powerful and Extensible WFST Framework for RNN-Transducer Losses. https://doi.org/10.1109/icassp49357.2023.10096679

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