RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition
Abstract
Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only. For closed-vocabulary scenarios, public tools supporting lexical-constrained decoding are usually only for classical ASR, or do not support all S2S models. To eliminate this restriction on research possibilities such as modeling unit choice, we present RASR2 in this work, a research-oriented generic S2S decoder implemented in C++. It offers a strong flexibility/compatibility for various S2S models, language models, label units/topologies and neural network architectures. It provides efficient decoding for both open- and closed-vocabulary scenarios based on a generalized search framework with rich support for different search modes and settings. We evaluate RASR2 with a wide range of experiments on both switchboard and Librispeech corpora. Our source code is public online.
Cite
@article{arxiv.2305.17782,
title = {RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition},
author = {Wei Zhou and Eugen Beck and Simon Berger and Ralf Schlüter and Hermann Ney},
journal= {arXiv preprint arXiv:2305.17782},
year = {2023}
}
Comments
accepted at Interspeech 2023