  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.
