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.

本文提出了一种名为 RASR2 的研究性通用 S2S 解码器，它支持多种 S2S 模型、语言模型、标签单元 / 拓扑和神经网络架构，并提供了对不同搜索模式和设置的丰富支持，因此可以有效地对开放词汇和闭合词汇场景进行解码。作者通过在 Switchboard 和 Librispeech 语料库上进行了广泛的实验证明了 RASR2 的效果，并在线公开了其源代码。