This paper introduces FunASR, an open-source speech recognition toolkit
designed to bridge the gap between academic research and industrial
applications. FunASR offers models trained on large-scale industrial corpora
and the ability to deploy them in applications. The toolkit's flagship model,
Paraformer, is a non-autoregressive end-to-end speech recognition model that
has been trained on a manually annotated Mandarin speech recognition dataset
that contains 60,000 hours of speech. To improve the performance of Paraformer,
we have added timestamp prediction and hotword customization capabilities to
the standard Paraformer backbone. In addition, to facilitate model deployment,
we have open-sourced a voice activity detection model based on the Feedforward
Sequential Memory Network (FSMN-VAD) and a text post-processing punctuation
model based on the controllable time-delay Transformer (CT-Transformer), both
of which were trained on industrial corpora. These functional modules provide a
solid foundation for building high-precision long audio speech recognition
services. Compared to other models trained on open datasets, Paraformer
demonstrates superior performance.

FunASR 是一种开源的语音识别工具箱，其中最具代表性的 Paraformer 模型是一种非自回归端到端语音识别模型，经过手动注释的中文语音识别数据集的训练，同时还开放了基于 FSMN-VAD 和 CT-Transformer 的语音活动检测和文本后处理标点符号模型，这些模块为建立高精度长音频语音识别服务提供了坚实的基础，相较于在公开数据集上训练的其他模型表现更佳。