FunASR: A Fundamental End-to-End Speech Recognition Toolkit
Zhifu Gao Zerui Li Jiaming Wang Haoneng Luo Xian Shi Mengzhe Chen Yabin Li Lingyun Zuo Zhihao Du Zhangyu Xiao Shiliang Zhang
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.