Papers › FunASR: A Fundamental End-to-End Speech Recognition Toolkit
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.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | AISHELL-1 | Paraformer-large | Params(M) | 220 | #5 of 18 | Archive leaderboard | report |
| Speech Recognition | AISHELL-1 | Paraformer-large | Word Error Rate (WER) | 1.95 | #5 of 18 | Archive leaderboard | report |
| Speech Recognition | AISHELL-1 | Paraformer | Params(M) | 46.3 | #13 of 18 | Archive leaderboard | report |
| Speech Recognition | AISHELL-1 | Paraformer | Word Error Rate (WER) | 4.95 | #13 of 18 | Archive leaderboard | report |
| Speech Recognition | AISHELL-2 | Paraformer-large | Word Error Rate (WER) | 2.85 | #1 of 2 | Archive leaderboard | report |
| Speech Recognition | AISHELL-2 | Paraformer | Word Error Rate (WER) | 5.73 | #2 of 2 | Archive leaderboard | report |
| Speech Recognition | WenetSpeech | Paraformer-large | Character Error Rate (CER) | 6.97 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections