FunASR: A Fundamental End-to-End Speech Recognition Toolkit
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Abstract (arXiv)
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.
Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). Builds on 27 of its 31 references that are in the map. First posted on arXiv 18 May 2023.
Builds on
27 of 31 references.
References · 27
- 2017Attention Is All You Needcited by 60,361
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
- 2018ESPnet: End-to-End Speech Processing Toolkitcited by 558
- 2015Listen, Attend and Spellcited by 543
- 2017AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baselinecited by 373
- 2021WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognitioncited by 197
- 2022Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognitioncited by 144
- 2018Deep context: end-to-end contextual speech recognitioncited by 137
- 2018AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scalecited by 128
- 2021WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkitcited by 125
- 2019CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognitioncited by 77
15 more
- 2022WeNet 2.0: More Productive End-to-End Speech Recognition Toolkitcited by 47
- 2018Deep-FSMN for Large Vocabulary Continuous Speech Recognitioncited by 28
- 2020Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detectioncited by 23
- 2020SAN-M: Memory Equipped Self-Attention for End-to-End Speech Recognitioncited by 16
- 2020Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognitioncited by 12
- 2022Fast and parallel decoding for transducercited by 12
- 2021DISC: A Dynamic Shape Compiler for Machine Learning Workloadscited by 10
- 2023Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Modelcited by 10
- 2020Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Modelcited by 9
- 2022PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkitcited by 9
- 2022ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognitioncited by 8
- 2021Extremely Low Footprint End-to-End ASR System for Smart Devicecited by 7
- 2022Memory-Efficient Training of RNN-Transducer with Sampled Softmaxcited by 3
- 2022Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speechcited by 3
- 2022Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducercited by 1
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Citing papers per year: 2023: 5; 2024: 21; 2025: 35; 2026: 34.
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