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| | | [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) |
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| | | | [**Highlights**](#highlights) |
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| | | | [**Installation**](#installation) |
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| | | | [**Docs_EN**](https://alibaba-damo-academy.github.io/FunASR/en/index.html) |
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| | | | [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html) |
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| | | | [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C) |
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| | | | [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations) |
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| | | | [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime) |
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| | | | [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md) |
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| | | | [**Contact**](#contact) |
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| | | [**M2MET2.0 Guidence_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html) |
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| | | | [**M2MET2.0 Guidence_EN**](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html) |
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| | | ## Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge |
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| | | We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)). |
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| | | ## What's new: |
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| | | |
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| | | For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases) |
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| | |
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| | | ## Citations |
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| | | |
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| | | ``` bibtex |
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| | | @inproceedings{gao2020universal, |
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| | | title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model}, |
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| | | author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian}, |
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| | | booktitle={arXiv preprint arXiv:2010.14099}, |
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| | | year={2020} |
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| | | } |
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| | | @inproceedings{gao2022paraformer, |
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| | | title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}, |
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| | | author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie}, |
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| | | booktitle={INTERSPEECH}, |
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| | | year={2022} |
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| | | } |
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| | | @inproceedings{gao2020universal, |
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| | | title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model}, |
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| | | author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian}, |
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| | | booktitle={arXiv preprint arXiv:2010.14099}, |
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| | | year={2020} |
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| | | } |
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| | | @inproceedings{Shi2023AchievingTP, |
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| | | title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model}, |
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| | | author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan}, |
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