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| | | [//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>) |
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| | | # FunASR: A Fundamental End-to-End Speech Recognition Toolkit |
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| | | <p align="left"> |
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| | | <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a> |
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| | | <a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a> |
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| | | <a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a> |
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| | | </p> |
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| | | <strong>FunASR</strong> hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！ |
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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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| | | For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases) |
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| | | |
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| | | ## Highlights |
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| | | - Many types of typical models are supported, e.g., [Tranformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100), [Paraformer](https://arxiv.org/abs/2206.08317). |
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| | | - FunASR supports speech recognition(ASR), Multi-talker ASR, Voice Activity Detection(VAD), Punctuation Restoration, Language Models, Speaker Verification and Speaker diarization. |
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| | | - We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) |
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| | | - The pretrained model [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) obtains the best performance on many tasks in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard) |
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| | | - FunASR supplies a easy-to-use pipeline to finetune pretrained models from [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) |
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