From c699b484b10b9b6ce8f22367ec448b042b3676d4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 27 四月 2023 21:25:56 +0800
Subject: [PATCH] docs
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+[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)
+
+# FunASR: A Fundamental End-to-End Speech Recognition Toolkit
+<p align="left">
+ <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
+ <a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
+ <a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a>
+</p>
+
+<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锛�
+
+[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
+| [**Highlights**](#highlights)
+| [**Installation**](#installation)
+| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
+| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
+| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
+| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
+| [**Contact**](#contact)
+| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)
+
+## What's new:
+### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
+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)).
+### Release notes
+For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
+
+## Highlights
+- FunASR supports speech recognition(ASR), Multi-talker ASR, Voice Activity Detection(VAD), Punctuation Restoration, Language Models, Speaker Verification and Speaker diarization.
+- We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
+- 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)
+- FunASR supplies a easy-to-use pipeline to finetune pretrained models from [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
+- Compared to [Espnet](https://github.com/espnet/espnet) framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader.
+
+## Installation
+
+Install from pip
+```shell
+pip install -U funasr
+# For the users in China, you could install with the command:
+# pip install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
+```
+
+Or install from source code
+
+
+``` sh
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+pip install -e ./
+# For the users in China, you could install with the command:
+# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
+
+```
+If you want to use the pretrained models in ModelScope, you should install the modelscope:
+
+```shell
+pip install -U modelscope
+# For the users in China, you could install with the command:
+# pip install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
+```
+
+For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)
+
+[//]: # ()
+[//]: # (## Usage)
+
+[//]: # (For users who are new to FunASR and ModelScope, please refer to FunASR Docs([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)))
+
+## Contact
+
+If you have any questions about FunASR, please contact us by
+
+- email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com)
+
+|Dingding group | Wechat group |
+|:---:|:-----------------------------------------------------:|
+|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
+
+## Contributors
+
+| <div align="left"><img src="docs/images/damo.png" width="180"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div> | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/DeepScience.png" width="200"/> </div> |
+|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
+
+## Acknowledge
+
+1. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for data preparation.
+2. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet). FunASR follows up the training and finetuning pipelines of ESPnet.
+3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training.
+4. We acknowledge [ChinaTelecom](https://github.com/zhuzizyf/damo-fsmn-vad-infer-httpserver) for contributing the VAD runtime.
+5. We acknowledge [RapidAI](https://github.com/RapidAI) for contributing the Paraformer and CT_Transformer-punc runtime.
+6. We acknowledge [DeepScience](https://www.deepscience.cn) for contributing the grpc service.
+
+## License
+This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
+
+## Citations
+
+``` bibtex
+@inproceedings{gao2022paraformer,
+ title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
+ author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
+ booktitle={INTERSPEECH},
+ year={2022}
+}
+@inproceedings{gao2020universal,
+ title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model},
+ author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian},
+ booktitle={arXiv preprint arXiv:2010.14099},
+ year={2020}
+}
+@inproceedings{Shi2023AchievingTP,
+ title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model},
+ author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan},
+ booktitle={arXiv preprint arXiv:2301.12343}
+ year={2023}
+}
+```
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