From b15db52e4e67da8a133a67e8ffa415386de48b40 Mon Sep 17 00:00:00 2001
From: zhuyunfeng <10596244@qq.com>
Date: 星期二, 09 五月 2023 23:03:15 +0800
Subject: [PATCH] Add contributor

---
 README.md |   48 ++++++++++++++++++++++++++----------------------
 1 files changed, 26 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index af38341..64d6d89 100644
--- a/README.md
+++ b/README.md
@@ -1,27 +1,34 @@
 [//]: # (<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_EN**](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)
+| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**Tutorial_CN**](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)
+| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/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
-- 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).
-- We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
+- 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), ref to [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
 - 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.
@@ -53,12 +60,8 @@
 # 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://github.com/alibaba-damo-academy/FunASR/wiki)
+For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
 
-[//]: # ()
-[//]: # (## Usage)
-
-[//]: # (For users who are new to FunASR and ModelScope, please refer to FunASR Docs&#40;[CN]&#40;https://alibaba-damo-academy.github.io/FunASR/cn/index.html&#41; / [EN]&#40;https://alibaba-damo-academy.github.io/FunASR/en/index.html&#41;&#41;)
 
 ## Contact
 
@@ -72,15 +75,17 @@
 
 ## 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/DeepScience.png" width="200"/> </div> |
-|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:-----------------------------------------------------------:|
+| <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 [DeepScience](https://www.deepscience.cn) for contributing the grpc service.
+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.
@@ -88,19 +93,18 @@
 ## Citations
 
 ``` bibtex
-@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{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},

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