From 76e3e8445a72ece8dd3eb30a129ec141c2746e8a Mon Sep 17 00:00:00 2001 From: lyblsgo <lyblsgo@163.com> Date: 星期一, 08 五月 2023 11:28:45 +0800 Subject: [PATCH] update readme --- README.md | 20 ++++++++++---------- 1 files changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index b8e1b89..665f425 100644 --- a/README.md +++ b/README.md @@ -18,14 +18,12 @@ | [**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 Guidence_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html) -| [**M2MET2.0 Guidence_EN**](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html) +| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge) -## 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)). ## 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 @@ -62,7 +60,7 @@ # 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.html) [//]: # () [//]: # (## Usage) @@ -81,15 +79,17 @@ ## 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. -- Gitblit v1.9.1