From 1988fe85f6d4e2d2f809e705e13d69d0b57bd0fc Mon Sep 17 00:00:00 2001 From: shixian.shi <shixian.shi@alibaba-inc.com> Date: 星期四, 04 五月 2023 19:27:00 +0800 Subject: [PATCH] update --- README.md | 22 +++++++++++----------- 1 files changed, 11 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index b8e1b89..414eb9b 100644 --- a/README.md +++ b/README.md @@ -18,19 +18,17 @@ | [**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 - 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) +- 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://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html) - 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. @@ -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