From 1d1ef01b4e23630a99a3be7e9d1dce9550a793e9 Mon Sep 17 00:00:00 2001 From: yhliang <68215459+yhliang-aslp@users.noreply.github.com> Date: 星期四, 11 五月 2023 16:26:24 +0800 Subject: [PATCH] Merge branch 'main' into dev_smohan --- README.md | 16 ++++++---------- 1 files changed, 6 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 414eb9b..e9c6ef9 100644 --- a/README.md +++ b/README.md @@ -13,22 +13,22 @@ | [**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) +| [**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)). +### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MeT2.0) Challenge +We are pleased to announce that the M2MeT2.0 challenge has been accepted by the ASRU 2023 challenge special session. The registration is now open. 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), ref to [Model Zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html) +- 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. @@ -60,12 +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://alibaba-damo-academy.github.io/FunASR/en/installation.html) +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([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html))) ## Contact -- Gitblit v1.9.1