From 66b502164d0f9bef3cf367f2e588fffb1596e6bb Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 18 四月 2023 16:14:28 +0800
Subject: [PATCH] docs

---
 README.md |   71 ++++++++++++++++++++++++-----------
 1 files changed, 49 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index 3f63c6b..11aa88f 100644
--- a/README.md
+++ b/README.md
@@ -1,33 +1,31 @@
 [//]: # (<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/index.html)
+| [**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)
 | [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
 | [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
-| [**Model Zoo**](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
+| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
 | [**Contact**](#contact)
 
+
 ## What's new: 
-### 2023.1.16, funasr-0.1.6
-- We release a new version model [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), which integrate the [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) model, [ASR](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary),
- [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) model and timestamp together. The model could take in several hours long inputs.
-- We release a new type model, [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary), which could predict the duration of none-silence speech. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md).
-- We release a new type model, [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary), which could predict the punctuation of ASR models's results. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md).
-- We release a new model, [Data2vec](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary), an unsupervised pretraining model which could be finetuned on ASR and other downstream tasks.
-- We release a new model, [Paraformer-Tiny](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary), a lightweight Paraformer model which supports Mandarin command words recognition.
-- We release a new type model, [SV](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary), which could extract speaker embeddings and further perform speaker verification on paired utterances. It will be supported for speaker diarization in the future version.
-- We improve the pipeline of modelscope to speedup the inference, by integrating the process of build model into build pipeline.
-- Various new types of audio input types are now supported by modelscope inference pipeline, including wav.scp, wav format, audio bytes, wave samples...
+
+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).
+- 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)
@@ -35,14 +33,37 @@
 
 ## 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 --editable ./
+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://github.com/alibaba-damo-academy/FunASR/wiki)
 
-## Usage
-For users who are new to FunASR and ModelScope, please refer to [FunASR Docs](https://alibaba-damo-academy.github.io/FunASR/index.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
 
@@ -50,14 +71,14 @@
 
 - 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="222"/></div>|
+|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/DeepScience.png" width="250"/> |
-|:---:|
+| <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> |
+|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:-----------------------------------------------------------:|
 
 ## Acknowledge
 
@@ -85,4 +106,10 @@
   booktitle={INTERSPEECH},
   year={2022}
 }
-```
\ No newline at end of file
+@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}
+}
+```

--
Gitblit v1.9.1