docs

.gitignore

@@ -9,4 +9,7 @@ init_model/
 test_local/
 RapidASR
 export/*
-*.pyc
+*.pyc
+.eggs
+MaaS-lib
+.gitignore

README.md

@@ -1,6 +1,11 @@
 [//]: # (<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！
 
@@ -20,7 +25,7 @@
 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)

docs/installation.md

@@ -1,7 +1,13 @@
-# Installation
-FunASR is easy to install. The detailed installation steps are as follows:
+<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>
+
+## Installation
+
+### Install Conda (Optional):
 
-- Install Conda and create virtual environment:
 ```sh
 wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
 sh Miniconda3-latest-Linux-x86_64.sh
@@ -10,26 +16,38 @@ conda create -n funasr python=3.7
 conda activate funasr
 ```
 
-- Install Pytorch (version >= 1.7.0):
+### Install Pytorch (version >= 1.11.0):
+
 ```sh
 pip install torch torchaudio
 ```
 
-For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally)
+For more details about torch, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally)
 
-- Install ModelScope
+### Install funasr
 
-For users in China, you can configure the following mirror source to speed up the downloading:
-``` sh
-pip config set global.index-url https://mirror.sjtu.edu.cn/pypi/web/simple
-```
-Install or update ModelScope
-```sh
-pip install "modelscope[audio_asr]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+#### 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
 ```
 
-- Clone the repo and install other packages
+### 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
+```
+
+### Install modelscope (Optional)
+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
 ```