From e14b2f03e8d954906ff3e9017281a2fd08fe6d6f Mon Sep 17 00:00:00 2001 From: nl <3210346136@qq.com> Date: Mon, 7 Mar 2022 22:05:48 +0800 Subject: [PATCH] =?UTF-8?q?docs:=20=E6=9B=B4=E6=96=B0readme=E6=96=87?= =?UTF-8?q?=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 14 ++++++++------ README_EN.md | 15 ++++++++------- 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index e87a858..72ba8db 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ -# ASRT: A Deep-Learning-Based Chinese Speech Recognition System -ASRT是一个基于深度学习的中文语音识别系统，如果您觉得喜欢，请点一个 **"Star"** 吧~ +![](https://res.ailemon.net/common/asrt_title_header.png) [![GPL-3.0 Licensed](https://img.shields.io/badge/License-GPL3.0-blue.svg?style=flat)](https://opensource.org/licenses/GPL-3.0) [![TensorFlow Version](https://img.shields.io/badge/Tensorflow-1.15+-blue.svg)](https://www.tensorflow.org/) [![Python Version](https://img.shields.io/badge/Python-3.6+-blue.svg)](https://www.python.org/) -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5808435.svg)](https://doi.org/10.5281/zenodo.5808435) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5808434.svg)](https://doi.org/10.5281/zenodo.5808434) + +ASRT是一个基于深度学习的中文语音识别系统，如果您觉得喜欢，请点一个 **"Star"** 吧~ **ReadMe Language** | 中文版 | [English](https://github.com/nl8590687/ASRT_SpeechRecognition/blob/master/README_EN.md) | @@ -62,6 +63,7 @@ ASRT训练和部署教程请看： This project uses tensorFlow.keras based on deep convolutional neural network and long-short memory neural network, attention mechanism and CTC to implement. * **操作步骤** +以在Linux系统下的操作为例： 首先通过Git将本项目克隆到您的计算机上，然后下载本项目训练所需要的数据集，下载链接详见[文档末尾部分](https://github.com/nl8590687/ASRT_SpeechRecognition#data-sets-%E6%95%B0%E6%8D%AE%E9%9B%86)。 ```shell @@ -70,9 +72,9 @@ $ git clone https://github.com/nl8590687/ASRT_SpeechRecognition.git 或者您也可以通过 "Fork" 按钮，将本项目Copy一份副本，然后通过您自己的SSH密钥克隆到本地。 -通过git克隆仓库以后，进入项目根目录；并创建一个存储数据的子目录， 例如 `dataset/` (可使用软链接代替)，然后将下载好的数据集直接解压进去 +通过git克隆仓库以后，进入项目根目录；并创建一个存储数据的子目录， 例如 `/data/speech_data` (可使用软链接代替)，然后将下载好的数据集直接解压进去 -注意，当前版本中，在配置文件里，默认添加了Thchs30和ST-CMDS两个数据集，如果不需要请自行删除。如果要使用其他数据集需要自行添加数据配置，并提前使用ASRT支持的标准格式整理数据。 +注意，当前版本中，在配置文件里，默认添加了Thchs30、ST-CMDS、Primewords、aishell-1、aidatatang200、MagicData 六个数据集，如果不需要请自行删除。如果要使用其他数据集需要自行添加数据配置，并提前使用ASRT支持的标准格式整理数据。 ```shell $ cd ASRT_SpeechRecognition @@ -240,7 +242,7 @@ $ pip install -r requirements.txt ## 参考引用本项目 -[DOI: 10.5281/zenodo.5808435](https://doi.org/10.5281/zenodo.5808435) +[DOI: 10.5281/zenodo.5808434](https://doi.org/10.5281/zenodo.5808434) ## Contributors 贡献者们 diff --git a/README_EN.md b/README_EN.md index 9b5bc9b..18f14fa 100644 --- a/README_EN.md +++ b/README_EN.md @@ -1,9 +1,11 @@ -# ASRT: A Deep-Learning-Based Chinese Speech Recognition System +![](https://res.ailemon.net/common/asrt_title_header_en.png) [![GPL-3.0 Licensed](https://img.shields.io/badge/License-GPL3.0-blue.svg?style=flat)](https://opensource.org/licenses/GPL-3.0) [![TensorFlow Version](https://img.shields.io/badge/Tensorflow-1.15+-blue.svg)](https://www.tensorflow.org/) [![Python Version](https://img.shields.io/badge/Python-3.6+-blue.svg)](https://www.python.org/) -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5808435.svg)](https://doi.org/10.5281/zenodo.5808435) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5808434.svg)](https://doi.org/10.5281/zenodo.5808434) + +ASRT is A Deep-Learning-Based Chinese Speech Recognition System. If you like this project, please **star** it. **ReadMe Language** | [中文版](https://github.com/nl8590687/ASRT_SpeechRecognition/blob/master/README.md) | English | @@ -41,6 +43,7 @@ For more infomation please refer to author's blog website: [AILemon Blog](https: This project uses tensorFlow.keras based on deep convolutional neural network and long-short memory neural network, attention mechanism and CTC to implement. * **Steps** +Take the operation under the Linux system as an example: First, clone the project to your computer through Git, and then download the data sets needed for the training of this project. For the download links, please refer to [End of Document](https://github.com/nl8590687/ASRT_SpeechRecognition/blob/master/README_EN.md#data-sets) ```shell @@ -49,7 +52,7 @@ $ git clone https://github.com/nl8590687/ASRT_SpeechRecognition.git Or you can use the "Fork" button to copy a copy of the project and then clone it locally with your own SSH key. -After cloning the repository via git, go to the project root directory; create a subdirectory `dataset/` (you can use a soft link instead) for datasets, and then extract the downloaded datasets directly into it. +After cloning the repository via git, go to the project root directory; create a subdirectory `/data/speech_data` (you can use a soft link instead) for datasets, and then extract the downloaded datasets directly into it. ```shell $ cd ASRT_SpeechRecognition @@ -59,9 +62,7 @@ $ mkdir /data/speech_data $ tar zxf -C /data/speech_data/ ``` -Then, you need to copy all the files in the 'datalist' directory to the dataset directory, that is, put them together with the data set. - -Note that in the current version, in the configuration file, two data sets, Thchs30 and ST-CMDS, are added by default, please delete them if you don’t need them. If you want to use other data sets, you need to add data configuration yourself, and use the standard format supported by ASRT to organize the data in advance. +Note that in the current version, in the configuration file, six data sets, Thchs30, ST-CMDS, Primewords, aishell-1, aidatatang200, MagicData, are added by default, please delete them if you don’t need them. If you want to use other data sets, you need to add data configuration yourself, and use the standard format supported by ASRT to organize the data in advance. To download pinyin syllable list files for default dataset: ```shell @@ -211,7 +212,7 @@ If the provided dataset link cannot be opened and downloaded, click this link [O ## Cite this project -[DOI: 10.5281/zenodo.5808435](https://doi.org/10.5281/zenodo.5808435) +[DOI: 10.5281/zenodo.5808434](https://doi.org/10.5281/zenodo.5808434) ## Contributors