commit 93369dde25b5d18948cd9429aa29187d2b98fdfd Author: tangjun1990 <274802107@qq.com> Date: Fri Aug 4 14:26:05 2023 +0800 fc diff --git a/Dockerfile b/Dockerfile new file mode 100755 index 0000000..dc6e933 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,45 @@ +# Ubuntu 20.04 +FROM ubuntu:focal + +# 切换默认shell为bash +SHELL ["/bin/bash", "-c"] + +ADD ./ /asrt_server + +WORKDIR /asrt_server + +# 最小化源,缩短apt update时间(ca-certificates必须先安装才支持换tsinghua源) +RUN echo 'deb http://archive.ubuntu.com/ubuntu/ focal main restricted' > /etc/apt/sources.list + +RUN apt update && apt install -y ca-certificates + +RUN echo $'\ +deb http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse\ +\n# deb-src http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse\n\ +deb http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse\ +\n# deb-src http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse\n\ +deb http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse\ +\n# deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse\n\ +deb http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse\ +\n# deb-src http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse\n\ +deb http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse\ +\n# deb-src http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse'\ +> /etc/apt/sources.list + +RUN apt update && apt install -y python3 python3-pip + +RUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple + +RUN pip3 install wave scipy matplotlib tensorflow-cpu==2.5.3 numpy==1.19.2 requests flask waitress grpcio==1.34.0 grpcio-tools==1.34.0 protobuf + +RUN echo $'cd /asrt_server \n python3 asrserver_http.py & \n python3 asrserver_grpc.py' > /asrt_server/start.sh && chmod +x /asrt_server/start.sh + +# refer: https://docs.docker.com/engine/reference/builder/#expose +EXPOSE 20001/tcp 20002/tcp + +ENTRYPOINT ["/bin/bash", "/asrt_server/start.sh"] + +# https://docs.docker.com/engine/reference/commandline/build/#options +# docker build --progress plain --rm --build-arg TAG=1.3.0 --tag asrt/api_server:1.3.0 . +# https://docs.docker.com/engine/reference/commandline/run/#options +# docker run --rm -it -p 20001:20001 -p 20002:20002 --name asrt -d asrt/api_server:1.3.0 diff --git a/LICENSE b/LICENSE new file mode 100755 index 0000000..af11a8e --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + ASRT_SpeechRecognition Copyright (C) 2017 nl8590687 + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100755 index 0000000..248cc3b --- /dev/null +++ b/README.md @@ -0,0 +1,238 @@ +![](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) +[![Stars](https://img.shields.io/github/stars/nl8590687/ASRT_SpeechRecognition)](https://github.com/nl8590687/ASRT_SpeechRecognition) +[![TensorFlow Version](https://img.shields.io/badge/Tensorflow-2.5+-blue.svg)](https://www.tensorflow.org/) +[![Python Version](https://img.shields.io/badge/Python-3.7+-blue.svg)](https://www.python.org/) +[![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) | + +[**ASRT项目主页**](https://asrt.ailemon.net/) | +[**发布版下载**](https://wiki.ailemon.net/docs/asrt-doc/download) | +[**查看本项目的Wiki文档**](https://wiki.ailemon.net/docs/asrt-doc) | +[**实用效果体验Demo**](https://asrt.ailemon.net/demo) | +[**打赏作者**](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deo9u61unti9) + +如果程序运行期间或使用中有什么问题,可以及时在issue中提出来,我将尽快做出答复。本项目作者交流QQ群:**894112051** ,加微信群请先加AI柠檬微信号:**ailemon-me** ,并备注“ASRT语音识别” + +
+ +提问前请仔细查看[项目文档](https://asrt.ailemon.net/docs/)、 +[FAQ常见问题](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deoeud494h4f) +以及[Issues](https://github.com/nl8590687/ASRT_SpeechRecognition/issues) 避免重复提问 + +如果程序运行时有任何异常情况,在提问时请发出完整截图,并注明所使用的CPU架构,GPU型号,操作系统、Python,TensorFlow和CUDA版本,以及是否修改过任何代码或增删数据集等。 + +## Introduction 简介 + +本项目使用tensorFlow.keras基于深度卷积神经网络和长短时记忆神经网络、注意力机制以及CTC实现。 + +## 训练模型的最低软硬件要求 +### 硬件 +* CPU: 4核 (x86_64, amd64) + +* RAM: 16 GB + +* GPU: NVIDIA, Graph Memory 11GB+ (1080ti起步) +* 硬盘: 500 GB 机械硬盘(或固态硬盘) + +### 软件 +* Linux: Ubuntu 18.04 + / CentOS 7 + 或 Windows 10/11 +* Python: 3.7 - 3.10 及后续版本 +* TensorFlow: 2.5 - 2.11 及后续版本 + +## 快速开始 + +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 +$ 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 `/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、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 + +$ mkdir /data/speech_data + +$ tar zxf <数据集压缩文件名> -C /data/speech_data/ +``` + +下载默认数据集的拼音标签文件: +```shell +$ python download_default_datalist.py +``` + +目前可用的模型有24、25、251和251bn + +运行本项目之前,请安装必要的[Python3版依赖库](https://github.com/nl8590687/ASRT_SpeechRecognition#python-import) + +本项目开始训练请执行: +```shell +$ python3 train_speech_model.py +``` +本项目开始测试请执行: +```shell +$ python3 evaluate_speech_model.py +``` +测试之前,请确保代码中填写的模型文件路径存在。 + +预测单条音频文件的语音识别文本: +```shell +$ python3 predict_speech_file.py +``` + +启动ASRT HTTP协议的API服务器启动请执行: +```shell +$ python3 asrserver_http.py +``` + +本地测试调用HTTP协议API服务是否成功: +```shell +$ python3 client_http.py +``` + +启动ASRT GRPC协议的API服务器启动请执行: +```shell +$ python3 asrserver_grpc.py +``` + +本地测试调用GRPC协议API服务是否成功: +```shell +$ python3 client_grpc.py +``` + +请注意,开启API服务器之后,需要使用本ASRT项目对应的客户端软件来进行语音识别,详见Wiki文档[下载ASRT语音识别客户端SDK和Demo](https://wiki.ailemon.net/docs/asrt-doc/download)。 + +如果要训练和使用非251bn版模型,请在代码中 `from speech_model.xxx import xxx` 的相应位置做修改。 + +使用docker直接部署ASRT: +```shell +$ docker pull ailemondocker/asrt_service:1.3.0 +$ docker run --rm -it -p 20001:20001 -p 20002:20002 --name asrt-server -d ailemondocker/asrt_service:1.3.0 +``` +仅CPU运行推理识别,不作训练 + +## Model 模型 + +### Speech Model 语音模型 + +DCNN + CTC + +其中,输入的音频的最大时间长度为16秒,输出为对应的汉语拼音序列 + +* 关于下载已经训练好的模型的问题 + +已经训练好的模型包含在发布版服务端程序压缩包里面,发布版成品服务端程序可以在此下载:[ASRT下载页面](https://wiki.ailemon.net/docs/asrt-doc/download)。 + +Github本仓库下[Releases](https://github.com/nl8590687/ASRT_SpeechRecognition/releases)页面里面还包括各个不同版本的介绍信息,每个版本下方的zip压缩包也是包含已经训练好的模型的发布版服务端程序压缩包。 + +### Language Model 语言模型 + +基于概率图的最大熵隐马尔可夫模型 + +输入为汉语拼音序列,输出为对应的汉字文本 + +## About Accuracy 关于准确率 + +当前,最好的模型在测试集上基本能达到85%的汉语拼音正确率 + +## Python依赖库 + +* tensorFlow (2.5-2.11+) +* numpy +* wave +* matplotlib +* scipy +* requests +* flask +* waitress +* grpcio / grpcio-tools / protobuf + +不会安装环境的同学请直接运行以下命令(前提是有GPU且已经安装好 Python3.9、CUDA 11.2 和 cudnn 8.1): + +```shell +$ pip install -r requirements.txt +``` + +[依赖环境和性能配置要求](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deobk7bmlgd6) + +## Data Sets 数据集 + +完整内容请查看:[几个最新免费开源的中文语音数据集](https://blog.ailemon.net/2018/11/21/free-open-source-chinese-speech-datasets/) + +|数据集|时长|大小|国内下载|国外下载| +|-|-|-|-|-| +|THCHS30|40h|6.01G|[data_thchs30.tgz]()|[data_thchs30.tgz]()| +|ST-CMDS|100h|7.67G|[ST-CMDS-20170001_1-OS.tar.gz]()|[ST-CMDS-20170001_1-OS.tar.gz]()| +|AIShell-1|178h|14.51G|[data_aishell.tgz]()|[data_aishell.tgz]()| +|Primewords|100h|8.44G|[primewords_md_2018_set1.tar.gz]()|[primewords_md_2018_set1.tar.gz]()| +|aidatatang_200zh|200h|17.47G|[aidatatang_200zh.tgz]()|[aidatatang_200zh.tgz]()| +|MagicData|755h|52G/1.0G/2.2G| [train_set.tar.gz]() / [dev_set.tar.gz]() / [test_set.tar.gz]()|[train_set.tar.gz]() / [dev_set.tar.gz]() / [test_set.tar.gz]()| + + 注:AISHELL-1 数据集解压方法 + + ``` + $ tar xzf data_aishell.tgz + $ cd data_aishell/wav + $ for tar in *.tar.gz; do tar xvf $tar; done + ``` + +特别鸣谢!感谢前辈们的公开语音数据集 + +如果提供的数据集链接无法打开和下载,请点击该链接 [OpenSLR](http://www.openslr.org) + +## ASRT语音识别API客户端调用SDK + +ASRT为客户端通过RPC方式调用开发语音识别功能提供了不同平台和编程语言的SDK接入能力,对于其他平台,可直接通过调用通用RESTful Open API方式进行语音识别功能接入。具体接入步骤请看ASRT项目文档。 + +|客户端平台|项目仓库链接| +|-|-| +|Windows客户端SDK和Demo|[ASRT_SDK_WinClient](https://github.com/nl8590687/ASRT_SDK_WinClient)| +|跨平台Python3客户端SDK和Demo|[ASRT_SDK_Python3](https://github.com/nl8590687/ASRT_SDK_Python3)| +|跨平台Golang客户端SDK和Demo|[asrt-sdk-go](https://github.com/nl8590687/asrt-sdk-go)| +|Java客户端SDK和Demo|[ASRT_SDK_Java](https://github.com/nl8590687/ASRT_SDK_Java)| + +## ASRT相关资料 +* [查看ASRT项目的Wiki文档](https://wiki.ailemon.net/docs/asrt-doc) + +ASRT的原理请查看本文: +* [ASRT:一个中文语音识别系统](https://blog.ailemon.net/2018/08/29/asrt-a-chinese-speech-recognition-system/) + +ASRT训练和部署教程请看: +* [教你如何使用ASRT训练中文语音识别模型]() +* [教你如何使用ASRT部署中文语音识别API服务器]() + +关于经常被问到的统计语言模型原理的问题,请看: + +* [统计语言模型:从中文拼音到文本](https://blog.ailemon.net/2017/04/27/statistical-language-model-chinese-pinyin-to-words/) +* [统计N元语言模型生成算法:简单中文词频统计](https://blog.ailemon.net/2017/02/20/simple-words-frequency-statistic-without-segmentation-algorithm/) + +关于CTC的问题请看: + +* [[翻译]使用CTC进行序列建模]() + +更多内容请访问作者的博客:[AI柠檬博客](https://blog.ailemon.net/) + +或使用[AI柠檬站内搜索引擎](https://s.ailemon.net/)进行相关信息的搜索 + +## License 开源许可协议 + +[GPL v3.0](LICENSE) © [nl8590687](https://github.com/nl8590687) 作者:[AI柠檬](https://www.ailemon.net/) + +## 参考引用本项目 + +[DOI: 10.5281/zenodo.5808434](https://doi.org/10.5281/zenodo.5808434) + +## Contributors 贡献者们 + +[贡献者页面](https://github.com/nl8590687/ASRT_SpeechRecognition/graphs/contributors) + +@nl8590687 (repo owner) diff --git a/README_EN.md b/README_EN.md new file mode 100755 index 0000000..a7a2ace --- /dev/null +++ b/README_EN.md @@ -0,0 +1,236 @@ +![](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) +[![Stars](https://img.shields.io/github/stars/nl8590687/ASRT_SpeechRecognition)](https://github.com/nl8590687/ASRT_SpeechRecognition) +[![TensorFlow Version](https://img.shields.io/badge/Tensorflow-2.5+-blue.svg)](https://www.tensorflow.org/) +[![Python Version](https://img.shields.io/badge/Python-3.7+-blue.svg)](https://www.python.org/) +[![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 | + +[**ASRT Project Home Page**](https://asrt.ailemon.net/) | +[**Released Download**](https://wiki.ailemon.net/docs/asrt-doc/download) | +[**View this project's wiki document (Chinese)**](https://wiki.ailemon.net/docs/asrt-doc) | +[**Experience Demo**](https://asrt.ailemon.net/demo) | +[**Donate**](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deo9u61unti9) + +If you have any questions in your works with this project, welcome to put up issues in this repo and I will response as soon as possible. + +You can check the [FAQ Page (Chinese)](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deoeud494h4f) first before asking questions to avoid repeating questions. + +If there is any abnormality when the program is running, please send a complete screenshot when asking questions, and indicate the CPU architecture, GPU model, operating system, Python, TensorFlow and CUDA versions used, and whether any code has been modified or data sets have been added or deleted, etc. . + +## Introduction + +This project uses tensorFlow.keras based on deep convolutional neural network and long-short memory neural network, attention mechanism and CTC to implement. + +## Minimum requirements for training +### Hardware +* CPU: 4 Core (x86_64, amd64) + +* RAM: 16 GB + +* GPU: NVIDIA, Graph Memory 11GB+ (>1080ti) +* 硬盘: 500 GB HDD(or SSD) + +### Software +* Linux: Ubuntu 18.04 + / CentOS 7 + or Windows 10/11 +* Python: 3.7 - 3.10 and later +* TensorFlow: 2.5 - 2.11 and later + +## Quick Start +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 +$ 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 `/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 + +$ mkdir /data/speech_data + +$ tar zxf -C /data/speech_data/ +``` + +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 +$ python download_default_datalist.py +``` + +Currently available models are 24, 25, 251 and 251bn + +Before running this project, please install the necessary [Python3 version dependent library](https://github.com/nl8590687/ASRT_SpeechRecognition#python-import) + +To start training this project, please execute: +```shell +$ python3 train_speech_model.py +``` +To start the test of this project, please execute: +```shell +$ python3 evaluate_speech_model.py +``` +Before testing, make sure the model file path filled in the code files exists. + +To predict one wave audio file for speech recognition: +```shell +$ python3 predict_speech_file.py +``` + +To startup ASRT API Server with HTTP protocol please execute: +```shell +$ python3 asrserver_http.py +``` + +Please note that after opening the API server, you need to use the client software corresponding to this ASRT project for voice recognition. For details, see the Wiki documentation to [download ASRT Client SDK & Demo](https://wiki.ailemon.net/docs/asrt-doc/download). + + +To test whether it is successful or not that calls api service interface with HTTP protocol: +```shell +$ python3 client_http.py +``` + +To startup ASRT API Server with GRPC protocol please execute: +```shell +$ python3 asrserver_grpc.py +``` + +To test whether it is successful or not that calls api service interface with GRPC protocol: +```shell +$ python3 client_grpc.py +``` + +If you want to train and use other model(not Model 251bn), make changes in the corresponding position of the `from speech_model.xxx import xxx` in the code files. + +If there is any problem during the execution of the program or during use, it can be promptly put forward in the issue, and I will reply as soon as possible. + +Deploy ASRT by docker: +```shell +$ docker pull ailemondocker/asrt_service:1.3.0 +$ docker run --rm -it -p 20001:20001 -p 20002:20002 --name asrt-server -d ailemondocker/asrt_service:1.3.0 +``` +It will start a api server for recognition rather than training. + +## Model + +### Speech Model + +DCNN + CTC + +The maximum length of the input audio is 16 seconds, and the output is the corresponding Chinese pinyin sequence. + +* Questions about downloading trained models + +The released finished software that includes trained model weights can be downloaded from [ASRT download page](https://wiki.ailemon.net/docs/asrt-doc/download). + +Github [Releases](https://github.com/nl8590687/ASRT_SpeechRecognition/releases) page includes the archives of the various versions of the software released and it's introduction. Under each version module, there is a zip file that includes trained model weights files. + +### Language Model + +Maximum Entropy Hidden Markov Model Based on Probability Graph. + +The input is a Chinese pinyin sequence, and the output is the corresponding Chinese character text. + +## About Accuracy + +At present, the best model can basically reach 85% of Pinyin correct rate on the test set. + +## Python Dependency Library + +* tensorFlow (2.5-2.11+) +* numpy +* wave +* matplotlib +* math +* scipy +* requests +* flask +* waitress +* grpcio / grpcio-tools / protobuf + +If you have trouble when install those packages, please run the following script to do it as long as you have a GPU and python 3.9, CUDA 11.2 and cudnn 8.1 have been installed: + +```shell +$ pip install -r requirements.txt +``` + +[Dependent Environment Details and Hardware Requirement](https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1deobk7bmlgd6) + +## ASRT Client SDK for Calling Speech Recognition API + +ASRT provides the abilities to import client SDKs for several platform and programing language for client develop speech recognition features , which work by RPC. Please refer ASRT project documents for detail. + +|Client Platform|Project Repos Link| +|-|-| +|Windows Client SDK & Demo|[ASRT_SDK_WinClient](https://github.com/nl8590687/ASRT_SDK_WinClient)| +|Python3 Client SDK & Demo (Any Platform)|[ASRT_SDK_Python3](https://github.com/nl8590687/ASRT_SDK_Python3)| +|Golang Client SDK & Demo|[asrt-sdk-go](https://github.com/nl8590687/asrt-sdk-go)| +|Java Client SDK & Demo|[ASRT_SDK_Java](https://github.com/nl8590687/ASRT_SDK_Java)| + +## Data Sets + +For full content please refer: [Some free Chinese speech datasets (Chinese)](https://blog.ailemon.net/2018/11/21/free-open-source-chinese-speech-datasets/) + +|Dataset|Time|Size|Download (CN Mirrors)|Download (Source)| +|-|-|-|-|-| +|THCHS30|40h|6.01G|[data_thchs30.tgz]()|[data_thchs30.tgz]()| +|ST-CMDS|100h|7.67G|[ST-CMDS-20170001_1-OS.tar.gz]()|[ST-CMDS-20170001_1-OS.tar.gz]()| +|AIShell-1|178h|14.51G|[data_aishell.tgz]()|[data_aishell.tgz]()| +|Primewords|100h|8.44G|[primewords_md_2018_set1.tar.gz]()|[primewords_md_2018_set1.tar.gz]()| +|aidatatang_200zh|200h|17.47G|[aidatatang_200zh.tgz]()|[aidatatang_200zh.tgz]()| +|MagicData|755h|52G/1.0G/2.2G| [train_set.tar.gz]() / [dev_set.tar.gz]() / [test_set.tar.gz]()|[train_set.tar.gz]() / [dev_set.tar.gz]() / [test_set.tar.gz]()| + + + Note:The way to unzip AISHELL-1 dataset + + ``` + $ tar xzf data_aishell.tgz + $ cd data_aishell/wav + $ for tar in *.tar.gz; do tar xvf $tar; done + ``` + +Special thanks! Thanks to the predecessors' public voice data set. + +If the provided dataset link cannot be opened and downloaded, click this link [OpenSLR](http://www.openslr.org) + +## ASRT Docuemnts + +* [ASRT project's Wiki document](https://wiki.ailemon.net/docs/asrt-doc) + +A post about ASRT's introduction +* [ASRT: Chinese Speech Recognition System (Chinese)](https://blog.ailemon.net/2018/08/29/asrt-a-chinese-speech-recognition-system/) + +About how to use ASRT to train and deploy: +* [Teach you how to use ASRT to train Chinese ASR model (Chinese)]() +* [Teach you how to use ASRT to deploy Chinese ASR API Server (Chinese)]() + +For questions about the principles of the statistical language model that are often asked, see: +* [Simple Chinese word frequency statistics to generate N-gram language model (Chinese)](https://blog.ailemon.net/2017/02/20/simple-words-frequency-statistic-without-segmentation-algorithm/) +* [Statistical Language Model: Chinese Pinyin to Words (Chinese)](https://blog.ailemon.net/2017/04/27/statistical-language-model-chinese-pinyin-to-words/) + +For questions about CTC, see: + +* [[Translation] Sequence Modeling with CTC (Chinese)]() + +For more infomation please refer to author's blog website: [AILemon Blog](https://blog.ailemon.net/) (Chinese) + +## License + +[GPL v3.0](LICENSE) © [nl8590687](https://github.com/nl8590687) Author: [ailemon](https://www.ailemon.net/) + +## Cite this project + +[DOI: 10.5281/zenodo.5808434](https://doi.org/10.5281/zenodo.5808434) + +## Contributors + +[Contributors Page](https://github.com/nl8590687/ASRT_SpeechRecognition/graphs/contributors) + +@nl8590687 (repo owner) diff --git a/asrserver_grpc.py b/asrserver_grpc.py new file mode 100755 index 0000000..db6994c --- /dev/null +++ b/asrserver_grpc.py @@ -0,0 +1,180 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别基于gRPC协议的API服务器程序 +""" + +import argparse +import time +from concurrent import futures +import grpc + +from assets.asrt_pb2_grpc import AsrtGrpcServiceServicer, add_AsrtGrpcServiceServicer_to_server +from assets.asrt_pb2 import SpeechResponse, TextResponse +from speech_model import ModelSpeech +from model_zoo.speech_model.keras_backend import SpeechModel251BN +from speech_features import Spectrogram +from language_model3 import ModelLanguage +from utils.ops import decode_wav_bytes + +API_STATUS_CODE_OK = 200000 # OK +API_STATUS_CODE_OK_PART = 206000 # 部分结果OK,用于stream +API_STATUS_CODE_CLIENT_ERROR = 400000 +API_STATUS_CODE_CLIENT_ERROR_FORMAT = 400001 # 请求数据格式错误 +API_STATUS_CODE_CLIENT_ERROR_CONFIG = 400002 # 请求数据配置不支持 +API_STATUS_CODE_SERVER_ERROR = 500000 +API_STATUS_CODE_SERVER_ERROR_RUNNING = 500001 # 服务器运行中出错 + +parser = argparse.ArgumentParser(description='ASRT gRPC Protocol API Service') +parser.add_argument('--listen', default='0.0.0.0', type=str, help='the network to listen') +parser.add_argument('--port', default='20002', type=str, help='the port to listen') +args = parser.parse_args() + +AUDIO_LENGTH = 1600 +AUDIO_FEATURE_LENGTH = 200 +CHANNELS = 1 +# 默认输出的拼音的表示大小是1428,即1427个拼音+1个空白块 +OUTPUT_SIZE = 1428 +sm251bn = SpeechModel251BN( + input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS), + output_size=OUTPUT_SIZE +) +feat = Spectrogram() +ms = ModelSpeech(sm251bn, feat, max_label_length=64) +ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') + +ml = ModelLanguage('model_language') +ml.load_model() + +_ONE_DAY_IN_SECONDS = 60 * 60 * 24 + + +class ApiService(AsrtGrpcServiceServicer): + """ + 继承AsrtGrpcServiceServicer,实现hello方法 + """ + + def __init__(self): + pass + + def Speech(self, request, context): + """ + 具体实现Speech的方法, 并按照pb的返回对象构造SpeechResponse返回 + :param request: + :param context: + :return: + """ + wav_data = request.wav_data + wav_samples = decode_wav_bytes(samples_data=wav_data.samples, + channels=wav_data.channels, byte_width=wav_data.byte_width) + result = ms.recognize_speech(wav_samples, wav_data.sample_rate) + print("语音识别声学模型结果:", result) + return SpeechResponse(status_code=API_STATUS_CODE_OK, status_message='', + result_data=result) + + def Language(self, request, context): + """ + 具体实现Language的方法, 并按照pb的返回对象构造TextResponse返回 + :param request: + :param context: + :return: + """ + print('Language收到了请求:', request) + result = ml.pinyin_to_text(list(request.pinyins)) + print('Language结果:', result) + return TextResponse(status_code=API_STATUS_CODE_OK, status_message='', + text_result=result) + + def All(self, request, context): + """ + 具体实现All的方法, 并按照pb的返回对象构造TextResponse返回 + :param request: + :param context: + :return: + """ + wav_data = request.wav_data + wav_samples = decode_wav_bytes(samples_data=wav_data.samples, + channels=wav_data.channels, byte_width=wav_data.byte_width) + result_speech = ms.recognize_speech(wav_samples, wav_data.sample_rate) + result = ml.pinyin_to_text(result_speech) + print("语音识别结果:", result) + return TextResponse(status_code=API_STATUS_CODE_OK, status_message='', + text_result=result) + + def Stream(self, request_iterator, context): + """ + 具体实现Stream的方法, 并按照pb的返回对象构造TextResponse返回 + :param request_iterator: + :param context: + :return: + """ + result = list() + tmp_result_last = list() + beam_size = 100 + + for request in request_iterator: + wav_data = request.wav_data + wav_samples = decode_wav_bytes(samples_data=wav_data.samples, + channels=wav_data.channels, + byte_width=wav_data.byte_width) + result_speech = ms.recognize_speech(wav_samples, wav_data.sample_rate) + + for item_pinyin in result_speech: + tmp_result = ml.pinyin_stream_decode(tmp_result_last, item_pinyin, beam_size) + if len(tmp_result) == 0 and len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + print("流式语音识别结果:", ''.join(result)) + yield TextResponse(status_code=API_STATUS_CODE_OK, status_message='', + text_result=''.join(result)) + result = list() + + tmp_result = ml.pinyin_stream_decode([], item_pinyin, beam_size) + tmp_result_last = tmp_result + yield TextResponse(status_code=API_STATUS_CODE_OK_PART, status_message='', + text_result=''.join(tmp_result[0][0])) + + if len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + print("流式语音识别结果:", ''.join(result)) + yield TextResponse(status_code=API_STATUS_CODE_OK, status_message='', + text_result=''.join(result)) + + +def run(host, port): + """ + gRPC API服务启动 + :return: + """ + server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) + add_AsrtGrpcServiceServicer_to_server(ApiService(), server) + server.add_insecure_port(''.join([host, ':', port])) + server.start() + print("start service...") + try: + while True: + time.sleep(_ONE_DAY_IN_SECONDS) + except KeyboardInterrupt: + server.stop(0) + + +if __name__ == '__main__': + run(host=args.listen, port=args.port) diff --git a/asrserver_http.py b/asrserver_http.py new file mode 100755 index 0000000..09e9358 --- /dev/null +++ b/asrserver_http.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别基于HTTP协议的API服务器程序 +""" + +import argparse +import base64 +import json +from flask import Flask, Response, request + +from speech_model import ModelSpeech +from model_zoo.speech_model.keras_backend import SpeechModel251BN +from speech_features import Spectrogram +from language_model3 import ModelLanguage +from utils.ops import decode_wav_bytes + +API_STATUS_CODE_OK = 200000 # OK +API_STATUS_CODE_CLIENT_ERROR = 400000 +API_STATUS_CODE_CLIENT_ERROR_FORMAT = 400001 # 请求数据格式错误 +API_STATUS_CODE_CLIENT_ERROR_CONFIG = 400002 # 请求数据配置不支持 +API_STATUS_CODE_SERVER_ERROR = 500000 +API_STATUS_CODE_SERVER_ERROR_RUNNING = 500001 # 服务器运行中出错 + +parser = argparse.ArgumentParser(description='ASRT HTTP+Json RESTful API Service') +parser.add_argument('--listen', default='0.0.0.0', type=str, help='the network to listen') +parser.add_argument('--port', default='20001', type=str, help='the port to listen') +args = parser.parse_args() + +app = Flask("ASRT API Service") + +AUDIO_LENGTH = 1600 +AUDIO_FEATURE_LENGTH = 200 +CHANNELS = 1 +# 默认输出的拼音的表示大小是1428,即1427个拼音+1个空白块 +OUTPUT_SIZE = 1428 +sm251bn = SpeechModel251BN( + input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS), + output_size=OUTPUT_SIZE +) +feat = Spectrogram() +ms = ModelSpeech(sm251bn, feat, max_label_length=64) +ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') + +ml = ModelLanguage('model_language') +ml.load_model() + + +class AsrtApiResponse: + """ + ASRT语音识别基于HTTP协议的API接口响应类 + """ + + def __init__(self, status_code, status_message='', result=''): + self.status_code = status_code + self.status_message = status_message + self.result = result + + def to_json(self): + """ + 类转json + """ + return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True) + + +# api接口根url:GET +@app.route('/', methods=["GET"]) +def index_get(): + """ + 根路径handle GET方法 + """ + buffer = '' + with open('assets/default.html', 'r', encoding='utf-8') as file_handle: + buffer = file_handle.read() + return Response(buffer, mimetype='text/html; charset=utf-8') + + +# api接口根url:POST +@app.route('/', methods=["POST"]) +def index_post(): + """ + 根路径handle POST方法 + """ + json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'ok') + buffer = json_data.to_json() + return Response(buffer, mimetype='application/json') + + +# 获取分类列表 +@app.route('/', methods=["POST"]) +def recognition_post(level): + """ + 其他路径 POST方法 + """ + # 读取json文件内容 + try: + if level == 'speech': + request_data = request.get_json() + samples = request_data['samples'] + wavdata_bytes = base64.urlsafe_b64decode(bytes(samples, encoding='utf-8')) + sample_rate = request_data['sample_rate'] + channels = request_data['channels'] + byte_width = request_data['byte_width'] + + wavdata = decode_wav_bytes(samples_data=wavdata_bytes, + channels=channels, byte_width=byte_width) + result = ms.recognize_speech(wavdata, sample_rate) + + json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'speech level') + json_data.result = result + buffer = json_data.to_json() + print('output:', buffer) + return Response(buffer, mimetype='application/json') + elif level == 'language': + request_data = request.get_json() + + seq_pinyin = request_data['sequence_pinyin'] + + result = ml.pinyin_to_text(seq_pinyin) + + json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'language level') + json_data.result = result + buffer = json_data.to_json() + print('output:', buffer) + return Response(buffer, mimetype='application/json') + elif level == 'all': + request_data = request.get_json() + + samples = request_data['samples'] + wavdata_bytes = base64.urlsafe_b64decode(samples) + sample_rate = request_data['sample_rate'] + channels = request_data['channels'] + byte_width = request_data['byte_width'] + + wavdata = decode_wav_bytes(samples_data=wavdata_bytes, + channels=channels, byte_width=byte_width) + result_speech = ms.recognize_speech(wavdata, sample_rate) + result = ml.pinyin_to_text(result_speech) + + json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'all level') + json_data.result = result + buffer = json_data.to_json() + print('ASRT Result:', result, 'output:', buffer) + return Response(buffer, mimetype='application/json') + else: + request_data = request.get_json() + print('input:', request_data) + json_data = AsrtApiResponse(API_STATUS_CODE_CLIENT_ERROR, '') + buffer = json_data.to_json() + print('output:', buffer) + return Response(buffer, mimetype='application/json') + except Exception as except_general: + request_data = request.get_json() + # print(request_data['sample_rate'], request_data['channels'], + # request_data['byte_width'], len(request_data['samples']), + # request_data['samples'][-100:]) + json_data = AsrtApiResponse(API_STATUS_CODE_SERVER_ERROR, str(except_general)) + buffer = json_data.to_json() + # print("input:", request_data, "\n", "output:", buffer) + print("output:", buffer, "error:", except_general) + return Response(buffer, mimetype='application/json') + + +if __name__ == '__main__': + # for development env + # app.run(host='0.0.0.0', port=20001) + # for production env + import waitress + + waitress.serve(app, host=args.listen, port=args.port) diff --git a/asrt_config.json b/asrt_config.json new file mode 100755 index 0000000..8641a8d --- /dev/null +++ b/asrt_config.json @@ -0,0 +1,122 @@ +{ + "dict_filename": "dict.txt", + + "dataset":{ + "train":[ + { + "name": "thchs30_train", + "data_list": "datalist/thchs30/train.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/thchs30/train.syllable.txt" + }, + { + "name": "stcmds_train", + "data_list": "datalist/st-cmds/train.wav.txt", + "data_path": "/data/speech_data", + "label_list": "datalist/st-cmds/train.syllable.txt" + }, + { + "name": "primewords_train", + "data_list": "datalist/primewords/train.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/primewords/train.syllable.txt" + }, + { + "name": "aishell_train", + "data_list": "datalist/aishell/train.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aishell/train.syllable.txt" + }, + { + "name": "aidatatang_train", + "data_list": "datalist/aidatatang_lst/train.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aidatatang_lst/train.syllable.txt" + }, + { + "name": "magicdata_train", + "data_list": "datalist/magicdata_lst/train.wav.lst", + "data_path": "/data/speech_data/magicdata", + "label_list": "datalist/magicdata_lst/train.syllable.txt" + } + ], + + "dev":[ + { + "name": "thchs30_dev", + "data_list": "datalist/thchs30/cv.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/thchs30/cv.syllable.txt" + }, + { + "name": "stcmds_dev", + "data_list": "datalist/st-cmds/dev.wav.txt", + "data_path": "/data/speech_data", + "label_list": "datalist/st-cmds/dev.syllable.txt" + }, + { + "name": "primewords_dev", + "data_list": "datalist/primewords/dev.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/primewords/dev.syllable.txt" + }, + { + "name": "aishell_dev", + "data_list": "datalist/aishell/dev.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aishell/dev.syllable.txt" + }, + { + "name": "aidatatang_dev", + "data_list": "datalist/aidatatang_lst/dev.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aidatatang_lst/dev.syllable.txt" + }, + { + "name": "magicdata_dev", + "data_list": "datalist/magicdata_lst/dev.wav.lst", + "data_path": "/data/speech_data/magicdata", + "label_list": "datalist/magicdata_lst/dev.syllable.txt" + } + ], + + "test":[ + { + "name": "thchs30_test", + "data_list": "datalist/thchs30/test.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/thchs30/test.syllable.txt" + }, + { + "name": "stcmds_test", + "data_list": "datalist/st-cmds/test.wav.txt", + "data_path": "/data/speech_data", + "label_list": "datalist/st-cmds/test.syllable.txt" + }, + { + "name": "primewords_test", + "data_list": "datalist/primewords/test.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/primewords/test.syllable.txt" + }, + { + "name": "aishell_test", + "data_list": "datalist/aishell/test.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aishell/test.syllable.txt" + }, + { + "name": "aidatatang_test", + "data_list": "datalist/aidatatang_lst/test.wav.lst", + "data_path": "/data/speech_data", + "label_list": "datalist/aidatatang_lst/test.syllable.txt" + }, + { + "name": "magicdata_test", + "data_list": "datalist/magicdata_lst/test.wav.lst", + "data_path": "/data/speech_data/magicdata", + "label_list": "datalist/magicdata_lst/test.syllable.txt" + } + ] + } +} \ No newline at end of file diff --git a/assets/asrt.proto b/assets/asrt.proto new file mode 100755 index 0000000..f328fe8 --- /dev/null +++ b/assets/asrt.proto @@ -0,0 +1,55 @@ +/* Copyright 2016-2099 Ailemon.net + +This file is part of ASRT Speech Recognition Tool. + +ASRT is free software: you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. +ASRT is distributed in the hope that it will be useful, + +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with ASRT. If not, see . +============================================================================ */ + +syntax = "proto3"; +package asrt; + +//定义服务接口 +service AsrtGrpcService { + rpc Speech (SpeechRequest) returns (SpeechResponse) {} //一个服务中可以定义多个接口,也就是多个函数功能 + rpc Language (LanguageRequest) returns (TextResponse) {} + rpc All (SpeechRequest) returns (TextResponse) {} + rpc Stream (stream SpeechRequest) returns (stream TextResponse) {} +} + +message SpeechRequest { + WavData wav_data = 1; +} + +message SpeechResponse { + int32 status_code = 1; + string status_message = 2; + repeated string result_data = 3; // 拼音结果 +} + +message LanguageRequest { + repeated string pinyins = 1; +} + +message TextResponse { + int32 status_code = 1; + string status_message = 2; + string text_result = 3; +} + +message WavData{ + bytes samples = 1; // wav样本点字节 + int32 sample_rate = 2; // wav采样率 + int32 channels = 3; // wav通道数 + int32 byte_width = 4; // wav样本字节宽度 +} diff --git a/assets/asrt_pb2.py b/assets/asrt_pb2.py new file mode 100755 index 0000000..69659bd --- /dev/null +++ b/assets/asrt_pb2.py @@ -0,0 +1,325 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: asrt.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database + +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + +DESCRIPTOR = _descriptor.FileDescriptor( + name='asrt.proto', + package='asrt', + syntax='proto3', + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\nasrt.proto\x12\x04\x61srt\"0\n\rSpeechRequest\x12\x1f\n\x08wav_data\x18\x01 \x01(\x0b\x32\r.asrt.WavData\"R\n\x0eSpeechResponse\x12\x13\n\x0bstatus_code\x18\x01 \x01(\x05\x12\x16\n\x0estatus_message\x18\x02 \x01(\t\x12\x13\n\x0bresult_data\x18\x03 \x03(\t\"\"\n\x0fLanguageRequest\x12\x0f\n\x07pinyins\x18\x01 \x03(\t\"P\n\x0cTextResponse\x12\x13\n\x0bstatus_code\x18\x01 \x01(\x05\x12\x16\n\x0estatus_message\x18\x02 \x01(\t\x12\x13\n\x0btext_result\x18\x03 \x01(\t\"U\n\x07WavData\x12\x0f\n\x07samples\x18\x01 \x01(\x0c\x12\x13\n\x0bsample_rate\x18\x02 \x01(\x05\x12\x10\n\x08\x63hannels\x18\x03 \x01(\x05\x12\x12\n\nbyte_width\x18\x04 \x01(\x05\x32\xec\x01\n\x0f\x41srtGrpcService\x12\x35\n\x06Speech\x12\x13.asrt.SpeechRequest\x1a\x14.asrt.SpeechResponse\"\x00\x12\x37\n\x08Language\x12\x15.asrt.LanguageRequest\x1a\x12.asrt.TextResponse\"\x00\x12\x30\n\x03\x41ll\x12\x13.asrt.SpeechRequest\x1a\x12.asrt.TextResponse\"\x00\x12\x37\n\x06Stream\x12\x13.asrt.SpeechRequest\x1a\x12.asrt.TextResponse\"\x00(\x01\x30\x01\x62\x06proto3' +) + +_SPEECHREQUEST = _descriptor.Descriptor( + name='SpeechRequest', + full_name='asrt.SpeechRequest', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='wav_data', full_name='asrt.SpeechRequest.wav_data', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=20, + serialized_end=68, +) + +_SPEECHRESPONSE = _descriptor.Descriptor( + name='SpeechResponse', + full_name='asrt.SpeechResponse', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='status_code', full_name='asrt.SpeechResponse.status_code', index=0, + number=1, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='status_message', full_name='asrt.SpeechResponse.status_message', index=1, + number=2, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='result_data', full_name='asrt.SpeechResponse.result_data', index=2, + number=3, type=9, cpp_type=9, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=70, + serialized_end=152, +) + +_LANGUAGEREQUEST = _descriptor.Descriptor( + name='LanguageRequest', + full_name='asrt.LanguageRequest', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='pinyins', full_name='asrt.LanguageRequest.pinyins', index=0, + number=1, type=9, cpp_type=9, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=154, + serialized_end=188, +) + +_TEXTRESPONSE = _descriptor.Descriptor( + name='TextResponse', + full_name='asrt.TextResponse', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='status_code', full_name='asrt.TextResponse.status_code', index=0, + number=1, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='status_message', full_name='asrt.TextResponse.status_message', index=1, + number=2, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='text_result', full_name='asrt.TextResponse.text_result', index=2, + number=3, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=190, + serialized_end=270, +) + +_WAVDATA = _descriptor.Descriptor( + name='WavData', + full_name='asrt.WavData', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='samples', full_name='asrt.WavData.samples', index=0, + number=1, type=12, cpp_type=9, label=1, + has_default_value=False, default_value=b"", + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='sample_rate', full_name='asrt.WavData.sample_rate', index=1, + number=2, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='channels', full_name='asrt.WavData.channels', index=2, + number=3, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='byte_width', full_name='asrt.WavData.byte_width', index=3, + number=4, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=272, + serialized_end=357, +) + +_SPEECHREQUEST.fields_by_name['wav_data'].message_type = _WAVDATA +DESCRIPTOR.message_types_by_name['SpeechRequest'] = _SPEECHREQUEST +DESCRIPTOR.message_types_by_name['SpeechResponse'] = _SPEECHRESPONSE +DESCRIPTOR.message_types_by_name['LanguageRequest'] = _LANGUAGEREQUEST +DESCRIPTOR.message_types_by_name['TextResponse'] = _TEXTRESPONSE +DESCRIPTOR.message_types_by_name['WavData'] = _WAVDATA +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +SpeechRequest = _reflection.GeneratedProtocolMessageType('SpeechRequest', (_message.Message,), { + 'DESCRIPTOR': _SPEECHREQUEST, + '__module__': 'asrt_pb2' + # @@protoc_insertion_point(class_scope:asrt.SpeechRequest) +}) +_sym_db.RegisterMessage(SpeechRequest) + +SpeechResponse = _reflection.GeneratedProtocolMessageType('SpeechResponse', (_message.Message,), { + 'DESCRIPTOR': _SPEECHRESPONSE, + '__module__': 'asrt_pb2' + # @@protoc_insertion_point(class_scope:asrt.SpeechResponse) +}) +_sym_db.RegisterMessage(SpeechResponse) + +LanguageRequest = _reflection.GeneratedProtocolMessageType('LanguageRequest', (_message.Message,), { + 'DESCRIPTOR': _LANGUAGEREQUEST, + '__module__': 'asrt_pb2' + # @@protoc_insertion_point(class_scope:asrt.LanguageRequest) +}) +_sym_db.RegisterMessage(LanguageRequest) + +TextResponse = _reflection.GeneratedProtocolMessageType('TextResponse', (_message.Message,), { + 'DESCRIPTOR': _TEXTRESPONSE, + '__module__': 'asrt_pb2' + # @@protoc_insertion_point(class_scope:asrt.TextResponse) +}) +_sym_db.RegisterMessage(TextResponse) + +WavData = _reflection.GeneratedProtocolMessageType('WavData', (_message.Message,), { + 'DESCRIPTOR': _WAVDATA, + '__module__': 'asrt_pb2' + # @@protoc_insertion_point(class_scope:asrt.WavData) +}) +_sym_db.RegisterMessage(WavData) + +_ASRTGRPCSERVICE = _descriptor.ServiceDescriptor( + name='AsrtGrpcService', + full_name='asrt.AsrtGrpcService', + file=DESCRIPTOR, + index=0, + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_start=360, + serialized_end=596, + methods=[ + _descriptor.MethodDescriptor( + name='Speech', + full_name='asrt.AsrtGrpcService.Speech', + index=0, + containing_service=None, + input_type=_SPEECHREQUEST, + output_type=_SPEECHRESPONSE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + _descriptor.MethodDescriptor( + name='Language', + full_name='asrt.AsrtGrpcService.Language', + index=1, + containing_service=None, + input_type=_LANGUAGEREQUEST, + output_type=_TEXTRESPONSE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + _descriptor.MethodDescriptor( + name='All', + full_name='asrt.AsrtGrpcService.All', + index=2, + containing_service=None, + input_type=_SPEECHREQUEST, + output_type=_TEXTRESPONSE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + _descriptor.MethodDescriptor( + name='Stream', + full_name='asrt.AsrtGrpcService.Stream', + index=3, + containing_service=None, + input_type=_SPEECHREQUEST, + output_type=_TEXTRESPONSE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + ]) +_sym_db.RegisterServiceDescriptor(_ASRTGRPCSERVICE) + +DESCRIPTOR.services_by_name['AsrtGrpcService'] = _ASRTGRPCSERVICE + +# @@protoc_insertion_point(module_scope) diff --git a/assets/asrt_pb2_grpc.py b/assets/asrt_pb2_grpc.py new file mode 100755 index 0000000..a9e197c --- /dev/null +++ b/assets/asrt_pb2_grpc.py @@ -0,0 +1,169 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc + +import assets.asrt_pb2 as asrt__pb2 + + +class AsrtGrpcServiceStub(object): + """定义服务接口 + """ + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.Speech = channel.unary_unary( + '/asrt.AsrtGrpcService/Speech', + request_serializer=asrt__pb2.SpeechRequest.SerializeToString, + response_deserializer=asrt__pb2.SpeechResponse.FromString, + ) + self.Language = channel.unary_unary( + '/asrt.AsrtGrpcService/Language', + request_serializer=asrt__pb2.LanguageRequest.SerializeToString, + response_deserializer=asrt__pb2.TextResponse.FromString, + ) + self.All = channel.unary_unary( + '/asrt.AsrtGrpcService/All', + request_serializer=asrt__pb2.SpeechRequest.SerializeToString, + response_deserializer=asrt__pb2.TextResponse.FromString, + ) + self.Stream = channel.stream_stream( + '/asrt.AsrtGrpcService/Stream', + request_serializer=asrt__pb2.SpeechRequest.SerializeToString, + response_deserializer=asrt__pb2.TextResponse.FromString, + ) + + +class AsrtGrpcServiceServicer(object): + """定义服务接口 + """ + + def Speech(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def Language(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def All(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def Stream(self, request_iterator, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_AsrtGrpcServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'Speech': grpc.unary_unary_rpc_method_handler( + servicer.Speech, + request_deserializer=asrt__pb2.SpeechRequest.FromString, + response_serializer=asrt__pb2.SpeechResponse.SerializeToString, + ), + 'Language': grpc.unary_unary_rpc_method_handler( + servicer.Language, + request_deserializer=asrt__pb2.LanguageRequest.FromString, + response_serializer=asrt__pb2.TextResponse.SerializeToString, + ), + 'All': grpc.unary_unary_rpc_method_handler( + servicer.All, + request_deserializer=asrt__pb2.SpeechRequest.FromString, + response_serializer=asrt__pb2.TextResponse.SerializeToString, + ), + 'Stream': grpc.stream_stream_rpc_method_handler( + servicer.Stream, + request_deserializer=asrt__pb2.SpeechRequest.FromString, + response_serializer=asrt__pb2.TextResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'asrt.AsrtGrpcService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + + +# This class is part of an EXPERIMENTAL API. +class AsrtGrpcService(object): + """定义服务接口 + """ + + @staticmethod + def Speech(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/asrt.AsrtGrpcService/Speech', + asrt__pb2.SpeechRequest.SerializeToString, + asrt__pb2.SpeechResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def Language(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/asrt.AsrtGrpcService/Language', + asrt__pb2.LanguageRequest.SerializeToString, + asrt__pb2.TextResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def All(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/asrt.AsrtGrpcService/All', + asrt__pb2.SpeechRequest.SerializeToString, + asrt__pb2.TextResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def Stream(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream(request_iterator, target, '/asrt.AsrtGrpcService/Stream', + asrt__pb2.SpeechRequest.SerializeToString, + asrt__pb2.TextResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, + metadata) diff --git a/assets/default.html b/assets/default.html new file mode 100755 index 0000000..a13d213 --- /dev/null +++ b/assets/default.html @@ -0,0 +1,25 @@ + + + + + ASRT Speech Recognition API + + + +

ASRT Speech Recognition API

+

framework version: 1.0

+

If you see this page, the ASRT api server is successfully installed and working.

+

For online documentation and support please refer to ASRT Project Page.

+

Please call this web api by post menthod.

+ Thank you for using ASRT. + + + \ No newline at end of file diff --git a/client_grpc.py b/client_grpc.py new file mode 100755 index 0000000..0262200 --- /dev/null +++ b/client_grpc.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别asrserver grpc协议测试专用客户端 +""" + +import grpc +import time + +from assets.asrt_pb2_grpc import AsrtGrpcServiceStub +from assets.asrt_pb2 import SpeechRequest, LanguageRequest, WavData + +from utils.ops import read_wav_bytes + + +def run_speech(): + """ + 请求ASRT服务Speech方法 + :return: + """ + conn = grpc.insecure_channel('127.0.0.1:20002') + client = AsrtGrpcServiceStub(channel=conn) + + wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('assets/A11_0.wav') + print('sample_width:', sample_width) + wav_data = WavData(samples=wav_bytes, sample_rate=sample_rate, + channels=channels, byte_width=sample_width) + + request = SpeechRequest(wav_data=wav_data) + time_stamp0 = time.time() + response = client.Speech(request) + time_stamp1 = time.time() + print('time:', time_stamp1 - time_stamp0, 's') + print("received:", response.result_data) + + +def run_lan(): + """ + 请求ASRT服务Language方法 + :return: + """ + conn = grpc.insecure_channel('127.0.0.1:20002') + client = AsrtGrpcServiceStub(channel=conn) + pinyin_data = ['ni3', 'hao3', 'ya5'] + request = LanguageRequest(pinyins=pinyin_data) + time_stamp0 = time.time() + response = client.Language(request) + time_stamp1 = time.time() + print('time:', time_stamp1 - time_stamp0, 's') + print("received:", response.text_result) + + +def run_all(): + """ + 请求ASRT服务All方法 + :return: + """ + conn = grpc.insecure_channel('127.0.0.1:20002') + client = AsrtGrpcServiceStub(channel=conn) + + wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('assets/A11_0.wav') + print('sample_width:', sample_width) + wav_data = WavData(samples=wav_bytes, sample_rate=sample_rate, + channels=channels, byte_width=sample_width) + + request = SpeechRequest(wav_data=wav_data) + time_stamp0 = time.time() + response = client.All(request) + time_stamp1 = time.time() + print("received:", response.text_result) + print('time:', time_stamp1 - time_stamp0, 's') + + +def run_stream(): + """ + 请求ASRT服务Stream方法 + :return: + """ + conn = grpc.insecure_channel('127.0.0.1:20002') + client = AsrtGrpcServiceStub(channel=conn) + + wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('assets/A11_0.wav') + print('sample_width:', sample_width) + wav_data = WavData(samples=wav_bytes, sample_rate=sample_rate, + channels=channels, byte_width=sample_width) + + # 先制造一些客户端能发送的数据 + def make_some_data(): + for _ in range(1): + time.sleep(1) + yield SpeechRequest(wav_data=wav_data) + + try: + status_response = client.Stream(make_some_data()) + for ret in status_response: + print("received:", ret.text_result, " , status:", ret.status_code) + time.sleep(0.1) + except Exception as any_exception: + print(f'err in send_status:{any_exception}') + return + + +if __name__ == '__main__': + # run_all() + run_stream() diff --git a/client_http.py b/client_http.py new file mode 100755 index 0000000..3d7fc37 --- /dev/null +++ b/client_http.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +''' +@author: nl8590687 +ASRT语音识别asrserver http协议测试专用客户端 +''' +import base64 +import json +import time +import requests +from utils.ops import read_wav_bytes + +URL = 'http://127.0.0.1:20001/all' + +wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('assets/A11_0.wav') +datas = { + 'channels': channels, + 'sample_rate': sample_rate, + 'byte_width': sample_width, + 'samples': str(base64.urlsafe_b64encode(wav_bytes), encoding='utf-8') +} +headers = {'Content-Type': 'application/json'} + +t0=time.time() +r = requests.post(URL, headers=headers, data=json.dumps(datas)) +t1=time.time() +r.encoding='utf-8' + +result = json.loads(r.text) +print(result) +print('time:', t1-t0, 's') diff --git a/data_loader.py b/data_loader.py new file mode 100755 index 0000000..ffda87a --- /dev/null +++ b/data_loader.py @@ -0,0 +1,102 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +加载语音数据集用的数据加载器的定义 +""" +import os +import random +import numpy as np +from utils.config import load_config_file, DEFAULT_CONFIG_FILENAME, load_pinyin_dict +from utils.ops import read_wav_data + + +class DataLoader: + """ + 数据加载器 + + 参数:\\ + config: 配置信息字典 + dataset_type: 要加载的数据集类型,包含('train', 'dev', 'test')三种 + """ + def __init__(self, dataset_type:str): + self.dataset_type = dataset_type + + self.data_list = list() + self.wav_dict = dict() + self.label_dict = dict() + self.pinyin_list = list() + self.pinyin_dict = dict() + self._load_data() + + def _load_data(self): + config = load_config_file(DEFAULT_CONFIG_FILENAME) + + self.pinyin_list, self.pinyin_dict = load_pinyin_dict(config['dict_filename']) + + for index in range(len(config['dataset'][self.dataset_type])): + filename_datalist = config['dataset'][self.dataset_type][index]['data_list'] + filename_datapath = config['dataset'][self.dataset_type][index]['data_path'] + with open(filename_datalist, 'r', encoding='utf-8') as file_pointer: + lines = file_pointer.read().split('\n') + for line in lines: + if len(line) == 0: + continue + tokens = line.split(' ') + self.data_list.append(tokens[0]) + self.wav_dict[tokens[0]] = os.path.join(filename_datapath, tokens[1]) + + filename_labellist = config['dataset'][self.dataset_type][index]['label_list'] + with open(filename_labellist, 'r', encoding='utf-8') as file_pointer: + lines = file_pointer.read().split('\n') + for line in lines: + if len(line) == 0: + continue + tokens = line.split(' ') + self.label_dict[tokens[0]] = tokens[1:] + + def get_data_count(self) -> int: + """ + 获取数据集总数量 + """ + return len(self.data_list) + + def get_data(self, index: int) -> tuple: + """ + 按下标获取一条数据 + """ + mark = self.data_list[index] + + wav_signal, sample_rate, _, _ = read_wav_data(self.wav_dict[mark]) + labels = list() + for item in self.label_dict[mark]: + if len(item) == 0: + continue + labels.append(self.pinyin_dict[item]) + + data_label = np.array(labels) + return wav_signal, sample_rate, data_label + + def shuffle(self) -> None: + """ + 随机打乱数据 + """ + random.shuffle(self.data_list) diff --git a/datalist/st-cmds/dev.syllable.txt b/datalist/st-cmds/dev.syllable.txt new file mode 100755 index 0000000..57493b0 --- /dev/null +++ b/datalist/st-cmds/dev.syllable.txt @@ -0,0 +1,600 @@ +20170001P00142A0070 mei2 qian2 yi1 fen1 qian2 mei2 you3 lao3 yue1 han4 ka3 li3 +20170001P00196A0072 jiu4 shi4 gan3 jue2 la5 jin1 tian1 he2 xue3 qin2 gei3 wo3 da3 dian4 hua4 +20170001P00337A0004 gao1 zhong1 er4 ban1 du2 ning2 da4 de5 xue1 rui4 qiu1 +20170001P00148I0088 piao1 xue3 ni3 zen3 me5 tui4 chu1 qun2 le5 ne5 +20170001P00131I0047 ling4 wai4 jiu4 jie1 ge4 wen3 shen2 me5 de5 mo1 mo1 er2 yi3 +20170001P00368I0013 wo3 gei3 ni3 wen4 wen4 yun2 xia2 ba5 +20170001P00367A0059 jin1 yin2 cai2 bao3 san1 ge4 ye2 hai2 you3 ge4 di4 di5 jiao4 jun1 bao3 +20170001P00213A0115 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index 0000000..b134b9a --- /dev/null +++ b/download_default_datalist.py @@ -0,0 +1,99 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +用于下载ASRT语音识别系统声学模型训练默认用的数据集列表程序 +""" + +import os +import logging +import json +import requests + +logging.basicConfig( + format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.INFO) + +DEFAULT_DATALIST_PATH = 'datalist/' +if not os.path.exists(DEFAULT_DATALIST_PATH): + os.makedirs(DEFAULT_DATALIST_PATH) + +URL_DATALIST_INDEX = "https://d.ailemon.net/asrt_assets/datalist/index.json" +rsp_index = requests.get(URL_DATALIST_INDEX) +rsp_index.encoding = 'utf-8' +if rsp_index.ok: + logging.info('Has connected to ailemon\'s download server...') +else: + logging.error('%s%s', 'Can not connected to ailemon\'s download server.', + 'please check your network connection.') + +index_json = json.loads(rsp_index.text) +if index_json['status_code'] != 200: + raise Exception(index_json['status_message']) + +body = index_json['body'] +logging.info('start to download datalist from ailemon\'s download server...') + +url_prefix = body['url_prefix'] +for i in range(len(body['datalist'])): + print(i, body['datalist'][i]['name']) +print(len(body['datalist']), 'all datalist') +num = input('Please choose which you select: (default all)') +if len(num) == 0: + num = len(body['datalist']) +else: + num = int(num) + + +def deal_download(datalist_item, url_prefix_str, datalist_path): + """ + to deal datalist file download + """ + logging.info('%s%s', 'start to download datalist ', datalist_item['name']) + save_path = os.path.join(datalist_path, datalist_item['name']) + if not os.path.exists(save_path): + os.makedirs(save_path) + logging.info('%s`%s`', 'Created directory ', save_path) + + for filename in datalist_item['filelist']: + tmp_url = url_prefix_str + datalist_item['name'] + '/' + filename + save_filename = os.path.join(save_path, filename) + rsp_listfile = requests.get(tmp_url) + + with open(save_filename, "wb") as file_pointer: + file_pointer.write(rsp_listfile.content) + if rsp_listfile.ok: + logging.info('%s `%s` %s', 'Download', filename, 'complete') + else: + logging.error('%s%s%s%s%s', 'Can not download ', filename, + ' from ailemon\'s download server. ', + 'http status ok is ', str(rsp_listfile.ok)) + + +if num == len(body['datalist']): + for i in range(len(body['datalist'])): + deal_download(body['datalist'][i], body['url_prefix'], DEFAULT_DATALIST_PATH) +else: + deal_download(body['datalist'][num], body['url_prefix'], DEFAULT_DATALIST_PATH) + +logging.info('%s%s%s', 'Datalist files download complete. ', + 'Please remember to download these datasets from ', + body['dataset_download_page_url']) diff --git a/evaluate_speech_model.py b/evaluate_speech_model.py new file mode 100755 index 0000000..7f23768 --- /dev/null +++ b/evaluate_speech_model.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +用于测试语音识别系统语音模型的程序 +""" + +import os + +from speech_model import ModelSpeech +from model_zoo.speech_model.keras_backend import SpeechModel251BN +from data_loader import DataLoader +from speech_features import Spectrogram + +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + +AUDIO_LENGTH = 1600 +AUDIO_FEATURE_LENGTH = 200 +CHANNELS = 1 +# 默认输出的拼音的表示大小是1428,即1427个拼音+1个空白块 +OUTPUT_SIZE = 1428 +sm251bn = SpeechModel251BN( + input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS), + output_size=OUTPUT_SIZE +) +feat = Spectrogram() +evalue_data = DataLoader('dev') +ms = ModelSpeech(sm251bn, feat, max_label_length=64) + +ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') +ms.evaluate_model(data_loader=evalue_data, data_count=-1, + out_report=True, show_ratio=True, show_per_step=100) diff --git a/language_model3.py b/language_model3.py new file mode 100755 index 0000000..9d09b2f --- /dev/null +++ b/language_model3.py @@ -0,0 +1,123 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别的语言模型 + +基于N-Gram的语言模型 +""" + +import os + +from utils.ops import get_symbol_dict, get_language_model + + +class ModelLanguage: + """ + ASRT专用N-Gram语言模型 + """ + + def __init__(self, model_path: str): + self.model_path = model_path + self.dict_pinyin = dict() + self.model1 = dict() + self.model2 = dict() + + def load_model(self): + """ + 加载N-Gram语言模型到内存 + """ + self.dict_pinyin = get_symbol_dict('dict.txt') + self.model1 = get_language_model(os.path.join(self.model_path, 'language_model1.txt')) + self.model2 = get_language_model(os.path.join(self.model_path, 'language_model2.txt')) + model = (self.dict_pinyin, self.model1, self.model2) + return model + + def pinyin_to_text(self, list_pinyin: list, beam_size: int = 100) -> str: + """ + 拼音转文本,一次性取得全部结果 + """ + result = list() + tmp_result_last = list() + for item_pinyin in list_pinyin: + tmp_result = self.pinyin_stream_decode(tmp_result_last, item_pinyin, beam_size) + if len(tmp_result) == 0 and len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + tmp_result = self.pinyin_stream_decode([], item_pinyin, beam_size) + if len(tmp_result) > 0: + result.append(tmp_result[0][0]) + tmp_result = [] + tmp_result_last = tmp_result + + if len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + + return ''.join(result) + + def pinyin_stream_decode(self, temple_result: list, + item_pinyin: str, + beam_size: int = 100) -> list: + """ + 拼音流式解码,逐字转换,每次返回中间结果 + """ + # 如果这个拼音不在汉语拼音字典里的话,直接返回空列表,不做decode + if item_pinyin not in self.dict_pinyin: + return [] + + # 获取拼音下属的字的列表,cur_words包含了该拼音对应的所有的字 + cur_words = self.dict_pinyin[item_pinyin] + # 第一个字做初始处理 + if len(temple_result) == 0: + lst_result = list() + for word in cur_words: + # 添加该字到可能的句子列表,设置初始概率为1.0 + lst_result.append([word, 1.0]) + return lst_result + + # 开始处理已经至少有一个字的中间结果情况 + new_result = list() + for sequence in temple_result: + for cur_word in cur_words: + # 得到2-gram的汉字子序列 + tuple2_word = sequence[0][-1] + cur_word + if tuple2_word not in self.model2: + # 如果2-gram子序列不存在 + continue + # 计算状态转移概率 + prob_origin = sequence[1] # 原始概率 + count_two_word = float(self.model2[tuple2_word]) # 二字频数 + count_one_word = float(self.model1[tuple2_word[-2]]) # 单字频数 + cur_probility = prob_origin * count_two_word / count_one_word + new_result.append([sequence[0] + cur_word, cur_probility]) + + new_result = sorted(new_result, key=lambda x: x[1], reverse=True) + if len(new_result) > beam_size: + return new_result[0:beam_size] + return new_result + + +if __name__ == '__main__': + ml = ModelLanguage('model_language') + ml.load_model() + + _str_pinyin = ['zhe4', 'zhen1', 'shi4', 'ji2', 'hao3', 'de5'] + _RESULT = ml.pinyin_to_text(_str_pinyin) + print('语音转文字结果:\n', _RESULT) diff --git a/model_language/language_model1.txt b/model_language/language_model1.txt new file mode 100755 index 0000000..8be1ddd --- /dev/null +++ b/model_language/language_model1.txt @@ -0,0 +1,6880 @@ +3941753 +的 213994 +一 72404 +是 69047 +在 63563 +不 61699 +了 60856 +有 59005 +人 52706 +中 52005 +国 50797 +我 48825 +大 45439 +上 39507 +年 37962 +为 35373 +日 34764 +和 34386 +个 33555 +你 32699 +网 31057 +这 30804 +会 30280 +新 30104 +来 29692 +时 29282 +公 28564 +发 28408 +以 28300 +生 27753 +要 27273 +业 27124 +行 26852 +出 26498 +到 26489 +能 25061 +对 24333 +下 23178 +作 23163 +家 23126 +就 22752 +可 22633 +成 22287 +用 22093 +们 21609 +信 21375 +分 21249 +多 21249 +后 21091 +月 20948 +经 20808 +于 20536 +方 20097 +动 20036 +市 20010 +者 19994 +也 19903 +全 19557 +地 19486 +学 19286 +天 18970 +过 18839 +现 18730 +他 18655 +机 18457 +好 18418 +本 18266 +报 18105 +进 17950 +自 17942 +都 17939 +| 17935 +高 17912 +考 17686 +还 17516 +场 17343 +前 17327 +体 17173 +开 17170 +车 17162 +子 16765 +关 16739 +面 16739 +最 16319 +务 16141 +去 16139 +将 16101 +力 15732 +得 15534 +金 15473 +看 15460 +电 15372 +合 15351 +说 15327 +点 15278 +资 15195 +主 15175 +小 15141 +工 15051 +部 14941 +实 14817 +产 14750 +与 14722 +司 14679 +美 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1 +薛若 1 +觉么 1 +准哈 1 +近频 1 +繁去 1 +算微 1 +腐女 1 +和莴 1 +笋和 1 +安迷 1 +迷修 1 +沙御 1 +湾刘 1 +媛熟 1 +悉么 1 +雄得 1 +伦直 1 +谁宣 1 +扬官 1 +啊混 1 +说忘 1 +我孤 1 +蔡院 1 +壳遭 1 +遭卷 1 +凶嗦 1 +嗦安 1 +金音 1 +色琴 1 +琴行 1 +行朱 1 +素怀 1 +首约 1 +房三 1 +询君 1 +台故 1 +获者 1 +跟游 1 +么涉 1 +奥从 1 +要傅 1 +要姐 1 +奖英 1 +寻壮 1 +安点 1 +布拥 1 +俩中 1 +毒患 1 +孝闻 1 +闻街 1 +街换 1 +泳充 1 +交慈 1 +云九 1 +葵院 1 +角长 1 +颗向 1 +怀黄 1 +驹逝 1 +乐玛 1 +玛返 1 +返涯 1 +涯队 1 +娃断 1 +池护 1 +坡措 1 +池循 1 +好介 1 +卫勇 1 +勇够 1 +盟点 1 +知研 1 +讯特 1 +王双 1 +险疯 1 +贼格 1 +林白 1 diff --git a/model_zoo/speech_model/keras_backend.py b/model_zoo/speech_model/keras_backend.py new file mode 100755 index 0000000..d9980b2 --- /dev/null +++ b/model_zoo/speech_model/keras_backend.py @@ -0,0 +1,509 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +若干声学模型模型的定义 +""" + +import tensorflow as tf +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Dense, Dropout, Input, Reshape, BatchNormalization +from tensorflow.keras.layers import Lambda, Activation, Conv2D, MaxPooling2D +from tensorflow.keras import backend as K +import numpy as np +from utils.ops import ctc_decode_delete_tail_blank + + +class BaseModel: + """ + 定义声学模型类型的接口基类 + """ + def __init__(self): + self.input_shape = None + self.output_shape = None + self.model = None + self.model_base = None + self._model_name = None + + def get_model(self) -> tuple: + return self.model, self.model_base + + def get_train_model(self) -> Model: + return self.model + + def get_eval_model(self) -> Model: + return self.model_base + + def summary(self) -> None: + self.model.summary() + + def get_model_name(self) -> str: + return self._model_name + + def load_weights(self, filename: str) -> None: + self.model.load_weights(filename) + + def save_weights(self, filename: str) -> None: + self.model.save_weights(filename + '.model.h5') + self.model_base.save_weights(filename + '.model.base.h5') + + f = open('epoch_'+self._model_name+'.txt', 'w') + f.write(filename) + f.close() + + def get_loss_function(self): + raise Exception("method not implemented") + + def forward(self, x): + raise Exception("method not implemented") + + +def ctc_lambda_func(args): + y_pred, labels, input_length, label_length = args + y_pred = y_pred[:, :, :] + return K.ctc_batch_cost(labels, y_pred, input_length, label_length) + + +class SpeechModel251BN(BaseModel): + """ + 定义CNN+CTC模型,使用函数式模型 + + 输入层:200维的特征值序列,一条语音数据的最大长度设为1600(大约16s)\\ + 隐藏层:卷积池化层,卷积核大小为3x3,池化窗口大小为2 \\ + 隐藏层:全连接层 \\ + 输出层:全连接层,神经元数量为self.MS_OUTPUT_SIZE,使用softmax作为激活函数, \\ + CTC层:使用CTC的loss作为损失函数,实现连接性时序多输出 + + 参数: \\ + input_shape: tuple,默认值(1600, 200, 1) \\ + output_shape: tuple,默认值(200, 1428) + """ + def __init__(self, input_shape: tuple = (1600, 200, 1), output_size: int = 1428) -> None: + super().__init__() + self.input_shape = input_shape + self._pool_size = 8 + self.output_shape = (input_shape[0] // self._pool_size, output_size) + self._model_name = 'SpeechModel251bn' + self.model, self.model_base = self._define_model(self.input_shape, self.output_shape[1]) + + def _define_model(self, input_shape, output_size) -> tuple: + label_max_string_length = 64 + + input_data = Input(name='the_input', shape=input_shape) + + layer_h = Conv2D(32, (3, 3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv0')(input_data) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN0')(layer_h) + layer_h = Activation('relu', name='Act0')(layer_h) + + layer_h = Conv2D(32, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv1')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN1')(layer_h) + layer_h = Activation('relu', name='Act1')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(64, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv2')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN2')(layer_h) + layer_h = Activation('relu', name='Act2')(layer_h) + + layer_h = Conv2D(64, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv3')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN3')(layer_h) + layer_h = Activation('relu', name='Act3')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv4')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN4')(layer_h) + layer_h = Activation('relu', name='Act4')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv5')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN5')(layer_h) + layer_h = Activation('relu', name='Act5')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv6')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN6')(layer_h) + layer_h = Activation('relu', name='Act6')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv7')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN7')(layer_h) + layer_h = Activation('relu', name='Act7')(layer_h) + + layer_h = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv8')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN8')(layer_h) + layer_h = Activation('relu', name='Act8')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv9')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN9')(layer_h) + layer_h = Activation('relu', name='Act9')(layer_h) + + layer_h = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h) # 池化层 + + # test=Model(inputs = input_data, outputs = layer_h12) + # test.summary() + + layer_h = Reshape((self.output_shape[0], input_shape[1] // self._pool_size * 128), name='Reshape0')(layer_h) # Reshape层 + + layer_h = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal', name='Dense0')(layer_h) # 全连接层 + + layer_h = Dense(output_size, use_bias=True, kernel_initializer='he_normal', name='Dense1')(layer_h) # 全连接层 + y_pred = Activation('softmax', name='Activation0')(layer_h) + + model_base = Model(inputs = input_data, outputs = y_pred) + # model_data.summary() + + labels = Input(name='the_labels', shape=[label_max_string_length], dtype='float32') + input_length = Input(name='input_length', shape=[1], dtype='int64') + label_length = Input(name='label_length', shape=[1], dtype='int64') + # Keras doesn't currently support loss funcs with extra parameters + # so CTC loss is implemented in a lambda layer + loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) + + model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) + + return model, model_base + + def get_loss_function(self) -> dict: + return {'ctc': lambda y_true, y_pred: y_pred} + + def forward(self, data_input): + batch_size = 1 + in_len = np.zeros((batch_size,), dtype=np.int32) + + in_len[0] = self.output_shape[0] + + x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64) + + for i in range(batch_size): + x_in[i, 0:len(data_input)] = data_input + + base_pred = self.model_base.predict(x=x_in) + r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1) + + if tf.__version__[0:2] == '1.': + r1 = r[0][0].eval(session=tf.compat.v1.Session()) + else: + r1 = r[0][0].numpy() + + speech_result = ctc_decode_delete_tail_blank(r1[0]) + return speech_result + + +class SpeechModel251(BaseModel): + """ + 定义CNN+CTC模型,使用函数式模型 + + 输入层:200维的特征值序列,一条语音数据的最大长度设为1600(大约16s)\\ + 隐藏层:卷积池化层,卷积核大小为3x3,池化窗口大小为2 \\ + 隐藏层:全连接层 \\ + 输出层:全连接层,神经元数量为self.MS_OUTPUT_SIZE,使用softmax作为激活函数, \\ + CTC层:使用CTC的loss作为损失函数,实现连接性时序多输出 + + 参数: \\ + input_shape: tuple,默认值(1600, 200, 1) \\ + output_shape: tuple,默认值(200, 1428) + """ + def __init__(self, input_shape: tuple = (1600, 200, 1), output_size: int = 1428) -> None: + super().__init__() + self.input_shape = input_shape + self._pool_size = 8 + self.output_shape = (input_shape[0] // self._pool_size, output_size) + self._model_name = 'SpeechModel251' + self.model, self.model_base = self._define_model(self.input_shape, self.output_shape[1]) + + def _define_model(self, input_shape, output_size) -> tuple: + label_max_string_length = 64 + + input_data = Input(name='the_input', shape=input_shape) + + layer_h1 = Conv2D(32, (3,3), use_bias=False, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层 + layer_h1 = Dropout(0.05)(layer_h1) + layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层 + layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层 + layer_h3 = Dropout(0.05)(layer_h3) # 随机中断部分神经网络连接,防止过拟合 + + layer_h4 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层 + layer_h4 = Dropout(0.1)(layer_h4) + layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层 + layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层 + + layer_h6 = Dropout(0.1)(layer_h6) + layer_h7 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层 + layer_h7 = Dropout(0.15)(layer_h7) + layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层 + layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层 + + layer_h9 = Dropout(0.15)(layer_h9) + layer_h10 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h9) # 卷积层 + layer_h10 = Dropout(0.2)(layer_h10) + layer_h11 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h10) # 卷积层 + layer_h12 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h11) # 池化层 + + layer_h12 = Dropout(0.2)(layer_h12) + layer_h13 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h12) # 卷积层 + layer_h13 = Dropout(0.2)(layer_h13) + layer_h14 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h13) # 卷积层 + layer_h15 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h14) # 池化层 + + # test=Model(inputs = input_data, outputs = layer_h12) + # test.summary() + + layer_h16 = Reshape((self.output_shape[0], input_shape[1] // self._pool_size * 128))(layer_h15) # Reshape层 + layer_h16 = Dropout(0.3)(layer_h16) # 随机中断部分神经网络连接,防止过拟合 + layer_h17 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h16) # 全连接层 + layer_h17 = Dropout(0.3)(layer_h17) + layer_h18 = Dense(output_size, use_bias=True, kernel_initializer='he_normal')(layer_h17) # 全连接层 + y_pred = Activation('softmax', name='Activation0')(layer_h18) + + model_base = Model(inputs=input_data, outputs=y_pred) + # model_data.summary() + + labels = Input(name='the_labels', shape=[label_max_string_length], dtype='float32') + input_length = Input(name='input_length', shape=[1], dtype='int64') + label_length = Input(name='label_length', shape=[1], dtype='int64') + # Keras doesn't currently support loss funcs with extra parameters + # so CTC loss is implemented in a lambda layer + loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) + + model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) + + return model, model_base + + def get_loss_function(self) -> dict: + return {'ctc': lambda y_true, y_pred: y_pred} + + def forward(self, data_input): + batch_size = 1 + in_len = np.zeros((batch_size,), dtype=np.int32) + + in_len[0] = self.output_shape[0] + + x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64) + + for i in range(batch_size): + x_in[i, 0:len(data_input)] = data_input + + base_pred = self.model_base.predict(x = x_in) + r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1) + + if tf.__version__[0:2] == '1.': + r1 = r[0][0].eval(session=tf.compat.v1.Session()) + else: + r1 = r[0][0].numpy() + + speech_result = ctc_decode_delete_tail_blank(r1[0]) + return speech_result + + +class SpeechModel25(BaseModel): + """ + 定义CNN+CTC模型,使用函数式模型 + + 输入层:200维的特征值序列,一条语音数据的最大长度设为1600(大约16s)\\ + 隐藏层:卷积池化层,卷积核大小为3x3,池化窗口大小为2 \\ + 隐藏层:全连接层 \\ + 输出层:全连接层,神经元数量为self.MS_OUTPUT_SIZE,使用softmax作为激活函数, \\ + CTC层:使用CTC的loss作为损失函数,实现连接性时序多输出 + + 参数: \\ + input_shape: tuple,默认值(1600, 200, 1) \\ + output_shape: tuple,默认值(200, 1428) + """ + def __init__(self, input_shape: tuple = (1600, 200, 1), output_size: int = 1428) -> None: + super().__init__() + self.input_shape = input_shape + self._pool_size = 8 + self.output_shape = (input_shape[0] // self._pool_size, output_size) + self._model_name = 'SpeechModel25' + self.model, self.model_base = self._define_model(self.input_shape, self.output_shape[1]) + + def _define_model(self, input_shape, output_size) -> tuple: + label_max_string_length = 64 + + input_data = Input(name='the_input', shape=input_shape) + + layer_h1 = Conv2D(32, (3, 3), use_bias=False, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层 + layer_h1 = Dropout(0.05)(layer_h1) + layer_h2 = Conv2D(32, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层 + layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层 + layer_h3 = Dropout(0.05)(layer_h3) # 随机中断部分神经网络连接,防止过拟合 + + layer_h4 = Conv2D(64, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层 + layer_h4 = Dropout(0.1)(layer_h4) + layer_h5 = Conv2D(64, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层 + layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层 + + layer_h6 = Dropout(0.1)(layer_h6) + layer_h7 = Conv2D(128, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层 + layer_h7 = Dropout(0.15)(layer_h7) + layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层 + layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层 + + layer_h9 = Dropout(0.15)(layer_h9) + layer_h10 = Conv2D(128, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h9) # 卷积层 + layer_h10 = Dropout(0.2)(layer_h10) + layer_h11 = Conv2D(128, (3, 3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h10) # 卷积层 + layer_h12 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h11) # 池化层 + + # test=Model(inputs = input_data, outputs = layer_h12) + # test.summary() + + layer_h12 = Reshape((self.output_shape[0], input_shape[1] // self._pool_size * 128))(layer_h12) # Reshape层 + layer_h12 = Dropout(0.3)(layer_h12) # 随机中断部分神经网络连接,防止过拟合 + layer_h13 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h12) # 全连接层 + layer_h13 = Dropout(0.3)(layer_h13) + layer_h14 = Dense(output_size, use_bias=True, kernel_initializer='he_normal')(layer_h13) # 全连接层 + y_pred = Activation('softmax', name='Activation0')(layer_h14) + + model_base = Model(inputs=input_data, outputs=y_pred) + # model_data.summary() + + labels = Input(name='the_labels', shape=[label_max_string_length], dtype='float32') + input_length = Input(name='input_length', shape=[1], dtype='int64') + label_length = Input(name='label_length', shape=[1], dtype='int64') + # Keras doesn't currently support loss funcs with extra parameters + # so CTC loss is implemented in a lambda layer + loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) + + model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) + + return model, model_base + + def get_loss_function(self) -> dict: + return {'ctc': lambda y_true, y_pred: y_pred} + + def forward(self, data_input): + batch_size = 1 + in_len = np.zeros((batch_size,), dtype=np.int32) + + in_len[0] = self.output_shape[0] + + x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64) + + for i in range(batch_size): + x_in[i, 0:len(data_input)] = data_input + + base_pred = self.model_base.predict(x=x_in) + r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1) + + if tf.__version__[0:2] == '1.': + r1 = r[0][0].eval(session=tf.compat.v1.Session()) + else: + r1 = r[0][0].numpy() + + speech_result = ctc_decode_delete_tail_blank(r1[0]) + return speech_result + + +class SpeechModel24(BaseModel): + """ + 定义CNN+CTC模型,使用函数式模型 + + 输入层:200维的特征值序列,一条语音数据的最大长度设为1600(大约16s)\\ + 隐藏层:卷积池化层,卷积核大小为3x3,池化窗口大小为2 \\ + 隐藏层:全连接层 \\ + 输出层:全连接层,神经元数量为self.MS_OUTPUT_SIZE,使用softmax作为激活函数, \\ + CTC层:使用CTC的loss作为损失函数,实现连接性时序多输出 + + 参数: \\ + input_shape: tuple,默认值(1600, 200, 1) \\ + output_shape: tuple,默认值(200, 1428) + """ + def __init__(self, input_shape :tuple=(1600, 200, 1), output_size: int = 1428) -> None: + super().__init__() + self.input_shape = input_shape + self._pool_size = 8 + self.output_shape = (input_shape[0] // self._pool_size, output_size) + self._model_name = 'SpeechModel24' + self.model, self.model_base = self._define_model(self.input_shape, self.output_shape[1]) + + def _define_model(self, input_shape, output_size) -> tuple: + label_max_string_length = 64 + + input_data = Input(name='the_input', shape=input_shape) + + layer_h1 = Conv2D(32, (3,3), use_bias=False, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层 + layer_h1 = Dropout(0.1)(layer_h1) + layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层 + layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层 + layer_h3 = Dropout(0.2)(layer_h3) # 随机中断部分神经网络连接,防止过拟合 + + layer_h4 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层 + layer_h4 = Dropout(0.2)(layer_h4) + layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层 + layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层 + + layer_h6 = Dropout(0.3)(layer_h6) + layer_h7 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层 + layer_h7 = Dropout(0.3)(layer_h7) + layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层 + layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层 + + # test=Model(inputs = input_data, outputs = layer_h12) + # test.summary() + + layer_h10 = Reshape((self.output_shape[0], input_shape[1] // self._pool_size * 128))(layer_h9) # Reshape层 + layer_h10 = Dropout(0.3)(layer_h10) # 随机中断部分神经网络连接,防止过拟合 + layer_h11 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h10) # 全连接层 + layer_h11 = Dropout(0.3)(layer_h11) + layer_h12 = Dense(output_size, use_bias=True, kernel_initializer='he_normal')(layer_h11) # 全连接层 + y_pred = Activation('softmax', name='Activation0')(layer_h12) + + model_base = Model(inputs=input_data, outputs=y_pred) + # model_data.summary() + + labels = Input(name='the_labels', shape=[label_max_string_length], dtype='float32') + input_length = Input(name='input_length', shape=[1], dtype='int64') + label_length = Input(name='label_length', shape=[1], dtype='int64') + # Keras doesn't currently support loss funcs with extra parameters + # so CTC loss is implemented in a lambda layer + loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) + + model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) + + return model, model_base + + def get_loss_function(self) -> dict: + return {'ctc': lambda y_true, y_pred: y_pred} + + def forward(self, data_input): + batch_size = 1 + in_len = np.zeros((batch_size,), dtype=np.int32) + + in_len[0] = self.output_shape[0] + + x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64) + + for i in range(batch_size): + x_in[i, 0:len(data_input)] = data_input + + base_pred = self.model_base.predict(x=x_in) + r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1) + + if tf.__version__[0:2] == '1.': + r1 = r[0][0].eval(session=tf.compat.v1.Session()) + else: + r1 = r[0][0].numpy() + + speech_result = ctc_decode_delete_tail_blank(r1[0]) + return speech_result diff --git a/predict_speech_file.py b/predict_speech_file.py new file mode 100755 index 0000000..d54fe37 --- /dev/null +++ b/predict_speech_file.py @@ -0,0 +1,55 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +用于通过ASRT语音识别系统预测一次语音文件的程序 +""" + +import os + +from speech_model import ModelSpeech +from model_zoo.speech_model.keras_backend import SpeechModel251BN +from speech_features import Spectrogram +from language_model3 import ModelLanguage + +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + +AUDIO_LENGTH = 1600 +AUDIO_FEATURE_LENGTH = 200 +CHANNELS = 1 +# 默认输出的拼音的表示大小是1428,即1427个拼音+1个空白块 +OUTPUT_SIZE = 1428 +sm251bn = SpeechModel251BN( + input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS), + output_size=OUTPUT_SIZE +) +feat = Spectrogram() +ms = ModelSpeech(sm251bn, feat, max_label_length=64) + +ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') +res = ms.recognize_speech_from_file('filename.wav') +print('*[提示] 声学模型语音识别结果:\n', res) + +ml = ModelLanguage('model_language') +ml.load_model() +str_pinyin = res +res = ml.pinyin_to_text(str_pinyin) +print('语音识别最终结果:\n', res) diff --git a/requirements.txt b/requirements.txt new file mode 100755 index 0000000..e80ba3c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,59 @@ +absl-py==1.4.0 +astunparse==1.6.3 +cachetools==5.3.0 +certifi==2022.12.7 +charset-normalizer==3.0.1 +click==8.1.3 +colorama==0.4.6 +contourpy==1.0.7 +cycler==0.11.0 +Flask==2.2.2 +flatbuffers==23.1.21 +fonttools==4.38.0 +gast==0.5.3 +google-auth==2.16.0 +google-auth-oauthlib==0.4.6 +google-pasta==0.2.0 +grpcio==1.51.1 +h5py==3.8.0 +idna==3.4 +importlib-metadata==6.0.0 +itsdangerous==2.1.2 +Jinja2==3.1.2 +keras==2.8.0 +Keras-Preprocessing==1.1.2 +kiwisolver==1.4.4 +libclang==15.0.6.1 +Markdown==3.4.1 +MarkupSafe==2.1.2 +matplotlib==3.6.3 +numpy==1.24.1 +oauthlib==3.2.2 +opt-einsum==3.3.0 +packaging==23.0 +Pillow==9.4.0 +protobuf==3.19.6 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pyparsing==3.0.9 +python-dateutil==2.8.2 +requests==2.28.2 +requests-oauthlib==1.3.1 +rsa==4.9 +scipy==1.10.0 +six==1.16.0 +tensorboard==2.8.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +tensorflow-estimator==2.8.0 +tensorflow-gpu==2.8.4 +tensorflow-io-gcs-filesystem==0.30.0 +termcolor==2.2.0 +typing_extensions==4.4.0 +urllib3==1.26.14 +waitress==2.1.2 +Wave==0.0.2 +Werkzeug==2.2.2 +wincertstore==0.2 +wrapt==1.14.1 +zipp==3.12.0 diff --git a/speech_features/__init__.py b/speech_features/__init__.py new file mode 100755 index 0000000..42155d1 --- /dev/null +++ b/speech_features/__init__.py @@ -0,0 +1,26 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别声学特征模块 +""" + +from .speech_features import * diff --git a/speech_features/base.py b/speech_features/base.py new file mode 100755 index 0000000..59ace41 --- /dev/null +++ b/speech_features/base.py @@ -0,0 +1,238 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +# calculate filterbank features. Provides e.g. fbank and mfcc features for use in ASR applications +# Author: James Lyons 2012 + +""" +@author: nl8590687 +ASRT语音识别声学特征基础库模块,一些基础函数实现 +""" + +from __future__ import division +import numpy +from scipy.fftpack import dct + +from .sigproc import preemphasis, framesig, powspec + + +def calculate_nfft(samplerate, winlen): + """Calculates the FFT size as a power of two greater than or equal to + the number of samples in a single window length. + + Having an FFT less than the window length loses precision by dropping + many of the samples; a longer FFT than the window allows zero-padding + of the FFT buffer which is neutral in terms of frequency domain conversion. + :param samplerate: The sample rate of the signal we are working with, in Hz. + :param winlen: The length of the analysis window in seconds. + """ + window_length_samples = winlen * samplerate + nfft = 1 + while nfft < window_length_samples: + nfft *= 2 + return nfft + + +def mfcc(signal, samplerate=16000, winlen=0.025, winstep=0.01, numcep=13, + nfilt=26, nfft=None, lowfreq=0, highfreq=None, preemph=0.97, ceplifter=22, appendEnergy=True, + winfunc=lambda x: numpy.ones((x,))): + """Compute MFCC features from an audio signal. + :param signal: the audio signal from which to compute features. Should be an N*1 array + :param samplerate: the sample rate of the signal we are working with, in Hz. + :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) + :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds) + :param numcep: the number of cepstrum to return, default 13 + :param nfilt: the number of filters in the filterbank, default 26. + :param nfft: the FFT size. Default is None, which uses the calculate_nfft function to choose the smallest size that does not drop sample data. + :param lowfreq: lowest band edge of mel filters. In Hz, default is 0. + :param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2 + :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97. + :param ceplifter: apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22. + :param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming + :returns: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector. + """ + nfft = nfft or calculate_nfft(samplerate, winlen) + feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft, lowfreq, highfreq, preemph, winfunc) + feat = numpy.log(feat) + feat = dct(feat, type=2, axis=1, norm='ortho')[:, :numcep] + feat = lifter(feat, ceplifter) + if appendEnergy: feat[:, 0] = numpy.log(energy) # replace first cepstral coefficient with log of frame energy + return feat + + +def fbank(signal, samplerate=16000, winlen=0.025, winstep=0.01, + nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97, + winfunc=lambda x: numpy.ones((x,))): + """Compute Mel-filterbank energy features from an audio signal. + :param signal: the audio signal from which to compute features. Should be an N*1 array + :param samplerate: the sample rate of the signal we are working with, in Hz. + :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) + :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds) + :param nfilt: the number of filters in the filterbank, default 26. + :param nfft: the FFT size. Default is 512. + :param lowfreq: lowest band edge of mel filters. In Hz, default is 0. + :param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2 + :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming + :returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The + second return value is the energy in each frame (total energy, unwindowed) + """ + highfreq = highfreq or samplerate / 2 + signal = preemphasis(signal, preemph) + frames = framesig(signal, winlen * samplerate, winstep * samplerate, winfunc) + pspec = powspec(frames, nfft) + energy = numpy.sum(pspec, 1) # this stores the total energy in each frame + energy = numpy.where(energy == 0, numpy.finfo(float).eps, energy) # if energy is zero, we get problems with log + + fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq) + feat = numpy.dot(pspec, fb.T) # compute the filterbank energies + feat = numpy.where(feat == 0, numpy.finfo(float).eps, feat) # if feat is zero, we get problems with log + + return feat, energy + + +def logfbank(signal, samplerate=16000, winlen=0.025, winstep=0.01, + nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97, + winfunc=lambda x: numpy.ones((x,))): + """Compute log Mel-filterbank energy features from an audio signal. + :param signal: the audio signal from which to compute features. Should be an N*1 array + :param samplerate: the sample rate of the signal we are working with, in Hz. + :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) + :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds) + :param nfilt: the number of filters in the filterbank, default 26. + :param nfft: the FFT size. Default is 512. + :param lowfreq: lowest band edge of mel filters. In Hz, default is 0. + :param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2 + :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming + :returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. + """ + feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft, lowfreq, highfreq, preemph, winfunc) + return numpy.log(feat) + + +def ssc(signal, samplerate=16000, winlen=0.025, winstep=0.01, + nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97, + winfunc=lambda x: numpy.ones((x,))): + """Compute Spectral Subband Centroid features from an audio signal. + :param signal: the audio signal from which to compute features. Should be an N*1 array + :param samplerate: the sample rate of the signal we are working with, in Hz. + :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) + :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds) + :param nfilt: the number of filters in the filterbank, default 26. + :param nfft: the FFT size. Default is 512. + :param lowfreq: lowest band edge of mel filters. In Hz, default is 0. + :param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2 + :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming + :returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. + """ + highfreq = highfreq or samplerate / 2 + signal = preemphasis(signal, preemph) + frames = framesig(signal, winlen * samplerate, winstep * samplerate, winfunc) + pspec = powspec(frames, nfft) + pspec = numpy.where(pspec == 0, numpy.finfo(float).eps, pspec) # if things are all zeros we get problems + + fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq) + feat = numpy.dot(pspec, fb.T) # compute the filterbank energies + R = numpy.tile(numpy.linspace(1, samplerate / 2, numpy.size(pspec, 1)), (numpy.size(pspec, 0), 1)) + + return numpy.dot(pspec * R, fb.T) / feat + + +def hz2mel(hz): + """Convert a value in Hertz to Mels + :param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise. + :returns: a value in Mels. If an array was passed in, an identical sized array is returned. + """ + return 2595 * numpy.log10(1 + hz / 700.) + + +def mel2hz(mel): + """Convert a value in Mels to Hertz + :param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise. + :returns: a value in Hertz. If an array was passed in, an identical sized array is returned. + """ + return 700 * (10 ** (mel / 2595.0) - 1) + + +def get_filterbanks(nfilt=20, nfft=512, samplerate=16000, lowfreq=0, highfreq=None): + """Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond + to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1) + :param nfilt: the number of filters in the filterbank, default 20. + :param nfft: the FFT size. Default is 512. + :param samplerate: the sample rate of the signal we are working with, in Hz. Affects mel spacing. + :param lowfreq: lowest band edge of mel filters, default 0 Hz + :param highfreq: highest band edge of mel filters, default samplerate/2 + :returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter. + """ + highfreq = highfreq or samplerate / 2 + assert highfreq <= samplerate / 2, "highfreq is greater than samplerate/2" + + # compute points evenly spaced in mels + lowmel = hz2mel(lowfreq) + highmel = hz2mel(highfreq) + melpoints = numpy.linspace(lowmel, highmel, nfilt + 2) + # our points are in Hz, but we use fft bins, so we have to convert + # from Hz to fft bin number + bin = numpy.floor((nfft + 1) * mel2hz(melpoints) / samplerate) + + fbank = numpy.zeros([nfilt, nfft // 2 + 1]) + for j in range(0, nfilt): + for i in range(int(bin[j]), int(bin[j + 1])): + fbank[j, i] = (i - bin[j]) / (bin[j + 1] - bin[j]) + for i in range(int(bin[j + 1]), int(bin[j + 2])): + fbank[j, i] = (bin[j + 2] - i) / (bin[j + 2] - bin[j + 1]) + return fbank + + +def lifter(cepstra, L=22): + """Apply a cepstral lifter the the matrix of cepstra. This has the effect of increasing the + magnitude of the high frequency DCT coeffs. + :param cepstra: the matrix of mel-cepstra, will be numframes * numcep in size. + :param L: the liftering coefficient to use. Default is 22. L <= 0 disables lifter. + """ + if L > 0: + nframes, ncoeff = numpy.shape(cepstra) + n = numpy.arange(ncoeff) + lift = 1 + (L / 2.) * numpy.sin(numpy.pi * n / L) + return lift * cepstra + else: + # values of L <= 0, do nothing + return cepstra + + +def delta(feat, N): + """Compute delta features from a feature vector sequence. + :param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector. + :param N: For each frame, calculate delta features based on preceding and following N frames + :returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector. + """ + if N < 1: + raise ValueError('N must be an integer >= 1') + NUMFRAMES = len(feat) + denominator = 2 * sum([i ** 2 for i in range(1, N + 1)]) + delta_feat = numpy.empty_like(feat) + padded = numpy.pad(feat, ((N, N), (0, 0)), mode='edge') # padded version of feat + for t in range(NUMFRAMES): + delta_feat[t] = numpy.dot(numpy.arange(-N, N + 1), + padded[t: t + 2 * N + 1]) / denominator # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1] + return delta_feat diff --git a/speech_features/sigproc.py b/speech_features/sigproc.py new file mode 100755 index 0000000..da17a79 --- /dev/null +++ b/speech_features/sigproc.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +# This file includes routines for basic signal processing including framing and computing power spectra. +# Author: James Lyons 2012 + +''' +@author: nl8590687 +ASRT语音识别声学特征计算的信号处理计算的函数库 +''' + +import decimal +import logging +import math +import numpy + + +def round_half_up(number): + return int(decimal.Decimal(number).quantize(decimal.Decimal('1'), rounding=decimal.ROUND_HALF_UP)) + + +def rolling_window(a, window, step=1): + # http://ellisvalentiner.com/post/2017-03-21-np-strides-trick + shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) + strides = a.strides + (a.strides[-1],) + return numpy.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)[::step] + + +def framesig(sig, frame_len, frame_step, winfunc=lambda x: numpy.ones((x,)), stride_trick=True): + """Frame a signal into overlapping frames. + :param sig: the audio signal to frame. + :param frame_len: length of each frame measured in samples. + :param frame_step: number of samples after the start of the previous frame that the next frame should begin. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. + :param stride_trick: use stride trick to compute the rolling window and window multiplication faster + :returns: an array of frames. Size is NUMFRAMES by frame_len. + """ + slen = len(sig) + frame_len = int(round_half_up(frame_len)) + frame_step = int(round_half_up(frame_step)) + if slen <= frame_len: + numframes = 1 + else: + numframes = 1 + int(math.ceil((1.0 * slen - frame_len) / frame_step)) + + padlen = int((numframes - 1) * frame_step + frame_len) + + zeros = numpy.zeros((padlen - slen,)) + padsignal = numpy.concatenate((sig, zeros)) + if stride_trick: + win = winfunc(frame_len) + frames = rolling_window(padsignal, window=frame_len, step=frame_step) + else: + indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile( + numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T + indices = numpy.array(indices, dtype=numpy.int32) + frames = padsignal[indices] + win = numpy.tile(winfunc(frame_len), (numframes, 1)) + + return frames * win + + +def deframesig(frames, siglen, frame_len, frame_step, winfunc=lambda x: numpy.ones((x,))): + """Does overlap-add procedure to undo the action of framesig. + :param frames: the array of frames. + :param siglen: the length of the desired signal, use 0 if unknown. Output will be truncated to siglen samples. + :param frame_len: length of each frame measured in samples. + :param frame_step: number of samples after the start of the previous frame that the next frame should begin. + :param winfunc: the analysis window to apply to each frame. By default no window is applied. + :returns: a 1-D signal. + """ + frame_len = round_half_up(frame_len) + frame_step = round_half_up(frame_step) + numframes = numpy.shape(frames)[0] + assert numpy.shape(frames)[1] == frame_len, '"frames" matrix is wrong size, 2nd dim is not equal to frame_len' + + indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile( + numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T + indices = numpy.array(indices, dtype=numpy.int32) + padlen = (numframes - 1) * frame_step + frame_len + + if siglen <= 0: siglen = padlen + + rec_signal = numpy.zeros((padlen,)) + window_correction = numpy.zeros((padlen,)) + win = winfunc(frame_len) + + for i in range(0, numframes): + window_correction[indices[i, :]] = window_correction[ + indices[i, :]] + win + 1e-15 # add a little bit so it is never zero + rec_signal[indices[i, :]] = rec_signal[indices[i, :]] + frames[i, :] + + rec_signal = rec_signal / window_correction + return rec_signal[0:siglen] + + +def magspec(frames, NFFT): + """Compute the magnitude spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1). + :param frames: the array of frames. Each row is a frame. + :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded. + :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the magnitude spectrum of the corresponding frame. + """ + if numpy.shape(frames)[1] > NFFT: + logging.warn( + 'frame length (%d) is greater than FFT size (%d), frame will be truncated. Increase NFFT to avoid.', + numpy.shape(frames)[1], NFFT) + complex_spec = numpy.fft.rfft(frames, NFFT) + return numpy.absolute(complex_spec) + + +def powspec(frames, NFFT): + """Compute the power spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1). + :param frames: the array of frames. Each row is a frame. + :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded. + :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the power spectrum of the corresponding frame. + """ + return 1.0 / NFFT * numpy.square(magspec(frames, NFFT)) + + +def logpowspec(frames, NFFT, norm=1): + """Compute the log power spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1). + :param frames: the array of frames. Each row is a frame. + :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded. + :param norm: If norm=1, the log power spectrum is normalised so that the max value (across all frames) is 0. + :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the log power spectrum of the corresponding frame. + """ + ps = powspec(frames, NFFT) + ps[ps <= 1e-30] = 1e-30 + lps = 10 * numpy.log10(ps) + if norm: + return lps - numpy.max(lps) + else: + return lps + + +def preemphasis(signal, coeff=0.95): + """perform preemphasis on the input signal. + :param signal: The signal to filter. + :param coeff: The preemphasis coefficient. 0 is no filter, default is 0.95. + :returns: the filtered signal. + """ + return numpy.append(signal[0], signal[1:] - coeff * signal[:-1]) diff --git a/speech_features/speech_features.py b/speech_features/speech_features.py new file mode 100755 index 0000000..78231a5 --- /dev/null +++ b/speech_features/speech_features.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别内置声学特征提取模块,定义了几个常用的声学特征类 +""" + +import random +import numpy as np +from scipy.fftpack import fft +from .base import mfcc, delta, logfbank + + +class SpeechFeatureMeta: + """ + ASRT语音识别中所有声学特征提取类的基类 + """ + + def __init__(self, framesamplerate=16000): + self.framesamplerate = framesamplerate + + def run(self, wavsignal, fs=16000): + ''' + run method + ''' + raise NotImplementedError('[ASRT] `run()` method is not implemented.') + + +class MFCC(SpeechFeatureMeta): + """ + ASRT语音识别内置的mfcc声学特征提取类 + + Compute MFCC features from an audio signal. + + :param framesamplerate: the sample rate of the signal we are working with, in Hz. + :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) + :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds) + :param numcep: the number of cepstrum to return, default 13 + :param nfilt: the number of filters in the filterbank, default 26. + :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97. + """ + + def __init__(self, framesamplerate=16000, + winlen=0.025, + winstep=0.01, + numcep=13, + nfilt=26, + preemph=0.97): + self.framesamplerate = framesamplerate + self.winlen = winlen + self.winstep = winstep + self.numcep = numcep + self.nfilt = nfilt + self.preemph = preemph + super().__init__(framesamplerate) + + def run(self, wavsignal, fs=16000): + """ + 计算mfcc声学特征,包含静态特征、一阶差分和二阶差分 + + :returns: A numpy array of size (NUMFRAMES by numcep * 3) containing features. Each row holds 1 feature vector. + """ + wavsignal = np.array(wavsignal, dtype=np.float64) + # 获取输入特征 + feat_mfcc = mfcc(wavsignal[0], samplerate=self.framesamplerate, winlen=self.winlen, + winstep=self.winstep, numcep=self.numcep, nfilt=self.nfilt, preemph=self.preemph) + feat_mfcc_d = delta(feat_mfcc, 2) + feat_mfcc_dd = delta(feat_mfcc_d, 2) + # 返回值分别是mfcc特征向量的矩阵及其一阶差分和二阶差分矩阵 + wav_feature = np.column_stack((feat_mfcc, feat_mfcc_d, feat_mfcc_dd)) + return wav_feature + + +class Logfbank(SpeechFeatureMeta): + """ + ASRT语音识别内置的logfbank声学特征提取类 + """ + + def __init__(self, framesamplerate=16000, nfilt=26): + self.nfilt = nfilt + super().__init__(framesamplerate) + + def run(self, wavsignal, fs=16000): + wavsignal = np.array(wavsignal, dtype=np.float64) + # 获取输入特征 + wav_feature = logfbank(wavsignal, fs, nfilt=self.nfilt) + return wav_feature + + +class Spectrogram(SpeechFeatureMeta): + """ + ASRT语音识别内置的语谱图声学特征提取类 + """ + + def __init__(self, framesamplerate=16000, timewindow=25, timeshift=10): + self.time_window = timewindow + self.window_length = int(framesamplerate / 1000 * self.time_window) # 计算窗长度的公式,目前全部为400固定值 + self.timeshift = timeshift + + ''' + # 保留将来用于不同采样频率 + self.x=np.linspace(0, self.window_length - 1, self.window_length, dtype = np.int64) + self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (self.window_length - 1) ) # 汉明窗 + ''' + + self.x = np.linspace(0, 400 - 1, 400, dtype=np.int64) + self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (400 - 1)) # 汉明窗 + super().__init__(framesamplerate) + + def run(self, wavsignal, fs=16000): + if fs != 16000: + raise ValueError( + f"[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this " + f"audio is {fs} Hz.") + + # wav波形 加时间窗以及时移10ms + time_window = 25 # 单位ms + window_length = int(fs / 1000 * time_window) # 计算窗长度的公式,目前全部为400固定值 + + wav_arr = np.array(wavsignal) + # wav_length = len(wavsignal[0]) + # wav_length = wav_arr.shape[1] + + range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1 # 计算循环终止的位置,也就是最终生成的窗数 + data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64) # 用于存放最终的频率特征数据 + data_line = np.zeros((1, window_length), dtype=np.float64) + + for i in range(0, range0_end): + p_start = i * 160 + p_end = p_start + 400 + + data_line = wav_arr[0, p_start:p_end] + data_line = data_line * self.w # 加窗 + data_line = np.abs(fft(data_line)) + + data_input[i] = data_line[0: window_length // 2] # 设置为400除以2的值(即200)是取一半数据,因为是对称的 + + data_input = np.log(data_input + 1) + return data_input + + +class SpecAugment(SpeechFeatureMeta): + """ + 复现谷歌SpecAugment数据增强特征算法,基于Spectrogram语谱图基础特征 + """ + + def __init__(self, framesamplerate=16000, timewindow=25, timeshift=10): + self.time_window = timewindow + self.window_length = int(framesamplerate / 1000 * self.time_window) # 计算窗长度的公式,目前全部为400固定值 + self.timeshift = timeshift + + ''' + # 保留将来用于不同采样频率 + self.x=np.linspace(0, self.window_length - 1, self.window_length, dtype = np.int64) + self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (self.window_length - 1) ) # 汉明窗 + ''' + + self.x = np.linspace(0, 400 - 1, 400, dtype=np.int64) + self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (400 - 1)) # 汉明窗 + super().__init__(framesamplerate) + + def run(self, wavsignal, fs=16000): + if fs != 16000: + raise ValueError( + f"[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this " + f"audio is {fs} Hz.") + + # wav波形 加时间窗以及时移10ms + time_window = 25 # 单位ms + window_length = int(fs / 1000 * time_window) # 计算窗长度的公式,目前全部为400固定值 + + wav_arr = np.array(wavsignal) + # wav_length = len(wavsignal[0]) + # wav_length = wav_arr.shape[1] + + range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1 # 计算循环终止的位置,也就是最终生成的窗数 + data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64) # 用于存放最终的频率特征数据 + data_line = np.zeros((1, window_length), dtype=np.float64) + + for i in range(0, range0_end): + p_start = i * 160 + p_end = p_start + 400 + + data_line = wav_arr[0, p_start:p_end] + data_line = data_line * self.w # 加窗 + data_line = np.abs(fft(data_line)) + + data_input[i] = data_line[0: window_length // 2] # 设置为400除以2的值(即200)是取一半数据,因为是对称的 + + # print(data_input.shape) + data_input = np.log(data_input + 1) + + # 开始对得到的特征应用SpecAugment + mode = random.randint(1, 100) + h_start = random.randint(1, data_input.shape[0]) + h_width = random.randint(1, 100) + + v_start = random.randint(1, data_input.shape[1]) + v_width = random.randint(1, 100) + + if mode <= 60: # 正常特征 60% + pass + elif 60 < mode <= 75: # 横向遮盖 15% + data_input[h_start:h_start + h_width, :] = 0 + elif 75 < mode <= 90: # 纵向遮盖 15% + data_input[:, v_start:v_start + v_width] = 0 + else: # 两种遮盖叠加 10% + data_input[h_start:h_start + h_width, :v_start:v_start + v_width] = 0 + + return data_input diff --git a/speech_model.py b/speech_model.py new file mode 100755 index 0000000..cff6820 --- /dev/null +++ b/speech_model.py @@ -0,0 +1,262 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +声学模型基础功能模板定义 +""" +import os +import time +import random +import numpy as np + +from utils.ops import get_edit_distance, read_wav_data +from utils.config import load_config_file, DEFAULT_CONFIG_FILENAME, load_pinyin_dict +from utils.thread import threadsafe_generator + + +class ModelSpeech: + """ + 语音模型类 + + 参数: + speech_model: 声学模型类型 (BaseModel类) 实例对象 + speech_features: 声学特征类型(SpeechFeatureMeta类)实例对象 + """ + + def __init__(self, speech_model, speech_features, max_label_length=64): + self.data_loader = None + self.speech_model = speech_model + self.trained_model, self.base_model = speech_model.get_model() + self.speech_features = speech_features + self.max_label_length = max_label_length + + @threadsafe_generator + def _data_generator(self, batch_size, data_loader): + """ + 数据生成器函数,用于Keras的generator_fit训练 + batch_size: 一次产生的数据量 + """ + labels = np.zeros((batch_size, 1), dtype=np.float64) + data_count = data_loader.get_data_count() + index = 0 + + while True: + X = np.zeros((batch_size,) + self.speech_model.input_shape, dtype=np.float64) + y = np.zeros((batch_size, self.max_label_length), dtype=np.int16) + input_length = [] + label_length = [] + + for i in range(batch_size): + wavdata, sample_rate, data_labels = data_loader.get_data(index) + data_input = self.speech_features.run(wavdata, sample_rate) + data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1) + # 必须加上模pool_size得到的值,否则会出现inf问题,然后提示No valid path found. + # 但是直接加又可能会出现sequence_length <= xxx 的问题,因此不能让其超过时间序列长度的最大值,比如200 + pool_size = self.speech_model.input_shape[0] // self.speech_model.output_shape[0] + inlen = min(data_input.shape[0] // pool_size + data_input.shape[0] % pool_size, + self.speech_model.output_shape[0]) + input_length.append(inlen) + + X[i, 0:len(data_input)] = data_input + y[i, 0:len(data_labels)] = data_labels + label_length.append([len(data_labels)]) + + index = (index + 1) % data_count + + label_length = np.matrix(label_length) + input_length = np.array([input_length]).T + + yield [X, y, input_length, label_length], labels + + def train_model(self, optimizer, data_loader, epochs=1, save_step=1, batch_size=16, last_epoch=0, call_back=None): + """ + 训练模型 + + 参数: + optimizer:tensorflow.keras.optimizers 优化器实例对象 + data_loader:数据加载器类型 (SpeechData) 实例对象 + epochs: 迭代轮数 + save_step: 每多少epoch保存一次模型 + batch_size: mini batch大小 + last_epoch: 上一次epoch的编号,可用于断点处继续训练时,epoch编号不冲突 + call_back: keras call back函数 + """ + save_filename = os.path.join('save_models', self.speech_model.get_model_name(), + self.speech_model.get_model_name()) + + self.trained_model.compile(loss=self.speech_model.get_loss_function(), optimizer=optimizer) + print('[ASRT] Compiles Model Successfully.') + + yielddatas = self._data_generator(batch_size, data_loader) + + data_count = data_loader.get_data_count() # 获取数据的数量 + # 计算每一个epoch迭代的次数 + num_iterate = data_count // batch_size + iter_start = last_epoch + iter_end = last_epoch + epochs + for epoch in range(iter_start, iter_end): # 迭代轮数 + try: + epoch += 1 + print('[ASRT Training] train epoch %d/%d .' % (epoch, iter_end)) + data_loader.shuffle() + self.trained_model.fit_generator(yielddatas, num_iterate, callbacks=call_back) + except StopIteration: + print('[error] generator error. please check data format.') + break + + if epoch % save_step == 0: + if not os.path.exists('save_models'): # 判断保存模型的目录是否存在 + os.makedirs('save_models') # 如果不存在,就新建一个,避免之后保存模型的时候炸掉 + if not os.path.exists(os.path.join('save_models', self.speech_model.get_model_name())): # 判断保存模型的目录是否存在 + os.makedirs( + os.path.join('save_models', self.speech_model.get_model_name())) # 如果不存在,就新建一个,避免之后保存模型的时候炸掉 + + self.save_model(save_filename + '_epoch' + str(epoch)) + + print('[ASRT Info] Model training complete. ') + + def load_model(self, filename): + """ + 加载模型参数 + """ + self.speech_model.load_weights(filename) + + def save_model(self, filename): + """ + 保存模型参数 + """ + self.speech_model.save_weights(filename) + + def evaluate_model(self, data_loader, data_count=-1, out_report=False, show_ratio=True, show_per_step=100): + """ + 评估检验模型的识别效果 + """ + data_nums = data_loader.get_data_count() + + if data_count <= 0 or data_count > data_nums: # 当data_count为小于等于0或者大于测试数据量的值时,则使用全部数据来测试 + data_count = data_nums + + try: + ran_num = random.randint(0, data_nums - 1) # 获取一个随机数 + words_num = 0 + word_error_num = 0 + + nowtime = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time())) + if out_report: + txt_obj = open('Test_Report_' + data_loader.dataset_type + '_' + nowtime + '.txt', 'w', + encoding='UTF-8') # 打开文件并读入 + txt_obj.truncate((data_count + 1) * 300) # 预先分配一定数量的磁盘空间,避免后期在硬盘中文件存储位置频繁移动,以防写入速度越来越慢 + txt_obj.seek(0) # 从文件首开始 + + txt = '' + i = 0 + while i < data_count: + wavdata, fs, data_labels = data_loader.get_data((ran_num + i) % data_nums) # 从随机数开始连续向后取一定数量数据 + data_input = self.speech_features.run(wavdata, fs) + data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1) + # 数据格式出错处理 开始 + # 当输入的wav文件长度过长时自动跳过该文件,转而使用下一个wav文件来运行 + if data_input.shape[0] > self.speech_model.input_shape[0]: + print('*[Error]', 'wave data lenghth of num', (ran_num + i) % data_nums, 'is too long.', + 'this data\'s length is', data_input.shape[0], + 'expect <=', self.speech_model.input_shape[0], + '\n A Exception raise when test Speech Model.') + i += 1 + continue + # 数据格式出错处理 结束 + + pre = self.predict(data_input) + + words_n = data_labels.shape[0] # 获取每个句子的字数 + words_num += words_n # 把句子的总字数加上 + edit_distance = get_edit_distance(data_labels, pre) # 获取编辑距离 + if edit_distance <= words_n: # 当编辑距离小于等于句子字数时 + word_error_num += edit_distance # 使用编辑距离作为错误字数 + else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字 + word_error_num += words_n # 就直接加句子本来的总字数就好了 + + if i % show_per_step == 0 and show_ratio: + print('[ASRT Info] Testing: ', i, '/', data_count) + + txt = '' + if out_report: + txt += str(i) + '\n' + txt += 'True:\t' + str(data_labels) + '\n' + txt += 'Pred:\t' + str(pre) + '\n' + txt += '\n' + txt_obj.write(txt) + + i += 1 + + # print('*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率:', word_error_num / words_num * 100, '%') + print('*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ', + word_error_num / words_num * 100, '%') + if out_report: + txt = '*[ASRT Test Result] Speech Recognition ' + data_loader.dataset_type + ' set word error ratio: ' + str( + word_error_num / words_num * 100) + ' %' + txt_obj.write(txt) + txt_obj.truncate() # 去除文件末尾剩余未使用的空白存储字节 + txt_obj.close() + + except StopIteration: + print('[ASRT Error] Model testing raise a error. Please check data format.') + + def predict(self, data_input): + """ + 预测结果 + + 返回语音识别后的forward结果 + """ + return self.speech_model.forward(data_input) + + def recognize_speech(self, wavsignal, fs): + """ + 最终做语音识别用的函数,识别一个wav序列的语音 + """ + # 获取输入特征 + data_input = self.speech_features.run(wavsignal, fs) + data_input = np.array(data_input, dtype=np.float64) + # print(data_input,data_input.shape) + data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1) + r1 = self.predict(data_input) + # 获取拼音列表 + list_symbol_dic, _ = load_pinyin_dict(load_config_file(DEFAULT_CONFIG_FILENAME)['dict_filename']) + + r_str = [] + for i in r1: + r_str.append(list_symbol_dic[i]) + + return r_str + + def recognize_speech_from_file(self, filename): + """ + 最终做语音识别用的函数,识别指定文件名的语音 + """ + wavsignal, sample_rate, _, _ = read_wav_data(filename) + r = self.recognize_speech(wavsignal, sample_rate) + return r + + @property + def model(self): + """ + 返回tf.keras model + """ + return self.trained_model diff --git a/speech_recorder.py b/speech_recorder.py new file mode 100755 index 0000000..24adac0 --- /dev/null +++ b/speech_recorder.py @@ -0,0 +1,109 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +一个配置为可用于ASRT语音识别系统的录音程序 +""" + +import wave +import pyaudio + + +def record_wave(wavfile, + duration=10, + channels=1, + sampling_rate=16000, + sampling_bits=16, + chunk_size=1024, + keyboard_interrupt='keep_audio'): + """Record audio using the default audio device by PyAudio and Wave""" + + format_ = None + if sampling_bits == 8: + format_ = pyaudio.paInt8 + if sampling_bits == 16: + format_ = pyaudio.paInt16 + elif sampling_bits == 24: + format_ = pyaudio.paInt24 + elif sampling_bits == 32: + format_ = pyaudio.paFloat32 + else: + raise ValueError('Unsupported sampling bits') + + audio = pyaudio.PyAudio() + stream = audio.open(format=format_, + channels=channels, + rate=sampling_rate, + input=True, + frames_per_buffer=chunk_size) + + frames = [] + + print('Start to record with {}-seconds audio\n' + 'Type Ctrl-C to get an early stop (a shorter audio)' + .format(duration)) + try: + for _ in range(0, int(sampling_rate / chunk_size * duration)): + data = stream.read(chunk_size) + frames.append(data) + print('.', end='', flush=True) + except KeyboardInterrupt: + if keyboard_interrupt == 'keep_audio': + used_seconds = int(len(frames) * chunk_size / sampling_rate) + print('\n-*- Early stop with {} seconds'.format(used_seconds)) + else: + raise + print('\nRecording finished') + + stream.stop_stream() + stream.close() + audio.terminate() + + print('Convert PCM frames to WAV... ', end='') + wavfp = wave.open(wavfile, 'wb') + wavfp.setnchannels(channels) + wavfp.setsampwidth(audio.get_sample_size(format_)) + wavfp.setframerate(sampling_rate) + wavfp.writeframes(b''.join(frames)) + wavfp.close() + print('OK') + + +if __name__ == "__main__": + from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter + + parser = ArgumentParser(description='Simple Wave Audio Recorder', + formatter_class=ArgumentDefaultsHelpFormatter) + parser.add_argument('-d', '--duration', type=int, + default=10, help='maximum duration in seconds') + parser.add_argument('-r', '--sampling-rate', type=int, + default=16000, help='sampling rate') + parser.add_argument('-b', '--sampling-bits', type=int, + default=16, choices=(8, 16, 24, 32), help='sampling bits') + parser.add_argument('-c', '--channels', type=int, + default=1, help='audio channels') + parser.add_argument('output', nargs='?', default='output.wav', + help='audio file to store audio stream') + args = parser.parse_args() + record_wave(args.output, duration=args.duration, + channels=args.channels, + sampling_bits=args.sampling_bits, + sampling_rate=args.sampling_rate) diff --git a/train_speech_model.py b/train_speech_model.py new file mode 100755 index 0000000..18685f7 --- /dev/null +++ b/train_speech_model.py @@ -0,0 +1,53 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +用于训练语音识别系统语音模型的程序 +""" + +import os +from tensorflow.keras.optimizers import Adam + +from speech_model import ModelSpeech +from model_zoo.speech_model.keras_backend import SpeechModel251BN +from data_loader import DataLoader +from speech_features import SpecAugment + +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + +AUDIO_LENGTH = 1600 +AUDIO_FEATURE_LENGTH = 200 +CHANNELS = 1 +# 默认输出的拼音的表示大小是1428,即1427个拼音+1个空白块 +OUTPUT_SIZE = 1428 +sm251bn = SpeechModel251BN( + input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS), + output_size=OUTPUT_SIZE +) +feat = SpecAugment() +train_data = DataLoader('train') +opt = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, decay=0.0, epsilon=10e-8) +ms = ModelSpeech(sm251bn, feat, max_label_length=64) + +# ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') +ms.train_model(optimizer=opt, data_loader=train_data, + epochs=50, save_step=1, batch_size=16, last_epoch=0) +ms.save_model('save_models/' + sm251bn.get_model_name()) diff --git a/utils/config.py b/utils/config.py new file mode 100755 index 0000000..71d3def --- /dev/null +++ b/utils/config.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +加载ASRT配置文件相关 +""" + +import json + +DEFAULT_CONFIG_FILENAME = 'asrt_config.json' +_config_dict = None +_pinyin_dict = None +_pinyin_list = None + + +def load_config_file(filename: str) -> dict: + """ + 加载json配置文件 + + 参数:\\ + filename: 文件名 + + 返回:\\ + 配置信息字典 + """ + global _config_dict + if _config_dict is not None: + return _config_dict + + with open(filename, 'r', encoding="utf-8") as file_pointer: + _config_dict = json.load(file_pointer) + return _config_dict + + +def load_pinyin_dict(filename: str) -> tuple: + """ + 加载拼音列表和拼音字典 + + 拼音列表:用于下标索引转拼音 \\ + 拼音字典:用于拼音索引转下标 + """ + global _pinyin_list, _pinyin_dict + if _pinyin_dict is not None and _pinyin_list is not None: + return _pinyin_list, _pinyin_dict + + _pinyin_list = list() + _pinyin_dict = dict() + with open(filename, 'r', encoding='utf-8') as file_pointer: + lines = file_pointer.read().split('\n') + for line in lines: + if len(line) == 0: + continue + tokens = line.split('\t') + _pinyin_list.append(tokens[0]) + _pinyin_dict[tokens[0]] = len(_pinyin_list) - 1 + return _pinyin_list, _pinyin_dict diff --git a/utils/ops.py b/utils/ops.py new file mode 100755 index 0000000..8ab58c0 --- /dev/null +++ b/utils/ops.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +一些常用操作函数的定义 +""" + +import wave +import difflib +import matplotlib.pyplot as plt +import numpy as np + + +def read_wav_data(filename: str) -> tuple: + """ + 读取一个wav文件,返回声音信号的时域谱矩阵和播放时间 + """ + wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流 + num_frame = wav.getnframes() # 获取帧数 + num_channel=wav.getnchannels() # 获取声道数 + framerate=wav.getframerate() # 获取帧速率 + num_sample_width=wav.getsampwidth() # 获取实例的比特宽度,即每一帧的字节数 + str_data = wav.readframes(num_frame) # 读取全部的帧 + wav.close() # 关闭流 + wave_data = np.fromstring(str_data, dtype = np.short) # 将声音文件数据转换为数组矩阵形式 + wave_data.shape = -1, num_channel # 按照声道数将数组整形,单声道时候是一列数组,双声道时候是两列的矩阵 + wave_data = wave_data.T # 将矩阵转置 + return wave_data, framerate, num_channel, num_sample_width + + +def read_wav_bytes(filename: str) -> tuple: + """ + 读取一个wav文件,返回声音信号的时域谱矩阵和播放时间 + """ + wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流 + num_frame = wav.getnframes() # 获取帧数 + num_channel=wav.getnchannels() # 获取声道数 + framerate=wav.getframerate() # 获取帧速率 + num_sample_width=wav.getsampwidth() # 获取实例的比特宽度,即每一帧的字节数 + str_data = wav.readframes(num_frame) # 读取全部的帧 + wav.close() # 关闭流 + return str_data, framerate, num_channel, num_sample_width + + +def get_edit_distance(str1, str2) -> int: + """ + 计算两个串的编辑距离,支持str和list类型 + """ + leven_cost = 0 + sequence_match = difflib.SequenceMatcher(None, str1, str2) + for tag, index_1, index_2, index_j1, index_j2 in sequence_match.get_opcodes(): + if tag == 'replace': + leven_cost += max(index_2-index_1, index_j2-index_j1) + elif tag == 'insert': + leven_cost += (index_j2-index_j1) + elif tag == 'delete': + leven_cost += (index_2-index_1) + return leven_cost + + +def ctc_decode_delete_tail_blank(ctc_decode_list): + """ + 处理CTC解码后序列末尾余留的空白元素,删除掉 + """ + p = 0 + while p < len(ctc_decode_list) and ctc_decode_list[p] != -1: + p += 1 + return ctc_decode_list[0:p] + + +def visual_1D(points_list, frequency=1): + """ + 可视化1D数据 + """ + # 首先创建绘图网格,1个子图 + fig, ax = plt.subplots(1) + x = np.linspace(0, len(points_list)-1, len(points_list)) / frequency + + # 在对应对象上调用 plot() 方法 + ax.plot(x, points_list) + fig.show() + + +def visual_2D(img): + """ + 可视化2D数据 + """ + plt.subplot(111) + plt.imshow(img) + plt.colorbar(cax=None, ax=None, shrink=0.5) + plt.show() + + +def decode_wav_bytes(samples_data: bytes, channels: int = 1, byte_width: int = 2) -> list: + """ + 解码wav格式样本点字节流,得到numpy数组 + """ + numpy_type = np.short + if byte_width == 4: + numpy_type = np.int + elif byte_width != 2: + raise Exception('error: unsurpport byte width `' + str(byte_width) + '`') + wave_data = np.fromstring(samples_data, dtype=numpy_type) # 将声音文件数据转换为数组矩阵形式 + wave_data.shape = -1, channels # 按照声道数将数组整形,单声道时候是一列数组,双声道时候是两列的矩阵 + wave_data = wave_data.T # 将矩阵转置 + return wave_data + + +def get_symbol_dict(dict_filename): + """ + 读取拼音汉字的字典文件 + 返回读取后的字典 + """ + txt_obj = open(dict_filename, 'r', encoding='UTF-8') # 打开文件并读入 + txt_text = txt_obj.read() + txt_obj.close() + txt_lines = txt_text.split('\n') # 文本分割 + + dic_symbol = {} # 初始化符号字典 + for i in txt_lines: + list_symbol = [] # 初始化符号列表 + if i != '': + txt_l=i.split('\t') + pinyin = txt_l[0] + for word in txt_l[1]: + list_symbol.append(word) + dic_symbol[pinyin] = list_symbol + + return dic_symbol + + +def get_language_model(model_language_filename): + """ + 读取语言模型的文件 + 返回读取后的模型 + """ + txt_obj = open(model_language_filename, 'r', encoding='UTF-8') # 打开文件并读入 + txt_text = txt_obj.read() + txt_obj.close() + txt_lines = txt_text.split('\n') # 文本分割 + + dic_model = {} # 初始化符号字典 + for i in txt_lines: + if i != '': + txt_l = i.split('\t') + if len(txt_l) == 1: + continue + dic_model[txt_l[0]] = txt_l[1] + + return dic_model + + +def ctc_decode_stream(tokens): + i = 0 + while i < len(tokens): + while i+1 < len(tokens) and tokens[i] == tokens[i+1]: + i += 1 + if i+1 == len(tokens) and tokens[i] != -1: + return tokens[0], [] + if tokens[i] != -1: + return tokens[i], tokens[i+1:] + i += 1 + return -1, [] diff --git a/utils/ops_test.py b/utils/ops_test.py new file mode 100755 index 0000000..9dc1ead --- /dev/null +++ b/utils/ops_test.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ops.py单元测试 +""" + +import pytest +from ops import get_edit_distance, ctc_decode_delete_tail_blank, ctc_decode_stream + + +class TestGetEditDistance: + def test_1(self): + examle_input = ["abc", "abc"] + examle_output = 0 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + def test_2(self): + examle_input = ["abc", "adc"] + examle_output = 1 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + def test_3(self): + examle_input = ["abc", "a"] + examle_output = 2 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + def test_4(self): + examle_input = ["abc", "addce"] + examle_output = 3 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + def test_5(self): + examle_input = ["abc", ""] + examle_output = 3 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + def test_6(self): + examle_input = ["", ""] + examle_output = 0 + result = get_edit_distance(examle_input[0], examle_input[1]) + assert result == examle_output + + +class TestCtcDecodeDeleteTailBlank: + def test_1(self): + examle_input = [1, 2, 3, 4, 5, -1, -1, -1, -1, -1, -1] + examle_output = [1, 2, 3, 4, 5] + result = ctc_decode_delete_tail_blank(examle_input) + assert result == examle_output + + def test_2(self): + examle_input = [1, 2, 3, 4, 5, -1] + examle_output = [1, 2, 3, 4, 5] + result = ctc_decode_delete_tail_blank(examle_input) + assert result == examle_output + + def test_3(self): + examle_input = [1, 2, 3, 4, 5] + examle_output = [1, 2, 3, 4, 5] + result = ctc_decode_delete_tail_blank(examle_input) + assert result == examle_output + + def test_4(self): + examle_input = [-1, -1, -1, -1] + examle_output = [] + result = ctc_decode_delete_tail_blank(examle_input) + assert result == examle_output + + def test_5(self): + examle_input = [-1] + examle_output = [] + result = ctc_decode_delete_tail_blank(examle_input) + assert result == examle_output + + +class TestCtcDecodeStream: + def test_1(self): + examle_input = [-1, -1, -1, 1, 1, 1, -1, -1, 2, 2, 2, 3, 3, -1, -1, -1] + examle_output = [1, 2, 3, -1] + result = [] + res, tmp = ctc_decode_stream(examle_input) + result.append(res) + while len(tmp) > 0: + res, tmp = ctc_decode_stream(tmp) + result.append(res) + assert result == examle_output + + def test_2(self): + examle_input = [-1, -1, -1, -1, -1] + examle_output = [-1] + result = [] + res, tmp = ctc_decode_stream(examle_input) + result.append(res) + while len(tmp) > 0: + res, tmp = ctc_decode_stream(tmp) + result.append(res) + assert result == examle_output diff --git a/utils/thread.py b/utils/thread.py new file mode 100755 index 0000000..72e9636 --- /dev/null +++ b/utils/thread.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +解决python生成器多线程的线程安全问题 +""" + +import threading +''' + A generic iterator and generator that takes any iterator and wrap it to make it thread safe. + This method was introducted by Anand Chitipothu in http://anandology.com/blog/using-iterators-and-generators/ + but was not compatible with python 3. This modified version is now compatible and works both in python 2.8 and 3.0 +''' + + +class threadsafe_iter: + """Takes an iterator/generator and makes it thread-safe by + serializing call to the `next` method of given iterator/generator. + """ + def __init__(self, it): + self.it = it + self.lock = threading.Lock() + + def __iter__(self): + return self + + def __next__(self): + with self.lock: + return self.it.__next__() + + +def threadsafe_generator(f): + """A decorator that takes a generator function and makes it thread-safe. + """ + def g(*a, **kw): + return threadsafe_iter(f(*a, **kw)) + return g