Saravanan666/pypi-org huggingface dataset:
Package: danspeech
DanSpeechAn open-source python package for Danish speech recognition.You can find all relevant information in the documentation and we provide you with some extra links below.DemoTo experience the danspeech package, we've created a simple demo with a nice GUI. It depends on danspeech
and django.The demo resides athttps://github.com/danspeech/danspeechdemo.Train modelsIf you wish to train your own models, or perhaps finetune a DanSpeech model to your specific use case, we then
refer to theDanSpeech training repository.SupportIf you require help with the software, then feel free to create issues here on github. We will continually solve issues
and answer any questions that you might have.Authors and acknowledgmentMain authors:Martin Carsten Nielsen  (mcnielsen4270@gmail.com)Rasmus Arpe Fogh Jensen (rasmus.arpe@gmail.com)This project is supported by Innovation Foundation Denmark through the projects DABAI and ATELOther acknowledgements:We've trained the models based on the code fromhttps://github.com/SeanNaren/deepspeech.pytorch.The audio handling and recognizing flow is based onhttps://github.com/Uberi/speech_recognition.Handling of the pretrained models is based onkeras.We've trained all models with the aid of DTU using data from Sprakbanken (NST).LicenceThe software is in general licenced under the Apache-2 licence and may be used commercially.NOTE:Some code parts in DanSpeech contain links to other original sources. If this is the case, then the specific code
part is licenced differently depending on the source and if you wish to redistribute DanSpeech code, then you must
make sure you also comply with the original code licences.The flac binaries are licenced under GPLv2. See more information atFlac licence.
Package: dante-tokenizer
Twitter Portuguese TokenizerTokenizador de Tweets para o português, voltado ao mercado de ações (Dataset Dante).RequerimentosPython >= 3.7Como usarExecute o seguinte comando para instalar as dependências e instalar o pacotedante_tokenizer.makeinstallO pacote será adicionado ao seu ambiente python. Para tokenizar textos, basta seguir os seguintes passos:>>>fromdante_tokenizerimportDanteTokenizer>>>tokenizer=DanteTokenizer()>>>tokenizer.tokenize("A DANT3 está em alta!")['A','DANT3','está','em','alta','!']O métodotokenizeirá retornar uma lista de strings contendo os respectivos tokens detectados. Outros exemplos estão disponíveis na pastanotebooks.DanteTokenizerO DanteTokenizer é uma modificação doTweetTokenizerdo pacote nltk. Adicionando regras para comportar as variedades de tokens existentes nos tweets relacionados ao mercado de ações.Obs: Como a classe atual do TweetTokenizer não permite uma extensão de forma fácil, o código foi extraído e modificado dentro dodante_tokenizer.AgradecimentosAo Prof. Thiago A. S. Pardo pela orientação no programa de mestrado, à Profa. Ariani Di Felippo por definir as regras de tokenização e à Dra. Lucelene Lopes pelas contribuições técnicas.
Package: dantico
danticoTools to convert Django ORM models to Pydantic models.Documentation:https://xshapira.github.io/dantico/The key features are:Custom Field Support: Create Pydantic Schemas from Django Models with default field type validations out of the box.Field Validator: Fields can be validated with Pydanticvalidatororroot_validator.RequirementsPython 3.7+Django 3.0+Pydantic 1.6+Installationpip install danticoUsageAssume we have the following user model definition:# models.pyfromdjango.dbimportmodelsclassUser(models.Model):GENDER_MALE="male"GENDER_FEMALE="female"GENDER_OTHER="other"GENDER_CHOICES=((GENDER_MALE,"Male"),(GENDER_FEMALE,"Female"),(GENDER_OTHER,"Other"),)username=models.CharField(max_length=20)age=models.IntegerField()gender=models.CharField(choices=GENDER_CHOICES,max_length=10,blank=True,)password=models.CharField(max_length=100)company=models.ForeignKey(Company,on_delete=models.CASCADE,)languages=models.ManyToManyField(Language)def__str__(self):returnself.nameUsing theModelSchemaclass will automatically generate schemas from ourUsermodel.# schemas.pyfromdanticoimportModelSchemafromusers.modelsimportUserclassUserSchema(ModelSchema):classConfig:model=Userjson_output=json.dumps(UserSchema.schema(),indent=4)print(json_output)# Output:{"title":"UserSchema","type":"object","properties":{"id":{"title":"Id","extra":{},"type":"integer"},"Username":{"title":"Username","maxLength":20,"type":"string"},"age":{"title":"Age","type":"integer"},"gender":{"title":"Gender","allOf":[{"$ref":"#/definitions/GenderEnum"}]},"password":{"title":"Password","maxLength":100,"type":"string"},"company_id":{"title":"Company","type":"integer"},"languages":{"title":"Languages","type":"array","items":{"type":"integer"}}},"required":["Username","age","password","company_id","languages"],"definitions":{"GenderEnum":{"title":"GenderEnum","description":"An enumeration.","enum":["male","female","other"]}}}LicenseThis project is licensed under the terms of the MIT license.
Package: dantro
dantro– fromdataanddentro(Greek fortree) – is a Python
package that provides a uniform interface for hierarchically structured
and semantically heterogeneous data. It is built around three main
features:data handling:loading heterogeneous data into a tree-like data
structure, providing a uniform interface to itdata transformation:performing arbitrary operations on the data,
if necessary using lazy evaluationdata visualization:creating a visual representation of the
processed dataTogether, these stages constitute adata processing pipeline: an
automated sequence of predefined, configurable operations. Akin to a
Continuous Integration pipeline, a data processing pipeline provides a
uniform, consistent, and easily extensible infrastructure that
contributes to more efficient and reproducible workflows. This can be
beneficial especially in a scientific context, for instance when
handling data that was generated by computer simulations.dantrois meant to beintegratedinto projects and be used to set up
such a data processing pipeline, customized to the needs of the project.
It is designed to beeasily customizableto the requirements of the project
it is integrated in, even if the involved data is hierachically structured or
semantically heterogeneous.
Furthermore, it allows aconfiguration-based specificationof all
operations viaYAMLconfiguration
files; the resulting pipeline can then be controlled entirely via these
configuration files and without requiring code changes.Thedantropackage isopen source softwarereleased under theLGPLv3+license.
It was developed alongside theUtopia project(a modelling framework for complex and adaptive systems), but is an
independent package.Learn moreDocumentationProject pageREADME and installation instructionsPublication in the Journal of Open Source SoftwareUtopia Project Website
Package: dantzig
Dantzig: A Rust-powered LP library for PythonDantzig is a lightweight and concise linear programming solver suitable for small
and large-scale problems alike.Dantzig is implemented in both Rust and Python, meaning you get the expressiveness
and flexibility of a Python frontend plus the raw computing speed of a Rust backend.Dantzig supportsA solver featuring a parametric self-dual algorithmArbitrarily restricted variables, including completely unrestricted free variables==,<=, and>=constraintsBoth minimization and maximization problemsA numerically stable LU factorization with partial pivoting routine for robust linear algebra operationsMemory-efficient sparse matrix representationsModern Python type-checking:warning: Dantzig is under active development. Please help us improve the library by reporting any issues!.InstallationDantzig supports Python 3.10+ and can be installed withpip.pipinstalldantzigDesign PhilosophiesDantzig prides itself on being bothlightweight(zero-dependency) andconcise.
The API is designed to be extremely expressive and terse, saving you keystrokes without
sacrificing clarity. To this end, Dantzig provides several short aliases for common
classes and methods.A few examples are listed below,Var == VariableMin == MinimizeMax == MaximizeVar.free() == Variable(lb=0.0, ub=0.0)Var.nn() == Var.nonneg() == Variable(lb=0.0, ub=None)Var.np() == Var.nonpos() == Variable(lb=None, ub=0.0)Examplesimportdantzigasdzx=dz.Variable(lb=0.0,ub=None)y=dz.Variable(lb=0.0,ub=None)z=dz.Variable(lb=0.0,ub=None)soln=dz.Minimize(x+y-z).subject_to(x+y+z==1).solve()assertsoln.objective_value==-1.0assertsoln[x]==0.0assertsoln[y]==0.0assertsoln[z]==1.0Using aliases, the previous example can alternately be writtenfromdantzigimportMin,Varx=Var.nn()y=Var.nn()z=Var.nn()soln=Min(x+y-z).st(x+y+z==1)Road MapMixed integer linear programing (MILP)SIMD-accelerated linear algebra operationsGeneral optimizations to make the library competitive withortools
Package: danube-delta
Honza Javorek’sPelicansetup.Installation$pipinstalldanube-deltaWarning:The library is only for Python 3.SetupCreate basic scaffolding of yourPelicanblog:$mkdir./my-awesome-blog$cd./my-awesome-blog$gitinit$mkdir./content./output$echo'/output'>.gitignoreCreate./settings.py:fromdanube_delta.settingsimport*AUTHOR='Zuzka & Honza'SITENAME='Pražení'ifPRODUCTION:SITEURL='http://example.com'Installdanube_delta, globally:$sudo-Hpipinstalldanube_deltaIn the root of your blog directory you can now use theblogCLI:$blogwriteUsage$blogupdate# Gets other people's changes from GitHub$blogwrite# Starts a new article$blogphotos# Adds images to the last article$blogpreview# Opens local preview of your blog website$blogpublish# Saves changes and sends them to GitHub$bloglint# Looks for errors in source code of your blog$blogdeploy# Uploads new version of the blog websiteTravis CIcan doblog deployfor you every time you performblog publish:language:"python"python:-"3.5"cache:"pip"script:-"bloglint"env:global:-secure:"..."# gem install travis && travis encrypt GITHUB_TOKEN=...deploy:provider:"script"script:"blogdeploy"on:branch:"master"repo:"honzajavorek/my-awesome-blog"sudo:falseHead to theuser guidefor more information on how to use a blog based on Danube Delta.Developing Danube DeltaClone Danube Delta to a directory of your choice, e.g.~/danube-delta.Install Danube Delta from your local clone:pip install-e~/danube-delta.NameIn 2012 I’ve seen somepelicansin theDanube Delta:Photo: © 2012 Honza Javorek
Package: danutils
My name is Daniel Lepage (http://www.dplepage.com), and you probably don’t need
this module. It’s full of random hacks that I’ve accumulated and needed
somewhere to stick. Many of them probably no longer work. Some that do may be
stupid ways of doing things, or obsoleted by real libraries.Some parts are also copied directly from blogs or whatnot; such code is marked
accordingly.I’m only putting this online so that I can use it when I need to work with some
old thing I developed that relies on some random function from here. Feel free
to use it, but do so at your own risk.danutils.do.interactHonestly, the only part of this library that I ever use on a regular basis is
the moduledanutils.do.interact, which, upon import, harvests the locals and
globals at that point, callscode.interactwith that namespace, and then
deletes itself from the module registry so that it can be imported again. I use
it when debugging:def foo(x):
  from danutils.do.interact import doit
  return x*xIf you run the above, you’ll suddenly find yourself at a command line at that
point where you imported interact.Some other things, at least the ones that I remember what they do, are:danutils.cudaSome automation tools for building modules with pyCuda. I’m pretty sure various
other libraries have made this ENTIRELY OBSOLETE.danutils.imgLooks like a bunch of image manipulation functions that are probably already
provided by some library or other. Also includes a python wrapper aroundChristopher de Coro’s image debuggerdanutils.libAutomatically compile really simple c projects and then import them via ctypes.danutils.pandatoolsFrom one time when I was playing around with thePanda3Dgame engine.danutils.sciA few tools I used back when I did a lot of numpy/scipy work. These days you’re
probably better off with IPython’s notebooks, and tools like PyTables or Blaze.danutils.wxSome wx widgets I built, also when I did a lot of numpy/scipy stuff.danutils.miscCould be pretty much anything in here.
Package: danvers
danvers: simple file-based data version controlDanversis a Python data version management tool, which helps you to
maintain and reference current and previous versions of data files.This means you can ensure code and models run consistently when they reference
datasets that may change and that you can ensure you keep older copies of data
files used as part of decision making, so logic and code can be rerun.New versions are only created if the new file is different to previous
versions. This allows infrequently updated data sets to be easily maintained
and versioned - if it's new, Danvers will keep a copy, if it hasn't changed,
it will ignore and keep its current version.FeaturesDanvers is simple, rather than feature-rich, but here are some of the things
it can do:Access previous versions of data filesMaintain a fixed number of verions (or all versions)Different trimming strategies are available (first-in-first-out,
last-used-first-out)Automatic duplicate check for data files against all known versionsDependenciesDanvers has no dependencies, just what comes with Python.Example Usagefrom danvers import Danvers

# instantiate with the location the data is stored
vers = Danvers(r'data')

# create the dataset if it doesn't exist already
if not 'marvel_movies' in vers.read_datasets():
    vers.create_dataset('marvel_movies')

# add the first data file, should return verion 1
version = vers.create_data_file('marvel_movies', r'test_data\movies_phase_1.csv')
print(version)

# adding a new data file should return version 2
version = vers.create_data_file('marvel_movies', r'test_data\movies_phase_1+2.csv')
print(version)

# get the filename for the latest version of the data
filename = vers.get_data_file('marvel_movies')
print(filename)

# get the filename for version 1 of the data
filename = vers.get_data_file('marvel_movies', 1)
print(filename)LicenseApache 2.0
Package: dao0
dao道将中国传统哲学思想结合到python编程思想中，用于优化代码逻辑结构，增强可读性，增加以往代码可序列化的可能性道、法、术、器、势、志传统的辩证哲学思想跟现代企业管理的对应关系: 好在“道、法、术、器、势、志”,先进于外国的管理思想很多年。是目前唯一可以解释整个世界由上到下的3D立体、无死角的逻辑关系的利器,是有效整合其它管理理论的理论道生一(志),一生二(法、术),二生三（器）,三生（势）万物志：产生类要达到的目的势： 函数生产者法： 函数如何调度消费者的方法，汇聚结果术： 消费者具体实现处理逻辑器： 消费者载体（进程、线程、函数、属性等）万物： 每个消费者产生的函数结果道：就是核心思想、理念、本质规律。很多时候有人纠结是自然规律，还是人定的思想，其实王阳明早说了“天理即人心”，我说“天理因人而存在”。这六个字，完全是因为人类存在而存在的，包括外星人。
法：就是法律、规章、制度、方法。是以“道”为基础制定的不可违背的原则，比如有些经常挂在嘴边却不知所云的“大道自然”或者“以人为本”。
术：行为与技巧，是可以以道为原则，做出反应和选择的，是以“道”为指导原则的。即“以道御术”，出自老子的《道德经》，很多人把“御”翻译成“承载”，实际上更贴切的是“驾驭”。以联想企业文化为例，联想提出“如果遇到公司没有相关规定的事，就按‘企业文化’办！”，这就是以道御术。
器：是指工具，比如桌椅板凳或者企业里的打卡机，还有EXCEL管理表格之类的。总之“器”用来体现道的思想，是人体器官的延伸，从而简化问题，更快达成目标，是体现“器以载道”的应用。
势：是从“道、法、术、器”体现的势能，比如“军魂、班风”之类的气场、气势、执行力。这个在“图1”里表现的是一个圈，其实画出来更应该像是太阳的光芒一样，感觉的到，摸 不到。以某个人为例，状态可以达到“善、诚、美、大、圣、神”的六重境界，善为别人以为你拥有美德，诚为你确实有美德，充盈为美，溢出为大，影响数个时代，众人为圣。影响千秋万代不同民族为神，比如佛教、基督教、伊斯兰教的创始人。
志：主要是指目标，只有通过势能才能达到，势就是火箭助推器形成的力量。更多详细可见 example.py 文件fromdaoimportDdefhandle(data):print(data)returndata+1if__name__=='__main__':# 志(物质) = 道(势, 术+器， 法)(意识)# 结果 = 目的(生产者, 消费者)(过程)res=D(range,handle)(1,10)print(res)res=D(range,handle).WD(list,handle).WD(list,handle).WD(list,handle)(1,10)print(res)
Package: daod
dao道将中国传统哲学思想结合到python编程思想中，用于优化代码逻辑结构，增强可读性，增加以往代码可序列化的可能性道、法、术、器、势、志传统的辩证哲学思想跟现代企业管理的对应关系: 好在“道、法、术、器、势、志”,先进于外国的管理思想很多年。是目前唯一可以解释整个世界由上到下的3D立体、无死角的逻辑关系的利器,是有效整合其它管理理论的理论道生一(志),一生二(势、法),二生三（术）,三生万物志：产生类要达到的目的势： 函数生产者法： 函数如何调度消费者的方法，汇聚结果术： 消费者具体实现处理逻辑器： 消费者载体（进程、线程、函数、属性等）万物： 每个消费者产生的函数结果道：就是核心思想、理念、本质规律。很多时候有人纠结是自然规律，还是人定的思想，其实王阳明早说了“天理即人心”，我说“天理因人而存在”。这六个字，完全是因为人类存在而存在的，包括外星人。
法：就是法律、规章、制度、方法。是以“道”为基础制定的不可违背的原则，比如有些经常挂在嘴边却不知所云的“大道自然”或者“以人为本”。
术：行为与技巧，是可以以道为原则，做出反应和选择的，是以“道”为指导原则的。即“以道御术”，出自老子的《道德经》，很多人把“御”翻译成“承载”，实际上更贴切的是“驾驭”。以联想企业文化为例，联想提出“如果遇到公司没有相关规定的事，就按‘企业文化’办！”，这就是以道御术。
器：是指工具，比如桌椅板凳或者企业里的打卡机，还有EXCEL管理表格之类的。总之“器”用来体现道的思想，是人体器官的延伸，从而简化问题，更快达成目标，是体现“器以载道”的应用。
势：是从“道、法、术、器”体现的势能，比如“军魂、班风”之类的气场、气势、执行力。这个在“图1”里表现的是一个圈，其实画出来更应该像是太阳的光芒一样，感觉的到，摸 不到。以某个人为例，状态可以达到“善、诚、美、大、圣、神”的六重境界，善为别人以为你拥有美德，诚为你确实有美德，充盈为美，溢出为大，影响数个时代，众人为圣。影响千秋万代不同民族为神，比如佛教、基督教、伊斯兰教的创始人。
志：主要是指目标，只有通过势能才能达到，势就是火箭助推器形成的力量。更多详细可见 example.py 文件fromdaoimportDif__name__=='__main__':defhandle(data):print(data)returndata+1# 志(物质) = 道(势, 术+器， 法)(意识)# 结果 = 目的(生产者, 消费者)(过程)res=D(range,handle)(1,10)print(res)
Package: daops
daops - data-aware operationsThedaopslibrary (pronounced “day-ops”) provides a python interface to a
set of operations suitable for working with climate simulation outputs. It is
typically used with ESGF data sets that are described in NetCDF files.daopsis unique in that it accesses a store offixesdefined for datasets that are
irregular when compared with others in theirpopulation.When adaopsoperation, such assubset, is requested, the library will look
up a database of known fixes before performing and calculations or transformations.
The data will be loaded andfixedusing thexarraylibrary before the any actual operations are sent to its sister libraryclisops.Free software: BSDDocumentation:https://daops.readthedocs.ioFeaturesThe package has the following features:Ability to rundata-reductionoperations on large climate data sets.Knowledge of irregularities/anomalies in some climate data sets.Ability to applyfixesto those data sets before operating on them.
This process is callednormalisationof the data sets.CreditsThis package was created withCookiecutterand thecedadev/cookiecutter-pypackageproject template.Cookiecutter:https://github.com/audreyr/cookiecuttercookiecutter-pypackage:https://github.com/cedadev/cookiecutter-pypackage
Package: daovoice-sdk
Installationpip install daovoice-sdkBasic Usagefromdaovoice.clientimportClientdaovoice_client=Client(token="Your Token Here")Note that you can find your token  inhttp://dashboard.daovoice.io/app/{app_id}/apps/settings/open-apiResourcesResources this API supports:https://api.daovoice.io/v1/conversationshttps://api.daovoice.io/v1/adminshttps://api.daovoice.io/v1/usersExamplesConversations# Get Conversation by idconversation=daovoice_client.conversations.get(id="conversation_id")printconversation.conversation_partsprintconversation.conversation_message# Replydaovoice_client.conversations.reply(id="conversation_id",admin_id="***",body="reply msg")Admins# list adminsadmins=daovoice_client.admins.all()foradinadmins:printad.nameUser# create useruser=daovoice_client.user.create(user_id="123456",name="example",email="example@daovoice.io")
Package: dap
Implementation of theData Access Protocol.This is a Python implementation of the Data Access Protocol, a
scientific protocol for data access developed by the OPeNDAP team
(http://opendap.org). This implementation is developed from scratch,
following the latest specification of the protocol (DAP 2.0 Draft
Community Standard 2005/04/27) and based on my experience with
OPeNDAP servers on the wild.Using this module one can access hundreds of scientific datasets
from Python programs, accessing data in an efficient, transparent
and pythonic way. Arrays are manipulated like normal multi-dimensional
arrays (like numpy.array, e.g.), with the fundamental difference
that data is downloaded on-the-fly when a variable is sliced.
Sequential data can be filtered on the server side before being
downloaded, saving bandwith and time.The module also implements a DAP server, allowing datasets from a
multitude of formats (netCDF, Matlab, CSV, GrADS/GRIB files, SQL
RDBMS) to be served on the internet. The server specifies a plugin
API for supporting new data formats in an easy way. The DAP server
is implemented as a WSGI application (see PEP 333), running on a
variery of servers, and can be combined with WSGI middleware to
support authentication, gzip compression and much more.The latest version is available in aSubversion repository.
Package: dap2rpm
Generate RPM specfiles for DevAssistant DAP packages.Usage:dap2rpm revealjs
dap2rpm revealjs -v 0.2
dap2rpm revealjs -L LICENSE NOTICE
dap2rpm Downloads/revealjs-0.2.dapNote for non-Fedora users: As dap2rpm is intended primarily for use in
Fedora, it automatically strips off the pre-release tags from the version
string (e. g.0.1devbecomes0.1), and attaches it to the Release tag
in the SPEC (in this case,Release:0.1.dev%{?dist}). This behaviour is
mandated by the Fedora Packaging Guidelines. If you want to avoid it, specify
the argument--keep-formatwhen running dap2rpm.
Package: dapc
Duyu Audio Processor CoreSoftware Introduction  软件简介软件全称：Duyu Audio Processor Core软件简称：DAPC (或 dapc)软件中文全称：DuYu音频处理器核心程序软件功能：简单处理主流格式的音频文件，诸如：变换采样格式，增益衰减，声道混缩，混响回声效果，调整速度与音调，剪切，混合，添加静音，淡入淡出效果，FFT滤波，3D环绕声音效等。项目开工时间：2020年05月02日最后更新时间与最新版本：2023年03月14日 v3.5.2.项目中使用的第三方开源软件：Sonic Library, FFmpeg.软件发行版下载地址：https://github.com/duyu09/Duyu-Audio-Processor-Core/releases/中文官方网站：https://www.duyu09.site/dapc/dapc.html简单使用方法:基本使用方法：dapc -i <inputFile> -o [outputFile] --output-option [sampleRate] [bitDepth]更多用法和细节：dapc -hCopyright Statement  著作权声明Python source code of Duyu Audio Processor Core (DAPC) software system:Copyright © 2020~2023 DuYu (No.202103180009), Faculty of Computer Science & Technology, Qilu University of Technology. All rights reserved. All of the DAPC source code is licensed under the Apache 2.0 license.DAPC软件系统Python源代码： © 2020~2023 齐鲁工业大学 计算机科学与技术学部 杜宇 保留所有权利。 DAPC软件系统的所有Python源代码都是在Apache 2.0协议的许可下授权的。C source code of Sonic Library:Copyright © 2010 Bill Cox. All of the Sonic library source code is licensed under the Apache 2.0 license.Sonic动态链接库C语言源代码： © 2010 比尔-考克斯。Sonic动态链接库的所有C语言源代码都是在Apache 2.0协议的许可下授权的。C source code of FFmpeg:Copyright © 2000~2021 the FFmpeg developers. All of the ffmpeg source code is licensed under the LGPL/GPL.FFmpeg音视频转码单元C语言源代码： © 2000~2021 FFmpeg 全体开发者。FFmpeg的所有C语言源代码都是在LGPL/GPL协议的许可下授权的。Software Update Logs  软件更新日志Update on May. 2nd, 2020基于实现音频消除人声的目的，项目开工。v1.0.0版本：实现了基于声道“相抵消”方法的音频消除人声和保留伴奏的功能。Update on Dec. 31st, 2021v1.0版本：初步完成了基于Numpy与Scipy模块进行WAV格式的音频声道混缩处理效果。Update on July 11th, 2022v2.0, v3.0与v3.1版本：添加了增益，混响和变换采样等函数，丰富了程序的功能，并且重构了代码，改进了算法，在性能上做了一定的优化。Update on July 12th, 2022v3.2版本：使用了Sonic链接库，可以实现快速的变调与变速处理。使用PyInstaller打包时可以将Sonic添加到根目录下。Update on July 18th, 2022v3.3版本：规范了代码的书写格式。添加了命令行解析的功能，DAPC不仅可以作为Python模块被调用，也可以解析命令行参数，单独运行。Update on Sept. 14th, 2022v3.4版本：添加了调用FFmpeg转码的功能。这样可以使任何主流格式的音频文件都能被解码为封装了PCM数据的WAV波形音频，供DAPC处理。使用PyInstaller打包时可以将FFmpeg添加到根目录下。Update on Jan. 28th, 2023v3.5版本：添加了淡入淡出，3D环绕声和FFT滤波的功能。重构了代码，减少了音频数据在处理时因计算机字长而造成的差错。Update on March 14th, 2023v3.5.1与v3.5.2版本：修复了程序中出现的部分Bug；为后续兼容即时编译(JIT)的Numba库与Cython相关用法做了“铺垫”。LINKS 友情链接齐鲁工业大学(山东省科学院) 计算机科学与技术学部http://jsxb.scsc.cn/山东省计算中心(国家超级计算济南中心)https://www.nsccjn.cn/FFMPEG官方网站https://ffmpeg.org/DuYu的个人网站https://www.duyu09.site/DuYu的GitHub网站https://github.com/duyu09/Statistics of Visiting Numbers  访问次数统计Number of Total Visits (All of Duyu09's GitHub Projects):Number of Total Visits (DAPC):