Saravanan666/pypi-org huggingface dataset:
Package: auditok
auditokis anAudio Activity Detectiontool that can process online data
(read from an audio device or from standard input) as well as audio files.
It can be used as a command-line program or by calling its API.The documentation for this version can be found onreadthedocs.InstallationA basic version ofauditokwill run with standard Python (>=3.4). However,
without installing additional dependencies,auditokcan only deal with audio
files inwavorrawformats. if you want more features, the following
packages are needed:pydub: read audio files in popular audio formats (ogg, mp3, etc.) or extract audio from a video file.pyaudio: read audio data from the microphone and play audio back.tqdm: show progress bar while playing audio clips.matplotlib: plot audio signal and detections.numpy: required by matplotlib. Also used for some math operations instead of standard python if available.Install the latest stable version with pip:sudopipinstallauditokInstall the latest development version from github:pipinstallgit+https://github.com/amsehili/auditokorgitclonehttps://github.com/amsehili/auditok.gitcdauditokpythonsetup.pyinstallBasic exampleimportauditok# split returns a generator of AudioRegion objectsaudio_regions=auditok.split("audio.wav",min_dur=0.2,# minimum duration of a valid audio event in secondsmax_dur=4,# maximum duration of an eventmax_silence=0.3,# maximum duration of tolerated continuous silence within an eventenergy_threshold=55# threshold of detection)fori,rinenumerate(audio_regions):# Regions returned by `split` have 'start' and 'end' metadata fieldsprint("Region{i}:{r.meta.start:.3f}s --{r.meta.end:.3f}s".format(i=i,r=r))# play detection# r.play(progress_bar=True)# region's metadata can also be used with the `save` method# (no need to explicitly specify region's object and `format` arguments)filename=r.save("region_{meta.start:.3f}-{meta.end:.3f}.wav")print("region saved as:{}".format(filename))output example:Region0:0.700s--1.400sregionsavedas:region_0.700-1.400.wavRegion1:3.800s--4.500sregionsavedas:region_3.800-4.500.wavRegion2:8.750s--9.950sregionsavedas:region_8.750-9.950.wavRegion3:11.700s--12.400sregionsavedas:region_11.700-12.400.wavRegion4:15.050s--15.850sregionsavedas:region_15.050-15.850.wavSplit and plotVisualize audio signal and detections:importauditokregion=auditok.load("audio.wav")# returns an AudioRegion objectregions=region.split_and_plot(...)# or just region.splitp()output figure:LimitationsCurrently, the core detection algorithm is based on the energy of audio signal.
While this is fast and works very well for audio streams with low background
noise (e.g., podcasts with few people talking, language lessons, audio recorded
in a rather quiet environment, etc.) the performance can drop as the level of
noise increases. Furthermore, the algorithm makes now distinction between speech
and other kinds of sounds, so you shouldn’t use it for Voice Activity Detection
if your audio data also contain non-speech events.LicenseMIT.
Package: auditorium
AuditoriumA Python-powered slideshow creator with steroids.See the demo atauditorium-demo.apiad.net.What's this aboutAuditorium is a Python-powered slideshow generator.
You write pure Python code, and obtain an HTML+JavaScript slideshow (using the amazingreveal.jslibrary).
The awesomeness comes from the fact that your Python backend is connected to the slideshow frontend, which allows your slideshow to dynamically change according to user input or automatically.This opens the door to a few interesting use cases:The slides content itself can be generated by code. For example, long and repetitive sets of slides can be automatically generated, or tables and graphs embedded in slides can be generated on-the-fly withmatplotlib,bokeh,altair,plotly, or any visualization package that produces HTML output.You can insert components in the slides that respond to user input, and execute a Python code in response. For example, you can generate an interactive graph that can be modified by moving sliders in a slideshow.You can create beautiful animations with simple Python code, that automatically play on a slide, using visualization libraries or simple HTML markup.And all of this without writing a single line of HTML or JavaScript.Alternatively, if you need little to no Python code, you can author your slideshow in pure Markdown and add some Python sprinkless here and there when necessary.InstallationSimply run:pip install auditorium[server]To see a quick demo run:auditorium demoAnd point your browser atlocalhost:6789.Quick Start and TutorialsIf you want to quickly grokauditorium, the best option is tolook at the demo onlineand thenread the source code. This way you will both see the end result and what effort it takes to get there.Authoring a slideshow with PythonAuthoring a slideshow with MarkdownRendering a slideshow as purely static HTMLHosting a slideshow online for freeMade with AuditoriumAuditorium Demo- a slideshow showcasing the most important features fromauditorium.Your Contributions HereIf you have a slideshow to showcase here, feel free toedit this Readmeand send a pull request. Add your website, Github repository, and any other information.If you feel like sending some support please consider adding a badge somewhere in your website or repository:<ahref="https://apiad.net/auditorium"><imgalt="Made with Auditorium"src="https://img.shields.io/badge/made--with-auditorium-blue"></img></a>It looks like this:HistorySeethe docs.Collaboration and LicenseLicense is MIT, so you know the drill: fork, develop, add tests, pull request, rinse and repeat.
See collaboration detailsin the docs.
Package: auditpol
AuditPolTheauditpolmodule allows you to easily parse and create Windows Audit Policy CSV files in Python.InstallationTo install theauditpolmodule via pip, run the command:$pipinstallauditpolUsageStart by importing theauditpolmodule.>>>importauditpolThe functionauditpol.load, loads an audit policy CSV file.>>>withopen('example.csv','r')asfile:...auditpol.load(file)In addition to loading an existing audit policy, policies created using the relevant subcategory settings, audit options or global object access audit settings can be dumped to a CSV file using theauditpol.dumpfunction.>>>withopen('example.csv','w')asfile:...auditpol.dump(policy,file)SubcategorySettingTo create a system subcategory setting as part of an audit policy, aauditpol.subcategories.Subcategoryand aauditpol.settings.SettingValuemust be created.This can then be used to create aauditpol.settings.SubcategorySetting.>>>fromauditpol.subcategoriesimportSubcategory>>>fromauditpol.settingsimportSettingValue,SubcategorySetting>>>subcategory=Subcategory(...id='{0CCE922B-69AE-11D9-BED3-505054503030}',...name='Process Creation'...)>>>inclusion_setting=SettingValue(...success=True,...failure=True...)>>>subcategory_setting=SubcategorySetting(...subcategory=subcategory,...inclusion_setting=inclusion_setting...)AuditOptionTo create an audit option as part of an audit policy, aauditpol.settings.OptionValuemust be created.This can then be used to create aauditpol.settings.AuditOption.>>>fromauditpol.settingsimportOptionValue,AuditOption>>>value=OptionValue(...enabled=True...)>>>audit_option=AuditOption(...type='CrashOnAuditFail'...value=value...)GlobalObjectAccessAuditSettingTo create a global object access audit setting, aauditpol.settings.GlobalObjectAccessAuditSettingmust be created.>>>fromauditpol.settingsimportGlobalObjectAccessAuditSetting>>>global_object_access_audit_setting=GlobalObjectAccessAuditSetting(...type='RegistryGlobalSacl'...sacl='S:(AU;SA;FA;;;WD)'...)AuditPolicyTo create an audit policy one or more subcategory settings, audit options or global object access audit settings must be created as described above.These settings can then be used to create anauditpol.policy.AuditPolicy.>>>fromauditpol.policyimportAuditPolicy>>>policy=AuditPolicy(...settings=[...subcategory_setting,...audit_option,...global_object_access_audit_setting...]...)
Package: auditwheel-symbols
auditwheel-symbolsFind out which symbols are causing auditwheel too-recent versioned symbols error, resolvespypa/auditwheel#36.Installationpipinstallauditwheel-symbolsUsage❯auditwheel-symbols--help
auditwheel-symbols0.1.4

USAGE:auditwheel-symbols[OPTIONS]<FILE>

FLAGS:-h,--helpPrintshelpinformation-V,--versionPrintsversioninformation

OPTIONS:-m,--manylinux<manylinux>[possiblevalues:1,2010,2014,2_24]ARGS:<FILE>

❯auditwheel-symbols--manylinux2014~/Downloads/rjieba-0.1.5-cp36-abi3-manylinux2010_x86_64.whl
rjieba/rjieba.abi3.soismanylinux2014compliant.

❯auditwheel-symbols--manylinux1~/Downloads/rjieba-0.1.5-cp36-abi3-manylinux2010_x86_64.whl
rjieba/rjieba.abi3.soisnotmanylinux1compliantbecauseitlinksthefollowingforbiddenlibraries:
libc.so.6offendingsymbols:memcpy@@GLIBC_2.14LicenseThis work is released under the MIT license. A copy of the license is provided in theLICENSEfile.
Package: audius-py
audius-pyA Python SDK and CLI for the Audius Platform.InstallationFrom pip:pipinstallaudius-pyFrom source (from the root project directory):pipinstall.NOTE: In order to user the media player functionality of the SDK, you must haveVLC media playerinstalled.Quick UsageTo create anaudiusSDK instance, do:fromaudius.sdkimportAudiusaudius=Audius()It is recommended that you set a custom app name (the default isaudius-py).
One way to do this is via an environment variable:exportAUDIUS_APP_NAME="My_Audius_App"Then, when you create an Audius SDK object, it will automatically use this value instead.You can also specify an app name (and other configuration) when creating the SDK, like:fromaudius.configimportConfigfromaudius.sdkimportAudiusconfig=Config(app_name="my_app")sdk=Audius(config=config)Another example config value is the host, e.g.:exportAUDIUS_HOST_NAME="https://audius.example.com"or:fromaudius.configimportConfigConfig(host="https://audius.exmaple.com")If you don't specify a host,audius-pywill select a random host from the list of known hosts to the Audius app.
To see all available hosts, run the following command:audiushostsCLISee all commands by doing:audius--helpThis guide will show how to stream one of the top songs on Audius directly into your terminal.
First, browse top artists using the CLI:audiususerstopIt should show output like this:1:Zedd(id=XlJby)2:Skrillex(id=eAZl3)3:Aluna(id=5j9VM)4:kennybeats(id=DrZwG)5:trillsammy(id=NzMW8)6:ODESZA(id=2oNg1)7:noodles(id=b9w8J)8:kayzo(id=LMdyZ)9:Disclosure(id=E2O1R)10:FatNick(id=oGKZd)Next, select one of the user IDs by copying it and using it in the following command:audiususerstrackseAZl3It should output track information like this:Track:KliptownEmpyrean(id=G0wyE)Finally, play the track by using its ID in the following command:audiustracksplayG0wyEThe song should now be streaming into your terminal!
And if you really enjoy the track, you can download it by doing:audiustracksdownloadG0wyEsong.mp3By default,audius-pytries to find the best player.
However, specify your player of choice using the--playerflag:audiustracksplayG0wyE--playervlcPython SDKUse the Python SDK directly:fromaudius.sdkimportAudiussdk=Audius(app="my_app")forartistinsdk.users.top():print(artist["name"])
Package: audl
AUDL APIUnofficial AUDL api for python usersWhat it does:Fetch Data directly fromaudl websiteUpdate database from Web Scrapping using workflowGenerate pdf reports for player, team and gamesSee DocumentationTo download the package:pip install audlUsageHow to get player profilefromaudl.stats.endpoints.playerprofileimportPlayerProfile# Fetching dataframe from "https://theaudl.com/league/players/cbrock"player=PlayerProfile('cbrock')career=player.get_career_stats()reg=player.get_regular_seasons_career()playoffs=player.get_playoffs_career()season=player.get_season_games_stats(2019)games_stats=player.get_career_games_stats()How to get Team StatsSeason=['career', 2022, ..., 2012]
Per=['total', 'game']
Team=['team', 'opponent']fromaudl.stats.endpoints.teamstatsimportTeamStats# fetching from "https://theaudl.com/stats/team"team_stats=TeamStats('career','game','opponent')# TeamStats(season, per, team)df=team_stats.get_table()How to get Player StatsSeason=['career', 2022, ..., 2012]
Per=['total', 'game', 'points', 'possessions', 'minutes']fromaudl.stats.endpoints.playerstatsimportPlayerStats# from "https://theaudl.com/stats/player-stats"playerstats=PlayerStats('career','total','breeze').fetch_table()# PlayerStats(season, per, team)How to fetch season schedulefromaudl.stats.endpoints.seasonscheduleimportSeasonSchedule,TeamSeasonSchedule,AllSchedule,TeamSeasonAgainstOpponentSchedule# Fetch complete season schedule from "https://theaudl.com/league/game-search"fromaudl.stats.endpoints.seasonscheduleimportSeasonSchedule,TeamSeasonSchedule,AllSchedule,TeamSeasonAgainstOpponentSchedule# Fetch complete season schedule from "https://theaudl.com/league/game-search"season_schedule=SeasonSchedule(2022).get_schedule()team_season_schedule=TeamSeasonSchedule(2022,'royal').get_schedule()all_schedule=AllSchedule().get_schedule()team_season_against_opponent=TeamSeasonAgainstOpponentSchedule(2022,'royal','rush').get_schedule()How to get game statisticsfromaudl.stats.endpoints.gamestatsimportGameStats# Fetching Box Scores from "https://theaudl.com/stats/game/2021-06-05-RAL-ATL"game=GameStats("2021-06-05-RAL-ATL")teams=game.get_teams_metadata()players=game.get_players_metadata()game_metadata=game.get_game_metadata()team_stats=game.get_team_stats()roster=game.get_roster_stats()scoring_time=game._get_scoring_time()boxscores=game.get_boxscores()events=game.get_events()throw_selection=game.get_throw_selection()thrower_receiver_count=game.get_thrower_receiver_count(True)lineup=game.get_lineup_frequency(True)teamates=game.get_teamates_selection('ataylor',True)df_throws=game.get_throws_dataframe()# print game eventsgame.print_team_events(True)game.print_team_events(False)How to get Team Game Statsfromaudl.stats.endpoints.teamgamestatsimportAllTeamGameStats,SeasonGameStats,TeamSeasonGameStatsteam_season=TeamSeasonGameStats(2022,'royal').get_game_stats()all_games=AllTeamGameStats().get_game_stats()season_games=SeasonGameStats(2022).get_game_stats()
Package: audlib
audlibA speech signal processing library in Python with emphasis on deep learning.audlib provides a collection of utilities for developing speech-related applications using both signal processing and deep learning. The package offers the following high-level features:Speech signal processing utilities with ready-to-use applicationsDeep learning architectures for speech processing tasks inPyTorchPyTorch-compatible interface (similar to torchvision) for batch processingA command-line interface with a unix-pipe-like syntaxSome use cases of audlib are:Extracting common speech features for your backendDeveloping your own deep-learning-based tools for speech tasksQuickly try out speech processors and visualize the spectrogram in command lineaudlib focuses on correctness, efficiency, and simplicity. Signal processing functionalities are mathematically checked whenever possible (e.g. constant overlap-add,istft(stft(X))==X). Deep neural networks follow thePyTorch's convention.Breaking Changes0.0.3sig.util.freqzbecomessig.util.fftfreqznn.strfnetis removed. See the official repositoryhere.transform.stlogmis removed0.0.2audioreadfollows the interface ofsoundfile.readaudiowritefollows the interface ofsoundfile.writeThe argumentsris removed from all short-time transformsInstallationpipinstallaudlibDeveloper InstallationIn the source directory, install the library with test dependencies:pipinstall".[tests]"Run test:python-mpytesttestsRelease flowBump version in setup.py.Package release:python setup.py sdist bdist_wheelUpload release:twine upload --repository-url https://upload.pypi.org/legacy/ dist/*Usage exampleMore extensive examples can be found inexamples/.Release history0.0.3First release of the command-line toolaudpipe0.0.2Streamlines optional installationImproves API (see breaking changes)Adds coverage test0.0.1First release on PyPIContactPlease reach out to Raymond Xia (raymondxia@cmu.edu) for comments.More information about our research groups can be found on atCMU Robust Speech Recognition GroupMachine Learning for Signal Processing GroupLicenseDistributed under the MIT license. SeeLICENSEfor more information.
Package: audman
Info------------AudmanA simple wsgi web page to control a running audacious media player process.Audman uses the standard python wsgi module to mount a web app,using mako templates, andIPC over dbus to access a runnning audacious processManual------------1. Install virtualenv$ virtualenv --system-site-package audman && source audman/bin/activate3. Install this package(audman)$ pip install audman4. Run audman(audman)$ runaudmanAudman: Audacious Audio Player Control listening on http://192.168.H.HHH:80515. Use audmanbrowse to http://192.168.H.HHH:8051, replacing 192.168.H.HHH with the actualip address printed on the console by the runaudman command.Notes------------* alternate system install by wheel./setup.py bdist_wheelsudo pip install --no-index -v -v -v dist/audman-0.2.1-py2.py3-none-any.whlrunaudman* adhoc source installation./setup.py sdistcp dist/audman-0.2.0.tar.gz ~cd ~tar ztvf audman-0.2.0.tar.gzcd audman./audws.py* quick setup and test./setup.py bdist_wheelPYTHONPATH=dist/audman-0.2.1-py2.7.whl python -m audman.audws* options"runaudman -e" or "runaudman --enque" => default to enqueing rather than playing"runaudman -p <port>" or "runaudman --port=<port>" => listen on specified port"runaudman -l" or "runaudman --playlists_locked" => no playlist select options* todo- get the browsers to cache images files (even on meta refresh)-? mute button<button type="button" id="mute">Mute</button>- apache fcgi integration; other wsgi adapters (dbus permissions)?-? async controls ala ajax-? tracklist scroll bars; reacting in real-time vie ajax?* issues- switching to view a second playlist in the audacious gui causesaudacious<=3.5 to report the wrong current track- can other playlist be scrolled through without making them active?** playlist selection- could we list the names other playlists without making them active?- could we list the names of tracks (or the complete "playlist-display")in playlists without making them active?Note that the icons are fromhttps://github.com/google/material-design-iconsa much appreciated resource.
Package: audoma
audoma - API Automatic Documentation MakerThe main goal of this project is to make Django Rest Framework documentation easier and more automatic.
Audoma is an extension ofdrf-spectacular.
Main goals of this project are:Make documentation more accurate, make schema include more information about views.Include some external library solutions as default, and document them properly.Make code and documentation more consistent, and avoid situations when documentation differs from code.Extend basic rest-framework functionalitiesGetting StartedInstallation and configurationFeaturesMore explicit documentation of permissions and filters/search fields.More ways of defining serializer_class on viewset.Native documentation of MoneyField, PhoneField.Possibility to define examples on the serializer field.New decorator@audoma_actionfor more consistent documentation, modifying action behavior.Modified@extend_schema_fielddecorator behavior, now it's updating field info, not overriding it.Added Bulk create/delete/update serializer mixins.Running example applicationYou can easily test audoma functionalities with our example application.
This can be done easily by usingdockeranddocker-compose.You simply have to:clone the repositoryenter cloned project root foldergot todocker/rundocker-compose up example_appNow the example application should be running underhttp://localhost:8000.
If you want to visit example documentation you should visit/redocor/swagger-ui.If you want to modify the example application you'll find its content inside/audoma_examplesfolder.DevelopmentProject has defined code style and pre-commit hooks for effortless code formatting.To setup pre-commit hooks inside repo run:$ pip install -r requirements_dev.txt
    $ pre-commit installFrom now on your commits will fail if your changes break code conventions of the project.To format project and apply all code style conventions run:$ ./autoformat.shThis command can be triggered automatically by IDE on file save or on file change.
Package: audonnx
audonnxdeploys machine learning models stored inONNXformat.Machine learning models can be trained
in a variety of frameworks,
e.g.scikit-learn,TensorFlow,Torch.
To be independent of the training framework
and its version models can be exported inONNXformat,
which enables you to deploy and combine them easily.audonnxallows you to name inputs and outputs
of your model,
and automatically loads the correct feature extraction
from a YAML file stored with your model.Have a look at theinstallationandusageinstructions.
Package: audplot
audplotincludes plotting functions targeted at
signal processing (e.g. spectral images)
and machine learning problems (e.g. confusion matrix).ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog,
and this project adheres toSemantic Versioning.Version 1.4.6 (2022-12-21)Changed: split API documentation into sub-pages
for each functionVersion 1.4.5 (2022-12-06)Fixed: x-axis scaling foraudplot.waveform().
x-axis values now always correspond
to the actual number of samples
of the signalVersion 1.4.4 (2022-12-02)Added: support for Python 3.10Changed: increase speed ofaudplot.waveform()by factor >100 for long signalsVersion 1.4.3 (2022-10-14)Fixed: requirematplotlib!=3.6.1to avoid failing ofaudplot.distribution()Version 1.4.2 (2022-01-07)Fixed: makeaudplot.scatter(...,fit=True)plots reproducible
by using a fixed seed for bootstrappingVersion 1.4.1 (2022-01-04)Added: Python 3.9 supportFixed: contributing textRemoved: Python 3.6 supportVersion 1.4.0 (2021-12-10)Added:fitandorderarguments toaudplot.scatter()Changed:audplot.scatter()shows just a scatter plot with default
argumentsVersion 1.3.2 (2021-11-17)Changed: show frequency instead of counts on the y-axis
foraudplot.distribution()Fixed:audplot.distribution()bins now independently
for each distribution,
which ensures the same distribution
is always plotted the same wayVersion 1.3.1 (2021-11-03)Fixed: building public documentation in CI pipelineVersion 1.3.0 (2021-11-03)Added:audplot.waveform()Added:label_aliasesargument toaudplot.confusion_matrix()Changed: show grid lines and remove top ad right axes as defaultChanged: Useseaborn.histplot()inaudplot.distribution()Fixed: xticks position for time axesVersion 1.2.0 (2021-07-30)Added:audplot.detection_error_tradeoff()Version 1.1.0 (2021-07-27)Added:audplot.human_format()Added:show_bothargument toaudplot.confusion_matrix()which allows showing percentage and absolute numbers
in the same figureChanged: switch fromTruetoFalseas default
forpercentageargument ofaudplot.confusion_matrix()Version 1.0.3 (2021-07-22)Fixed: install missinglibsndfile1when publishing docsFixed: calculate minimum and maximum inscatter()andseries()Version 1.0.2 (2021-07-13)Added:cepstrum(),signal(),spectrum()Version 1.0.1 (2021-07-05)Fixed: URLs to documentation and source code inside Python packageVersion 1.0.0 (2021-06-28)Added: initial release
Package: audresample
audresampleremixes or resamples your signals.Resampling is supported
for signals in single precision floating-point format,
and based on thesoxrimplementation
as provided byaudresamplelib.Have a look at theinstallationandusageinstructions.>>>importnumpyasnp>>>importaudresample>>>signal=np.zeros((2,8000),dtype='float32')>>>signal.shape(2,8000)>>>audresample.remix(signal,mixdown=True).shape(1,8000)>>>audresample.remix(signal,channels=[0,0,1,1]).shape(4,8000)>>>audresample.resample(signal,8000,16000).shape(2,16000)