Saravanan666/pypi-org huggingface dataset:
Package: audiotoolbox
audiotoolsA python package for creating acoustic stimuli primariliy aimed at auditory research.How to install audiotoolsUsing pipIf you usepip, you can install audiotools with:pip install audiotoolboxFrom GitHubClone the repository:git clone https://github.com/Jencke/audiotools.gitInstall the package:pip install ./Optionally run the tests:pytestRequirementsaudiotools depends on the libsndfile library. The libray should be installed automatically when using pip.DocumentationThe package documentation can be found at:https://audiotools.readthedocs.io/en/master/However, it is a bit outdated and does not document all the features. Help would be highly appreciatedContributionsThe Project is happy about any contribution. If you find bugs, think the documentation could be improved, or have comments or ideas, open a ticket within the issue trackerhttps://github.com/Jencke/audiotools/issuesor contact me via email. If you implement new features, I'm happy to receive merge requests or code-snippets.AcknowledgmentsThis software package was developed during my PHD in the group of Prof. Werner Hemmert at the Technical University of Munich and further developed during my time as a post-doc in the Group of Prof. Mathias Dietz at the University of Oldenburg.
Package: audio-transcriber
Audio-TranscriberVersion: 0.5.37Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio!This repository is actively maintained - Contributions are welcome!Contribution Opportunities:Support new modelsUsage:Short FlagLong FlagDescription-h--helpSee Usage-b--bitrateBitrate to use during recording-c--channelsNumber of channels to use during recording-d--directoryDirectory to save recording-e--exportExport txt, srt, and vtt files-f--fileFile to transcribe-l--languageLanguage to transcribe-m--modelModel to use: <tiny, base, small, medium, large>-n--nameName of recording-r--recordSpecify number of seconds to record to record from microphoneExample:audio-transcriber--file'~/Downloads/Federal_Reserve.mp4'--model'large'audio-transcriber--record60--directory'~/Downloads/'--name'my_recording.wav'--model'tiny'Model Information:Courtesy of and Credits to OpenAI: Whisper.aiSizeParametersEnglish-only modelMultilingual modelRequired VRAMRelative speedtiny39 Mtiny.entiny~1 GB~32xbase74 Mbase.enbase~1 GB~16xsmall244 Msmall.ensmall~2 GB~6xmedium769 Mmedium.enmedium~5 GB~2xlarge1550 MN/Alarge~10 GB1xInstallation Instructions:Install Python Packagepython-mpipinstallaudio-transcriberUbuntu Dependenciesaptinstall-ylibasound-devportaudio19-devlibportaudio2libportaudiocpp0ffmpegRepository Owners:
Package: audiotranser
Transcribes audio filespip install audiotranserTested against Windows 10 / Python 3.10 / AnacondaUses the models fromhttps://huggingface.co/ggerganov/whisper.cpp/tree/mainArgs:inputfile:pathtotheinputaudiofilesmall_large:modelsize(smallorlarge)blas:useBLASlibraryforfasterdecodingsilence_threshold:silencethresholdinmillisecondsmin_silence_len:minimumsilencelengthinmillisecondskeep_silence:minimumsilencelengthtokeepaftersilenceremovalthreads:numberofthreadstouseprocessors:numberofprocessorstouseoffset_t:timeoffsetinmillisecondsoffset_n:segmentindexoffsetduration:durationofaudiotoprocessinmillisecondsmax_context:maximumnumberoftextcontexttokenstostoremax_len:maximumsegmentlengthincharactersbest_of:numberofbestcandidatestokeepbeam_size:beamsizeforbeamsearchword_thold:wordtimestampprobabilitythresholdentropy_thold:entropythresholdfordecoderfaillogprob_thold:logprobabilitythresholdfordecoderfailspeed_up:speedupaudiobyx2(reducedaccuracy)translate:translatefromsourcelanguagetoenglishdiarize:stereoaudiodiarizationlanguage:spokenlanguage('auto'forauto_detect)Returns:PandasDataFramewiththeresultsoftheinferenceorthepathtotheoutputCSVfileifpd.read_csvfails.fromaudiotranserimporttranscribe_audiodf=transcribe_audio(inputfile=r"C:\untitled.wav",small_large="large",blas=True,silence_threshold=-30,# ignored if == 0 or Nonemin_silence_len=500,# ignored if silence_threshold == 0 or Nonekeep_silence=1000,# ignored if silence_threshold == 0 or Nonethreads=3,# number of threads to use during computationprocessors=1,# number of processors to use during computationoffset_t=0,# time offset in millisecondsoffset_n=0,# segment index offsetduration=0,# duration of audio to process in millisecondsmax_context=-1,# maximum number of text context tokens to storemax_len=0,# maximum segment length in charactersbest_of=5,# number of best candidates to keepbeam_size=-1,# beam size for beam searchword_thold=0.01,# word timestamp probability thresholdentropy_thold=2.40,# entropy threshold for decoder faillogprob_thold=-1.00,# log probability threshold for decoder failspeed_up=True,# speed up audio by x2 (reduced accuracy)translate=False,# translate from source language to englishdiarize=False,# stereo audio diarizationlanguage="en",# spoken language ('auto' for auto_detect))print(df)
Package: audiotsm
AudioTSM is a python library for real-time audio time-scale modification
procedures, i.e. algorithms that change the speed of an audio signal without
changing its pitch.Documentation:https://audiotsm.readthedocs.io/Examples:https://muges.github.io/audiotsm/Source code repository and issue tracker:https://github.com/Muges/audiotsm/Python Package Index:https://pypi.python.org/pypi/audiotsm/License:MIT – see the fileLICENSEfor details.InstallationAudiotsm should work with python 2.7 and python 3.4+.You can install the latest version of audiotsm with pip:pip install audiotsmIf you want to use the gstreamer plugins, you should installPyGObjectandpython-gst, and use the following command to install audiotsm:pip install audiotsm[gstreamer]If you want to play the output of the TSM procedures in real time, or to use
the examples, you should install audiotsm as follow:pip install audiotsm[stream]Basic usageThe audiotsm package implements several time-scale modification procedures:OLA (Overlap-Add);WSOLA (Waveform Similarity-based Overlap-Add);Phase Vocoder.The OLA procedure should only be used on percussive audio signals. The WSOLA
and the Phase Vocoder procedures are improvements of the OLA procedure, and
should both give good results in most cases.If you are unsure which procedure to choose, the Phase Vocoder should sound
best in most cases. You can listen to the output of the different procedures on
various audio files and at various speeds on theexamples page.Below is a basic example showing how to reduce the speed of a wav file by half
using the WSOLA procedure:from audiotsm import phasevocoder
from audiotsm.io.wav import WavReader, WavWriter

with WavReader(input_filename) as reader:
    with WavWriter(output_filename, reader.channels, reader.samplerate) as writer:
        tsm = phasevocoder(reader.channels, speed=0.5)
        tsm.run(reader, writer)ThanksIf you are interested in time-scale modification procedures, I highly recommend
readingA Review of Time-Scale Modification of Music Signalsby Jonathan
Driedger and Meinard Müller.
Package: audiotsm2
AudioTSM 2A real-time audio time-scale modification librarypip install audiotsm2Basic usageAudioTSM 2 is a light weight audio speed modification library. Below is a basic example showing how to reduce the speed of a wav file by half using the phase vocoder procedure.fromaudiotsm2importphasevocoderfromaudiotsm2.io.wavimportWavReader,WavWriterwithWavReader(input_filename)asreader:withWavWriter(output_filename,reader.channels,reader.samplerate)aswriter:phasevocoder(reader.channels,speed=0.5).run(reader,writer)
Package: audiotube
Helpful tool for getting YouTube information using Python!
You can do:search YouTubeget information of videosget transcription of videoInstallationusing setup.pypython setup.py installusing pippip install audiotubeUsageimport audiotube

youtube = audiotube.YouTube()
results = youtube.search('palo alto', search_num=10)
print(results)
# [<YouTube Videos> Num: 10 [<Object: YouTubeAudio, url: https://www.youtube.com/watch?v=wr691AYEu4A>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=Dmg-HU-kbV0>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=sTqMUu1iTIo>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=5G2a93C8pPM>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=yuT5IzuXgBw>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=Q1Wm0_fOON4>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=c-oIfq5mw_g>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=xomlcQrefZ8>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=N8h34bbQ_2s>, <Object: YouTubeAudio, url: https://www.youtube.com/watch?v=3wKwHX54P9s>] 

print(type(results))  # <class 'audiotube.objects.YouTubeAudios'>
print(type(results[0]))  # <class 'audiotube.objects.YouTubeAudio'>

print(results[1].transcription)
# [{'text': 'ah', 'start': 0.0, 'duration': 2.0}, {'text': 'you', 'start': 4.69, 'duration': 2.06}, {'text': "there's a lot of great attractions in", 'start'$ 12.82, 'duration': 4.39}, {'text': 'San Francisco alone but at the same time', 'start': 15.02, 'duration': 4.17}, {'text': 'there is a lot of different pl$ces you', 'start': 17.21, 'duration': 3.12}, {'text': 'can check out throughout the San', 'start': 19.19, 'duration': 4.35}, {'text': 'Francisco Bay region one of them is Palo', 'start': 20.33, 'duration': 6.21}, {'text': 'Alto California welcome this is Scott', 'start': 23.54, 'duration': 7.91}, {'text': "and we're live here in front of", 'start': 26.54, 'duration': 4.91}, {'text': "Rinconada Park we're at the Embarcadero", 'start': 32.17, 'duration': 6.01}, {'t$xt': 'Road not be confused with the one in', 'start': 36.2, 'duration': 4.98}, {'text': 'downtown San Francisco and then middle', 'start': 38.18, 'duration': 4.8}, {'text': 'field roads over at that light over', 'start': 41.18, 'duration': 6.45},
Package: audio-utils
Audio utilsMiscellaneous audio processing tools.For usersAutogenerated Docs on readthedocs (might be
broken).Manually and periodically generated docshereFor detailed examples and help, please see individual module files in
this package.Installation or upgrade:sudo pip install audio_utils-Usudo pip installgit+https://github.com/sanskrit-coders/audio_utils/@master-UWeb.For contributorsContactHave a problem or question? Please head togithub.Packaging~/.pypirc should have your pypi login credentials.python setup.py bdist_wheel
twine upload dist/* --skip-existingBuild documentationsphinx html docs can be generated withcd docs; make htmlTestingRunpytestin the root directory.Auxiliary toolspyup
Package: audiovisualizer
Audio VisualizerVisual representation of audio stream in terminal. Also provides helpful functions for
turning an audio stream into a representation that is friendly for graphical representation.
This is done by scaling the values in stream and interpolating them to create a stream
of frames for given fps, width and height.usage: visualizer [-h] [-t TYPE] [-f FPS] [-w WIDTH] [-he HEIGHT] [-c CHAR]
                  [-d] [-ns]
                  filename

visualize audio

positional arguments:
  filename              path to audio file

options:
  -h, --help            show this help message and exit
  -t TYPE, --type TYPE  file type of the audio file
  -f FPS, --fps FPS     frames per second for the graphical representation of
                        audio
  -w WIDTH, --width WIDTH
                        width of graphical representation of audio
  -he HEIGHT, --height HEIGHT
                        height of the graphical representation of audio
  -c CHAR, --char CHAR  char used to represent graph point
  -d, --debug           prints the number of skipped frames
  -ns, --nosync         do not sync graph with audio
Package: audioviz
audioviz-colabAn user-friendly music information retrieval visualization API interfacing with Google ColabCoordinated by Prof.Li Su, Prof.Yu-Fen Huangfrom theMusic and Culture Technology Lab, Academia Sinica, Taipei, TaiwanA collaration project with graduate students from National Tsing Hua University, Hsinchu, TaiwanIntroduction to this packagesCustom visualization wrappers for open source modulelibrosaUsing open source graphing libraryPlotlyto enhance visualization capabilitiesDirecctly integrated with Google Colab functions to provide platform-free user experiencePlease refer toofficial pagefor more detailsInstallation and DependencyInstallationPyPi installationpipinstallaudiovizsetup.pyDeveloper DemoFuture WorksColab upload solutionDirect instructionProgramming Copilot
Package: audiowalkman
walkmanWalkman is a minimalistic, robust software to trigger (audio) cues in performance contexts.
It usespyoas its backend andpysimpleguias its frontend.
It can be configured bytomlfiles.RationaleLive-electronic setups tend to be messy, difficult to maintain and difficult to test.
Furthermore many compositions with live-electronics make use of cue-based pattern, but in most of electronic music frameworks (Pd, Max/MSP, ...) no default implementation exists.walkmanaims to improve the situation by providing a simple, declarative configuration language to setup programs based on cues.
The actual software is implemented in Python3, can be extended in python3 and can be tested with unit tests.Installationwalkman is available on pip:pip3installaudiowalkmanAlternatively you can useNixto install walkman.Configuration file# ./my_composition.toml.j2

[configure]
name            = string                                              (default to "Project")
logging_level   = "info" | "error" | "warning" | "debug" | "notset"   (default to "info")

[configure.audio]
audio           = string                                              (default to "jack")
midi            = string                                              (default to "jack")
sampling_rate   = integer                                             (default to 44100)
buffer_size     = integer                                             (default to 1024)
channel_count   = integer                                             (default to 2)

[configure.module.MODULE_NAME.REPLICATION_KEY]

[cue.CUE_NAME.MODULE_NAME.REPLICATION_KEY]Usagewalkmanmy_composition.toml.j2TestsWith nix tests can be run with a newly compiled isolated setup:nix-builddefault.nix
Package: audisto-exporter
This package exportsAudistoweb crawler report data asPrometheusmetrics.UsageStart HTTP serviceStart the HTTP server like this:$ AUDISTO_USERNAME=APIUSER AUDISTO_PASSWORD=APISECRET audisto_exporter --host=127.0.0.1 --port=9307Pass--ttl=SECONDSto cache API results for the given time or -1 to disable (default is 600).
Prometheus considers metrics stale after 300s, so that’s the highest scrape_interval one should use.
However it’s usually unnecessary to hit the API that often, since the information does not change that rapidly.Configure Prometheusscrape_configs:
  - job_name: 'audisto'
    scrape_interval: 300s
    static_configs:
      - targets: ['localhost:9307']We export two metrics, firstly a gaugehttp_requests_totalwith labels{service="http://www.zeit.de/index",code="200"}.
Status codes 601/602/603 are used for indexable/robots noindex/canonical.
Status codes 700/701/702/703 are used for duplicate title/description/body/similar urls.Secondly we export a histogramresponse_time,
with labels{service="http://www.zeit.de/index"}and buckets as determined by Audisto.Additionally, aaudisto_scrape_duration_secondsgauge is exported.CHANGES1.0.1 (2019-11-20)Import code=410 as well.1.0.0 (2019-11-20)First release.
Package: audit-cuos
audit-cuosGenerate activity reports for functionaries on the English Wikipedia.Author:Molly WhiteLicense:MITVersion:0.2To useYou will need to havecheckuserandoversightpermissions on the
English Wikipedia to successfully run this script. You will also need to generate abot password, which you will use
to log in when prompted. Trying to log in with your main account username and password
will not work.Install withpip:pip install audit-cuosWhen prompted, enter your bot username and password. Make sure you use the bot
username (e.g., "GorillaWarfare@cuos") and not your main username
("GorillaWarfare").The script will output its results to a file calledstats.txt. Although it does its
best to find people who have been added/removed from CU/OS groups, it doesn't try to
determinewhysomeone was added or removed, and so those notations will need to
be added manually. You should also give the results a once-over to ensure that any
users who have been renamed are not showing up under both names.Copy the results to theAudit Statisticspage.
Package: auditee
auditeeWORK IN PROGRESS -- DO NOT USE -- DO NOT TRUSTTool to help with auditing an SGX enclave that has been remotely attested.
The idea is as follows:Given:a signed enclave binary file (e.g.Enclave.signed.so),the source code used to build the enclave, andan attestation report,an auditor verifies:whether the signed enclave build can be reproduced, using the source code, and
a nix & docker based toolchain to rebuild the enclave binaryWhether the MRENCLAVE and ATTRIBUTES of the provided signed Enclave match that of
the rebuilt oneWhether the MRENCLAVE and ATTRIBUTES match the ones from the provided reportRoughly speaking the goal is to establish that the source code was indeed the code
used to build the signed enclave that has been successfully (remotely) attested.Such an audit can help external users gain trust into a system which depends on
Intel SGX and remote attestation. Without such an audit, remote attestation is not
sufficient because the fact that an MRENCLAVE (enclave hash) matches that of the
attestation verification report does not prove to a user of the system that the
MRENCLAVE corresponds to a version of source code that they are willing to trust.MRENCLAVE (enclave hash)The MRENCLAVE, also known as enclave hash, from three sources must all match. The
enclave hash is obtained via ameasurmentof the enclave code and data when an
enclave is initialized. This measurement can also be simulated, and thus a verifier
can obtain the MRENCLAVE using a build toolchain, and the SGX signing tool to get
the SIGSTRUCT which contains the MRENCLAVE.Hash extracted from the signed enclave (Enclave.signed.so) that is remotely attestatedHash in the attestation report, from IASHash extracted from the enclave, which was rebuilt from source code using a
nix+docker based toolchainIf (1) and (3) match then the auditor can trust that they are auditing the correct
code, and can trust that the report is for the enclave that is under audit, and
consequently the successful attesation can be trusted in sofar that the attestation
is for source code that has been audited.
Package: auditing
This library provides a simple interface for auditing of user actions in Flask-based web applications.ExampleTo setup auditing, simply import the package and run the setup method with the appropriate arguments. As an example, let's consider a postgres database. First, spin up the database via dockerdocker run --name my-postgres -p 5432:5432 -d postgresNext, setup the connection to the database,import auditing

args_to_postgres = dict(database="mydb", user="postgres", host="127.0.0.1")
auditing.setup("postgres_auto", args=args_to_postgres)The special "postgres_auto" driver creates databases and tables dynamically. Furthermore, it extends tables when new data entries are added to the audit call. If this behaviour is not desired, simply use the "postgres" driver instead. Subsequently, auditing can be performed asfrom auditing import audit

audit("mytag", a="some data", c="some other data", b=0, username="me")where "mytag" identifies the audit collection (in postgres sql it will map to the table name) while the following keyword arguments denotes the data. The current datetime is always appended unless overwritten via the "dt" argument. Querying the databasedocker exec -it my-postgres bash
psql -U postgres
\c mydb
select * from mytag;now yieldsusername |          datetime          |     a     |        c        | b 
----------+----------------------------+-----------+-----------------+---
 me       | 2019-12-05 13:55:55.636742 | some data | some other data | 0If the username is not provided, an attempt is made to extract it from the session context. If the user cannot be identified, the audit is cancelled. If the audit is made within a request context, the following data is appendedpath, host, ip (from "X-Forwarded-For" header)[*] In addition to postgres, elastic search is supported. Other databases can also be used, simply inject a driver via the "inject_driver" method.DeploymentBump the version number in setup.py and runpython3 setup.py sdist
pip3 install twine
twine upload dist/*
Package: auditless-py-solc-x
py-solc-x wrapper for AuditlessThis wrapper modifiespy-solcx-xto produce output on every compilation that can be consumed by Auditless.Can I use this for my projectAs long as you are usingpy-solc-xto compile Solidity files, you can use this wrapper.How to useAdd the the following snippet to your code:frompathlibimportPathfromauditless_solcximportsolcx_start_saving_debugging_output_to_pathpath=Path(__file__).parent# This will save files in a folder ./artifacts/build-infosolcx_start_saving_debugging_output_to_path(path)# This needs to appear before any modules consuming `py-solc-x` are loaded# See below "Important note about patching"Important note about patchingThe patching functionsolcx_start_saving_debugging_output_to_path(<path>)needs to be called either beforesolcxis used directly or before any module that imports and usessolcx.SeePython Mock Gotchasfor more information about patching
order.LicenseMIT
Package: auditlog
[![Build Status](https://travis-ci.org/jjkester/django-auditlog.svg?branch=master)](https://travis-ci.org/jjkester/django-auditlog)
[![Docs](https://readthedocs.org/projects/django-auditlog/badge/?version=latest)](http://django-auditlog.readthedocs.org/en/latest/?badge=latest)Please remember that this app is still in development.Test this app before deploying it in production environments.`django-auditlog`(Auditlog) is a reusable app for Django that makes logging object changes a breeze. Auditlog tries to use as much as Python and Django’s built in functionality to keep the list of dependencies as short as possible. Also, Auditlog aims to be fast and simple to use.Auditlog is created out of the need for a simple Django app that logs changes to models along with the user who made the changes (later referred to as actor). Existing solutions seemed to offer a type of version control, which was found excessive and expensive in terms of database storage and performance.The core idea of Auditlog is similar to the log from Django’s admin. Unlike the log from Django’s admin (`django.contrib.admin`) Auditlog is much more flexible. Also, Auditlog saves a summary of the changes in JSON format, so changes can be tracked easily.DocumentationThe documentation for`django-auditlog`can be found onhttp://django-auditlog.readthedocs.org. The source files are available in the`docs`folder.LicenseAuditlog is licensed under the MIT license (see the`LICENSE`file for details).ContributeIf you have great ideas for Auditlog, or if you like to improve something, feel free to fork this repository and/or create a pull request. I’m open for suggestions. If you like to discuss something with me (about Auditlog), please open an issue.ReleasesMake sure all tests onmasterare green.Create a new branchvX.Y.Zfrom master for that specific release.Bump versions insetup.pyanddocs/source/conf.py(docs have 2 places where the versions need to be changed!)Pull requestvX.Y.Z->stable. Merging policy is very strict. This triggers a new release.Pull requeststable->master. Now everything is back in sync.Opening a pull request frommasterdirectly tostableis discouraged asmastermay be updated while the PR is open, thus changing the contents of the release.