Saravanan666/pypi-org huggingface dataset:
Package: whisper-mps
whisper-mpsAn opinionated CLI to transcribe Audio files w/ Whisper on-device! Powered byMLX,Whisper&Apple M seriesTL;DR- After our actual testing. The Whisper supported by MPS achieves speeds comparable to 4090!80 mins audio file only need 80s on APPLE M1 MAX 32G! ONLY 80 SECONDS🆕 Blazingly fast transcriptions via your terminal! ⚡️We've added a CLI to enable fast transcriptions. Here's how you can use it:Installwhisper-mpswithpip:pipinstallwhisper-mpsRun inference from any path on your computer:# filename should be wav/mp3/mp4 etcwhisper-mps--file-name<filename>Run inference from specfic model size:# for example,with base model size, others:["tiny", "base", "small", "medium", "large"]# Larger models require more loading time# filename should be wav/mp3/mp4 etcwhisper-mps--file-name<filename>--model-namebaseRun inference from YOUTUBE URL on your computer:# default download behavior is to fetch the video as a mp3 file to save timewhisper-mps--youtube-urlhttps://www.youtube.com/watch\?v\=jaM02mb6JFM[!NOTE]
The CLI is highly opinionated and only works on Apple MPS.CLI OptionsThewhisper-mpsrepo provides an all round support for running Whisper in various settings. More command-line support will be provided later--file-name FILE_NAME
                  Path or URL to the audio file to be transcribed.
  --model-name MODEL_NAME
                  size of the OPENAI Whisper model name, like tiny(default),base,small,etc      
  --youtube-url URL_ADDRESS
                  the youtube play url
Package: whisper-normalizer
whisper_normalizerInstallation of packagepipinstallwhisper_normalizeror from github repositorypipinstallgit+https://github.com/kurianbenoy/whisper_normalizer.gitWhy should we normalize/standardize text?In ASR systems it’s important to normalize the text to reduce
unintentional penalties in metrics like WER, CER etc.Text normalization/standardization is process of converting texts in
different styles into a standardized form, which is a best-effort
attempt to penalize only when a word error is caused by actually
mistranscribing a word, and not by formatting or punctuation
differences.(fromWhisper
paper)Why use this python package?This package is a python implementation of the text
standardisation/normalization approach which is being used in OpenAI
whisper text normalizer. If you want to use just text normalization
alone, it’s better to use this instead reimplementing the same thing.
OpenAI approach of text normalization is very helpful and is being used
as normalization step when evaluating competitive models likeAssemblyAI Conformer-1
model.Models evaluated using Whisper normalizationMassively Multilingual Speech (MMS) models by MetaConformer 1 by AssemblyAIConformer 2 by AssemblyAIHow to useOpenAI open source approach of text normalization/standardization is
mentioned in detail Appendix Section C pp.21 the paperRobust Speech
Recognition via Large-Scale Weak
Supervision.Whisper Normalizer by default comes with two classesBasicTextNormalizerandEnglishTextNormalizerYou can use the same thing in this package as follows:fromwhisper_normalizer.basicimportBasicTextNormalizernormalizer=BasicTextNormalizer()normalizer("I'm a little teapot, short and stout. Tip me over and pour me out!")'i m a little teapot short and stout tip me over and pour me out 'fromwhisper_normalizer.englishimportEnglishTextNormalizerenglish_normalizer=EnglishTextNormalizer()english_normalizer("I'm a little teapot, short and stout. Tip me over and pour me out!")'i am a little teapot short and stout tip me over and pour me out'This model extends Whisper_normalizer to support Indic languages as well.The logic for normalization in Indic languages is derived fromindic-nlp-library.
The logic for Malayalam normalization is expanded beyond the Indic NLP
library byMalayalamNormalizer.fromwhisper_normalizer.indic_normalizerimportMalayalamNormalizernormalizer=MalayalamNormalizer()normalizer("എന്റെ കമ്പ്യൂട്ടറിനു് എന്റെ ഭാഷ.")'എന്റെ കമ്പ്യൂട്ടറിനു് എന്റെ ഭാഷ.'
Package: whisper-pandas
WhisperDB Python Pandas ReaderGetting startedInstall:pip install whisper-pandasUse as Python package:# Simple to read any Whisper file
>>> from whisper_pandas import WhisperFile
>>> wsp = WhisperFile.read("example.wsp")

# Simple to work with metadata as objects
>>> wsp.meta.archives
[WhisperArchiveMeta(index=0, offset=52, seconds_per_point=10, points=1555200, retention=15552000),
 WhisperArchiveMeta(index=1, offset=18662452, seconds_per_point=60, points=5256000, retention=315360000),
 WhisperArchiveMeta(index=2, offset=81734452, seconds_per_point=3600, points=87601, retention=315363600)]

# Simple to work with data as `pandas.Series`
>>> wsp.data[1]
2017-02-10 07:07:00+00:00    0.000000
2017-02-10 07:08:00+00:00    0.000000
2017-02-10 07:09:00+00:00    0.000000
2017-02-10 07:10:00+00:00    0.000000
2017-02-10 07:11:00+00:00    0.000000
                               ...   
2021-07-20 13:35:00+00:00    4.099915
2021-07-20 13:36:00+00:00    4.104024
2021-07-20 13:37:00+00:00    4.099772
2021-07-20 13:38:00+00:00    4.101358
2021-07-20 13:39:00+00:00    4.099854
Length: 2331015, dtype: float32Use as command line tool:whisper-pandas example.wspDescriptionWhisperDB is a fixed-size time series format (seedocs).The official Python package is here:whisperYou should use it, except if you likePandasand only need
to read (not write) Whisper files, then you should usewhisper-pandas.Why?Mucho simpler to useMucho less likely you will shoot yourself in the footMucho speedyCurrently we usewhisper.infointernally to read the metadata,
and then Numpy & Pandas to read the data,
using objects for the metadata and some conveniences like quickly
showing what data is available in a given file or reading zipped files.Partial reading is not implemented, a given file is always read completely
and the data directly converted to columnarpandas.Seriesformat with
a datetime index.DevelopmentContributions are welcome via Github pull requests.You can run the tests viapytest.The packaging follows the recommendation inPackaging Python Projects.
To make a release increase the version number insetup.cfgand run the commands there.
Package: whisper_spln
WhisperWhisper SPLN is a remote service that allows a user to transcrive audio files to text using whisper from openai.
The user can make various requests to the server, such as transcribing a file, getting the status of the queue, and getting the logs of the application.
It is also possible to translate the transcrived text to a language chosen by the user.Possible Commands:
Transcribe: transcribes an audio file to text.
Variables:
input_file (obrigatory): path for the audio file to transcrive.
dest (-d, --dest, optional): defines the path for the output file.
inputLang (-il, --inputLang, optional): defines the language of the input file if not defined, the whisper assumes the language.
outputLang (-ol, --outputLang, optional): defines the language of the output text, if not defined the output language is the one used in the audio file.queue (-q, --queue, optional): shows the audio conversion queue.
logs (-l, --logs, optional): shows the execution logs.
clearLogs (-cl, --clearLogs, optional): clear the execution logs.Examplewhisper_spln input_file
whisper_spln input_file -d output_file -il input_language -ol output_languageSource CodeTo work whisper will do 1 of 2 things:whisper will first create 2 threads, one for the listener and one for the worker.If the 2 threads are already running, whisper will send the request to the queue.Files:listener.py -> listener class that listens for requests and sends them to the queue.lockedQueue.py -> locked queue class that stores the requests whit locks to secure the access from both threads.worker.py -> worker class that gets the requests from the queue and executes them.main.py -> argparse of the service, it has different classes to handle different argumentsparserTasks.py -> functions that handle different arguments of the argparsestartThreads.py -> tries to access or initializes the threads that will run Whisper
Package: whisper-transcribe
whisper-transcribePython video transcriber that uses OpenAI's WhisperAI.How to installpip install whisper-transcribeTrasncribe a video file:from whisper_transcribe import Transcriber

api_key = "sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

with Transcriber(api_key=api_key) as tb:
    trasncription = tb.transcribe("path/to/a/local/video.mp4")

print(transcription)Create a subtitle of a video:with Transcriber(api_key=api_key) as tb:
    tb.transcribe("path/to/a/local/video.mp4", output="srt")Transcribe a YouTube video:with Transcriber(api_key=api_key) as tb:
    tb.translate("https://youtube.com/myVideo")Translate a video file:with Transcriber(api_key=api_key) as tb:
    translation = tb.translate("path/to/a/local/video.mp4")

print(translation)Summarize a text:with Transcriber(api_key=api_key) as tb:
    transcription = tb.transcribe("path/to/a/local/video.mp4", output="text")
    summary = tb.summarize(transcription)
Package: whisper-voice-commands
Whisper MicThis repo is based on the work donehereby OpenAI.  This repo allows you use use a mic to run scripts. This repo copies some of the README from original project.Video TutorialSee the video tutorial for this repohere. This is a fork ofherethe video may not be relevantProfessional AssistanceIf are in need of paid professional help, that is available through thisemailSetupNow a pip package!Create a venv of your choice.Runpip install whisper-voice-commandsExample usagewhisper-voice-commands --model tiny --script_path ~youruser/scripts/ --english --ambient --dynamic_energyCheckwhisper-voice-commands --helpfor more detailsAvailable models and languagesThere are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.SizeParametersEnglish-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 GB1xFor English-only applications, the.enmodels tend to perform better, especially for thetiny.enandbase.enmodels. We observed that the difference becomes less significant for thesmall.enandmedium.enmodels.Microphone DemoYou can use the model with a microphone using thewhisper-voice-commandsprogram.  Use-hto see flag options.Some of the more important flags are the--modeland--englishflags.TroubleshootingIf you are having issues with thecli.pynot running try the following:sudo apt install portaudio19-dev python3-pyaudioContributingCurrently, this is just a cli demo.  I forsee that this pip package could become more than that for example:fromwhisper_mic.micimportWhisperMicmic=WhisperMic(timeout=5)command=mic.listen()LicenseThe model weights of Whisper are released under the MIT License. See their repo for more information.This code under this repo is under the MIT license.  SeeLICENSEfor further details.ThanksUntil recently, access to high performing speech to text models was only available through paid serviecs.  With this release, I am excited for the many applications that will come.
Package: whispool
WhispoolFast in memory word frequency count, with RustInstallationpipinstallwhispoolBasic usageWe assume we have n sentences, with each sentence broken into words. And each sentence is# each sentence could have diffrent length of wordssentence=[['This','is','a','sentence'],['This','is','another','sentence'],['This','is','a','third','sentence','here'],...]# each info list has to be IN THE SAME LENGTHinfo=list([['id1','category1','sci-fi','movie','Jan.'],['id2','category1','romance','movie','Jan.'],['id3','category2','sci-fi','tv','Feb.'],...])Put in dataNow we put in datafromwhispoolimportWhispoolfrompathlibimportPathwhisper_multi=Whispool(directory=Path("test_data"),threads=2,capacity=200)whisper_multi.consume(sentence,info)whisper_multi.consume(sentence_batch2,info_batch2)...whisper_multi.consume(sentence_batchN,info_batchN)Fetch the result dataNow we want to see the hot key words ofSci-Fi movies in January.df=whisper_multi.final_stats([None,None,'sci-fi','movie','Jan.'],top_n=2)Build from rustmaturinbuild--release
Package: whispyr
whispyra python client library for whispir.ioFree software: MIT licenseDocumentation:https://whispyr.readthedocs.io.FeaturesVery thin client forwhispir.ioAPI.Adding a new whispir endpoint as easy as declaring a new class (almost)Configurable retry policiesDecent test code coverageHistory0.3.0 (2018-08-14)Adjust client to new api gateway changes0.2.2 (2018-08-06)Fix message statuses pagination with detailed view0.2.1 (2018-08-03)Workaround broken pagination for messagestatus endpoint0.2.0 (2018-08-03)Add python 2.7 support0.1.0 (2018-07-15)First release on PyPI.
Package: whist
pywhistA package containing the logic for the card game Whist and its variants.How to installpip install whistHow to usewhistcontains a wide range of utility to work with playing cards and perform Whist game logic.Playing with cards>>>fromwhist.cardsimportDeck>>>deck=Deck.full_deck()>>>deck.shuffle()>>>cards=[card.symbolforcardindeck.cards]>>>print(cards[:3])['🃙','🂥','🂦']Interactive play$ python -m whist.game

=== Turn: north ===

  east: 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠
 south: 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠
  west: 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠 🂠

Your partner: south

Pile:

Your hand: 🃖 🃛 🃂 🃆 🃇 🃋 🂴 🂹 🂺 🂽 🂢 🂩 🂫

Actions:
 0: 🃖
 1: 🃛
 2: 🃂
 3: 🃆
 4: 🃇
 5: 🃋
 6: 🂴
 7: 🂹
 8: 🂺
 9: 🂽
10: 🂢
11: 🂩
12: 🂫
>
Package: whist-core
Whist-CoreWhist rules implementationDevelopmentSetupYou needPoetryfor development.# Create venv and install depspoetryinstallThe Python virtual environment will be created in the.venvdirectory.Run tests/lint# Run tests (in venv)python-mpytest# or pylint...# ORpoetryrunpython-mpytestBuildGeneratessdistandbdist_wheel.poetrybuildPublishYou need the environment variablePOETRY_PYPI_TOKEN_PYPIfilled with a PyPI token.poetrybuild
poetrypublish# ORpoetrypublish--build
Package: whistle
Whistleis a lightweight python library that allow your application components to communicate with each other by
dispatching events and listening to them.Homepage:https://python-whistle.github.io/Issues:https://github.com/python-whistle/whistle/issuesInstallpipinstallwhistleQuick startInstall thewhistlepackage:$pipinstallwhistleCreate an event dispatcher:fromwhistleimportEventDispatcherdispatcher=EventDispatcher()Add a listener to react to eventsdefon_spectacle_starts(event):print('Please turn down your phones!')dispatcher.add_listener('spectacle.starts',on_spectacle_starts)</code></pre>Dispatch!dispatcher.dispatch('spectacle.starts')Made with ♥ byRomain Dorgueilandcontributors.
Package: whistleaio
WhistleaioAsynchronous Python library for Whistle's API using Whistle account email and password.This package provides an API client for the Whistle API to be used with a Whistle account email and password.
This API is not published, so there is a chance it may break in the future.InstallationPyPIpip3 install whistleaioThis package depends onaiohttp, and requiresPython 3.7or greater.
Package: whistler
Whistler Bot is an XMPP bot written in python usingSleekXMPP, which is
a requirement. The bot is designed to handle some commands, and it is easy to
extend.Simple usage:$whistlermyconfig.confCreate bot programmatically:fromwhistler.botimportWhistlerBotbot=WhistlerBot("myjid@myserver.com","mypassword")bot.start()Extending botYou can extend the bot functionalities, just see for example the code of the
whistler console script. In short you can add commands creating a new class
from WhistlerBot, and define new functions in the formcmd_<command>plus the
command name, for example, to handle the commandping.fromwhistler.botimportWhistlerBotclassMyBot(WhistlerBot):defcmd_ping(self,msg,args):return"pong"Example chatWhistler allows to send private commands in usual jabber (and of course
GTalk and Google Hangouts) chat.Also public commands are available, prefixed them with “!” (exclamation mark),
as also reply to a direct mention in the form “whistler:” or “whistler,”, where
“whistler” is the nickname of the bot in the MUC:(Me) whistler: uptime
(whistler) 17:33:01 up 2 days, ...Currently Whistler support a number of features, including tweet from
command, shorting url action and so on…You can see themixinsdirectory to see a number of examples for whistler
and how easy is to extend.ContributionsAny new mixin or functionality is welcome to Whistler. If you has been
developed new one or want new feature, pleasesend me a messageor apull
request.
Package: whist-score
OverviewDescriptionwhist_scoreis a CLI tool that focusses on scorekeeping for the 4-man card game color whist.This Python package which can be installed via the commandpip install whist_score.The program can be started by executingwhist-scorein a terminal after installation.FeaturesScorekeepingSaving/loading gamesModifying a game recordRemoving the last game recordModifying the scoring rulesMoving to a full CLI tool with separate commandsTranslations
Package: whist-server
Whist-ServerThis is the REST API server of a Whist game. It provides user management, session organization and
a convenient interface for the rules' implementation ofWhist-Core.DevelopmentSetupYou needPoetryfor development.# Create venv and install depspoetryinstallThe Python virtual environment will be created in the.venvdirectory.Run tests/lint# Run tests (in venv)python-mpytest# or pylint...# ORpoetryrunpython-mpytestBuildGeneratessdistandbdist_wheel.poetrybuildPublishYou need the environment variablePOETRY_PYPI_TOKEN_PYPIfilled with a PyPI token.poetrybuild
poetrypublish# ORpoetrypublish--buildRunIn order to use GitHub SSO you need to set two environment variables. At the moment they are
mandatory.GITHUB_CLIENT_ID # This is the GitHub Identifier
GITHUB_CLIENT_SECRET # During creation this secret is generated.
GITHUB_REDIRECT_URL=http://HOST:PORT/oauth2/github/ # Only required for Browser Application with the ability to redirect.SplunkIf you want to use Splunk you require an environment variable with the authentication token:SPLUNK_TOKENand you have to start the server with optional arguments--splunk_hostand--splunk-port.Splunk support is currently disabled.In order to run the application it must be started like this:python-mwhist_server--reload--admin_name=root--admin_pwd=password0.0.0.08080:warning: A mongodb instance is required to run before launching theWhist-Server.
Package: white
White: Black, but brighter (PEP8–inspired)Blackis an amazing tool for auto–formatting
Python code in a style that I prefer. I use it in all my projects.It hasoneconfiguration option — to change its default line–length of88chars to, say,79, likePEP8recommends.That is exactly what this tool does. It invokes$ black--line-length79on your behalf.Usage$ white myapp.py
reformatted myapp.pyInstallation$ pipenv install white
✨🍰✨
Package: whitebeam
whitebeamwhitebeamis a python module for decision trees without the use of sklearn and SciPydocstestpackageThe project was started in 2020 by Kieran Molloy as a coursework component for SCC461 at Lancaster UniversityInstallationDependencieswhitebeam requires:Python (>= 3.6)NumPy (>= 1.14)Cython (>= 0.29.21)joblib (>= 1.0.0)User InstallationIf you already have a working directory, the easiest installation is viapippip install whitebeamHowever it is advisable to install within a virtual environmentvirtualenv venv
venv/bin/activate
pip install whitebeamThere will soon be more detailed installation instructions in the docs.ChangelogSee the changelog for a history of notable changes to whitebeamDevelopmentDevelopment is planned to be ongoing however this may change due to university requirements.Source InstallationInstalling from source requirespython-devwhich can be installed usingsudo apt-get install python3-devthen cloning this repo, installing requirements and building which is handled by poetrygit clone https://github.com/K-Molloy/whitebeam
pip install -r requirements.txt
python setup.py build_ext --inplaceagain, it is advisable to use a virtual environment.TestingAfter installation, the test suite can be launched from outside the source directory ( althoughpytest>= 5.01 must be installed)pytest whitebeam
Package: whiteboard-bin
Copyright (c) 2021 Garrett HowardPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: # whiteboardA simple package counter for Arch Linux.Platform: UNKNOWN
Package: whitebox-adversarial-toolbox
WHite-box Adversarial Toolbox (WHAT)A Python Library for Deep Learning Security that focuses on Real-time White-box Attacks.Installationpipinstallwhitebox-adversarial-toolboxUsage (CLI)Usage: what [OPTIONS] COMMAND [ARGS]...

  The CLI tool for WHitebox-box Adversarial Toolbox (what).

Options:
  --help  Show this message and exit.

Commands:
  attack   Manage Attacks
  example  Manage Examples
  model    Manage Deep Learning ModelsUseful commands:# List supported models
$ what model list

# List supported Attacks
$ what attack list

# List available examples
$ what example listAvailable models:[x] 1 : YOLOv3      (    Darknet    )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
[x] 2 : YOLOv3      (   Mobilenet   )   Object Detection        YOLOv3 pretrained on MS COCO dataset.
[x] 3 : YOLOv3 Tiny (    Darknet    )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
[x] 4 : YOLOv3 Tiny (   MobileNet   )   Object Detection        YOLOv3 Tiny pretrained on MS COCO dataset.
[x] 5 : YOLOv4      (    Darknet    )   Object Detection        YOLOv4 pretrained on MS COCO dataset.
[x] 6 : YOLOv4 Tiny (    Darknet    )   Object Detection        YOLOv4 Tiny pretrained on MS COCO dataset.
[x] 7 : SSD         ( MobileNet  v1 )   Object Detection        SSD pretrained on VOC-2012 dataset.
[x] 8 : SSD         ( MobileNet  v2 )   Object Detection        SSD pretrained on VOC-2012 dataset.
[x] 9 : FasterRCNN  (     VGG16     )   Object Detection        Faster-RCNN pretrained on VOC-2012 dataset.A Man-in-the-Middle Hardware AttackThe Universal Adversarial Perturbation (UAP) can be deployed using a Man-in-the-Middle Hardware Attack.[ Talk ][ Video ][ Paper ][ Code ]The Man-in-the-Middle Attack consists of two steps:Step 1:Generating the perturbation.Step 2:Deploying the perturbation.