Saravanan666/pypi-org huggingface dataset:
Package: disvoice-phonological
Phonological featuresPhonological featuresphonological.pyCompute phonological features from continuous speech files.18 descriptors are computed, bases on 18 different phonological classes from the phonet toolkithttps://phonet.readthedocs.io/en/latest/?badge=latestIt computes the phonological log-likelihood ratio features from phonetStatic or dynamic matrices can be computed:Static matrix is formed with 108 features formed with (18 descriptors) x (6 functionals: mean, std, skewness, kurtosis, max, min)Dynamic matrix is formed with the 18 descriptors computed for frames of 25 ms with a time-shift of 10 ms.RunningScript is called as followspythonphonological.py<file_or_folder_audio><file_features><static(trueorfalse)><plots(trueorfalse)><format(csv,txt,npy,kaldi,torch)>Examples:Extract features in the command linepythonphonological.py"../audios/001_ddk1_PCGITA.wav""phonologicalfeaturesAst.txt""true""true""txt"pythonphonological.py"../audios/001_ddk1_PCGITA.wav""phonologicalfeaturesUst.csv""true""true""csv"pythonphonological.py"../audios/001_ddk1_PCGITA.wav""phonologicalfeaturesUdyn.pt""false""true""torch"pythonphonological.py"../audios/""phonologicalfeaturesst.txt""true""false""txt"pythonphonological.py"../audios/""phonologicalfeaturesst.csv""true""false""csv"pythonphonological.py"../audios/""phonologicalfeaturesdyn.pt""false""false""torch"pythonphonological.py"../audios/""phonologicalfeaturesdyn.csv""false""false""csv"KALDI_ROOT=/home/camilo/Camilo/codes/kaldi-master2exportPATH=$PATH:$KALDI_ROOT/src/featbin/
pythonphonological.py"../audios/001_ddk1_PCGITA.wav""phonologicalfeaturesddk1dyn""false""false""kaldi"pythonphonological.py"../audios/""phonologicalfeaturesdyn""false""false""kaldi"Extract features directly in Pythonphonological=Phonological()
file_audio="../audios/001_ddk1_PCGITA.wav"
features1=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="npy")
features2=phonological.extract_features_file(file_audio, static=True, plots=True, fmt="dataframe")
features3=phonological.extract_features_file(file_audio, static=False, plots=True, fmt="torch")
phonological.extract_features_file(file_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test")

path_audio="../audios/"
features1=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="npy")
features2=phonological.extract_features_path(path_audio, static=True, plots=False, fmt="csv")
features3=phonological.extract_features_path(path_audio, static=False, plots=True, fmt="torch")
phonological.extract_features_path(path_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test.ark")Jupyter notebookResults:Phonological analysis
!!!References[1]Vásquez-Correa, J. C., Klumpp, P., Orozco-Arroyave, J. R., & Nöth, E. (2019). Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. In INTERSPEECH (pp. 549-553).[2] Diez, M., Varona, A., Penagarikano, M., Rodriguez-Fuentes, L. J., & Bordel, G. (2014). On the projection of PLLRs for unbounded feature distributions in spoken language recognition. IEEE Signal Processing Letters, 21(9), 1073-1077.
Package: diswarm-handler
DiSwarm HandlerBasic DiSwarm bot handling class, and standardized communication protocolUsageMain class:Handler(channel, token, swarm_id, bot_id, lead_timeout=5, role=None)Channel: Discord server channel to run swarm in
Token: Discord bot token
Swarm_id: Unique id of entire swarm
Bot_id: Unique id of bot
Lead_timeout: Seconds before bot declares itself swarm leader
Role: Role in swarm. Changing this from None overrides leader/drone selection.Subclass this, and redefine the functionprocess_one(self, response)to perform different tasks based on input. This is much like thesocketserver.BaseRequestHandler.handle()method.Other functions:request(req, args=())Requests data from swarm.
Req: Request text
Args: tuple of arguments to pass to swarmprocess(): Processes swarm queue and returns list ofprocess_one()outputsNotesSwarm ID: the same for all bots in your swarm, but it should be unique from any other swarm, at least on your swarm channel. To be safe, use a randomly generated string or number. Make sure every bot in your swarm has the same id, because bots with different ids cannot see eachother's messages.Requirespip install DiSwarm(Will install all dependenciesAs an alternative, runpip install diswarm-handlerto download this and all dependencies.
Package: dit-activity-stream
DIT Activity StreamInstallationRead theDjango Hawk installationdocumentation.Add the package to yoururls.pyfile.fromdjango.urlsimportinclude,pathurlpatterns=[...path("dit-activity-stream/",include("dit_activity_stream.urls")),...]How to implement?Write your custom client, here is an example client for returning all users:fromtypingimportAny,Dictfromdjango.contrib.authimportget_user_modelfromdjango.db.modelsimportQuerySetfromdjango.httpimportHttpRequestfromdit_activity_stream.clientimportActivityStreamClientUser=get_user_model()classActivityStreamUserClient(ActivityStreamClient):object_uuid_field:str="user_id"object_last_modified_field:str="last_modified"defget_queryset(self,request:HttpRequest)->QuerySet:returnUser.objects.all()defrender_object(self,object:User)->Dict:return{"id":object.id,"username":object.username,"first_name":object.first_name,"last_name":object.last_name,}Where the following attributes:object_uuid_fieldis a field on the Object that is a Unique Identifier for the object.This will be output in the URL GET parameter so it should be a UUID.object_last_modified_fieldus a field on the Object that holds a datetime value of when the object was last modified.This will be output in the URL GET parameter.SetDIT_ACTIVITY_STREAM_CLIENT_CLASSin your django settings file:DIT_ACTIVITY_STREAM_CLIENT_CLASS="package.client.ActivityStreamUserClient"Pushing to PyPIPyPI PackageTest PyPI PackageRunningmake buildwill build the package into thedist/directory.
Runningmake push-pypi-testwill push the built package to Test PyPI.
Runningmake push-pypiwill push the built package to PyPI.Setting up poetry for pushing to PyPIFirst you will need to add the test pypy repository to your poetry config:poetry config repositories.test-pypi https://test.pypi.org/legacy/Then go tohttps://test.pypi.org/manage/account/token/and generate a token.Then add it to your poetry config:poetry config pypi-token.test-pypi XXXXXXXXThen you also need to go tohttps://pypi.org/manage/account/token/to generate a token for the real PyPI.Then add it to your poetry config:poetry config pypi-token.pypi XXXXXXXXNow the make commands should work as expected.
Package: ditchcarbon-python
ditchcarbon-pythonWhat is it?ditchcarbon-pythonis the official Python wrapper for the DitchCarbon API. DitchCarbon calculates the carbon impact of almost anything using a combination of GHG protocol approved calculations and an unparalleled database of company and product disclosures.Where to get it?You can install the library via PyPI (hostedhere):pip3installditchcarbon-pythonThe source code is currently hosted on GitHub,here.How to use it?First, import and initialise the library with your API token:fromditchcarbon_pythonimportClientditchcarbon=Client(token="YOUR_TOKEN")Then, use it:# Activitiesditchcarbon.activities.retrieve(1)ditchcarbon.activities.retrieve_many(**params)ditchcarbon.activities.retrieve_assessment(1,**params)ditchcarbon.activities.retrieve_categories(**params)# Categoriesditchcarbon.categories.search(**params)# Expensesditchcarbon.expenses.calculate_emissions(**params)# Productsditchcarbon.products.calculate_emissions(**params)# Serversditchcarbon.servers.find(**params)ditchcarbon.servers.retrieve(1)ditchcarbon.servers.calculate_emissions(1,**params)# Suppliersditchcarbon.suppliers.calculate_emissions(**params)Documentation and HelpView our API referencehere. For usage questions, feel free to contact ushere.
Package: dithering
Dithering is an open-source image processing library written in Rust, designed to perform image dithering operations. It provides a collection of algorithms that can be used to convert images to a limited color palette, resulting in the illusion of additional colors. This library is licensed under the MIT license.FeaturesPerform image dithering operations to reduce the number of colors in an image.Support for ordered dithering algorithm.Convert images to a limited color palette.Integration with Python through Rust's Foreign Function Interface (FFI).InstallationTo install the library using pip, run the following command:pip install ditheringUsageHere's an example usage of the Dithering library in Python:fromPILimportImagefromditheringimportordered_ditherimportnumpyasnpinput_image=np.array(Image.open('path/to/image.jpeg'))output_image=ordered_dither(input_image,"Bayer2x2")OriginalDitheredIn the example above, we first import the necessary modules. Then, we load an image using the PIL library and convert it to a NumPy array. We pick a dither matrix ("Dither2x2") and pass it along with the input image to theordered_ditherfunction from theditheringmodule. The function returns the dithered output image as a NumPy array.ContributingContributions to the Dithering library are welcome! If you want to contribute, please follow these steps:Fork the repository on GitHub.Create a new branch from themainbranch.Make your changes and ensure the tests pass.Add/update tests if applicable.Commit your changes and push them to your forked repository.Submit a pull request to themainbranch of the original repository.Please make sure to follow the code style and conventions used in the project.LicenseThe Dithering library is licensed under the MIT license. See theLICENSEfile for more details.ContactIf you have any questions, suggestions, or feedback regarding the Dithering library, you can reach out to us by creating an issue on the GitHub repository or contacting us via email atinfo@backyardml.se.Feel free to explore the repository, submit bug reports, or request new features. We appreciate your interest and support in advancing the Dithering library.
Package: ditingtools
DiTingTools [谛听--用于天然地震数据处理的深度学习工具包]This module is undergoing rapid development.该模块仍在开发阶段。Description [简介]DiTingTools aims to provide modules for developing, training, evaluating, and applying deep-learning-based methods to solve data processing tasks in earthquake seismology.谛听工具包主要为开发、训练、测评和应用基于深度学习的天然地震数据处理方法提供帮助。Getting Started [教程]Right now there are three example notebooks: (1) How to read DiTing50Hz dataset;(2) How to train an earthquake detection and phase picking model with DiTingTools; (3) How to train a first-motion polarity determination model with DiTingTools. More examples will be updated in the futrue.目前有三个例子：（1）如何读取谛听数据集；（2）如何训练地震检测和震相拾取模型；（3）如何训练一个初动极性判别模型。Dependenciestensorflow == 2.2.0keras == 2.4.3scipynumpypyyamlh5pytqdmscikit-learnobspyInstalling-- To be updatedAuthorsXiao zhuowei, Zhao ming, Chen shi.Version History-- To be updatedLicense-- To be updatedAcknowledgments-- To be updated
Package: ditolo
ditolodiscord token login extension packegeexample code!!!import ditolo
from ditolo import *

ditolo.join("アカウントのトークンbotではない","inviteリンク")
ditolo.join(token="アカウントのトークンbotではない",link="inviteリンク")

ditolo.send("アカウントのトークンbotではない","チャンネルid","送信するメッセージ")
ditolo.send(token="アカウントのトークンbotではない",channel="チャンネルid",content="送信するメッセージ")


ditolo.leave(アカウントのトークンbotではない,"サーバーid","trueかfalse")
ditolo.leave(token="アカウントのトークンbotではない",guild_id="サーバーid",print="trueかfalse")


ditolo.check("アカウントのトークンbotではない","trueかfalse")
ditolo.check(token="アカウントのトークンbotではない",print="trueかfalse")
Package: ditti-boost
Ditti BoostDitti Boost is a command-line tool that automates following and unfollowing users on Farcaster. It provides a simple way to grow your network, find new users, and manage your connections on the platform.FeaturesFollow users based on different criteria:Copy a user's followingFollow new usersFollow collection ownersUnfollow users based on different criteria:Unfollow all usersUnfollow non-follow back usersUnfollow collection ownersUnfollow users with no castsUnfollow no casts within x daysInstallationThis project requires python3.10To install Ditti Boost, run the following command:pip install ditti-boost

ditti-boostUsageTo use Ditti Boost, first navigate to the project directory and run the following command:poetry env use 3.10
poetry install
poetry run python ditti_boost/cli.pyYou will be prompted to enter either an access token or a mnemonic phrase. Then, you can choose from various follow and unfollow options.ContributingContributions are welcome! If you'd like to help improve Ditti Boost, please feel free to open an issue, submit a pull request, or suggest new features.LicenseDitti Boost is licensed under the MIT License.
Package: dittoffi
FFI Module for Ditto.Need alibdittoffi.soin the folder to work atmIts path must be specified using:export DITTOFFI_SEARCH_PATH=...
export LD_LIBRARY_PATH=...Update HeadersWhen the FFI signatures change, the headers inside this directory must be updated.dittoffi.hcan be replaced by the same-titled generated file, thanks to safer-ffi.
Usemake build-pythonat the root of the project in order to do so.
Thedittoffi.cffimust be manually updated at the moment. Safer-ffi we generate it
in the future.Need alibdittoffi.solibrary to work.
Package: dittohunt
Ditto Hunt is a duplicate file finder that quickly finds duplicate files
recursively under a folder and allows you to preview and then select
which versions should be deleted or moved to another folder. It does not
use filenames for comparison, and instead does a binary comparison of
all files.This utility is handy, for example, if you have a bunch of images and
want to find and get rid of duplicate images. However, it can be used
with any file type.FeaturesQuick duplicate file search.Multi platform.Easy and straightforward to use.Runtime RequirementsThis is just an overview of runtime requirements. All major versions of
both the Python interpreter and QT API are supported.Python2 or Python3PyQt4 or
PyQt5orPySideQtInstallationFor simplicity, instructions for only PyQt4 and Python2 are listed here.
However, you can adjust to use any combination of Python2/Python3 and
PyQt4/PyQt5/PySide if you prefer when it comes to runtime dependencies.On Ubuntu or Debian, first install dependencies using the system package
manager.sudo apt-get install python python-qt4 python-pipThen, use pip to install dittohunt.pip install [--user] dittohuntOr, if you’re installing from source:pip install [--user] dittohunt-<version>.tar.bz2The –user option causes dittohunt to be installed in your home
directory under ~/.local. You may have to add this to your path in some
environments.Then, if you somehow get fed up with Ditto Hunt’s awesomeness, uninstall
it.pip uninstall dittohuntRunningJust executedittohunt.ScreenshotsMain WindowPyQt4/PyQt5/PySideDitto Hunt can use PyQt4, PyQt5, or PySide for its Qt API. As long as
you have one installed, it will be automatically detected and used at
runtime. However, if you wish to force a specific Qt API, you can set
the QT_API environment variable to one of the following values when
running.QT_API=pyqt4 dittohunt
QT_API=pyqt5 dittohunt
QT_API=pyside dittohuntLicenseDitto Hunt is licensed under GPL Version 3. See theLICENSE.txtfile.qt.pyis licensed under 3-clause BSD.pyside_dyanmic.pyis
MIT licensed.Ditto Hunt Release NotesVersion 1.1     16 July 2017Fix README install instructions.Show number of files in confirmation dialogsRework options for searchingAdd refresh buttonAdd menu option to copy path to clipboardAdd handling of FindThread errorsFix PyQt5 issue where progress dialog sometimes ghostsFix bug where status is not updatedAdd expand/collapse actionsAdd move buttonHandle and show errors from FindThreadShow open menu for all tree itemsShow PREVIEW in preview label when no imageAdd tooltips for GUI widgetsMake statusbar message permanentVersion 1.0     15 July 2017Initial alpha release.
Package: diTTS
gTTSgTTS(Google Text-to-Speech), a Python library and CLI tool to interface with Google Translate's text-to-speech API.
Write spokenmp3data to a file, a file-like object (bytestring) for further audio manipulation, orstdout.https://gtts.readthedocs.io/FeaturesCustomizable speech-specific sentence tokenizer that allows for unlimited lengths of text to be read, all while keeping proper intonation, abbreviations, decimals and more;Customizable text pre-processors which can, for example, provide pronunciation corrections;Installation$ pip install gTTSQuickstartCommand Line:$ gtts-cli 'hello' --output hello.mp3Module:>>> from gtts import gTTS
>>> tts = gTTS('hello')
>>> tts.save('hello.mp3')Seehttps://gtts.readthedocs.io/for documentation and examples.DisclaimerThis project isnotaffiliated with Google or Google Cloud. Breaking upstream changescanoccur without notice. This project is leveraging the undocumentedGoogle Translatespeech functionality and isdifferentfromGoogle Cloud Text-to-Speech.ProjectQuestions & communityChangelogContributingLicenceThe MIT License (MIT)Copyright 짤 2014-2024 Pierre Nicolas Durette &Contributors
Package: ditty
DittyA simple fine-tune.WhatA very simple library for finetuning Huggingface Pretrained AutoModelForCausalLM such as GPTNeoX, leveraging Huggingface Accelerate, Transformers, Datasets and PeftDitty has support for LORA, 8bit, and fp32 cpu offloading right out of the box and assumes you are running with a single GPU or distributed over multiple GPUs by default.Checkpointing supported, currently a bug with pushing to HF model hub though so checkpoints are all local.FP16, BFLOAT16 now supported.QLORA 4bit supported under experimental, and requires installing development branches of accelerate, peft, transformers and the latest bitsandbytes.What NotDitty does not support ASICs like TPU or Trainium.Ditty does not handle SagemakerDitty does not by default run with the CPUDitty does not handle evaluation sets or benchmarking, this may or may not change.SoonDitty may handle distributed cluster finetuningDitty will support DeepSpeedClassesPipelinePipeline is responsible for running the entire show. Simply subclass Pipeline and implement thedatasetmethod for your custom data, this must return atorch.utils.data.DataLoaderInstantiate with your chosen config and then simply callrun.TrainerTrainer does what it's name implies, which is to train the model. You may never need to touch this if you're not interested in customizing the training loop.DataData wraps an HF Dataset and can configure length grouped sampling and random sampling, as well as handling collation, batching, seeds, removing unused columns and a few other things.The primary way of using this class is through thepreparemethod which takes a list of operations to perform against the dataset. These are normal operations likemapandfilter.Example:data=Data(load_args=(self.dataset_name,self.dataset_language),tokenizer=self.tokenizer,seed=self.seed,batch_size=self.batch_size,grad_accum=self.grad_accum,)....sicdataloader=data.prepare([("filter",filter_longer,{}),("map",do_something,dict(batched=True,remove_columns=columns)),("map",truncate,{}),])This can be used to great effect when overriding thedatasetmethod in a subclass ofPipeline.Setuppip install dittyTipshttps://github.com/google/python-fireis a tool for autogenerating CLIs from Python functions, dicts and objects.It can be combined with Pipeline to make a very quick cli for launching your process.Attribution / Statement of ChangesPortions of this library look to Huggingface's transformers Trainer class as a reference and in some cases re-implements functions from Trainer, simplified to only account for GPU based work and overall narrower supported scope.This statement is both to fulfill the obligations of the ApacheV2 licencse, but also because those folks do super cool work and I appreciate all they've done for the community and its just right to call this out.LicenseApache V2 see the LICENSE file for full text.
Package: divar-scraper
DivarPython package for crawling from Divar website.
divar.ir is the biggest website in the middle-east that people post their properties and try to sell them online.InstallationSimply you can install it from PyPi by following command:pipinstall-Udivar-scraperor if you prefer the latest development version, you can install it from the source:gitclonehttps://github.com/softcoder24/divar.gitcddivar
pythonsetup.pyinstallUsageA very simple usage for getting the phone number could be like:fromdivarimportclientsession='eyJ0eXAiOiJKV1QiLCJhbGciOiJI................UzI1NiJ9zRumpqq1wrfGpYaD'divar=client.Divar()divar.load(session=session)divar.contact('wXSTyUdO')Sample response for contact:09991112233fromdivarimportclientdivar=client.Divar()divar.post('wXSTyUdO')Sample response for post:{
    'title': '۳خوابه امامت۱۴', 
    'description': 'کلا ۴ واحد\nسرامیک\nام دی اف\n۰۹۱۵۱۰۰۳۵۲۴\n۰۹۱۵۲۰۰۳۵۲۴\nرجایی\nهیچ جا خونه ی خود آدم نمیشه',
    'web_url': 'https://divar.ir/v/wXSTyUdO', 
    'price': None, 
    'rent': 1000, 
    'credit': 245000000, 
    'category': 'apartment-rent', 
    'business_type': 'personal', 
    'images': ['https://s100.divarcdn.com/static/pictures/1607624286/wXSTyUdO.webp'], 
    'city': 'مشهد',
    'district': 'آزادشهر'
}fromdivarimportclientdivar=client.Divar()divar.search(city_code=3,category='real-estate')Sample response for search:{
    'last_post_date': 613049590941897, 
    'posts': [
              {'token': 'wXLnEJOQ', 'title': 'فروش زمین باغی در مجتمع شاهنامه محدوده زاک ماریان', 'image': 'https://s100.divarcdn.com/static/thumbnails/1607791740/wXLnEJOQ.webp', 'description': 'توافقی', 'city': 'مشهد', 'district': 'بلوار توس', 'category': 'زمین و کلنگی', 'normal_text': 'لحظاتی پیش در بلوار توس'}, 
              {'token': 'wXULcoir', 'title': 'دفترکار 140متری حاشیه بلوارفردوسی', 'image': '', 'description': 'ودیعه: ۱۰۰,۰۰۰,۰۰۰ تومان\nاجاره ماهیانه: ۴,۰۰۰,۰۰۰ تومان', 'city': 'مشهد', 'district': 'فرامرز عباسی', 'category': 'دفتر کار، اتاق اداری و مطب', 'normal_text': 'لحظاتی پیش در فرامرز عباسی'},
              ...
              {'token': 'wXUL8s-R', 'title': '160متر اپارتمان نوساز حاشیه گلشن ()', 'image': '', 'description': 'ودیعه: ۱۰۰,۰۰۰,۰۰۰ تومان\nاجاره ماهیانه: ۷,۰۰۰,۰۰۰ تومان', 'city': 'مشهد', 'district': 'محله هنرستان', 'category': 'آپارتمان', 'normal_text': 'لحظاتی پیش در محله هنرستان'},
              {'token': 'wXCaNi92', 'title': 'اجاره باغ ویلا واستخرسرپوشیده', 'image': 'https://s100.divarcdn.com/static/thumbnails/1607791726/wXCaNi92.webp', 'description': 'ودیعه: توافقی\nاجاره ماهیانه: توافقی', 'city': 'مشهد', 'district': 'بلوار توس', 'category': 'خانه و ویلا', 'normal_text': 'لحظاتی پیش در بلوار توس'}
             ]
}
Package: divbasiccalculator
DivcalcThis is a basic calculator that either adds, subtracts, multiplies or divides 2 numbers.Usage:This is how you can use the calculatorimport divcalc

divcalc.add(2, 3)
divcalc.sub(2, 3)
divcalc.mul(2, 3)
divcalc.div(2, 3)Change Log0.0.1 (2/9/21)--First release.0.0.2 (3/9/21)--Fixed a few bugs that didn't allow successful pip installs.0.0.3 (3/9/21)--Provided better instructions on how to use the calculator.0.0.4 (3/9/21)--Finalised usage instructions.