Saravanan666/pypi-org huggingface dataset:
Package: autosub-baidu
Autosub-baiduAuto-generated subtitles for any videoAutosub是用于自动语音识别和字幕生成的实用程序。但是这个项目已经不再维护了，而且在中国使用有些困难。
于是我克隆了这个项目的代码，在内部把Google Web Speech API替换为了百度短语音识别API。项目结构也做出了一些修改，使其即可以在命令行使用，也可以在其他代码中导入使用（主要目的是方便在代码中使用）。语言也改为了仅支持中文。运行参数有些不一样，要想顺利使用这个新项目你必须提供API_KEY、SECRET_KEY和APP_ID。这里贴出了一些链接，可以跟随这些链接去自行探索：短语音识别标准版产品首页百度AI开放平台语音技术新手入门（通过阅读可以了解如何获得API_KEY、SECRET_KEY和APP_ID）Autosubis a utility for automatic speech recognition and subtitle generation.But this project is no longer maintained, and it is somewhat difficult to use in China.
So I cloned the code of this project and replaced Google Web Speech API with Baidu Short Speech Recognition API internally.Now Autosub-baidu only supports Chinese.InstallationInstallffmpeg.Runpip install autosub-baidu.Usagepython -m autosubb -h
usage: __main__.py [-h] [-C CONCURRENCY] [-o OUTPUT] [-F FORMAT] [-L LANG] [-K API_KEY] [-S SECRET_KEY] [-A APP_ID]
                   [--list-formats] [--list-languages]
                   [source_path]

positional arguments:
  source_path           Path to the video or audio file to subtitle

optional arguments:
  -h, --help            show this help message and exit
  -C CONCURRENCY, --concurrency CONCURRENCY
                        Number of concurrent API requests to make
  -o OUTPUT, --output OUTPUT
                        Output path for subtitles (by default, subtitles are saved in the same directory and name as
                        the source path)
  -F FORMAT, --format FORMAT
                        Destination subtitle format
  -L LANG, --lang LANG  Language spoken in source file. default 1537.
  -K API_KEY, --api-key API_KEY
                        The Baidu Cloud API key to be used.
  -S SECRET_KEY, --secret-key SECRET_KEY
                        The Baidu Cloud Secret Key to be used.
  -A APP_ID, --app-id APP_ID
                        The Baidu Cloud AppID to be used.
  --list-formats        List all available subtitle formats
  --list-languages      List all available source/destination languagesLicenseMIT
Package: autosubmaia
Autosub is a utility for automatic speech recognition and subtitle generation. It takes a video or an audio file as input, performs voice activity detection to find speech regions, makes parallel requests to Speech Recognition API to generate transcriptions for those regions, (optionally) translates them to a different language, and finally saves the resulting subtitles to disk. It supports a variety of input and output languages (to see which, run the utility with –list-src-languages and –list-dst-languages as arguments respectively) and can currently produce subtitles in either the SRT format or simple JSON.
Package: autosubmit
Autosubmit is a lightweight workflow manager and meta-scheduler. It was originally designed in 2011 for use in climate research for configuring and running experiments. In the last few years, it has been extended to cover additional use cases, and today it is used as a general orchestration tool. It supports scheduling jobs to remote batch servers (via SSH) such as PBS, LSF, SLURM, and SGE.Autosubmit is a Python package provided in PyPI, which facilitates easy and fast integration and relocation on new platforms. Conda recipes are available on the project website. A containerized version for testing purposes is also available but not public yet.The features found in Autosubmit characterize it as both an experiment manager and also as a workflow orchestrator. The experiment manager allows users to define and configure experiments, supported by a hierarchical database that ensures reproducibility and traceability. The orchestrator is designed to run complex workflows in research and operational mode by managing their dependencies and interfacing with local and remote hosts.These multi-scale workflows can contain from a few steps to thousands of steps, and from a single platform to multiple platforms. Platform is a concept in Autosubmit to abstract servers. A workflow configuration can include one or multiple platforms, allowing the workflow to run on any number of servers via password-less SSH without any external deployment.Due to its robustness it can handle different eventualities such as networking connectivity issues or I/O errors. The monitoring capabilities extend beyond the command-line application through a REST API that allows communication with workflow monitoring tools such as the Autosubmit web GUI.It has contributed to various European research projects and runs different operational systems. It will support the Earth Digital Twins as the Digital Twin Ocean over the next years.It is currently used at the Barcelona Supercomputing Centre (BSC) to run models (EC-Earth, MONARCH, NEMO, CALIOPE, HERMES, and others), operational toolchains (S2S4E), data-download workflows (ECMWF MARS), and for many other use cases. Autosubmit has been used to run workflows in different supercomputers at BSC, ECMWF, IC3, CESGA, EPCC, PDC, and OLCF.
Package: autosubmitconfigparser
Simple library that allows to read the data of an Autosubmit experiment.Usage:#import the main config libraryfromautosubmitconfigparser.config.configcommonimportAutosubmitConfigimportos# Init the configuration object where expid = experiment identifier that you want to loadexpid="a01y"as_conf=AutosubmitConfig("a01y")# This will load the data from the experimentas_conf.reload(True)#all data is stored in the as_conf.experiment_data dictionaryas_conf.experiment_data# Obtain only section dataas_conf.jobs_data# Obtain only platforms dataas_conf.platforms_data# Obtain all data in parameter format( %SECTION%.%SUBSECTION%.%SUBSECTION% )parameters=as_conf.deep_parameters_export(as_conf.experiment_data)# To parse the placeholders from a file use the following function#write sample textwithopen("as_sample.txt","w")asf:f.write("This is a sample text with a placeholder %DEFAULT.EXPID%")#write the parsed textwithopen("as_sample_parsed.txt","w")asf:f.write(as_conf.parse_placeholders(open("as_sample.txt","r").read(),parameters))# print the file contentwithopen("as_sample.txt","r")asf:print(f.read())# print the file contentwithopen("as_sample_parsed.txt","r")asf:print(f.read())# Result must be:# This is a sample text with a placeholder %DEFAULT.EXPID%# This is a sample text with a placeholder expid# delete samplesos.remove("as_sample.txt")os.remove("as_sample_parsed.txt")
Package: autosubset
autosubset – Automatically subset a font based on actual textautosubsetcreates a subset font exactly for the set of characters that you
actually use.UsageJust pipe the text intoautosubsetand specify the font.Assuming all your text is in the HTML files in the current directory:html2text*.html|autosubset.pyMyFont.woff2autosubsetanalyzes the content, extracts the characters used and usespyftsubstfromfonttoolsto create a matching
subset.After that, just add lines like the following to your web site:HTML: Preload the font for faster availability.<linkrel="preload"href="MyFont.subset.woff2"as="font"type="font/woff2">CSS: Define and use the font.@font-face{font-family:"My Font";src:local("Tenor Sans"),url("MyFont.subset.woff2")format("woff2");font-display:fallback;}@body{font-family:"My Font","System Fallback Font",sans;}Whenever you change your text, make sure to rerunautosubst. This ensures
that the browser does not need to substitute characters from the fallback
fonts.
Package: autosubsync
Automatically synchronize SRT subtitles with audio.
Requires ffmpeg (sudoapt-getinstall ffmepg):autosubsync [input movie] [input subtitles] [output subs]

# for example
autosubsync plan-9-from-outer-space.avi \
  plan-9-out-of-sync-subs.srt \
  plan-9-subtitles-synced.srtSeeautosubsync--helpfor more details.
Package: auto-subtitle
Automatic subtitles in your videosThis repository usesffmpegandOpenAI's Whisperto automatically generate and overlay subtitles on any video.InstallationTo get started, you'll need Python 3.7 or newer. Install the binary by running the following command:pip install git+https://github.com/m1guelpf/auto-subtitle.gitYou'll also need to installffmpeg, which is available from most package managers:# on Ubuntu or Debiansudoaptupdate&&sudoaptinstallffmpeg# on MacOS using Homebrew (https://brew.sh/)brewinstallffmpeg# on Windows using Chocolatey (https://chocolatey.org/)chocoinstallffmpegUsageThe following command will generate asubtitled/video.mp4file contained the input video with overlayed subtitles.auto_subtitle /path/to/video.mp4 -o subtitled/The default setting (which selects thesmallmodel) works well for transcribing English. You can optionally use a bigger model for better results (especially with other languages). The available models aretiny,tiny.en,base,base.en,small,small.en,medium,medium.en,large.auto_subtitle /path/to/video.mp4 --model mediumAdding--task translatewill translate the subtitles into English:auto_subtitle /path/to/video.mp4 --task translateRun the following to view all available options:auto_subtitle --helpLicenseThis script is open-source and licensed under the MIT License. For more details, check theLICENSEfile.
Package: auto-subtitles-generator
Auto-subtitles video tool using AssemblyAI API and ffmpegRequirementsYou need an AssemblyAI API key. You can get onehere.
API key can be set using the--api_keyoption or by setting theAAI_API_KEYenvironment variable.InstallPippip3installauto-subtitles-generatorFrom sourcegitclonecdauto-subtitles-generator
poetryinstallUsageUsage:auto_subtitles[OPTIONS]Options:-i,--inputPATHPathtovideofile[required]-o,--outputPATHOutputvideofile[required]--api_keyTEXTAssemblyAIAPIkey--helpShowthismessageandexit.BuildpoetrybuildCoveragecoveragerun-munittest&&coveragereport-m
Package: autosuper
This is an (awful, hacky, wtf-were-you-thinking) attempt to port the magical
zero-argument super() call from python3 to python2.In standard python2 usage of the super() builtin, you have to repeat both the
class and instance objects when you call super, like this:class Hello(Base):def hello(self):super(Hello,self).hello()Using autosuper, you can get the friendlier behaviour from python3 where it
just figures out the correct call at runtime:class Hello(Base):def hello(self):super().hello()Of course, you can still explicitly pass in the arguments if you want to do
something strange.  Sometimes you really do want that, e.g. to skip over
some classes in the method resolution order.How does it work?  By inspecting the calling frame to determine the function
object being executed and the object on which it’s being called, and then
walking the object’s __mro__ chain to find out where that function was
defined.  Yuck, but it seems to work…
Package: auto-surprise
Auto-SurpriseAuto-Surprise is built as a wrapper around the PythonSurpriserecommender-system library. It automates algorithm selection and hyper parameter optimization in a highly parallelized manner.Documentation is available atAuto-Surprise ReadTheDocsAutoSurprise is currently in development.SetupAuto-Surprise is easy to install with Pip, and required Python>=3.6 installed on a linux system. Currently not supported in windows, but can be used usingWSL.$pipinstallauto-surpriseUsageBasic usage of AutoSurprise is given below.fromsurpriseimportDatasetfromauto_surprise.engineimportEngine# Load the datasetdata=Dataset.load_builtin('ml-100k')# Intitialize auto surprise engineengine=Engine(verbose=True)# Start the trainerbest_algo,best_params,best_score,tasks=engine.train(data=data,target_metric='test_rmse',cpu_time_limit=60*60,max_evals=100)In the above example, we first initialize theEngine. We then runengine.train()to begin training our model. To train the model we need to pass the followingdata: The data as an instance ofsurprise.dataset.DatasetAutoFolds. Please readSurprise Dataset docstarget_metric: The metric we seek to minimize. Available options aretest_rmseandtest_mae.cpu_time_limit: The time limit we want to train. This is in seconds. For datasets like Movielens 100k, 1 hour is sufficient. But you may want to increase this based on the size of your datasetmax_evals: The maximum number of evaluations each algorithm gets for hyper parameter optimization.hpo_algo: Auto-Surprise uses Hyperopt for hyperparameter tuning. By default, it's set to use TPE, but you can change this to any algorithm supported by hyperopt, such as Adaptive TPE or Random search.Setting the Hyperparameter Optimization AlgorithmAuto-Surprise uses Hyperopt. You can change the HPO algo as shown below.# Example for setting the HPO algorithm to adaptive TPEimporthyperopt...engine=Engine(verbose=True)engine.train(data=data,target_metric='test_rmse',cpu_time_limit=60*60,max_evals=100,hpo_algo=hyperopt.atpe.suggest)Building back the best modelYou can build a pickelable model as shown.model=engine.build_model(best_algo,best_params)BenchmarksIn my testing, Auto-Surprise performed anywhere from 0.8 to 4% improvement in RMSE compared to the best performing default algorithm configuration. In the table below are the results for the Jester 2 dataset. Benchmark results for Movielens and Book-Crossing dataset are also availablehereAlgorithmRMSEMAETimeNormal Predictor7.2775.88600:00:01SVD4.9053.9700:00:13SVD++5.1024.05500:00:29NMF------Slope One5.1893.94500:00:02KNN Basic5.0784.03400:02:14KNN with Means5.1243.95500:02:16KNN with   Z-score5.2193.95500:02:20KNN Baseline4.8983.89600:02:14Co-clustering5.1533.91700:00:12Baseline Only4.8493.93400:00:01GridSearch4.74093.814780:52:35Auto-Surprise (TPE)4.64893.683702:00:10Auto-Surprise (ATPE)4.65553.690602:00:01PapersAuto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Package: autosweep
AutoSweep Test Framework 0.0.3The AutoSweep project is designed to run automated device testing.
It's designed to be highly modular, giving a framework with which to organize test sequences and manage instruments, while keeping track of device metadata, saving raw data, generating reports and handling errors.InstallationAfter cloning the repository, you can install it usingpip install ..
If you want to develop features within Test Automation, you'll find the pip-eoption useful,pip install -e ., this allows you to make edits to the code and execute tests from the python environment the package was installed in without reinstalling it via pip.After installation, execute the python script intests/test_exec/run_test_exec.py. You'll see a a folder of virtual test data generated in the foldertests/test_exec/data.
Open the script and browse the test data to become more familiar with the operation of the code.
Package: autosysloguru
Auto-LoguruFancy defaults for the awesome loguru logs!Simple!Install:pip install as_loguruUse in Python script:from as_loguru import loggerThis is a wrapper for the marvlouslogurulogging package. Below is the information from the original project repo.Loguruis a library which aims to bring enjoyable logging in Python.Did you ever feel lazy about configuring a logger and usedprint()instead?... I did, yet logging is fundamental to every application and
eases the process of debugging. UsingLoguruyou have no excuse not
to use logging from the start, this is as simple asfromloguruimportloggerlogger.info("I'm logging now!")Also, this library is intended to make Python logging less painful by
adding a bunch of useful functionalities that solve caveats of the
standard loggers. Using logs in your application should be an
automatism,Logurutries to make it both pleasant and powerful.Installationpip install loguruFeaturesReady to use out of the box without boilerplate[No Handler, no Formatter, no Filter: one function to rule them
all][ready to use out of the box without boilerplate][Easier file logging with rotation / retention /
compression][ready to use out of the box without boilerplate][Modern string formatting using braces
style][ready to use out of the box without boilerplate][Exceptions catching within threads or
main][ready to use out of the box without boilerplate][Pretty logging with
colors][ready to use out of the box without boilerplate][Asynchronous, Thread-safe,
Multiprocess-safe][ready to use out of the box without boilerplate][Fully descriptive
exceptions][ready to use out of the box without boilerplate][Structured logging as
needed][ready to use out of the box without boilerplate][Lazy evaluation of expensive
functions][ready to use out of the box without boilerplate][Customizable
levels][ready to use out of the box without boilerplate][Better datetime
handling][ready to use out of the box without boilerplate][Suitable for scripts and
libraries][ready to use out of the box without boilerplate]`Entirely compatible with standard loggin[Ready to use out of the box without boilerplate]:
Package: autota
AutoTA提供BookRoll中的Marker/Memo評分功能，以及教材推薦功能準備以下四個服務需要先以docker在本地端或遠端建立Question generation service(問題生成才要)BERT pre-trained model serviceBERT fine-tuned model service(簡答題評分才要)Google cloud translation service(問題生成才要)安裝pip install autota使用獲取Marker/Memo分數fromautota.graderimportGradergrader=Grader(pdf_path='./test.pdf',bert_api_port=PRETRAINED_BERT_SERVICE_PORT,bert_api_url='PRETRAINED_BERT_SERVICE_HOST')print(grader.grade_marker('marker text'))#得到單一marker分數print(grader.grade_memo('memo text'))#得到單一memo分數獲取教材推薦頁數fromautota.recommenderimportRecommender#num_page指定要推薦多少頁recommender=Recommender(pdf_path='./test.pdf',num_page=2,api_port=PRETRAINED_BERT_SERVICE_PORT,api_url='PRETRAINED_BERT_SERVICE_HOST')print(recommender.guiding_from(ta_ans='要推薦的概念'))#輸出為[(2, 0.0778473040773201), (1, 0.08752984923065377)]#tuple第一項元素即為頁數，第二項為該頁與ta_ans概念間的餘弦距離從教材自動生成問題fromautota.generatorimportGenerator#num_page指定要推薦多少頁generator=Generator(pdf_path='./test.pdf',,translate_api_port=TRANSLATE_SERVICE_PORT,translate_api_url='TRANSLATE_SERVICE_HOST',gpt2_api_port=GPT2_SERVICE_PORT,gpt2_api_url='GPT2_SERVICE_HOST')print(generator.get_qa())#輸出為[('What is the first thing that can be a variable name?', '變數名稱的第一個字不可為數字')]#list中每個tuple為一組QA pair開發中簡答題自動評分
Package: autotab
autotaboptimize pipeline for any machine learning mdoel using hierarchical optimization
method for tabular datasets.InstallationThis package can be installed using pip from pypi using following commandpip install autotabor using github link for the latest codepython -m pip install git+https://github.com/Sara-Iftikhar/autotab.gitor using setup file, go to folder where this repoitory is downloadedpython setup.py installExampleClick here toor cick here tofromai4water.datasetsimportbusan_beachfromskopt.plotsimportplot_objectivefromautotabimportOptimizePipelinedata=busan_beach()input_features=data.columns.tolist()[0:-1]output_features=data.columns.tolist()[-1:]transformations=['minmax','zscore','log','log10','sqrt','robust','quantile','none','scale']pl=OptimizePipeline(inputs_to_transform=data.columns.tolist()[0:-1],parent_iterations=400,child_iterations=20,parent_algorithm='bayes',child_algorithm="random",cv_parent_hpo=True,eval_metric='mse',monitor=['r2','nse'],input_transformations=transformations,output_transformations=transformations,models=["LinearRegression","LassoLars","Lasso","RandomForestRegressor","HistGradientBoostingRegressor","CatBoostRegressor","XGBRegressor","LGBMRegressor","GradientBoostingRegressor","ExtraTreeRegressor","ExtraTreesRegressor"],input_features=data.columns.tolist()[0:-1],output_features=data.columns.tolist()[-1:],cross_validator={"KFold":{"n_splits":5}},split_random=True,)get version informationpl._version_info()perform optimizationresults=pl.fit(data=data,process_results=False)print optimization reportprint(pl.report())show convergence plotpl.optimizer_._plot_convergence(save=False)pl.optimizer_._plot_parallel_coords(figsize=(16,8),save=False)_=pl.optimizer_._plot_distributions(save=False)pl.optimizer_.plot_importance(save=False)pl.optimizer_.plot_importance(save=False,plot_type="bar")_=plot_objective(results)pl.optimizer._plot_evaluations(save=False)pl.optimizer._plot_edf(save=False)pl.dumbbell_plot(data=data)pl.dumbbell_plot(data=data,metric_name='r2')pl.taylor_plot(data=data,save=False,figsize=(6,6))pl.compare_models()pl.compare_models(plot_type="bar_chart")pl.compare_models("r2",plot_type="bar_chart")get best pipeline with respect to evaluation metricpl.get_best_pipeline_by_metric('r2')build fit and evaluate the best pipelinemodel=pl.bfe_best_model_from_scratch(data=data)pl.evaluate_model(model,data=data)pl.evaluate_model(model,data=data,metric_name='nse')pl.evaluate_model(model,data=data,metric_name='r2')get best pipeline with respect to $R^2$pl.get_best_pipeline_by_metric('r2')model=pl.bfe_best_model_from_scratch(data=data,metric_name='r2')pl.evaluate_model(model,data=data,metric_name='r2')print(f"all results are save in{pl.path}folder")
Package: auto-tabnet
Auto-TabnetAuto-TabNet is the implementation of Google's TabNet model using dreamquark-ai's pytorch implementation along with hyperparameter search with Optuna.Google's TabNet was proposed in 2019 with the idea of effectively using deep neural networks for tabular data.TabNet is a complex model composed of a feature transformer, attentive transformer, and feature masking, that soft feature selection with controllable sparsity in end-to-end learning. The reason for the high performance of TabNet is that it focuses on the most important features that have been considered by the Attentive Transformer. The Attentive Transformer performs feature selection to select which model features to reason from at each step in the model, and a Feature Transformer processes feature into more useful representations and learn complex data patterns, which improve interpretability and help it learn more accurate models.Requirementspython3.7>pip(pythonpackagemanager)InstallationWith pip:pipinstallauto-tabnetSource CodeIf you want to use it locally within a pip virtualenv:Clone the repositorygitclonehttps://github.com/Femme-js/auto-tabnet.gitCreate a pip virtual environment.virtualenvenvInstall the dependencies from requirements.txt file.pipinstall-rrequirements.txtUsagefromauto_tabnetimportAutoTabnetClassifierclf=AutoTabnetClassifier(X,y,X_test)To get the prediction on test data.results=clf.predict()To get the auc_roc_score:results=clf.get_roc_auc_score()To get the best hyperparamters tuned by optuna:results=clf.get_best_params()The targets on y_train should contain a unique type (e.g. they must all be strings or integers).