Saravanan666/pypi-org huggingface dataset:
Package: vod-tudelft
The View-of-Delft datasetExample frame from our dataset with camera, LiDAR, 3+1D radar, and annotations overlaid.<\figure>We present the novel View-of-Delft (VoD) automotive dataset. It contains 10.000 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of more than 76000 3D bounding box annotations, including more than 15000 pedestrian, 7100 cyclist and 17000 car labels.Sensors and dataThe LiDAR sensor is a Velodyne 64 operating at 10 Hz. The camera provides colored images of 1936 × 1216 pixels at around 12 Hz. The horizontal field of view is ~64° (± 32°), vertical field of view is ~ 44° (± 22°). Odometry is a filtered combination of several inputs: RTK GPS, IMU, and wheel odometry with a frame rate around 100 Hz. Odometry is given relative to the starting position at the beginning of the current sequence, not in absolute coordinates. Note that while camera and odometry operate at a higher frequency than the LiDAR sensor, timestamps of the LiDAR sensor were chosen as “lead”, and we provide the closest camera frame and odometry information available.The provided LiDAR point-clouds are already ego-motion compensated. More specifically, misalignment of LiDAR and camera data originating from ego-motion during the scan (i.e. one full rotation of the LiDAR sensor) and ego-motion between the capture of LiDAR and camera data has been compensated. See an example frame for our data with calibrated and ego-motion compensated LiDAR point-cloud overlaid on the camera image.We also provide intrinsic calibration for the camera and extrinsic calibration of all three sensors in a format specified by the annotating party.IntroductionGoalUse-CasesAnnotation intstructions
Package: vo-fabutils
Utilities for creating better fabric tasks.Install$pipinstallvo-fabutilsUsageImport the proper fabutils modules inside your fabfile an hack a nice
day.ExamplesDefine environments in JSON formatFirst you must create a JSON file containing your environment
configuration.#/path/to/environments/file.json{"devel":{"user":"devel-user","hosts":["dev.host.com"],"site_dir":"/path/to/devel/site/www/","command_prefixes":["/path/to/devel/site/env/bin/activate"]},"production":{"user":"prod-user","hosts":["host.com"],"site_dir":"/path/to/production/site/www/","command_prefixes":["/path/to/production/site/env/bin/activate"]}}Note that:You can define any arbitrary string as env properties and these will be passed to the task’s env.Properties with names “reserved” by fabric will be recognized by it and treated with the special meanig that fabric has for them.The only caveat is that if you define an array ofcommand_prefixesyou must only list the path to the script and this will be automatically prepended to with the word ‘souce’. For example if you define../some_scriptin your command_prefixes, it will be tranformed tosource../some_script.Every directory path must end with a trailing slash.Next, importfabutils.env.set_envin your fabfile and create a task that calls it with the path to your JSON file.# fabfile.pyfromfabric.apiimporttaskfromfabutils.envimportset_env@taskdefenvironment(env_name):set_env(env_name,'/path/to/environments/file.json')Now you can run tasks in the environment by callingenvironment:namebeforeyour task.# Run a task in devel environment$fabenvironment:develsome_task# Run a task in production environment$fabenvironment:productionsome_taskArbitrary options and parametersSome times you just need to pass an unknown number of
options/arguments to acommand, in this case you can usejoin,optionsandargumentsutils totranslete the pythonic*argsto command arguments and**kwargstocommand options.fromfabric.apiimporttask,runfromfabutilsimportjoin,arguments,options@taskdefsome_task(*args,**kwargs):run(join('some_command',arguments(*args,**kwargs))And then call your task using the fabric’s notation$fabsome_task:arg1,arg2,kwarg1=val1,kwarg2=val2# The above will be translated to:
# some_command arg1 arg2 kwarg1=val1 kwarg2=val2If you pass your*kwargstooptionsthe keys that are
evaluated to a booleanTrue will be translated to--{key}notation....@taskdefanother_task(**kwargs):return(join('another_command',options(**kwargs)))...$fabanother_task:option1=True,option2=No,option3=1,option4=False# The above will be translated to:
# another_command --option1 --option3Of course, you can combine the two approaches....fromfabutilsimportboolean@taskdefthe_task(*args,**kwargs):options={}arguments={}fork,vinkwargs:ifboolean(v):options[k]=velse:arguments[k]=vrun(join('the_command',arguments(*args,**arguments),options(**options)))...$fabthe_task:arg1,arg2=val2,option1=True# The above will be translated to:
# the_command arg1 arg2=val2 --option1
Package: vofotensors
V(ariety) o(f) F(iber) O(rientation) TensorsThis repository contains a Python package calledvofotensorswhich contains most of the parameterizations of fiber orientation tensors
defined in the paper@article{title={Varietyoffiberorientationtensors},author={Bauer, Julian Karl and B{\"o}hlke, Thomas},journal={Mathematics and Mechanics of Solids},publisher={SAGE Publications Sage UK: London, England},doi={10.1177/10812865211057602},volume={27},number={7},pages={1185-1211},year={2022},}Please seelicense,acknowledgmentand cite thepaper given aboveand the latestZenodo-DOI.InstallationInstall directly from githubpython -m pip install git+https://github.com/JulianKarlBauer/vofotensors.gitInstall frompipinstallvofotensorsInstall from local cloneClonethis repository to your machineOpen a terminal and navigate to your local cloneInstall the package from the local clone into the currentenvironmentin develop mode:pythonsetup.pydevelopNote:Develop vs. installExamplesRendered example notebooks are inthe docs.
These notebooks and corresponding scripts can be foundhere.AcknowledgmentThe research documented in this repository has been funded by theGerman Research Foundation (DFG, Deutsche Forschungsgemeinschaft)- project number255730231.
The support by the German Research Foundation within the International Research Training Group“Integrated engineering of continuous-discontinuous long fiber reinforced polymer structures“ (GRK 2078)is gratefully acknowledged.
Package: vofs
vosfs is a python module that allows a VOSpace service to be used as a
file system.The default installation of vosfs is tuned for accessing the VOSpace
provided by theCanadian Advanced Network For Astronomical
Research(CANFAR)VOSpace is a Distributed Cloud storage service for use in Astronomy.There are two ways to use vos:make VOSpace appear as mounted filesystem:mountvofsuse the vosfs module inside a Python script:import vosfsAuthentication to the CANFAR VOSpace service is performed using X509
security certificates, header tokens or username/password pairs. The
authentication system is managed by the CADC Group Management Service
(GMS).To retrieve an X509 security certificate for use with thevosfstools use thecadc-get-certscript included with the cadcutils
package.Additional information is available in theCANFAR
documentationSystem RequirmentsA CANFAR VOSpace account (required for WRITE access, READ access can
be anonymous)fuse OR OSX-FUSE (see additional documentation, only required for
filesystem based access, not for command line or programmatic)python2.6 or laterInstallationvosfs is distributed viaPyPI/vosand
PyPI is the most direct way to get the latest stable release:pip install vosfs--upgrade--userOr, you can retrieve thegithubdistribution and usepython setup.py install--userTutorialGet aCANFAR
accountInstall the vosfs package.Retrieve a X509/SSL certificate using the installed incadc-get-certscript.Example Usage.For filesystem usage:mountvofsmounts the CADC VOSpace root
Container Node at /tmp/vospace and initiates a 5GB cache in the
users home directory (${HOME}/vos_).fusermount-u/tmp/vospace(LINUX) orumount /tmp/vospace(OS-X) unmounts the file system.VOSpace does not have a mapping
of your unix users IDs and thus files appear to be owned by the
user who issued the ‘mountvofs’ command.Integration TestsThe integration tests are, at present, designed to run only with the
CADC VOSpace. Tests assume that the vofs package has been installed.Run the tests:``` $ ./test/scripts/vospace-mountvospace-atest.tcsh
Package: vogen
PyVogenGithub|Gitee|Gitlab|Bitbucket|CodingPyVogen是开源歌声合成引擎Vogen的python实现本python库依赖以下库：wgettqdmnumpypyworldjyutpingtabulatepypinyinpypercliponnxruntimesimpleaudiomore-itertools安装pip install vogen示例以下是文件读写示例，音频合成等更多示例参见文档importvogenasvg#打开文件vf=vg.openvog("myproject.vog")# 获取第0乐句的歌词与拼音列表u=vf.utts[0]lyrics=[i.lyricforiinu.notes]roms=[i.romforiinu.notes]print(lyrics)print(roms)#保存文件vf.save("myproject2.vog")相关链接vogen仓库vogen作者的b站空间（vogen试听页面）
Package: vohlc
Python VOHLCVersion 0.0.1Creates Volume-based candlesticks from time-based ohlcv data retrieved from cryptocurrency exchanges.Output can be either sqlite or csv (for version 0.0.1)Primarily just a utility for something that I'm working onShould not be very complex. Keep it simple, just do what it needs to doDoes not keep the timestampsPotentially give the option to not write the output, just keep in memory
Package: voica
Это библиотека для создания голосового асистентавам нужно установить SpeechRecognition через комманду pip install SpeechRecognition а также pyttsx3 через команду pip install pyttsx3Для начало в вашем коде напишитеfrom voica import gs это для произношения, скана потом раскажу что это а также считывания и распознования текста с микрофона но для этого надо ниже написать from voica import r
from voica import Main для считыванияя времениgs.speak("здесь текст который будет произносится") соответсвено для произношения текста
command = r.recognize_google(gs.read_microphone(), language="ru-RU") вместо command можно поставить жругое названиеMain.time("minutes") эта команда считывает минуты и возрощает значение
Main.time("hours") почти тоже самое только часы
Package: voice100-runtime
Voice100 RuntimeVoice100 Android App is a TTS/ASR sample app that usesONNX Runtime,WORLDandVoice100neural TTS/ASR models
on Python.
Inference of Voice100 is low cost as its models are tiny and only depend
on CNN without recursion.Beginnings are apt to be determinative and when reinforced
by continuous applications of similar influence.Sample audiomata toojinoyoonigodaimyooootoyobarerushuyoonamyoooonochuuoonihaisarerukotomoooiJapanese sample audioInstallUsepipto install from GitHub.pipinstallgit+https://github.com/kaiidams/voice100-runtime.gitList available modelsimportvoice100_runtimeprint(voice100_runtime.list_models())Using TTSThis downloads ONNX files in~/.cache/voice100_runtime/if not
available.importsoundfileassfimportvoice100_runtimetts=voice100_runtime.load("tts_en")waveform,sample_rate=tts("Hello, world!")sf.write("output.wav",waveform,sample_rate,"PCM_16")Using ASRThis downloads an ONNX file in~/.cache/voice100_runtime/if not
available.importsoundfileassfimportvoice100_runtimeasr=voice100_runtime.load("asr_en")waveform,sample_rate=sf.read("output.wav")text=asr(waveform,sample_rate)print(text)Using multi-task TTSThis downloads ONNX files in~/.cache/voice100_runtime/if not
available.importsoundfileassfimportvoice100_runtimetts=voice100_runtime.load("tts_en_mt")waveform,sample_rate,align=tts("Hello, world!",return_align=True)print("/".join(align))sf.write("output.wav",waveform,sample_rate,"PCM_16")
Package: voice2text-deltabot
Voice To Text BotA voice-to-text converter bot for Delta Chat.Installpipinstallvoice2text-deltabotThe bot usesWhisperto extract the text from voice messages,
Whisper requires the command-line toolffmpegto be installed on your system, which is available
from most package managers:# on Ubuntu or Debiansudoaptupdate&&sudoaptinstallffmpegUsageTo configure the bot:voice2text-botinitbot@example.orgSuperHardPassword(Optional)To customize the bot name, avatar and status/signature:voice2text-botconfigselfavatar"/path/to/avatar.png"voice2text-botconfigdisplayname"Voice To Text"voice2text-botconfigselfstatus"Hi, send me some voice message to convert it to text"Finally you can start the bot with:voice2text-botserveTo see the available options, run in the command line:voice2text-bot --help
Package: voiceads
voiceads---------# Install the packageyou can install the package locally in the current folder by using the following command:#from main public PIP repopip install -t ./ voiceads----------## SDK usage:from voiceads import VoiceAdsAIappToken = '<YOUR APP TOKEN>'appType = '<YOUR APP TYPE>'va = VoiceAdsAI()params = {'appName' : '<YOUR APP NAME>','appId': '<YOUR APP ID>','appType': '<YOUR APP TYPE>','appCategory' : '<YOUR APP CATEGORY>'}va.initialize(appToken, appType, params)resp = va.getAd()
Package: voice-analyzer
Voice-analyzerHuman voice analyzer for determining human parameters.Requirementspython 3.7 x64pip (for installation)Installation$ pip install voice-analyzerUsageImportfromvoice_analyzerimport*SexTo determine sex, a ml model trained on the basis of voice frequency is used.
The success rate is ~87%.va_sex=analyzer.Sex()You can use your own model.va_sex=analyzer.Sex("path\\to\\model.h5")Predictionva_sex.predict("path\\to\\file.mp3")The function returns the gender as a string:male (0)female (1)TrainYou can train the model based on your own dataset.fromvoice_analyzerimport*importpandasaspdva_sex=analyzer.Sex()data=[{'sex':0,'frequency':180},{'sex':1,'frequency':220}]df=pd.DataFrame(data)va_sex.train(df,"path\\to\\save\\model.h5")TestsYou can check the percent accuracy of the model on your dataset.fromvoice_analyzerimport*importpandasaspdva_sex=analyzer.Sex()data=[{'sex':0,'frequency':180},{'sex':1,'frequency':220}]df=pd.DataFrame(data)va_sex.tests(df)
Package: voice-annotation-tool
Voice Annotation ToolThis program allows annotation of a list of audio files and exporting it in a similar format asMozilla's CommonVoice dataset.InstallationInstall the package system-wide:pipinstallvoice-annotation-toolUsageLaunch the program:voice-annotation-toolTo get started, create a project by providing the folder with the audio samples and the location of the .tsv file in which the annotations will be stored.The tsv file is exported when the project is saved. Samples added to the audio folder after the project is created are added when the project is opened.For a more detailed overview of the interface and API please refer to thedocumentation.ScreenshotsTranslationTo generate the translation file, run the following command:pyside6-lupdatesrc/voice_annotation_tool/*.-tstranslations/<LANGUAGE>.tsThen open the file in QT Linguist and update the missing fields. After reinstalling the package the translation is updated.
Package: voiceassistant
Voice AssistantOpen-source voice assistant with emphasis on customizability. Create your own skills and personalize your assistant in a simple YAML configuration. Perfect to use in combination withHome Assistant.Unique FeaturesSay trigger word e.g. "Jarvis" anywhere in a sentence, for example,"play music, Jarvis". Few last seconds of audio input are recoded into memory, so when trigger word is said, pre-recorded audio data is also processedSpeech is processed continuously while user is speaking which, for certain requests, results in a very fast action/responseVoice Assistant REST API enables remote control. For example, it can be used to make Voice Assistant say something when Home Assistant automation is triggeredDefine custom skills and extend the nlp "vocubulary" in a simple configuration fileAvailable IntegrationsHow they work?Getting StartedInstallationFirst time configurationPersonalizationEnabling integrationsCreating custom skills
Package: voice-assistant-linux
Voice Assistant for LinuxSay hi to VAL (Voice Assistant for Linux)!DescriptionA virtual assistant for Linux for controlling the operating system graphical user interface by using voice commands.Project task list statusHuman voice recognition.Human voice transcription.Detects voice commands from a defined list and executes it.Debug mode.Works on i3 tiling window manager.Works on Gnome desktop environment.Works on KDE Plasma desktop environment.Works on XFCE desktop environment.Infinite while loop should be implement.WarningThe software run only at once. This should be fixed by implementing the infinity while loop. The problem here is to define a strategy to invoke only the voice command after the user say 'Val'. Furthermore the source code must be returned to wait the 'Val' to be called again.RequirementsLinux kernel.Python >= 3.10 (only because the match case and 'f' strings).PIP3.Shell Script Library.For all Linux distributionsInstall Shell Script Library$curl -fsSL github.com/henrikbeck95/shell_script_library/releases/latest/download/shell-script-library -O$mv ./shell-script-library /usr/local/bin/shell-script-libraryFor a specific Linux distributionArchLinux$sudo pacman -S festival festival-english festival-us python-pyaudioInstallationDevelopment modeInstall Python libraries$pip3 install SpeechRecognition$pip3 install yaml-1.3$pip3 install setuptools$pip3 install wheel$pip3 install twine$pip3 install voice-assistant-linuxClone this repository.$git clone https://github.com/henrikbeck95/voice_assistant_linux.gitBuild the package for PyPi$python3 setup.py sdist bdist_wheelUpload the package to PyPi repository$twine upload dist/*Normal usage modeInstall from PyPi repository$pip3 install voice-assistant-linuxUsageDevelopment modeRun it.$python3 ./src/__init__.pyUninstallationDevelopment modeRemove the Val project folder$rm -fr ./voice_assistant_linux/Normal usage modeUninstall it.$pip3 uninstall voice-assistant-linux
Package: voice-bot-demo
voice-bot-demoAI conversational botInstallationInstall this tool usingpip:pip install voice-bot-demoUsageFor help, run:voice-bot-demo --helpYou can also use:python -m voice_bot_demo --helpFor now, only theAssembly AIspeech-to-text model works fully. To run:export ASSEMBLYAI_API_SECRET=YOUR_KEY_HERE
voice-bot-demo start --speech-text-model assemblyDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd voice-bot-demo
python -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: voicechangerio
RoadmapFeature 1Feature 2Feature 3Nested FeatureSee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the Mozilla Public License 2.0. SeeLICENSE.txtfor more information.(back to top)
Package: voiceClassifier
voiceClassifierA module for classifying voice samples. The layout/Class diagram of package is as follows.Installation:Run this command in terminalpip install voiceclassifierThis package has dependancy on librosa and other audio related packages. Please check the requirement.txt for further details.Please note that this package is extensively tested on Linux enviourment(Ubuntu.)Examples:Let's have a look at some examples to gain more understanding.1. Trainning on your own data :For this you can simply run evaluate.py file from the package if you downloaded zip from github. For this please keep your voice_samples ready. The name of the voice file will be considerd as a class label.Pass list of all your voice_samples path when asked.If you have downloaded the package using pip, then you can train as follow...from voiceClassifier import evaluate
evaluate.train(
               ["/home/rshinde/Desktop/data/Shekhar.m4a","/home/rshinde/Desktop/data/Shubham.m4a",
                "/home/rshinde/Desktop/data/Dhruvesh.m4a","/home/rshinde/Desktop/data/Rohit.m4a"]2. classifying the voice sampleHerewith the code...from voiceClassifier.voiceClassify import VoiceClassify

vc = VoiceClassify()
# to get classes
print(vc.classes_())

# pass voice_samples for actual prediciton...
prediciton,probabilities,classes_ = vc.predict("/home/rshinde/Desktop/Dhruvesh.wav")OutputAudio preprocessed..
(array(['Dhruvesh'], dtype=object),
 array([[0.55461039, 0.05390575, 0.03683959, 0.35464427]]),
 array(['Dhruvesh', 'Rohit', 'Shekhar', 'Shubham'], dtype=object))
Package: voice-comm
Voice CommunicationWith this package you can create programs which can speak to you and recognize your voice adn run commands. (easy eg.: You can create J.A.R.V.I.S from Iron Man :))Supported language is python 3. (Package wrote in 3.9.6)InstallationUse this command:pip install voice-commChange Log0.0.4 (10/09/2021)Added modules option | Load modules / Stop modulesFixing some issueHelpHelp:import voice_comm

voice = voice_comm.VoiceComm(language="en-EN") # Here you can specify rate, volume, voice (this voice is choose from voice tokens [HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens])

voice.load_modules("test")

@voice.add_command(or_required_words=["hi", "hello"], words=["Da4ndo"]) #Here you can specify is_capital_letters_matter [default: False], required_words, words and or_required_words (or_required_words is very similar to required_words but if its contaion more, its don't require all words, just one)
def hello():
    voice.say("Hello word!")

text = voice.recognize() # Here you can specify phrase_time_limit, timeout, snowboy_configuration. Advanced: only ``phrase_time_limit`` and value ``5``

voice.recognize_command(text) # hello function will call if you say ``hello`` or ``hi``How to load module:# File name ``test.py`` | Can be modified.

import datetime

class Mod():
    def __init__(self, engine):
        self.engine = engine
    
    def run(self):
        @self.engine.add_command(is_capital_letters_matter=True, words=["Microsoft"])
        def test(text):
            print(text)
            self.engine.say("Test sikeres!")

        @self.engine.add_command(or_required_words=["idĹ‘", "Ăłra"], words=["hĂˇny", "mennyi"])
        def time():
            time = str(datetime.datetime.now().replace(microsecond=0)).replace("-", ".")
            print(f"[RESPOND] {time}")
            self.engine.say(time)
    
def setup(engine):
    Mod(engine).run()
Package: voice_engine
Voice EngineThe library is used to create voice interface applications.
It includes building blocks such as KWS (keyword spotting), DOA (Direction Of Arrival). There are also elements to measure RMS (dBFS or dB(A)).RequirementspyaudionumpysnowboyInstallationInstall pyaudio, numpy and snowboy, use virtualenv a virtual python environment.sudo apt install python-pyaudio python-numpy python-virtualenv
sudo apt-get install swig python-dev libatlas-base-dev build-essential make
git clone --depth 1 https://github.com/Kitt-AI/snowboy.git
cd snowboy
virtualenv --system-site-packages env
source env/bin/activate
python setup.py build
python setup.py bdist_wheel
pip install dist/snowboy*.whl
cd ..
git clone https://github.com/voice-engine/voice-engine.git
cd voice-engine
python setup.py bdist_wheel
pip install dist/*.whlGet startedTo record audio and search keyword "snowboy", see alsokws_snowboy.pyimporttimefromvoice_engine.kwsimportKWSfromvoice_engine.sourceimportSourcesrc=Source()kws=KWS()src.link(kws)defon_detected(keyword):print('found{}'.format(keyword))kws.on_detected=on_detectedkws.start()src.start()whileTrue:try:time.sleep(1)exceptKeyboardInterrupt:breakkws.stop()src.stop()Building blocksThe library uses gstreamer-likeelementswhich can be linked together as an audio pipeline.
One element can connect to more than one other elements.The topology can be:Source --> ChannelPicker --> KWS          Source --> ChannelPicker --> KWS --> Alexa
  |                          /\
  V                        /   \
 DOA                   Alexa   Google Asissitant