Saravanan666/pypi-org huggingface dataset:
Package: audiomass
Audiomass - wrapper for multiple audio conversion libraries.Audiomassis a command line audio wrapper of the Flac, Lame, Vorbis-tools,
and FFmpeg libraries. It supports conversions of different audio file formats at
a time and the ability to convert even groups of files in a directory, saving the
output in a specific folder.For each input file, audiomass writes to a filename based on the name of the
input file. If the (-o) option is not specified, it save any file at
same destination as input file.It is possible to choose one or more conversion formats, based on the imported
files, and then choose the respective bitrates.Dependenciespython >=3.6flaclamevorbis-tools(include: oggenc, oggdec)ffmpegUsageaudiomass [-h]
          [--version]
          [-c]
          [-d INPUT_FOLDER, --directory INPUT_FOLDER]
          [-f FILE [FILE.. FILE.. ...], --files FILE [FILE.. FILE.. ...]]
          [-o OUTPUT_FOLDER, --output OUTPUT_FOLDER]ExamplesConvert a single audio file and save it into same directory:audiomass -f '/home/Name/my Music/audiofile.wav'Convert a bunch of audio files inside a folder and save them in another folder:audiomass -d '/MyDirName/Music' -o '/MyOtherDir/converted'Convert a queue of audio files and save them in a specified folder:audiomass -f '..FILE 1.wav' '..FILE 2.mp3' '..FILE 3.flac' '...' -o '/MyOtherDir/converted'Installationpython3 -m pip install audiomassLicense and CopyrightCopyright © 2010 - 2022 Gianluca PernigottoAuthor and Developer: Gianluca PernigottoMail:jeanlucperni@gmail.comLicense: GPL3 (see LICENSE file in the docs folder)
Package: audiomath
audiomath is a package for Python programmers who want to record, manipulate,
visualize or play sound waveforms. It allows you to:Represent sounds as numeric arrays via the third-party Python packagenumpy(required). The arrays are contained within high-level objects, allowing common
operations to be performed with minimal coding—for example: slicing and
concatenation in time, selection and stacking of channels, resampling, mixing,
rescaling and modulation.Plot the resulting waveforms, via the third-party Python packagematplotlib(optional).Read and write uncompressed.wavfiles (via the Python standard library).Read other audio formats using the third-party AVbin library (binaries are
included in the package, for a selection of platforms).Record and play back sounds using the third-party PortAudio library (binaries
are included in the package, for a selection of platforms).Play sounds using the alternative PsychPortAudio back-end, from the (optional)psychtoolboxpackage, allowing very precise control of latencyPlug in other recording/playback back-ends with moderate development effort.Seehttps://audiomath.readthedocs.iofor full documentation and installation instructions.If you use audiomath in your work, please cite:Hill NJ, Mooney SWJ & Prusky GT (2021). audiomath: a neuroscientist’s sound
toolkit. Heliyon 7(2):e06236.https://doi.org/10.1016/j.heliyon.2021.e06236@article{hill2020audiomath,
    author  = {Hill, N. Jeremy  and  Mooney, Scott W. J.  and  Prusky, Glen T.},
    title   = {{audiomath}: a Neuroscientist's Sound Toolkit},
    journal = {Heliyon},
    volume  = {7},
    number  = {2},
    pages   = {e06236},
    month   = {February},
    year    = {2021},
    date    = {2021-02-10},
    doi     = {10.1016/j.heliyon.2021.e06236},
    url     = {https://doi.org/10.1016/j.heliyon.2021.e06236},
}
Package: audio-metadata
audio-metadataaudio-metadatais a library for reading and, in the future, writing audio metadata.Why another audio metadata library? / Why not just use mutagen et al?Clean and understandable code, nice API, and good UX (user experience) are the focal points of audio-metadata.
One or more of these things I feel are lacking from already existing alternatives
enough to want to write something from scratch with them in mind.
Also, there are certain features not present in other solutions that would be prohibitively painful to add.So, why should I use it?Features and functionality that set it apart:Uses the Python standard load(s)/dump(s) API.Can load filepaths, os.PathLike objects, file-like objects, and bytes-like objects.Metadata objects look like a dictandact like a dict.Some common libraries shadow the representation of a dict
and/or dict methods but do not behave like a dict.Supports attribute-style access that can be mixed with dict key-subscription.All metadata objects have user-friendly representations.This includeshumanizedrepresentations of certain values
like filesize, bitrate, duration, and sample rate.>>> import audio_metadata

>>> metadata = audio_metadata.load('05 - Heart of Hearts.flac')

>>> metadata
<FLAC ({
	'filepath': '05 - Heart of Hearts.flac',
	'filesize': '44.23 MiB',
	'pictures': [],
	'seektable': <FLACSeekTable (37 seekpoints)>,
	'streaminfo': <FLACStreamInfo ({
		'bit_depth': 16,
		'bitrate': '1022 Kbps',
		'channels': 2,
		'duration': '06:03',
		'md5': '3ae700893d099a5d281a5d8db7847671',
		'sample_rate': '44.1 KHz',
	})>,
	'tags': <VorbisComment ({
		'album': ['Myth Takes'],
		'artist': ['!!!'],
		'bpm': ['119'],
		'date': ['2007'],
		'genre': ['Dance Punk'],
		'title': ['Heart of Hearts'],
		'tracknumber': ['05'],
	})>,
})>

>>> metadata['streaminfo']
<FLACStreamInfo ({
	'bit_depth': 16,
	'bitrate': '1022 Kbps',
	'channels': 2,
	'duration': '06:03',
	'md5': '3ae700893d099a5d281a5d8db7847671',
	'sample_rate': '44.1 KHz',
})>

>>> metadata.streaminfo.bitrate
1022134.0362995076

>>> metadata.streaminfo['duration']
362.9066666666667

>>> metadata['streaminfo'].sample_rate
44100Installationpip install -U audio-metadataUsageFor the release version, see thestable docs.For the development version, see thelatest docs.The high-level API and basic usage are covered, but more advanced features/functionality need documentation.TODOIf you're willing tocontributeyour time to work onaudio-metadata, you can:Post in theDevelopmentcategory on theDiscourse forum.Browse and comment onissuesorpull requests.Open an issuewith a bug report or feature request.See currentprojects.Contact me by email atmail@thebigmunch.me.AppreciationShowing appreciation is always welcome.ThankGet your own thanks inbox atSayThanks.io.ContributeContributeby submitting bug reports, feature requests, or code.Help Others/Stay InformedDiscourse forumReferrals/Donations
Package: audiomind
AudioMindOverviewAudioMind is a Python-based solution designed to extract meaningful insights from audio files. By leveraging whisper and LLMs, the platform transcribes and summarizes audio content, making it easier to derive actionable information.StackLLMOpenAISpeech to TextWhisper (Openai API) [DEFAULT]Whisper (On-Device)Current SolutionsCreate a journal entry from your voice note.GoalsTranscribe audio files to text.Summarize the transcribed text.Easy to integrate and use.Get Insights from any audio file, including podcasts , interviews, lectures, etc.Solve actual problems.InstallationPrerequisitesPython 3.xpipUse PIP PackageSteps to InstallClone the Repositorygitclonehttps://github.com/onlyoneaman/audiomind.gitcdaudiomindCreate a Virtual Environmentpython3-mvenv.venvActivate the virtual environment:Unix or MacOSsource.venv/bin/activateWindows.\.venv\Scripts\activateInstall Dependenciespipinstall-rrequirements.txtEnvironment VariablesCopy.env.templateto.env.cp.env.template.envOpen.envand provide your OpenAI API key:OPENAI_API_KEY=your_openai_api_key_here
DREAMBOAT_API_KEY=your_dreamboat_api_key_here // optionalRun the Applicationpython3-maudiomindUsagePlace the audio files in the/exmaplesfolder and run theaudio_to_journal.pyscript. The script will transcribe the audio and summarize it.python3-maudiomind--fileexamples/1.mp3You can add some information about yourself inperson.txtfile.
Audiomind will use this information too while creating the journal entry.RoadmapTranscribe audio files to text.Summarize the transcribed text.Easy to integrate and use.Get Insights from any audio file, including podcasts , interviews, lectures, etc.Create a journal entry from your voice note.Improve the journal entry.Create a summary of a podcast episode.Create a summary of a lecture.Create a summary of a meeting.ContributingFeel free to submit issues and enhancement requests.LicenseMITEnjoy using AudioMind!
Package: audio-mixer
# 项目作用
为wav格式音频增加binaural beats双耳节拍效果# 使用说明
## 快速制作双耳节拍音乐
### 1. git clone 项目至本地，并切换至项目路径### 2. 安装audio_mixer包
执行`pip install audio_mixer`### 3. 执行`bianural_beats_generator`模块
执行`python -m audio_mixer.workflow.bianural_beats_generator`### 4. 按照命令行提示符输入指定参数PS:若在未安装audio_mixer包的情况下使用：需要先切换至audio_mixer包所在的父目录下，并执行以下命令：`python -m audio_mixer.workflow.bianural_beats_generator`（注：当前仅支持wav格式文件，请进行格式转换后再使用)# 开发使用的python版本
python –version: Python 3.6.6# pip requirement 依赖包
依赖包文件：audio_mixer/config/requirement.txt## 项目代码拓扑
![代码拓扑](https://github.com/willerhehehe/audio_mixer/blob/master/img/audio_mixer.svg)
## 工作流
![工作流](https://github.com/willerhehehe/audio_mixer/blob/master/img/workflow.svg)
Package: audiomodels
Audio Models Stattus4Audio Models and statistical tools used in Devs for Stattus4 Start-Up of Water Wasting DetectionNote: this is a Alpha Version.Statistical AnalysisFilteringSpectralInverse FilteringInverse Filtering with Tensorflow Optimization and Stochastic Gradient DescentAudio Deep LearningDeep Neural Networks for Tagging Audio based on binary tagged database using Audio Features Models.TODO: Road-Map Milestones:Implementar unidades.escolher um conjunto de parametros (tamanho de filtro da convolução, número de layers, tamanho do batch de dados por atualização da apredizagem).Validação do funcionamento do modelo com poucos dados triviais (ruido de duas frequencias diferentes a principio). OverfitAquiCross-validation.Check-pointing.Neural Network Selection Criteria.Installation
Package: audio-mouse
audio-mouseControl mouse with audio input.This project is intended to use a guitar as an input source and use the notes
played on it to simulate mouse movements. But you can always use your voice and
control the mouse movements as long as you can hit those pitches which are
configured to trigger the mouse eventsInstallpip install audio-mouseExamplesBasic examplefrommouseimportLineIn,Mousedefmain():line_in=LineIn()mouse_handler=Mouse()stream=line_in.record()line_in.detect_pitch(mouse_handler,stream)if__name__=="__main__":main()Configuring notes/pitchesYou can configure the notes you want to act as trigger for specific mouse
movements for example, you can configure that when a pitch of A2 is played the
mouse pointer moves up etc.frommouseimportLineIn,Mousedefmain():line_in=LineIn()mouse_handler=Mouse()mouse_handler.UP="A2"mouse_handler.DOWN="B2"mouse_handler.RIGHT="G3"# Events that are not configured will use the defaults (check constants.py)stream=line_in.record()line_in.detect_pitch(mouse_handler,stream)if__name__=="__main__":main()Configuring X/Y mouse speedyou can configureMouse.MOUSE_XandMouse.MOUSE_Yto update the speed of
the mouse.frommouseimportLineIn,Mousedefmain():line_in=LineIn()mouse_handler=Mouse()mouse_handler.MOUSE_X=10mouse_handler.MOUSE_Y=10stream=line_in.record()line_in.detect_pitch(mouse_handler,stream)if__name__=="__main__":main()In actionCheckout the 30 seconds video linked below for a demo. I've played few of the
notes from the major pentatonic scale on 5th fret of a guitar and each note is
a mouse event.https://imgur.com/a/ECJwEFyScribblesLooks like we can make a visual representation of what we play and speak and
use it for some analysis maybe? IDK. Play something and without listening to it
one can visually analyze some patterns maybe? Can make a note transcriber.
Maybe something like converting the recorded pitches to notes and then tabs? IDKLicenseMIT
Package: audio-normalizer
AudioNormalizerThis is a normalizer for mp3 and wav files. Its a final project for Havard CS50P.The approach in this program to normalize audio differs in comparison to most other programs that implement this task. Most normalizers amplify an audio signal  until the highest transient reaches 0 dB. The downside of this approach is that the audio signal can only be amplified based on a single maximum transient.     In other words, the whole normalizing process depends on one transient, which is inefficient.This program, however, doesn't depend on a single transient. It rather splits the original signal into four separate signals (left positive, left negative,     right positive, and right negative). Then it uses an algorithm that finds several transients that exceed a calculated value based on the audio file's           duration and actual volume. For each split signal, a copy is being created, a dummy signal. In the dummy signals, the found transients are turned into the       value of zero. The next task is to find the new maximum transient in each modified dummy signal like any other simple normalizer. The new maximum value is       then saved for the final amplification.At this stage, the program finds all amplitudes in the original split signals where the previously found transients are sitting. The split audio signals are     then amplified based on the new maximum transient. All amplitudes that exceed (clip) above 0 dB are lowered to 0 dB without manipulating the original signal     except for the volume.Finally, all the amplified split signals are merged together and are saved as the original file name and type. All the ID3 tags, including the album cover,     are also maintained in the new normalized file.As a result, the user gets an audiofile normalized to 0 dB without losing noticeable dynamic range or the overall audio quality.Installationpipinstallaudio_normalizerUsageimportaudio_normalizeraudio_normalizer.open_window()
Package: audioperm
Audioperm, a python library for generating different permutations of audible segments from audio files.AudiopermA python library for generating different permutations of audible segments from audio files.pip install audiopermUse:Silence Removal from AudioAudio / Speech augmentationWord segmentationWord level permutation generationAdd new synthetic data for deep learningSpeaker recognition, Speaker verification, Audio classification, Audio fingerprintingDocumentation:https://zabir-nabil.github.io/audioperm/Source Code:https://github.com/zabir-nabil/audiopermWord segmentationfromaudiopermimportAudioPermfromaudioperm.utilsimportsave_audioap=AudioPerm("i_love_cats.m4a")label="i love cats"words=ap.word_segments()label_words=label.split()fori,winenumerate(words):save_audio(w,label_words[i]+".wav")cats.wav  i_love_cats.m4a  i.wav  love.wavWord-level permutationimportnumpyasnpfromaudiopermimportAudioPermfromaudioperm.utilsimportsave_audioap=AudioPerm("i_love_cats.m4a")ap.word_segments(return_words=False)perm_sentences=ap.permute(n_permutations=5)fori,sinenumerate(perm_sentences):save_audio(s,f"perm_{i}.wav")cats.wav	   i.wav       perm_1.wav    perm_4.wav
i_love_cats.m4a    love.wav    perm_2.wav    perm_0.wav  
perm_3.wavpermutationson multiple filesfromaudiopermimportread_audio,word_segments,permutationsap=read_audio(["bangla_demo.wav","i_love_cats.m4a"])out=word_segments(ap)perms=permutations(out,n_permutations=5)Fixed-length segmentsGenerate fixed length audible segments (with permutation/augmentation)fromaudiopermimportfixed_len_segmentsfixed_len_segments("bangla_demo.wav",return_segments=False,save_path="fls_out",save=True,segment_size=0.5)out=fixed_len_segments("bangla_demo.wav",return_segments=True,max_segments=5,permute=True,save=False,segment_size=0.5)OthersTo run the code:Google ColabAny contribution is welcome.ContributorsContribution guideTested with:python3.7python3.8Internal audio representation:PCM 16float32TO-DO:multi-channel audioaugmentationmulti-processinggpu-support
Package: audio-plot
Converts a line graph to sound and returns an object that can be played
in Jupyter notebook or Google Colab.Values are represented by pitches, and the timeline is represented by left and right pans.It was created to make data science fun for the visually impaired.Dependencyffmpeg library is needed to provide a descriptive guide to the graph.Google ColabNo additional installation is required.Linux$ sudo apt-get install libavformat-dev libavfilter-dev libavdevice-dev ffmpegOthersIt's under investigation. If you know of any, please make a pull request. Thanks.Install$ pip install audio-plot  # !pip install audio-plot for Colab or NotebookUsageSee and run demo notebook also.y = np.sin(np.arange(0, np.pi*2, 0.1))
yy = np.array([y, -y]).T
plot(yy)  # audio control will be appeared on notebook.[The audio is as follows for this example]tts > minimum value is -1.0
(Low sinusoidal sound)
tts > maximum value is 1.0
(High sinusoidal sound)
tts > line 1
(Sound in response to changes in the line 1 graph)
tts > line 2
(Sound in response to changes in the line 2 graph)Example Results PageSupplementIf using Jupyter Notebook with a screen reader is inconvenient for you, you may want to consider using the following NVDA add-on.https://github.com/mltony/nvda-browser-navUse in scriptfrom pydub.playback import play

# generate graph sound
audio = audio_plot.plot(two_inverted_sin)

# play
play(audio)

# save to audio file
audio.export("graph.wav", format="wav")For contributerUpdate PyPI$ nosetests -vs
$ pip install twine # if necessary
$ cat ~/.pypirc  # if necessary
[distutils]
index-servers = pypi

[pypi]
repository: https://upload.pypi.org/legacy/
username: YOUR_USERNAME
password: YOUR_PASSWORD
$ rm -rf audio_plot.egg-info dist # if necessary
$ python setup.py sdist
$ twine upload --repository pypi dist/*
$ pip --no-cache-dir install --upgrade audio-plothttps://pypi.org/project/audio-plot/ContributingFork the repository on GithubCreate a named feature branch (like add_component_x)Write your changeWrite tests for your change (if applicable)Run the tests, ensuring they all passSubmit a Pull Request using GithubLicenseMIT
Package: audio-plot-lib
audio-plot-libThis library provides graph sonification functions
and has been developed for a project named "Data science and machine learning resources for screen reader users".
Please refer to the project page for more details.https://hassaku.github.io/DS-and-ML-with-screen-reader/Use in Google Colab (recommended)Try the following example.Use in script (option)InteractiveGenerate html with graphs that can be displayed interactively in a browser.Dependency$ pip install audio-plot-lib
$ pip install beautifulsoup4Exampleimport audio_plot_lib as apl
# apl.interactive.set_language("ja")  # Option to change language. Default is "en"
apl.interactive.plot([0, 1, 2, 3, 2, 1, 0], script_name=__file__)Open the html file with the same name as the script name in your browser, and if you find a button that reads 'push here to unmute graph', try moving the mouse cursor appropriately after pressing the button. If not, try reloading the page.PlayableGenerate an audio file with sonified graphs.Dependency$ sudo apt-get install libavformat-dev libavfilter-dev libavdevice-dev ffmpeg
$ pip install audio-plot-libExampleimport audio_plot_lib as apl
from pydub.playback import play

# generate graph sound
audio = apl.playable.plot([0, 1, 2, 3, 2, 1, 0], duration=300, autoplay=False)

# play
play(audio)

# save to audio file
audio.export("graph.wav", format="wav")For contributerUpdate PyPI$ python -m unittest discover tests
$ pip install twine # if necessary
$ cat ~/.pypirc  # if necessary
[distutils]
index-servers = pypi

[pypi]
repository: https://upload.pypi.org/legacy/
username: YOUR_USERNAME
password: YOUR_PASSWORD
$ rm -rf audio-plot-lib.egg-info dist # if necessary
$ python setup.py sdist
$ twine upload --repository pypi dist/*
$ pip --no-cache-dir install --upgrade audio-plot-libhttps://pypi.org/project/audio-plot-lib/ContributingFork the repository on GithubCreate a named feature branch (like add_component_x)Write your changeWrite tests for your change (if applicable)Run the tests, ensuring they all passSubmit a Pull Request using GithubLicenseMIT
Package: audioProcessingWisrovi
ProcessAudioLibreria python para extraer caracteristicas a audiosInitializationpipinstallaudioProcessingWisroviAProcessAudioobject should be created and use its attributes.fromaudioProcessingWisroviimportProcessAudioprocessAudio=ProcessAudio()processAudio.set_data("audio_file.wav")caracteristicas=processAudio.get_all()# Extrayendo caracteristicas audios, salen 26 caracteristicasProcessAudio methodsget_all(): returns all featuresconfigured folder, returns the new filename.
Package: audio-purifier
Audio PurifierAudio Purifieris a utility package that enables silent parts
removal from audio, exporting as new files. Inspired byfaster-whisper, which used SileroVAD for audio processing..RequirementsPython 3.9 or greaterYou can open and saveWAVfiles with pure python. For opening
and saving non-wav files – like mp3 – you'll needffmpegorlibavin order to install extra package
(PyDub).InstallationThe module can be installed fromPyPI:pipinstallaudio-purifierTo have the ability to export output file as other than 'wav' format, please installffmpegto your run time and install PyDub library as well.pipinstallaudio-purifier[extra]Other installation methods (click to expand)Install the latest dev version from github (or replace@masterwith a [@release_version]pipinstallgit+https://github.com/huynguyengl99/audio-purifier@masterDocumentPlease visitAudio Purifier docfor
documentation.
Package: audio-recorder-streamlit
Audio record streamlitThis streamlit component allows to register an audio utterence from a user.Installationpip install audio-recorder-streamlitUsageimportstreamlitasstfromaudio_recorder_streamlitimportaudio_recorderaudio_bytes=audio_recorder()ifaudio_bytes:st.audio(audio_bytes,format="audio/wav")Recording parametersYou can adjust the recording parametersenergy_thresholdandpause_threshold:energy_threshold: The energy recording sensibility above which we consider
that the user is speaking. If it is a float, then this is the energy
threshold used to automatically detect recording start and recording end.
You can provide a tuple for specifying different threshold for recording
start detection and recording end detection.pause_threshold: The number of seconds to spend belowenergy_levelto
automatically stop the recording.sample_rate: Sample rate of the recorded audio. If not provided, this will use the
default sample rate (https://developer.mozilla.org/en-US/docs/Web/API/AudioContext/AudioContext).# The recording will stop automatically# 2 sec after the utterance endaudio_bytes=audio_recorder(pause_threshold=2.0,sample_rate=41_000)Styling parametersYou can adjust the button style parameters:Text: specify the text displayed next to the iconIcon: specify the icon among the font awesome solid icons
(https://fontawesome.com/search?o=r&s=solid)Color: specify the neutral color and recording color.Size: specify the icon size using font awesome sizing
(https://fontawesome.com/docs/web/style/size).audio_bytes=audio_recorder(text="",recording_color="#e8b62c",neutral_color="#6aa36f",icon_name="user",icon_size="6x",)Frequently Asked QuestionHow can I record for a fixed duration ?You can record for a fixed duration by setting theenergy_threshold=(-1.0, 1.0)so that the recorder considers that you are speaking at the beginning and then
you are never speaking from this point on.Then simply setpause_thresholdto your desired recording length.# Records 3 seconds in any caseaudio_bytes=audio_recorder(energy_threshold=(-1.0,1.0),pause_threshold=3.0,)