Saravanan666/pypi-org huggingface dataset:
Package: audioanalytics
Components of the AudioAnalytics Package: 1) Extract Feature - It will return the feature values in a Dataframe. 2) Model_Load - It loads the model from backend.3) Predict - Predicts the Emotion by conducting the analysis of the provided audio.4) Summary Report - A report where probabilities are displayed of 4 Emotions.NOTE : THE AUDIO FILE INPUT FORMAT SHOULD BE .wav EXTENSION. ANY OTHER AUDIO FILE FORMAT IS NOT SUPPORTED FOR 0.1 VERSION.
Package: audio-augmentations
Audio AugmentationsAudio augmentations library for PyTorch for audio in the time-domain, with support for stochastic data augmentations as used often in self-supervised / contrastive learning.UsageWe can define several audio augmentations, which will be applied sequentially to a raw audio waveform:from audio_augmentations import *

audio, sr = torchaudio.load("tests/classical.00002.wav")

num_samples = sr * 5
transforms = [
    RandomResizedCrop(n_samples=num_samples),
    RandomApply([PolarityInversion()], p=0.8),
    RandomApply([Noise(min_snr=0.3, max_snr=0.5)], p=0.3),
    RandomApply([Gain()], p=0.2),
    RandomApply([HighLowPass(sample_rate=sr)], p=0.8),
    RandomApply([Delay(sample_rate=sr)], p=0.5),
    RandomApply([PitchShift(
        n_samples=num_samples,
        sample_rate=sr
    )], p=0.4),
    RandomApply([Reverb(sample_rate=sr)], p=0.3)
]We can return either one or many versions of the same audio example:transform = Compose(transforms=transforms)
transformed_audio =  transform(audio)
>> transformed_audio.shape[0] = 1audio = torchaudio.load("testing/classical.00002.wav")
transform = ComposeMany(transforms=transforms, num_augmented_samples=4)
transformed_audio = transform(audio)
>> transformed_audio.shape[0] = 4Similar to thetorchvision.datasetsinterface, an instance of theComposeorComposeManyclass can be supplied to a torchaudio dataloaders that accepttransform=.OptionalInstall WavAugment for reverberation / pitch shifting:pip install git+https://github.com/facebookresearch/WavAugment
Package: audiobook
👉 CLI tool to listen to your favourite book in Python 👈InstallationInstall usingpypiUsing pippip install audiobookDocumentationCheck the full documentation here:-Read the docsContributingCheck theContributingfor the contribution guidelines.Supported File FormatsFile FormatSupportedPDF✅TXT✅EPUB✅MOBI✅HTML✅DOCX✅ODT✅Changelog and Release NotesCheck the changelog and release noteshereContributors
Package: audiobook-dl
audiobook-dlCLI tool for downloading audiobooks from online sources.Supported Servicesaudiobook-dl currently supports downloading from the following sources:audiobooks.comBlinkistChirpeReolenEverand (previously Scribd)LibrivoxNextoryOverdrive / LibbyPodimoSaxoStorytel/MofiboYourCloudLibraryMore infoInstallationaudiobook-dl can be installed from the repo itself or through pip.To get the newest stable version with pip run:pipinstallaudiobook-dlIf you want to use the newest version (can be unstable) run:pipinstall"git+https://github.com/jo1gi/audiobook-dl.git"orgitclonehttps://github.com/jo1gi/audiobook-dl.gitcdaudiobook-dl
python3setup.pyinstallSome features requireffmpegwhich can be installed
through most package managers or fromffmpeg.org/download.html.AuthenticationCookiesaudiobook-dl uses Netscape cookie files for authentication in most cases. I usethisextension to export my cookies
from the browser.Cookies can be placed in current dir ascookies.txtor be given with the--cookieargument.LoginSome sourcessupport authentication through login with
username and password (and sometimes library). Use the--usernameand--passwordarguments or enter them through an interactive prompt.Downloading audiobooksaudiobook-dl-c<cookiefile><url>Most sites require you to provide the listening page not not just the
information pageArgumentsArgumentValueurlThe url of the page where you listen to the audiobook-c/--cookiePath to a Netscape cookie file--combineCombine all output files into a single file (requires ffmpeg)--coverOnly download cover-d/--debugPrint debug information-o/--outputOutput location--remove-charsList of characters that will be removed from output path--no-chaptersDon't include chapters in output file--output-formatOutput file format--verbose--ffmpegShow ffmpeg output in terminal--usernameUsername to source (Required when using login)--passwordPassword to source (Required when using login)--librarySpecific library on service (Sometimes required when using login)OutputBy default, audiobook-dl saves all audiobooks to{title}relative to the
current path. This can be changed with the--outputargument. Path can be
customized by audiobook with the following fields:titleauthorseriesnarratorNot all fields are available for all audiobooks.The file extension can be changed with the--output-formatargument.ContributionsIssues, bug-reports, pull requests or ideas for features and improvements arevery welcome.DonationsIf you like the project, please consider donating:Ko-fiBuy me a CoffeeCryptocurrenciesBitcoin: bc1qrh8hcnw0fd22y7rmljlmrztwrz2nd5tqckrt44Bitcoin Cash: qp6rt9zx7tfyu9e4alxcn5yf4re5pfztvu8yx0rywhDash: XfgopGkj4BBpuzsUvrbj9jenXUZ6dXsr3JEtherium: 0x8f5d2eb6d2a4d4615d2b9b1cfa28b4c5b9d18f9fLitecoin: ltc1qfz2936a04m2h7t0srxftygjrq759auav7ndfd3Monero: 853tLAbK5wQ93mdj884C31JGKBUEJCpM25gEjGGLnuVDc8PEDMJi6uC5Vcz9g37K2PeT8FY1bjEveUWqJXNPotFRLwLnn9a
Package: audiobook-tags
Audiobooks from mp3 with broken tagsFix mp3 files tags to convert them into iTunes/iPhone audiobooks..Fixes sort order.Supports messed encoding like cyrillic Win1251.Details inmy blog's article.InstallationYou should have Python installed.python -m pip install audiobook-tagsUsageaudiobook-tags [-h] [--encoding ENCODING] [--extension EXTENSION] [--set-tag [SET_TAG ...]] [--track-num TRACK_NUM] [--title-prefix TITLE_PREFIX] [--dry] [folder]

Fixes mp3 tags for iOS audiobooks.

positional arguments:
  folder                Folder to process. By default current folder.

options:
  -h, --help            show this help message and exit
  --suffix SUFFIX, -s SUFFIX
                        Files suffix. By default mp3
  --encoding ENCODING, -e ENCODING
                        mp3 tags encoding. "none" if you do not need mp3 tags encoding fix. By default "cp1251".
  --tag [SET_TAG ...], -t [SET_TAG ...]
                        Change mp3 tag to specified string. Format "tag-name/tag-value".
  --num TRACK_NUM, -n TRACK_NUM
                        Sort files and set mp3 tag `track_num`:
                          --num="name" - sort by names;
                          --num="tag-<TAG>" - sort by mp3 tag with name <TAG>.
                            For example to sort by title tag use --num="tag-title".
  --prefix TITLE_PREFIX, -p TITLE_PREFIX
                        Add prefix to title tags. By default `{track:04} - ` if `--num` and no prefix if not.
  --dry, -d             Dry run without changing files.Example:audiobook-tags --tag="album_artist/Юрий Заборовский (Ардис)" --num="name" --prefix=""converts allmp3files in current folder and subfoldersfix encoding supposing that original encoding wasWindows 1251change tag album artist.settrack_nummp3 tag to file number as ordered by file name.
But do not add the track number to the title (--prefix=""").DevelopmentOS DependenciesMacOSbrew update
brew install libmagicPython dependenciesNote the dot before./activate.sh:. ./activate.shWe useeyeD3to work with mp3 tags.
Package: audiocaps-download
AudioCaps DownloadDISCLAIMER: This repository is a modified version of theAudioSet Downloadrepository.This repository contains code for downloading theAudioCapsdataset.
The repository isnot officially affiliatedwith the AudioCaps dataset.RequirementsPython 3.9 (it may work with other versions, but it has not been tested)Installation# Install ffmpegsudoaptinstallffmpeg# Install audiocaps-downloadpipinstallaudiocaps-downloadUsageThe following code snippet downloads the complete dataset in WAV format, and stores it in thetestdirectory.fromaudiocaps_downloadimportDownloaderd=Downloader(root_path='audiocaps/',n_jobs=16)d.download(format='wav')# it will cross-check the files with the csv files in the original repositoryDownload updated Dec 2023As of December 2023, the repository has been used to download the dataset in WAV format. The following table shows the number of files downloaded.Difference between the original training set and the updated one: 4651
Difference between the original validation set and the updated one: 255
Difference between the original test set and the updated one: 460
Training set: 45187
Validation set: 2220
Test set: 4415SplitNumber of files (using this repo)Number of files (original)Training4518749838Validation22202475Test44154875The missing files may be due to the missing youtube videos, please open an issuehereif you find any bug.ImplementationThe main class isaudiocaps_download.Downloader. It is initialized using the following parameters:root_path: the path to the directory where the dataset will be downloaded.n_jobs: the number of parallel downloads. Default is 1.The methods of the class are:download(format='vorbis', quality=5): downloads the dataset.The format can be one of the following (supported byyt-dlp--audio-formatparameter):vorbis: downloads the dataset in Ogg Vorbis format. This is the default.wav: downloads the dataset in WAV format.mp3: downloads the dataset in MP3 format.m4a: downloads the dataset in M4A format.flac: downloads the dataset in FLAC format.opus: downloads the dataset in Opus format.webm: downloads the dataset in WebM format.... and many more.The quality can be an integer between 0 and 10. Default is 5.load_dataset(): reads the csv files from the original repository. It is not used externally.download_file(...): downloads a single file. It is not used externally.
Package: audio-caption-crawler-and-processor
Audio, Caption Crawler and ProcessorDownloads and processes the audios and captions(subtitles) from Youtube videos for Speech AIRequirementsCurrently requires python >= 3.6FFmpegTo Usefrom accp import ACCP

  playlist_name=""
  playlist_url = ""

  accp = ACCP(playlist_name, playlist_url)
  accp.download_audio()    #download audio from youtube

  accp.download_caption()  #download captions from youtube

  accp.audio_split()       #splitResultsdatasets
    |- playlist name
        |- metadata.csv
        |- alignment.json
        |- wavs
             ├── 1.wav
             ├── 2.wav
             ├── 3.wav
             └── ...andmetadata.csvshould look like:{
    0001.wav|그래서 사람들도 날 핍이라고 불렀다.,
    0002.wav|크리스마스 덕분에 부엌에 먹을게 가득했다.,
    0003.wav|조가 자신이 그 사람이라고 나섰다.,
    ...
}andalignment.jsonshould look like:{
    "./datasets/playlist name/wavs/0001.wav": "그래서 사람들도 날 핍이라고 불렀다.",
    "./datasets/playlist name/wavs/0002.wav": "크리스마스 덕분에 부엌에 먹을게 가득했다.",
    "./datasets/playlist name/wavs/0003.wav": "조가 자신이 그 사람이라고 나섰다.",
}
Package: audio-cast
Application to cast streams to chromecastNote: Designed to work with Rpi 4. Any installation process works for the Rpi4.Production installationUsing pip (Windows/Linux/osX)This requires to havepython3installed andpipinstalled. Optionally alsovenv.(Optional) Create virtual environment to avoid dependency issues.Example using venv:Create:python3 -m venv <DIR>Start: linux -source <DIR>/bin/activate- windows -<DIR>/Scripts/activateStop:deactivateTo clear delete the directory and create againrm -r <DIR>Install package from pip:Linux/osX:python3 -m pip install audio_castWindows:py -m pip install audio_castInstall required applications:Install vlc:Linux/osX has 2 alternatives:Install required libraries:sudo apt install vlc-binsudo apt install vlc-plugin-baseInstall VLCsudo apt install vlcWindows: Install vlc from their website.(Only Linux/osX) Installlibportaudio2:sudo apt install libportaudio2Install a WSGI server.Waitress:pip install waitressRun WSGI server.Example:waitress-serve --call --host=localhost --port=5000 'audio_cast:create_app'Using apt (Linux)Dev UsageWhen using VS Code, just run the debugger.Optional: Use a virtual environment.When starting from termimal.Set environment variable forFLASK_APPtoaudio_cast.Linux/osX:export set FLASK_APP=audio_castWindows cmd:set FLASK_APP=audio_castWindows powershell:$Env:FLASK_APP = "audio_cast"Launch the server withpython -m flask run.CheatsheetTo upload to pypi:Build:py -m buildDeploy:py -m twine upload dist/*To run locally:Set environment variableFLASK_APP=audio_castTo run after installation from package:Install waitresspython3 -m pip install waitressRun waitresswaitress-serve --call --host=localhost --port=5000 'audio_cast:create_app'Virtual environment:Create:python3 -m venv <DIR>Start: linux -source <DIR>/bin/activate- windows -<DIR>/Scripts/activateStop:deactivateTo clear delete the directory and create againrm -r <DIR>Install packages fromrequirements.txtpip install -r requirements.txt.Rpi stepsInstall pip using aptsudo apt install python3-pipInstall venv using aptsudo apt install python3-venv.Create virtual environment and activate it.Install waitress.Run waitress, run it without host so it is exposed.Install vlcsudo apt install vlcHopefully this can be changed to either incorporating the libvlc libraries or installing a part of vlc. Right now it's around 1 gb, which seems too much for the use needed.Or install vlc-bin and vlc-plugin-base.sudo apt install vlc-binandsudo apt install vlc-plugin-base. It's smaller but still big. Aroung 700MB.Still there might be a better way.Install PortAudio using aptsudo apt install libportaudio2.
Package: audio-cat
audio-cathigh-level api to split and categorize audio samples, intended for data collectionInstallationpip install audio-catorgit clone https://github.com/nathanielCherian/audio-cat.git
pip install -e audio-cataudio-cat implements ffmpeg to proccess download ithereand add it to your path (from enviornment variables)CLI Usageaudio-cat can be used by command line for quick and easy sound wrangling$ audio-cat [command] --optionalsDownloadand chop sound from a youtube video$ audio-cat download [URL] [TITLE]

URL       video url
TITLE     name to save audio

--des     destination default=audio
--split   split interval default=2500 (set -1 for download only)
--blurb   full audio storage default=FULL_AUDIO
--dataset create dataset for samplesCreate Datasetfrom chopped samples$ audio-cat dataset [PATH] [TITLE]

PATH      path to directory containing segments
TITLE     desired title of dataset

--des     destination default=datasets
Package: audio-classification-features
Audio Classification FeaturesIt is made to extract the features from any audio dataset. User's have to provide location of the dataset folder and this library will produce x and y npy files. We also provide custom built Keras model for training.Installation$pipinstallaudio_classification_featuresUsageMaking Training Datasetfromaudio_classification_featuresimportaudio_featuresasafaf().extractor('dataset')These npy files are loaded with numpy using following commands:importnumpyx=np.load('x.npy')y=np.load('y.npy')Training Custom Built Model#input_shape generated by this package is of shape (9,13,1)input_shape=(9,13,1)Making Predictionsfromaudio_classification_featuresimportaudio_featuresasaf#filename example: audio_test.wav#num_classes same as above#modelaf().make_prediction(filename,num_classes,model)Dataset Structurefolder name of audio will be used as label.datasetaudio class 1audiofile.wavaudio class 2audiofile.wavLICENSEGNU General Public License v3.0
Package: audio-classification-models
Audio Classification Models in Tensorflow 2.0This library utilizes some automatic speech recognition architectures such as ContextNet, Conformer, etc for audio classification.Installationpipinstall-Uaudio_classification_modelsorpipinstallgit+https://github.com/awsaf49/audio_classification_modelsUsageimportaudio_classification_modelsasacmmodel=acm.Conformer(pretrain=True)AcknowledgementTensorflowASR
Package: audiocodes-exporter
AudioCodes SBC Prometheus exporterThis is an exporter that exposes information gathered from AudioCodes SBC for use by the Prometheus monitoring system.This is a work in progress!!Installationpip install audiocodes-exporterUsageDefault port is statically set to 9954!The parameters can be set with env variables as well.audiocodes-exporter [-h] -H API_URL -u API_USERNAME -p API_PASSWORDRequirementsAudioCodes SBC v7.4
Python 3.10+
Package: audio-connected
Watches PulseAudio events for new sinks connected, and if the name matches the configured name, make that sink the default sink.InstallationProbably works best when installed withpipx:pipx install audio-connectedTo make it run on startup, you can add a XDG Autostart entry in$XDG_CONFIG_HOME/autostart(~/.config/autostartby default).Example:[Desktop Entry]Version=1.5Type=ApplicationName=Audio ConnectedComment=Set EPOS ADAPT 660 headset as default sink when connectedExec=audio-connected "EPOS ADAPT 660"OnlyShowIn=XFCE;StartupNotify=falseTerminal=falseHidden=false
Package: audioconverter
AudioConverterA simple CLI to convert a directory of audio files from  one format
to another. This CLI sits ontop ofpydubandffmpegMotivationI have some old music in a lossless format. Now that I am constantly
jumping between computers, I wanted it to be converted in
a more universal format such asmp3so that I can play it with
the simplest of players. I also wanted to avoid having
to stream my music on cloud platforms. Upon a cursory and naive scan
on the web, I found that existing scripts are defunct (again cursory)
or was not as simple as I would like it to be. I did not want to download
a GUI for a one time use or upload a directory of music online to have it
be converted on some server and download it again either. Instead, I wrote
this quick CLI to do it for me.SetupInstallffmpegGo follow thepydubtutorialon how to set upffmpegon the various platforms.Install CLIpipinstall--upgradeAudioConverterUsageaudioconvert[--verbose/-v]convertINPUT_DIRECTORYOUTPUT_DIRECTORY[--output-format/-oTARGET_FORMAT]This will recursively search theINPUT_DIRECTORYfor files with music
extensions. Each file found will then be converted to theTARGET_FORMATand
placed in theOUTPUT_DIRECTORYwith the same name but updated extension.The--verbose/-vflag must be provided before theconvertcommand. This
will enable debugging logs and allow you to monitor progress.For example - to convert the contents of the directoryinput/, containing
files of type.m4aand.flac, outputting to directoryoutput/, converting
to type.mp3run:audioconvertconvertinput/output/--output-format.mp3ExperimentalAudio can be passed to be converted to specific codecs. This is an experimental now feature
as it has no error checking that certain codecs are compatible with your desired output
audio format. Depending onffmpegand/orpydub, there may or may not be error logging.To use the new experimental feature:audioconvertconvertinput/output/--output-format.wav--codecpcm_mulawAccepted FormatsDue to not being super savvy with audio formats, I hard coded the extensions
that are searched for in theINPUT_DIRECTORYand acceptableTARGET_FORMAT.
Here is a list of formats I thought were popular:.mp3.flac.aiff.mp4.m4a.wav.oggSupported Codecpcm_mulaw
Package: audio-convertio
File ConverterTable of ContentsAboutRequirementsHow to useSetting up ProgramTechnologiesAboutAn audio converter that allows you to convert your audio files with a single command to any audio format that you want.RequirementsTo run this repository by yourself you will need to install python3 in your machine.How to useProgram Setup# Clone this repository$gitclone<https://github.com/AudioConvertio/file-converter># Access the project page on your terminal$cdfile-converter# Install all the requirements$pipinstall.# Execute the program with parameters$audio_convertio<path_to_audios_directory><input_format><output_format># Them it's just wait for the code run and the converted audio file will be in the same folder of the audio file passed as argumentTechnologiesPython3PytestPydubMultiprocessing
Package: audiodata
Audio Metadata ExtractorThis script extracts metadata from audio files usingpydubandmutagenlibraries with customised and fine-tuned outputs.InstallationTo use this script, you need to have Python installed on your system. You can install the required dependencies using pip:pip install pydub mutagenUsagefrom audiodata import metadata

file_path = "path/to/audio.extension"
length_seconds, metadata = metadata.get_audio_metadata(file_path)

print(f"Audio Length: {length_seconds} seconds")
print("Metadata: \n" + metadata)Sample OutputExample:
----------------
Audio Length: 26.006 seconds
Metadata: 
tracknumber: 9, title: Komplexe Zahl, album: Funktionentheorie, artist: Engelbert NiehausSupportIf you find this script helpful, please do consider supporting the developer atGitHub SponsorsLicenseThis project is licensed under the MIT License. See theLICENSEfile for details.IssuesHave any suggestions for imptovement ? Open an issue here - "https://github.com/Shreyan1/WhisperMe-Audio-Transcriber/issues"
Package: audiodb
TheAudioDBTheAudioDB API client forPython.DownloadPyPIpip install audiodbExamplefromaudiodbimportsearch_albums_by_artist_idforalbuminsearch_albums_by_artist_id(111674):print(album.strAlbum)DocumentationpdocLicenseTheAudioDB is released under theApache License 2.0.Copyright 2022 XXIV

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
Package: audio-denoiser
This Python library reduces substantial background noisein audio files containing speech. It uses a machinelearning model (38m parameters) trained to handledifferent types of ambient noise.Installationpip install audio-denoiserUsageBasic:from audio_denoiser.AudioDenoiser import AudioDenoiser



denoiser = AudioDenoiser()

in_audio_file = '/content/input-audio-with-noise.wav'

out_audio_file = '/content/output-denoised-audio.wav'

denoiser.process_audio_file(in_audio_file, out_audio_file)With additional options:from audio_denoiser.AudioDenoiser import AudioDenoiser

import torch

import torchaudio



# Use the "soundfile" audio backend, used in training.

torchaudio.set_audio_backend("soundfile")

# Use a CUDA device for inference if available

device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')

denoiser = AudioDenoiser(device=device)

in_audio_file = '/content/input-audio-with-noise.wav'

out_audio_file = '/content/output-denoised-audio.wav'

auto_scale = True # Recommended for low-volume input audio

denoiser.process_audio_file(in_audio_file, out_audio_file, auto_scale=auto_scale)You can also provide your own waveform tensor:from audio_denoiser.AudioDenoiser import AudioDenoiser



noisy_waveform, sample_rate = torchaudio.load('/content/input-audio-with-noise.wav')

denoiser = AudioDenoiser()

denoised_waveform = denoiser.process_waveform(noisy_waveform, sample_rate, auto_scale=False)

print('Tensor shape: ', denoised_waveform.shape)