Saravanan666/pypi-org huggingface dataset:
Package: asrecognition
ASRecognitionASRecognition: just an easy-to-use library for Automatic Speech Recognition.I have no intention of building a very complex toolkit for speech recognition here.
In fact, this library uses a lot of things already built by theHugging Face(Thank you guys!).
I just wanna have an easy-to-use interface to add speech recognition features to my apps.
I hope this library could be useful for someone else too :)The currently supported languages are Arabic (ar), German (de), Greek (el), English (en), Spanish (es), Persian (fa), Finnish (fi), French (fr), Hungarian (hu), Italian (it), Japanese (ja), Dutch (nl), Polish (pl), Portuguese (pt), Russian (ru), Chinese (zh-CN).RequirementsPython 3.7+Installation$pipinstallasrecognitionHow to use it?fromasrecognitionimportASREngine# 1 - Load the ASR engine for a given language (on the first run it may take a while)asr=ASREngine("en")# 2 - Use the loaded ASR engine to transcribe a list of audio filesaudio_paths=["/path/to/sagan.mp3","/path/to/asimov.wav"]transcriptions=asr.transcribe(audio_paths)# 3 - Voilà!print(transcriptions)# [#  {"path": "/path/to/sagan.mp3", "transcription": "EXTRAORDINARY CLAIMS REQUIRE EXTRAORDINARY EVIDENCE"},#  {"path": "/path/to/asimov.wav", "transcription": "VIOLENCE IS THE LAST REFUGE OF THE INCOMPETENT"}# ]Want to help?See thecontribution guidelinesif you'd like to contribute to ASRecognition project.You don't even need to know how to code to contribute to the project. Even the improvement of our documentation is an outstanding contribution.If this project has been useful for you, please share it with your friends. This project could be helpful for them too.If you like this project and want to motivate the maintainers, give us a :star:. This kind of recognition will make us very happy with the work that we've done with :heart:CitationIf you want to cite the tool you can use this:@misc{grosman2021asrecognition,title={ASRecognition},author={Grosman, Jonatas},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/jonatasgrosman/asrecognition}},year={2021}}
Package: asre-compute
ARE package from the Saint-Malo poster “Evaluating the Effect of Individualized Treatment Rules Using Observational Data.”Implementation details will are available athttps://github.com/fcgrolleau/Emulated-ITRTheoretical argument including extensive simulations will be posted on Arxiv soon.Change Log0.0.12 (10/25/2021)New class release of the ASRE package0.0.11 (07/28/2021)Fix ASRE printing and returned parameters when est=’ASRE_cb’0.0.10 (07/28/2021)Fix Docstring, README and program structure for computational efficiency.0.0.7 (07/27/2021)Fix module structure0.0.1 (07/27/2021)First Release
Package: asrequests
This module contains an asynchronous method to send get and post http requests.Use 'AsRequests' to send them.Simple and effective.:)Usage is simple:```from  asrequests import AsRequestsurls = ['http://nodomain/','https://github.com','https://www.python.org/']```Send them all the same time.```>>> with AsRequests() as ar:for i in urls:ar.get(i)...>>>print(ar.result)[<Response [200]>, ErrorRequest(url='http://nodomain/', text='', ...), <Response [200]>]```Using like requests:```from asrequests import asrequestsasync def get_http():for i in urls:response = await asrequests.get(i)print(response)....asyncio.run_until_complete(get_http)===<Response [200]>ErrorRequest(url='http://nodomain/', text='', ...)<Response [200]>```
Package: asrertools
当前版本0.0.0说明这是一些ASR/Speech工作人员常用的函数组成的包，可以简化一些读写操作，使代码更加可读。主要包括两个部分：基本的读写工具和机器学习/深度学习工作中常用的数据处理函数。baseio基本的读写工具。包括了文件读写、文件夹读写、数据读写等功能。baseml机器学习/深度学习工作中常用的数据处理函数。包括划分十折交叉数据、常见json格式数据读取等功能。Emamplepass
Package: asr_evaluation
asr_evaluationPython module for evaluting ASR hypotheses (i.e. word error rate and word
recognition rate).This module depends on theeditdistanceproject, for computing edit distances between arbitrary sequences.The formatting of the output of this program is very loosely based around the
same idea as the align.c program commonly used within the Sphinx ASR community.
This may run a bit faster if neither instances nor confusions are printed.Please let me know if you have any comments, questions, or problems.OutputThe program outputs three standard measurements:Word error rate (WER)Word recognition rate (the number ofmatchedwords in the alignment divided by the number of words in the reference).Sentence error rate (SER) (the number of incorrect sentences divided by the total number of sentences).Installing & uninstallingThe easiest way to install is using pip:pip install asr-evaluationAlternatively you can clone this git repo and install using distutils:git clone git@github.com:belambert/asr-evaluation.git
cd asr-evaluation
python setup.py installTo uninstall with pip:pip uninstall asr-evaluationCommand line usageFor command line usage, see:wer --helpIt should display something like this:usage: wer [-h] [-i | -r] [--head-ids] [-id] [-c] [-p] [-m count] [-a] [-e]
           ref hyp

Evaluate an ASR transcript against a reference transcript.

positional arguments:
  ref                   Reference transcript filename
  hyp                   ASR hypothesis filename

optional arguments:
  -h, --help            show this help message and exit
  -i, --print-instances
                        Print all individual sentences and their errors.
  -r, --print-errors    Print all individual sentences that contain errors.
  --head-ids            Hypothesis and reference files have ids in the first
                        token? (Kaldi format)
  -id, --tail-ids, --has-ids
                        Hypothesis and reference files have ids in the last
                        token? (Sphinx format)
  -c, --confusions      Print tables of which words were confused.
  -p, --print-wer-vs-length
                        Print table of average WER grouped by reference
                        sentence length.
  -m count, --min-word-count count
                        Minimum word count to show a word in confusions.
  -a, --case-insensitive
                        Down-case the text before running the evaluation.
  -e, --remove-empty-refs
                        Skip over any examples where the reference is empty.Contributing and code of conductFor contributions, it's best to Github issues and pull requests. Proper
testing and documentation suggested.Code of conduct is expected to be reasonable, especially as specified by
theContributor Covenant
Package: asreview-notes-export
ASReview Notes Export ExtensionThis is a simple extension forASReviewthat can be used to export notes from the ASReview GUI while exporting the labeled or partially labeled dataset as .csv file.InstallationASReview notes-export can be installed from PyPI:pipinstallasreview-notes-exportAfter installation, check if theasreview-notes-exportpackage is listed as an
extension. Use the following command:asreview--helpIt should list thenotes_exportsubcommand.UsageTo export all the records from the dataset with the current state of labels and notes, we can use the following command:asreview notes_export YOUR_ASREVIEW_FILE.asreviewTo export only the records with notes from the dataset, we can use the following command:asreview notes_export --only-with-notes YOUR_ASREVIEW_FILE.asreviewTo include the labeling order in the exported dataset, use following optional argument:asreview notes_export --labeling-order YOUR_ASREVIEW_FILE.asreviewBy default, the filename of the output .csv is the same as the name of .asreview file. To save the output to .csv file with a custom filename, we can use the following command. We can either specify the extension:asreview notes_export --only-with-notes YOUR_ASREVIEW_FILE.asreview -o YOUR_OUTPUT_FILENAME.csvor only the filename will suffice, without an extension, using the following command:asreview notes_export --only-with-notes YOUR_ASREVIEW_FILE.asreview -o YOUR_OUTPUT_FILENAMENote that currently only .csv output is supported.LicenseThe extension is published under theApache 2.0 license.ContactThis is an unofficial extension of ASReview and is developed and maintained byRohit Garud.
Package: asreview-statistics
ASReview-statisticsASReview extension for generating statistics on state files and datasets.GeneralInstall the package with:pipinstallasreview-statisticsThe general usage of the package is to inspect files related to the systematic review done
with ASReview. It can be used to inspect your dataset that you would like to review (or have
reviewed).General usage:asreviewstatpath_to_fileDatasetsUse the following command on your command line:asreviewstatpath_to_your_datasetIt should give some general properties of the dataset, e.g.:************  PTSD_VandeSchoot_18.csv  ************

Number of papers:            5782
Number of inclusions:        38 (0.66%)
Number of exclusions:        5744 (99.34%)
Number of unlabeled:         0 (0.00%)
Average title length:        101
Average abstract length:     1339
Average number of keywords:  8.8
Number of missing titles:    64 (of which 0 included)
Number of missing abstracts: 747 (of which 0 included)Your dataset should be in a format that is readable by the ASReview software. Documentation
on how to create such a dataset is in the main project.State filesAnother use is the quick analysis of either one state file, or multiple state files in the same
directory:asreviewstatpath_to_your_state_filesThis will give output similar to:************  ptsd_nb  *******************

-----------  general  -----------
Number of runs            : 16
Number of papers          : 5782
Number of included papers : 38
Number of excluded papers : 5744
Number of unlabeled papers: 0
Number of queries         : 233

-----------  settings  -----------
model             : nb
query_strategy    : max_random
balance_strategy  : double
feature_extraction: tfidf
n_instances       : 25
n_prior_included  : 1
n_prior_excluded  : 1
mode              : simulate
model_param       : {'alpha': 3.822}
query_param       : {'strategy_1': 'max', 'strategy_2': 'random', 'mix_ratio': 0.95}
feature_param     : {}
balance_param     : {'a': 2.155, 'alpha': 0.94, 'b': 0.789, 'beta': 1.0}
abstract_only     : False

-----------    ATD    -----------
 0.0195

-----------  WSS/RRF  -----------
WSS@95 : 91.49 %
WSS@100: 87.54 %
RRF@5  : 97.30 %
RRF@10 : 97.64 %Currently, the amount of information displayed is growing; help and suggestions are welcome!
Package: asreview-wordcloud
ASReview-wordcloudASReview-wordcloud is an extension for theASReview
LABsoftware. It offers an easy way to
create a visual impression of the contents of datasets.InstallationThe easiest way to install the wordcloud extension is to install from PyPI:pipinstallasreview-wordcloudAfter installation of the wordcloud extension,asreviewshould automatically
detect it. Test this by:asreview--helpIt should list thewordcloudsubcommand.Basic usageThe dataset should contain a column containing titles and/or abstracts.
For specific requirements check theASReview documentation.
To use your data use:asreview wordcloud MY_DATA.csvThe following shows theSchoot et al. (2017) dataset:To make a wordcloud on titles only, use thetitleflag.asreview wordcloud MY_DATA.csv --titleTo make a wordcloud on abstracts only, use theabstractflag.asreview wordcloud MY_DATA.csv --abstractTo make a wordcloud on relevant (inclusions) only, use therelevantflag.asreview wordcloud MY_DATA.csv --relevantSave figureSave the wordcloud to a file with the-oflag.asreview wordcloud MY_DATA.csv -o MY_DATA_WORDCLOUD.pngLicenseThis extension is published under theMIT license.ContactThis extension is part of the ASReview project (asreview.ai). It is maintained by the
maintainers of ASReview LAB. SeeASReview
LABfor contact information and more
resources.
Package: asrl-pylgmath
pylgmathpylgmath is a Python library for handling geometry in state estimation problems in robotics.
It is used to store, manipulate, and apply three-dimensional rotations and transformations and their associated uncertainties.There are no minimal, constraint-free, singularity-free representations for these quantities, so lgmath exploits two different representations for the nominal and noisy parts of the uncertain random variable.Nominal rotations and transformations are represented using their composable, singularity-freematrix Lie groups,SO(3)andSE(3).Their uncertainties are represented as multiplicative perturbations on the minimal, constraint-free vectorspaces of theirLie algebras,*so**(3)* and*se**(3)*.This library uses concepts and mathematics described in Timothy D. Barfoot's bookState Estimation for Robotics.
It is used for robotics research at the Autonomous Space Robotics Lab; most notably in the STEAM Engine, a library for Simultaneous Trajectory Estimation and Mapping.Installation## PyPIpipinstallasrl-pylgmath## Sourcegitclonehttps://github.com/utiasASRL/pylgmath.git
pipinstall-epylgmath# may need to replace `pip` with `pip3` if not using a virtual environment.License
Package: asrl-pysteam
pysteampysteam is the Python implementation of STEAM (Simultaneous Trajectory Estimation and Mapping). It is an optimization library aimed at solving batch nonlinear optimization problems involving both SO(3)/SE(3) and continuous-time components. This is accomplished by using an iterative Gauss-Newton-style estimator in combination with techniques developed and used by ASRL. With respect to SO(3) and SE(3) components, we make use of the constraint sensitive perturbation schemes discussed in Barfoot and Furgale [1]. STEAM Engine is by no means intended to be the fastest car on the track; the intent is simply to be fast enough for the types of problems we wish to solve, while being both readable and easy to use by people with a basic background in robotic state estimation.[1] Barfoot, T. D. and Furgale, P. T., “Associating Uncertainty with Three-Dimensional Poses for use in Estimation Problems,” IEEE Transactions on Robotics, 2014.Installation## PyPIpipinstallasrl-pysteam## Sourcegitclonehttps://github.com/utiasASRL/pysteam.git
pipinstall-epysteam# may need to replace `pip` with `pip3` if not using a virtual environment.License
Package: asr-phoneme-assessment
Author: Jonathan Lim Wei Siang
Email:Jonathanlimws@gmail.com# Main Function
This Python package allows you to assess the phonetic error rate and visualise them.# Workflow of package
1. TIMIT file is loaded
2. ASR Model converts audio.wav file into phoneme string
3. ASR Phoneme string is standardized to TIMIT standard to allow for comparison
4. Phonetic Error Rate is generate## Additional
Noise can be added to audio.wav fiel to test the ASR model and step 2-4 is repeated# Types of Plots
1. Boxplot of accuracy rate for each phoneme across selected TIMIT files
2. Stacked boxplot of accuracy rate across varying added noise
3. Time/frequency Plot any given TIMIT audio showing the timing/phoneme which was incorrected predicted (substitution and deletion only)
![alt text](/Users/jonathanlim/Desktop/Code_2022/ASR_Assessment/Screenshot 2022-08-12 at 16.57.12.png?raw=True)# Requirements
TIMIT file: TIMIT is a corpus of phonemically and lexically transcribed speech of American English speakers of different sexes and dialects
link:https://en.wikipedia.org/wiki/TIMITChange Log0.0.1 (12/08/2022)First Release
Package: asrpy
ASRpyArtifact Subspace Reconstruction for PythonIntroductionInstallationExamplesDocumentationIntroductionArtifact subspace reconstruction (ASR) is an automated, online,
component-based artifact removal method for removing transient or
large-amplitude artifacts in multi-channel EEG recordings (Kothe & Jung,
2016). This repository provides a Python implementation of the standard
ASR algorithm, similar to the original MATLAB implementation in EEGLab'sclean_rawdataplugin.
As of now, this repository only implements the standard version of
the ASR algorithm. A valid version of the improvedriemannianASR
(Blum et al., 2019) might be added in the future.This implementation aims to follow the original ASR algorithm as close
as possible. Using the according parameters, it should be perfectly
equivalent to the original implementation, except for a few imprecisions
introduced by different solvers implemented in Python and MATLAB, e.g.
when calculating the eigenspace. However, this implementation is
based onpython_meegkit.
For an alternative implementation check their repository.ReferencesKothe, C. A. E., & Jung, T. P. (2016). U.S. Patent Application No.
14/895,440.https://patents.google.com/patent/US20160113587A1/enBlum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019).
A Riemannian Modification of Artifact Subspace Reconstruction for EEG
Artifact Handling. Frontiers in Human Neuroscience, 13.https://doi.org/10.3389/fnhum.2019.00141InstallationYou can install the latest ASRpy release using:pip install asrpyor install the current working version directly from GitHub, using:pip install git+https://github.com/DiGyt/asrpy.gitExamplesASRpy applies the Artifact Subspace Reconstruction method directly to MNE-Python'smne.io.Rawobjects. It's usage should be as simple as:importasrpyasr=asrpy.ASR(sfreq=raw.info["sfreq"],cutoff=20)asr.fit(raw)raw=asr.transform(raw)To get started, we recommend going through theexample notebook. You can simply run them via your internet browser (on Google Colab's hosted runtime) by clicking the  button below.DocumentationThe ASRpy documentation is created usingpdoc3andGitHub Pages. Click on the link below to view the documentation.DocumentationIn most Python IDEs, you can also read them by e.g. typingasrpy.ASR?
Package: asrpy-eh
ASRpyArtifact Subspace Reconstruction for PythonIntroductionInstallationExamplesDocumentationIntroductionArtifact subspace reconstruction (ASR) is an automated, online,
component-based artifact removal method for removing transient or
large-amplitude artifacts in multi-channel EEG recordings (Kothe & Jung,
2016). This repository provides a Python implementation of the standard
ASR algorithm, similar to the original MATLAB implementation in EEGLab'sclean_rawdataplugin.
As of now, this repository only implements the standard version of
the ASR algorithm. A valid version of the improvedriemannianASR
(Blum et al., 2019) might be added in the future.This implementation aims to follow the original ASR algorithm as close
as possible. Using the according parameters, it should be perfectly
equivalent to the original implementation, except for a few imprecisions
introduced by different solvers implemented in Python and MATLAB, e.g.
when calculating the eigenspace. However, this implementation is
based onpython_meegkit.
For an alternative implementation check their repository.ReferencesKothe, C. A. E., & Jung, T. P. (2016). U.S. Patent Application No.
14/895,440.https://patents.google.com/patent/US20160113587A1/enBlum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019).
A Riemannian Modification of Artifact Subspace Reconstruction for EEG
Artifact Handling. Frontiers in Human Neuroscience, 13.https://doi.org/10.3389/fnhum.2019.00141InstallationYou can install the latest ASRpy release using:pip install asrpyor install the current working version directly from GitHub, using:pip install git+https://github.com/DiGyt/asrpy.gitExamplesASRpy applies the Artifact Subspace Reconstruction method directly to MNE-Python'smne.io.Rawobjects. It's usage should be as simple as:importasrpyasr=asrpy.ASR(sfreq=raw.info["sfreq"],cutoff=20)asr.fit(raw)raw=asr.transform(raw)To get started, we recommend going through theexample notebook. You can simply run them via your internet browser (on Google Colab's hosted runtime) by clicking the  button below.DocumentationThe ASRpy documentation is created usingpdoc3andGitHub Pages. Click on the link below to view the documentation.DocumentationIn most Python IDEs, you can also read them by e.g. typingasrpy.ASR?
Package: asrt
ASRT is a high-level deep learning API for speech recognition,
written in Python and capable of running on top of
Keras, TensorFlow, or MxNet.
Use ASRT if you need a deep learning library that:Allows for easy and fast prototyping for speech recognition
(through user friendliness, modularity, and extensibility).Supports both Keras and other Deep learning framework(on future).Runs seamlessly on CPU and GPU.Contains a api server module for developers to test models easily.
Read the documentation at:https://github.com/nl8590687/ASRT_SpeechRecognition/wikiFor a detailed overview of what makes ASRT special, see:https://asrt.ailemon.meASRT is compatible with Python 3.0-3.7
and is distributed under the GPL v3.0 license.
Package: asrt-sdk
ASRT_SDK is a client sdk package for ASRT.
ASRT is a high-level deep learning API for speech recognition,
written in Python and capable of running on top of
Keras, TensorFlow, or MxNet.
Use ASRT SDK if you need a speech recognition library that:Allows for easy and fast prototyping for speech recognition
(through user friendliness, modularity, and extensibility).Supports both TensorFlow and other Deep learning framework(on future) for speech recognition.Applications can only run on CPU.Can call ASRT api server module for developers to test models easily.
Read the documentation at:https://wiki.ailemon.net/docs/asrt-doc/asrt-doc-1dkgqc3871kttFor a detailed overview of what makes ASRT special, see:https://asrt.ailemon.netASRT is compatible with Python 3.0-3.10
and is distributed under the GPL v3.0 license.
Package: ass2srt
ass2srtA tool that convert .ass subtitles to .srtInstallpip install ass2srtUsage$ ass2srt -h

usage: ass2srt.py [-h] [-s {zh,en,fr,de}] [-l {0,1,2}] [-i] file

positional arguments:
  file                  .ass file to convert

optional arguments:
  -h, --help            show this help message and exit
  -s {zh,en,fr,de}, --suffix {zh,en,fr,de}
                        add suffix to subtitles name
  -l {0,1,2}, --line {0,1,2}
                        keep double subtitles
  -i, --info            display subtitles infomation
  -o OUT, --out OUT     output file nameExample :ass2srt Movies.S01.E01.Name.assUsed as a LibraryYou can use asstosrt on your program easily.import ass2srt
subtitles=ass2srt.Ass2srt("Movies.S01E01.Name.ass")
subtitles.to_srt()LicenseMIT LicenseBugs and IssuesPlease visitGitHub.
Package: assart
assartAccording to Wikipedia,Assartingis the act of clearing forested lands for use in agriculture or other purposes. In English land law, it was illegal to assart any part of a royal forest without permission.In our case,assartisA simple S3 analytic reporting tool_Note: This is an experimental project and the codebase is not yet ready for serious usage.Getting startedInstallationDue to the experimental nature of the project, it is recommended thatassartbe installed into a virtual environment like so:$ cd ~/venvs
$ python3 -m venv assartenv
$ source assartenv/bin/activate
$ pip install assartConfigurationassartutilizes the AWS Python SDK (boto3). By convention,boto3sources your AWS credentials and default region from the following locations:$ cat ~/.aws/credentials
[default]
aws_access_key_id = YOUR_AWS_ACCESS_KEY_ID
aws_secret_access_key = YOUR_AWS_SECRET_ACCESS_KEY

$ cat ~/.aws/config
[default]
region = us-east-1Usage$ assart -h
usage: assart [-h] [-u {KB,MB,GB}]

A simple S3 analytics reporting tool

optional arguments:
  -h, --help            show this help message and exit
  -u {KB,MB,GB}, --unit {KB,MB,GB}
                        Unit of SizeRunningWith your credentials configured, the program can be run like so:$ assartBy default the program will output a list of the user's s3 buckets, and for each bucket will provide:The Bucket nameThe Creation date of the bucketThe Number of files in the bucketThe Total size of the files in the bucketThe Last modified date of the most recent fileContributingTheassartpackage is available under a permissive MIT License. Feedback and suggestions are welcome. The package can be built like so:python setup.py sdist bdist_wheel