Speech summarizer

by kandikits

Speech summarization help us in generating a gist of a speech by solving the problem of transcribing and summarization.To install this kit, scroll down to refer 'Installation Guide' section and follow instructions.Below are the steps involved in building a speech summarizer. The speech summarizer takes an audio file as an input and generates text or audio as an output1. Transform audio to meet the following spec a. '.wav' file format b. 16KHz sample rate c. Mono channel2. Transcribe transformed audio file3. Process transcribed text if necessary4. Summarize transcribed text using pretrained state-of-the-art models5. Generate audio out of summarized textSpeech summarizer can also be used to comprehend Podcasts on variety of topics.

Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.Jupyter Notebook is used for our development.
n

notebookby jupyter

Jupyter Interactive Notebook

JavaScript Updated: 3 mo ago License: Proprietary

Support
Quality
Security
License
Reuse
v

vscodeby microsoft

Visual Studio Code

TypeScript Updated: 0 d ago License: Permissive

Support
Quality
Security
License
Reuse

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.
p

pandasby pandas-dev

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Python Updated: 4 d ago License: Permissive

Support
Quality
Security
License
Reuse
n

numpyby numpy

The fundamental package for scientific computing with Python.

Python Updated: 8 d ago License: Permissive

Support
Quality
Security
License
Reuse

Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.
s

spaCyby explosion

💫 Industrial-strength Natural Language Processing (NLP) in Python

Python Updated: 2 d ago License: Permissive

Support
Quality
Security
License
Reuse
n

nltkby nltk

NLTK Source

Python Updated: 3 mo ago License: Permissive

Support
Quality
Security
License
Reuse

Transcribing

Transcribing libraries help in converting speech to text.
D

DeepSpeechby mozilla

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

C++ Updated: 4 mo ago License: Weak Copyleft

Support
Quality
Security
License
Reuse

Machine Learning

Machine learning libraries and frameworks here are helpful in generating state-of-the-art summarization.
s

scikit-learnby scikit-learn

scikit-learn: machine learning in Python

Python Updated: 1 d ago License: Permissive

Support
Quality
Security
License
Reuse
t

transformersby huggingface

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Python Updated: 1 d ago License: Permissive

Support
Quality
Security
License
Reuse

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.
f

fastapiby tiangolo

FastAPI framework, high performance, easy to learn, fast to code, ready for production

Python Updated: 4 d ago License: Permissive

Support
Quality
Security
License
Reuse
c

composeby docker

Define and run multi-container applications with Docker

Go Updated: 1 d ago License: Permissive

Support
Quality
Security
License
Reuse
k

kubernetesby kubernetes

Production-Grade Container Scheduling and Management

Go Updated: 1 d ago License: Permissive

Support
Quality
Security
License
Reuse
w

waitressby Pylons

Waitress - A WSGI server for Python 2 and 3

Python Updated: 6 mo ago License: Proprietary

Support
Quality
Security
License
Reuse
f

flaskby pallets

The Python micro framework for building web applications.

Python Updated: 10 d ago License: Permissive

Support
Quality
Security
License
Reuse

Kit Solution Source

Speech Summarizer created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
s

speech-summarizerby kandikits

Transcribes and summarizes speech or audio

Jupyter Notebook Updated: 10 d ago License: Permissive

Support
Quality
Security
License
Reuse

Installation Guide

Follow below instructions to install the libraries required for building speech summarizer. 1. Download, extract and double-click the python-installer file to install python 2. Download, extract and double-click the modules-installer file to install dependencies 3. Download and extract the speech-summarizer file to explore Speech Summarizer repo

Kit Deployment Instructions

Follow below instructions to deploy and run the solution. 1. Navigate to the folder 'speech-summarizer' 2. Launch Jupyter Notebook by running the command 'jupyter notebook' in the terminal 3. Click 'Speech Summarizer.ipynb' to open the notebook 4. Execute cells in the notebookIf there're any challenges while installing dependencies, run the command below to upgrade pip and try again. python -m pip install --upgrade pip

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items