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Education: Starter Kit - Course Shorts

by kandikits Updated: Jan 24, 2022

Course Shorts help students get introductions to courses or refresh topics or even transmit summaries over low bandwidth connections. In this challenge, we are inviting to build a solution for creating summaries from video/audio course content. You can choose any course of your choice. Please see below a sample solution kit to jumpstart your solution on creating a course shorts. To install this kit, scroll down to refer sections Kit Deployment Instructions and Instruction to Run. Complexity : Simple This kit transcribes audio and creates a summary out of transcription.

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.

jupyterby jupyter

Python star image 12379 Version:Current

License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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jupyterby jupyter

Python star image 12379 Version:Current License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat
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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2 License: Permissive (MIT)

Visual Studio Code
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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.

pandasby pandas-dev

Python star image 33259 Version:v1.4.1

License: Permissive (BSD-3-Clause)

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

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pandasby pandas-dev

Python star image 33259 Version:v1.4.1 License: Permissive (BSD-3-Clause)

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

Python star image 20101 Version:v1.22.3

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 20101 Version:v1.22.3 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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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.

spaCyby explosion

Python star image 23063 Version:v3.1.6

License: Permissive (MIT)

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

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spaCyby explosion

Python star image 23063 Version:v3.1.6 License: Permissive (MIT)

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

Python star image 10427 Version:Current

License: Permissive (Apache-2.0)

NLTK Source

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nltkby nltk

Python star image 10427 Version:Current License: Permissive (Apache-2.0)

NLTK Source
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Transcribing

Transcribing libraries help in converting speech to text.

DeepSpeechby mozilla

C++ star image 18003 Version:v0.10.0-alpha.3

License: Weak Copyleft (MPL-2.0)

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.

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DeepSpeechby mozilla

C++ star image 18003 Version:v0.10.0-alpha.3 License: Weak Copyleft (MPL-2.0)

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.
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Machine Learning

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

scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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transformersby huggingface

Python star image 61400 Version:v4.18.0

License: Permissive (Apache-2.0)

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

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transformersby huggingface

Python star image 61400 Version:v4.18.0 License: Permissive (Apache-2.0)

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

speech-summarizerby kandikits

Jupyter Notebook star image 0 Version:v1.0.0

License: Permissive (Apache-2.0)

Transcribes and summarizes speech or audio

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speech-summarizerby kandikits

Jupyter Notebook star image 0 Version:v1.0.0 License: Permissive (Apache-2.0)

Transcribes and summarizes speech or audio
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Deployment Information

Course shorts application built using this kit are added in here. The entire solution is available as a package to download from the source code repository.

For Windows OS, Download, extract and double-click kit_installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth. 1. When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar to see how the speech summariser works. It is loaded with sample audio file. 2. To run the app manually, press N when you're prompted and locate the zip file speech-summarizer.zip 3. Extract the zip file and navigate to the directory speech-summarizer-main 4. Open command prompt in the extracted directory speech-summarizer-main and run the command jupyter notebook For other Operating System, 1. Click here to install python 2. Click here to download the repo 3. Extract the zip file and navigate to the directory speech-summarizer-main 4. Open terminal in the extracted directory speech-summarizer-main 5. Install dependencies by executing the command pip install -r requirements.txt 6. Run the command jupyter notebook

Instruction to Run

Follow below instructions to run the solution. 1. Locate and open the Course Shorts App.ipynb notebook from the Jupyter Notebook browser window. 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar For using with your audio file, 1. In Jupyter Notebook, set the variable INPUT_AUDIO_FILE to an audio file of your choice meeting below criteria. a) wav file format b) sample rate of 16KHz c) mono type audio channel 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar 3. The output file will be generated in the directory speech-summarizer-main/output/ from the kit_installer.bat location Sample Input: speech-summarizer-main/input/speech.wav - an audio file matching aforementioned criteria Output: speech-summarizer-main/output/summarised_text.txt - a text file containing summary of the input audio You can additionally build interfaces to the speech summariser and other enhancements for additional score. For any support, you can direct message us at #help-with-kandi-kits

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway 2. During kit installer, if you encounter Windows security alert, click Allow

Support

For any support, you can direct message us at #help-with-kandi-kits

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