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Speech Summarizer

by kandikits Updated: Oct 20, 2022


Speech summarization help us in generating a gist of a speech by solving the problem of transcribing and summarization. Speech summarizer can also be used to comprehend Podcasts on variety of topics.


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 output.


  1. Transform audio to meet the following spec a. '.wav' file format b. 16KHz sample rate c. Mono channel
  2. Transcribe transformed audio file
  3. Process transcribed text if necessary
  4. Summarize transcribed text using pre-trained state-of-the-art models
  5. Generate audio out of summarized text.

Deployment Information

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.


  1. Download, extract and double-click the kit installer file to install the kit. Do ensure to extract the zip file before running it.
  2. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
  3. To run the kit manually, press 'N' and locate the zip file 'speech-summarizer.zip'
  4. Extract the zip file and navigate to the directory 'speech-summarizer'
  5. Open command prompt in the extracted directory 'speech-summarizer' and run the command 'jupyter notebook'
  6. Locate and open the 'Speech Summarizer.ipynb' notebook from the Jupyter Notebook browser window.
  7. Execute cells in the notebook


Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Speech Summarizer App.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

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.

notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2

License: Others (Non-SPDX)

Jupyter Interactive Notebook

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notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2 License: Others (Non-SPDX)

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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)

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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.

Transcribing

Transcribing libraries help in converting speech to text.

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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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.

Kit Solution Source

speech-summarizerby kandi1clickkits

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 kandi1clickkits

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

Transcribes and summarizes speech or audio
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Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

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