Speech Summarizer 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 pretrained state-of-the-art models 5. Generate audio out of summarized text kandi kit provides you with a fully deployable Speech Summarizer. Source code included so that you can customize it for your requirement. To install this kit, scroll down to refer 'Kit Deployment Instructions' section and follow instructions.

Kit Deployment Instructions

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

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.

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.

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.

Machine Learning

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

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 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.


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.