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Ai Hulks App Kit

by ddmasterdon

SPEAKER COUNTING It enhances understanding through automatic speech recognition Beneficial for real - world applications like call-center transcription and meeting transcription analytics Speaker Diarization is a developing field of study, with new approaches being published on a frequent basis. The Problem Not many studies have been done for estimating a large number of speakers. Diarization becomes extremely difficult when the number of speakers is huge. Providing the number of speakers to the diarization system can be advantageous Complete solution Architecture - Machine Learning model - To predict the no. of speakers and the time stamps of the speaker. Web App - Frontend for the user to use the feature. Middleware Flask Api - To connect Frontend and ML Model. We have build a Web App that a user can use to communicate and leverage the advantages of the our Machine learning model. Since the model we build and the web app are build on different platforms, we used REST API as a middleware to connect frontend and model.

ML Model Solution Process

These are used to create our Web UI using node as backend and VueJs as front end. 1. Preprocessing: Denoising -> Speech separation 2. Embedding Extraction: YAMNet sound & classification model 3. Speaker Counting: Machine learning model selection -> Model training -> Model prediction

Data Preprocessing

Technologies used for pre processing the audio data.

Audio Pre Processing

The additional libaries are use to processing the audio which are needed to be fed into the classifier model.

Model Trainning

This libaries are used to create the two classifier models which are then both combined into one.

Kit Solution Source

https://github.com/aihulks69/Speaker-dirization

Kit Deployment Instructions

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