The confidence model that you'll rizzspect
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About The Project
The Rizzerator is a machine learning classification model designed to help improve your speech. Using advanced machine learning techniques it provides a confidence score to the inputed audio file, that being how much rizz was detected. The model was trained on hundreds of thousands of speeches and has found nuances in word choice, candence, and emotions to accurately rate the confidence (rizz) of an audio file. The Rizzerator will take you beyond your potential, allowing you to analyze and improve your speech has never been easier - with rizzz.
About our model:
- Bank customer service caller audio and agent audio, along with transcipt, used to train model
- We used Whisper (OpenAI Library) to transcribe the audio to text
- Linguistic feature extractors used: Word2Vec, Glove, BERT
- Acoustic feature extractors used: pitch, zero-crossing rate, energy, centroid, spectral spread, spectral entropy
All data used is public access.
Use the link or www.rizzerator.io clone the repo to get started
This is an example of how to list things you need to use the software and how to install them (when cloning).
sh npm install
sh go get
We believe that everyone can improve with the Rizzerator, allowing people to analyze the see the easy corrections to make would allow those who can improve, to improve.
Some use cases:
- You have a presentation and would like to do a test run, The Rizzerator wouuld generate a confidence score and suggest possible changes to improve said score based on the input audio.
- Job interview coming up soon and want to practice? Run some small talk topics over with the Rizzerator and maximize your chances of employment
- Nervous to talk to someone you like? Confess your feelings to them first with the Rizzerator, and boom you're in
Distributed under the MIT License. See
LICENSE.txt for more information.
Project Link: https://github.com/JJX30/Rizzerator-App
Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!