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In this notebook, we aim to recognize speech commands using classification. For this purpose, we used the SPEECHCOMMANDS dataset and the deep convolutional model M5. The code is written in Python and designed for the PyTorch platform.
CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline
A convolutional neural network for gender classification, which achieved an F1-score of 94.3% when tested on the RAVDESS dataset. Created as postgraduate coursework, the report is included. The report also discusses Sodiq Adebiy's CNN, which I'd recommend looking at to anyone interested in emotion classification.
In this challenge, the goal is to learn to recognize which of several English words is pronounced in an audio recording. This is a multiclass classification task.
It is a full-fetched web application.Based on sentiment classification, by using nltk library it predicts that a speech is how much toxic, sever toxic, insult, obscene, threat.