Popular GitHub Repositories for Sad

TEN VAD: low-latency high-performance Voice Activity Detector

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

A curated list of awesome voice activity detection

Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task.

This is all in one chatbot. When you start conversation it sees your face and identifies if it has previously talked to you(Not supported in latest versions). It also checks for your emotion in between conversation. This emotion data is then used to help you whenever it detects you are sad or angry. It can search anything you ask on the internet.

Given a voice detecting emotion it holds. Such as - Angry, Happy, Sad, Disgust, Neutral, Surprise & Pleasant. Etc...

CHEERBOT is a special chatbot with whom you can text-chat, voice chat. It will cheer you up if you're sad and make you happier if you're happy.

The Machine learning project where user express there emotion and classify at the following (Sad, Joy, Love, Anger, Fear, Surprice).

The goal of this project is to predict the expression on the face. The expression labels are standard ones used in psychology research: angry, disgusted, fearful, happy, sad, surprised, neutral.

A sad excuse for a Chess AI using a CNN value function and Minimax alpha-beta pruning

Built a real time system which is able to capture facial emotions of a person and classify human faces in real time into a fixed number of emotions. Trained a CNN to identify whether the face is happy, angry, sad, surprised etc.

Deep Learning Image Classification model which detects whether a person is happy or sad

Emotion Recognition in Speech: This project leverages advanced machine learning techniques to classify emotions from speech using the Toronto Emotional Speech Set (TESS). By extracting Mel-Frequency Cepstral Coefficients (MFCC) and utilizing an LSTM-based deep learning model, the project accurately identifies emotions like anger, happiness, and sad

Emotion and Facial Key-Point Detection Classify emotions and detect facial key-points using deep learning! This project combines CNNs and Residual Blocks to predict 15 facial key-points and categorize facial expressions into five emotions: Angry, Disgust, Sad, Happy, and Surprise.

VoxShift is a text-to-speech app that turns your text into lifelike speech with different voice tones like happy, sad, angry, calm, and robotic. Just type, pick a mood, and hear it out loud perfect for voiceovers, games, or fun!

I Build Deep CNN Image Classifier Using Deep Learning And Python To Identify Whether A Image Is Happy Or Sad.

building a model to classify the emotion of an image face: Angry, Disgust, Fear, happy, Sad, Surprise, Neutral.

This project implements a Speech Emotion Recognition (SER) model using TensorFlow Lite, specifically designed for deployment on microcontrollers like the Arduino Nano BLE33. The model is trained on the RAVDESS dataset and can recognize seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.

? ? the third day task for ODC internship where user upload an image or record a video and the app will tell him the person emotion happy, sad, angry ...etc