Real vs Fake Human Voice – Deepfake Audio Dataset

AI Avatar Dataset provides 1,215 high-resolution videos and 405 speech clips of 45 individuals, annotated with gender, ethnicity, and multi-emotion labels. Designed for AI avatar generation, identity verification, and computer vision tasks, this emotional video and speech dataset supports creating realistic avatars, training deep learning models, and advancing applications in virtual assistants, facial recognition, and digital identity.

This music genres dataset contains 500,000 studio-grade music tracks in lossless FLAC format, designed for music genre classification and detection tasks. It provides rich music metadata, including detailed genre labels, instruments, and artist information, making it ideal training data for machine learning and deep learning models in audio analysis.

The dataset includes 1,136 hours of annotated telephone dialogues from 1,416 native speakers across the United States, providing high-quality audio recordings, transcriptions, and speaker metadata to support speech recognition systems, NLP tasks, and machine learning models requiring diverse American speech datasets

The dataset consists of 200 hours of high-quality telephone dialogues from 310 native speakers in the UK, with detailed annotations (transcriptions, timestamps, speaker ID, gender, and background noise) to support speech recognition systems, NLP tasks, and machine learning models requiring diverse British English audio datasets.

Speech Emotion Recognition Dataset comprises over 30,000 audio recordings labeled with four distinct speech emotions: euphoria, joy, sadness, and surprise. It is designed to train emotion recognition and speech recognition systems using rich audio features, human-labeled metadata, and diverse emotional expressions for advanced machine learning and sentiment analysis tasks.