Omkar2118/datasets huggingface dataset:
CREMA-D
Full name: CREMA-D

**CREMA-D** is an emotional multimodal actor data set of 7,442 original clips from 91 actors. These clips were from 48 male and 43 female actors between the ages of 20 and 74 coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified).

Actors spoke from a selection of 12 sentences. The sentences were presented using one of six different emotions (Anger, Disgust, Fear, Happy, Neutral, and Sad) and four different emotion levels (Low, Medium, High, and Unspecified).

Participants rated the emotion and emotion levels based on the combined audiovisual presentation, the video alone, and the audio alone. Due to the large number of ratings needed, this effort was crowd-sourced and a total of 2443 participants each rated 90 unique clips, 30 audio, 30 visual, and 30 audio-visual. 95% of the clips have more than 7 ratings.
DementiaBank
Full name: DementiaBank

DementiaBank is a shared database of multimedia interactions for the study of communication in dementia. The dataset contains 117 people diagnosed with Alzheimer Disease, and 93 healthy people, reading a description of an image. The principal task and benchmark is to classify each group.
FUSS
Full name: Free Universal Sound Separation

The **Free Universal Sound Separation (FUSS)** dataset is a database of arbitrary sound mixtures and source-level references, for use in experiments on arbitrary sound separation. FUSS is based on FSD50K corpus.

Introducing paper: What's All the FUSS About Free Universal Sound Separation Data?
Groove
Full name: Groove MIDI Dataset

The **Groove MIDI Dataset (GMD)** is composed of 13.6 hours of aligned MIDI and (synthesized) audio of human-performed, tempo-aligned expressive drumming. The dataset contains 1,150 MIDI files and over 22,000 measures of drumming.

Introducing paper: Learning to Groove with Inverse Sequence Transformations
GTZAN

The **gtzan8** audio dataset contains 1000 tracks of 30 second length. There are 10 genres, each containing 100 tracks which are all 22050Hz Mono 16-bit audio files in .wav format. The genres are:

- blues
- classical
- country
- disco
- hiphop
- jazz
- metal
- pop
- reggae
- rock
gtzan_music_speech
Full name: gtzan_music_speech

**gtzan_music_speech** is a dataset for music/speech discrimination. It consists of 120 tracks of 30 second length. Each class (music/speech) has 60 samples. The tracks are all 22050Hz Mono 16-bit audio files in .wav format.
iVQA
Full name: Instructional Video Question Answering

An open-ended VideoQA benchmark that aims to: i) provide a well-defined evaluation by including five correct answer annotations per question and ii) avoid questions which can be answered without the video. 

iVQA contains 10,000 video clips with one question and five corresponding answers per clip. Moreover, we manually reduce the language bias by excluding questions that could be answered without watching the video.  

Source: [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://antoyang.github.io/just-ask.html)

Introducing paper: Just Ask: Learning to Answer Questions from Millions of Narrated Videos
LibriTTS
Full name: LibriTTS

**LibriTTS** is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus. The main differences from the LibriSpeech corpus are listed below:

- The audio files are at 24kHz sampling rate.
- The speech is split at sentence breaks.
- Both original and normalized texts are included.
- Contextual information (e.g., neighbouring sentences) can be extracted.
- Utterances with significant background noise are excluded.

Introducing paper: LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
SAVEE
Full name: Surrey Audio-Visual Expressed Emotion

The **Surrey Audio-Visual Expressed Emotion (SAVEE)** dataset was recorded as a pre-requisite for the development of an automatic emotion recognition system. The database consists of recordings from 4 male actors in 7 different emotions, 480 British English utterances in total. The sentences were chosen from the standard TIMIT corpus and phonetically-balanced for each emotion. The data were recorded in a visual media lab with high quality audio-visual equipment, processed and labeled. To check the quality of performance, the recordings were evaluated by 10 subjects under audio, visual and audio-visual conditions. Classification systems were built using standard features and classifiers for each of the audio, visual and audio-visual modalities, and speaker-independent recognition rates of 61%, 65% and 84% achieved respectively.