datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
emotion
Dataset Card for "emotion"
Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
An example looks as follows.
{
"text": "im feeling quite sad and sorry for myself but… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.pg19
Dataset Card for "pg19"
Paraquet version of pg19
Statistics (in # of characters): total_len: 11425076324, average_len: 399450.2595622684
emotion** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**
go_emotions
Dataset Card for GoEmotions
Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
Languages
The data is in English.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions.reachy-mini-emotions-library
Reachy Mini Emotions Library
Curated emotion recordings for the Reachy Mini robot, maintained by
Pollen Robotics. Each move is a JSON trajectory (head pose, antennas,
body yaw, sampled over time) paired with an Opus audio track.
Motion is sampled at 50 Hz; audio is mono Ogg/Opus (decoded natively by
the robot). Requires reachy_mini ≥ v1.8.4 (its move loader resolves
non-.wav audio sidecars).
File layout
Files live at the root of the dataset, named <emotion>.json +… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-emotions-library.emotion
EmotionClassification
An MTEB dataset
Massive Text Embedding Benchmark
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
Task category
t2c
Domains
Social, Written
Reference
https://www.aclweb.org/anthology/D18-1404
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["EmotionClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/emotion.emotionsquality
Dataset Card for "quality"
More Information needed
emova-alignment-7m
EMOVA-Alignment-7M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-Alignment-7M is a comprehensive dataset curated for omni-modal pre-training, including vision-language and speech-language alignment.
This dataset is created using open-sourced image-text pre-training datasets, OCR datasets, and 2,000 hours of ASR and TTS data.
This dataset is part of the EMOVA-Datasets… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-alignment-7m.emova-sft-4m
EMOVA-SFT-4M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-SFT-4M is a comprehensive dataset curated for omni-modal instruction tuning, including textual, visual, and audio interactions. This dataset is created by gathering open-sourced multi-modal instruction datasets and synthesizing high-quality omni-modal conversation data to enhance user experience. This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-sft-4m.laions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuningLAION's Got Talent: Generated Voice Acting Dataset
Overview
"LAION's Got Talent" is a synthetic voice acting dataset designed to offer a broad range of emotional expressions, vocal bursts, and multi-language utterances. This dataset is a component of the BUD-E project, led by LAION with support from Intel, and aims to drive forward research in context-aware and empathetic AI voice assistants.
Updated Composition
Voices and Languages
English: 11 OpenAI voices, each… See the full description on the dataset page: https://huggingface.co/datasets/laion/laions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuning.Robot-EQ
RobotEQ-Data
Official dataset release for RobotEQ.
Evaluation & Scripts
For inference scripts, evaluation scripts, and data production tooling, see the RobotEQ code repository.
Dataset Statistics
Item
Count
Behavior judgment scenarios (synthetic)
1,812
Behavior judgment scenarios (real POV)
223
Behavior judgment scenarios (total)
2,035
Behavior judgment behavior annotations
3,171
Spatial grounding questions
825… See the full description on the dataset page: https://huggingface.co/datasets/Tongji-Emotion/Robot-EQ.pg19-test
Dataset Card for "pg19-test"
More Information needed
dialogs-ru-emotional-conversations
Dialogs: A Studio-Quality Expressive Conversational Russian Speech Corpus
Dialogs is a 20.6-hour studio-quality corpus of expressive, conversational
Russian speech, designed for dialog-oriented and emotional text-to-speech.
Unlike existing Russian corpora — mostly single-speaker read speech or large but
low-quality web-mined audio — Dialogs was recorded by professional theatre actors
performing scripted dialogs face-to-face, capturing natural turn-taking,
timing, and expressive… See the full description on the dataset page: https://huggingface.co/datasets/langswap/dialogs-ru-emotional-conversations.Emolia
Dataset Card for Emolia
Dataset Description
This dataset is an enhanced version of the Emilia dataset, enriched with detailed emotion annotations. The annotations were generated using models from the EmoNet suite to provide deeper insight into the emotional content of speech. This work is based on the research and models described in the blog post "Do They See What We See?".
The annotations include 54 scores for each sample, covering a wide range of emotional and… See the full description on the dataset page: https://huggingface.co/datasets/laion/Emolia.Long-Data-Collections-Pretrain-Without-Books
Dataset Card for "Long-Data-Collections-Pretrain-Without-Books"
Paraquet version of the pretrain split of togethercomputer/Long-Data-Collections WITHOUT books
Statistics (in # of characters): total_len: 236088622215, average_len: 25159.041601590307
BRIGHTER-emotion-categories
BRIGHTER Emotion Categories Dataset
This dataset contains the emotion categories data from the BRIGHTER paper: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages.
Dataset Description
The BRIGHTER Emotion Categories dataset is a comprehensive multi-language, multi-label emotion classification dataset with separate configurations for each language. It represents one of the largest human-annotated emotion datasets across multiple… See the full description on the dataset page: https://huggingface.co/datasets/brighter-dataset/BRIGHTER-emotion-categories.emo_webds_2emo_parleremo_webdssat-reading
Dataset Card for "sat-reading"
This dataset contains the passages and questions from the Reading part of ten publicly available SAT Practice Tests.
For more information see the blog post Language Models vs. The SAT Reading Test.
For each question, the reading passage from the section it is contained in is prefixed.
Then, the question is prompted with Question #:, followed by the four possible answers.
Each entry ends with Answer:.
Questions which reference a diagram, chart, table… See the full description on the dataset page: https://huggingface.co/datasets/emozilla/sat-reading.microduck-emotions
Microduck Emotions
A collection of emotions for the Microduck robot. Each one is a motion and a sound designed together, beat by
beat, with the beak opening on the sound, rendered in the physics simulation and validated on the real robot. Every
emotion is three files: the motion (emotions/<name>.json, keyframes at 30 fps: head and body offsets played on
top of whichever trained policy is active, plus the policy hand-overs, such as the sit that devastated and play dead
start)… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/microduck-emotions.Emilia-with-Emotion-Annotations4EmoFake_test
EmoFake Test
Benchmark-ready packaging of the EmoFake test set for speech anti-spoofing.
Overview
Emotional speech deepfake detection test set. Contains bonafide emotional utterances and spoofed samples with emotion conversion.
License
CC BY 4.0. See LICENSE.txt.
Schema
Column
Type
Description
path
string
Audio filename
audio
Audio(16000)
Audio waveform, 16 kHz mono
label
ClassLabel
bonafide (index 0) or spoof (index 1)… See the full description on the dataset page: https://huggingface.co/datasets/SpeechAntiSpoofingBenchmarks/EmoFake_test.EmoSpoofTTS
EmoSpoofTTS
A spoof-only attack corpus of emotional text-to-speech (TTS) synthesis:
36,000 clips spanning 3 modern TTS systems, 10 speakers, and 4 emotions, all
synthesized from transcripts of the Emotional Speech Dataset (ESD).
Overview
EmoSpoof-TTS (Mahapatra et al., "Can Emotion Fool Anti-spoofing?", Interspeech
2025, arXiv:2505.23962) was built to study whether emotionally expressive TTS
is harder for anti-spoofing systems to detect than neutral TTS. For 10… See the full description on the dataset page: https://huggingface.co/datasets/SpeechAntiSpoofingBenchmarks/EmoSpoofTTS.qwen3-tts-multilingual-emotional-speechEmilia-with-Emotion-Annotations5eMotions
eMotions Dataset
The proposed eMotions dataset in our paper entitled Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline (ACM ICMR'25).
If you find our dataset useful, please cite our paper:
@inproceedings{wu2025towards,
title={Towards emotion analysis in short-form videos: A large-scale dataset and baseline},
author={Wu, Xuecheng and Sun, Heli and Xue, Junxiao and Nie, Jiayu and Kong, Xiangyan and Zhai, Ruofan and Huang, Danlei and He, Liang}… See the full description on the dataset page: https://huggingface.co/datasets/Conna/eMotions.emo_speech_filtered_v12 second filtered emotional speech in webdataset format
https://huggingface.co/datasets/EQ4You/Emotional_Speech
laion-emotional-trajectory-t80
LAION Emotional-Trajectory Speech — tier T≥0.80
319,765 crossfaded speech trajectories · 4,482 audio-hours · 1,598,825 source clips
A trajectory is a short sequence of 5 consecutive utterances by one
speaker whose measured emotion or voice character moves monotonically from one end of
the corpus distribution to the other. The clips are joined into one continuous audio file
with equal-power crossfades, the joined audio is re-tokenized with MOSS-Audio-
Tokenizer-v2, and every… See the full description on the dataset page: https://huggingface.co/datasets/laion/laion-emotional-trajectory-t80.