datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.emo_webds_2emo_parleremo_webdsEmilia-with-Emotion-Annotations4qwen3-tts-multilingual-emotional-speechEmilia-with-Emotion-Annotations5emo_speech_filtered_v12 second filtered emotional speech in webdataset format
https://huggingface.co/datasets/EQ4You/Emotional_Speech
Emotiontalk
EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations
Introduction
EmotionTalk is an interactive Chinese multimodal emotion dataset with rich annotations. This dataset provides multimodal information from 19 actors participating in dyadic conversation settings, incorporating acoustic, visual, and textual modalities. It includes 23.6 hours of speech (19,250 utterances), annotations for 7 utterance-level emotion categories (happy… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Emotiontalk.Emilia-with-Emotion-Annotations3emolia
emolia-balanced-5M-subset · flac 48 kHz · WebDataset (paired)
This is the emolia-balanced-5M-subset corpus repackaged for high-quality
audio–text contrastive training. Audio is re-encoded as mono FLAC at 48 kHz
(PCM 16-bit) and stored as a WebDataset of paired <key>.flac + <key>.json
samples.
The JSON sidecar carries the full annotation stack:
Original metadata (id, text, duration, speaker, language, dnsmos).
A free-text emotion_caption derived from the emotion-annotation scalars.… See the full description on the dataset page: https://huggingface.co/datasets/VoiceNet/emolia.Emilia-with-Emotion-Annotations2emonet-face-big
EmoNet-Face: A Fine-Grained, Expert-Annotated Benchmark for Facial Emotion Recognition
Dataset Summary
EmoNet-Face is a comprehensive benchmark suite designed to address critical gaps in facial emotion recognition (FER). Current benchmarks often have a narrow emotional spectrum, lack demographic diversity, and use uncontrolled imagery. EmoNet-Face provides a robust foundation for developing and evaluating AI systems with a deeper, more nuanced understanding of human… See the full description on the dataset page: https://huggingface.co/datasets/laion/emonet-face-big.EmoArt-130k
EmoArt: A Large-Scale Emotion-Annotated Artistic Dataset
Overview
EmoArt is a comprehensive, large-scale emotion-annotated artistic dataset containing 132,664 high-resolution artworks spanning 56 painting styles across 7 thematic categories. This dataset bridges the gap between visual art and emotional computing, enabling groundbreaking research in emotion-aware AI systems.
Key Statistics
📊 132,664 artworks with rich emotional annotations
🎨 56 distinct… See the full description on the dataset page: https://huggingface.co/datasets/printblue/EmoArt-130k.emolia-hq
Emolia-HQ
Emolia-HQ is a high-quality, speaker-paired subset of the LAION Emolia dataset. Each sample includes a target utterance and a reference utterance from the same speaker, enabling speaker-conditioned tasks such as voice conversion, expressive TTS, and speaker-aware emotion recognition.
Source
Derived from laion/Emolia by:
Quality filtering: Only samples with dnsmos >= 3.0 are retained.
Speaker pairing: Each target sample is matched with a reference audio from the… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/emolia-hq.AffectDF_EmotionSDD
AffectDF: Emotionally Expressive Speech Deepfake Benchmark
Overview
AffectDF is a large-scale benchmark for speech deepfake detection under emotionally expressive spoofing conditions. The dataset is designed to evaluate whether current speech deepfake detection (SDD) systems can generalize beyond conventional neutral-speech benchmarks to modern emotional and expressive speech attacks.
AffectDF contains approximately 260 hours of audio generated using 21 spoofing… See the full description on the dataset page: https://huggingface.co/datasets/AffectDF/AffectDF_EmotionSDD.Emotion-Voice-Attribute-Reference-Snippets-DACVAE-Wave
Emotion and Voice Attribute Reference Snippets - DACVAE and Wave
Merged dataset combining TTS-AGI/enhanced-emo-snippets-balanced-DACVAE and
TTS-AGI/emotion-attribute-conditioning-dacvae with decoded WAV audio.
Overview
Total samples: 606,178
Filtered out: 363,331 (samples with speech_quality < 1.8)
Total tar files: 328
Total size: 1.54 TB
Audio format: WAV, 48kHz, PCM 16-bit mono
Latents: DAC-VAE float16 [T, 128] at 25 frames/sec
Dimensions: 57 (40 emotions + 15 voice… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/Emotion-Voice-Attribute-Reference-Snippets-DACVAE-Wave.emolia-3k-speaker-clusters-DACVAE
Emolia 3K Speaker Clusters
A curated set of 3,000 diverse speaker clusters derived from the TTS-AGI/emolia-hq dataset, with up to 20 representative audio samples per cluster.
Overview
The original emolia-hq dataset contains hundreds of thousands of speech samples with 128-dimensional WavLM speaker timbre embeddings. These were first clustered into 10,000 centroids, then intelligently pruned to 3,000 using density-aware farthest-point sampling to ensure:
Outlier… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/emolia-3k-speaker-clusters-DACVAE.emotion_bias
Emotion Bias in Synthetic Face Generation
Description
This dataset accompanies the paper "Happy Young Women, Grumpy Old Men? Emotion Prompts as Demographic Selectors in AI Image Generation".
It contains 56,000 synthetic face images generated by eight state-of-the-art text-to-image (T2I) models across seven emotion prompt conditions, along with demographic attribute annotations (gender, race, age) and perceived attractiveness labels for each image.
The dataset is designed… See the full description on the dataset page: https://huggingface.co/datasets/mengtingwei/emotion_bias.emolia-3k-speaker-clusters
Emolia 3K Speaker Clusters
A curated set of 3,000 diverse speaker clusters derived from the TTS-AGI/emolia-hq dataset, with up to 20 representative audio samples per cluster.
Overview
The original emolia-hq dataset contains hundreds of thousands of speech samples with 128-dimensional WavLM speaker timbre embeddings. These were first clustered into 10,000 centroids, then intelligently pruned to 3,000 using density-aware farthest-point sampling to ensure:
Outlier… See the full description on the dataset page: https://huggingface.co/datasets/laion/emolia-3k-speaker-clusters.emolia-balanced-5M-subset
emolia-balanced-5M-subset
A balanced ~5.26M-sample subset of laion/Emolia (80.5M speech samples), packaged as WebDataset-compatible tar shards for direct use in training pipelines.
How this subset was filtered
Samples were selected if they met either of two criteria:
1. Emotion thresholds
Each sample carries 40 emotion annotation scores (from the Emonet taxonomy) in its metadata. A sample qualifies for an emotion bucket if its score for that emotion meets or… See the full description on the dataset page: https://huggingface.co/datasets/laion/emolia-balanced-5M-subset.emoji-tts-22k
Emoji-TTS 22K Training Data
Emoji-TTS 22K is the training corpus used to build Emoji-TTS, an emoji-conditioned expressive text-to-speech model. Each example pairs an English transcript, an emoji control label, and a synthetic WAV utterance spoken with the fixed Kore voice.
The corpus contains 21,940 utterances: 19,945 emoji-conditioned samples and 1,995 neutral/no-emoji samples. The control inventory covers ten emoji labels plus the neutral <none> label.
How It Was Built… See the full description on the dataset page: https://huggingface.co/datasets/emoji-tts/emoji-tts-22k.voice-emo-cloning-dataset
Emotion-Cloning TTS Training Dataset
Location
/home/deployer/laion/echo-tts-training-main/emotion_eval/dataset_output/
Overview
This dataset contains ~22,518 training triplets for fine-tuning a zero-shot voice+emotion cloning TTS model. Each sample provides everything needed to train a model that can clone both a speaker's voice identity AND their emotional delivery from separate reference audio clips.
The data is stored as WebDataset .tar shards, partitioned… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/voice-emo-cloning-dataset.enhanced-emo-snippets-balanced-DACVAE
Enhanced Emotion Snippets — Balanced DACVAE
A balanced, emotion-bucketed subset of TTS-AGI/enhanced-audiosnippets-DACVAE,
organized by Empathic Insight Voice+ emotion and voice attribute categories.
Overview
This dataset provides up to 100 samples per magnitude bucket for each of the
40 emotion categories and 15 voice attribute dimensions scored by
Empathic Insight Voice+.
Selection Criteria
Emotion Categories (40 dimensions)
For each emotion (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/enhanced-emo-snippets-balanced-DACVAE.Emoticemotion-attribute-conditioning-dacvae
Echo TTS - Emotion & Attribute Conditioning Dataset (DAC-VAE Latents)
Pre-bucketed speech dataset with DAC-VAE latent representations organized by 40 emotion categories and 13 vocal/audio attributes. Built for conditioning fine-tuning of Echo TTS and similar DiT-based TTS models.
Overview
Total emotion samples: 163,271 (across 40 emotions, 10K cap per emotion)
Total attribute samples: ~785K (across 13 attributes x 7 buckets, 10K cap per bucket)
Format: WebDataset .tar… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/emotion-attribute-conditioning-dacvae.en_and_de_reference_voice_files_for_emotion_cloningvoxbox-vb-wds-emotion-fullemo_subset_webdsEmotion-Voice-Attribute-Reference-Snippets-DACVAE
Emotion and Voice Attribute Reference Snippets - DACVAE and Wave
Merged dataset combining TTS-AGI/enhanced-emo-snippets-balanced-DACVAE and
TTS-AGI/emotion-attribute-conditioning-dacvae with decoded WAV audio.
Overview
Total samples: 606,178
Filtered out: 363,331 (samples with speech_quality < 1.8)
Total tar files: 328
Total size: ~98 GB (latents-only, no WAV)
Audio format: WAV, 48kHz, PCM 16-bit mono
Latents: DAC-VAE float16 [T, 128] at 25 frames/sec
Dimensions: 57 (40… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/Emotion-Voice-Attribute-Reference-Snippets-DACVAE.