datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Emilia-Dataset
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline.
News 🔥
2025/02/26: The Emilia-Large dataset, featuring over 200,000 hours of data, is now available!!! Emilia-Large combines the original 101k-hour Emilia dataset (licensed under CC BY-NC 4.0) with the brand-new 114k-hour Emilia-YODAS… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset.Emilia-YODAS-ENClotho-Moment
Clotho-Moment
This repository provides wav files used in Language-based Audio Moment Retrieval.
Each sample includes long audio containing some audio events with the temporal and textual annotation.
Project page: https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/
Code: https://github.com/line/lighthouse
Split
Train
train/train-{000..715}.tar
37930 audio samples
Valid
valid/valid-{000..108}.tar
5741 audio samples
Test
test/test-{000..142}.tar
7569… See the full description on the dataset page: https://huggingface.co/datasets/lighthouse-emnlp2024/Clotho-Moment.emilia-yodasA mirror of the Emilia-YODAS dataset. Only includes the YODAS subset from the original dataset.
https://huggingface.co/datasets/amphion/Emilia-Dataset
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.Emilia-ENemo_webds_2emo_parleremo_webdslaions_got_talent_enhanced_no_metadataEmilia-with-Emotion-Annotations4NeMo
NVIDIA NeMo Speech
Checkout our HuggingFace🤗 collection for the latest open
weight checkpoints and demos!
Updates
2026-03: Nemotron 3 VoiceChatis now released in Early Access. Built on the Nemotron Nano v2 LLM backbone with Nemotron speech and TTS decoder, VoiceChat delivers full-duplex, natural, interruptible conversations with low latency. Try out the demo and apply for early access.
2026-03: Nemotron-Speech-Streaming v2603 has been
updated. It has been… See the full description on the dataset page: https://huggingface.co/datasets/echodict/NeMo.Emilia-with-Emotion-Annotations5qwen3-tts-multilingual-emotional-speechJapanese-Eroge-Voice
Japanese-Eroge-Voice
Description
This dataset contains pairs of audio data and corresponding transcriptions extracted from Japanese eroge (adult games) that I have personally purchased. The transcriptions are generated using the litagin/anime-whisper model.
Preprocessing Steps
The raw audio data has undergone the following preprocessing steps:
Loudness Normalization:
Audio loudness is normalized using ffmpeg's 2-pass loudnorm filter to target parameters of… See the full description on the dataset page: https://huggingface.co/datasets/NandemoGHS/Japanese-Eroge-Voice.emolia
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-Annotations3ESpeech-webinars2
Webinar Audio Dataset
Dataset Description
This dataset contains 850 hours processed webinar audio segments with corresponding metadata. Each audio file represents a segment extracted from webinar recordings, processed at 44.1kHz sample rate.
Dataset Summary
Language: Russian
Task: TTS, ASR, Quality Asessment
Audio format: MP3, 44.1kHz sample rate
Structure: Segmented audio files with JSON metadata
Dataset Structure
Data Fields… See the full description on the dataset page: https://huggingface.co/datasets/ESpeech/ESpeech-webinars2.Emilia-with-Emotion-Annotations2musanMUSAN
Identifier: SLR17
Summary: A corpus of music, speech, and noise
Category: Audio
License: Attribution 4.0 International (CC BY 4.0)
Downloads (use a mirror closer to you):
musan.tar.gz [11G] ( The corpus ) Mirrors: [EU] [EU] [CN]
About this resource:
MUSAN is a corpus of music, speech, and noise recordings.
This work was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1232825 and by Spoken Communications.
You can cite the data using the… See the full description on the dataset page: https://huggingface.co/datasets/EaseZh/musan.Emilia-NV
NVSpeech Dataset
Overview
The NVSpeech dataset provides extensive annotations of paralinguistic vocalizations for Mandarin Chinese speech, aimed at enhancing the capabilities of automatic speech recognition (ASR) and text-to-speech (TTS) systems. The dataset features explicit word-level annotations for 18 categories of paralinguistic vocalizations, including non-verbal sounds like laughter and breathing, as well as lexicalized interjections like "uhm" and "oh."… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-NV.mls-enhanced-dacvae
Multilingual LibriSpeech converted to DAC VAE latents
Source
facebook/multilingual_librispeech
Format
Each tar shard (~2GB) contains samples with three files per sample:
{sample_key}.audio.flac # Original audio (FLAC, original sample rate)
{sample_key}.dacvae.npy # DAC VAE latent [T_latent, 128] numpy float32
{sample_key}.metadata.json # All metadata + duration_seconds + chars_per_second
DAC VAE Latent Format
Model:… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/mls-enhanced-dacvae.emo_speech_filtered_v12 second filtered emotional speech in webdataset format
https://huggingface.co/datasets/EQ4You/Emotional_Speech
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.EchoSetEmilia-YODAS-DEAffectDF_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.Lyra-Evalcosyvoice2_encommon_voice_english