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-ENemilia-yodasA mirror of the Emilia-YODAS dataset. Only includes the YODAS subset from the original dataset.
https://huggingface.co/datasets/amphion/Emilia-Dataset
Emilia-ENEmilia-with-Emotion-Annotations4Emilia-with-Emotion-Annotations5Emilia-dataset-french-with-gender
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/AdrienB134/Emilia-dataset-french-with-gender.Emilia-DE
Emilia - DE
Clean version with only text and audio from the Emilia Dataset.
Samples: 653,109
Language: DE
Usage
from datasets import load_dataset
dataset = load_dataset("Vyvo/Emilia-DE")
sample = dataset['train'][0]
text = sample['text']
audio = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']
emilia-yodas-fr-parquetEmilia-dataset-french-splitEmilia-Annotated-WIPStill a WIP, full dataset is still being annotated
Emilia-with-Emotion-Annotations3Emilia-YODAS-Voice-Conversion
Emilia-YODAS-Voice-Conversion
We sample https://huggingface.co/datasets/amphion/Emilia-Dataset YODAS set for voice conversion.
Filter transcriptions based on character repetitiveness and word ngrams.
Filter speaker similarity using https://huggingface.co/nvidia/speakerverification_en_titanet_large during speaker permutation.
Convert audio to speech tokens using https://huggingface.co/neuphonic/neucodec
We also upload the full permutation as zip files.
Speech Tokenizer… See the full description on the dataset page: https://huggingface.co/datasets/Scicom-intl/Emilia-YODAS-Voice-Conversion.Emilia-with-Emotion-Annotations2Emilia-YODAS-DE
Emilia-YODAS - DE
Clean version with only text and audio from the Emilia Dataset.
Samples: 2,005,364
Language: DE
Usage
from datasets import load_dataset
dataset = load_dataset("Vyvo/Emilia-YODAS-DE")
sample = dataset['train'][0]
text = sample['text']
audio = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']
emilia_mfa_correctEmilia-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.Emilia-DEemilia-subset
emilia-yodas-english-neucodec
Dataset Card for NeuCodec Emilia-YODAS
Dataset Summary
The NeuCodec Emilia-YODAS dataset is an English-language dataset containing >30M audio samples (>78k hours), taken from the English-language subset of Emilia-YODAS and compressed with NeuCodec.
Usage
import torch
from datasets import load_dataset
from neucodec import NeuCodec
# load dataset and model
dataset = load_dataset("neuphonic/emilia-yodas-english-neucodec", split="train"… See the full description on the dataset page: https://huggingface.co/datasets/neuphonic/emilia-yodas-english-neucodec.JA_Emilia_Yodas_266hthe dataset is 266h out of the japanese part from https://huggingface.co/datasets/amphion/Emilia-Dataset ( Emilia Yodas - cc by 4.0)
audio event classified via scribe v1 (elevenlabs stt/asr)
facebook audio aestetics to be used as prefilter
the dataset is very much at a v1 -
if you want to help - lets talk
https://discord.gg/RUs3uzBdW3 (nsfw is fully opt in only - as sfw)
if you want full transaction timestamps as they come from scribe v1 - they are cc by 4.0 NC and can be found here… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/JA_Emilia_Yodas_266h.Emilia-KO
Emilia - KO
Clean version with only text and audio from the Emilia Dataset.
Samples: 92,182
Language: KO
Usage
from datasets import load_dataset
dataset = load_dataset("Vyvo/Emilia-KO")
sample = dataset['train'][0]
text = sample['text']
audio = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']
Emilia-YODAS-ZH
Emilia-YODAS - ZH
Clean version with only text and audio from the Emilia Dataset.
Samples: 130,460
Language: ZH
Usage
from datasets import load_dataset
dataset = load_dataset("Vyvo/Emilia-YODAS-ZH")
sample = dataset['train'][0]
text = sample['text']
audio = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']
dedup-Malaysian-Emilia
Dedup Malaysian Emilia
Dedup mesolitica/Malaysian-Emilia at 95% similarity threshold.
You can use this for voice profiling or public voice synthetic generation.
download the dataset
huggingface-cli download \
mesolitica/dedup-Malaysian-Emilia \
--include "*.zip" \
--repo-type "dataset" \
--local-dir './'
wget https://gist.githubusercontent.com/huseinzol05/2e26de4f3b29d99e993b349864ab6c10/raw/9b2251f3ff958770215d70c8d82d311f82791b78/unzip.py
python3 unzip.py… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/dedup-Malaysian-Emilia.DE_Emilia_Yodas_680hthe dataset is 680h out of the german part from
https://huggingface.co/datasets/amphion/Emilia-Dataset ( Emilia Yodas - cc by 4.0)
audio event classified via scribe v1 (elevenlabs stt/asr)
facebook audio aestetics to be used as prefilter
the dataset is very much at a v1 -
if you want to help - lets talk
https://discord.gg/RUs3uzBdW3 (nsfw is fully opt in only - as sfw)
if you want full transaction timestamps as they come from scribe v1 - they are cc by 4.0 NC
and can be found here… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/DE_Emilia_Yodas_680h.Emilia-YODAS-DEEmo-EmiliaC2SER: Paper | Code | HuggingFace
Emo-Emilia Dataset
To better simulate real-world context, we introduce a new SER test set, Emo-Emilia.
Specifically, we apply the automated labeling approach to annotate Emilia, a large-scale multilingual and diverse speech generation resource with over 100,000 hours of speech data that captures a wide range of emotional contexts.
We then manually verify the accuracy of the emotion labels. Each utterance is checked by at least two experts to ensure… See the full description on the dataset page: https://huggingface.co/datasets/ASLP-lab/Emo-Emilia.EN_Emilia_Yodas_616hthe dataset is 616h out of the English part from https://huggingface.co/datasets/amphion/Emilia-Dataset ( Emilia Yodas - cc by 4.0)
audio event classified via scribe v1 (elevenlabs stt/asr)
facebook audio aestetics to be used as prefilter
the dataset is very much at a v1 -
if you want to help - lets talk
https://discord.gg/RUs3uzBdW3 (nsfw is fully opt in only - as sfw)
if you want full transaction timestamps as they come from scribe v1 - they are cc by 4.0 NC and can be found here… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/EN_Emilia_Yodas_616h.Malaysian-Emilia-Nonverbal-Tags
Malaysian-Emilia Non-verbal Tags
Inline non-verbal event tags (laughter, cough, sigh, sneeze, burping, humming) mined from
Scicom-intl/Malaysian-Emilia
podcast segments, for expressive-TTS training. Tags follow the
<|sfx:family|> + onomatopoeia convention (e.g. <|sfx:laughter|>Haha), placed inline at the
word boundary nearest the event onset.
Example:
Dato' cakap ni macam ada pengalaman dengan orang berusia. Ada ke tau? <|sfx:laughter|>Hahaha Tak adalah...
Pipeline… See the full description on the dataset page: https://huggingface.co/datasets/Scicom-intl/Malaysian-Emilia-Nonverbal-Tags.Emilia-DE-B000000