datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
common_voice_17_0yodas3
YODAS v3
Paper
YODAS v3 is a large web-crawled dataset containing over 1.1 million hours of audio that were originally released under a CC-BY-3.0 license. The dataset contains audio in over 100 languages. YODAS v3 can be used for a variety of multi-modal tasks, including Automatic Speech Recognition, Text-to-Speech, and Audio Representation Learning. We crawl a distinct set of videos from the v1 and v2 versions of YODAS, to guarantee that there are no overlaps in the data.
For… See the full description on the dataset page: https://huggingface.co/datasets/espnet/yodas3.cml-tts
Dataset Card for CML-TTS
Dataset Summary
CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG).
CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish… See the full description on the dataset page: https://huggingface.co/datasets/ylacombe/cml-tts.fleurs
FLEURS
Fleurs is the speech version of the FLoRes machine translation benchmark.
We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs.librispeech_asr_dummycovost2This is a partial copy of CoVoST2 dataset.
The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer.
The limitation of this method is that all audio samples of the EN_XX subsets are duplicated, as such the size of the dataset is larger.
As such, not all the data is included: Only the validation and test subsets are available.
From the XX_EN subsets, only fr, es, and zh-CN are included.
svq
Simple Voice Questions
Simple Voice Questions (SVQ) is a set of short audio questions recorded in 26 locales across 17 languages under multiple audio conditions. It serves as a core evaluation componenet for Massive Sound Embedding Benchmark (MSEB).
Technical Specifications
Feature
Details
Locales
26
Languages
17
Total Speakers
~700 (Capped at 250 recordings per speaker)
Audio Conditions
Clean, Background Speech, Media, Traffic Noise
Gender… See the full description on the dataset page: https://huggingface.co/datasets/google/svq.librispeech_asr
Dataset Card for librispeech_asr
Dataset Summary
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic… See the full description on the dataset page: https://huggingface.co/datasets/openslr/librispeech_asr.whisper_transcriptions.reazon_speech_allAudioSet
Dataset Card for AudioSet
Dataset Summary
AudioSet is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.
Supported Tasks and Leaderboards
audio-classification: Classify audio clips into categories. The leaderboard is available here
Languages
The class labels in the dataset are in English.
Dataset Structure
Data Instances
Example… See the full description on the dataset page: https://huggingface.co/datasets/agkphysics/AudioSet.multilingual_librispeech
Dataset Card for MultiLingual LibriSpeech
Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from OpenSLR to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multilingual_librispeech.peoples_speech
Dataset Card for People's Speech
Dataset Summary
The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.
Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/MLCommons/peoples_speech.EuroSpeech
EuroSpeech Dataset
Dataset Description
EuroSpeech is a large-scale multilingual speech corpus containing high-quality aligned parliamentary speech across 22 European languages. The dataset was constructed by processing parliamentary proceedings using a robust alignment pipeline that handles diverse audio formats and non-verbatim transcripts. More information can be found in the paper.
This dataset is 16 kHz, the 24 kHz version of EuroSpeech can be found at… See the full description on the dataset page: https://huggingface.co/datasets/disco-eth/EuroSpeech.voxpopuli
Dataset Card for Voxpopuli
Dataset Summary
VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native… See the full description on the dataset page: https://huggingface.co/datasets/facebook/voxpopuli.VaaniVAANI is an India-representative multi-modal multi-lingual dataset.
The current version (phase 1- 80 districts, phase 2- 85 districts) contains ~31278 hours of spontaenous,image-prompted speech by 156K speakers across 165 districts, talking about 288K images covering 105 languages.
From this audio data, 2,122 hours of transcribed data(text) is available, spanning almost evenly across the 165 districts.
Project Vaani, by IISc, Bangalore and ARTPARK, is capturing the true diversity of India’s… See the full description on the dataset page: https://huggingface.co/datasets/ARTPARK-IISc/Vaani.Omni-DuplexEval
Omni-DuplexEval
📖 arXiv | GitHub
Omni-DuplexEval is a benchmark for evaluating real-time duplex multimodal interaction. Unlike conventional offline video understanding benchmarks, Omni-DuplexEval focuses on streaming settings where models must continuously process evolving multimodal inputs and decide what to respond and when to respond.
The benchmark contains two scenarios:
Real-Time Description (RTD): evaluates continuous streaming description ability.
Proactive Reminder (PR):… See the full description on the dataset page: https://huggingface.co/datasets/Hothan/Omni-DuplexEval.IndicVoices
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
Updates
[23 December 2025] We now have 11,200 hours of transcribed data! 🎉
Overview
INDICVOICES is a dataset of natural and spontaneous speech containing a total of 23.7K hours of read (8%), extempore (76%) and conversational (15%) audio from 51K speakers covering 400+ Indian districts and 22 languages. Of these 23.7K hours, 11.2K hours have… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/IndicVoices.EgoIT-99KCheckout the paper EgoLife (https://arxiv.org/abs/2503.03803) for more information.
Multitask-National-Speech-Corpus-v1Multitask-National-Speech-Corpus (MNSC v1) is derived from IMDA's NSC Corpus.
MNSC is a multitask speech understanding dataset derived and further annotated from IMDA NSC Corpus. It focuses on the knowledge of Singapore's local accent, localised terms, and code-switching.
ASR: Automatic Speech Recognition
SQA: Speech Question Answering
SDS: Spoken Dialogue Summarization
PQA: Paralinguistic Question Answering
from datasets import load_dataset
data =… See the full description on the dataset page: https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1.parliament_hearings_processed
Preprocessed parliament hearings ASR dataset to truecased form.
Original dataset: https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3126
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
sequence: string
splits:
- name: train
num_bytes: 53645064353.18
num_examples: 191455
- name: test
num_bytes: 740331298.0
num_examples: 2726… See the full description on the dataset page: https://huggingface.co/datasets/jkot/parliament_hearings_processed.vp-er-10l
Dataset Card for "vp-er-10l"
More Information needed
NatureLM-audio-training
Dataset card for NatureLM-audio-training
Overview
NatureLM-audio-training is a large and diverse audio-language dataset designed for training bioacoustic models that can generate a natural language answer to a natural language query on a reference bioacoustic audio recording.
For example, for an in-the-wild audio recording of a bird species, a relevant query might be "What is the common name for the focal species in the audio?" to which an audio-language model trained… See the full description on the dataset page: https://huggingface.co/datasets/EarthSpeciesProject/NatureLM-audio-training.symile-m3
Dataset Card for Symile-M3
Symile-M3 is a multilingual dataset of (audio, image, text) samples. The dataset is specifically designed to test a model's ability to capture higher-order information between three distinct high-dimensional data types: by incorporating multiple languages, we construct a task where text and audio are both needed to predict the image, and where, importantly, neither text nor audio alone would suffice.
Paper: https://arxiv.org/abs/2411.01053
GitHub:… See the full description on the dataset page: https://huggingface.co/datasets/arsaporta/symile-m3.genshin-voice
Genshin Voice
Genshin Voice is a dataset of voice lines from the popular game Genshin Impact.
Hugging Face 🤗 Genshin-Voice
ModelScope Genshin-Voice
Per-speaker downloads are grouped by language and ZIP size. Browse every archive in the ZIP index.
Last update at 2026-08-13
654252 wavs
7291 without speaker (1%)
52693 without transcription (8%)
1088 without inGameFilename (0%)
Dataset Details
Dataset Description
The dataset contains voice lines… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/genshin-voice.wenetspeechami
Dataset Card for AMI
Dataset Description
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were… See the full description on the dataset page: https://huggingface.co/datasets/edinburghcstr/ami.whisper_transcriptions.mls.wer_10.0minds14
MInDS-14
MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14
intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
Example
MInDS-14 can be downloaded and used as follows:
from datasets import load_dataset
minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French
# to download all data for multi-lingual fine-tuning uncomment following… See the full description on the dataset page: https://huggingface.co/datasets/PolyAI/minds14.hit-asr
HIT-ASR — data and results
The data behind HIT-ASR: Hierarchical Transformer Routing for Adaptive ASR Expert Selection (Huseyin Karaca,
A. Samil Namli, Suleyman S. Kozat — Bilkent University): what the pretrained ASR experts of the paper produce on its
four English corpora, and the stored results every notebook of the code repository reads.
Code and notebooks: github.com/huseyin-karaca/hit-asr
Documentation: huseyin-karaca.github.io/hit-asr
What is here… See the full description on the dataset page: https://huggingface.co/datasets/huseyin-karaca/hit-asr.LibriS2S
LibriS2S
This repo contains scripts and alignment data to create a dataset build further upon librivoxDeEn such that it contains (German audio, German transcription, English audio, English transcription) quadruplets and can be used for Speech-to-Speech translation research. Because of this, the alignments are released under the same Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
These alignments were collected by downloading the English audiobooks… See the full description on the dataset page: https://huggingface.co/datasets/PedroDKE/LibriS2S.