datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
librivox-mirror
LibriVox Mirror
Fast, structured, continuously updated LibriVox audio mirror.
Current snapshot
Metric
Value
Published books
21,728
Published sections
493,262
Audio hours
132,580.7
Audio languages
86
Quarantined books
606
Last updated (UTC)
2026-09-23T13:55:55.728817Z
Audio by language
Language
Hours
English
131,631.3
German
417.0
Spanish
160.9
French
103.8
Portuguese
37.4
Polish
34.1
Dutch
25.8… See the full description on the dataset page: https://huggingface.co/datasets/twangodev/librivox-mirror.mosel
Dataset Description, Collection, and Source
The MOSEL corpus is a multilingual dataset collection including up to 950K hours of open-source speech recordings covering the 24 official languages of the European Union. We collect data by surveying labeled and unlabeled speech corpora under open-source compliant licenses.
In particular, MOSEL includes the automatic transcripts of 441k hours of unlabeled speech from VoxPopuli and LibriLight. The data is transcribed using Whisper large… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/mosel.ParsVoice
ParsVoice
A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
📣 Accepted to the EMNLP 2026 Main Conference.
ParsVoice is the largest publicly available Persian speech–text corpus tailored for
training multi-speaker text-to-speech (TTS) systems. It is built from long-form
Persian audiobook recordings using a fully automated pipeline combining sentence-aware
segmentation, ASR transcription, a ParsBERT sentence-completion classifier, binary-search… See the full description on the dataset page: https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice.parler-tts_mls_eng_10k_snac_token_old
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/blanchon/parler-tts_mls_eng_10k_snac_token_old.NEXUS-temporal_hierarchical_multi-modal
NEXUS: Neural Evolution for eXtensible Universal Semantics Dataset
(Temporal Multimodal Slices)
This dataset is a multi-modal, hierarchical, temporal representation derived from HuggingFaceFV/finevideo. It is designed for streaming training where the primary unit is a 10 ms "slice" that aggregates upward into moments (100 ms), seconds (1 s), experiences (10 s), and minutes (60 s).
It is meant to represent an extensible stream of "experience" as there are… See the full description on the dataset page: https://huggingface.co/datasets/Ardea/NEXUS-temporal_hierarchical_multi-modal.MyMentorLLM-dataset
Dataset Card for MyMentorLLM
This dataset accompanies the MyMentorLLM paper, arXiv:2607.25667, which describes the simulation environment, generation procedure, experimental design and validation analyses. If you use this dataset, please cite the paper (see Citation Information).
Dataset Summary
MyMentorLLM is a multimodal voice- and text-based deliberate-practice environment used to generate 2,100 simulated Cognitive Behavioural Therapy (CBT) training sessions… See the full description on the dataset page: https://huggingface.co/datasets/RodolfoRizzi/MyMentorLLM-dataset.omniasr-molge
OmniASR Molge Aligned
Training-friendly re-segmentation of Meta’s facebook/omnilingual-asr-corpus: long utterances are segmented / aligned into ≤30s clips with transcripts, then packed as Parquet shards with embedded FLAC.
Source
facebook/omnilingual-asr-corpus
Configs
omniasr_aligned_v1, omniasr_aligned_v2
Splits
train / validation (dev-*.parquet) / test
Scale
~2.56M utts · ~839 shards · ~439GB
If this dataset is useful for your work, we’d appreciate a… See the full description on the dataset page: https://huggingface.co/datasets/Sanghyang00/omniasr-molge.enhanced-audiosnippets-long-2-8M
Enhanced Audiosnippets Long 2.8M
Enhanced version of mitermix/audiosnippets_long_2_8M with speech enhancement, emotion annotations, speaker embeddings, and comprehensive metadata analysis.
Dataset Summary
Metric
Value
Total samples
2,633,037
Total audio hours
4,932 h
Duration range
3.0s - 1124.3s
Mean duration
6.7s
Audio format
WAV, 48kHz mono
Tar files
1,410
Processing Pipeline
Each audio sample was processed through:
Speech… See the full description on the dataset page: https://huggingface.co/datasets/ai-music4you3/enhanced-audiosnippets-long-2-8M.mls-eng-speaker-descriptions
Dataset Card for Annotations of English MLS
This dataset consists in annotations of the English subset of the Multilingual LibriSpeech (MLS) dataset.
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. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/parler-tts/mls-eng-speaker-descriptions.Inkling-Small-Multimodal-Calibration
Inkling-Small Multimodal Calibration
The exact 1,663 samples used for BF16 routed-expert importance collection
for Inkling-Small Mixed Quant GGUF.
This is calibration material, not a held-out evaluation benchmark.
The primary balanced pass is:
Category
Samples
Valid decoder tokens
Share
Text / reasoning
462
471,858
44.976%
Code / tool-oriented source text
205
209,715
19.989%
Real image / document
486
262,476
25.018%
Real speech audio
309
105,080
10.016%
Total… See the full description on the dataset page: https://huggingface.co/datasets/Baekpica/Inkling-Small-Multimodal-Calibration.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.
Data Collection
Speakers were presented with recording instructions specifying the recording environment and text query to be recorded.
They recorded using their own phones or tablets under four conditions:
clean: Record in quiet environment
background speech noise: Record while audio from sources like podcasts… See the full description on the dataset page: https://huggingface.co/datasets/mteb/svq.multilingual-tts-benchmark
Multilingual Speech Benchmark for Zero-Shot TTS
A voice-cloning and intelligibility benchmark for six language variants, built
from Common Voice 17.0 by coverage-driven selection rather than random sampling.
Every example pairs a reference clip of one speaker with a target text that
speaker never read, so a system is asked to clone a voice and produce new
speech, which is what zero-shot TTS is actually for.
Pipeline source code:… See the full description on the dataset page: https://huggingface.co/datasets/nineninesix/multilingual-tts-benchmark.MCIF-ST
MCIF-ST: Context-aware Speech Recognition and Speech Translation from MCIF
MCIF-ST provides both long-form and short-form ready-to-use
Automatic Speech Recogniton (ASR) and Speech Translation (ST) data derived
from MCIF (Multimodal
Crosslingual Instruction Following), a multilingual benchmark based on
scientific talks. While the original MCIF release packages its content as
instruction-following rows (multimodal context + prompt + expected
answer, for… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/MCIF-ST.tadabur
Tadabur: A Large-Scale Quran Audio Dataset
The most comprehensive and richly annotated Qur'anic recitation corpus to date
Faisal Alherran
✦ Overview
Tadabur is a large-scale, high-diversity Qur'anic speech dataset designed to advance research in Qur'anic Automatic Speech Recognition (ASR), reciter modeling, tajwīd-aware speech processing, and prosodic analysis. It is the most comprehensive publicly available collection of… See the full description on the dataset page: https://huggingface.co/datasets/MShakir7137/tadabur.fleurs-r-neucodec-all-languages
FLEURS-R NeuCodec All Languages
FLEURS-R metadata, source audio and precomputed NeuCodec speech tokens for 102
locales, plus a speaker label FLEURS itself does not ship.
Layout
data/{locale}-{split}.parquet — metadata, one row per utterance (this is what the
viewer shows).
audio/{locale}-{split}.zip — source FLEURS-R audio, 24kHz mono PCM16 WAV, members
named audio/{locale}/{split}/{id}.wav (the path column).
neucodec/{locale}-{split}-rank{N}.zip — NeuCodec… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/fleurs-r-neucodec-all-languages.MM-ContextASR-Bench
MM-ContextASR Bench
Metadata and evaluation splits for Multimodal Conversational Context for
LLM-Based ASR: Data Construction, Training, and Benchmark.
Dataset summary
Config
Examples
Audio
Context
Primary metric
mm_contextasr
1,250 (250 current utterances × 5 histories)
1,439 WAV files included
Controlled user-assistant dialogue
entity Recall
kespeech
19,212
Source ID only
Same-speaker speech and transcript
CER, SER, entity Recall
cv_yue
3,525… See the full description on the dataset page: https://huggingface.co/datasets/lilonghao/MM-ContextASR-Bench.maleo-short-1.5H
Dataset Card for Maleo Short 1.5H
Dataset Description
Dataset Summary
Maleo Short 1.5H is a manually curated, rigorously annotated speaker diarization dataset designed to benchmark State-of-the-Art (SOTA) models against complex, "in-the-wild" media domains. While modern diarization pipelines excel in controlled acoustic environments (like telephony or reading corpora), they heavily struggle with the overlapping speech, sound effects, and rapid speaker shifts… See the full description on the dataset page: https://huggingface.co/datasets/maleo-ai/maleo-short-1.5H.sqp-tts-en
SQP TTS (English)
Synthesized speech for SQPsychConv_qwen-2.5, a synthetic CBT therapist-client
dialogue dataset (English).
Each configuration below corresponds to one TTS model. Load a single model
with:
from datasets import load_dataset
ds = load_dataset("sinselm/sqp-tts-en", "qwen3-tts")
Models included
qwen3-tts: https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base
cosyvoice: https://huggingface.co/FunAudioLLM/Fun-CosyVoice3-0.5B-2512
fishaudio:… See the full description on the dataset page: https://huggingface.co/datasets/marleen-snsl/sqp-tts-en.fama-data
Dataset Description, Collection, and Source
The FAMA training data is the collection of English and Italian datasets for automatic speech recognition (ASR) and speech translation (ST)
used to train the FAMA models family.
The ASR section of FAMA is derived from the MOSEL data collection, including the automatic
transcripts obtained with Whisper and available in the HuggingFace MOSEL Dataset.
The ASR is further augmented with automatically transcribed speech from the… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/fama-data.arabic-msa-25k-saudi-male-tashkeel
Arabic MSA 25K — Saudi Male (Tashkeel)
25,000 fully-diacritized Arabic MSA text + audio pairs, rendered with a single
Saudi male neural voice at 48 kHz / 16-bit PCM, across 10 thematic categories.
Dataset Summary
arabic-msa-25k-saudi-male-tashkeel is a 25,000-clip Modern Standard Arabic (MSA)
speech corpus with matching diacritized text (full tashkeel / ḥarakāt). Every clip
is synthesized by the single voice ar-SA-HamedNeural (Azure Neural TTS, Saudi
Arabic male) at 48… See the full description on the dataset page: https://huggingface.co/datasets/HeshamHaroon/arabic-msa-25k-saudi-male-tashkeel.multichannel-meetings-10h
GroundTruth Multi-Channel Meeting Audio Dataset (10h)
Summary
This dataset contains approximately 10 hours of co-located, multi-speaker meeting recordings, each captured simultaneously via a room (built-in) microphone and individual close-talk lapel microphones worn by each participant.
Each meeting includes:
One full meeting recording (room microphone)
Individual close-talk recordings for each participant (one file per speaker)
Structured metadata describing speakers… See the full description on the dataset page: https://huggingface.co/datasets/ground-truth/multichannel-meetings-10h.africanvoices-naija-batch1-summary
African Voices Naija Train Metadata Summary
This dataset contains a compact summary of metadata for the Naija training split, provided as CSV tables for inspection and analysis.
Files included:
batch_summary.csv
domain_distribution.csv
The repository contains metadata summaries only and does not include raw audio.
massive-yt-edu-queue
Massive YouTube Educational Video Queue
Full metadata and content classification for 4,489,228 YouTube educational videos totaling 3,975,157 hours.
Description
This dataset contains metadata, content categorization, and license risk assessment for ~4.5M YouTube videos identified as potentially educational. It serves as the discovery and processing queue for the massive-yt-edu-transcriptions project, which aims to create the world's largest open educational transcript… See the full description on the dataset page: https://huggingface.co/datasets/thepowerfuldeez/massive-yt-edu-queue.mls-eng-10k-tags_tagged_10k_generated
Dataset Card for Annotations of 10K hours of English MLS
This dataset consists in annotations of a 10K hours subset of English version of the Multilingual LibriSpeech (MLS) dataset.
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. It includes about 44.5K hours of English and a total of about 6K hours… See the full description on the dataset page: https://huggingface.co/datasets/parler-tts/mls-eng-10k-tags_tagged_10k_generated.marathi-phonology-matrices
मराठी व्याकरण आणि ध्वनी मॅट्रिक्स
Marathi Phonology Matrices
गणितीय ध्वनी संश्लेषणासाठी (Mathematical Speech Synthesis) तयार केलेला सर्वसमावेशक मराठी फोनोलॉजी डेटासेट.
🎯 उद्देश्य
हा डेटासेट मराठी भाषेच्या:
फोनोलॉजिकल विश्लेषण
मॉर्फोलॉजी (लिंग, वचन, काळ)
संधि व श्व नियम
युक्तक्षर (Clusters)
Duration & Pitch नियम
Loanword adaptation
या सर्वांसाठी संरचित डेटा पुरवतो. TTS, ASR, G2P आणि Computational Linguistics संशोधनासाठी उपयुक्त.
📊… See the full description on the dataset page: https://huggingface.co/datasets/kalpesh77/marathi-phonology-matrices.multilingual-speech
Multilingual Indian Conversational Speech
A dataset of naturalistic, spontaneous two-speaker conversations across
13 Indian languages, with segment-level transcripts, speaker profiles,
timestamps, and recording metadata. Designed for ASR, TTS, speaker
diarization, and conversational speech research.
Languages (13)
Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali,
Odia, Punjabi, Tamil, Telugu, Urdu.
Content
Conversations… See the full description on the dataset page: https://huggingface.co/datasets/eQOURSE/multilingual-speech.Moroccan-Arabic-Multimodal-Emotion-Recognition
MDER-MA — Moroccan Arabic Multimodal Emotion Recognition (TTS-aligned repackaging)
A repackaging of the MDER-MA dataset that pairs every audio clip with its Arabic (Moroccan dialect / Darija) transcript and ships speaker-disjoint train/validation/test splits.
Original dataset: Ouali, S. & El Garouani, S. (2025). MDER-MA: A multimodal dataset for emotion recognition in low-resource Moroccan Arabic language. Data in Brief. DOI: 10.1016/j.dib.2025.112005. Mendeley:… See the full description on the dataset page: https://huggingface.co/datasets/FatimahEmadEldin/Moroccan-Arabic-Multimodal-Emotion-Recognition.ljspeech-mimi-codes
LJSpeech — Mimi Codes
Pre-extracted Kyutai Mimi neural-codec tokens for the
LJSpeech corpus — 13,100 English utterances
from a single female speaker reading public-domain audiobook passages (~24 hours).
This dataset contains codes only, not audio. For waveforms, go to the original LJSpeech
release; these codes are designed to be loaded alongside it for training Mimi-based speech
models without paying the ~1 hour of GPU extraction cost.
Schema
One row per utterance:… See the full description on the dataset page: https://huggingface.co/datasets/shangeth/ljspeech-mimi-codes.massive-yt-edu-transcriptions
Massive YouTube Educational Transcriptions
Large-scale educational content transcribed from YouTube using distil-whisper/distil-large-v3.5.
Stats
Videos: 59,355
Characters: 1,539,022,925 (~384M tokens)
Audio hours: 35,890
Model: faster-whisper (CTranslate2) with distil-large-v3.5
Hardware: 2x RTX 5090 + 2x RTX 4090 at 165-185x realtime
Fields
Field
Description
video_id
YouTube video ID
title
Video title
text
Full transcript… See the full description on the dataset page: https://huggingface.co/datasets/thepowerfuldeez/massive-yt-edu-transcriptions.example_mmdata_mnbvc
mnbvc mm dataset v2.1
MNBVC 多模态语料数据格式。原链接：https://huggingface.co/datasets/wanng/example_mmdata_mnbvc
参考实现：mm_template_mnbvc
的 mmdata_block.BLOCK_SCHEMA。schema 以那份代码为准，这个数据集是它的示例产物。
字段
字段名称
类型
字段说明
可选
实体ID
string
数据的唯一标识符。用于在数据集中确定是哪一条数据。在单个数据集中确定一条数据的实体对象。
必选
md5
string
内容的 md5，用于去重与完整性校验
必选
块ID
int32
一个实体对象内的标识符。用于确定一条数据内的一个部分数据。parquet 行的最小单元。
必选
块类型
string
用于保存块的类别。类别的含义为「模态」。取值见下
必选
扩展字段
string
用于保存块的元信息。为可以被成功 load 的 json 字符串。后期可继续扩展
必选… See the full description on the dataset page: https://huggingface.co/datasets/miracleyin/example_mmdata_mnbvc.