datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bolAIndia
bolAIndia
Human-side speech from production call recordings, cut into utterance-level
chunks by a two-engine VAD (Silero + TEN) and transcribed by third-party ASR
providers. Each row keeps the transcript, the provider's confidence, and full
provenance back to the source recording.
Sources
One config per transcription system, so their output stays separable.
config (source_id)
provider
model
hours
rows
shards
vendor-a
vendor-a
undisclosed
420.03
480774… See the full description on the dataset page: https://huggingface.co/datasets/kapturecx/bolAIndia.rixvox-v2
RixVox-v2: A Swedish parliamentary speech dataset
RixVox-v2 is a parliamentary speech dataset spanning nearly 23000 hours of speech. The dataset was built by matching and force aligning speeches in parliamentary protocols to media recordings of debates. Each observation contains metadata about the speaker's name, gender, district, role, party affiliation, and the date the speech was given. We include identifiers for protocols, speeches and speakers that allow linking observations in… See the full description on the dataset page: https://huggingface.co/datasets/KBLab/rixvox-v2.waxholmThe Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH.maa
Multilingual MFA-Aligned Speech Dataset (UNDER DEVELOPMENT)
A large-scale multilingual speech dataset with word-level and phoneme-level alignments produced using the Montreal Forced Aligner (MFA).
Dataset Description
This dataset consolidates multiple speech corpora across various languages, all processed through MFA to provide precise phoneme and word alignments. Each sample includes the original audio, transcript, and detailed timing information for both words and… See the full description on the dataset page: https://huggingface.co/datasets/khursani8/maa.knesset-committees
About
This dataset is derived from raw a/v recordings and human-generated protocols of the Knesset (the Israeli house of representatives) committee sessions as part of the ivrit.ai project.
Consider visiting the preview space for this dataset here
Method
Data dumps from the Knesset contain A/V recordings of committee sessions, alongside human-generated protocols.
We extract the audio stream, abd produce weakly time stamped segmentation of the protocol text (we… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/knesset-committees.central-kurdish-pseudolabel
Central Kurdish → English Pseudo-Labeled Speech Translation Corpus
Dataset Summary
This repository contains a large-scale pseudo-labeled speech translation corpus for Central Kurdish (Sorani Kurdish).
The dataset was automatically generated using a pipeline composed of:
Speech segmentation
Automatic Speech Recognition (ASR)
Machine Translation (MT)
The objective is to provide training data for end-to-end Speech-to-Text Translation (S2TT) in a language with very… See the full description on the dataset page: https://huggingface.co/datasets/aranemini/central-kurdish-pseudolabel.khmer-speech-dataset
Khmer ASR Cultural Dataset
727.94 hours of manually curated speech-text pairs by native speakers in the Khmer language about Cambodian cultural topics. On average, each recording is 8 seconds. Speaker metadata (gender, age group, and origin city) is provided.
Language: Khmer (khm).
Source(s): Native speakers from Cambodia (5 females, 7 males). The utterances were manually generated based on topics and subtopics listed in metadata.
Domain(s): Cultural domain, with a total of 61… See the full description on the dataset page: https://huggingface.co/datasets/Digital-Divide-Data/khmer-speech-dataset.IndicSynth
IndicSynth: Indian Multilingual Audio Deepfake Detection & Anti-Spoofing Dataset
A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages to facilitate audio deepfake detection and anti-spoofing research
🏆 Outstanding Paper Award, ACL 2025
🧠 Overview
IndicSynth is a novel multilingual synthetic speech dataset designed to advance multilingual audio deepfake detection (ADD) and anti-spoofing research. It covers 12 low-resource Indian… See the full description on the dataset page: https://huggingface.co/datasets/ksmashhero/IndicSynth.kasem-speech-text-parallel
This dataset is shared under CC BY-NC 4.0, which means you are free to use, share, and adapt it for non-commercial research and educational purposes with attribution. You can read the full license at https://creativecommons.org/licenses/by-nc/4.0/.
Kasem Speech-Text Parallel Dataset
Dataset Description
This dataset contains 75990 parallel speech-text pairs for Kasem, a language spoken primarily in Ghana. The dataset consists of audio recordings paired with their… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/kasem-speech-text-parallel.voicehub-arena-seed-tts-eval
VoiceHub Arena — full English Seed-TTS-Eval
35,904 synthesized WAV files: 33 model families × the same 1,088 target texts.
The campaign completed on 15 September 2026 on one NVIDIA A100-SXM4 40 GB.
All 198 shards and every WAV SHA256 were verified after backup.
Interactive leaderboard and all audio samples
· Source repository (access required).
Contents
audio_shards/<model>.tar: 33 WebDataset shards, each containing 1,088 original WAVs and matching JSON metadata.… See the full description on the dataset page: https://huggingface.co/datasets/kadirnar/voicehub-arena-seed-tts-eval.Kamba-ASR-Data-Subset-484H
Kamba ASR Data Subset 484H
Kamba speech dataset for automatic speech recognition.
Chaashini
Chaashini (चाशनी)
Chaashini — Hindi/Urdu for sugar syrup — is a continuously growing corpus of clean, single-speaker,
studio-grade Indian-language speech built for training speech models (text-to-speech, speech
recognition, speech language models). Every clip in the corpus has passed a strict multi-stage
quality gate; the aim is purity over volume.
Total: 1,378,589 clips · 2928.24 hours · 33 languages
Format: mono 24 kHz FLAC (audio column) with a verbatim transcript and rich… See the full description on the dataset page: https://huggingface.co/datasets/kapturecx/Chaashini.ISSAI_KSC_335RS_v_1_1
Dataset Card for "ISSAI_KSC_335RS_v_1_1"
Kazakh Speech Corpus (KSC)
Identifier: SLR102
Summary: A crowdsourced open-source Kazakh speech corpus developed by ISSAI (330 hours)
Category: Speech
License: Attribution 4.0 International (CC BY 4.0)
Downloads (use a mirror closer to you):
ISSAI_KSC_335RS_v1.1_flac.tar.gz [19G] (speech, transcripts and metadata ) Mirrors: [US] [EU] [CN]
About this resource:
A crowdsourced open-source speech corpus for the Kazakh language. The KSC… See the full description on the dataset page: https://huggingface.co/datasets/Shirali/ISSAI_KSC_335RS_v_1_1.Latin-Audio
Dataset Summary
Vox Classica is a Latin speech corpus of ~73 hours of audio, segmented into short audio clips by sentence. Vox Classica is a large-scale, ML-ready dataset of human-read Classical Latin. It was designed to address the absence of a publicly available human-read Latin corpus large enough for model training.
Alignment and curation: Kaiyuan Zhao
Language: Latin (Classical)
Uses
This dataset is built for training and evaluating speech processing models… See the full description on the dataset page: https://huggingface.co/datasets/Ken-Z/Latin-Audio.spgispeech
Dataset Card for SPGISpeech
Dataset Description
Dataset Summary
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
Data Fields
Data Splits
Dataset Creation
Curation Rationale
Source Data
Annotations
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
Contributions
Terms of Usage… See the full description on the dataset page: https://huggingface.co/datasets/kensho/spgispeech.kiraat
KIRAAT — A Turkish Read-Speech Corpus
A sentence-aligned read-speech corpus built from publicly available
recordings on Turkish audiobook YouTube channels. The channel credits are
in the table at the end of this card; every clip carries the channel it came
from in the channel column.
clips
1,840,404
duration
3,105.7 hours
recommended subset
1,547,494 clips / 2,575.2 hours
channels
27
speakers (clustered)
90
source recordings
2,680
words (ASR)
21,695,774… See the full description on the dataset page: https://huggingface.co/datasets/serdarcaglar/kiraat.Kazakh_Speech_Corpus_2
Kazakh Speech Corpus 2 (KSC2)
This dataset card describes the KSC2, an industrial-scale, open-source speech corpus for the Kazakh language.
Paper: KSC2: An Industrial-Scale Open-Source Kazakh Speech Corpus
Summary: KSC2 corpus subsumes the previously introduced two corpora: Kazakh Speech Corpus and Kazakh Text-To-Speech 2, and supplements additional data from other sources like tv programs, radio, senate, and podcasts. In total, KSC2 contains around 1.2k hours of high-quality… See the full description on the dataset page: https://huggingface.co/datasets/issai/Kazakh_Speech_Corpus_2.SPGISpeech2.0
Dataset Card for SPGISpeech 2.0
Dataset Details
Dataset Overview
We are excited to present SPGISpeech 2.0, a dataset suitable for speaker-tagged transcription in the financial domain. SPGISpeech 2.0 improves the diversity of applicable modeling tasks while maintaining the core characteristic of the original SPGISpeech dataset: audio snippets and their corresponding fully formatted text transcriptions, usable for end-to-end automatic speech recognition (ASR).… See the full description on the dataset page: https://huggingface.co/datasets/kensho/SPGISpeech2.0.Abjad-Kids
Abjad-Kids: An Arabic Speech Classification Dataset for Primary Education
Abjad-Kids is an Arabic speech classification dataset designed for primary education applications. It contains spoken recordings of the Arabic alphabet, numbers, and colors from multiple child speakers, supporting research in automatic speech recognition, audio classification, and educational technology for Arabic-speaking children.
This dataset is related to the work presented in:
Abjad-Kids: An Arabic… See the full description on the dataset page: https://huggingface.co/datasets/Aziz-snoubra/Abjad-Kids.propagator-multimodal-pretraining-data
Propagator Multimodal Pretraining Data
This public dataset contains tokenized multimodal pretraining data prepared for the Propagator model family. It combines language, image-grounded, and speech/audio-token examples into a single training format.
This is not a raw text or image browsing dataset. The examples have already been converted into compact binary token frames for model training, with a manifest that records the source groups and file layout.
Source Code… See the full description on the dataset page: https://huggingface.co/datasets/ken-sungmin/propagator-multimodal-pretraining-data.Urdu-ONYX-WAV-kanade-Annotated
Urdu-ONYX-WAV-real-Annotated
Enhanced version of Urdu-ONYX-WAV-real with phoneme annotations and Kanade tokenizer features.
Dataset Statistics
Total Samples: 26,217
Total Duration: 42.77 hours
Average Duration: 5.87 seconds
Duration Range: 0.65s - 122.23s
Average Phonemes: 18.5 per sample
Average Kanade Tokens: 151.1 per sample
Global Embedding Dimension: 128
New Columns
This dataset adds the following columns:
duration (float): Audio duration in seconds… See the full description on the dataset page: https://huggingface.co/datasets/humair025/Urdu-ONYX-WAV-kanade-Annotated.khm-asr-cultural
Khmer ASR Cultural Dataset
134.6 hours manually curated speech-text pairs by native speakers in Khmer language about Cambodian cultural topics. On average, each recording is 8.54 seconds with the standard deviation of 3.37. Speaker metadata (gender, age group, and origin city) is provided.
Language: Khmer (khm).
Source(s): Native speakers from Cambodia (4 females, 4 males). The utterances were manually generated based on topics and subtopics listed in metadata.
Domain(s):… See the full description on the dataset page: https://huggingface.co/datasets/Digital-Divide-Data/khm-asr-cultural.khmer-speech-dataset
Khmer ASR Cultural Dataset
727.94 hours of manually curated speech-text pairs by native speakers in the Khmer language about Cambodian cultural topics. On average, each recording is 8 seconds. Speaker metadata (gender, age group, and origin city) is provided.
Language: Khmer (khm).
Source(s): Native speakers from Cambodia (5 females, 7 males). The utterances were manually generated based on topics and subtopics listed in metadata.
Domain(s): Cultural domain, with a total of 61… See the full description on the dataset page: https://huggingface.co/datasets/phonsobon/khmer-speech-dataset.lj_speechThis is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
from 1 to 10 seconds and have a total length of approximately 24 hours.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .wav format and is not converted to a float32 array. To convert the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
```YO-CPT-kk
YO-CPT-kk
YouTube-Oriented dataset for Continual Pre-Training (Kazakh). A heavily
quality-filtered corpus of Kazakh speech mined from YouTube and processed into clean, single-speaker,
TTS-grade utterances. Every utterance ships with an ensemble-verified transcription, a
punctuated/denormalized and stress-marked text variant, word-level forced alignment, within- and
cross-video speaker identities, an audio-quality (MOS) score, and a speaker persona built from the
voice and, where… See the full description on the dataset page: https://huggingface.co/datasets/NCSpeech/YO-CPT-kk.kupe-asr-en-data
kupe-asr-en-mini-150m — data
Two loadable configs, packed into ~20-25 bunch_*.parquet files each (Hub-quota friendly):
raw — 24 kHz mono English audio (flac bytes) + text. The encode stage reads this.
mimi — Mimi c0..c7 codes (12.5 Hz) + text. Training reads this.
Ledgers under ledger/ (data.json, mimi.json) track collected/encoded hours and resume state.
from datasets import load_dataset
ds = load_dataset("anuj-inavlabs/kupe-asr-en-data", "mimi", split="train")
zeroth-korean
Zeroth-Korean
Zeroth-Korean
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor).
Zeroth project introduces free Korean speech corpus and aims to make Korean… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/zeroth-korean.SUBAK.KO
Dataset Card for SUBAK.KO
Dataset Summary
SUBAK.KO (সুবাক্য), a publicly available annotated Bangladeshi standard Bangla speech corpus, is compiled for automatic speech recognition research.
This corpus contains 241 hours of high-quality speech data, including 229 hours of read speech data and 12 hours of broadcast speech data.
The read speech segment is recorded in a noise-proof studio environment from 33 male and 28 female native Bangladeshi Bangla speakers… See the full description on the dataset page: https://huggingface.co/datasets/SUST-CSE-Speech/SUBAK.KO.nst
NST Swedish ASR Database (16 kHz) – reorganized
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and… See the full description on the dataset page: https://huggingface.co/datasets/KTH/nst.seamless-interaction-jefferson-annotations
Seamless Interaction Jefferson-Style Annotations
An automatic, turn-oriented annotation layer for the
Meta Seamless Interaction Dataset.
It compares the dataset's traditional transcript with an ASR-derived
Jefferson-style condition and supplies speech-act, communicative-purpose,
interactional-signal, alignment, and quality fields.
This is a derived noncommercial research dataset. It does not redistribute
the source audio. Every record retains the original interaction ID, split… See the full description on the dataset page: https://huggingface.co/datasets/kennethli319/seamless-interaction-jefferson-annotations.