datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
xperience-10m
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Interactive Intelligence from Human Xperience
Xperience-10M
Dataset Summary
Xperience-10M is a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial… See the full description on the dataset page: https://huggingface.co/datasets/ropedia-ai/xperience-10m.whisper_transcriptions.reazon_speech_allDaimon-Infinity
Daimon-Infinity mirror
This repository is a file-preserving mirror of
daimonrobotics/Daimon-Infinity on ModelScope.
Source and license
Upstream: daimonrobotics/Daimon-Infinity
License: CC BY-NC-SA 4.0
Attribution: Daimon Robotics / Daimon-Infinity
This mirror keeps the upstream directory layout and is distributed under the
same CC BY-NC-SA 4.0 license. No data is altered; files are transferred with
integrity checks supplied by ModelScope and the Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/ml-resources/Daimon-Infinity.Quranic-Recitation-Data
🌟 Overview
Quranic Recitation Dataset (Word-by-Word Sync) is a highly optimized, production-ready dataset containing high-quality audio recitations of the Holy Quran synchronized at the word-by-word level.
This dataset features 135 world-renowned reciters, with every Surah (114 chapters) mapped precisely to millisecond-accurate word timestamps. It is designed for modern Islamic mobile and web applications — served via a Cloudflare Edge CDN with native… See the full description on the dataset page: https://huggingface.co/datasets/zaibihassan/Quranic-Recitation-Data.Complete_Data_Source_100K_HOURS
Multi-Language Audio Collection (100K Hours)
This repository is physically reorganized for Absolute 100% Data Visibility.
🏗️ Global Consolidator
Select your language subset to listen to high-quality waveform audio. All shards from legacy and modern pipelines are automatically routed here.
MIT_environmental_impulse_responsesMIT Environmental Impulse Response Dataset
The audio recordings in this dataset are originally created by the Computational Audition Lab at MIT. The source of the data can be found at: https://mcdermottlab.mit.edu/Reverb/IR_Survey.html.
The audio files in the dataset have been resampled to a sampling rate of 16 kHz. This resampling was done to reduce the size of the dataset while making it more suitable for various tasks, including data augmentation.
The dataset consists of 271 audio files… See the full description on the dataset page: https://huggingface.co/datasets/davidscripka/MIT_environmental_impulse_responses.speech_robust_bench
Dataset Card for "speech_robust_bench"
More Information needed
MQ-RAVSBench
MQ-RAVSBench
MQ-RAVSBench is a benchmark for mask-quality auditing in referring audio-visual segmentation. Each example links a video clip, audio, a referring expression, the ground-truth object mask, and candidate masks with different error patterns. The benchmark is used by MQ-Auditor to assess whether a candidate mask should be accepted, revised, or rejected.
All paths stored in the metadata files are relative to the dataset root.
Dataset Layout
MQ-RAVSBench/… See the full description on the dataset page: https://huggingface.co/datasets/Jinxing1/MQ-RAVSBench.reachy-mini-emotions-library
Reachy Mini Emotions Library
Curated emotion recordings for the Reachy Mini robot, maintained by
Pollen Robotics. Each move is a JSON trajectory (head pose, antennas,
body yaw, sampled over time) paired with an Opus audio track.
Motion is sampled at 50 Hz; audio is mono Ogg/Opus (decoded natively by
the robot). Requires reachy_mini ≥ v1.8.4 (its move loader resolves
non-.wav audio sidecars).
File layout
Files live at the root of the dataset, named <emotion>.json +… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-emotions-library.YO-CPT-ru
YO-CPT-ru
YouTube-Oriented dataset for Continual Pre-Training (Russian). A large, heavily
quality-filtered corpus of Russian speech mined from YouTube (via YODAS2)
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… See the full description on the dataset page: https://huggingface.co/datasets/NCSpeech/YO-CPT-ru.Rasa
Rasa: Towards Building an Expressive Multilingual Text-To-Speech Dataset for Indian Languages
Funded by: Bhashini, Ministry of Electronics and Information Technology, Government of IndiaSupported by: EkStep Foundation and Nilekani Philanthropies
Overview
We introduce Rasa, the first high-quality multilingual expressive Text-to-Speech (TTS) dataset for any Indian language. It comprises a minimum of 20 hours per speaker with a target of covering
a female and male… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/Rasa.Raon-OpenTTS-Pool
Raon-OpenTTS-Pool
Technical Report
Raon-OpenTTS-Pool is a large-scale open English speech corpus for text-to-speech (TTS) training,
constructed from 8 publicly available speech corpora and a set of web-sourced recordings.
It is the training data behind Raon-OpenTTS,
an open TTS model that performs on par with state-of-the-art closed-data systems.
615K hours of speech audio
239.7M speech segments
11 source datasets aggregated into a unified format
All… See the full description on the dataset page: https://huggingface.co/datasets/KRAFTON/Raon-OpenTTS-Pool.OmniReasoner-SFT
OmniReasoner-SFT
OmniReasoner-SFT is a mixed-source, research-only supervised fine-tuning dataset
for audio-visual and long-video reasoning. It contains two-stage cold-start SFT
trajectories with interval selection, zoom-in evidence, and final answers.
Contents
data/train.jsonl: HF-ready training JSONL with repo-relative media paths.
media/: raw and derived media referenced by train.jsonl.
manifests/media_manifest.jsonl: media inventory with repo paths, source
family… See the full description on the dataset page: https://huggingface.co/datasets/Rocky131/OmniReasoner-SFT.libritts_r
Dataset Card for LibriTTS-R
LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus
(http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate, published in 2019.
Overview
This is the LibriTTS-R dataset, adapted for the datasets library.
Usage
Splits
There are 7 splits (dots replace dashes from the original dataset, to comply with… See the full description on the dataset page: https://huggingface.co/datasets/mythicinfinity/libritts_r.indicvoices_r
IndicVoices-R: Multilingual, Multi-Speaker Speech Corpus for Indian TTS
Dataset Summary
IndicVoices-R (IV-R) is the largest multilingual Indian text-to-speech (TTS) dataset derived from an automatic speech recognition (ASR) dataset. It contains 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages. This dataset is designed to enhance the development of robust Indian TTS models by providing diverse speaker demographics, natural… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/indicvoices_r.ict_s2s_refactoredwhisper_transcriptions.reazonspeech.alltadabur-align-references
tadabur-align-references
Precomputed reference embeddings powering tadabur-align — word-level timestamp extraction for Quranic recitation via DTW alignment transfer (no ASR).
What this is
For 5,481 of the Quran's 6,236 ayahs, this dataset holds frame-level tadabur-embedding features for up to 8 reference reciters, plus each reference's word-level timestamps and internal-pause intervals. No audio is included — only model outputs and timing data. tadabur-align… See the full description on the dataset page: https://huggingface.co/datasets/FaisaI/tadabur-align-references.STT_MODEL
Multilingual STT Dataset
Audio and transcript pairs for 50 languages. Each language is a Dataset Viewer configuration with train, validation, and test splits.
Language configurations
amharic: Amharic
arabic_msa: Arabic MSA
assamese: Assamese
bengali: Bengali
czech: Czech
dutch: Dutch
egyptian_arabic: Egyptian Arabic
english: English
farsi_persian: Farsi - Persian
filipino_tagalog: Filipino - Tagalog
french: French
german: German
greek: Greek
gujarati: Gujarati… See the full description on the dataset page: https://huggingface.co/datasets/RidheshBhati/STT_MODEL.telegram-audiobook-chizzled
Telegram Persian Audiobook Chizzled
1,555,434 Persian audiobook clips · 14,430.384 hours · 16 kHz mono PCM WAV · public Parquet release
This is a large, provenance-preserving collection of Persian audiobook audio gathered from 26 Telegram channels accessible to the collector account. Each source message is retained as message-level provenance and segmented with Silero voice-activity detection (VAD) into pause-aware clips. The audio bytes are embedded in Parquet files, so the… See the full description on the dataset page: https://huggingface.co/datasets/Reza2kn/telegram-audiobook-chizzled.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.Audio2Tool
Audio2Tool: Speak, Call, Act — A Dataset for Benchmarking Speech Tool Use
Authors: Ramit Pahwa1,∗,∗∗, Apoorva Beedu1,∗, Parivesh Priye1, Rutu Gandhi†1, Saloni Takawale†1, Aruna Baijal1, Zengli Yang1
1 Rivian & Volkswagen Technologies · ∗ equal contribution · ∗∗ corresponding author · † equal contribution
📄 Project page / demo: https://audio2tool.github.io/
📦 Dataset: https://huggingface.co/datasets/RVtech/Audio2Tool
✉️ Contact (corresponding… See the full description on the dataset page: https://huggingface.co/datasets/RVtech/Audio2Tool.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K.Codemixed_New
Codemixed ASR Dataset
Unified collection of code-mixed ASR datasets.
ja_asr.reazon_speech_alltraining-movies-test-stuffOmnimodal-Agent-SFT-2K
OmniGAIA: Omni-Modal General AI Assistant Benchmark
📄 Paper
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💻 Code & Demo
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🤗 Dataset & Model
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📈 Leaderboard
This dataset contains omni-modal agent supervised fine-tuning (SFT) trajectories in the LlamaFactory SFT data format. You can directly follow LlamaFactory's instructions to fine-tune your omni-modal LLMs.OmniGAIA is a benchmark for Omni-Modal General AI Assistants that jointly reason over vision, audio, and language with external tools. It is… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/Omnimodal-Agent-SFT-2K.libritts_r_filtered
Dataset Card for Filtered LibriTTS-R
This is a filtered version of LibriTTS-R. It has been filtered based on two sources:
LibriTTS-R paper [1], which lists samples for which speech restoration have failed
LibriTTS-P [2] list of excluded speakers for which multiple speakers have been detected.
LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate… See the full description on the dataset page: https://huggingface.co/datasets/parler-tts/libritts_r_filtered.Emilia-YODAS-ENDeepDialogue-orpheus
DeepDialogue-orpheus
DeepDialogue-orpheus is a large-scale multimodal dataset containing 40,150 high-quality multi-turn dialogues spanning 41 domains and incorporating 20 distinct emotions with coherent emotional progressions. This repository contains the Orpheus variant of the dataset, where speech is generated using Orpheus, a state-of-the-art TTS model that infers emotional expressions implicitly from text.
🚨 Important Notice
This dataset is large (~180GB) due to… See the full description on the dataset page: https://huggingface.co/datasets/SALT-Research/DeepDialogue-orpheus.