Blame for data/datasets.jsonl

{"slug":"ami","name":"AMI Meeting Corpus","maintainer":"University of Edinburgh / Idiap","tagline":"Multi-party meeting recordings with close-talking mics, video and overlap annotation. One of the few CC-BY corpora rich in overlapping speech.","hours":"100 h","size_hours":100,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2006-06","url":"https://groups.inf.ed.ac.uk/ami/corpus/","paper_url":"https://doi.org/10.1007/11677482_3","page_url":"https://www.fullduplex.ai/datasets/ami"}
{"slug":"switchboard","name":"Switchboard-1 (LDC97S62)","maintainer":"LDC","tagline":"2,400 U.S. telephone conversations, the original turn-taking corpus on which the field was built.","hours":"260 h","size_hours":260,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":8,"license":"LDC (paid)","license_class":"gated","commercial_use":"restricted","released":"1993-01","url":"https://catalog.ldc.upenn.edu/LDC97S62","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/switchboard"}
{"slug":"candor","name":"CANDOR","maintainer":"BetterUp Labs (Reece, Cooney et al.)","tagline":"Naturalistic video-chat conversation across 1,656 dyads, the go-to open corpus for full-duplex turn-taking research.","hours":"850 h","size_hours":850,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2023-03","url":"https://betterup-data-requests.herokuapp.com/","paper_url":"https://www.science.org/doi/10.1126/sciadv.adf3197","page_url":"https://www.fullduplex.ai/datasets/candor"}
{"slug":"fisher","name":"Fisher English","maintainer":"LDC","tagline":"Ten-minute telephone conversations, a classic for conversational ASR and turn-taking baselines.","hours":"2,000 h","size_hours":2000,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":null,"license":"LDC (paid)","license_class":"gated","commercial_use":"restricted","released":"2004-12","url":"https://catalog.ldc.upenn.edu/LDC2004T19","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/fisher"}
{"slug":"magicdata-ramc","name":"MagicData-RAMC","maintainer":"Magic Data Technology / OpenSLR SLR-123","tagline":"Mandarin spontaneous two-party conversation, free for academic use. The canonical Chinese analogue of Switchboard.","hours":"180 h","size_hours":180,"languages":["zh"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-ND-4.0","license_class":"non-commercial","commercial_use":"no","released":"2022-03","url":"https://www.openslr.org/123/","paper_url":"https://arxiv.org/abs/2203.16844","page_url":"https://www.fullduplex.ai/datasets/magicdata-ramc"}
{"slug":"ksponspeech","name":"KsponSpeech","maintainer":"ETRI / Korean AI Hub","tagline":"Korean spontaneous dyadic conversation, the canonical Korean analogue of Fisher and Switchboard.","hours":"969 h","size_hours":969,"languages":["ko"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"AI Hub (registration)","license_class":"gated","commercial_use":"restricted","released":"2020-03","url":"https://aihub.or.kr/aidata/105","paper_url":"https://aclanthology.org/2020.lrec-1.125/","page_url":"https://www.fullduplex.ai/datasets/ksponspeech"}
{"slug":"indicvoices","name":"IndicVoices (conversational slice)","maintainer":"AI4Bharat (IIT Madras)","tagline":"Conversational speech across all 22 scheduled Indic languages, the single largest permissive source for Hindi, Bengali, Tamil and Telugu dialogue.","hours":"~3,500 h (conv.)","size_hours":3500,"languages":["hi","bn","ta","te","mr","gu","kn","ml","pa","or","as","ur","ne","sd","ks","kok","brx","doi","mai","mni","sa","sat"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2024-03","url":"https://huggingface.co/datasets/ai4bharat/IndicVoices","paper_url":"https://arxiv.org/abs/2403.01926","page_url":"https://www.fullduplex.ai/datasets/indicvoices"}
{"slug":"dailytalk","name":"DailyTalk","maintainer":"KAIST","tagline":"Two-speaker daily conversations with emotion and dialog-act labels, a staple for dialogue TTS.","hours":"20 h","size_hours":20,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-SA-4.0","license_class":"share-alike","commercial_use":"restricted","released":"2022-07","url":"https://github.com/keonlee9420/DailyTalk","paper_url":"https://arxiv.org/abs/2207.01063","page_url":"https://www.fullduplex.ai/datasets/dailytalk"}
{"slug":"callhome","name":"CALLHOME (JA / ZH / ES / DE / AR …)","maintainer":"LDC","tagline":"Native-speaker telephone conversations across Japanese, Mandarin, Spanish, German, Arabic, and English — the classic non-English turn-taking set.","hours":"~229 h (2026 Second Editions)","size_hours":229,"languages":["ja","zh","es","de","ar","en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":8,"license":"LDC (paid, per-release)","license_class":"gated","commercial_use":"restricted","released":"1996-01","url":"https://catalog.ldc.upenn.edu/search?query=callhome","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/callhome"}
{"slug":"chime-6","name":"CHiME-6","maintainer":"CHiME Challenge","tagline":"Real dinner-party conversation from 20 parties, captured with multiple arrays and close-talk mics. Overlap-rich multi-party speech.","hours":"40 h","size_hours":40,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"Research use (CHiME Challenge terms)","license_class":"non-commercial","commercial_use":"no","released":"2020-04","url":"https://www.chimechallenge.org/challenges/chime6/","paper_url":"https://arxiv.org/abs/2004.09249","page_url":"https://www.fullduplex.ai/datasets/chime-6"}
{"slug":"spokenwoz","name":"SpokenWOZ","maintainer":"Alibaba DAMO Academy","tagline":"5.7K human-to-human spoken task-oriented dialogues (203K turns, 249 h) — the canonical spoken task-oriented corpus.","hours":"249 h","size_hours":249,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2023-05","url":"https://spokenwoz.github.io/","paper_url":"https://arxiv.org/abs/2305.13040","page_url":"https://www.fullduplex.ai/datasets/spokenwoz"}
{"slug":"yodas","name":"YODAS / YODAS2","maintainer":"WavLab (CMU)","tagline":"Creative Commons YouTube speech across 100+ languages. The larger YODAS v3 is listed separately.","hours":"500k h","size_hours":500000,"languages":["100+"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-3.0","license_class":"permissive","commercial_use":"yes","released":"2024-06","url":"https://huggingface.co/datasets/espnet/yodas","paper_url":"https://arxiv.org/abs/2406.00899","page_url":"https://www.fullduplex.ai/datasets/yodas"}
{"slug":"yodas-v3","name":"YODAS v3","maintainer":"ESPnet / WavLab (CMU)","tagline":"48 kHz multi-channel YouTube speech under CC-BY-3.0, crawled from videos that do not overlap YODAS v1 or v2.","hours":"1.1M h","size_hours":1100000,"languages":["100+"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":48,"license":"CC-BY-3.0","license_class":"permissive","commercial_use":"yes","released":"2026-09","url":"https://huggingface.co/datasets/espnet/yodas3","paper_url":"https://arxiv.org/abs/2609.29448","page_url":"https://www.fullduplex.ai/datasets/yodas-v3"}
{"slug":"voxpopuli","name":"VoxPopuli","maintainer":"Meta AI","tagline":"Unlabelled European Parliament speech in 23 languages, plus 1.8k hours transcribed and 17.3k hours of interpreter audio. Released under CC0.","hours":"400k h","size_hours":400000,"languages":["en","de","fr","es","+19"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC0 (data) / CC BY-NC 4.0 (code & models)","license_class":"permissive","commercial_use":"yes","released":"2021-01","url":"https://github.com/facebookresearch/voxpopuli","paper_url":"https://arxiv.org/abs/2101.00390","page_url":"https://www.fullduplex.ai/datasets/voxpopuli"}
{"slug":"emilia","name":"Emilia / Emilia-Large","maintainer":"Amphion (CUHK-Shenzhen)","tagline":"In-the-wild multilingual speech under two licences: Emilia (101.7k h, non-commercial) and Emilia-YODAS (113.9k h, CC-BY-4.0).","hours":"215.6k h (Emilia-Large)","size_hours":215600,"languages":["en","zh","ja","ko","de","fr"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0 (Emilia) / CC-BY-4.0 (Emilia-YODAS)","license_class":"non-commercial","commercial_use":"restricted","released":"2024-07","url":"https://huggingface.co/datasets/amphion/Emilia-Dataset","paper_url":"https://arxiv.org/abs/2407.05361","page_url":"https://www.fullduplex.ai/datasets/emilia"}
{"slug":"mls","name":"Multilingual LibriSpeech (MLS)","maintainer":"Meta AI","tagline":"LibriSpeech-style audiobooks in 8 languages, the permissive multilingual pretraining baseline.","hours":"50k h","size_hours":50000,"languages":["en","de","nl","fr","es","it","pt","pl"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2020-12","url":"https://www.openslr.org/94","paper_url":"https://arxiv.org/abs/2012.03411","page_url":"https://www.fullduplex.ai/datasets/mls"}
{"slug":"granary","name":"Granary","maintainer":"NVIDIA","tagline":"Pseudo-labelled ASR and speech-translation data for 25 European languages under CC-BY-4.0: 192k hours with audio included and 451k hours of new transcripts for existing corpora.","hours":"643k h (192k h with audio)","size_hours":643000,"languages":["en","de","fr","es","+21"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0 (MOSEL labels) / CC-BY-3.0 (YODAS-Granary); source audio under original licences","license_class":"permissive","commercial_use":"restricted","released":"2025-05","url":"https://huggingface.co/datasets/nvidia/Granary","paper_url":"https://arxiv.org/abs/2505.13404","page_url":"https://www.fullduplex.ai/datasets/granary"}
{"slug":"common-voice","name":"Common Voice (Scripted Speech 27.0)","maintainer":"Mozilla","tagline":"Crowdsourced multilingual read speech under CC0: 42.6k total and 29.3k validated hours in Scripted Speech 27.0 (September 2026).","hours":"42.6k h (29.3k validated)","size_hours":42593,"languages":["295"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC0-1.0","license_class":"permissive","commercial_use":"yes","released":"2019-06","url":"https://mozilladatacollective.com/organization/cmfh0j9o10006ns07jq45h7xk","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/common-voice"}
{"slug":"peoples-speech","name":"People's Speech","maintainer":"MLCommons","tagline":"Diverse English speech harvested from Archive.org, the largest permissive English ASR corpus.","hours":"30k+ h","size_hours":30000,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-SA-4.0 / CC-BY-4.0","license_class":"share-alike","commercial_use":"yes","released":"2021-11","url":"https://mlcommons.org/datasets/peoples-speech/","paper_url":"https://arxiv.org/abs/2111.09344","page_url":"https://www.fullduplex.ai/datasets/peoples-speech"}
{"slug":"gigaspeech","name":"GigaSpeech","maintainer":"SpeechColab","tagline":"English from audiobooks, podcasts and YouTube. Broad acoustic coverage, but audio access is research-only.","hours":"10k h","size_hours":10000,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"Research-only (gated, Apache-2.0 code)","license_class":"gated","commercial_use":"no","released":"2021-06","url":"https://github.com/SpeechColab/GigaSpeech","paper_url":"https://arxiv.org/abs/2106.06909","page_url":"https://www.fullduplex.ai/datasets/gigaspeech"}
{"slug":"librispeech","name":"LibriSpeech","maintainer":"Vassil Panayotov et al.","tagline":"Read English audiobooks, the canonical ASR benchmark since 2015.","hours":"960 h","size_hours":960,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2015-04","url":"https://www.openslr.org/12","paper_url":"https://www.danielpovey.com/files/2015_icassp_librispeech.pdf","page_url":"https://www.fullduplex.ai/datasets/librispeech"}
{"slug":"reazonspeech","name":"ReazonSpeech","maintainer":"Reazon Holdings","tagline":"Japanese TV broadcasts pseudo-labelled with Whisper, the largest practical Japanese training pool.","hours":"35k h","size_hours":35000,"languages":["ja"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CDLA-Sharing-1.0","license_class":"share-alike","commercial_use":"restricted","released":"2023-03","url":"https://research.reazon.jp/projects/ReazonSpeech/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/reazonspeech"}
{"slug":"csj","name":"Corpus of Spontaneous Japanese (CSJ)","maintainer":"NINJAL / NICT / Tokyo Tech","tagline":"Japanese spontaneous speech, the canonical academic Japanese corpus for prosody, POS and dependency.","hours":"661 h","size_hours":661,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"NINJAL (tiered)","license_class":"gated","commercial_use":"restricted","released":"2004-01","url":"https://clrd.ninjal.ac.jp/csj/en/index.html","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/csj"}
{"slug":"jvs","name":"JVS Corpus","maintainer":"Takamichi Lab (University of Tokyo)","tagline":"Japanese parallel readings across 100 speakers, the staple for multi-speaker Japanese TTS.","hours":"30 h","size_hours":30,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-SA-4.0 (with conditions)","license_class":"custom","commercial_use":"restricted","released":"2019-08","url":"https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/jvs"}
{"slug":"iemocap","name":"IEMOCAP","maintainer":"USC SAIL","tagline":"Scripted and improvised dyadic acting, the emotion-recognition classic.","hours":"12 h","size_hours":12,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"USC SAIL (request)","license_class":"gated","commercial_use":"no","released":"2008-12","url":"https://sail.usc.edu/iemocap/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/iemocap"}
{"slug":"expresso","name":"Expresso","maintainer":"Meta AI","tagline":"Studio-quality expressive speech from 4 speakers, with 8 read styles and 26 improvised dialogue styles including laughter and whisper.","hours":"40 h","size_hours":40,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":48,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2023-08","url":"https://speechbot.github.io/expresso/","paper_url":"https://arxiv.org/abs/2308.05725","page_url":"https://www.fullduplex.ai/datasets/expresso"}
{"slug":"fleurs","name":"FLEURS","maintainer":"Google Research","tagline":"Few-shot Learning Evaluation of Universal Representations of Speech — 102 languages, ~10 h each, built on FLoRes translation pairs.","hours":"~1k h","size_hours":1020,"languages":["102+"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2022-05","url":"https://huggingface.co/datasets/google/fleurs","paper_url":"https://arxiv.org/abs/2205.12446","page_url":"https://www.fullduplex.ai/datasets/fleurs"}
{"slug":"covost2","name":"CoVoST-2","maintainer":"Meta AI","tagline":"Multilingual speech-to-text translation built on Common Voice, the standard supervised S2ST base.","hours":"2,880 h","size_hours":2880,"languages":["21 + en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC0-1.0","license_class":"permissive","commercial_use":"yes","released":"2020-07","url":"https://github.com/facebookresearch/covost","paper_url":"https://arxiv.org/abs/2007.10310","page_url":"https://www.fullduplex.ai/datasets/covost2"}
{"slug":"cvss","name":"CVSS","maintainer":"Google Research","tagline":"Massively multilingual S2ST corpus — 21 source languages into English, with synthetic target speech — CC-BY-4.0.","hours":"3,809 h","size_hours":3809,"languages":["21 → en"],"speech":"mixed","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2022-01","url":"https://github.com/google-research-datasets/cvss","paper_url":"https://arxiv.org/abs/2201.03713","page_url":"https://www.fullduplex.ai/datasets/cvss"}
{"slug":"cvss-x","name":"CVSS-X","maintainer":"Gris et al.","tagline":"Synthetic English-to-28-language speech translation pairs that reverse CVSS, about 16,000 hours.","hours":"~16,070 h (synthetic target speech)","size_hours":16070,"languages":["en","+28"],"speech":"mixed","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0 (synthetic translations and speech); CC0-1.0 (Common Voice source)","license_class":"non-commercial","commercial_use":"no","released":"2026-09","url":"https://huggingface.co/datasets/lgris/CVSS-X","paper_url":"https://arxiv.org/abs/2609.13413","page_url":"https://www.fullduplex.ai/datasets/cvss-x"}
{"slug":"move-en-zh","name":"MoVE (paired EN/ZH emotional speech)","maintainer":"Chen et al.","tagline":"About 1,056 hours of synthetic English-Chinese emotional speech pairs, laughter and crying included, for expressive speech translation.","hours":"1,056 h (English side)","size_hours":1055.67,"languages":["en","zh"],"speech":"synthetic","speaker_consent":"unknown","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2026-04","url":"https://huggingface.co/datasets/47z/MoVE","paper_url":"https://arxiv.org/abs/2604.17435","page_url":"https://www.fullduplex.ai/datasets/move-en-zh"}
{"slug":"must-c","name":"MuST-C","maintainer":"FBK (Fondazione Bruno Kessler)","tagline":"English TED-talk speech translated into 14 target languages — hundreds of hours per pair, the reference training corpus for ST.","hours":"~4k h","size_hours":4000,"languages":["en → 14"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-ND-4.0","license_class":"non-commercial","commercial_use":"no","released":"2019-06","url":"https://aclanthology.org/N19-1202/","paper_url":"https://www.sciencedirect.com/science/article/pii/S0885230820300887","page_url":"https://www.fullduplex.ai/datasets/must-c"}
{"slug":"j-chat","name":"J-CHAT","maintainer":"Sarulab (UTokyo) / Keio","tagline":"Japanese spoken dialogue scraped from podcasts and YouTube, the first Japanese corpus aimed at dialogue-oriented speech LMs.","hours":"76k h","size_hours":76000,"languages":["ja"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"Research-only (JP Copyright Art. 30-4)","license_class":"non-commercial","commercial_use":"no","released":"2024-07","url":"https://huggingface.co/datasets/sarulab-speech/J-CHAT","paper_url":"https://arxiv.org/abs/2407.15828","page_url":"https://www.fullduplex.ai/datasets/j-chat"}
{"slug":"multidialog","name":"MultiDialog","maintainer":"IVLLab (Yonsei / Naver)","tagline":"9k audio-visual dialogues between 12 fluent English speakers, with emotion annotations on top of TopicalChat.","hours":"340 h","size_hours":340,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-SA-4.0","license_class":"non-commercial","commercial_use":"no","released":"2024-06","url":"https://multidialog.github.io/","paper_url":"https://arxiv.org/abs/2406.07867","page_url":"https://www.fullduplex.ai/datasets/multidialog"}
{"slug":"instructs2s-200k","name":"InstructS2S-200K","maintainer":"ICTNLP (LLaMA-Omni team)","tagline":"200k multi-turn speech-to-speech conversations tailored for instruction-following speech models.","hours":null,"size_hours":null,"languages":["en"],"speech":"synthetic","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2024-09","url":"https://huggingface.co/datasets/ICTNLP/InstructS2S-200K","paper_url":"https://arxiv.org/abs/2409.06666","page_url":"https://www.fullduplex.ai/datasets/instructs2s-200k"}
{"slug":"sd-eval","name":"SD-Eval","maintainer":"Amphion (CUHK-Shenzhen)","tagline":"Curated evaluation across emotion, accent, age and environment. A non-commercial, eval-only release under CC-BY-NC-4.0.","hours":"8.76 h","size_hours":9,"languages":["en"],"speech":"mixed","speaker_consent":"mixed","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2024-06","url":"https://huggingface.co/datasets/amphion/SD-Eval","paper_url":"https://arxiv.org/abs/2406.13340","page_url":"https://www.fullduplex.ai/datasets/sd-eval"}
{"slug":"voxdialogue","name":"VoxDialogue","maintainer":"VoxDialogue authors (ICLR 2025)","tagline":"4,500 multi-turn spoken dialog samples × 12 acoustic attributes — probing whether spoken dialog systems catch what text can't.","hours":null,"size_hours":null,"languages":["en"],"speech":"synthetic","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0 (Hugging Face card metadata)","license_class":"permissive","commercial_use":"yes","released":"2025-05","url":"https://huggingface.co/datasets/Exgc/VoxDialogue","paper_url":"https://openreview.net/forum?id=vbmSSIhKAM","page_url":"https://www.fullduplex.ai/datasets/voxdialogue"}
{"slug":"mm-f2f","name":"MM-F2F","maintainer":"Xiamen University","tagline":"Multi-modal face-to-face conversation with word-level turn-taking and backchannel labels.","hours":"210 h","size_hours":210,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":null,"license":"Research (see repo)","license_class":"custom","commercial_use":"restricted","released":"2025-07","url":"https://github.com/Linyx1125/MM-F2F","paper_url":"https://aclanthology.org/2025.acl-long.743/","page_url":"https://www.fullduplex.ai/datasets/mm-f2f"}
{"slug":"jp-duplex-magichub","name":"Japanese Duplex Conversation Dataset","maintainer":"MagicData","tagline":"Channel-separated two-speaker Japanese conversation. Tiny, but one of the only open Japanese corpora with per-speaker tracks.","hours":"10.35 h","size_hours":10,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":16,"license":"CC-BY-NC-ND-4.0","license_class":"non-commercial","commercial_use":"no","released":"2025-04","url":"https://magichub.com/datasets/japanese-duplex-conversation-training-dataset/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/jp-duplex-magichub"}
{"slug":"olespeech-iv","name":"OleSpeech-IV","maintainer":"Olewave / Stanford / Microsoft / PingAn / KU / CMU","tagline":"English conversational speech with accents from all world regions — human-refined speaker turns and transcripts, 100-hour open subset.","hours":"100 h (open subset)","size_hours":100,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"Non-commercial research","license_class":"non-commercial","commercial_use":"no","released":"2025-09","url":"https://huggingface.co/datasets/olewave/OleSpeech-IV-2025-EN-AR-100","paper_url":"https://arxiv.org/abs/2509.04702","page_url":"https://www.fullduplex.ai/datasets/olespeech-iv"}
{"slug":"interactspeech","name":"InteractSpeech","maintainer":"InteractSpeech authors (EMNLP 2025)","tagline":"English speech-interaction data, synthesised plus filtered real dialogues, with precise speaker timestamps for interruptions and backchannels.","hours":"150 h (audio not released)","size_hours":null,"languages":["en"],"speech":"mixed","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"Research (see repo)","license_class":"custom","commercial_use":"restricted","released":"2025-11","url":"https://interactspeech.github.io/","paper_url":"https://aclanthology.org/2025.findings-emnlp.424/","page_url":"https://www.fullduplex.ai/datasets/interactspeech"}
{"slug":"mlc-slm","name":"MLC-SLM (Interspeech 2025)","maintainer":"Nexdata / Interspeech 2025 challenge","tagline":"Two-speaker conversational speech in 11 languages, the first genuinely multilingual public benchmark for conversational speech LMs.","hours":"1,604 h","size_hours":1604,"languages":["en","fr","de","it","pt","es","ja","ko","ru","th","vi"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"Challenge access (CC-BY-SA-4.0 for eval)","license_class":"gated","commercial_use":"restricted","released":"2025-09","url":"https://www.nexdata.ai/competition/mlc-slm","paper_url":"https://arxiv.org/abs/2509.13785","page_url":"https://www.fullduplex.ai/datasets/mlc-slm"}
{"slug":"magicdata-multistream","name":"Multi-stream Spontaneous Conversation (zh+en)","maintainer":"Magic Data Technology","tagline":"Dual-track two-speaker conversation in Mandarin and English, with a channel per speaker. A rare open corpus built for full duplex.","hours":"15 h (10 zh + 5 en)","size_hours":15,"languages":["zh","en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":16,"license":"CC-BY-NC-ND-4.0","license_class":"non-commercial","commercial_use":"no","released":"2024-11","url":"https://magichub.com/datasets/multi-stream-spontaneous-conversation-training-datasets_chinese/","paper_url":"https://arxiv.org/abs/2509.04093","page_url":"https://www.fullduplex.ai/datasets/magicdata-multistream"}
{"slug":"behavior-sd","name":"Behavior-SD","maintainer":"Seoul National University (NAACL 2025)","tagline":"108K LLM-synthesised full-duplex dialogues (2,164 h) with explicit backchannel / interruption / filler labels — the largest publicly-downloadable FD-labeled corpus.","hours":"2,164 h","size_hours":2164,"languages":["en"],"speech":"synthetic","speaker_consent":"unknown","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2025-04","url":"https://huggingface.co/datasets/yhytoto12/behavior-sd","paper_url":"https://aclanthology.org/2025.naacl-long.484/","page_url":"https://www.fullduplex.ai/datasets/behavior-sd"}
{"slug":"otospeech-fd-280h","name":"otoSpeech-full-duplex-280h","maintainer":"otoearth","tagline":"Channel-separated two-speaker English conversation at 48 kHz under CC-BY-4.0. Among the largest permissive, commercially usable real-recorded FD corpora indexed on Hugging Face as of 2026-04.","hours":"280 h","size_hours":280,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-02","url":"https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-280h","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-fd-280h"}
{"slug":"otospeech-fd-processed-141h","name":"otoSpeech-full-duplex-processed-141h","maintainer":"otoearth","tagline":"Human-reviewed, denoised, channel-separated English FD conversation. The processed sibling of otoSpeech-280h.","hours":"141 h","size_hours":141,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":44.1,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-02","url":"https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-fd-processed-141h"}
{"slug":"otospeech-fd-turn-104h","name":"otoSpeech-full-duplex-turn-104h","maintainer":"otoearth","tagline":"Dyadic English conversation with hand-made turn-taking labels on every speaker channel. The training split of Sesame's TurnBench.","hours":"105 h","size_hours":104.94,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"oto Speech License v1.0","license_class":"gated","commercial_use":"no","released":"2026-06","url":"https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-turn-104h","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-fd-turn-104h"}
{"slug":"duplexchat","name":"DuplexChat","maintainer":"Nakata, Saito, Saruwatari (U. Tokyo / AIST)","tagline":"Speaker-separated two-channel dialogue mined from public podcasts, distributed as metadata rather than audio.","hours":"415k h","size_hours":415357,"languages":["en","ja"],"speech":"human","speaker_consent":"public","full_duplex":true,"sample_rate_khz":null,"license":"MIT (manifest only); audio rights stay with each podcast's rights holders","license_class":"custom","commercial_use":"restricted","released":"2026-07","url":"https://huggingface.co/datasets/sarulab-speech/DuplexChat","paper_url":"https://arxiv.org/abs/2607.04941","page_url":"https://www.fullduplex.ai/datasets/duplexchat"}
{"slug":"apptek-callcenter-dialogues","name":"AppTek Call-Center Dialogues","maintainer":"AppTek","tagline":"Role-played agent and customer calls in 14 English accent groups, each speaker in a separate file, released for evaluation.","hours":"128.6 h","size_hours":128.6,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":16,"license":"CC-BY-SA-4.0","license_class":"share-alike","commercial_use":"yes","released":"2026-04","url":"https://huggingface.co/datasets/apptek-com/apptek_callcenter_dialogues","paper_url":"https://arxiv.org/abs/2604.27543","page_url":"https://www.fullduplex.ai/datasets/apptek-callcenter-dialogues"}
{"slug":"perit-duplex-support-en","name":"Perit Duplex Support Conversations (English)","maintainer":"Perit AI","tagline":"Performed English customer-support calls with one speaker per channel at 48 kHz, under CC-BY-4.0.","hours":"5.1 h","size_hours":5.1,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-10","url":"https://huggingface.co/datasets/perit-ai/perit-duplex-support-en","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/perit-duplex-support-en"}
{"slug":"real-turnturk","name":"Real-TurnTurk","maintainer":"Ata Technology Platforms","tagline":"Unscripted Turkish video-call conversations with each speaker's audio on its own track, plus turn labels and facial features.","hours":"1.94 h (public subset)","size_hours":1.94,"languages":["tr"],"speech":"human","speaker_consent":"unknown","full_duplex":true,"sample_rate_khz":48,"license":"Apache-2.0","license_class":"permissive","commercial_use":"yes","released":"2026-08","url":"https://huggingface.co/datasets/tugrulbayrak/Real-TurnTurk","paper_url":"https://arxiv.org/abs/2608.22071","page_url":"https://www.fullduplex.ai/datasets/real-turnturk"}
{"slug":"dialogs-ru","name":"Dialogs (Russian)","maintainer":"Shigabeev, Latyshev","tagline":"Studio-recorded expressive Russian dialogue with per-utterance emotion labels.","hours":"20.6 h","size_hours":20.6,"languages":["ru"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":44.1,"license":"OpenRAIL","license_class":"custom","commercial_use":"yes","released":"2026-07","url":"https://huggingface.co/datasets/langswap/dialogs-ru-emotional-conversations","paper_url":"https://arxiv.org/abs/2607.14310","page_url":"https://www.fullduplex.ai/datasets/dialogs-ru"}
{"slug":"korean-fd-synthetic-preview","name":"Korean full-duplex synthetic (preview)","maintainer":"Wi-Fi (Hugging Face)","tagline":"A 100-conversation CC-BY-4.0 taster in front of a claimed 2,000-hour synthetic Korean full-duplex corpus.","hours":"preview only (2,000.5 h claimed)","size_hours":null,"languages":["ko"],"speech":"synthetic","speaker_consent":"unknown","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0 (preview subset)","license_class":"permissive","commercial_use":"yes","released":"2026-08","url":"https://huggingface.co/datasets/Wi-Fi/korean-full-duplex-synthetic-dataset-preview","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/korean-fd-synthetic-preview"}
{"slug":"naijas2st","name":"NaijaS2ST","maintainer":"Maltais et al.","tagline":"Parallel speech-to-speech translation across four Nigerian languages plus accented English.","hours":"~200 h","size_hours":200,"languages":["ig","ha","yo","pcm","en"],"speech":"mixed","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-04","url":"https://huggingface.co/datasets/McGill-NLP/NaijaS2ST","paper_url":"https://arxiv.org/abs/2604.16287","page_url":"https://www.fullduplex.ai/datasets/naijas2st"}
{"slug":"otospeech-fd-task-20h","name":"otoSpeech-full-duplex-task-oriented-20h","maintainer":"otoearth","tagline":"Two-speaker English conversation recorded while the pair worked on a task, with task state, interface events and reference answers on the same timeline as the audio.","hours":"20 h","size_hours":20,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-08","url":"https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-task-oriented-20h","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-fd-task-20h"}
{"slug":"otospeech-fd-task-v2","name":"otoSpeech-full-duplex-task-oriented-v2","maintainer":"otoearth","tagline":"Two-speaker English conversation recorded during collaborative tasks, all from sessions after the 20-hour release.","hours":"17.4 h","size_hours":17.38,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-09","url":"https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-task-oriented-v2","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-fd-task-v2"}
{"slug":"otospeech-1125h","name":"otoSpeech 1,125h","maintainer":"otoearth","tagline":"Casual two-speaker English conversation across 3,878 sessions, one speaker per channel, under a commercial licence.","hours":"1,125 h","size_hours":1125,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"Commercial licence (on request)","license_class":"custom","commercial_use":"yes","released":null,"url":"https://www.oto.earth/datasets/english","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-1125h"}
{"slug":"otospeech-video","name":"otoSpeech Video","maintainer":"otoearth","tagline":"Two-speaker English conversation with a camera on each speaker, synced to channel-separated audio.","hours":null,"size_hours":null,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/video","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-video"}
{"slug":"otospeech-face-to-face","name":"otoSpeech Face-to-Face","maintainer":"otoearth","tagline":"In-person English conversation among two to five people in one room, recorded on 16 channels with video.","hours":null,"size_hours":null,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/multi-speaker","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-face-to-face"}
{"slug":"otospeech-3-speaker","name":"otoSpeech 3-Speaker","maintainer":"otoearth","tagline":"English conversation among three people, with a separate mono recording for each speaker.","hours":null,"size_hours":null,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/multi-speaker","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-3-speaker"}
{"slug":"otospeech-korean","name":"otoSpeech Korean","maintainer":"otoearth","tagline":"Korean free conversation between two people on a given topic, one speaker per channel.","hours":null,"size_hours":null,"languages":["ko"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":44.1,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/korean","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-korean"}
{"slug":"otospeech-japanese","name":"otoSpeech Japanese","maintainer":"otoearth","tagline":"Japanese free conversation between two people on a given topic, one speaker per channel.","hours":null,"size_hours":null,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/japanese","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-japanese"}
{"slug":"otospeech-role-play","name":"otoSpeech Role-Play","maintainer":"otoearth","tagline":"Japanese two-speaker role-play in set scenarios, from shop visits to business talks, in the speakers' own words.","hours":null,"size_hours":null,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/japanese","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-role-play"}
{"slug":"otospeech-scripted","name":"otoSpeech Scripted","maintainer":"otoearth","tagline":"Japanese two-speaker dialogue read from scripts, each recording with its script and a timed transcript.","hours":null,"size_hours":null,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/japanese","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-scripted"}
{"slug":"otospeech-egocentric","name":"otoSpeech Egocentric","maintainer":"otoearth","tagline":"English conversation with first-person video while two to four people fold towels, tidy objects or play Jenga.","hours":null,"size_hours":null,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":null,"license":"On request","license_class":"custom","commercial_use":"restricted","released":null,"url":"https://www.oto.earth/datasets/egocentric","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/otospeech-egocentric"}
{"slug":"open-yap-1k","name":"Open Yap 1K","maintainer":"The Agentic Data Company","tagline":"Two-speaker English conversation between people who already know each other, one 48 kHz track per speaker, under a data use agreement.","hours":"1,000 h (8.9 h open sample)","size_hours":1000,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"Open Yap 1K Data Use Agreement (full corpus); CC-BY-4.0 (sample)","license_class":"gated","commercial_use":"yes","released":"2026-09","url":"https://huggingface.co/datasets/TheAgenticDataCompany/open-yap-1k","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/open-yap-1k"}
{"slug":"basis-conversations-1500","name":"Basis Conversations 1500","maintainer":"Basis","tagline":"Multi-party conversation in 22 languages, with every speaker on a separate 48 kHz track.","hours":"1,502 h","size_hours":1502,"languages":["en","es","fr","de","it","nl","pl","pt","ru","uk","tr","ar","he","hi","ja","ko","zh","ka","kk","xmf","xh","zu"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"basis-data-license-1.0 (custom, gated)","license_class":"gated","commercial_use":"restricted","released":"2026-09","url":"https://huggingface.co/datasets/basis-ai/basis-conversations-1500","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/basis-conversations-1500"}
{"slug":"smoothconv-duplexconv","name":"SmoothConv / DuplexConv","maintainer":"ASLP@NPU / QualiaLabs","tagline":"Mandarin multi-party conversation with a synchronized track per participant: 100 hours human-annotated, 2,000 hours LLM-annotated.","hours":"2,100 h (100 h + 2,000 h)","size_hours":2100,"languages":["zh"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":null,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2026-06","url":"https://github.com/qualialabsAI/SmoothConv-DuplexConv","paper_url":"https://arxiv.org/abs/2610.11150","page_url":"https://www.fullduplex.ai/datasets/smoothconv-duplexconv"}
{"slug":"multitalk","name":"MultiTalkPT / MultiTalkFT","maintainer":"CUHK MMLab","tagline":"Synthetic long-form, multi-party English and Chinese full-duplex dialogue, plus a benchmark built from real recordings.","hours":"57.6k h","size_hours":57600,"languages":["en","zh"],"speech":"synthetic","speaker_consent":"unknown","full_duplex":true,"sample_rate_khz":null,"license":"CC-BY-NC-4.0 (PT, FT); CC-BY-SA-4.0 (Bench)","license_class":"non-commercial","commercial_use":"no","released":"2026-09","url":"https://huggingface.co/datasets/MultiTalk/MultiTalkPT","paper_url":"https://arxiv.org/abs/2609.36903","page_url":"https://www.fullduplex.ai/datasets/multitalk"}
{"slug":"seamless-interaction","name":"Seamless Interaction","maintainer":"Meta FAIR","tagline":"Face-to-face dyadic conversation from more than 4,000 participants, with each participant's audio and video kept separate.","hours":"4,000+ h","size_hours":4000,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":48,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2025-06","url":"https://huggingface.co/datasets/facebook/seamless-interaction","paper_url":"https://arxiv.org/abs/2506.22554","page_url":"https://www.fullduplex.ai/datasets/seamless-interaction"}
{"slug":"vartalaap","name":"Vartalaap","maintainer":"Kapture CX","tagline":"Synthetic Hindi-English two-party calls, customer support plus open-topic chat, with one stereo channel per role, built for full-duplex speech-to-speech training.","hours":"5,446 h","size_hours":5446.1,"languages":["hi","en"],"speech":"synthetic","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":24,"license":"CC-BY-NC-4.0 (gated, no redistribution)","license_class":"non-commercial","commercial_use":"no","released":"2026-08","url":"https://huggingface.co/datasets/kapturecx/Vartalaap","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/vartalaap"}
{"slug":"indic-diarbench","name":"Indic DiarBench","maintainer":"Sarvam AI","tagline":"Human-annotated multi-speaker conversation across all 22 scheduled Indian languages, for joint diarization and speaker-attributed ASR.","hours":"~108 h","size_hours":108,"languages":["hi","bn","ta","te","+18"],"speech":"human","speaker_consent":"mixed","full_duplex":false,"sample_rate_khz":null,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2026-07","url":"https://huggingface.co/datasets/sarvamai/indic-diarbench","paper_url":"https://arxiv.org/abs/2607.23808","page_url":"https://www.fullduplex.ai/datasets/indic-diarbench"}
{"slug":"dai-asr-i18n","name":"DAI-ASR-I18N","maintainer":"David AI","tagline":"Everyday two-speaker conversation in 21 languages, each speaker on a separate channel, licensed for evaluation only.","hours":"42.1 h","size_hours":42.1,"languages":["en","zh","ja","ko","hi","bn","mr","ta","te","th","vi","id","tl","ar","tr","ru","de","fr","es","it","pt"],"speech":"human","speaker_consent":"consented","full_duplex":true,"sample_rate_khz":null,"license":"DAI-ASR-I18N Data Use Agreement v1.1 (evaluation only)","license_class":"gated","commercial_use":"restricted","released":"2026-10","url":"https://huggingface.co/datasets/davidai-labs/DAI-ASR-I18N","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/dai-asr-i18n"}
{"slug":"timit","name":"TIMIT","maintainer":"DARPA / LDC","tagline":"The phonetically transcribed corpus every acoustic-model paper cited for two decades.","hours":"5.4 h","size_hours":5.4,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"LDC User Agreement (paid)","license_class":"gated","commercial_use":"restricted","released":"1993-10","url":"https://catalog.ldc.upenn.edu/LDC93S1","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/timit"}
{"slug":"wsj","name":"Wall Street Journal (WSJ0 / WSJ1)","maintainer":"LDC","tagline":"Read newspaper text that defined large-vocabulary ASR benchmarking in the 1990s.","hours":"~80 h","size_hours":80,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"LDC User Agreement (paid)","license_class":"gated","commercial_use":"restricted","released":"1993-01","url":"https://catalog.ldc.upenn.edu/LDC93S6A","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/wsj"}
{"slug":"ted-lium-3","name":"TED-LIUM 3","maintainer":"LIUM, Université du Mans","tagline":"TED talks with aligned transcripts, the standard long-form English ASR set.","hours":"452 h","size_hours":452,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-NC-ND-3.0","license_class":"non-commercial","commercial_use":"no","released":"2018-05","url":"https://www.openslr.org/51/","paper_url":"https://arxiv.org/abs/1805.04699","page_url":"https://www.fullduplex.ai/datasets/ted-lium-3"}
{"slug":"libri-light","name":"Libri-Light","maintainer":"Meta AI (FAIR)","tagline":"Unlabelled English audiobooks, the corpus that made self-supervised speech practical.","hours":"60k h","size_hours":60000,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"MIT","license_class":"permissive","commercial_use":"yes","released":"2019-12","url":"https://github.com/facebookresearch/libri-light","paper_url":"https://arxiv.org/abs/1912.07875","page_url":"https://www.fullduplex.ai/datasets/libri-light"}
{"slug":"aishell-1","name":"AISHELL-1","maintainer":"Beijing Shell Shell Technology","tagline":"Read Mandarin under Apache-2.0, the open baseline for Chinese ASR.","hours":"178 h","size_hours":178,"languages":["zh"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"Apache-2.0","license_class":"permissive","commercial_use":"yes","released":"2017-09","url":"https://www.openslr.org/33/","paper_url":"https://arxiv.org/abs/1709.05522","page_url":"https://www.fullduplex.ai/datasets/aishell-1"}
{"slug":"aishell-2","name":"AISHELL-2","maintainer":"Beijing Shell Shell Technology","tagline":"Clean read Mandarin, free to academia on application.","hours":"1,000 h","size_hours":1000,"languages":["zh"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"Academic research licence (application required)","license_class":"gated","commercial_use":"no","released":"2018-08","url":"https://www.aishelltech.com/aishell_2","paper_url":"https://arxiv.org/abs/1808.10583","page_url":"https://www.fullduplex.ai/datasets/aishell-2"}
{"slug":"wenetspeech","name":"WenetSpeech","maintainer":"WeNet community / NPU-ASLP","tagline":"10,000+ labelled hours of multi-domain Mandarin scraped from YouTube and podcasts.","hours":"10k h (labelled)","size_hours":10000,"languages":["zh"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-4.0 (non-commercial usage only)","license_class":"non-commercial","commercial_use":"no","released":"2021-10","url":"https://github.com/wenet-e2e/WenetSpeech","paper_url":"https://arxiv.org/abs/2110.03370","page_url":"https://www.fullduplex.ai/datasets/wenetspeech"}
{"slug":"spgispeech","name":"SPGISpeech","maintainer":"Kensho (S&P Global)","tagline":"Transcribed earnings calls with fully formatted, orthographic transcripts.","hours":"5,000 h","size_hours":5000,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"Kensho user agreement (registration required)","license_class":"gated","commercial_use":"restricted","released":"2021-04","url":"https://huggingface.co/datasets/kensho/spgispeech","paper_url":"https://arxiv.org/abs/2104.02014","page_url":"https://www.fullduplex.ai/datasets/spgispeech"}
{"slug":"earnings-22","name":"Earnings-22","maintainer":"Rev.com","tagline":"Global-accent English earnings calls, released for accent-robustness work.","hours":"119 h","size_hours":119,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-SA-4.0","license_class":"share-alike","commercial_use":"yes","released":"2022-03","url":"https://github.com/revdotcom/speech-datasets","paper_url":"https://arxiv.org/abs/2203.15591","page_url":"https://www.fullduplex.ai/datasets/earnings-22"}
{"slug":"multilingual-tedx","name":"Multilingual TEDx","maintainer":"Johns Hopkins / CMU","tagline":"TEDx talks in 8 languages with speech-translation pairs.","hours":"760 h","size_hours":760,"languages":["es","fr","pt","it","ru","el","ar","de"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-NC-ND-4.0","license_class":"non-commercial","commercial_use":"no","released":"2021-02","url":"https://www.openslr.org/100/","paper_url":"https://arxiv.org/abs/2102.01757","page_url":"https://www.fullduplex.ai/datasets/multilingual-tedx"}
{"slug":"icsi-meeting","name":"ICSI Meeting Corpus","maintainer":"ICSI / University of Edinburgh (AMI site)","tagline":"75 real research meetings recorded with close-talking and tabletop mics.","hours":"72 h","size_hours":72,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2004-02","url":"https://groups.inf.ed.ac.uk/ami/icsi/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/icsi-meeting"}
{"slug":"ljspeech","name":"LJSpeech","maintainer":"Keith Ito","tagline":"One speaker reading public-domain books, the default single-speaker TTS set.","hours":"24 h","size_hours":24,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":22.05,"license":"Public Domain","license_class":"permissive","commercial_use":"yes","released":"2017-07","url":"https://keithito.com/LJ-Speech-Dataset/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/ljspeech"}
{"slug":"vctk","name":"VCTK Corpus","maintainer":"CSTR, University of Edinburgh","tagline":"109 English speakers across accents — the multi-speaker and voice-cloning staple.","hours":"44 h","size_hours":44,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":48,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2019-11","url":"https://datashare.ed.ac.uk/handle/10283/3443","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/vctk"}
{"slug":"libritts","name":"LibriTTS","maintainer":"Google / Heiga Zen et al.","tagline":"LibriSpeech re-cut at 24 kHz with punctuation restored, for synthesis.","hours":"585 h","size_hours":585,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":24,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2019-04","url":"https://www.openslr.org/60/","paper_url":"https://arxiv.org/abs/1904.02882","page_url":"https://www.fullduplex.ai/datasets/libritts"}
{"slug":"libritts-r","name":"LibriTTS-R","maintainer":"Google","tagline":"LibriTTS restored with speech enhancement — same transcripts, cleaner audio.","hours":"585 h","size_hours":585,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":24,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2023-05","url":"https://www.openslr.org/141/","paper_url":"https://arxiv.org/abs/2305.18802","page_url":"https://www.fullduplex.ai/datasets/libritts-r"}
{"slug":"hi-fi-tts","name":"Hi-Fi TTS","maintainer":"NVIDIA","tagline":"Studio-grade audiobook speech from 10 speakers at 44.1 kHz.","hours":"292 h","size_hours":292,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":44.1,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2021-04","url":"https://www.openslr.org/109/","paper_url":"https://arxiv.org/abs/2104.01497","page_url":"https://www.fullduplex.ai/datasets/hi-fi-tts"}
{"slug":"jsut","name":"JSUT","maintainer":"Shinnosuke Takamichi (U. Tokyo)","tagline":"Single-speaker Japanese covering everyday vocabulary, recorded in an anechoic room.","hours":"10 h","size_hours":10,"languages":["ja"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":48,"license":"Custom (research free; commercial terms apply, no redistribution)","license_class":"custom","commercial_use":"restricted","released":"2017-11","url":"https://sites.google.com/site/shinnosuketakamichi/publication/jsut","paper_url":"https://arxiv.org/abs/1711.00354","page_url":"https://www.fullduplex.ai/datasets/jsut"}
{"slug":"aishell-3","name":"AISHELL-3","maintainer":"Beijing Shell Shell Technology","tagline":"Multi-speaker Mandarin TTS data from 218 speakers, under Apache-2.0.","hours":"85 h","size_hours":85,"languages":["zh"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":44.1,"license":"Apache-2.0","license_class":"permissive","commercial_use":"yes","released":"2020-10","url":"https://www.openslr.org/93/","paper_url":"https://arxiv.org/abs/2010.11567","page_url":"https://www.fullduplex.ai/datasets/aishell-3"}
{"slug":"css10","name":"CSS10","maintainer":"Kyubyong Park et al.","tagline":"Single-speaker TTS data in 10 languages, cut from public-domain audiobooks.","hours":"140 h","size_hours":140,"languages":["de","el","es","fi","fr","hu","ja","nl","ru","zh"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":22.05,"license":"Apache-2.0","license_class":"permissive","commercial_use":"yes","released":"2019-03","url":"https://github.com/Kyubyong/css10","paper_url":"https://arxiv.org/abs/1903.11269","page_url":"https://www.fullduplex.ai/datasets/css10"}
{"slug":"voxceleb1","name":"VoxCeleb1","maintainer":"VGG, University of Oxford","tagline":"1,251 celebrities speaking in the wild — the reference speaker-verification set.","hours":"352 h","size_hours":352,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-SA-4.0 (annotations; source video rights retained)","license_class":"share-alike","commercial_use":"restricted","released":"2017-06","url":"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/","paper_url":"https://arxiv.org/abs/1706.08612","page_url":"https://www.fullduplex.ai/datasets/voxceleb1"}
{"slug":"voxceleb2","name":"VoxCeleb2","maintainer":"VGG, University of Oxford","tagline":"6,112 speakers and over a million utterances — the scale tier of speaker recognition.","hours":"2,442 h","size_hours":2442,"languages":["multi"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-SA-4.0 (annotations; source video rights retained)","license_class":"share-alike","commercial_use":"restricted","released":"2018-06","url":"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/","paper_url":"https://arxiv.org/abs/1806.05622","page_url":"https://www.fullduplex.ai/datasets/voxceleb2"}
{"slug":"voxlingua107","name":"VoxLingua107","maintainer":"Tallinn University of Technology","tagline":"Spoken language identification data covering 107 languages.","hours":"6,628 h","size_hours":6628,"languages":["multi"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2020-11","url":"https://cs.taltech.ee/staff/tanel.alumae/data/voxlingua107/","paper_url":"https://arxiv.org/abs/2011.12998","page_url":"https://www.fullduplex.ai/datasets/voxlingua107"}
{"slug":"cn-celeb","name":"CN-Celeb","maintainer":"CSLT, Tsinghua University","tagline":"Chinese speaker recognition in eleven genres — deliberately harder than VoxCeleb.","hours":"274 h","size_hours":274,"languages":["zh"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-SA-4.0, no commercial usage","license_class":"non-commercial","commercial_use":"no","released":"2019-11","url":"https://www.openslr.org/82/","paper_url":"https://arxiv.org/abs/1911.01799","page_url":"https://www.fullduplex.ai/datasets/cn-celeb"}
{"slug":"ravdess","name":"RAVDESS","maintainer":"Ryerson University","tagline":"24 actors performing eight emotions at two intensities, audio and video.","hours":"~3 h","size_hours":3,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":48,"license":"CC-BY-NC-SA-4.0","license_class":"non-commercial","commercial_use":"no","released":"2018-04","url":"https://zenodo.org/record/1188976","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/ravdess"}
{"slug":"crema-d","name":"CREMA-D","maintainer":"University of Pennsylvania","tagline":"7,442 acted clips from 91 ethnically diverse actors, rated by crowdworkers.","hours":"~5 h","size_hours":5,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"Open Database License (ODbL)","license_class":"share-alike","commercial_use":"yes","released":"2014-08","url":"https://github.com/CheyneyComputerScience/CREMA-D","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/crema-d"}
{"slug":"msp-podcast","name":"MSP-Podcast","maintainer":"UT Dallas (MSP Lab)","tagline":"The largest naturalistic emotional speech corpus — podcast audio, continuously growing.","hours":"409 h (v2.0)","size_hours":409,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"Academic licence (application required)","license_class":"gated","commercial_use":"restricted","released":"2017-01","url":"https://www.lab-msp.com/MSP/MSP-Podcast.html","paper_url":"https://arxiv.org/abs/2509.09791","page_url":"https://www.fullduplex.ai/datasets/msp-podcast"}
{"slug":"meld","name":"MELD","maintainer":"DeCLaRe Lab / SUTD","tagline":"Emotion recognition in multi-party conversation, cut from a sitcom script.","hours":"~12 h","size_hours":12,"languages":["en"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"Not stated (repository code is GPL-3.0)","license_class":"custom","commercial_use":"restricted","released":"2018-10","url":"https://affective-meld.github.io/","paper_url":"https://arxiv.org/abs/1810.02508","page_url":"https://www.fullduplex.ai/datasets/meld"}
{"slug":"esd","name":"ESD (Emotional Speech Dataset)","maintainer":"NUS / HLT Lab","tagline":"Parallel emotional speech in English and Mandarin from 20 speakers.","hours":"29 h","size_hours":29,"languages":["en","zh"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"Research use (see repository terms)","license_class":"custom","commercial_use":"restricted","released":"2021-05","url":"https://hltsingapore.github.io/ESD/","paper_url":"https://arxiv.org/abs/2105.14762","page_url":"https://www.fullduplex.ai/datasets/esd"}
{"slug":"musan","name":"MUSAN","maintainer":"Johns Hopkins University","tagline":"Music, speech and noise — the corpus behind almost every augmentation recipe.","hours":"109 h","size_hours":109,"languages":["multi"],"speech":"human","speaker_consent":"public","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2015-10","url":"https://www.openslr.org/17/","paper_url":"https://arxiv.org/abs/1510.08484","page_url":"https://www.fullduplex.ai/datasets/musan"}
{"slug":"wham","name":"WHAM! / WHAMR!","maintainer":"Whisper.ai / MERL","tagline":"Real ambient noise recorded in cafés and bars, paired with speech separation mixtures.","hours":"~80 h","size_hours":80,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-NC-4.0","license_class":"non-commercial","commercial_use":"no","released":"2019-07","url":"http://wham.whisper.ai/","paper_url":"https://arxiv.org/abs/1907.01160","page_url":"https://www.fullduplex.ai/datasets/wham"}
{"slug":"demand","name":"DEMAND","maintainer":"Inria / University of Edinburgh","tagline":"Multi-channel recordings of 18 real acoustic environments.","hours":"~5 h","size_hours":5,"languages":["multi"],"speech":null,"speaker_consent":null,"full_duplex":false,"sample_rate_khz":48,"license":"CC-BY-SA-3.0","license_class":"share-alike","commercial_use":"yes","released":"2013-06","url":"https://zenodo.org/record/1227121","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/demand"}
{"slug":"myst-children","name":"MyST Children's Conversational Speech","maintainer":"Boulder Learning / LDC","tagline":"Children talking to a virtual science tutor, the largest child-speech corpus.","hours":"393 h","size_hours":393,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-NC-SA-4.0 (non-commercial); commercial licence available","license_class":"non-commercial","commercial_use":"restricted","released":"2021-06","url":"https://catalog.ldc.upenn.edu/LDC2021S05","paper_url":"https://arxiv.org/abs/2309.13347","page_url":"https://www.fullduplex.ai/datasets/myst-children"}
{"slug":"l2-arctic","name":"L2-ARCTIC","maintainer":"Texas A&M / Iowa State","tagline":"Non-native English from 24 speakers across six first languages, with error annotation.","hours":"27 h","size_hours":27,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":44.1,"license":"Research use (registration required)","license_class":"custom","commercial_use":"restricted","released":"2018-09","url":"https://psi.engr.tamu.edu/l2-arctic-corpus/","paper_url":null,"page_url":"https://www.fullduplex.ai/datasets/l2-arctic"}
{"slug":"primock57","name":"PriMock57","maintainer":"Babylon Health","tagline":"57 mock primary-care consultations released for clinical speech research.","hours":"~9 h","size_hours":9,"languages":["en"],"speech":"human","speaker_consent":"consented","full_duplex":false,"sample_rate_khz":16,"license":"CC-BY-4.0","license_class":"permissive","commercial_use":"yes","released":"2022-05","url":"https://github.com/babylonhealth/primock57","paper_url":"https://aclanthology.org/2022.acl-short.65/","page_url":"https://www.fullduplex.ai/datasets/primock57"}