TheAgenticDataCompany/open-yap-1k

Open Yap 1K sample

Download the sample

Download all 16 conversations (8.9 hours)

One TAR file contains both speaker tracks, transcripts, metadata, and preview audio.

The world's largest free conversational dataset: 1000 hours of two-speaker English speech, open to labs and research teams worldwide.

With this open release, we aim to close a gap in the literature: speech recorded as it happens in real life. Fisher and Switchboard assigned partners and topics to maximize variety, but the phone network capped their audio near 4 kHz. Newer corpora record at full bandwidth but still pair strangers. Open Yap 1K assigns nothing: each speaker invites someone they already know and talks freely among friends and family.

That design choice shows in the data quality. People who know each other interrupt more, backchannel more, and leave shorter gaps between turns: the behaviour a full-duplex model has to learn.

These 1000 hours are one part of a larger licensed corpus we build with frontier labs and research teams. We release them because progress in conversational AI is slower than it needs to be - and open data is the fastest way to change that for everyone. Contact us to discuss licensing the full corpus.

The HuggingFace repository only holds a sample. The full corpus is publicly available for commercial and research use, under the Open Yap 1K Data Use Agreement. Request it at theagenticdatacompany.com/open-yap-1k.

Corpus composition

	HuggingFace	Full Corpus
Size	8.9 h, 16 conversations	1000 h, 1602 conversations
Speakers	8	239
License	CC-BY-4.0	Open Yap 1K Data Use Agreement
Access	This page	Request form
Audio	48 kHz, 16-bit FLAC, one file per speaker	48 kHz, 16-bit PCM, one file per speaker

Contact

Website: theagenticdatacompany.com

Email: christian@theagenticdatacompany.com

Files and loading

open-yap-1k/
├── README.md
├── LICENSE.txt
├── metadata.jsonl
├── open-yap-1k-sample.tar
├── preview/
│   ├── metadata.jsonl
│   └── <key>.mp3
└── shard-00000.tar to shard-00001.tar
    ├── <key>.preview.mp3
    ├── <key>.a.flac
    ├── <key>.b.flac
    └── <key>.json

Load both speaker tracks and the full conversation record in Python:

from datasets import load_dataset

ds = load_dataset(
    "webdataset",
    data_files={"train": "hf://datasets/TheAgenticDataCompany/open-yap-1k/shard-*.tar"},
    split="train",
)
sample = ds[0]
a, b = sample["a.flac"]["array"], sample["b.flac"]["array"]
assert len(a) == len(b)   # one timeline, by construction
record = sample["json"]   # metadata and both transcripts

Or fetch the files and read them with any tar reader:

hf download TheAgenticDataCompany/open-yap-1k --repo-type dataset --include "shard-*.tar" --local-dir open-yap-1k

Collection method and quality assurance

We collect the audio with our own app, which works like a phone call. A speaker invites someone they know, and the two of them simply talk. This gives us natural conversation and a straightforward way to scale collection. The trade-off is less control over the recording environment, so real-world noise appears more often. We keep the subtle noise, because it teaches a model robustness. However, we remove the long tail of noisy recordings with several ML models combined into one pipeline.

	Other datasets	Open Yap 1K
Speakers	Strangers, paired by the collector	Friends and family, self-paired
Setting	A treated room	Real rooms, on their own devices
Conversation	An assigned topic	Free talk, any topic
What you hear	Clean turns, little overlap	Overlap, quick turns, backchannel, laughter
Background	Pristine	Slightly noisier, kept on purpose

Consent and privacy

• All audio was recorded on our own platform.
• Speakers register, give explicit consent before their first recording, and are paid for their time.
• Demographics are self-reported at registration, before any recording, and are never inferred from audio.
• Speaker identifiers are pseudonymous and stable within the release. Names, contact details and account identifiers are excluded.

Known limitations

• Hand-picked, not a random draw. 16 friends; nothing about this sample's distribution generalises to the corpus.
• Transcripts are machine-generated (Deepgram Nova-3, word-level) and not human-verified. Expect errors on overlapping speech and names.
• 8 speakers. Several appear in more than one conversation, so this is a handful of voices, not a population.
• 8 of 32 tracks carry no energy above 8 kHz. Those microphones were Bluetooth headsets, but the file is still 48 kHz; read effective_bandwidth_hz per track before assuming full bandwidth.

Metadata

File name	Field	Meaning
<key>.json	conversation_id	stable pseudonym
	language	BCP-47
	relationship	self-reported
	duration_seconds	conversation length
	topics[]	subject tags
	turns_per_minute	speaker handoffs per minute, excluding short backchannels
	turn_taking_gap_ms	median gap between turns
	speech_dominance	speaker A's share of spoken words, 0-1 (0.5 = equal)
	sample_rate	48000 Hz, as delivered
	speakers.{a,b}	per-speaker metadata
	transcripts.{a,b}	per-speaker transcript
speakers.{a,b}	speaker_id	stable pseudonym
	age_range	bucketed, e.g. "25-34"
	gender	self-reported
	country	ISO-3166 alpha-2, self-reported
	education_level	self-reported
	native_language	BCP-47, self-reported
	accent	derived from self-reported demographics, not measured from audio
	recording	track format and measured levels
	avg_wpm	words per minute
	headphones	exact on mobile, inferred on web
	echo_cancellation	applied at capture
	device	capture device or audio route
	audio_metrics	measured from this track, at the rate this archive ships
transcripts.{a,b}	conversation_id	stable pseudonym
	speaker_index	"a" | "b"
	language	BCP-47
	text	full transcript
	words[]	{ word, start, end, type }
	corrections_applied	a reviewer edited the ASR output

Audio is delivered un-normalised. Integrated loudness and true peak are measured and reported so a target level can be applied without probing every file.

Citation

@misc{openyap1k,
  title     = {Open Yap 1K: Channel-Separated English Natural Two-Speaker Conversations},
  author    = {The Agentic Data Company},
  year      = {2026},
  version   = {1.0},
  publisher = {The Agentic Data Company},
  url       = {https://theagenticdatacompany.com/open-yap-1k},
}