---
language:
- en
task_categories:
- automatic-speech-recognition
- text-to-speech
- audio-classification
pretty_name: American English Full-Duplex Two-Speaker Conversational Dataset (Prompt-Based Sample)
tags:
- audio
- speech
- conversational
- multi-speaker
- full-duplex
- turn-taking
- prompt-based
- open-topic
- english
- sample
size_categories:
- n<1K
extra_gated_prompt: >-
  This is a free prompt-based preview sample of OcularAI's full
  American English Full-Duplex Two-Speaker Conversational Dataset. It contains
  recordings of identifiable human speakers and self-reported demographic
  attributes. By requesting access you agree to use the data for evaluation,
  research, and AI development only, to follow the terms of use, and to make no
  attempt to identify or contact any speaker. For commercial licensing or access
  to the full dataset, contact OcularAI.
extra_gated_fields:
  Full name: text
  Company / Affiliation: text
  Work email: text
  Intended use: text
  Interested in the full dataset: checkbox
  I agree to the data use terms: checkbox
---

# American English Full-Duplex Two-Speaker Conversational Dataset — Prompt-Based Sample

> **This is a free prompt-based preview** (~5 hours) of OcularAI's full
> American English Full-Duplex Two-Speaker Conversational Dataset. Each
> conversation is an **open, natural discussion seeded by a single prompt** — the
> pair was given one topic and talked freely. (For conversations *directed* by a
> designed scenario targeting a specific turn-taking / voice-dynamics behavior,
> see the companion **Scenario-Based** sample in the same collection.) It is built
> from a diverse, representative slice of the full corpus so you can evaluate audio
> quality, structure, and metadata before licensing the complete dataset.
> **For full-dataset access or commercial licensing, contact OcularAI.**

## What's in this sample

- **21 conversations** (~5.0 hours of full-duplex conversation content)
- **42 isolated speaker tracks** (one per speaker, Opus)
- **21 distinct conversation topics** — no repeated prompts
- **20 distinct speaker locations** across the US/Canada
- Mixed speaker demographics (gender, location, ethnicity — all self-reported)

## Dataset summary

Natural, **unscripted, two-speaker English conversations** recorded by fluent
English speakers based in the **United States and Canada**. Each session is a
~15-minute spontaneous discussion between a matched pair of speakers on everyday
topics — personal experiences, hobbies, workplace challenges, or opinions that
have changed over time.

The recordings support the development of next-generation AI systems — helping
them better understand **natural speech patterns, conversational flow,
turn-taking, and real-world human interaction**. Each speaker is captured on an
**independent, isolated audio track**, enabling per-speaker analysis,
diarization, full-duplex modeling, ASR, and TTS.

## Dataset structure

**One row per speaker track**, stacked and ordered by `room_name` so a
conversation's two speakers sit adjacent. Each row is one isolated voice with its
own metadata; join on `room_name` to reconstruct the conversation. Audio is the
original Opus capture in a `.opus` container.

### Fields (per row = one speaker's track)

| field | description |
|-------|-------------|
| `file_name` | this speaker's isolated audio track |
| `room_name` | conversation/session key — shared by both speakers of a conversation |
| `conversation_id` | conversation identifier |
| `role` | `SPEAKER_A` or `SPEAKER_B` |
| `speaker_id` | stable speaker identifier |
| `duration_seconds` | track duration |
| `language` | spoken language of the session (`en-US`) |
| `prompt` | the conversation topic the pair discussed |
| `gender` | self-reported |
| `city`, `country` | self-reported location |
| `ethnicity` | self-reported |
| `fluent_languages` | languages the speaker is fluent in |

Rows are ordered by `room_name` so a conversation's two speakers appear
adjacent. Join on `room_name` to reconstruct a full conversation.

## The full dataset

This 5-hour sample is a small, representative slice of OcularAI's full
American English Full-Duplex Two-Speaker Conversational Dataset. The complete
corpus is substantially larger and continues to grow.

Contact OcularAI for access to the full dataset and for commercial licensing
terms.

## Privacy & consent

Speaker **names and emails are removed**. Demographic fields are self-reported.
Recordings were collected from consenting, compensated participants for AI
research. Access requires agreeing to evaluation/research-only use and no
re-identification.

## Audio note

Tracks are Opus in a `.opus` container. Decode with `datasets>=4.0`
(torchcodec/FFmpeg). For older stacks, losslessly rewrap
(`ffmpeg -i in.opus -c:a copy out.ogg`).

## Licensing

© OcularAI. All rights reserved. This sample is provided for evaluation only.
Licensing terms — including full commercial usage rights — are by agreement.
Contact OcularAI for licensing.