| --- |
| 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. |
|
|
