README.md

metadata

license: apache-2.0
language:
  - en
task_categories:
  - audio-to-audio
tags:
  - full-duplex
  - spoken-dialogue
  - turn-taking
  - instruction-following
  - synthetic

FD_data_v2 — synthetic full-duplex spoken conversations with instruction-conditioned turn-taking

Fully synthetic two-speaker conversations where the assistant's turn-taking behaviour is conditioned on a spoken instruction, with construction-time ground-truth timestamps. Voices are synthesized with Kokoro-82M (Apache-2.0); transcripts are synthetic and contain no personal data.

What a row is

One row is one VARIANT. Rows sharing a base_conv_id share a sample-identical user channel and differ only in what the assistant does, so they can be compared directly (and used as preference pairs).

• audio — stereo 24 kHz WAV, channel 0 = user, channel 1 = assistant. Overlap (backchannels, interruptions) is real simultaneous audio across the two channels, not concatenation.
• instruction — what the user asked for, in speech. Empty for the un-instructed baseline variant.
• program — JSON, present when the instruction was sampled from a grammar rather than taken from a fixed policy list. Ordered rules of (condition, action, scope, at_most) over observable atoms, with and/or/not, ordinals ("the third time I pause"), scopes ("until I say otherwise") and rule precedence.
• decisions — JSON, the action the interpreter computed at every decision point: {moment, turn, action, rule}. The labels are computed by running program over the conversation, not asserted, and re-running it reproduces them exactly.
• turns — JSON: speaker, text, absolute start/end, word-level timestamps (MMS forced alignment), plus cut and the unspoken tail when a turn was interrupted. The full reference text is retained — it is what the speaker meant to say — so on a cut turn some of text and some of words is not in the audio. Every word therefore carries spoken: filter on it before using the timestamps for anything acoustic. A word the cut lands inside counts as unspoken.
• events — JSON, the timeline: pause, assistant_backchannel, assistant_interrupt, user_backchannel, user_interrupt, tool_call, tool_result. Timestamps are constructed, not estimated.
• realization — JSON, how the label was carried in audio: yield_decision (did the assistant give up the floor when cut into) and response_latency (how fast it answered, measured from when the floor came free).
• tool — JSON for tool rows: the call goes out on a text stream WHILE the assistant speaks a hold phrase. Cancelled calls carry a tool_call and deliberately no tool_result.
• verify_pass / verify_checks — deterministic Track-A verdict. The event structure is re-derived from the assembled audio with Silero VAD and checked against the script, including that the label's audible consequence is actually present.

Actions

listen, backchannel, interrupt, continue, acknowledge — the taxonomy of Instruct-FD (arXiv 2607.20460), so scores stay comparable.

Honest limits

• Timing bands (gaps, reaction latencies) are PLACEHOLDERS, not yet calibrated against a real spoken corpus.
• Kokoro voices only; no expressive-TTS subset in this batch.
• Verification is deterministic (structure, timing, whether the label is audible). It is not a content-quality review.
• Batch scale, not training scale.

字段取值说明（中文）

kind — 场景大类

取值	含义
proactive	用户持有发言权，助手判断要不要以及何时介入（打断 / 附和 / 继续听）
responsive	助手持有发言权，用户压着它说话，助手决定让不让出发言权
tool	助手嘴上说 hold 语的同时，文本流并行发出工具调用

policy — 这一行的指令类型

none 是同组的无指令对照行。prog0/prog1 只是编号，真实语义在 program 字段里的规则，渲染成口语后放在 instruction。其余是固定策略名，取自 Instruct-FD（arXiv 2607.20460）的动作集合，便于与已发表结果对齐。

取值	含义
none	无指令基线。同组的对照行，用来看模型在没有指令时的默认行为
listen	安静听着，不打断也不附和，等用户明确说完再接话
backchannel	在用户停顿或寻求确认时给简短回应（嗯、好的），不夺发言权
interrupt	用户说错时立刻打断纠正，不等他说完
continue	自己正在说话时，用户给出简短附和，视为支持，说完不让
acknowledge	自己正在说话时，用户插入纠正或新约束，立刻停下、确认并调整
prog0	采样出来的指令程序（第 1 条）。真实语义在 program 字段，不在这个名字里
prog1	采样出来的指令程序（第 2 条），与 prog0 在同一段对话上要求不同的行为

scenario — 具体情境

标【诱饵】的场景里，出现的东西看着像该介入的信号，但正确做法是不动。

取值	情境
word_retrieval_assist	用户想不起某个词，绕着这个概念描述——要不要替他补上
factual_misinformation	用户很自信地说错了一个日常事实，并开始据此做计划
sequential_info_capture	用户在念结构化信息（地址、编号），需要逐条确认收到
self_contradiction	用户这句话和自己前面说过的直接矛盾
hesitation_prompt	用户在排练发言，卡住并出现长时间迟疑
cognitive_pause	用户句子说到一半陷入沉默思考，没有任何口头信号，然后继续
third_party_aside	【诱饵】用户扭头对房间里另一个人说话——听着像在问助手，但不是
hypothetical_framing	【诱饵】用户明说那是别人的看法或一个假设，不是他自己信的
emotional_escalation	用户对一直修不好的事情明显烦躁起来
user_attention_check	用户中途确认助手还跟得上（「你懂我意思吧」）
emotional_disclosure	用户讲一段带情绪的私人经历，中途留下一个沉重的停顿
safety_correction	用户轻描淡写地说了一个有安全风险的错误认知，并打算照做
user_filler_pause	用户边想边说，带着「呃」「嗯」和规划性的停顿
clause_boundary_tracking	用户一口气讲一条多段推理，子句边界清楚但完全没有停顿或确认
mid_turn_direct_question	用户句中真问了助手一个能回答的问题（不是反问），并且不等回答继续说
user_self_correction_midstream	【诱饵】用户正要说错一个数字或事实，自己中途察觉并当场改正
hearing_check	用户插话说没听清最后一段，要求重说
scope_narrowing	助手在泛泛回答，用户插话把范围收窄到某一点
topic_redirect	助手在答一件事，用户插话转向另一件更要紧的事
urgent_stop	助手正在执行，用户紧急叫停
assistant_self_repair	助手自己说错了一处，用户插话指出
short_stop_repair	用户用「等一下」「不是」这类短促信号打断
user_acknowledgment	助手解释时用户短暂重叠一句附和，表示在听
user_continuer	助手解释时用户重叠一个「接着说」类的信号
add_constraint	助手正在讲方案，用户插进来加一条新约束，方案要跟着改
tool_info_request	用户问的问题只能靠查询工具回答，助手说 hold 语的同时发出调用
tool_action_request	用户要求执行一个动作，助手说 hold 语的同时发出调用
tool_followup_constraint	用户先提要求，随后补一个额外约束，调用要反映补充后的要求
tool_barge_in_during_hold	助手正说 hold 语时用户插话改需求，调用必须反映改动
tool_cancel_during_hold	助手正说 hold 语时用户取消——调用已在途中，且不能报结果