Add Ego4D AV assisted annotation review workflow

PROJECT_CONTEXT.md

@@ -308,7 +308,9 @@ Do not reorganize them physically without compatibility wrappers.
 | `audit_easycom_floor_events.py` | Count EOT/takeover/backchannel candidates |
 | `prepare_easycom_eot_int_review.py` | Retain every raw wearer onset and offset as an unresolved semantic candidate; attach nearby participant boundaries, activity relations, transcripts, and target-of-speech facts without entry/takeover gating; also scan independent hard-negative regions; semantic labels remain AI/human review outputs |
 | `adjudicate_easycom_eot_int_candidates.py` | Add optional DeepSeek transcript/timing suggestions; never human ground truth |
-| `review_floor_events.py` | Serve the local video/transcript workspace and persist human event reviews |
+| `prepare_ego4d_av_eot_int_review.py` | Build the equivalent unresolved wearer onset/offset and hard-negative review queue from official Ego4D AV voice, transcription and annotation-only `is_at_me` fields without changing source boundaries |
+| `adjudicate_ego4d_av_eot_int_candidates.py` | Apply the checkpointed strict LLM pre-label client with an Ego4D AV-specific evidence policy; every suggestion still requires human review |
+| `review_floor_events.py` / `review_ego4d_av_events.py` | Serve the local EasyCom or Ego4D AV video/transcript workspace and atomically persist human event reviews |
 | `prepare_easycom_oracle_features.py` | Build explicit past-only oracle person-activity sidecars and analysis-only addressee controls |
 
 ### Local features

adjudicate_ego4d_av_eot_int_candidates.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""Add auditable LLM suggestions to Ego4D AV floor-event candidates.
+
+This reuses the checkpointing, strict JSON validation, retry/bisection, and
+privacy opt-in machinery of the EasyCom annotator, but supplies an Ego4D AV
+specific evidence policy.  Output remains a suggestion for human review, never
+ground truth.
+"""
+
+from __future__ import annotations
+
+import adjudicate_easycom_eot_int_candidates as engine
+
+
+PROMPT_VERSION = "ego4d-av-wearer-boundary-semantic-prelabel-v1"
+
+SYSTEM_PROMPT = """You conservatively pre-label Ego4D AV candidate regions for a scientific study of wearer-relative conversational floor control.
+
+Evidence policy:
+1. Use only the supplied clip-local timestamps, participant roles, voice activity, transcriptions, and addressed_to_wearer facts. Never alter timestamps or infer unseen gaze, prosody, intent, identity, or acoustic evidence.
+2. Ego4D AV voice segments are sound-activity annotations and may join sounds separated by less than one second. A WEARER_ONSET is not automatically a floor entry and a WEARER_OFFSET is not automatically an interruption yield. Overlap is not automatically interruption. Duration and lexical form alone do not establish conversational function.
+3. addressed_to_wearer comes from annotation-only social_segments_talking.is_at_me. Treat true as supporting evidence that the other addresses the wearer, false as evidence against direct address, and null as unknown. It is evidence for annotation only, never a model input.
+4. For WEARER_ONSET, label POSITIVE/WEARER_ENTRY only when the wearer genuinely enters or re-enters the floor with a contextually complete response or substantive contribution. A brief complete answer to a question can be positive. Use HARD_NEGATIVE/ENTRY_WEARER_BACKCHANNEL when the wearer merely acknowledges or feeds back while an other retains the floor. Use EXCLUDE/OUT_OF_SCOPE_WITHIN_TURN_CONTINUATION for a within-turn resumption.
+5. For WEARER_OFFSET, label POSITIVE/INT_YIELD only when a non-wearer genuinely takes over during wearer speech and the wearer relinquishes the floor. A natural completion, later other onset, or end of a wearer backchannel is not INT_YIELD. Crossing the offset is descriptive timing evidence, not proof.
+6. Deterministic SAME_SPEAKER_PAUSE and OTHER_TO_OTHER rows are proposed hard-negative regions. Confirm their conversational meaning from the chronology and transcript; do not blindly accept the automatic label.
+7. EXCLUDE is the only non-scoring outcome and is appropriate for clear out-of-scope events, insufficient semantic evidence, missing context, recording boundaries, annotation errors, non-speech artifacts, or timestamp corruption. Every item remains subject to human review.
+
+Top-level verdicts and closed labels:
+- POSITIVE event_type: WEARER_ENTRY or INT_YIELD.
+- HARD_NEGATIVE subtype: ENTRY_OTHER_MIDTURN_PAUSE, ENTRY_OTHER_TO_OTHER, ENTRY_WEARER_BACKCHANNEL, INT_OTHER_BACKCHANNEL, INT_NON_TAKEOVER_OVERLAP, INT_WEARER_HOLDS_FLOOR, or OTHER_HARD_NEGATIVE. Use only ENTRY_* for wearer_entry tasks and INT_* for int_yield tasks, except OTHER_HARD_NEGATIVE.
+- EXCLUDE reason: OUT_OF_SCOPE_NORMAL_COMPLETION, OUT_OF_SCOPE_WEARER_BACKCHANNEL_OFFSET, OUT_OF_SCOPE_WITHIN_TURN_CONTINUATION, OUT_OF_SCOPE_POST_COMPLETION_ACTIVITY, OUT_OF_SCOPE_NON_EVENT, INSUFFICIENT_EVIDENCE, DATA_ANNOTATION_ERROR, DATA_RECORDING_BOUNDARY, DATA_MISSING_CONTEXT, DATA_NON_SPEECH_ARTIFACT, or DATA_TIMESTAMP_CORRUPTION.
+
+For a positive WEARER_ENTRY, selected_opportunity_uid may be exactly one uid from temporal_evidence.opportunity_options when that supplied other release clearly prompted the entry; otherwise null. It must be null for all other outcomes.
+
+Return exactly one JSON object and no markdown:
+{"items":[{"uid":"item-0000","verdict":"EXCLUDE","event_type":null,"subtype":null,"exclude_reason":"INSUFFICIENT_EVIDENCE","selected_opportunity_uid":null,"confidence":0.25,"reason":"brief evidence-based sentence"}]}
+Return every supplied uid exactly once. confidence must be a number in [0,1]."""
+
+
+def configure_engine() -> None:
+    engine.PROMPT_VERSION = PROMPT_VERSION
+    engine.SYSTEM_PROMPT = SYSTEM_PROMPT
+    engine.__doc__ = __doc__
+
+
+def main() -> None:
+    configure_engine()
+    engine.main()
+
+
+if __name__ == "__main__":
+    main()

docs/ego4d_av_llm_annotation_review.md

@@ -0,0 +1,136 @@
+# Ego4D AV LLM-assisted floor-event annotation
+
+This workflow mirrors the EasyCom boundary-review pipeline while using the
+official Ego4D AV annotation schema. It creates semantic review data for
+wearer floor entry and interruption-driven yield; it does not modify Ego4D's
+voice activity annotations and does not turn LLM output into ground truth.
+
+## Data and claim boundary
+
+Expected dataset layout:
+
+```text
+DATA_ROOT/
+  v1/
+    annotations/av_train.json
+    annotations/av_val.json
+    clips/<clip_uid>.mp4
+```
+
+The generator reads clip-local `voice_segments`, `transcriptions`, and
+`social_segments_talking.is_at_me`. The social label is annotation-only
+evidence. It must never be passed to a forecasting model or used after a
+strictly causal prediction cutoff. Ego4D's voice annotation guideline joins
+sounds separated by less than one second, so a stored segment boundary is an
+activity boundary, not automatically a semantic turn boundary.
+
+The output split remains the split declared by Ego4D (`train` or `val`). Do not
+rename validation data to test or tune annotation rules after inspecting the
+final evaluation set.
+
+## 1. Generate deterministic candidates
+
+Start with one clip and inspect the JSONL before running the full set:
+
+```bash
+python3 prepare_ego4d_av_eot_int_review.py \
+  --annotations "$DATA_ROOT/v1/annotations/av_train.json" \
+  --output reports/ego4d_av_event_candidates/ego4d_av_train_candidates.jsonl \
+  --limit 1
+```
+
+Then generate the selected splits:
+
+```bash
+python3 prepare_ego4d_av_eot_int_review.py \
+  --annotations \
+    "$DATA_ROOT/v1/annotations/av_train.json" \
+    "$DATA_ROOT/v1/annotations/av_val.json" \
+  --output reports/ego4d_av_event_candidates/ego4d_av_candidates.jsonl
+```
+
+The command also writes `<output_stem>_timelines.json` beside the JSONL. Each
+raw wearer onset and offset is retained. Same-other pauses and other-to-other
+handoffs are added as independently reviewable hard-negative proposals.
+Timing rules only select and describe candidates; they never assign semantic
+positive labels.
+
+## 2. Inspect what will be uploaded
+
+The LLM client refuses to upload transcript excerpts without an explicit
+opt-in. First inspect the exact prompt locally:
+
+```bash
+python3 adjudicate_ego4d_av_eot_int_candidates.py \
+  --input reports/ego4d_av_event_candidates/ego4d_av_candidates.jsonl \
+  --output reports/ego4d_av_event_candidates/ego4d_av_candidates_ai.jsonl \
+  --max-events 8 \
+  --dry-run
+```
+
+The prompt uses only timestamps, participant roles, provided transcripts, and
+provided `is_at_me` facts. It does not upload video or audio.
+
+## 3. Run LLM pre-labeling
+
+After confirming that external transcript upload is authorized:
+
+```bash
+export DEEPSEEK_API_KEY=...
+python3 adjudicate_ego4d_av_eot_int_candidates.py \
+  --input reports/ego4d_av_event_candidates/ego4d_av_candidates.jsonl \
+  --output reports/ego4d_av_event_candidates/ego4d_av_candidates_ai.jsonl \
+  --passes 2 \
+  --batch-size 8 \
+  --allow-external-transcript-upload
+```
+
+The client checkpoints after every successful batch. Two passes must agree;
+disagreement becomes `EXCLUDE/INSUFFICIENT_EVIDENCE`. Malformed singleton
+responses are written to a pending-human JSONL rather than silently dropped.
+Every successful LLM decision has `needs_human_review=true`.
+
+## 4. Review in the browser
+
+```bash
+python3 review_ego4d_av_events.py \
+  --events reports/ego4d_av_event_candidates/ego4d_av_candidates_ai.jsonl \
+  --dataset-root "$DATA_ROOT" \
+  --host 127.0.0.1 \
+  --port 8765
+```
+
+Open `http://127.0.0.1:8765`. The UI shows the local video, clip-level speaker
+timeline, participant-aligned transcripts, LLM rationale, and the closed human
+label taxonomy. Reviews are atomically saved to
+`reports/ego4d_av_event_reviews/reviews.jsonl` with a CSV companion and can be
+resumed. The server rejects media paths outside `DATA_ROOT` and supports byte
+ranges for browser seeking.
+
+For an SSH host, keep the service on loopback and forward the port:
+
+```bash
+ssh -L 8765:127.0.0.1:8765 USER@HOST
+```
+
+## 5. Freeze and audit
+
+Before using these annotations in a paper or evaluator:
+
+1. Freeze candidate windows and the closed taxonomy on training data.
+2. Double-review a random sample plus every low-confidence/disagreement item.
+3. Report raw candidates, positives, hard negatives, exclusions, and exclusion
+   reasons separately for train and validation.
+4. Print and independently inspect example timelines for entry, yield,
+   backchannel, same-speaker pause, and other-to-other cases.
+5. Keep the final label named human-adjudicated semantic annotation. LLM output
+   alone is never ground truth.
+
+## Tests
+
+```bash
+python3 -m unittest -v \
+  test_prepare_ego4d_av_eot_int_review.py \
+  test_review_floor_events.py \
+  test_adjudicate_easycom_eot_int_candidates.py
+```

floor_event_review_web/app.js

@@ -102,8 +102,14 @@ function formatTime(value) {
   return `${String(minutes).padStart(2, "0")}:${(seconds % 60).toFixed(1).padStart(4, "0")}`;
 }
 
+function participantCode(value) {
+  const raw = String(value ?? "?");
+  return /^(p|person|wearer)/i.test(raw) ? raw : `P${raw}`;
+}
+
 function participantIdentity(row, verbose = false) {
-  const participant = `P${row.participant_id ?? "?"}`;
+  const raw = String(row.participant_id ?? "?");
+  const participant = participantCode(raw);
   if (row.participant_role === "wearer") {
     return verbose ? `W · ${participant} · wearer` : `W · ${participant}`;
   }
@@ -282,7 +288,7 @@ function renderDetail(detail) {
   for (const option of opportunityOptions) {
     const control = document.createElement("option");
     control.value = option.uid;
-    control.textContent = `${formatTime(option.anchor_seconds)} · P${option.participant_id ?? "?"} · ${option.transcription || "无转写"}`;
+    control.textContent = `${formatTime(option.anchor_seconds)} · ${participantCode(option.participant_id)} · ${option.transcription || "无转写"}`;
     elements.opportunitySelect.append(control);
   }
   elements.saveState.textContent = detail.review ? "已保存" : "未保存";
@@ -501,7 +507,7 @@ function renderSpeakerTimeline(detail) {
       const opportunity = document.createElement("i");
       opportunity.className = "opportunity-anchor";
       opportunity.style.left = `${100 * (Number(option.anchor_seconds) - left) / span}%`;
-      opportunity.title = `可选 release · ${formatTime(option.anchor_seconds)} · P${option.participant_id ?? "?"}`;
+      opportunity.title = `可选 release · ${formatTime(option.anchor_seconds)} · ${participantCode(option.participant_id)}`;
       track.append(opportunity);
     }
     const anchor = document.createElement("i");

floor_event_review_web/index.html

@@ -3,19 +3,19 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1" />
-  <title>EasyCom Floor Event Review</title>
+  <title>Egocentric Floor Event Review</title>
   <link rel="stylesheet" href="/app.css" />
 </head>
 <body>
   <header class="topbar">
-    <div class="brand"><span class="brand-mark"></span><div><h1>Floor Event Review</h1><p>AI 粗标注 → 人工确认</p></div></div>
+    <div class="brand"><span class="brand-mark"></span><div><h1>Floor Event Review</h1><p>EasyCom / Ego4D AV · AI 粗标注 → 人工确认</p></div></div>
     <div class="progress-block"><div class="progress-copy"><strong id="progressText">载入中</strong><span id="progressMeta">—</span></div><div class="progress-track"><span id="progressBar"></span></div></div>
     <nav class="export-actions"><a href="/api/export.jsonl" download>导出 JSONL</a><a href="/api/export.csv" download>导出 CSV</a></nav>
   </header>
 
   <section class="filters" aria-label="候选筛选">
     <label><span>Session</span><select id="sessionFilter"><option value="all">全部</option></select></label>
-    <label><span>Split</span><select id="splitFilter"><option value="all">全部</option><option value="train">train</option><option value="test">test</option></select></label>
+    <label><span>Split</span><select id="splitFilter"><option value="all">全部</option><option value="train">train</option><option value="val">val</option><option value="test">test</option></select></label>
     <label><span>任务</span><select id="familyFilter"><option value="all">全部</option><option value="entry">WEARER ENTRY</option><option value="int">INT / YIELD</option></select></label>
     <label><span>审核状态</span><select id="statusFilter"><option value="unreviewed">未审核</option><option value="all">全部</option><option value="reviewed">已审核</option></select></label>
     <label class="search-field"><span>搜索</span><input id="searchFilter" type="search" placeholder="事件来源、AI 标签、session" /></label>

prepare_ego4d_av_eot_int_review.py

@@ -0,0 +1,533 @@
+#!/usr/bin/env python3
+"""Build Ego4D AV wearer-entry/INT candidates for LLM and human review.
+
+The input is one or more official ``av_train.json``/``av_val.json`` files.
+Official voice boundaries are never moved or merged.  This deterministic stage
+only attaches nearby speaker activity, transcriptions, and social ``is_at_me``
+facts.  Floor-entry and interruption-yield semantics remain unresolved until
+LLM pre-labeling and human review.
+"""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from dataclasses import asdict, dataclass
+import json
+from pathlib import Path
+from typing import Any, Iterable, Sequence
+
+
+SCHEMA_VERSION = 1
+PROTOCOL_VERSION = "ego4d-av-wearer-entry-int-candidates-v1"
+
+
+@dataclass(frozen=True)
+class CandidateConfig:
+    context_seconds: float = 4.0
+    relation_window_seconds: float = 3.0
+    same_speaker_pause_max_seconds: float = 2.0
+    other_handover_max_seconds: float = 1.5
+
+
+@dataclass(frozen=True)
+class SpeechSegment:
+    uid: str
+    person_id: str
+    role: str
+    start: float
+    end: float
+    transcription: str
+    addressed_to_wearer: bool | None
+    source: dict[str, Any]
+
+
+def _number(value: Any, *, field: str) -> float:
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        raise ValueError(f"{field} must be numeric, got {value!r}")
+    result = float(value)
+    if result < 0:
+        raise ValueError(f"{field} must be non-negative, got {result}")
+    return result
+
+
+def _segment_times(row: dict[str, Any]) -> tuple[float, float]:
+    if row.get("start_time") is not None and row.get("end_time") is not None:
+        start = _number(row["start_time"], field="start_time")
+        end = _number(row["end_time"], field="end_time")
+    elif row.get("start_time_sec") is not None and row.get("end_time_sec") is not None:
+        start = _number(row["start_time_sec"], field="start_time_sec")
+        end = _number(row["end_time_sec"], field="end_time_sec")
+    else:
+        raise ValueError("voice segment lacks clip-local start/end time")
+    if end <= start:
+        raise ValueError(f"voice segment must have positive duration: {start}..{end}")
+    return start, end
+
+
+def _person_key(value: Any) -> str:
+    if value is None:
+        return ""
+    return str(value).strip()
+
+
+def _best_transcription(
+    rows: Sequence[dict[str, Any]], person_id: str, start: float, end: float
+) -> str:
+    matches: list[tuple[float, float, str]] = []
+    for row in rows:
+        if _person_key(row.get("person_id", row.get("person"))) != person_id:
+            continue
+        try:
+            left = _number(row.get("start_time_sec", row.get("start_time")), field="transcript start")
+            right = _number(row.get("end_time_sec", row.get("end_time")), field="transcript end")
+        except ValueError:
+            continue
+        overlap = max(0.0, min(end, right) - max(start, left))
+        if overlap > 0:
+            matches.append((left, overlap, str(row.get("transcription", "")).strip()))
+    matches.sort()
+    return " ".join(text for _, _, text in matches if text)
+
+
+def _addressed_to_wearer(
+    rows: Sequence[dict[str, Any]], person_id: str, start: float, end: float
+) -> bool | None:
+    overlaps: list[bool] = []
+    for row in rows:
+        if _person_key(row.get("person", row.get("person_id"))) != person_id:
+            continue
+        try:
+            left, right = _segment_times(row)
+        except ValueError:
+            continue
+        if min(end, right) - max(start, left) > 0:
+            value = row.get("is_at_me")
+            if isinstance(value, bool):
+                overlaps.append(value)
+    if not overlaps:
+        return None
+    return any(overlaps)
+
+
+def load_clip_segments(clip: dict[str, Any]) -> tuple[list[SpeechSegment], str]:
+    """Return validated clip-local speaker segments and the wearer person ID."""
+    clip_uid = str(clip.get("clip_uid", "")).strip()
+    if not clip_uid:
+        raise ValueError("clip lacks clip_uid")
+    transcripts = clip.get("transcriptions") or []
+    social = clip.get("social_segments_talking") or []
+    if not isinstance(transcripts, list) or not isinstance(social, list):
+        raise ValueError(f"{clip_uid}: transcriptions/social_segments_talking must be lists")
+
+    people: list[dict[str, Any]] = []
+    wearer = clip.get("camera_wearer")
+    if isinstance(wearer, dict):
+        people.append(wearer)
+    raw_people = clip.get("persons") or []
+    if not isinstance(raw_people, list):
+        raise ValueError(f"{clip_uid}: persons must be a list")
+    people.extend(person for person in raw_people if isinstance(person, dict))
+
+    wearer_ids = {
+        _person_key(person.get("person_id"))
+        for person in people
+        if person.get("camera_wearer") is True
+    }
+    wearer_ids.discard("")
+    if len(wearer_ids) != 1:
+        raise ValueError(f"{clip_uid}: expected exactly one camera wearer, found {sorted(wearer_ids)}")
+    wearer_id = next(iter(wearer_ids))
+
+    segments: list[SpeechSegment] = []
+    seen: set[tuple[str, float, float]] = set()
+    ordinal = 0
+    for person in people:
+        person_id = _person_key(person.get("person_id"))
+        if not person_id:
+            raise ValueError(f"{clip_uid}: person lacks person_id")
+        voice = person.get("voice_segments") or []
+        if not isinstance(voice, list):
+            raise ValueError(f"{clip_uid}/{person_id}: voice_segments must be a list")
+        for source in voice:
+            if not isinstance(source, dict):
+                raise ValueError(f"{clip_uid}/{person_id}: voice segment must be an object")
+            start, end = _segment_times(source)
+            key = (person_id, start, end)
+            if key in seen:
+                continue
+            seen.add(key)
+            segments.append(
+                SpeechSegment(
+                    uid=f"{clip_uid}/speech/{ordinal:05d}",
+                    person_id=person_id,
+                    role="wearer" if person_id == wearer_id else "other",
+                    start=start,
+                    end=end,
+                    transcription=_best_transcription(transcripts, person_id, start, end),
+                    addressed_to_wearer=(
+                        None if person_id == wearer_id
+                        else _addressed_to_wearer(social, person_id, start, end)
+                    ),
+                    source=dict(source),
+                )
+            )
+            ordinal += 1
+    segments.sort(key=lambda row: (row.start, row.end, row.person_id, row.uid))
+    for left, right in zip(segments, segments[1:]):
+        if left.person_id == right.person_id and left.start == right.start and left.end == right.end:
+            raise ValueError(f"{clip_uid}: duplicate voice segment after normalization")
+    return segments, wearer_id
+
+
+def _timeline_row(row: SpeechSegment) -> dict[str, Any]:
+    return {
+        "uid": row.uid,
+        "participant_id": row.person_id,
+        "participant_role": row.role,
+        "start_seconds": row.start,
+        "end_seconds": row.end,
+        "duration_seconds": row.end - row.start,
+        "transcription": row.transcription,
+        "addressed_to_wearer": row.addressed_to_wearer,
+    }
+
+
+def _context(segments: Sequence[SpeechSegment], anchor: float, seconds: float) -> list[dict[str, Any]]:
+    return [
+        _timeline_row(row) for row in segments
+        if row.end > anchor - seconds and row.start < anchor + seconds
+    ]
+
+
+def _relation(segments: Sequence[SpeechSegment], anchor: float, wearer_id: str, window: float) -> dict[str, Any]:
+    others = [row for row in segments if row.person_id != wearer_id]
+    previous = [row for row in others if row.end <= anchor and anchor - row.end <= window]
+    following = [row for row in others if row.start >= anchor and row.start - anchor <= window]
+    active = [row for row in others if row.start < anchor < row.end]
+    prev = max(previous, key=lambda row: row.end, default=None)
+    nxt = min(following, key=lambda row: row.start, default=None)
+    return {
+        "previous_speaker_id": None if prev is None else prev.person_id,
+        "previous_speaker_offset_seconds": None if prev is None else prev.end,
+        "gap_from_previous_speaker_offset_seconds": None if prev is None else anchor - prev.end,
+        "active_other_ids": sorted({row.person_id for row in active}),
+        "next_speaker_id": None if nxt is None else nxt.person_id,
+        "next_other_onset_seconds": None if nxt is None else nxt.start,
+        "next_other_delay_seconds": None if nxt is None else nxt.start - anchor,
+    }
+
+
+def _opportunity_options(
+    segments: Sequence[SpeechSegment], wearer: SpeechSegment, wearer_id: str, window: float
+) -> list[dict[str, Any]]:
+    result = []
+    for row in segments:
+        if row.person_id == wearer_id or row.end > wearer.start:
+            continue
+        delay = wearer.start - row.end
+        if delay > window:
+            continue
+        intervening = [
+            other for other in segments
+            if other.person_id != wearer_id and other.uid != row.uid
+            and other.start >= row.end and other.start < wearer.start
+        ]
+        result.append({
+            "uid": row.uid,
+            "participant_id": row.person_id,
+            "anchor_seconds": row.end,
+            "response_delay_seconds": delay,
+            "transcription": row.transcription,
+            "addressed_to_wearer": row.addressed_to_wearer,
+            "other_onset_between_release_and_onset": bool(intervening),
+        })
+    return sorted(result, key=lambda row: (-float(row["anchor_seconds"]), str(row["uid"])))
+
+
+def _base_candidate(
+    *, clip: dict[str, Any], split: str, source: str, task: str, anchor_type: str,
+    anchor: float, region_start: float, region_end: float, sequence: int,
+    segments: Sequence[SpeechSegment], evidence: dict[str, Any], rule_type: str,
+    automatic_label: str = "candidate", automatic_subtype: str | None = None,
+) -> dict[str, Any]:
+    clip_uid = str(clip["clip_uid"])
+    uid = f"{clip_uid}/{source.lower()}/{sequence:05d}"
+    return {
+        "schema_version": SCHEMA_VERSION,
+        "protocol_version": PROTOCOL_VERSION,
+        "dataset": "ego4d_av",
+        "uid": uid,
+        "event_id": uid,
+        "session": clip_uid,
+        "clip_uid": clip_uid,
+        "video_uid": clip.get("video_uid"),
+        "split": split,
+        "task": task,
+        "event_type": rule_type,
+        "candidate_source": source,
+        "rule_candidate_type": rule_type,
+        "automatic_protocol_label": automatic_label,
+        "automatic_hard_negative_type": automatic_subtype,
+        "automatic_confidence": "deterministic-boundary-only",
+        "anchor_type": anchor_type,
+        "anchor_dense_seconds": anchor,
+        "event_time": anchor,
+        "region_start_seconds": region_start,
+        "region_end_seconds": region_end,
+        "clip_stem": clip_uid,
+        "temporal_evidence": evidence,
+        "speaker_timeline": _context(segments, anchor, max(anchor - region_start, region_end - anchor)),
+        "context": _context(segments, anchor, max(anchor - region_start, region_end - anchor)),
+        "ai_verdict": None,
+        "ai_event_type": None,
+        "ai_subtype": None,
+        "ai_confidence": None,
+        "ai_reason": None,
+        "human_status": "UNREVIEWED",
+        "human_verdict": None,
+        "human_event_type": None,
+        "human_subtype": None,
+        "exclude_reason": None,
+    }
+
+
+def derive_clip_candidates(
+    clip: dict[str, Any], *, split: str, config: CandidateConfig
+) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
+    segments, wearer_id = load_clip_segments(clip)
+    clip_end = _number(clip.get("clip_end_sec", max((row.end for row in segments), default=0.0)), field="clip_end_sec")
+    outside = [row for row in segments if row.end > clip_end + 1e-6]
+    if outside:
+        first = outside[0]
+        raise ValueError(
+            f"{clip.get('clip_uid')}: voice segment {first.uid} ends at {first.end:.6f}s "
+            f"after clip_end_sec={clip_end:.6f}; refusing to clamp or drop it"
+        )
+    candidates: list[dict[str, Any]] = []
+    wearer_segments = [row for row in segments if row.person_id == wearer_id]
+
+    for sequence, row in enumerate(wearer_segments):
+        previous_wearer = max(
+            (other for other in wearer_segments if other.end <= row.start),
+            key=lambda other: other.end,
+            default=None,
+        )
+        evidence = _relation(segments, row.start, wearer_id, config.relation_window_seconds)
+        options = _opportunity_options(segments, row, wearer_id, config.relation_window_seconds)
+        evidence.update({
+            "behavior_anchor_seconds": row.start,
+            "wearer_segment_uid": row.uid,
+            "wearer_transcription": row.transcription,
+            "wearer_speech_duration_after_onset_seconds": row.end - row.start,
+            "previous_wearer_offset_seconds": None if previous_wearer is None else previous_wearer.end,
+            "previous_wearer_silence_duration_seconds": None if previous_wearer is None else row.start - previous_wearer.end,
+            "opportunity_options": options,
+            "preceding_release_options_present": bool(options),
+            "official_voice_segments_are_unmodified": True,
+        })
+        candidates.append(_base_candidate(
+            clip=clip, split=split, source="WEARER_ONSET", task="wearer_entry",
+            anchor_type="wearer_onset", anchor=row.start,
+            region_start=max(0.0, row.start - config.context_seconds),
+            region_end=min(clip_end, row.start + config.context_seconds), sequence=sequence,
+            segments=segments, evidence=evidence, rule_type="WEARER_ENTRY_CANDIDATE",
+        ))
+
+        active_before = [other for other in segments if other.person_id != wearer_id and other.start < row.end < other.end]
+        new_during = [other for other in segments if other.person_id != wearer_id and row.start < other.start < row.end]
+        next_other = min(
+            (other for other in segments if other.person_id != wearer_id and other.start >= row.end),
+            key=lambda other: other.start,
+            default=None,
+        )
+        offset_evidence = _relation(segments, row.end, wearer_id, config.relation_window_seconds)
+        offset_evidence.update({
+            "behavior_anchor_seconds": row.end,
+            "wearer_activity_run_start_seconds": row.start,
+            "wearer_segment_uid": row.uid,
+            "wearer_transcription": row.transcription,
+            "other_crosses_wearer_offset": bool(active_before),
+            "crossing_other_ids": sorted({other.person_id for other in active_before}),
+            "new_other_onsets_during_wearer": [_timeline_row(other) for other in new_during],
+            "new_other_onset_during_wearer": bool(new_during),
+            "next_speaker_ids": [] if next_other is None else [next_other.person_id],
+            "next_speech_onset_seconds": None if next_other is None else next_other.start,
+            "official_voice_segments_are_unmodified": True,
+        })
+        candidates.append(_base_candidate(
+            clip=clip, split=split, source="WEARER_OFFSET", task="int_yield",
+            anchor_type="wearer_offset", anchor=row.end,
+            region_start=max(0.0, row.end - config.context_seconds),
+            region_end=min(clip_end, row.end + config.context_seconds), sequence=sequence,
+            segments=segments, evidence=offset_evidence, rule_type="YIELD_CANDIDATE",
+        ))
+
+    others = [row for row in segments if row.person_id != wearer_id]
+    control_sequence = 0
+    for previous in others:
+        following = sorted(
+            (row for row in others if row.start >= previous.end and row.uid != previous.uid),
+            key=lambda row: (row.start, row.end),
+        )
+        if not following:
+            continue
+        nxt = following[0]
+        gap = nxt.start - previous.end
+        wearer_in_gap = any(row.start < nxt.start and row.end > previous.end for row in wearer_segments)
+        subtype = None
+        source = None
+        max_gap = None
+        if nxt.person_id == previous.person_id:
+            source, subtype = "SAME_SPEAKER_PAUSE", "ENTRY_OTHER_MIDTURN_PAUSE"
+            max_gap = config.same_speaker_pause_max_seconds
+        elif nxt.person_id != previous.person_id:
+            source, subtype = "OTHER_TO_OTHER", "ENTRY_OTHER_TO_OTHER"
+            max_gap = config.other_handover_max_seconds
+        if gap > float(max_gap) or wearer_in_gap:
+            continue
+        evidence = _relation(segments, previous.end, wearer_id, config.relation_window_seconds)
+        evidence.update({
+            "previous_speaker_id": previous.person_id,
+            "incoming_speaker_id": nxt.person_id,
+            "previous_segment_uid": previous.uid,
+            "incoming_segment_uid": nxt.uid,
+            "gap_seconds": gap,
+            "previous_transcription": previous.transcription,
+            "incoming_transcription": nxt.transcription,
+            "wearer_activity_in_gap": False,
+            "official_voice_segments_are_unmodified": True,
+        })
+        candidates.append(_base_candidate(
+            clip=clip, split=split, source=str(source), task="wearer_entry",
+            anchor_type="other_offset", anchor=previous.end,
+            region_start=max(0.0, previous.end - config.context_seconds),
+            region_end=min(clip_end, previous.end + config.context_seconds),
+            sequence=control_sequence, segments=segments, evidence=evidence,
+            rule_type=str(subtype), automatic_label="hard_negative",
+            automatic_subtype=str(subtype),
+        ))
+        control_sequence += 1
+    candidates.sort(key=lambda row: (float(row["event_time"]), str(row["uid"])))
+    return candidates, [_timeline_row(row) for row in segments]
+
+
+def iter_clips(paths: Iterable[Path]) -> Iterable[tuple[dict[str, Any], str]]:
+    seen: set[str] = set()
+    for path in paths:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        videos = payload.get("videos") if isinstance(payload, dict) else None
+        if not isinstance(videos, list):
+            raise ValueError(f"{path}: expected a videos list")
+        for video in videos:
+            if not isinstance(video, dict):
+                continue
+            split = str(video.get("split") or path.stem.removeprefix("av_") or "unknown")
+            for clip in video.get("clips") or []:
+                if not isinstance(clip, dict) or clip.get("valid") is False:
+                    continue
+                uid = str(clip.get("clip_uid", ""))
+                if uid in seen:
+                    raise ValueError(f"duplicate clip_uid across inputs: {uid}")
+                seen.add(uid)
+                yield clip, split
+
+
+def _write_jsonl(path: Path, rows: Sequence[dict[str, Any]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".partial")
+    with temporary.open("w", encoding="utf-8") as handle:
+        for row in rows:
+            handle.write(json.dumps(row, ensure_ascii=False) + "\n")
+    temporary.replace(path)
+
+
+def _write_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".partial")
+    temporary.write_text(
+        json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+    )
+    temporary.replace(path)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--annotations", type=Path, nargs="+", required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--timeline-output", type=Path)
+    parser.add_argument("--clip-uid", action="append", help="Restrict to one or more clip UIDs")
+    parser.add_argument("--limit", type=int, help="Process at most N selected clips")
+    parser.add_argument("--context-seconds", type=float, default=4.0)
+    parser.add_argument("--relation-window-seconds", type=float, default=3.0)
+    parser.add_argument("--same-speaker-pause-max-seconds", type=float, default=2.0)
+    parser.add_argument("--other-handover-max-seconds", type=float, default=1.5)
+    parser.add_argument("--overwrite", action="store_true")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    numeric = [
+        args.context_seconds, args.relation_window_seconds,
+        args.same_speaker_pause_max_seconds, args.other_handover_max_seconds,
+    ]
+    if any(value < 0 for value in numeric):
+        raise ValueError("all time windows must be non-negative")
+    if args.limit is not None and args.limit <= 0:
+        raise ValueError("--limit must be positive")
+    paths = [path.expanduser().resolve() for path in args.annotations]
+    for path in paths:
+        if not path.is_file():
+            raise FileNotFoundError(path)
+    selected = list(iter_clips(paths))
+    if args.clip_uid:
+        requested = set(args.clip_uid)
+        known = {str(clip["clip_uid"]) for clip, _ in selected}
+        if requested - known:
+            raise ValueError(f"unknown clip UID(s): {sorted(requested - known)}")
+        selected = [(clip, split) for clip, split in selected if clip["clip_uid"] in requested]
+    if args.limit is not None:
+        selected = selected[: args.limit]
+    config = CandidateConfig(*numeric)
+    rows: list[dict[str, Any]] = []
+    timelines: dict[str, list[dict[str, Any]]] = {}
+    for clip, split in selected:
+        derived, timeline = derive_clip_candidates(clip, split=split, config=config)
+        rows.extend(derived)
+        timelines[str(clip["clip_uid"])] = timeline
+    if not rows:
+        raise ValueError("selected annotations produced no candidates")
+    missing_voice_count_by_clip = {
+        str(clip["clip_uid"]): len(clip.get("missing_voice_segments") or [])
+        for clip, _ in selected
+    }
+    for row in rows:
+        row["temporal_evidence"]["clip_missing_voice_segments_count"] = (
+            missing_voice_count_by_clip[row["clip_uid"]]
+        )
+    output = args.output.expanduser().resolve()
+    timeline_output = (
+        args.timeline_output.expanduser().resolve() if args.timeline_output
+        else output.with_name(output.stem + "_timelines.json")
+    )
+    existing = [path for path in (output, timeline_output) if path.exists()]
+    if existing and not args.overwrite:
+        raise FileExistsError(
+            "Refusing to overwrite existing output(s): "
+            + ", ".join(str(path) for path in existing)
+            + "; pass --overwrite"
+        )
+    _write_jsonl(output, rows)
+    _write_json(
+        timeline_output,
+        {"schema_version": 1, "dataset": "ego4d_av", "sessions": timelines},
+    )
+    counts = Counter(row["candidate_source"] for row in rows)
+    print(f"clips={len(selected)} candidates={len(rows)} output={output}")
+    print("candidate_sources=" + json.dumps(dict(sorted(counts.items())), ensure_ascii=False))
+    print(f"timelines={timeline_output}")
+
+
+if __name__ == "__main__":
+    main()

review_ego4d_av_events.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+"""Launch the browser-based review tool for Ego4D AV floor candidates."""
+
+from __future__ import annotations
+
+import sys
+
+import review_floor_events
+
+
+def main() -> None:
+    if "--dataset" not in sys.argv:
+        sys.argv.extend(["--dataset", "ego4d"])
+    if "--output" not in sys.argv:
+        sys.argv.extend([
+            "--output", "reports/ego4d_av_event_reviews/reviews.jsonl",
+        ])
+    review_floor_events.main()
+
+
+if __name__ == "__main__":
+    main()

review_floor_events.py

@@ -1,11 +1,10 @@
 #!/usr/bin/env python3
-"""Serve a local, event-centered EasyCom annotation review workspace.
+"""Serve a local, event-centered EasyCom or Ego4D AV review workspace.
 
-The server reads JSONL candidates produced by
-``derive_easycom_interaction_events.py`` or ``audit_easycom_floor_events.py``.
-It serves only media inside the supplied EasyCom dataset root and stores human
-reviews outside the source dataset as an atomically rewritten JSONL file plus
-a CSV companion.
+The server reads JSONL candidates produced by the EasyCom or Ego4D AV
+candidate generators. It serves only media inside the supplied dataset root
+and stores human reviews outside the source dataset as an atomically rewritten
+JSONL file plus a CSV companion.
 """
 
 from __future__ import annotations
@@ -94,7 +93,7 @@ class Candidate:
 
     @property
     def session(self) -> str:
-        return str(self.payload.get("session", "unknown"))
+        return str(self.payload.get("session") or self.payload.get("clip_uid") or "unknown")
 
     @property
     def split(self) -> str:
@@ -161,6 +160,7 @@ def expand_candidate_paths(values: Iterable[Path], *, output_path: Path) -> list
         if path.is_dir():
             paths.extend(sorted(path.glob("Session_*.events.jsonl")))
             paths.extend(sorted(path.glob("easycom_*_candidates.jsonl")))
+            paths.extend(sorted(path.glob("ego4d*_candidates.jsonl")))
         elif path.is_file():
             paths.append(path)
         else:
@@ -239,17 +239,35 @@ class CandidateRepository:
         candidates: list[Candidate],
         dataset_root: Path,
         *,
+        dataset: str = "auto",
         context_before_seconds: float,
         context_after_seconds: float,
     ) -> None:
         self.candidates = candidates
         self.by_id = {candidate.candidate_id: candidate for candidate in candidates}
         self.dataset_root = dataset_root.expanduser().resolve()
+        if dataset not in {"auto", "easycom", "ego4d"}:
+            raise ValueError(f"Unknown dataset: {dataset}")
+        declared = {str(candidate.payload.get("dataset", "")) for candidate in candidates}
+        kinds = {"ego4d" if value.startswith("ego4d") else "easycom" for value in declared}
+        if dataset == "auto" and len(kinds) != 1:
+            raise ValueError(f"Candidate inputs mix dataset layouts: {sorted(declared)}")
+        inferred = next(iter(kinds))
+        self.dataset = inferred if dataset == "auto" else dataset
         self.context_before_seconds = context_before_seconds
         self.context_after_seconds = context_after_seconds
         self._transcript_cache: dict[tuple[str, str], list[dict[str, Any]]] = {}
         self.session_timelines: dict[str, list[dict[str, Any]]] = {}
-        for path in sorted({candidate.source_path.parent / "easycom_session_timelines.json" for candidate in candidates}):
+        timeline_names = ("easycom_session_timelines.json", "ego4d_av_clip_timelines.json")
+        timeline_paths = {
+            candidate.source_path.parent / name
+            for candidate in candidates for name in timeline_names
+        }
+        timeline_paths.update(
+            candidate.source_path.with_name(candidate.source_path.stem + "_timelines.json")
+            for candidate in candidates
+        )
+        for path in sorted(timeline_paths):
             if not path.is_file():
                 continue
             payload = json.loads(path.read_text(encoding="utf-8"))
@@ -279,6 +297,14 @@ class CandidateRepository:
     def _location(self, candidate: Candidate) -> tuple[str | None, float | None, int | None]:
         row = candidate.payload
         clip_stem = row.get("clip_stem")
+        if self.dataset == "ego4d":
+            clip_uid = row.get("clip_uid") or clip_stem or candidate.session
+            event_time = row.get("event_time", row.get("anchor_dense_seconds"))
+            return (
+                None if clip_uid is None else str(clip_uid),
+                None if event_time is None else max(0.0, float(event_time)),
+                None,
+            )
         transcript_index: int | None = None
         uid = None if row.get("candidate_source") else (
             _uid_parts(row.get("uid")) or _uid_parts(row.get("holder_uid"))
@@ -308,9 +334,12 @@ class CandidateRepository:
         clip_stem, _, _ = self._location(candidate)
         if clip_stem is None:
             return None
-        path = self.dataset_root.joinpath(
-            *VIDEO_ROOT_PARTS, candidate.session, f"{clip_stem}.mp4"
-        ).resolve()
+        if self.dataset == "ego4d":
+            path = self.dataset_root.joinpath("v1", "clips", f"{clip_stem}.mp4").resolve()
+        else:
+            path = self.dataset_root.joinpath(
+                *VIDEO_ROOT_PARTS, candidate.session, f"{clip_stem}.mp4"
+            ).resolve()
         if not _within_root(path, self.dataset_root) or not path.is_file():
             return None
         return path
@@ -358,7 +387,37 @@ class CandidateRepository:
             for row in evidence_rows
             if isinstance(row, dict) and row.get("participant_role") == "wearer"
         }
-        if clip_stem is not None:
+        if self.dataset == "ego4d":
+            for index, row in enumerate(evidence_rows):
+                if not isinstance(row, dict):
+                    continue
+                try:
+                    start = max(0.0, float(row["start_seconds"]))
+                    end = max(start, float(row["end_seconds"]))
+                except (KeyError, TypeError, ValueError):
+                    continue
+                if local_seconds is not None and (
+                    end < local_seconds - self.context_before_seconds
+                    or start > local_seconds + self.context_after_seconds
+                ):
+                    continue
+                transcript_rows.append({
+                    "uid": row.get("uid", f"{candidate.session}/context/{index:04d}"),
+                    "index": index,
+                    "start_seconds": start,
+                    "end_seconds": end,
+                    "participant_id": row.get("participant_id"),
+                    "participant_role": row.get("participant_role", "other"),
+                    "target_of_speech": (
+                        "camera_wearer" if row.get("addressed_to_wearer") is True
+                        else "not_camera_wearer" if row.get("addressed_to_wearer") is False
+                        else None
+                    ),
+                    "addressed_to_wearer": row.get("addressed_to_wearer"),
+                    "transcription": str(row.get("transcription", "")),
+                    "is_anchor": start <= (local_seconds or -1) <= end,
+                })
+        elif clip_stem is not None:
             for index, row in enumerate(self._transcript(candidate.session, clip_stem)):
                 try:
                     start = max(0.0, (float(row.get("Start_Frame", 1)) - 1.0) / SOURCE_FPS)
@@ -915,6 +974,10 @@ def parse_args() -> argparse.Namespace:
         help="Candidate JSONL files or directories containing generated candidates.",
     )
     parser.add_argument("--dataset-root", type=Path, required=True)
+    parser.add_argument(
+        "--dataset", choices=("auto", "easycom", "ego4d"), default="auto",
+        help="Dataset layout; auto reads the candidate dataset field (default: %(default)s).",
+    )
     parser.add_argument(
         "--output",
         type=Path,
@@ -953,6 +1016,7 @@ def main() -> None:
     repository = CandidateRepository(
         candidates,
         dataset_root,
+        dataset=args.dataset,
         context_before_seconds=args.context_before_seconds,
         context_after_seconds=args.context_after_seconds,
     )

test_prepare_ego4d_av_eot_int_review.py

@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import tempfile
+import unittest
+
+from prepare_ego4d_av_eot_int_review import (
+    CandidateConfig, derive_clip_candidates, iter_clips, load_clip_segments,
+)
+from review_floor_events import CandidateRepository, load_candidates
+
+
+def segment(person: str, start: float, end: float) -> dict[str, object]:
+    return {"person": person, "start_time": start, "end_time": end}
+
+
+def fixture_clip() -> dict[str, object]:
+    return {
+        "clip_uid": "clip-001",
+        "video_uid": "video-001",
+        "clip_start_sec": 0,
+        "clip_end_sec": 12.0,
+        "valid": True,
+        "camera_wearer": {
+            "person_id": "wearer",
+            "camera_wearer": True,
+            "voice_segments": [segment("wearer", 2.0, 3.0), segment("wearer", 6.0, 8.0)],
+        },
+        "persons": [
+            {
+                "person_id": "p1", "camera_wearer": False,
+                "voice_segments": [segment("p1", 0.5, 1.8), segment("p1", 3.2, 4.0), segment("p1", 6.5, 8.8)],
+            },
+            {
+                "person_id": "p2", "camera_wearer": False,
+                "voice_segments": [segment("p2", 4.3, 5.2)],
+            },
+        ],
+        "transcriptions": [
+            {"person_id": "p1", "start_time_sec": 0.5, "end_time_sec": 1.8, "transcription": "Can you answer?"},
+            {"person_id": "wearer", "start_time_sec": 2.0, "end_time_sec": 3.0, "transcription": "Yes."},
+            {"person_id": "p1", "start_time_sec": 6.5, "end_time_sec": 8.8, "transcription": "Wait, one thing."},
+        ],
+        "social_segments_talking": [
+            {"person": "p1", "start_time": 0.5, "end_time": 1.8, "is_at_me": True},
+        ],
+    }
+
+
+class Ego4DAVCandidateTests(unittest.TestCase):
+    def test_loads_roles_transcript_and_social_evidence(self) -> None:
+        rows, wearer_id = load_clip_segments(fixture_clip())
+        self.assertEqual(wearer_id, "wearer")
+        question = next(row for row in rows if row.start == 0.5)
+        self.assertEqual(question.transcription, "Can you answer?")
+        self.assertIs(question.addressed_to_wearer, True)
+        self.assertEqual(next(row.role for row in rows if row.person_id == "wearer"), "wearer")
+
+    def test_derives_raw_wearer_boundaries_and_controls(self) -> None:
+        rows, timeline = derive_clip_candidates(
+            fixture_clip(), split="train", config=CandidateConfig()
+        )
+        sources = [row["candidate_source"] for row in rows]
+        self.assertEqual(sources.count("WEARER_ONSET"), 2)
+        self.assertEqual(sources.count("WEARER_OFFSET"), 2)
+        self.assertIn("OTHER_TO_OTHER", sources)
+        onset = next(row for row in rows if row["candidate_source"] == "WEARER_ONSET")
+        option = onset["temporal_evidence"]["opportunity_options"][0]
+        self.assertEqual(option["participant_id"], "p1")
+        self.assertIs(option["addressed_to_wearer"], True)
+        offset = next(row for row in rows if row["candidate_source"] == "WEARER_OFFSET" and row["event_time"] == 8.0)
+        self.assertTrue(offset["temporal_evidence"]["other_crosses_wearer_offset"])
+        self.assertTrue(offset["temporal_evidence"]["new_other_onset_during_wearer"])
+        self.assertTrue(all(row["start_seconds"] < row["end_seconds"] for row in timeline))
+
+    def test_duplicate_clip_uid_is_rejected(self) -> None:
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            payload = {"videos": [{"split": "train", "clips": [fixture_clip()]}]}
+            paths = []
+            for name in ("av_train.json", "av_val.json"):
+                path = root / name
+                path.write_text(json.dumps(payload), encoding="utf-8")
+                paths.append(path)
+            with self.assertRaisesRegex(ValueError, "duplicate clip_uid"):
+                list(iter_clips(paths))
+
+    def test_review_repository_uses_ego4d_media_and_embedded_transcript(self) -> None:
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            media = root / "v1" / "clips" / "clip-001.mp4"
+            media.parent.mkdir(parents=True)
+            media.write_bytes(b"not-a-real-video")
+            rows, _ = derive_clip_candidates(
+                fixture_clip(), split="val", config=CandidateConfig()
+            )
+            events = root / "ego4d_av_candidates.jsonl"
+            events.write_text("".join(json.dumps(row) + "\n" for row in rows), encoding="utf-8")
+            candidates = load_candidates([events])
+            repository = CandidateRepository(
+                candidates, root, dataset="auto",
+                context_before_seconds=3.0, context_after_seconds=3.0,
+            )
+            self.assertEqual(repository.dataset, "ego4d")
+            self.assertEqual(repository.media_path(candidates[0]), media.resolve())
+            detail = repository.detail(candidates[0])
+            self.assertTrue(detail["transcript_available"])
+            self.assertTrue(any(row["transcription"] == "Can you answer?" for row in detail["transcript"]))
+
+    def test_rejects_missing_camera_wearer(self) -> None:
+        clip = fixture_clip()
+        clip.pop("camera_wearer")
+        with self.assertRaisesRegex(ValueError, "exactly one camera wearer"):
+            load_clip_segments(clip)
+
+
+if __name__ == "__main__":
+    unittest.main()