Revamp EasyCom floor event review pipeline
This commit is contained in:
Xin Wang
+1
-1
@@ -304,7 +304,7 @@ Do not reorganize them physically without compatibility wrappers.
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| `derive_easycom_interaction_events.py` | Deterministic transcript/timing proxies |
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| `adjudicate_easycom_interaction_events.py` | Optional ambiguous-event adjudication |
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| `audit_easycom_floor_events.py` | Count EOT/takeover/backchannel candidates |
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| `prepare_easycom_eot_int_review.py` | Build manuscript-aligned EOT-response/INT-yield positive and hard-negative review regions |
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| `prepare_easycom_eot_int_review.py` | Build wearer-transition and independent hard-negative candidates with structured timing evidence; semantic labels remain AI/human review outputs |
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| `adjudicate_easycom_eot_int_candidates.py` | Add optional DeepSeek transcript/timing suggestions; never human ground truth |
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| `review_floor_events.py` | Serve the local video/transcript workspace and persist human event reviews |
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| `prepare_easycom_oracle_features.py` | Build explicit past-only oracle person-activity sidecars and analysis-only addressee controls |
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@@ -1,10 +1,10 @@
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#!/usr/bin/env python3
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"""Add conservative DeepSeek suggestions to EOT/INT review candidates.
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"""Add conservative DeepSeek pre-labels to wearer-centric EOT/INT candidates.
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Only transcript excerpts and timing metadata are sent. Suggestions are never
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treated as gold labels: the output remains input to ``review_floor_events.py``
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and requires an identified human reviewer. The API key is read only from the
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``DEEPSEEK_API_KEY`` environment variable and is never persisted.
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The model receives the candidate source, structured participant timing and the
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transcript excerpt. It cannot change any source timestamp. Its output is
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stored as an auditable suggestion beside the untouched rule candidate and must
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still be accepted, relabeled, excluded, or sent to second review by a human.
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"""
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from __future__ import annotations
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@@ -16,49 +16,65 @@ import json
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import os
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from pathlib import Path
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import random
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import re
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import time
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from typing import Any, Sequence
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import urllib.error
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import urllib.request
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PROMPT_VERSION = "easycom-manuscript-eot-int-adjudicator-v1"
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PROMPT_VERSION = "easycom-wearer-centric-eot-int-prelabel-v2"
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DEFAULT_MODEL = "deepseek-v4-flash"
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DEFAULT_BASE_URL = "https://api.deepseek.com"
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DISPOSITIONS = {"positive", "hard_negative", "exclude", "uncertain"}
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VERDICTS = {"POSITIVE", "HARD_NEGATIVE", "NOT_APPLICABLE", "EXCLUDE", "UNSURE"}
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EVENT_TYPES = {"EOT_RESPONSE", "INT_YIELD"}
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HARD_NEGATIVE_TYPES = {
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"eot_midturn_pause",
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"eot_other_to_other",
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"eot_no_response",
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"int_backchannel",
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"int_non_takeover_overlap",
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"int_retain_floor",
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"other",
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"not_applicable",
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"uncertain",
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"EOT_MIDTURN_PAUSE",
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"EOT_OTHER_TO_OTHER",
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"EOT_WEARER_BACKCHANNEL",
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"INT_OTHER_BACKCHANNEL",
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"INT_NON_TAKEOVER_OVERLAP",
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"INT_WEARER_HOLDS_FLOOR",
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"OTHER_HARD_NEGATIVE",
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}
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CONFIDENCES = {"high", "medium", "low"}
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SYSTEM_PROMPT = """You audit candidate regions for a scientific study of wearer-relative floor control.
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Use only the supplied participant-resolved transcript and timing evidence. Do not infer prosody, gaze, intention, or acoustic interruption cues that are not supplied.
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SYSTEM_PROMPT = """You conservatively pre-label candidate regions for a scientific study of wearer-relative conversational floor control.
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Tasks:
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- eot_response positive: a non-wearer releases the floor and the wearer makes a genuine floor-taking response.
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- int_yield positive: a non-wearer takes over during wearer speech and the wearer genuinely yields the floor.
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Evidence rules:
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1. Use candidate source, structured participant timing, per-speaker activity, and transcript together. Timing evidence has priority over a speculative transcript interpretation.
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2. Never alter or propose an event timestamp. Do not infer prosody, gaze, intention, or acoustic cues that were not supplied.
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3. A wearer onset is not automatically a floor take; a wearer offset is not automatically a yield; an other onset is not automatically a takeover. A pause is not automatically EOT, overlap is not automatically interruption, and words such as "yeah" are not automatically backchannels. Check who speaks sustainably afterward and who resumes.
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4. Use UNSURE when the evidence does not establish conversational function. Never force a label for coverage.
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Hard negatives:
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- eot_midturn_pause: the same non-wearer resumes after a within-turn pause.
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- eot_other_to_other: a different non-wearer takes the floor while the wearer remains silent.
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- eot_no_response: the wearer correctly remains silent for the labeled region.
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- int_backchannel: brief feedback during wearer speech that does not take the floor.
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- int_non_takeover_overlap: overlapping other speech without takeover.
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- int_retain_floor: the wearer correctly retains the floor for the labeled region.
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Top-level verdicts:
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- POSITIVE: the system should commit.
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- HARD_NEGATIVE: a valid, interpretable region where the system should not commit.
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- NOT_APPLICABLE: a real speech transition outside the committed EOT/INT task, such as a wearer starting after long silence or naturally ending into silence.
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- EXCLUDE: the region cannot be scored reliably because of annotation corruption, a recording boundary, missing context, a non-speech artifact, timestamp corruption, or another unresolvable defect.
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- UNSURE: evidence is insufficient and a second human review is needed.
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Use exclude for annotation corruption or an unusable boundary. Use uncertain whenever transcript/timing evidence cannot establish the semantic function. Timing alone does not prove EOT, interruption, backchannel, or takeover.
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POSITIVE event_type must be exactly EOT_RESPONSE or INT_YIELD. EOT_RESPONSE means a non-wearer genuinely releases the floor and the wearer then takes it with a sustained response. INT_YIELD means a non-wearer genuinely takes over during wearer speech and the wearer relinquishes the floor.
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HARD_NEGATIVE subtype must be exactly one of EOT_MIDTURN_PAUSE, EOT_OTHER_TO_OTHER, EOT_WEARER_BACKCHANNEL, INT_OTHER_BACKCHANNEL, INT_NON_TAKEOVER_OVERLAP, INT_WEARER_HOLDS_FLOOR, or OTHER_HARD_NEGATIVE.
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Return exactly one JSON object with this shape and no markdown:
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{"items":[{"uid":"...","protocol_disposition":"uncertain","hard_negative_type":"uncertain","confidence":"low","rationale":"brief evidence-based sentence"}]}
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Return every supplied uid exactly once."""
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{"items":[{"uid":"...","verdict":"UNSURE","event_type":null,"subtype":null,"confidence":0.25,"reason":"brief evidence-based sentence","needs_human_review":true}]}
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Return every supplied uid exactly once. Copy uid byte-for-byte and never add a backslash before an underscore. confidence must be a number from 0 to 1."""
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def _normalize_returned_uid(value: Any) -> Any:
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"""Undo DeepSeek's occasional Markdown escaping without weakening identity checks."""
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if not isinstance(value, str):
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return value
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return re.sub(r"\\+_", "_", value)
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def _participant_uid_suffix(row: dict[str, Any], ordinal: int) -> str:
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previous = row.get("previous_other_participant_ids") or []
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incoming = row.get("incoming_other_participant_ids") or []
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previous_key = "-".join(f"p{x}" for x in previous) or "none"
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incoming_key = "-".join(f"p{x}" for x in incoming) or "none"
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return f"prev-{previous_key}__in-{incoming_key}__row-{ordinal}"
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def read_candidates(path: Path) -> list[dict[str, Any]]:
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@@ -70,33 +86,50 @@ def read_candidates(path: Path) -> list[dict[str, Any]]:
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row = json.loads(line)
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if not isinstance(row, dict) or not isinstance(row.get("uid"), str):
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raise ValueError(f"Expected candidate object with uid at {path}:{line_number}")
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if row.get("task") not in {"eot_response", "int_yield"}:
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raise ValueError(f"Unsupported task at {path}:{line_number}: {row.get('task')!r}")
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if not (row.get("candidate_source") or row.get("task")):
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raise ValueError(f"Missing candidate source/task at {path}:{line_number}")
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rows.append(row)
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if not rows:
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raise ValueError(f"No candidates in {path}")
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by_uid: dict[str, list[int]] = {}
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for index, row in enumerate(rows):
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by_uid.setdefault(row["uid"], []).append(index)
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for original_uid, indices in by_uid.items():
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if len(indices) == 1:
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continue
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for ordinal, index in enumerate(indices, 1):
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row = dict(rows[index])
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row["uid_disambiguated_from"] = original_uid
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row["uid"] = f"{original_uid}/{_participant_uid_suffix(row, ordinal)}"
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row["event_id"] = row["uid"]
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rows[index] = row
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if len({row["uid"] for row in rows}) != len(rows):
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raise ValueError(f"Could not deterministically disambiguate duplicate candidate UIDs in {path}")
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return rows
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def candidate_for_prompt(row: dict[str, Any]) -> dict[str, Any]:
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fields = (
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"uid",
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"candidate_source",
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"rule_candidate_type",
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"task",
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"automatic_protocol_label",
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"automatic_hard_negative_type",
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"anchor_type",
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"anchor_dense_seconds",
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"behavior_anchor_type",
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"behavior_seconds",
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"event_time",
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"region_start_seconds",
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"region_end_seconds",
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"region_end_reason",
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"previous_other_participant_ids",
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"incoming_other_participant_ids",
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"automatic_reason",
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"temporal_evidence",
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"speaker_timeline",
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"context",
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)
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return {field: row.get(field) for field in fields}
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payload = {field: row.get(field) for field in fields if row.get(field) is not None}
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# Older v1 files remain reviewable and can still be pre-labeled.
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if "candidate_source" not in payload:
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payload["candidate_source"] = row.get("anchor_type", row.get("event_type", "UNKNOWN"))
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if "event_time" not in payload:
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payload["event_time"] = row.get("anchor_dense_seconds")
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return payload
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def build_user_prompt(rows: Sequence[dict[str, Any]], pass_index: int) -> str:
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@@ -112,35 +145,59 @@ def build_user_prompt(rows: Sequence[dict[str, Any]], pass_index: int) -> str:
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)
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def validate_decisions(payload: Any, expected_uids: Sequence[str]) -> list[dict[str, str]]:
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def _transport_rows(rows: Sequence[dict[str, Any]]) -> tuple[list[dict[str, Any]], dict[str, str]]:
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"""Use simple batch-local IDs so the model never has to copy path-like UIDs."""
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transported: list[dict[str, Any]] = []
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original_by_transport: dict[str, str] = {}
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for index, row in enumerate(rows):
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transport_uid = f"item-{index:04d}"
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original_by_transport[transport_uid] = row["uid"]
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transported.append({**row, "uid": transport_uid})
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return transported, original_by_transport
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def validate_decisions(payload: Any, expected_uids: Sequence[str]) -> list[dict[str, Any]]:
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if not isinstance(payload, dict) or not isinstance(payload.get("items"), list):
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raise ValueError("Response must be an object with an items list")
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expected = set(expected_uids)
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decisions: dict[str, dict[str, str]] = {}
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decisions: dict[str, dict[str, Any]] = {}
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for item in payload["items"]:
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if not isinstance(item, dict):
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raise ValueError("Every decision must be an object")
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uid = item.get("uid")
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disposition = item.get("protocol_disposition")
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negative_type = item.get("hard_negative_type")
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raw_uid = item.get("uid")
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uid = _normalize_returned_uid(raw_uid)
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verdict = item.get("verdict")
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event_type = item.get("event_type")
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subtype = item.get("subtype")
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confidence = item.get("confidence")
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rationale = item.get("rationale")
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reason = item.get("reason")
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needs_human_review = item.get("needs_human_review")
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if uid not in expected or uid in decisions:
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raise ValueError(f"Unexpected or duplicate uid: {uid!r}")
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if disposition not in DISPOSITIONS:
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raise ValueError(f"Invalid protocol_disposition for {uid}: {disposition!r}")
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if negative_type not in HARD_NEGATIVE_TYPES:
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raise ValueError(f"Invalid hard_negative_type for {uid}: {negative_type!r}")
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if confidence not in CONFIDENCES:
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raise ValueError(f"Invalid confidence for {uid}: {confidence!r}")
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if not isinstance(rationale, str) or not rationale.strip():
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raise ValueError(f"Missing rationale for {uid}")
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raise ValueError(f"Unexpected or duplicate uid: {raw_uid!r}")
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if verdict not in VERDICTS:
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raise ValueError(f"Invalid verdict for {uid}: {verdict!r}")
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if verdict == "POSITIVE" and event_type not in EVENT_TYPES:
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raise ValueError(f"POSITIVE requires EOT_RESPONSE or INT_YIELD for {uid}")
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if verdict != "POSITIVE" and event_type is not None:
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raise ValueError(f"Only POSITIVE may have event_type for {uid}")
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if verdict == "HARD_NEGATIVE" and subtype not in HARD_NEGATIVE_TYPES:
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raise ValueError(f"HARD_NEGATIVE requires a valid subtype for {uid}")
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if verdict != "HARD_NEGATIVE" and subtype is not None:
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raise ValueError(f"Only HARD_NEGATIVE may have subtype for {uid}")
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if isinstance(confidence, bool) or not isinstance(confidence, (int, float)) or not 0 <= confidence <= 1:
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raise ValueError(f"confidence must be in [0, 1] for {uid}")
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if not isinstance(reason, str) or not reason.strip():
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raise ValueError(f"Missing reason for {uid}")
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if not isinstance(needs_human_review, bool):
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raise ValueError(f"needs_human_review must be boolean for {uid}")
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decisions[str(uid)] = {
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"uid": str(uid),
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"protocol_disposition": str(disposition),
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"hard_negative_type": str(negative_type),
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"confidence": str(confidence),
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"rationale": rationale.strip(),
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"verdict": str(verdict),
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"event_type": event_type,
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"subtype": subtype,
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"confidence": float(confidence),
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"reason": reason.strip(),
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"needs_human_review": needs_human_review,
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}
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if set(decisions) != expected:
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raise ValueError(f"Response uid mismatch; missing={sorted(expected - set(decisions))}")
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@@ -156,12 +213,13 @@ def _post(
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base_url: str,
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timeout_seconds: float,
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max_retries: int,
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) -> tuple[list[dict[str, str]], dict[str, Any]]:
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) -> tuple[list[dict[str, Any]], dict[str, Any]]:
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transported, original_by_transport = _transport_rows(rows)
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body = {
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"model": model,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": build_user_prompt(rows, pass_index)},
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{"role": "user", "content": build_user_prompt(transported, pass_index)},
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],
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"response_format": {"type": "json_object"},
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"thinking": {"type": "disabled"},
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@@ -182,7 +240,10 @@ def _post(
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envelope = json.loads(response.read().decode("utf-8"))
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content = envelope["choices"][0]["message"]["content"]
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parsed = json.loads(content)
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return validate_decisions(parsed, [row["uid"] for row in rows]), envelope.get("usage", {})
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decisions = validate_decisions(parsed, list(original_by_transport))
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for decision in decisions:
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decision["uid"] = original_by_transport[decision["uid"]]
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return decisions, envelope.get("usage", {})
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except (
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urllib.error.HTTPError,
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urllib.error.URLError,
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@@ -206,14 +267,24 @@ def add_suggestions(
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model: str,
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base_url: str,
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passes: int,
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batch_size: int = 8,
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timeout_seconds: float,
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max_retries: int,
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) -> tuple[list[dict[str, Any]], Counter[str]]:
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by_uid: dict[str, list[dict[str, str]]] = {row["uid"]: [] for row in rows}
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by_uid: dict[str, list[dict[str, Any]]] = {row["uid"]: [] for row in rows}
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prompt_hashes: dict[str, list[str]] = {row["uid"]: [] for row in rows}
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usage: Counter[str] = Counter()
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if batch_size <= 0:
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raise ValueError("batch_size must be positive")
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for pass_index in range(passes):
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for start in range(0, len(rows), batch_size):
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batch = rows[start:start + batch_size]
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transported, _ = _transport_rows(batch)
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prompt_hash = hashlib.sha256(
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(SYSTEM_PROMPT + "\n" + build_user_prompt(transported, pass_index)).encode("utf-8")
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).hexdigest()
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decisions, pass_usage = _post(
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rows=rows,
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rows=batch,
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pass_index=pass_index,
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api_key=api_key,
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model=model,
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@@ -223,6 +294,7 @@ def add_suggestions(
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)
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for decision in decisions:
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by_uid[decision["uid"]].append(decision)
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prompt_hashes[decision["uid"]].append(prompt_hash)
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for key, value in pass_usage.items():
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if isinstance(value, int):
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usage[key] += value
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@@ -230,43 +302,45 @@ def add_suggestions(
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output: list[dict[str, Any]] = []
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for source in rows:
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decisions = by_uid[source["uid"]]
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labels = {
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(decision["protocol_disposition"], decision["hard_negative_type"])
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for decision in decisions
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}
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labels = {(d["verdict"], d["event_type"], d["subtype"]) for d in decisions}
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unanimous = len(labels) == 1
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if unanimous:
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disposition, negative_type = next(iter(labels))
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confidence = (
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"high" if all(item["confidence"] == "high" for item in decisions) else "medium"
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)
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rationale = " | ".join(item["rationale"] for item in decisions)
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verdict, event_type, subtype = next(iter(labels))
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confidence = min(float(item["confidence"]) for item in decisions)
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reason = " | ".join(item["reason"] for item in decisions)
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needs_human_review = any(item["needs_human_review"] for item in decisions)
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else:
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disposition = "uncertain"
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negative_type = "uncertain"
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confidence = "low"
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rationale = "The independent DeepSeek passes disagreed."
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prompt_payload = {
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"prompt_version": PROMPT_VERSION,
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"model": model,
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"passes": passes,
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"candidate": candidate_for_prompt(source),
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}
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verdict, event_type, subtype = "UNSURE", None, None
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confidence = min(float(item["confidence"]) for item in decisions)
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reason = "The independent DeepSeek passes disagreed."
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needs_human_review = True
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enriched = dict(source)
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enriched["llm_suggestion"] = {
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"protocol_disposition": disposition,
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"hard_negative_type": negative_type,
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"verdict": verdict,
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"event_type": event_type,
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"subtype": subtype,
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"confidence": confidence,
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"rationale": rationale,
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"reason": reason,
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"needs_human_review": needs_human_review,
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"unanimous": unanimous,
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}
|
||||
enriched.update(
|
||||
ai_verdict=verdict,
|
||||
ai_event_type=event_type,
|
||||
ai_subtype=subtype,
|
||||
ai_confidence=confidence,
|
||||
ai_reason=reason,
|
||||
human_status="UNREVIEWED",
|
||||
human_verdict=None,
|
||||
human_event_type=None,
|
||||
human_subtype=None,
|
||||
exclude_reason=None,
|
||||
)
|
||||
enriched["llm_adjudication"] = {
|
||||
"model": model,
|
||||
"prompt_version": PROMPT_VERSION,
|
||||
"passes": passes,
|
||||
"prompt_sha256": hashlib.sha256(
|
||||
json.dumps(prompt_payload, sort_keys=True, ensure_ascii=False).encode("utf-8")
|
||||
).hexdigest(),
|
||||
"request_prompt_sha256": prompt_hashes[source["uid"]],
|
||||
"decisions": decisions,
|
||||
"human_review_required": True,
|
||||
}
|
||||
@@ -281,6 +355,7 @@ def parse_args() -> argparse.Namespace:
|
||||
parser.add_argument("--model", default=os.environ.get("DEEPSEEK_MODEL", DEFAULT_MODEL))
|
||||
parser.add_argument("--base-url", default=os.environ.get("DEEPSEEK_BASE_URL", DEFAULT_BASE_URL))
|
||||
parser.add_argument("--passes", type=int, default=2)
|
||||
parser.add_argument("--batch-size", type=int, default=8)
|
||||
parser.add_argument("--max-events", type=int)
|
||||
parser.add_argument("--selection-seed", type=int, default=2027)
|
||||
parser.add_argument("--timeout-seconds", type=float, default=120.0)
|
||||
@@ -293,8 +368,8 @@ def parse_args() -> argparse.Namespace:
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
if args.passes <= 0 or args.max_retries < 0:
|
||||
raise ValueError("passes must be positive and max-retries non-negative")
|
||||
if args.passes <= 0 or args.batch_size <= 0 or args.max_retries < 0:
|
||||
raise ValueError("passes/batch-size must be positive and max-retries non-negative")
|
||||
rows = read_candidates(args.input.expanduser().resolve())
|
||||
if args.max_events is not None:
|
||||
if args.max_events < 0:
|
||||
@@ -324,6 +399,7 @@ def main() -> None:
|
||||
model=args.model,
|
||||
base_url=args.base_url,
|
||||
passes=args.passes,
|
||||
batch_size=args.batch_size,
|
||||
timeout_seconds=args.timeout_seconds,
|
||||
max_retries=args.max_retries,
|
||||
)
|
||||
|
||||
@@ -1,16 +1,19 @@
|
||||
# EasyCom EOT/INT review workspace
|
||||
# EasyCom wearer-centric EOT/INT pre-label and review pipeline
|
||||
|
||||
`review_floor_events.py` is a local annotation workspace for reviewing the
|
||||
manuscript-aligned regions produced by `prepare_easycom_eot_int_review.py`.
|
||||
It can also open the older timing/transcript proxy reports from
|
||||
`derive_easycom_interaction_events.py` and `audit_easycom_floor_events.py`, but
|
||||
those older reports do not have the directionality or hard-negative region
|
||||
contract required by the current manuscript. Raw EasyCom media stays read-only;
|
||||
human decisions are stored separately as JSONL and CSV.
|
||||
The workflow has three explicit stages. Speech activity timestamps are
|
||||
read-only throughout; reviewed events never modify the dense 5 Hz activity
|
||||
ground truth.
|
||||
|
||||
## Prepare manuscript-aligned candidates
|
||||
```text
|
||||
EasyCom participant speech intervals
|
||||
-> rule candidates + structured timing evidence
|
||||
-> DeepSeek semantic pre-label (optional but recommended)
|
||||
-> human Accept / Relabel / Exclude / Second review
|
||||
```
|
||||
|
||||
Develop the annotation guide on training-side Sessions 4--12:
|
||||
## 1. Generate candidates
|
||||
|
||||
Develop the rules on training-side Sessions 4--12:
|
||||
|
||||
```bash
|
||||
python3 prepare_easycom_eot_int_review.py \
|
||||
@@ -19,24 +22,39 @@ python3 prepare_easycom_eot_int_review.py \
|
||||
--output-dir reports/easycom_floor_event_candidates
|
||||
```
|
||||
|
||||
For a one-event pilot, add `--max-candidates 1`. The generator follows the
|
||||
manuscript direction exactly:
|
||||
The generator emits two independent sources:
|
||||
|
||||
- EOT-response begins at an aggregate-other offset while the wearer is silent.
|
||||
- INT-yield begins at an aggregate-other onset while the wearer is active.
|
||||
- An observed wearer transition before the competing other transition is a
|
||||
timing-positive candidate.
|
||||
- A competing other transition, or a fully observed no-transition window, is a
|
||||
timing hard-negative candidate.
|
||||
- A source boundary without a complete outcome/window is marked for exclusion.
|
||||
- every wearer `0->1` (`WEARER_ONSET`, `ENTRY_CANDIDATE`) and `1->0`
|
||||
(`WEARER_OFFSET`, `YIELD_CANDIDATE`) transition;
|
||||
- hard-negative regions that need no wearer transition:
|
||||
`SAME_SPEAKER_PAUSE`, `OTHER_TO_OTHER`, `OTHER_BACKCHANNEL`, and
|
||||
`NON_TAKEOVER_OVERLAP`.
|
||||
|
||||
Automatic labels remain candidates until independent human semantic review.
|
||||
Wearer transitions are initially `unsure`; timing alone is not a semantic
|
||||
positive label. Hard-negative scans are rule hints and still require AI and
|
||||
human review. The JSONL retains candidate source, original event time,
|
||||
participant-level activity, raw durations/gaps/overlap evidence, transcript,
|
||||
and `Target_of_Speech` when present. `Target_of_Speech` is annotation assistance
|
||||
only and cannot be a deployable model input.
|
||||
|
||||
## Optional DeepSeek suggestion
|
||||
All heuristic durations are configurable rather than frozen paper rules:
|
||||
|
||||
DeepSeek suggestions are triage metadata and never count as a human label. The
|
||||
script sends only the candidate transcript excerpt and timing fields. Inspect
|
||||
the exact request without network access first:
|
||||
```text
|
||||
--relation-window-seconds
|
||||
--same-speaker-pause-max-seconds
|
||||
--other-handover-max-seconds
|
||||
--short-vocalization-max-seconds
|
||||
--continuation-min-seconds
|
||||
```
|
||||
|
||||
Use `--max-candidates 1` for a one-event smoke test. `summary.json` records the
|
||||
exact configuration and candidate counts. `easycom_session_timelines.json`
|
||||
stores the read-only participant speech lanes used by the full-session web
|
||||
timeline; it is not sent to DeepSeek.
|
||||
|
||||
## 2. Add DeepSeek pre-labels
|
||||
|
||||
First inspect exactly what would be uploaded:
|
||||
|
||||
```bash
|
||||
python3 adjudicate_easycom_eot_int_candidates.py \
|
||||
@@ -47,51 +65,52 @@ python3 adjudicate_easycom_eot_int_candidates.py \
|
||||
--dry-run
|
||||
```
|
||||
|
||||
After setting `DEEPSEEK_API_KEY`, make the authorized request:
|
||||
After setting `DEEPSEEK_API_KEY` and obtaining authorization to upload the
|
||||
candidate excerpts:
|
||||
|
||||
```bash
|
||||
python3 adjudicate_easycom_eot_int_candidates.py \
|
||||
--input reports/easycom_floor_event_candidates/easycom_eot_int_candidates.jsonl \
|
||||
--output reports/easycom_floor_event_candidates/easycom_eot_int_deepseek.jsonl \
|
||||
--model deepseek-v4-flash \
|
||||
--max-events 1 \
|
||||
--allow-external-transcript-upload
|
||||
```
|
||||
|
||||
Point the review server at the DeepSeek-enriched JSONL to show its suggestion
|
||||
beside the video. Keep suggestions hidden from an initial independent human
|
||||
pass if anchoring bias is a concern.
|
||||
DeepSeek sees candidate source, structured timing, per-speaker activity, and
|
||||
transcript together. It returns one of `POSITIVE`, `HARD_NEGATIVE`,
|
||||
`NOT_APPLICABLE`, `EXCLUDE`, or `UNSURE`; positives additionally receive
|
||||
`EOT_RESPONSE` or `INT_YIELD`, and hard negatives receive a named subtype. It
|
||||
cannot modify timestamps. The output preserves both the original rule candidate
|
||||
and the complete AI decision/provenance.
|
||||
|
||||
## Start the local workspace
|
||||
## 3. Human review
|
||||
|
||||
Run the web workspace on the AI-enriched JSONL:
|
||||
|
||||
```bash
|
||||
python3 review_floor_events.py \
|
||||
--dataset-root /path/to/easycom \
|
||||
--events reports/easycom_floor_event_candidates/easycom_eot_int_candidates.jsonl \
|
||||
--events reports/easycom_floor_event_candidates/easycom_eot_int_deepseek.jsonl \
|
||||
--output reports/easycom_event_reviews/reviewer_1.jsonl
|
||||
```
|
||||
|
||||
Open `http://127.0.0.1:8765`. The page resolves the source MP4 and transcript,
|
||||
seeks to a six-second event context, and supports session/split/task/status
|
||||
filtering.
|
||||
Open `http://127.0.0.1:8765`. The page shows a session candidate overview,
|
||||
participant activity near the event, video/audio, transcript, structured timing,
|
||||
and the AI verdict/confidence/reason. The common path is one operation:
|
||||
|
||||
Each save atomically rewrites both JSONL and CSV. The review explicitly records
|
||||
`positive`, `hard_negative`, `exclude`, or `uncertain`; the hard-negative
|
||||
subtype; boundary validity; onset function; conversational routing; offset
|
||||
outcome; three 200 ms anchor/region corrections; confidence; and reviewer ID.
|
||||
- **Accept** keeps the AI verdict/type;
|
||||
- **Relabel** expands verdict and subtype controls;
|
||||
- **Exclude** requires a concrete data-quality reason;
|
||||
- **Unsure / Second review** preserves the event for adjudication.
|
||||
|
||||
Boundary correction is hidden under an optional disclosure and is limited to
|
||||
one 200 ms bin. The original timestamp is always retained. Each save atomically
|
||||
rewrites JSONL and CSV while preserving the rule candidate, AI decision, human
|
||||
decision, relabel flag, exclusion state, reviewer, and provenance.
|
||||
|
||||
## Freeze discipline
|
||||
|
||||
Develop the annotation guide and resolve category disagreements on training
|
||||
Sessions 4--12. Freeze the guide before reviewing official test Sessions 1--3.
|
||||
Do not use test reviews to change candidate windows, label categories, or the
|
||||
R=.8 threshold calibration rule.
|
||||
|
||||
For paper-quality labels, use two independent reviewers with separate
|
||||
`--output` files and adjudicate disagreements in a third output. A non-empty
|
||||
reviewer ID is required on every saved row. DeepSeek is not one of the two
|
||||
reviewers.
|
||||
|
||||
This workspace produces audited labels. It does not yet convert them into the
|
||||
final committed-event matching metrics; that integration follows only after
|
||||
the schema and real-data pilot are frozen.
|
||||
Develop the guide and resolve disagreements only on Sessions 4--12. Freeze
|
||||
categories and thresholds before reviewing official test Sessions 1--3. Use two
|
||||
independent human review files plus a separate adjudication output for paper-
|
||||
quality labels. DeepSeek is pre-label metadata, not an independent reviewer.
|
||||
|
||||
@@ -220,6 +220,27 @@ textarea { resize: vertical; line-height: 1.45; }
|
||||
.llm-suggestion strong { display: block; color: var(--accent); margin-bottom: 3px; }
|
||||
.event-position { color: var(--muted); font-size: 0.8rem; }
|
||||
|
||||
.timeline-section { padding: 0 18px 16px; display: grid; gap: 10px; }
|
||||
.timeline-section .section-heading { margin-top: 2px; }
|
||||
.local-heading { margin-top: 8px !important; }
|
||||
.session-timeline { min-height: 34px; border: 1px solid var(--line); border-radius: 8px; position: relative; background: #07101d; padding: 6px 7px 28px; display: grid; gap: 3px; overflow: hidden; }
|
||||
.session-lane { display: grid; grid-template-columns: 28px 1fr; gap: 5px; align-items: center; min-height: 10px; }
|
||||
.session-lane > strong { color: var(--muted); text-align: right; font-size: .58rem; }
|
||||
.session-track { height: 8px; position: relative; background: rgba(255,255,255,.025); }
|
||||
.session-speech { position: absolute; top: 1px; height: 6px; min-width: 1px; background: #60a5fa; opacity: .75; }
|
||||
.session-speech.wearer { background: var(--accent); }
|
||||
.session-events { position: absolute; left: 40px; right: 7px; bottom: 4px; height: 20px; border-top: 1px solid var(--line-strong); }
|
||||
.session-marker { position: absolute; top: 3px; width: 7px; height: 15px; margin-left: -3px; padding: 0; border: 0; border-radius: 4px; background: var(--amber); cursor: pointer; opacity: .68; }
|
||||
.session-marker.int { background: #a78bfa; }
|
||||
.session-marker.current { opacity: 1; box-shadow: 0 0 0 2px white; z-index: 2; }
|
||||
.speaker-timeline { display: grid; gap: 6px; color: var(--muted); font-size: .76rem; }
|
||||
.speaker-lane { display: grid; grid-template-columns: 34px 1fr; align-items: center; gap: 8px; }
|
||||
.speaker-lane > strong { color: var(--muted); text-align: right; font-size: .72rem; }
|
||||
.speaker-track { height: 22px; position: relative; border: 1px solid var(--line); border-radius: 5px; overflow: hidden; background: #07101d; }
|
||||
.speaker-track > i { position: absolute; top: 0; bottom: 0; width: 1px; background: var(--amber); z-index: 3; }
|
||||
.speech-segment { position: absolute; top: 5px; height: 10px; min-width: 2px; border-radius: 3px; background: #60a5fa; }
|
||||
.speech-segment.wearer { background: var(--accent); }
|
||||
|
||||
.video-shell { background: #02050a; aspect-ratio: 16 / 9; position: relative; border-block: 1px solid var(--line); }
|
||||
.video-shell video { width: 100%; height: 100%; display: block; object-fit: contain; }
|
||||
.empty-state { position: absolute; inset: 0; display: grid; place-content: center; text-align: center; gap: 7px; color: var(--muted); }
|
||||
@@ -273,6 +294,11 @@ legend { font-size: .84rem; font-weight: 750; margin-bottom: 8px; }
|
||||
.text-field input, .text-field textarea { font-size: .84rem; }
|
||||
.form-error { color: #fecdd3; background: rgba(251,113,133,.1); border: 1px solid rgba(251,113,133,.4); border-radius: 8px; padding: 9px; font-size: .8rem; }
|
||||
.review-actions { display: grid; grid-template-columns: .9fr 1.4fr; gap: 8px; }
|
||||
.conditional-fields { padding: 11px; border: 1px solid var(--line); border-radius: 9px; background: rgba(94,234,212,.035); display: grid; gap: 9px; }
|
||||
.conditional-fields label, .boundary-details label { display: grid; gap: 6px; }
|
||||
.conditional-fields label > span, .boundary-details label > span { color: var(--muted); font-size: .72rem; font-weight: 700; }
|
||||
.boundary-details { border-top: 1px solid var(--line); padding-top: 10px; }
|
||||
.boundary-details summary { color: var(--muted); cursor: pointer; font-size: .78rem; margin-bottom: 9px; }
|
||||
kbd { border: 1px solid rgba(3,24,22,.35); border-bottom-width: 2px; border-radius: 4px; padding: 1px 4px; font-size: .68rem; font-family: inherit; }
|
||||
.shortcuts { border-top: 1px solid var(--line); padding: 11px 15px 14px; display: flex; flex-wrap: wrap; gap: 8px 14px; color: var(--muted); font-size: .71rem; }
|
||||
.shortcuts kbd { color: #cbd7e7; border-color: var(--line-strong); margin-right: 2px; }
|
||||
|
||||
+117
-38
@@ -7,6 +7,7 @@ const state = {
|
||||
currentId: null,
|
||||
currentDetail: null,
|
||||
saving: false,
|
||||
sessionSpeech: {},
|
||||
};
|
||||
|
||||
const elements = Object.fromEntries(
|
||||
@@ -15,10 +16,11 @@ const elements = Object.fromEntries(
|
||||
"familyFilter", "statusFilter", "searchFilter", "clearFilters", "queueTitle",
|
||||
"queueCount", "queueList", "queueLimit", "familyBadge", "splitBadge",
|
||||
"autoConfidenceBadge", "eventTitle", "eventSubtitle", "eventPosition",
|
||||
"llmSuggestion", "llmSuggestionText",
|
||||
"llmSuggestion", "llmSuggestionText", "sessionTimeline", "speakerTimeline",
|
||||
"eventVideo", "mediaEmpty", "jumpEvent", "timeReadout", "clipLabel",
|
||||
"transcriptList", "rawCandidate", "reviewForm", "reviewerInput", "formError",
|
||||
"saveButton", "uncertainButton", "saveState", "toast",
|
||||
"saveButton", "uncertainButton", "saveState", "toast", "relabelFields",
|
||||
"excludeFields", "eventTypeField", "subtypeField",
|
||||
].map((id) => [id, document.getElementById(id)])
|
||||
);
|
||||
|
||||
@@ -86,7 +88,7 @@ function applyFilters({ keepCurrent = true } = {}) {
|
||||
(elements.splitFilter.value === "all" || item.split === elements.splitFilter.value) &&
|
||||
(elements.familyFilter.value === "all" || item.family === elements.familyFilter.value) &&
|
||||
(status === "all" || (status === "reviewed" ? reviewed : !reviewed)) &&
|
||||
(!search || `${item.uid} ${item.automatic_label} ${item.automatic_protocol_label || ""} ${item.automatic_hard_negative_type || ""} ${item.session}`.toLowerCase().includes(search))
|
||||
(!search || `${item.uid} ${item.candidate_source || ""} ${item.rule_candidate_type || ""} ${item.ai_verdict || ""} ${item.ai_event_type || ""} ${item.ai_subtype || ""} ${item.session}`.toLowerCase().includes(search))
|
||||
);
|
||||
});
|
||||
renderQueue();
|
||||
@@ -121,9 +123,9 @@ function renderQueue() {
|
||||
meta.textContent = `${item.session} · ${familyNames[item.family] || item.family} · ${formatTime(item.event_time_seconds)}`;
|
||||
const label = document.createElement("div");
|
||||
label.className = "queue-label";
|
||||
label.textContent = item.automatic_protocol_label
|
||||
? `${item.automatic_protocol_label} · ${item.automatic_hard_negative_type || item.automatic_label}`
|
||||
: item.automatic_label;
|
||||
label.textContent = item.ai_verdict
|
||||
? `AI ${item.ai_verdict} · ${item.ai_event_type || item.ai_subtype || "—"}`
|
||||
: `${item.rule_candidate_type || item.automatic_label} · 待 AI 粗标`;
|
||||
button.append(line, meta, label);
|
||||
button.addEventListener("click", () => selectCandidate(item.candidate_id));
|
||||
elements.queueList.append(button);
|
||||
@@ -171,7 +173,7 @@ function renderDetail(detail) {
|
||||
elements.familyBadge.textContent = familyNames[detail.family] || detail.family;
|
||||
elements.splitBadge.textContent = `${detail.split} · ${detail.session}`;
|
||||
elements.autoConfidenceBadge.textContent = detail.automatic_confidence ? `auto ${detail.automatic_confidence}` : "auto confidence —";
|
||||
elements.eventTitle.textContent = detail.automatic_label;
|
||||
elements.eventTitle.textContent = detail.candidate_source || detail.automatic_label;
|
||||
elements.eventSubtitle.textContent = `${detail.uid} · ${detail.source_file}:${detail.source_row}`;
|
||||
elements.clipLabel.textContent = detail.clip_stem ? `${detail.session} / ${detail.clip_stem}` : detail.session;
|
||||
elements.rawCandidate.textContent = JSON.stringify(detail.candidate, null, 2);
|
||||
@@ -181,13 +183,16 @@ function renderDetail(detail) {
|
||||
const suggestion = detail.llm_suggestion;
|
||||
elements.llmSuggestion.hidden = !suggestion;
|
||||
if (suggestion) {
|
||||
const disposition = suggestion.protocol_disposition || suggestion.final_protocol_disposition || "uncertain";
|
||||
const subtype = suggestion.hard_negative_type || suggestion.final_hard_negative_type || "uncertain";
|
||||
const disposition = suggestion.verdict || suggestion.protocol_disposition || "UNSURE";
|
||||
const subtype = suggestion.event_type || suggestion.subtype || suggestion.hard_negative_type || "—";
|
||||
const confidence = suggestion.confidence || suggestion.final_confidence || "—";
|
||||
const rationale = suggestion.rationale || suggestion.final_rationale || "";
|
||||
const rationale = suggestion.reason || suggestion.rationale || "";
|
||||
elements.llmSuggestionText.textContent = `${disposition} · ${subtype} · ${confidence}${rationale ? ` — ${rationale}` : ""}`;
|
||||
}
|
||||
|
||||
renderSessionTimeline(detail);
|
||||
renderSpeakerTimeline(detail);
|
||||
|
||||
elements.mediaEmpty.hidden = detail.media_available;
|
||||
if (detail.media_available) {
|
||||
elements.eventVideo.src = `/media?id=${encodeURIComponent(detail.candidate_id)}`;
|
||||
@@ -226,16 +231,87 @@ function renderDetail(detail) {
|
||||
updateTimeReadout();
|
||||
}
|
||||
|
||||
async function renderSessionTimeline(detail) {
|
||||
elements.sessionTimeline.replaceChildren();
|
||||
if (!state.sessionSpeech[detail.session]) {
|
||||
try {
|
||||
const payload = await request(`/api/session-timeline?session=${encodeURIComponent(detail.session)}`);
|
||||
state.sessionSpeech[detail.session] = payload.speech || [];
|
||||
} catch (_) {
|
||||
state.sessionSpeech[detail.session] = [];
|
||||
}
|
||||
}
|
||||
if (state.currentId !== detail.candidate_id) return;
|
||||
const events = state.candidates.filter((item) => item.session === detail.session);
|
||||
const speech = state.sessionSpeech[detail.session];
|
||||
const end = Math.max(1,
|
||||
...events.map((item) => Number(item.event_time_seconds) || 0),
|
||||
...speech.map((row) => Number(row.end_seconds) || 0));
|
||||
const speakers = [...new Set(speech.map((row) => row.participant_role === "wearer" ? "W" : `P${row.participant_id}`))];
|
||||
for (const speakerId of speakers) {
|
||||
const lane = document.createElement("div"); lane.className = "session-lane";
|
||||
const label = document.createElement("strong"); label.textContent = speakerId;
|
||||
const track = document.createElement("div"); track.className = "session-track";
|
||||
for (const row of speech.filter((row) => (row.participant_role === "wearer" ? "W" : `P${row.participant_id}`) === speakerId)) {
|
||||
const segment = document.createElement("span");
|
||||
segment.className = `session-speech${speakerId === "W" ? " wearer" : ""}`;
|
||||
segment.style.left = `${100 * Number(row.start_seconds) / end}%`;
|
||||
segment.style.width = `${Math.max(0.08, 100 * (Number(row.end_seconds) - Number(row.start_seconds)) / end)}%`;
|
||||
track.append(segment);
|
||||
}
|
||||
lane.append(label, track); elements.sessionTimeline.append(lane);
|
||||
}
|
||||
const eventTrack = document.createElement("div"); eventTrack.className = "session-events";
|
||||
for (const item of events) {
|
||||
const marker = document.createElement("button");
|
||||
marker.type = "button";
|
||||
marker.className = `session-marker ${item.family}${item.candidate_id === detail.candidate_id ? " current" : ""}`;
|
||||
marker.style.left = `${Math.min(100, 100 * (Number(item.event_time_seconds) || 0) / end)}%`;
|
||||
marker.title = `${formatTime(item.event_time_seconds)} · ${item.candidate_source || item.automatic_label} · ${item.ai_verdict || "未粗标"}`;
|
||||
marker.addEventListener("click", () => selectCandidate(item.candidate_id));
|
||||
eventTrack.append(marker);
|
||||
}
|
||||
elements.sessionTimeline.append(eventTrack);
|
||||
}
|
||||
|
||||
function renderSpeakerTimeline(detail) {
|
||||
elements.speakerTimeline.replaceChildren();
|
||||
const rows = detail.candidate.speaker_timeline || detail.candidate.context || [];
|
||||
if (!rows.length) {
|
||||
elements.speakerTimeline.textContent = "没有 participant activity evidence。";
|
||||
return;
|
||||
}
|
||||
const eventTime = Number(detail.candidate.event_time ?? detail.candidate.anchor_dense_seconds);
|
||||
const left = Math.min(...rows.map((row) => Number(row.start_seconds)), eventTime - 3);
|
||||
const right = Math.max(...rows.map((row) => Number(row.end_seconds)), eventTime + 3);
|
||||
const span = Math.max(0.001, right - left);
|
||||
const speakers = [...new Set(rows.map((row) => `${row.participant_role === "wearer" ? "W" : `P${row.participant_id}`}`))];
|
||||
for (const speakerId of speakers) {
|
||||
const lane = document.createElement("div");
|
||||
lane.className = "speaker-lane";
|
||||
const label = document.createElement("strong"); label.textContent = speakerId;
|
||||
const track = document.createElement("div"); track.className = "speaker-track";
|
||||
for (const row of rows.filter((row) => (row.participant_role === "wearer" ? "W" : `P${row.participant_id}`) === speakerId)) {
|
||||
const segment = document.createElement("span");
|
||||
segment.className = `speech-segment${speakerId === "W" ? " wearer" : ""}`;
|
||||
segment.style.left = `${100 * (Number(row.start_seconds) - left) / span}%`;
|
||||
segment.style.width = `${Math.max(0.8, 100 * (Number(row.end_seconds) - Number(row.start_seconds)) / span)}%`;
|
||||
segment.title = `${formatTime(row.start_seconds)}–${formatTime(row.end_seconds)} ${row.transcription || ""}`;
|
||||
track.append(segment);
|
||||
}
|
||||
const anchor = document.createElement("i"); anchor.style.left = `${100 * (eventTime - left) / span}%`; track.append(anchor);
|
||||
lane.append(label, track); elements.speakerTimeline.append(lane);
|
||||
}
|
||||
}
|
||||
|
||||
function restoreForm(review) {
|
||||
elements.reviewForm.reset();
|
||||
elements.formError.hidden = true;
|
||||
const reviewer = review?.reviewer || localStorage.getItem("floor-reviewer") || "";
|
||||
elements.reviewerInput.value = reviewer;
|
||||
if (!review) {
|
||||
elements.reviewForm.elements.anchor_adjustment_bins.value = "0";
|
||||
elements.reviewForm.elements.behavior_adjustment_bins.value = "0";
|
||||
elements.reviewForm.elements.region_end_adjustment_bins.value = "0";
|
||||
elements.reviewForm.elements.confidence.value = "medium";
|
||||
updateConditionalFields();
|
||||
return;
|
||||
}
|
||||
for (const [name, value] of Object.entries(review)) {
|
||||
@@ -244,24 +320,20 @@ function restoreForm(review) {
|
||||
if (control instanceof RadioNodeList) control.value = String(value);
|
||||
else control.value = value === null ? "" : String(value);
|
||||
}
|
||||
updateConditionalFields();
|
||||
}
|
||||
|
||||
function formPayload() {
|
||||
const data = new FormData(elements.reviewForm);
|
||||
const adjustment = (name) => {
|
||||
const value = data.get(name);
|
||||
return value === "" ? null : Number(value);
|
||||
};
|
||||
const nullable = (name) => data.get(name) || null;
|
||||
const correction = data.get("boundary_correction_seconds");
|
||||
return {
|
||||
protocol_disposition: data.get("protocol_disposition"),
|
||||
hard_negative_type: data.get("hard_negative_type"),
|
||||
validity: data.get("validity"),
|
||||
onset_function: data.get("onset_function"),
|
||||
context: data.get("context"),
|
||||
offset_outcome: data.get("offset_outcome"),
|
||||
anchor_adjustment_bins: adjustment("anchor_adjustment_bins"),
|
||||
behavior_adjustment_bins: adjustment("behavior_adjustment_bins"),
|
||||
region_end_adjustment_bins: adjustment("region_end_adjustment_bins"),
|
||||
human_action: data.get("human_action"),
|
||||
human_verdict: nullable("human_verdict"),
|
||||
human_event_type: nullable("human_event_type"),
|
||||
human_subtype: nullable("human_subtype"),
|
||||
exclude_reason: nullable("exclude_reason"),
|
||||
boundary_correction_seconds: correction === "" ? null : Number(correction),
|
||||
confidence: data.get("confidence"),
|
||||
reviewer: String(data.get("reviewer") || "").trim(),
|
||||
notes: String(data.get("notes") || "").trim(),
|
||||
@@ -270,16 +342,25 @@ function formPayload() {
|
||||
|
||||
function validateReview(review) {
|
||||
const missing = [];
|
||||
if (!review.protocol_disposition) missing.push("协议结论");
|
||||
if (!review.hard_negative_type) missing.push("hard-negative 类型");
|
||||
if (!review.validity) missing.push("活动边界");
|
||||
if (!review.onset_function) missing.push("onset 功能");
|
||||
if (!review.context) missing.push("对话场景");
|
||||
if (!review.offset_outcome) missing.push("offset 结果");
|
||||
if (!review.human_action) missing.push("主操作");
|
||||
if (review.human_action === "relabel" && !review.human_verdict) missing.push("一级 verdict");
|
||||
if (review.human_action === "relabel" && review.human_verdict === "POSITIVE" && !review.human_event_type) missing.push("positive event");
|
||||
if (review.human_action === "relabel" && review.human_verdict === "HARD_NEGATIVE" && !review.human_subtype) missing.push("hard-negative subtype");
|
||||
if (review.human_action === "exclude" && !review.exclude_reason) missing.push("排除原因");
|
||||
if (review.human_action === "accept" && !state.currentDetail?.candidate?.ai_verdict && !state.currentDetail?.llm_suggestion?.verdict) missing.push("AI 预标注（尚未运行，需 Relabel）");
|
||||
if (!review.reviewer) missing.push("审核者");
|
||||
return missing;
|
||||
}
|
||||
|
||||
function updateConditionalFields() {
|
||||
const action = elements.reviewForm.elements.human_action.value;
|
||||
const verdict = elements.reviewForm.elements.human_verdict.value;
|
||||
elements.relabelFields.hidden = action !== "relabel";
|
||||
elements.excludeFields.hidden = action !== "exclude";
|
||||
elements.eventTypeField.hidden = action !== "relabel" || verdict !== "POSITIVE";
|
||||
elements.subtypeField.hidden = action !== "relabel" || verdict !== "HARD_NEGATIVE";
|
||||
}
|
||||
|
||||
async function saveCurrent({ advance = true } = {}) {
|
||||
if (!state.currentId || state.saving) return;
|
||||
const review = formPayload();
|
||||
@@ -376,14 +457,12 @@ function bindEvents() {
|
||||
saveCurrent();
|
||||
});
|
||||
elements.uncertainButton.addEventListener("click", () => {
|
||||
elements.reviewForm.elements.protocol_disposition.value = "uncertain";
|
||||
elements.reviewForm.elements.hard_negative_type.value = "uncertain";
|
||||
elements.reviewForm.elements.validity.value = "uncertain";
|
||||
elements.reviewForm.elements.onset_function.value = "uncertain";
|
||||
elements.reviewForm.elements.context.value = "unknown";
|
||||
elements.reviewForm.elements.offset_outcome.value = "uncertain";
|
||||
elements.reviewForm.elements.human_action.value = "second_review";
|
||||
elements.reviewForm.elements.confidence.value = "low";
|
||||
updateConditionalFields();
|
||||
});
|
||||
elements.reviewForm.elements.human_action.forEach((control) => control.addEventListener("change", updateConditionalFields));
|
||||
elements.reviewForm.elements.human_verdict.addEventListener("change", updateConditionalFields);
|
||||
elements.jumpEvent.addEventListener("click", jumpToEvent);
|
||||
document.querySelectorAll("[data-seek]").forEach((button) =>
|
||||
button.addEventListener("click", () => seek(Number(button.dataset.seek)))
|
||||
|
||||
@@ -3,283 +3,80 @@
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<meta name="description" content="EasyCom wearer floor-event local review workspace" />
|
||||
<title>Floor Event Review</title>
|
||||
<link rel="icon" type="image/svg+xml" href="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 64 64'%3E%3Crect width='64' height='64' rx='14' fill='%230b1324'/%3E%3Cpath d='M14 39h8l5-17 9 29 6-22 4 10h5' fill='none' stroke='%235eead4' stroke-width='5' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E" />
|
||||
<title>EasyCom Floor Event Review</title>
|
||||
<link rel="stylesheet" href="/app.css" />
|
||||
</head>
|
||||
<body>
|
||||
<header class="topbar">
|
||||
<div class="brand">
|
||||
<span class="brand-mark" aria-hidden="true"></span>
|
||||
<div>
|
||||
<h1>Floor Event Review</h1>
|
||||
<p>EasyCom 事件审核</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="progress-block" aria-live="polite">
|
||||
<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" aria-label="导出审核结果">
|
||||
<a href="/api/export.jsonl" download>导出 JSONL</a>
|
||||
<a href="/api/export.csv" download>导出 CSV</a>
|
||||
</nav>
|
||||
<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="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>事件族</span>
|
||||
<select id="familyFilter">
|
||||
<option value="all">全部</option>
|
||||
<option value="eot">EOT-response</option>
|
||||
<option value="int">INT-yield</option>
|
||||
<option value="onset">wearer onset</option>
|
||||
<option value="offset">旧 EOT proxy</option>
|
||||
<option value="interaction">旧 interaction proxy</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="UID、类别、session" />
|
||||
</label>
|
||||
<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>任务</span><select id="familyFilter"><option value="all">全部</option><option value="eot">EOT / 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>
|
||||
<button id="clearFilters" class="button ghost" type="button">清除筛选</button>
|
||||
</section>
|
||||
|
||||
<main class="workspace">
|
||||
<aside class="queue-panel panel" aria-label="事件候选列表">
|
||||
<div class="panel-heading">
|
||||
<div>
|
||||
<span class="eyebrow">QUEUE</span>
|
||||
<h2 id="queueTitle">候选</h2>
|
||||
</div>
|
||||
<span id="queueCount" class="count-pill">0</span>
|
||||
</div>
|
||||
<div id="queueList" class="queue-list"></div>
|
||||
<p id="queueLimit" class="queue-limit" hidden></p>
|
||||
<aside class="queue-panel panel">
|
||||
<div class="panel-heading"><div><span class="eyebrow">QUEUE</span><h2 id="queueTitle">待审核</h2></div><span id="queueCount" class="count-pill">0</span></div>
|
||||
<div id="queueList" class="queue-list"></div><p id="queueLimit" class="queue-limit" hidden></p>
|
||||
</aside>
|
||||
|
||||
<section class="evidence-panel panel" aria-label="事件证据">
|
||||
<div class="event-header">
|
||||
<div>
|
||||
<div class="event-badges">
|
||||
<span id="familyBadge" class="badge accent">—</span>
|
||||
<span id="splitBadge" class="badge">—</span>
|
||||
<span id="autoConfidenceBadge" class="badge">—</span>
|
||||
</div>
|
||||
<h2 id="eventTitle">请选择一个候选事件</h2>
|
||||
<p id="eventSubtitle">—</p>
|
||||
</div>
|
||||
<div class="event-position"><strong id="eventPosition">0 / 0</strong></div>
|
||||
</div>
|
||||
<section class="evidence-panel panel">
|
||||
<div class="event-header"><div><div class="event-badges"><span id="familyBadge" class="badge accent">—</span><span id="splitBadge" class="badge">—</span><span id="autoConfidenceBadge" class="badge">—</span></div><h2 id="eventTitle">请选择候选</h2><p id="eventSubtitle">—</p></div><div class="event-position"><strong id="eventPosition">0 / 0</strong></div></div>
|
||||
|
||||
<div id="llmSuggestion" class="llm-suggestion" hidden>
|
||||
<strong>DeepSeek 预标注（仅供复核）</strong>
|
||||
<span id="llmSuggestionText">—</span>
|
||||
</div>
|
||||
<div id="llmSuggestion" class="llm-suggestion" hidden><strong>AI 预标注（不是最终标签）</strong><span id="llmSuggestionText">—</span></div>
|
||||
|
||||
<div class="video-shell">
|
||||
<video id="eventVideo" controls preload="metadata"></video>
|
||||
<div id="mediaEmpty" class="empty-state" hidden>
|
||||
<strong>当前候选未找到视频</strong>
|
||||
<span>检查 dataset root、session 和 clip stem。</span>
|
||||
</div>
|
||||
<div class="time-marker" aria-hidden="true"><span></span></div>
|
||||
</div>
|
||||
|
||||
<div class="transport" aria-label="视频定位">
|
||||
<button type="button" data-seek="-2">−2 s</button>
|
||||
<button type="button" data-seek="-0.2">−0.2 s</button>
|
||||
<button id="jumpEvent" class="jump-event" type="button">回到事件</button>
|
||||
<button type="button" data-seek="0.2">+0.2 s</button>
|
||||
<button type="button" data-seek="2">+2 s</button>
|
||||
<span id="timeReadout">—</span>
|
||||
</div>
|
||||
|
||||
<section class="transcript-section">
|
||||
<div class="section-heading">
|
||||
<div>
|
||||
<span class="eyebrow">TRANSCRIPT</span>
|
||||
<h3>事件上下文</h3>
|
||||
</div>
|
||||
<span id="clipLabel">—</span>
|
||||
</div>
|
||||
<div id="transcriptList" class="transcript-list">
|
||||
<div class="empty-copy">选择事件后显示 transcript。</div>
|
||||
</div>
|
||||
<section class="timeline-section">
|
||||
<div class="section-heading"><div><span class="eyebrow">SESSION OVERVIEW</span><h3>本 Session 候选分布</h3></div><span>点击 marker 跳转</span></div>
|
||||
<div id="sessionTimeline" class="session-timeline"></div>
|
||||
<div class="section-heading local-heading"><div><span class="eyebrow">LOCAL SPEAKER ACTIVITY</span><h3>事件附近 participant timeline</h3></div><span>原始标注，只读</span></div>
|
||||
<div id="speakerTimeline" class="speaker-timeline"></div>
|
||||
</section>
|
||||
|
||||
<details class="raw-details">
|
||||
<summary>查看自动规则与原始候选字段</summary>
|
||||
<pre id="rawCandidate">{}</pre>
|
||||
</details>
|
||||
<div class="video-shell"><video id="eventVideo" controls preload="metadata"></video><div id="mediaEmpty" class="empty-state" hidden><strong>未找到视频</strong><span>检查 dataset root 与 clip。</span></div></div>
|
||||
<div class="transport"><button type="button" data-seek="-2">−2 s</button><button type="button" data-seek="-0.2">−0.2 s</button><button id="jumpEvent" class="jump-event" type="button">回到事件</button><button type="button" data-seek="0.2">+0.2 s</button><button type="button" data-seek="2">+2 s</button><span id="timeReadout">—</span></div>
|
||||
|
||||
<section class="transcript-section"><div class="section-heading"><div><span class="eyebrow">TRANSCRIPT</span><h3>事件上下文</h3></div><span id="clipLabel">—</span></div><div id="transcriptList" class="transcript-list"><div class="empty-copy">选择事件后显示 transcript。</div></div></section>
|
||||
<details class="raw-details"><summary>查看 structured timing 与原始候选</summary><pre id="rawCandidate">{}</pre></details>
|
||||
</section>
|
||||
|
||||
<aside class="review-panel panel" aria-label="人工审核表单">
|
||||
<div class="panel-heading review-heading">
|
||||
<div>
|
||||
<span class="eyebrow">HUMAN REVIEW</span>
|
||||
<h2>审核结论</h2>
|
||||
</div>
|
||||
<span id="saveState" class="save-state">未保存</span>
|
||||
</div>
|
||||
|
||||
<aside class="review-panel panel">
|
||||
<div class="panel-heading review-heading"><div><span class="eyebrow">HUMAN REVIEW</span><h2>只做一个主操作</h2></div><span id="saveState" class="save-state">未保存</span></div>
|
||||
<form id="reviewForm">
|
||||
<fieldset>
|
||||
<legend>1. 稿件协议结论 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two compact">
|
||||
<label><input type="radio" name="protocol_disposition" value="positive" /><span><b>Positive</b><small>EOT-response / INT-yield 正例</small></span></label>
|
||||
<label><input type="radio" name="protocol_disposition" value="hard_negative" /><span><b>Hard negative</b><small>错误 commit 的计分区间</small></span></label>
|
||||
<label><input type="radio" name="protocol_disposition" value="exclude" /><span><b>Exclude</b><small>歧义或标注损坏</small></span></label>
|
||||
<label><input type="radio" name="protocol_disposition" value="uncertain" /><span><b>不确定</b><small>需要裁决</small></span></label>
|
||||
<fieldset><legend>确认 AI 判断，或改标 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two action-grid">
|
||||
<label><input type="radio" name="human_action" value="accept" /><span><b>Accept</b><small>接受 AI verdict/type</small></span></label>
|
||||
<label><input type="radio" name="human_action" value="relabel" /><span><b>Relabel</b><small>展开并选择正确标签</small></span></label>
|
||||
<label><input type="radio" name="human_action" value="exclude" /><span><b>Exclude</b><small>数据不可可靠评分</small></span></label>
|
||||
<label><input type="radio" name="human_action" value="second_review" /><span><b>Unsure / 二审</b><small>证据不足，交给二审</small></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
|
||||
<fieldset>
|
||||
<legend>2. Hard-negative 类型 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two compact">
|
||||
<label><input type="radio" name="hard_negative_type" value="eot_midturn_pause" /><span><b>EOT 句中停顿</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="eot_other_to_other" /><span><b>EOT other→other</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="eot_no_response" /><span><b>EOT 保持沉默</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="int_backchannel" /><span><b>INT backchannel</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="int_non_takeover_overlap" /><span><b>INT 非接管重叠</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="int_retain_floor" /><span><b>INT 保持话轮</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="other" /><span><b>其他</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="not_applicable" /><span><b>不适用</b></span></label>
|
||||
<label><input type="radio" name="hard_negative_type" value="uncertain" /><span><b>不确定</b></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
<section id="relabelFields" class="conditional-fields" hidden>
|
||||
<label><span>一级 verdict</span><select name="human_verdict"><option value="">请选择</option><option>POSITIVE</option><option>HARD_NEGATIVE</option><option>NOT_APPLICABLE</option><option>UNSURE</option></select></label>
|
||||
<label id="eventTypeField" hidden><span>Positive event</span><select name="human_event_type"><option value="">请选择</option><option>EOT_RESPONSE</option><option>INT_YIELD</option></select></label>
|
||||
<label id="subtypeField" hidden><span>Hard-negative subtype</span><select name="human_subtype"><option value="">请选择</option><option>EOT_MIDTURN_PAUSE</option><option>EOT_OTHER_TO_OTHER</option><option>EOT_WEARER_BACKCHANNEL</option><option>INT_OTHER_BACKCHANNEL</option><option>INT_NON_TAKEOVER_OVERLAP</option><option>INT_WEARER_HOLDS_FLOOR</option><option>OTHER_HARD_NEGATIVE</option></select></label>
|
||||
</section>
|
||||
|
||||
<fieldset>
|
||||
<legend>3. 活动边界是否真实？ <span class="required">必填</span></legend>
|
||||
<div class="choice-grid three">
|
||||
<label><input type="radio" name="validity" value="valid" /><span><b>真实</b><small>有效活动边界</small></span></label>
|
||||
<label><input type="radio" name="validity" value="artifact" /><span><b>伪边界</b><small>噪声、断点或错标</small></span></label>
|
||||
<label><input type="radio" name="validity" value="uncertain" /><span><b>不确定</b><small>证据不足</small></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
<section id="excludeFields" class="conditional-fields" hidden><label><span>排除原因</span><select name="exclude_reason"><option value="">请选择</option><option>ANNOTATION_ERROR</option><option>RECORDING_BOUNDARY</option><option>MISSING_CONTEXT</option><option>NON_SPEECH_ARTIFACT</option><option>TIMESTAMP_CORRUPTION</option><option>UNRESOLVABLE</option></select></label></section>
|
||||
|
||||
<fieldset>
|
||||
<legend>4. Onset 的 floor 功能 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two compact">
|
||||
<label><input type="radio" name="onset_function" value="floor_taking" /><span><b>取得话轮</b></span></label>
|
||||
<label><input type="radio" name="onset_function" value="backchannel" /><span><b>Backchannel</b></span></label>
|
||||
<label><input type="radio" name="onset_function" value="within_turn_resume" /><span><b>句内恢复</b></span></label>
|
||||
<label><input type="radio" name="onset_function" value="nonverbal" /><span><b>非语言发声</b></span></label>
|
||||
<label><input type="radio" name="onset_function" value="not_applicable" /><span><b>不适用</b></span></label>
|
||||
<label><input type="radio" name="onset_function" value="uncertain" /><span><b>不确定</b></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
|
||||
<fieldset>
|
||||
<legend>5. 对话指向/场景 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two compact">
|
||||
<label><input type="radio" name="context" value="addressed_wearer" /><span><b>对 wearer</b></span></label>
|
||||
<label><input type="radio" name="context" value="third_party" /><span><b>第三方交谈</b></span></label>
|
||||
<label><input type="radio" name="context" value="group" /><span><b>群体</b></span></label>
|
||||
<label><input type="radio" name="context" value="unknown" /><span><b>未知</b></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
|
||||
<fieldset>
|
||||
<legend>6. Offset 的结果 <span class="required">必填</span></legend>
|
||||
<div class="choice-grid two compact">
|
||||
<label><input type="radio" name="offset_outcome" value="yield_to_other" /><span><b>让给 other</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="completion_to_silence" /><span><b>结束后静音</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="temporary_pause" /><span><b>短暂停顿</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="backchannel_end" /><span><b>Backchannel 结束</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="cutoff" /><span><b>截断/边界</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="not_applicable" /><span><b>不适用</b></span></label>
|
||||
<label><input type="radio" name="offset_outcome" value="uncertain" /><span><b>不确定</b></span></label>
|
||||
</div>
|
||||
</fieldset>
|
||||
|
||||
<div class="inline-fields">
|
||||
<label>
|
||||
<span>other 锚点校正</span>
|
||||
<select name="anchor_adjustment_bins">
|
||||
<option value="">无法判断</option>
|
||||
<option value="-1">−1 bin（−200 ms）</option>
|
||||
<option value="0" selected>原 bin</option>
|
||||
<option value="1">+1 bin（+200 ms）</option>
|
||||
</select>
|
||||
</label>
|
||||
<label>
|
||||
<span>wearer 行为锚点校正</span>
|
||||
<select name="behavior_adjustment_bins">
|
||||
<option value="">不适用/无法判断</option>
|
||||
<option value="-1">−1 bin（−200 ms）</option>
|
||||
<option value="0" selected>原 bin</option>
|
||||
<option value="1">+1 bin（+200 ms）</option>
|
||||
</select>
|
||||
</label>
|
||||
<label>
|
||||
<span>负区间终点校正</span>
|
||||
<select name="region_end_adjustment_bins">
|
||||
<option value="">不适用/无法判断</option>
|
||||
<option value="-1">−1 bin（−200 ms）</option>
|
||||
<option value="0" selected>原 bin</option>
|
||||
<option value="1">+1 bin（+200 ms）</option>
|
||||
</select>
|
||||
</label>
|
||||
<label>
|
||||
<span>置信度</span>
|
||||
<select name="confidence" required>
|
||||
<option value="high">高</option>
|
||||
<option value="medium" selected>中</option>
|
||||
<option value="low">低</option>
|
||||
</select>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
<label class="text-field">
|
||||
<span>审核者</span>
|
||||
<input id="reviewerInput" name="reviewer" maxlength="100" placeholder="姓名或代号" required />
|
||||
</label>
|
||||
<label class="text-field">
|
||||
<span>备注</span>
|
||||
<textarea name="notes" maxlength="4000" rows="3" placeholder="记录歧义、说话对象或边界问题"></textarea>
|
||||
</label>
|
||||
<details class="boundary-details"><summary>仅在原 annotation 明显错误时校正边界</summary><label><span>可选校正（原时间始终保留）</span><select name="boundary_correction_seconds"><option value="">不校正</option><option value="-0.2">−0.2 s</option><option value="0.2">+0.2 s</option></select></label></details>
|
||||
|
||||
<div class="inline-fields"><label><span>置信度</span><select name="confidence"><option value="high">高</option><option value="medium" selected>中</option><option value="low">低</option></select></label><label><span>审核者</span><input id="reviewerInput" name="reviewer" maxlength="100" placeholder="姓名或代号" required /></label></div>
|
||||
<label class="text-field"><span>备注（可选）</span><textarea name="notes" maxlength="4000" rows="3" placeholder="只记录必要的歧义或证据"></textarea></label>
|
||||
<div id="formError" class="form-error" role="alert" hidden></div>
|
||||
<div class="review-actions">
|
||||
<button id="uncertainButton" class="button ghost" type="button">全部标为不确定</button>
|
||||
<button id="saveButton" class="button primary" type="submit">保存并看下一个 <kbd>⌘↵</kbd></button>
|
||||
</div>
|
||||
<div class="review-actions"><button id="uncertainButton" class="button ghost" type="button">送二审</button><button id="saveButton" class="button primary" type="submit">保存并看下一个 <kbd>⌘↵</kbd></button></div>
|
||||
</form>
|
||||
|
||||
<div class="shortcuts">
|
||||
<span><kbd>←</kbd><kbd>→</kbd> 前后事件</span>
|
||||
<span><kbd>[</kbd><kbd>]</kbd> ±200 ms</span>
|
||||
<span><kbd>Space</kbd> 播放/暂停</span>
|
||||
</div>
|
||||
<div class="shortcuts"><span><kbd>←</kbd><kbd>→</kbd> 前后事件</span><span><kbd>[</kbd><kbd>]</kbd> ±200 ms</span><span><kbd>Space</kbd> 播放/暂停</span></div>
|
||||
</aside>
|
||||
</main>
|
||||
|
||||
<div id="toast" class="toast" role="status" aria-live="polite"></div>
|
||||
<script src="/app.js" defer></script>
|
||||
<div id="toast" class="toast" role="status"></div><script src="/app.js" defer></script>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
+357
-421
@@ -1,18 +1,10 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Prepare manuscript-aligned EasyCom EOT/INT regions for human review.
|
||||
"""Generate wearer-centric EasyCom EOT/INT candidates for AI and human review.
|
||||
|
||||
The released EasyCom annotations provide participant-resolved speech intervals,
|
||||
not semantic EOT/INT ground truth. This script therefore creates auditable
|
||||
candidate regions from the original 20 Hz timeline. It keeps the two wearer-
|
||||
relative directions distinct:
|
||||
|
||||
* EOT-response: an aggregate non-wearer offset while the wearer is silent;
|
||||
* INT-yield: an aggregate non-wearer onset while the wearer is speaking.
|
||||
|
||||
Observed wearer transitions form timing-positive candidates. Competing other
|
||||
transitions and fully observed no-transition windows form hard-negative
|
||||
candidates. Source-boundary cases are retained as exclusions. Every output
|
||||
row remains an automatic candidate until it is independently human reviewed.
|
||||
This stage never changes EasyCom speech boundaries and never claims semantic
|
||||
ground truth. It emits every wearer transition plus independent participant-
|
||||
resolved hard-negative regions. All timing thresholds are CLI parameters, and
|
||||
raw evidence is retained for DeepSeek pre-labeling and human correction.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -26,22 +18,24 @@ import random
|
||||
from typing import Any, Iterable, Sequence
|
||||
|
||||
from derive_easycom_interaction_events import (
|
||||
CAMERA_WEARER_ID,
|
||||
SOURCE_FPS,
|
||||
Clip,
|
||||
Rules,
|
||||
Utterance,
|
||||
build_clips,
|
||||
is_lexical_feedback,
|
||||
is_pure_nonverbal,
|
||||
load_utterances,
|
||||
CAMERA_WEARER_ID, SOURCE_FPS, Clip, Rules, Utterance, build_clips,
|
||||
is_lexical_feedback, is_pure_nonverbal, load_utterances,
|
||||
)
|
||||
from easycom_manifest import ALL_SESSIONS, TEST_SESSIONS
|
||||
|
||||
|
||||
SCHEMA_VERSION = 1
|
||||
PROTOCOL_VERSION = "easycom-manuscript-eot-int-v1"
|
||||
DEFAULT_WINDOW_SECONDS = 2.0
|
||||
SCHEMA_VERSION = 2
|
||||
PROTOCOL_VERSION = "easycom-wearer-centric-eot-int-candidates-v2"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CandidateConfig:
|
||||
context_seconds: float = 3.0
|
||||
relation_window_seconds: float = 3.0
|
||||
same_speaker_pause_max_seconds: float = 2.0
|
||||
other_handover_max_seconds: float = 1.5
|
||||
short_vocalization_max_seconds: float = 1.0
|
||||
continuation_min_seconds: float = 0.4
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
@@ -49,40 +43,46 @@ class ProtocolCandidate:
|
||||
schema_version: int
|
||||
protocol_version: str
|
||||
uid: str
|
||||
event_id: str
|
||||
session: str
|
||||
split: str
|
||||
task: str
|
||||
event_type: str
|
||||
candidate_source: str
|
||||
rule_candidate_type: str
|
||||
automatic_protocol_label: str
|
||||
automatic_hard_negative_type: str
|
||||
automatic_hard_negative_type: str | None
|
||||
automatic_confidence: str
|
||||
anchor_type: str
|
||||
anchor_dense_frame: int
|
||||
anchor_dense_seconds: float
|
||||
behavior_anchor_type: str | None
|
||||
behavior_dense_frame: int | None
|
||||
behavior_seconds: float | None
|
||||
event_time: float
|
||||
region_start_seconds: float
|
||||
region_end_seconds: float
|
||||
region_end_reason: str
|
||||
previous_other_participant_ids: tuple[int, ...]
|
||||
incoming_other_participant_ids: tuple[int, ...]
|
||||
clip_stem: str
|
||||
source_start_frame: int
|
||||
previous_other_participant_ids: tuple[int, ...]
|
||||
incoming_other_participant_ids: tuple[int, ...]
|
||||
temporal_evidence: dict[str, Any]
|
||||
speaker_timeline: tuple[dict[str, Any], ...]
|
||||
context: tuple[dict[str, Any], ...]
|
||||
automatic_reason: str
|
||||
ai_verdict: None = None
|
||||
ai_event_type: None = None
|
||||
ai_subtype: None = None
|
||||
ai_confidence: None = None
|
||||
ai_reason: None = None
|
||||
human_status: str = "UNREVIEWED"
|
||||
human_verdict: None = None
|
||||
human_event_type: None = None
|
||||
human_subtype: None = None
|
||||
exclude_reason: None = None
|
||||
|
||||
|
||||
def _fill_state(
|
||||
rows: Sequence[Utterance],
|
||||
*,
|
||||
segment_start: int,
|
||||
segment_end: int,
|
||||
wearer_id: int,
|
||||
) -> tuple[bytearray, bytearray]:
|
||||
def _fill_states(rows: Sequence[Utterance], *, segment_start: int, segment_end: int,
|
||||
wearer_id: int) -> tuple[bytearray, bytearray]:
|
||||
length = segment_end - segment_start
|
||||
wearer = bytearray(length)
|
||||
others = bytearray(length)
|
||||
wearer, others = bytearray(length), bytearray(length)
|
||||
for row in rows:
|
||||
left = max(segment_start, row.dense_start_frame) - segment_start
|
||||
right = min(segment_end, row.dense_end_frame_exclusive) - segment_start
|
||||
@@ -93,371 +93,329 @@ def _fill_state(
|
||||
return wearer, others
|
||||
|
||||
|
||||
def _first_transition(
|
||||
state: bytearray,
|
||||
*,
|
||||
start: int,
|
||||
stop: int,
|
||||
rising: bool,
|
||||
) -> int | None:
|
||||
for index in range(max(1, start), min(stop, len(state))):
|
||||
before = bool(state[index - 1])
|
||||
after = bool(state[index])
|
||||
if (not before and after) if rising else (before and not after):
|
||||
return index
|
||||
return None
|
||||
def _run_after(state: bytearray, index: int, value: bool) -> int:
|
||||
end = index
|
||||
while end < len(state) and bool(state[end]) == value:
|
||||
end += 1
|
||||
return end - index
|
||||
|
||||
|
||||
def _active_other_ids(
|
||||
rows: Sequence[Utterance],
|
||||
dense_frame: int,
|
||||
*,
|
||||
wearer_id: int,
|
||||
) -> tuple[int, ...]:
|
||||
def _run_before(state: bytearray, index: int, value: bool) -> int:
|
||||
start = index - 1
|
||||
while start >= 0 and bool(state[start]) == value:
|
||||
start -= 1
|
||||
return index - start - 1
|
||||
|
||||
|
||||
def _all_state(state: bytearray, start: int, stop: int, value: bool) -> bool:
|
||||
left, right = max(0, start), min(len(state), stop)
|
||||
return right > left and all(bool(state[i]) == value for i in range(left, right))
|
||||
|
||||
|
||||
def _active_other_ids(rows: Sequence[Utterance], frame: int, *, wearer_id: int) -> tuple[int, ...]:
|
||||
return tuple(sorted({
|
||||
row.participant_id
|
||||
for row in rows
|
||||
row.participant_id for row in rows
|
||||
if row.participant_id != wearer_id
|
||||
and row.dense_start_frame <= dense_frame < row.dense_end_frame_exclusive
|
||||
and row.dense_start_frame <= frame < row.dense_end_frame_exclusive
|
||||
}))
|
||||
|
||||
|
||||
def _clip_for_frame(clips: Sequence[Clip], dense_frame: int) -> Clip | None:
|
||||
return next(
|
||||
(
|
||||
clip
|
||||
for clip in clips
|
||||
if clip.dense_start_frame <= dense_frame < clip.dense_start_frame + clip.num_frames
|
||||
),
|
||||
None,
|
||||
)
|
||||
def _clip_for_frame(clips: Sequence[Clip], frame: int) -> Clip | None:
|
||||
return next((clip for clip in clips
|
||||
if clip.dense_start_frame <= frame < clip.dense_start_frame + clip.num_frames), None)
|
||||
|
||||
|
||||
def _context_rows(
|
||||
rows: Sequence[Utterance],
|
||||
*,
|
||||
anchor_frame: int,
|
||||
context_frames: int,
|
||||
wearer_id: int,
|
||||
) -> tuple[dict[str, Any], ...]:
|
||||
left = anchor_frame - context_frames
|
||||
right = anchor_frame + context_frames
|
||||
selected = []
|
||||
for row in rows:
|
||||
if row.dense_end_frame_exclusive <= left or row.dense_start_frame >= right:
|
||||
continue
|
||||
selected.append(
|
||||
{
|
||||
def _context_rows(rows: Sequence[Utterance], *, frame: int, context_frames: int,
|
||||
wearer_id: int) -> tuple[dict[str, Any], ...]:
|
||||
left, right = frame - context_frames, frame + context_frames
|
||||
return tuple({
|
||||
"uid": row.uid,
|
||||
"participant_id": row.participant_id,
|
||||
"participant_role": "wearer" if row.participant_id == wearer_id else "other",
|
||||
"start_seconds": row.dense_start_seconds,
|
||||
"end_seconds": row.dense_end_seconds,
|
||||
"duration_seconds": row.duration_seconds,
|
||||
"transcription": row.transcription,
|
||||
"target_of_speech": list(row.target_of_speech),
|
||||
}
|
||||
)
|
||||
return tuple(selected)
|
||||
} for row in rows
|
||||
if row.dense_end_frame_exclusive > left and row.dense_start_frame < right)
|
||||
|
||||
|
||||
def _incoming_is_backchannel(
|
||||
rows: Sequence[Utterance],
|
||||
*,
|
||||
dense_frame: int,
|
||||
wearer_id: int,
|
||||
max_seconds: float,
|
||||
) -> bool:
|
||||
incoming = [
|
||||
row
|
||||
for row in rows
|
||||
if row.participant_id != wearer_id
|
||||
and row.dense_start_frame <= dense_frame < row.dense_end_frame_exclusive
|
||||
and row.dense_start_frame >= dense_frame - 1
|
||||
]
|
||||
return bool(incoming) and all(row.duration_seconds <= max_seconds for row in incoming) and any(
|
||||
is_lexical_feedback(row.transcription) or is_pure_nonverbal(row.transcription)
|
||||
for row in incoming
|
||||
)
|
||||
|
||||
|
||||
def _candidate(
|
||||
*,
|
||||
session: str,
|
||||
task: str,
|
||||
automatic_protocol_label: str,
|
||||
automatic_hard_negative_type: str,
|
||||
anchor_frame: int,
|
||||
behavior_frame: int | None,
|
||||
region_end_frame: int,
|
||||
region_end_reason: str,
|
||||
previous_ids: tuple[int, ...],
|
||||
incoming_ids: tuple[int, ...],
|
||||
clips: Sequence[Clip],
|
||||
rows: Sequence[Utterance],
|
||||
wearer_id: int,
|
||||
context_frames: int,
|
||||
reason: str,
|
||||
) -> ProtocolCandidate:
|
||||
clip = _clip_for_frame(clips, anchor_frame)
|
||||
if clip is None:
|
||||
raise ValueError(f"No source clip for {session} dense frame {anchor_frame}")
|
||||
event_type = "eot_response_candidate" if task == "eot_response" else "int_yield_candidate"
|
||||
anchor_type = "other_offset" if task == "eot_response" else "other_onset"
|
||||
behavior_type = None
|
||||
if behavior_frame is not None:
|
||||
behavior_type = "wearer_onset" if task == "eot_response" else "wearer_offset"
|
||||
return ProtocolCandidate(
|
||||
schema_version=SCHEMA_VERSION,
|
||||
protocol_version=PROTOCOL_VERSION,
|
||||
uid=f"{session}/{task}/{anchor_frame:08d}",
|
||||
session=session,
|
||||
split="test" if session in TEST_SESSIONS else "train",
|
||||
task=task,
|
||||
event_type=event_type,
|
||||
automatic_protocol_label=automatic_protocol_label,
|
||||
automatic_hard_negative_type=automatic_hard_negative_type,
|
||||
automatic_confidence="timing_candidate",
|
||||
anchor_type=anchor_type,
|
||||
anchor_dense_frame=anchor_frame,
|
||||
anchor_dense_seconds=anchor_frame / SOURCE_FPS,
|
||||
behavior_anchor_type=behavior_type,
|
||||
behavior_dense_frame=behavior_frame,
|
||||
behavior_seconds=None if behavior_frame is None else behavior_frame / SOURCE_FPS,
|
||||
region_start_seconds=anchor_frame / SOURCE_FPS,
|
||||
region_end_seconds=region_end_frame / SOURCE_FPS,
|
||||
region_end_reason=region_end_reason,
|
||||
previous_other_participant_ids=previous_ids,
|
||||
incoming_other_participant_ids=incoming_ids,
|
||||
clip_stem=clip.stem,
|
||||
source_start_frame=anchor_frame - clip.dense_start_frame + 1,
|
||||
context=_context_rows(
|
||||
rows,
|
||||
anchor_frame=anchor_frame,
|
||||
context_frames=context_frames,
|
||||
wearer_id=wearer_id,
|
||||
def _relation_evidence(rows: Sequence[Utterance], *, frame: int, wearer_id: int,
|
||||
window_frames: int) -> dict[str, Any]:
|
||||
previous = [row for row in rows if row.participant_id != wearer_id
|
||||
and row.dense_end_frame_exclusive <= frame
|
||||
and frame - row.dense_end_frame_exclusive <= window_frames]
|
||||
following = [row for row in rows if row.participant_id != wearer_id
|
||||
and row.dense_start_frame >= frame
|
||||
and row.dense_start_frame - frame <= window_frames]
|
||||
prev = max(previous, key=lambda row: row.dense_end_frame_exclusive, default=None)
|
||||
nxt = min(following, key=lambda row: row.dense_start_frame, default=None)
|
||||
active_rows = [row for row in rows if row.participant_id != wearer_id
|
||||
and row.dense_start_frame < frame < row.dense_end_frame_exclusive]
|
||||
overlapping = sorted({row.participant_id for row in active_rows})
|
||||
resumes = [] if prev is None else [row for row in following if row.participant_id == prev.participant_id]
|
||||
resumed = min(resumes, key=lambda row: row.dense_start_frame, default=None)
|
||||
nearby_onsets = [row for row in rows if row.participant_id != wearer_id
|
||||
and abs(row.dense_start_frame - frame) <= window_frames]
|
||||
closest_onset = min(nearby_onsets, key=lambda row: abs(row.dense_start_frame - frame), default=None)
|
||||
return {
|
||||
"previous_speaker_id": None if prev is None else prev.participant_id,
|
||||
"previous_speaker_offset_seconds": None if prev is None else prev.dense_end_seconds,
|
||||
"gap_from_previous_speaker_offset_seconds": (
|
||||
None if prev is None else (frame - prev.dense_end_frame_exclusive) / SOURCE_FPS),
|
||||
"overlapping_other_ids": overlapping,
|
||||
"other_overlap_at_event_seconds": max(
|
||||
[0.0] + [(row.dense_end_frame_exclusive - frame) / SOURCE_FPS for row in active_rows]
|
||||
),
|
||||
automatic_reason=reason,
|
||||
"next_speaker_id": None if nxt is None else nxt.participant_id,
|
||||
"next_other_onset_seconds": None if nxt is None else nxt.dense_start_seconds,
|
||||
"previous_speaker_resumes": resumed is not None,
|
||||
"previous_speaker_resume_delay_seconds": (
|
||||
None if resumed is None or prev is None
|
||||
else (resumed.dense_start_frame - prev.dense_end_frame_exclusive) / SOURCE_FPS
|
||||
),
|
||||
"closest_other_onset_relative_seconds": (
|
||||
None if closest_onset is None else (closest_onset.dense_start_frame - frame) / SOURCE_FPS
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def _candidate(*, session: str, task: str, source: str, rule_type: str,
|
||||
anchor_type: str, frame: int, region_start: int, region_end: int,
|
||||
label: str, subtype: str | None, previous_ids: tuple[int, ...],
|
||||
incoming_ids: tuple[int, ...], evidence: dict[str, Any], clips: Sequence[Clip],
|
||||
rows: Sequence[Utterance], wearer_id: int, context_frames: int,
|
||||
reason: str) -> ProtocolCandidate | None:
|
||||
clip = _clip_for_frame(clips, frame)
|
||||
if clip is None:
|
||||
return None
|
||||
participant_key = "prev-" + ("-".join(f"p{x}" for x in previous_ids) or "none")
|
||||
participant_key += "__in-" + ("-".join(f"p{x}" for x in incoming_ids) or "none")
|
||||
uid = f"{session}/{source.lower()}/{frame:08d}/{participant_key}"
|
||||
context = _context_rows(rows, frame=frame, context_frames=context_frames, wearer_id=wearer_id)
|
||||
return ProtocolCandidate(
|
||||
schema_version=SCHEMA_VERSION, protocol_version=PROTOCOL_VERSION,
|
||||
uid=uid, event_id=uid, session=session,
|
||||
split="test" if session in TEST_SESSIONS else "train", task=task,
|
||||
event_type=source.lower(), candidate_source=source, rule_candidate_type=rule_type,
|
||||
automatic_protocol_label=label, automatic_hard_negative_type=subtype,
|
||||
automatic_confidence="rule_candidate", anchor_type=anchor_type,
|
||||
anchor_dense_frame=frame, anchor_dense_seconds=frame / SOURCE_FPS,
|
||||
event_time=frame / SOURCE_FPS, region_start_seconds=region_start / SOURCE_FPS,
|
||||
region_end_seconds=region_end / SOURCE_FPS, clip_stem=clip.stem,
|
||||
source_start_frame=frame - clip.dense_start_frame + 1,
|
||||
previous_other_participant_ids=previous_ids,
|
||||
incoming_other_participant_ids=incoming_ids, temporal_evidence=evidence,
|
||||
speaker_timeline=context, context=context, automatic_reason=reason,
|
||||
)
|
||||
|
||||
|
||||
def derive_protocol_candidates(
|
||||
utterances: Sequence[Utterance],
|
||||
clips: Sequence[Clip],
|
||||
*,
|
||||
session: str,
|
||||
wearer_id: int = CAMERA_WEARER_ID,
|
||||
window_seconds: float = DEFAULT_WINDOW_SECONDS,
|
||||
context_seconds: float = 3.0,
|
||||
) -> list[ProtocolCandidate]:
|
||||
"""Construct timing candidates with manuscript-aligned directionality."""
|
||||
|
||||
if window_seconds <= 0 or context_seconds < 0:
|
||||
raise ValueError("window_seconds must be positive and context_seconds non-negative")
|
||||
window_frames = max(1, round(window_seconds * SOURCE_FPS))
|
||||
context_frames = max(0, round(context_seconds * SOURCE_FPS))
|
||||
def derive_protocol_candidates(utterances: Sequence[Utterance], clips: Sequence[Clip], *,
|
||||
session: str, wearer_id: int = CAMERA_WEARER_ID,
|
||||
config: CandidateConfig | None = None,
|
||||
window_seconds: float | None = None,
|
||||
context_seconds: float | None = None) -> list[ProtocolCandidate]:
|
||||
"""Return every wearer transition and independent hard-negative regions."""
|
||||
cfg = config or CandidateConfig()
|
||||
values = asdict(cfg)
|
||||
if window_seconds is not None:
|
||||
values["relation_window_seconds"] = window_seconds
|
||||
if context_seconds is not None:
|
||||
values["context_seconds"] = context_seconds
|
||||
cfg = CandidateConfig(**values)
|
||||
if any(value < 0 for value in asdict(cfg).values()) or cfg.relation_window_seconds == 0:
|
||||
raise ValueError("candidate timing parameters must be non-negative and relation window positive")
|
||||
context_frames = round(cfg.context_seconds * SOURCE_FPS)
|
||||
relation_frames = round(cfg.relation_window_seconds * SOURCE_FPS)
|
||||
pause_frames = round(cfg.same_speaker_pause_max_seconds * SOURCE_FPS)
|
||||
handover_frames = round(cfg.other_handover_max_seconds * SOURCE_FPS)
|
||||
short_frames = round(cfg.short_vocalization_max_seconds * SOURCE_FPS)
|
||||
continuation_frames = round(cfg.continuation_min_seconds * SOURCE_FPS)
|
||||
rows_by_segment: dict[int, list[Utterance]] = defaultdict(list)
|
||||
clips_by_segment: dict[int, list[Clip]] = defaultdict(list)
|
||||
for row in utterances:
|
||||
rows_by_segment[row.segment_id].append(row)
|
||||
for clip in clips:
|
||||
clips_by_segment[clip.segment_id].append(clip)
|
||||
|
||||
candidates: list[ProtocolCandidate] = []
|
||||
seen: set[tuple[str, int, tuple[int, ...]]] = set()
|
||||
|
||||
def add(item: ProtocolCandidate | None, key: tuple[str, int, tuple[int, ...]]) -> None:
|
||||
if item is not None and key not in seen:
|
||||
seen.add(key)
|
||||
candidates.append(item)
|
||||
|
||||
for segment_id, segment_clips in sorted(clips_by_segment.items()):
|
||||
segment_clips.sort(key=lambda clip: clip.dense_start_frame)
|
||||
segment_rows = sorted(
|
||||
rows_by_segment.get(segment_id, []),
|
||||
key=lambda row: (row.dense_start_frame, row.dense_end_frame_exclusive, row.uid),
|
||||
)
|
||||
rows = sorted(rows_by_segment.get(segment_id, []),
|
||||
key=lambda row: (row.dense_start_frame, row.dense_end_frame_exclusive, row.uid))
|
||||
segment_start = segment_clips[0].dense_start_frame
|
||||
segment_end = segment_clips[-1].dense_start_frame + segment_clips[-1].num_frames
|
||||
wearer, others = _fill_state(
|
||||
segment_rows,
|
||||
segment_start=segment_start,
|
||||
segment_end=segment_end,
|
||||
wearer_id=wearer_id,
|
||||
)
|
||||
wearer, others = _fill_states(rows, segment_start=segment_start,
|
||||
segment_end=segment_end, wearer_id=wearer_id)
|
||||
|
||||
for index in range(1, len(others)):
|
||||
dense_frame = segment_start + index
|
||||
if others[index - 1] and not others[index] and not wearer[index - 1]:
|
||||
competing = _first_transition(
|
||||
others,
|
||||
start=index + 1,
|
||||
stop=min(len(others), index + window_frames + 1),
|
||||
rising=True,
|
||||
# A: all wearer 0->1 and 1->0 transitions, without semantic guessing.
|
||||
for index in range(1, len(wearer)):
|
||||
frame = segment_start + index
|
||||
if not wearer[index - 1] and wearer[index]:
|
||||
evidence = _relation_evidence(rows, frame=frame, wearer_id=wearer_id,
|
||||
window_frames=relation_frames)
|
||||
evidence.update(
|
||||
wearer_speech_duration_after_onset_seconds=_run_after(wearer, index, True) / SOURCE_FPS,
|
||||
previous_wearer_silence_duration_seconds=_run_before(wearer, index, False) / SOURCE_FPS,
|
||||
original_timestamp_seconds=frame / SOURCE_FPS,
|
||||
)
|
||||
nominal_end = index + window_frames
|
||||
observable_end = min(nominal_end, len(others))
|
||||
effective_end = min(observable_end, competing if competing is not None else observable_end)
|
||||
outcome = _first_transition(
|
||||
wearer,
|
||||
start=index,
|
||||
stop=effective_end,
|
||||
rising=True,
|
||||
)
|
||||
previous_ids = _active_other_ids(
|
||||
segment_rows, dense_frame - 1, wearer_id=wearer_id
|
||||
)
|
||||
incoming_ids = (
|
||||
()
|
||||
if competing is None
|
||||
else _active_other_ids(
|
||||
segment_rows, segment_start + competing, wearer_id=wearer_id
|
||||
)
|
||||
)
|
||||
if outcome is not None:
|
||||
label = "positive"
|
||||
subtype = "not_applicable"
|
||||
end_index = outcome
|
||||
end_reason = "wearer_onset"
|
||||
reason = "wearer onset observed after other offset and before competing other onset"
|
||||
elif competing is not None:
|
||||
label = "hard_negative"
|
||||
subtype = (
|
||||
"eot_midturn_pause"
|
||||
if set(previous_ids) & set(incoming_ids)
|
||||
else "eot_other_to_other"
|
||||
)
|
||||
end_index = competing
|
||||
end_reason = "competing_other_onset"
|
||||
reason = "wearer remains silent until another non-wearer onset"
|
||||
elif nominal_end <= len(others):
|
||||
label = "hard_negative"
|
||||
subtype = "eot_no_response"
|
||||
end_index = nominal_end
|
||||
end_reason = "full_window"
|
||||
reason = "wearer remains silent for the full observable response window"
|
||||
else:
|
||||
label = "exclude"
|
||||
subtype = "other"
|
||||
end_index = len(others)
|
||||
end_reason = "right_censored"
|
||||
reason = "recording segment ends before a wearer outcome or full window"
|
||||
candidates.append(_candidate(
|
||||
session=session,
|
||||
task="eot_response",
|
||||
automatic_protocol_label=label,
|
||||
automatic_hard_negative_type=subtype,
|
||||
anchor_frame=dense_frame,
|
||||
behavior_frame=None if outcome is None else segment_start + outcome,
|
||||
region_end_frame=segment_start + end_index,
|
||||
region_end_reason=end_reason,
|
||||
previous_ids=previous_ids,
|
||||
incoming_ids=incoming_ids,
|
||||
clips=segment_clips,
|
||||
rows=segment_rows,
|
||||
wearer_id=wearer_id,
|
||||
add(_candidate(
|
||||
session=session, task="eot_response", source="WEARER_ONSET",
|
||||
rule_type="ENTRY_CANDIDATE", anchor_type="wearer_onset", frame=frame,
|
||||
region_start=frame, region_end=min(segment_end, frame + relation_frames),
|
||||
label="unsure", subtype=None,
|
||||
previous_ids=_active_other_ids(rows, frame - 1, wearer_id=wearer_id),
|
||||
incoming_ids=_active_other_ids(rows, frame, wearer_id=wearer_id),
|
||||
evidence=evidence, clips=segment_clips, rows=rows, wearer_id=wearer_id,
|
||||
context_frames=context_frames,
|
||||
reason=reason,
|
||||
))
|
||||
|
||||
if not others[index - 1] and others[index] and wearer[index - 1]:
|
||||
competing = _first_transition(
|
||||
others,
|
||||
start=index + 1,
|
||||
stop=min(len(others), index + window_frames + 1),
|
||||
rising=False,
|
||||
reason="observed wearer 0->1 transition; semantic EOT-response status is unresolved",
|
||||
), ("WEARER_ONSET", frame, ()))
|
||||
elif wearer[index - 1] and not wearer[index]:
|
||||
evidence = _relation_evidence(rows, frame=frame, wearer_id=wearer_id,
|
||||
window_frames=relation_frames)
|
||||
evidence.update(
|
||||
wearer_speech_duration_before_offset_seconds=_run_before(wearer, index, True) / SOURCE_FPS,
|
||||
wearer_silence_duration_after_offset_seconds=_run_after(wearer, index, False) / SOURCE_FPS,
|
||||
active_other_ids_before_offset=list(_active_other_ids(rows, frame - 1, wearer_id=wearer_id)),
|
||||
active_other_ids_after_offset=list(_active_other_ids(rows, frame, wearer_id=wearer_id)),
|
||||
original_timestamp_seconds=frame / SOURCE_FPS,
|
||||
)
|
||||
nominal_end = index + window_frames
|
||||
observable_end = min(nominal_end, len(others))
|
||||
effective_end = min(observable_end, competing if competing is not None else observable_end)
|
||||
outcome = _first_transition(
|
||||
wearer,
|
||||
start=index,
|
||||
stop=effective_end,
|
||||
rising=False,
|
||||
)
|
||||
previous_ids = _active_other_ids(
|
||||
segment_rows, dense_frame - 1, wearer_id=wearer_id
|
||||
)
|
||||
incoming_ids = _active_other_ids(
|
||||
segment_rows, dense_frame, wearer_id=wearer_id
|
||||
)
|
||||
if outcome is not None:
|
||||
label = "positive"
|
||||
subtype = "not_applicable"
|
||||
end_index = outcome
|
||||
end_reason = "wearer_offset"
|
||||
reason = "wearer offset observed after other onset and before competing other offset"
|
||||
elif competing is not None:
|
||||
label = "hard_negative"
|
||||
subtype = (
|
||||
"int_backchannel"
|
||||
if _incoming_is_backchannel(
|
||||
segment_rows,
|
||||
dense_frame=dense_frame,
|
||||
wearer_id=wearer_id,
|
||||
max_seconds=1.0,
|
||||
)
|
||||
else "int_non_takeover_overlap"
|
||||
)
|
||||
end_index = competing
|
||||
end_reason = "competing_other_offset"
|
||||
reason = "wearer retains the floor until the incoming other activity ends"
|
||||
elif nominal_end <= len(others):
|
||||
label = "hard_negative"
|
||||
subtype = "int_retain_floor"
|
||||
end_index = nominal_end
|
||||
end_reason = "full_window"
|
||||
reason = "wearer remains active through the full observable yield window"
|
||||
else:
|
||||
label = "exclude"
|
||||
subtype = "other"
|
||||
end_index = len(others)
|
||||
end_reason = "right_censored"
|
||||
reason = "recording segment ends before a wearer outcome or full window"
|
||||
candidates.append(_candidate(
|
||||
session=session,
|
||||
task="int_yield",
|
||||
automatic_protocol_label=label,
|
||||
automatic_hard_negative_type=subtype,
|
||||
anchor_frame=dense_frame,
|
||||
behavior_frame=None if outcome is None else segment_start + outcome,
|
||||
region_end_frame=segment_start + end_index,
|
||||
region_end_reason=end_reason,
|
||||
previous_ids=previous_ids,
|
||||
incoming_ids=incoming_ids,
|
||||
clips=segment_clips,
|
||||
rows=segment_rows,
|
||||
wearer_id=wearer_id,
|
||||
add(_candidate(
|
||||
session=session, task="int_yield", source="WEARER_OFFSET",
|
||||
rule_type="YIELD_CANDIDATE", anchor_type="wearer_offset", frame=frame,
|
||||
region_start=max(segment_start, frame - relation_frames),
|
||||
region_end=min(segment_end, frame + relation_frames), label="unsure", subtype=None,
|
||||
previous_ids=_active_other_ids(rows, frame - 1, wearer_id=wearer_id),
|
||||
incoming_ids=_active_other_ids(rows, frame, wearer_id=wearer_id),
|
||||
evidence=evidence, clips=segment_clips, rows=rows, wearer_id=wearer_id,
|
||||
context_frames=context_frames,
|
||||
reason=reason,
|
||||
))
|
||||
reason="observed wearer 1->0 transition; semantic INT-yield status is unresolved",
|
||||
), ("WEARER_OFFSET", frame, ()))
|
||||
|
||||
return sorted(candidates, key=lambda row: (row.anchor_dense_frame, row.task, row.uid))
|
||||
other_rows = [row for row in rows if row.participant_id != wearer_id]
|
||||
|
||||
# B1: same speaker pauses and resumes while the wearer remains silent.
|
||||
by_speaker: dict[int, list[Utterance]] = defaultdict(list)
|
||||
for row in other_rows:
|
||||
by_speaker[row.participant_id].append(row)
|
||||
for participant_id, speaker_rows in by_speaker.items():
|
||||
for first, resumed in zip(speaker_rows, speaker_rows[1:]):
|
||||
gap = resumed.dense_start_frame - first.dense_end_frame_exclusive
|
||||
left = first.dense_end_frame_exclusive - segment_start
|
||||
right = resumed.dense_start_frame - segment_start
|
||||
if (gap <= 0 or gap > pause_frames
|
||||
or not _all_state(wearer, left, right, False)
|
||||
or not _all_state(others, left, right, False)):
|
||||
continue
|
||||
frame = first.dense_end_frame_exclusive
|
||||
evidence = {
|
||||
"speaker_id": participant_id, "speaker_offset_seconds": first.dense_end_seconds,
|
||||
"speaker_resume_seconds": resumed.dense_start_seconds,
|
||||
"resume_delay_seconds": gap / SOURCE_FPS, "wearer_active_during_pause": False,
|
||||
"original_timestamp_seconds": frame / SOURCE_FPS,
|
||||
}
|
||||
add(_candidate(
|
||||
session=session, task="eot_response", source="SAME_SPEAKER_PAUSE",
|
||||
rule_type="EOT_HARD_NEGATIVE_REGION", anchor_type="other_offset", frame=frame,
|
||||
region_start=frame, region_end=resumed.dense_start_frame,
|
||||
label="hard_negative", subtype="EOT_MIDTURN_PAUSE",
|
||||
previous_ids=(participant_id,), incoming_ids=(participant_id,), evidence=evidence,
|
||||
clips=segment_clips, rows=rows, wearer_id=wearer_id, context_frames=context_frames,
|
||||
reason="same participant resumes after a configurable pause while wearer stays silent",
|
||||
), ("SAME_SPEAKER_PAUSE", frame, (participant_id,)))
|
||||
|
||||
# B2: participant-resolved non-wearer to non-wearer handover.
|
||||
for first in other_rows:
|
||||
options = [row for row in other_rows if row.participant_id != first.participant_id
|
||||
and row.dense_start_frame >= first.dense_end_frame_exclusive
|
||||
and row.dense_start_frame - first.dense_end_frame_exclusive <= handover_frames]
|
||||
if not options:
|
||||
continue
|
||||
incoming = min(options, key=lambda row: row.dense_start_frame)
|
||||
same_resumes = any(row.participant_id == first.participant_id
|
||||
and first.dense_end_frame_exclusive < row.dense_start_frame <= incoming.dense_start_frame
|
||||
for row in other_rows)
|
||||
left = first.dense_end_frame_exclusive - segment_start
|
||||
right = max(left + 1, incoming.dense_start_frame - segment_start + 1)
|
||||
if same_resumes or not _all_state(wearer, left, right, False):
|
||||
continue
|
||||
frame = first.dense_end_frame_exclusive
|
||||
evidence = {
|
||||
"previous_speaker_id": first.participant_id, "incoming_speaker_id": incoming.participant_id,
|
||||
"previous_offset_seconds": first.dense_end_seconds,
|
||||
"incoming_onset_seconds": incoming.dense_start_seconds,
|
||||
"handover_gap_seconds": (incoming.dense_start_frame - frame) / SOURCE_FPS,
|
||||
"wearer_active_during_handover": False, "original_timestamp_seconds": frame / SOURCE_FPS,
|
||||
}
|
||||
add(_candidate(
|
||||
session=session, task="eot_response", source="OTHER_TO_OTHER",
|
||||
rule_type="EOT_HARD_NEGATIVE_REGION", anchor_type="other_offset", frame=frame,
|
||||
region_start=frame, region_end=incoming.dense_start_frame,
|
||||
label="hard_negative", subtype="EOT_OTHER_TO_OTHER",
|
||||
previous_ids=(first.participant_id,), incoming_ids=(incoming.participant_id,),
|
||||
evidence=evidence, clips=segment_clips, rows=rows, wearer_id=wearer_id,
|
||||
context_frames=context_frames,
|
||||
reason="participant-level IDs show a non-wearer to non-wearer handover",
|
||||
), ("OTHER_TO_OTHER", frame, (first.participant_id, incoming.participant_id)))
|
||||
|
||||
# B3/B4: other activity ends while the wearer continuously holds and continues the floor.
|
||||
for other in other_rows:
|
||||
start = other.dense_start_frame - segment_start
|
||||
end = other.dense_end_frame_exclusive - segment_start
|
||||
if (start < 0 or end + continuation_frames > len(wearer)
|
||||
or not _all_state(wearer, start, end + continuation_frames, True)):
|
||||
continue
|
||||
feedback = (other.dense_end_frame_exclusive - other.dense_start_frame <= short_frames
|
||||
and (is_lexical_feedback(other.transcription) or is_pure_nonverbal(other.transcription)))
|
||||
source = "OTHER_BACKCHANNEL" if feedback else "NON_TAKEOVER_OVERLAP"
|
||||
subtype = "INT_OTHER_BACKCHANNEL" if feedback else "INT_NON_TAKEOVER_OVERLAP"
|
||||
evidence = {
|
||||
"other_speaker_id": other.participant_id, "other_onset_seconds": other.dense_start_seconds,
|
||||
"other_offset_seconds": other.dense_end_seconds, "other_duration_seconds": other.duration_seconds,
|
||||
"wearer_active_through_region": True,
|
||||
"wearer_continuation_after_other_seconds": continuation_frames / SOURCE_FPS,
|
||||
"lexical_or_nonverbal_feedback_hint": feedback,
|
||||
"original_timestamp_seconds": other.dense_start_seconds,
|
||||
}
|
||||
add(_candidate(
|
||||
session=session, task="int_yield", source=source,
|
||||
rule_type="INT_HARD_NEGATIVE_REGION", anchor_type="other_onset",
|
||||
frame=other.dense_start_frame, region_start=other.dense_start_frame,
|
||||
region_end=other.dense_end_frame_exclusive, label="hard_negative", subtype=subtype,
|
||||
previous_ids=_active_other_ids(rows, other.dense_start_frame - 1, wearer_id=wearer_id),
|
||||
incoming_ids=(other.participant_id,), evidence=evidence, clips=segment_clips,
|
||||
rows=rows, wearer_id=wearer_id, context_frames=context_frames,
|
||||
reason=("short feedback-like other activity ends while wearer holds the floor"
|
||||
if feedback else "other overlaps but wearer retains and continues the floor"),
|
||||
), (source, other.dense_start_frame, (other.participant_id,)))
|
||||
|
||||
return sorted(candidates, key=lambda row: (row.anchor_dense_frame, row.candidate_source, row.uid))
|
||||
|
||||
|
||||
def stratified_limit(
|
||||
rows: Sequence[ProtocolCandidate],
|
||||
limit: int | None,
|
||||
*,
|
||||
seed: int,
|
||||
) -> list[ProtocolCandidate]:
|
||||
def stratified_limit(rows: Sequence[ProtocolCandidate], limit: int | None, *, seed: int) -> list[ProtocolCandidate]:
|
||||
if limit is None or limit >= len(rows):
|
||||
return list(rows)
|
||||
if limit < 0:
|
||||
raise ValueError("--max-candidates must be non-negative")
|
||||
rng = random.Random(seed)
|
||||
buckets: dict[tuple[str, str, str], list[ProtocolCandidate]] = defaultdict(list)
|
||||
buckets: dict[tuple[str, str], list[ProtocolCandidate]] = defaultdict(list)
|
||||
for row in rows:
|
||||
buckets[(row.task, row.automatic_protocol_label, row.automatic_hard_negative_type)].append(row)
|
||||
keys = sorted(buckets)
|
||||
for key in keys:
|
||||
rng.shuffle(buckets[key])
|
||||
rng.shuffle(keys)
|
||||
buckets[(row.session, row.candidate_source)].append(row)
|
||||
for bucket in buckets.values():
|
||||
rng.shuffle(bucket)
|
||||
selected: list[ProtocolCandidate] = []
|
||||
while len(selected) < limit:
|
||||
progressed = False
|
||||
for key in keys:
|
||||
if buckets[key]:
|
||||
while len(selected) < limit and any(buckets.values()):
|
||||
for key in sorted(buckets):
|
||||
if buckets[key] and len(selected) < limit:
|
||||
selected.append(buckets[key].pop())
|
||||
progressed = True
|
||||
if len(selected) == limit:
|
||||
break
|
||||
if not progressed:
|
||||
break
|
||||
return sorted(selected, key=lambda row: (row.session, row.anchor_dense_frame, row.task))
|
||||
return sorted(selected, key=lambda row: (row.session, row.anchor_dense_frame, row.candidate_source))
|
||||
|
||||
|
||||
def _write_jsonl(path: Path, rows: Iterable[ProtocolCandidate]) -> None:
|
||||
@@ -473,20 +431,13 @@ def parse_args() -> argparse.Namespace:
|
||||
parser.add_argument("--output-dir", type=Path, required=True)
|
||||
parser.add_argument("--sessions", nargs="+", default=list(ALL_SESSIONS))
|
||||
parser.add_argument("--camera-wearer-id", type=int, default=CAMERA_WEARER_ID)
|
||||
parser.add_argument("--window-seconds", type=float, default=DEFAULT_WINDOW_SECONDS)
|
||||
parser.add_argument("--context-seconds", type=float, default=3.0)
|
||||
parser.add_argument(
|
||||
"--tasks",
|
||||
nargs="+",
|
||||
choices=("eot_response", "int_yield"),
|
||||
default=("eot_response", "int_yield"),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--automatic-labels",
|
||||
nargs="+",
|
||||
choices=("positive", "hard_negative", "exclude"),
|
||||
default=("positive", "hard_negative", "exclude"),
|
||||
)
|
||||
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("--short-vocalization-max-seconds", type=float, default=1.0)
|
||||
parser.add_argument("--continuation-min-seconds", type=float, default=0.4)
|
||||
parser.add_argument("--candidate-sources", nargs="+")
|
||||
parser.add_argument("--max-candidates", type=int)
|
||||
parser.add_argument("--selection-seed", type=int, default=2027)
|
||||
return parser.parse_args()
|
||||
@@ -497,74 +448,59 @@ def main() -> None:
|
||||
unknown = sorted(set(args.sessions) - set(ALL_SESSIONS))
|
||||
if unknown:
|
||||
raise ValueError(f"Unknown EasyCom sessions: {unknown}")
|
||||
dataset_root = args.dataset_root.expanduser().resolve()
|
||||
output_dir = args.output_dir.expanduser().resolve()
|
||||
config = CandidateConfig(
|
||||
context_seconds=args.context_seconds,
|
||||
relation_window_seconds=args.relation_window_seconds,
|
||||
same_speaker_pause_max_seconds=args.same_speaker_pause_max_seconds,
|
||||
other_handover_max_seconds=args.other_handover_max_seconds,
|
||||
short_vocalization_max_seconds=args.short_vocalization_max_seconds,
|
||||
continuation_min_seconds=args.continuation_min_seconds,
|
||||
)
|
||||
dataset_root, output_dir = args.dataset_root.expanduser().resolve(), args.output_dir.expanduser().resolve()
|
||||
all_rows: list[ProtocolCandidate] = []
|
||||
session_timelines: dict[str, list[dict[str, Any]]] = {}
|
||||
rules = Rules()
|
||||
for session in args.sessions:
|
||||
clips = build_clips(dataset_root, session, rules)
|
||||
utterances = load_utterances(session, clips)
|
||||
rows = derive_protocol_candidates(
|
||||
utterances,
|
||||
clips,
|
||||
session=session,
|
||||
wearer_id=args.camera_wearer_id,
|
||||
window_seconds=args.window_seconds,
|
||||
context_seconds=args.context_seconds,
|
||||
)
|
||||
session_timelines[session] = [{
|
||||
"uid": row.uid,
|
||||
"participant_id": row.participant_id,
|
||||
"participant_role": "wearer" if row.participant_id == args.camera_wearer_id else "other",
|
||||
"start_seconds": row.dense_start_seconds,
|
||||
"end_seconds": row.dense_end_seconds,
|
||||
} for row in utterances]
|
||||
rows = derive_protocol_candidates(utterances, clips, session=session,
|
||||
wearer_id=args.camera_wearer_id, config=config)
|
||||
all_rows.extend(rows)
|
||||
counts = Counter((row.task, row.automatic_protocol_label) for row in rows)
|
||||
print(f"{session}: candidates={len(rows)} counts={dict(sorted(counts.items()))}")
|
||||
|
||||
eligible = [
|
||||
row
|
||||
for row in all_rows
|
||||
if row.task in set(args.tasks)
|
||||
and row.automatic_protocol_label in set(args.automatic_labels)
|
||||
]
|
||||
print(f"{session}: candidates={len(rows)} sources={dict(sorted(Counter(row.candidate_source for row in rows).items()))}")
|
||||
sources = None if args.candidate_sources is None else set(args.candidate_sources)
|
||||
eligible = [row for row in all_rows if sources is None or row.candidate_source in sources]
|
||||
selected = stratified_limit(eligible, args.max_candidates, seed=args.selection_seed)
|
||||
output_path = output_dir / "easycom_eot_int_candidates.jsonl"
|
||||
_write_jsonl(output_path, selected)
|
||||
counts = Counter(
|
||||
(row.split, row.task, row.automatic_protocol_label, row.automatic_hard_negative_type)
|
||||
for row in selected
|
||||
)
|
||||
counts = Counter((row.split, row.candidate_source, row.rule_candidate_type) for row in selected)
|
||||
summary = {
|
||||
"schema_version": SCHEMA_VERSION,
|
||||
"protocol_version": PROTOCOL_VERSION,
|
||||
"dataset": "EasyCom",
|
||||
"semantic_warning": (
|
||||
"Automatic labels are timing candidates for independent human audit; "
|
||||
"they are not semantic EOT/INT ground truth."
|
||||
),
|
||||
"sessions": list(args.sessions),
|
||||
"camera_wearer_id": args.camera_wearer_id,
|
||||
"window_seconds": args.window_seconds,
|
||||
"context_seconds": args.context_seconds,
|
||||
"selection_seed": args.selection_seed,
|
||||
"schema_version": SCHEMA_VERSION, "protocol_version": PROTOCOL_VERSION, "dataset": "EasyCom",
|
||||
"semantic_warning": "Rules create candidates only; AI labels need human review and never modify dense activity ground truth.",
|
||||
"sessions": list(args.sessions), "camera_wearer_id": args.camera_wearer_id,
|
||||
"candidate_config": asdict(config), "selection_seed": args.selection_seed,
|
||||
"max_candidates": args.max_candidates,
|
||||
"tasks": list(args.tasks),
|
||||
"automatic_labels": list(args.automatic_labels),
|
||||
"num_candidates_before_filter": len(all_rows),
|
||||
"num_candidates_before_limit": len(eligible),
|
||||
"candidate_sources": None if sources is None else sorted(sources),
|
||||
"num_candidates_before_filter": len(all_rows), "num_candidates_before_limit": len(eligible),
|
||||
"num_candidates_written": len(selected),
|
||||
"counts": [
|
||||
{
|
||||
"split": key[0],
|
||||
"task": key[1],
|
||||
"automatic_protocol_label": key[2],
|
||||
"automatic_hard_negative_type": key[3],
|
||||
"count": value,
|
||||
}
|
||||
for key, value in sorted(counts.items())
|
||||
],
|
||||
"counts": [{"split": key[0], "candidate_source": key[1],
|
||||
"rule_candidate_type": key[2], "count": value}
|
||||
for key, value in sorted(counts.items())],
|
||||
}
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
(output_dir / "summary.json").write_text(
|
||||
json.dumps(summary, indent=2, ensure_ascii=False) + "\n",
|
||||
json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
|
||||
(output_dir / "easycom_session_timelines.json").write_text(
|
||||
json.dumps({"schema_version": 1, "sessions": session_timelines}, ensure_ascii=False) + "\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
print(f"Saved {len(selected)} manuscript-aligned review candidates to {output_path}")
|
||||
print(f"Saved {len(selected)} wearer-centric review candidates to {output_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
+123
-31
@@ -32,7 +32,7 @@ SOURCE_FPS = 20.0
|
||||
STATIC_ROOT = Path(__file__).resolve().parent / "floor_event_review_web"
|
||||
VIDEO_ROOT_PARTS = ("Main", "Video_Compressed")
|
||||
TRANSCRIPT_ROOT_PARTS = ("Main", "Speech_Transcriptions")
|
||||
REVIEW_SCHEMA_VERSION = 2
|
||||
REVIEW_SCHEMA_VERSION = 3
|
||||
MAX_REQUEST_BYTES = 128 * 1024
|
||||
|
||||
PROTOCOL_DISPOSITION_VALUES = {"positive", "hard_negative", "exclude", "uncertain"}
|
||||
@@ -67,6 +67,18 @@ OFFSET_OUTCOME_VALUES = {
|
||||
"uncertain",
|
||||
}
|
||||
CONFIDENCE_VALUES = {"high", "medium", "low"}
|
||||
HUMAN_ACTION_VALUES = {"accept", "relabel", "exclude", "second_review"}
|
||||
HUMAN_VERDICT_VALUES = {"POSITIVE", "HARD_NEGATIVE", "NOT_APPLICABLE", "EXCLUDE", "UNSURE"}
|
||||
HUMAN_EVENT_TYPE_VALUES = {"EOT_RESPONSE", "INT_YIELD"}
|
||||
HUMAN_SUBTYPE_VALUES = {
|
||||
"EOT_MIDTURN_PAUSE", "EOT_OTHER_TO_OTHER", "EOT_WEARER_BACKCHANNEL",
|
||||
"INT_OTHER_BACKCHANNEL", "INT_NON_TAKEOVER_OVERLAP", "INT_WEARER_HOLDS_FLOOR",
|
||||
"OTHER_HARD_NEGATIVE",
|
||||
}
|
||||
EXCLUDE_REASON_VALUES = {
|
||||
"ANNOTATION_ERROR", "RECORDING_BOUNDARY", "MISSING_CONTEXT", "NON_SPEECH_ARTIFACT",
|
||||
"TIMESTAMP_CORRUPTION", "UNRESOLVABLE",
|
||||
}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
@@ -95,7 +107,8 @@ class Candidate:
|
||||
@property
|
||||
def automatic_label(self) -> str:
|
||||
return str(
|
||||
self.payload.get("event_type")
|
||||
self.payload.get("candidate_source")
|
||||
or self.payload.get("event_type")
|
||||
or self.payload.get("category")
|
||||
or self.payload.get("proxy_group")
|
||||
or "unclassified"
|
||||
@@ -108,6 +121,8 @@ class Candidate:
|
||||
|
||||
@property
|
||||
def family(self) -> str:
|
||||
if self.payload.get("candidate_source"):
|
||||
return "eot" if self.task == "eot_response" else "int"
|
||||
if self.task == "eot_response":
|
||||
return "eot"
|
||||
if self.task == "int_yield":
|
||||
@@ -233,6 +248,16 @@ class CandidateRepository:
|
||||
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}):
|
||||
if not path.is_file():
|
||||
continue
|
||||
payload = json.loads(path.read_text(encoding="utf-8"))
|
||||
sessions = payload.get("sessions") if isinstance(payload, dict) else None
|
||||
if isinstance(sessions, dict):
|
||||
for session, rows in sessions.items():
|
||||
if isinstance(session, str) and isinstance(rows, list):
|
||||
self.session_timelines[session] = [row for row in rows if isinstance(row, dict)]
|
||||
|
||||
def _transcript(self, session: str, clip_stem: str) -> list[dict[str, Any]]:
|
||||
key = (session, clip_stem)
|
||||
@@ -255,7 +280,9 @@ class CandidateRepository:
|
||||
row = candidate.payload
|
||||
clip_stem = row.get("clip_stem")
|
||||
transcript_index: int | None = None
|
||||
uid = _uid_parts(row.get("uid")) or _uid_parts(row.get("holder_uid"))
|
||||
uid = None if row.get("candidate_source") else (
|
||||
_uid_parts(row.get("uid")) or _uid_parts(row.get("holder_uid"))
|
||||
)
|
||||
if uid is not None:
|
||||
uid_session, uid_stem, transcript_index = uid
|
||||
if uid_session == candidate.session:
|
||||
@@ -291,8 +318,10 @@ class CandidateRepository:
|
||||
def summary(self, candidate: Candidate, reviewed: bool) -> dict[str, Any]:
|
||||
row = candidate.payload
|
||||
event_time = row.get(
|
||||
"anchor_dense_seconds",
|
||||
"event_time",
|
||||
row.get("anchor_dense_seconds",
|
||||
row.get("dense_start_seconds", row.get("boundary_seconds")),
|
||||
),
|
||||
)
|
||||
return {
|
||||
"candidate_id": candidate.candidate_id,
|
||||
@@ -306,6 +335,11 @@ class CandidateRepository:
|
||||
"automatic_hard_negative_type": row.get("automatic_hard_negative_type"),
|
||||
"automatic_confidence": row.get("automatic_confidence", row.get("confidence")),
|
||||
"llm_suggestion": row.get("llm_suggestion"),
|
||||
"candidate_source": row.get("candidate_source"),
|
||||
"rule_candidate_type": row.get("rule_candidate_type"),
|
||||
"ai_verdict": row.get("ai_verdict"),
|
||||
"ai_event_type": row.get("ai_event_type"),
|
||||
"ai_subtype": row.get("ai_subtype"),
|
||||
"participant_role": row.get("participant_role") or row.get("holder_role"),
|
||||
"event_time_seconds": event_time,
|
||||
"reviewed": reviewed,
|
||||
@@ -389,15 +423,11 @@ class ReviewStore:
|
||||
if not isinstance(payload, dict):
|
||||
raise ValueError("review must be an object")
|
||||
|
||||
required_choices = {
|
||||
"protocol_disposition": PROTOCOL_DISPOSITION_VALUES,
|
||||
"hard_negative_type": HARD_NEGATIVE_TYPE_VALUES,
|
||||
"validity": VALIDITY_VALUES,
|
||||
"onset_function": ONSET_FUNCTION_VALUES,
|
||||
"context": CONTEXT_VALUES,
|
||||
"offset_outcome": OFFSET_OUTCOME_VALUES,
|
||||
"confidence": CONFIDENCE_VALUES,
|
||||
}
|
||||
# Schema v3 deliberately keeps the common path small. Legacy v2 review
|
||||
# payloads remain accepted so existing review files are not stranded.
|
||||
if "human_action" not in payload:
|
||||
return self._validated_legacy_review(payload)
|
||||
required_choices = {"human_action": HUMAN_ACTION_VALUES, "confidence": CONFIDENCE_VALUES}
|
||||
normalized: dict[str, Any] = {}
|
||||
for key, allowed in required_choices.items():
|
||||
value = payload.get(key)
|
||||
@@ -405,15 +435,48 @@ class ReviewStore:
|
||||
raise ValueError(f"Invalid {key}: {value!r}")
|
||||
normalized[key] = value
|
||||
|
||||
for key in (
|
||||
"anchor_adjustment_bins",
|
||||
"behavior_adjustment_bins",
|
||||
"region_end_adjustment_bins",
|
||||
):
|
||||
adjustment = payload.get(key)
|
||||
if adjustment not in {-1, 0, 1, None}:
|
||||
raise ValueError(f"{key} must be -1, 0, 1, or null")
|
||||
normalized[key] = adjustment
|
||||
action = normalized["human_action"]
|
||||
candidate = self.candidates[candidate_id].payload
|
||||
if action == "accept":
|
||||
verdict = candidate.get("ai_verdict") or (candidate.get("llm_suggestion") or {}).get("verdict")
|
||||
event_type = candidate.get("ai_event_type") or (candidate.get("llm_suggestion") or {}).get("event_type")
|
||||
subtype = candidate.get("ai_subtype") or (candidate.get("llm_suggestion") or {}).get("subtype")
|
||||
if verdict not in HUMAN_VERDICT_VALUES:
|
||||
raise ValueError("Accept requires an AI pre-label; use Relabel when no AI label is available")
|
||||
elif action == "exclude":
|
||||
verdict, event_type, subtype = "EXCLUDE", None, None
|
||||
elif action == "second_review":
|
||||
verdict, event_type, subtype = "UNSURE", None, None
|
||||
else:
|
||||
verdict, event_type, subtype = payload.get("human_verdict"), payload.get("human_event_type"), payload.get("human_subtype")
|
||||
if verdict not in HUMAN_VERDICT_VALUES:
|
||||
raise ValueError(f"Invalid human_verdict: {verdict!r}")
|
||||
if verdict == "POSITIVE" and event_type not in HUMAN_EVENT_TYPE_VALUES:
|
||||
raise ValueError("POSITIVE requires EOT_RESPONSE or INT_YIELD")
|
||||
if verdict != "POSITIVE" and event_type is not None:
|
||||
raise ValueError("Only POSITIVE may have a human_event_type")
|
||||
if verdict == "HARD_NEGATIVE" and subtype not in HUMAN_SUBTYPE_VALUES:
|
||||
raise ValueError("HARD_NEGATIVE requires a valid human_subtype")
|
||||
if verdict != "HARD_NEGATIVE" and subtype is not None:
|
||||
raise ValueError("Only HARD_NEGATIVE may have a human_subtype")
|
||||
exclude_reason = payload.get("exclude_reason")
|
||||
if action == "exclude" and exclude_reason not in EXCLUDE_REASON_VALUES:
|
||||
raise ValueError("Exclude requires an exclude_reason")
|
||||
if action != "exclude" and exclude_reason is not None:
|
||||
raise ValueError("exclude_reason is only valid for Exclude")
|
||||
correction = payload.get("boundary_correction_seconds")
|
||||
if correction not in {-0.2, 0, 0.2, None}:
|
||||
raise ValueError("boundary_correction_seconds must be -0.2, 0, 0.2, or null")
|
||||
normalized.update(
|
||||
human_status="SECOND_REVIEW" if action == "second_review" else "REVIEWED",
|
||||
human_verdict=verdict,
|
||||
human_event_type=event_type,
|
||||
human_subtype=subtype,
|
||||
exclude_reason=exclude_reason,
|
||||
relabeled=action == "relabel",
|
||||
boundary_correction_seconds=correction,
|
||||
original_event_time=candidate.get("event_time", candidate.get("anchor_dense_seconds")),
|
||||
)
|
||||
|
||||
reviewer = str(payload.get("reviewer", "")).strip()
|
||||
notes = str(payload.get("notes", "")).strip()
|
||||
@@ -427,6 +490,31 @@ class ReviewStore:
|
||||
normalized["notes"] = notes
|
||||
return normalized
|
||||
|
||||
def _validated_legacy_review(self, payload: dict[str, Any]) -> dict[str, Any]:
|
||||
required_choices = {
|
||||
"protocol_disposition": PROTOCOL_DISPOSITION_VALUES,
|
||||
"hard_negative_type": HARD_NEGATIVE_TYPE_VALUES,
|
||||
"validity": VALIDITY_VALUES, "onset_function": ONSET_FUNCTION_VALUES,
|
||||
"context": CONTEXT_VALUES, "offset_outcome": OFFSET_OUTCOME_VALUES,
|
||||
"confidence": CONFIDENCE_VALUES,
|
||||
}
|
||||
normalized: dict[str, Any] = {}
|
||||
for key, allowed in required_choices.items():
|
||||
value = payload.get(key)
|
||||
if value not in allowed:
|
||||
raise ValueError(f"Invalid {key}: {value!r}")
|
||||
normalized[key] = value
|
||||
for key in ("anchor_adjustment_bins", "behavior_adjustment_bins", "region_end_adjustment_bins"):
|
||||
value = payload.get(key)
|
||||
if value not in {-1, 0, 1, None}:
|
||||
raise ValueError(f"{key} must be -1, 0, 1, or null")
|
||||
normalized[key] = value
|
||||
reviewer, notes = str(payload.get("reviewer", "")).strip(), str(payload.get("notes", "")).strip()
|
||||
if not reviewer or len(reviewer) > 100 or len(notes) > 4000:
|
||||
raise ValueError("invalid reviewer or notes")
|
||||
normalized.update(reviewer=reviewer, notes=notes)
|
||||
return normalized
|
||||
|
||||
def save(self, candidate_id: str, review: Any) -> dict[str, Any]:
|
||||
normalized = self._validated_review(candidate_id, review)
|
||||
candidate = self.candidates[candidate_id]
|
||||
@@ -445,6 +533,9 @@ class ReviewStore:
|
||||
"automatic_hard_negative_type": candidate.payload.get(
|
||||
"automatic_hard_negative_type"
|
||||
),
|
||||
"ai_verdict": candidate.payload.get("ai_verdict"),
|
||||
"ai_event_type": candidate.payload.get("ai_event_type"),
|
||||
"ai_subtype": candidate.payload.get("ai_subtype"),
|
||||
"review": normalized,
|
||||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||||
"candidate": candidate.payload,
|
||||
@@ -497,15 +588,9 @@ class ReviewStore:
|
||||
"automatic_label",
|
||||
"automatic_protocol_label",
|
||||
"automatic_hard_negative_type",
|
||||
"protocol_disposition",
|
||||
"hard_negative_type",
|
||||
"validity",
|
||||
"onset_function",
|
||||
"context",
|
||||
"offset_outcome",
|
||||
"anchor_adjustment_bins",
|
||||
"behavior_adjustment_bins",
|
||||
"region_end_adjustment_bins",
|
||||
"ai_verdict", "ai_event_type", "ai_subtype",
|
||||
"human_action", "human_status", "human_verdict", "human_event_type", "human_subtype",
|
||||
"exclude_reason", "relabeled", "boundary_correction_seconds", "original_event_time",
|
||||
"confidence",
|
||||
"reviewer",
|
||||
"notes",
|
||||
@@ -621,6 +706,13 @@ class ReviewRequestHandler(BaseHTTPRequestHandler):
|
||||
)
|
||||
self._json(detail)
|
||||
return
|
||||
if parsed.path == "/api/session-timeline":
|
||||
session = query.get("session", [""])[0]
|
||||
if session not in self.server.repository.session_timelines:
|
||||
self._error("Session timeline unavailable", HTTPStatus.NOT_FOUND)
|
||||
return
|
||||
self._json({"session": session, "speech": self.server.repository.session_timelines[session]})
|
||||
return
|
||||
if parsed.path == "/api/export.jsonl":
|
||||
self._send_download(self.server.store.output_path, "application/x-ndjson")
|
||||
return
|
||||
|
||||
@@ -3,17 +3,25 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
import json
|
||||
import tempfile
|
||||
|
||||
from adjudicate_easycom_eot_int_candidates import build_user_prompt, validate_decisions
|
||||
from adjudicate_easycom_eot_int_candidates import (
|
||||
_transport_rows, build_user_prompt, read_candidates, validate_decisions,
|
||||
)
|
||||
|
||||
|
||||
class EOTINTAdjudicationTests(unittest.TestCase):
|
||||
def test_prompt_contains_task_and_context(self) -> None:
|
||||
prompt = build_user_prompt(
|
||||
[{"uid": "u1", "task": "eot_response", "context": [{"transcription": "Yes."}]}],
|
||||
[{"uid": "u1", "candidate_source": "WEARER_ONSET", "task": "eot_response",
|
||||
"temporal_evidence": {"wearer_duration": 2.0},
|
||||
"context": [{"transcription": "Yes."}]}],
|
||||
0,
|
||||
)
|
||||
self.assertIn("eot_response", prompt)
|
||||
self.assertIn("WEARER_ONSET", prompt)
|
||||
self.assertIn("wearer_duration", prompt)
|
||||
self.assertIn("Yes.", prompt)
|
||||
|
||||
def test_decisions_require_exact_uid_and_vocabulary(self) -> None:
|
||||
@@ -21,18 +29,56 @@ class EOTINTAdjudicationTests(unittest.TestCase):
|
||||
"items": [
|
||||
{
|
||||
"uid": "u1",
|
||||
"protocol_disposition": "hard_negative",
|
||||
"hard_negative_type": "int_backchannel",
|
||||
"confidence": "high",
|
||||
"rationale": "Brief feedback while the wearer continues.",
|
||||
"verdict": "HARD_NEGATIVE",
|
||||
"event_type": None,
|
||||
"subtype": "INT_OTHER_BACKCHANNEL",
|
||||
"confidence": 0.91,
|
||||
"reason": "Brief feedback while the wearer continues.",
|
||||
"needs_human_review": False,
|
||||
}
|
||||
]
|
||||
}
|
||||
decisions = validate_decisions(payload, ["u1"])
|
||||
self.assertEqual(decisions[0]["hard_negative_type"], "int_backchannel")
|
||||
self.assertEqual(decisions[0]["subtype"], "INT_OTHER_BACKCHANNEL")
|
||||
with self.assertRaisesRegex(ValueError, "mismatch"):
|
||||
validate_decisions({"items": []}, ["u1"])
|
||||
|
||||
def test_markdown_escaped_uid_is_normalized_but_still_unique(self) -> None:
|
||||
item = {
|
||||
"uid": "Session\\_4/wearer\\_onset/00000024",
|
||||
"verdict": "POSITIVE",
|
||||
"event_type": "EOT_RESPONSE",
|
||||
"subtype": None,
|
||||
"confidence": 0.9,
|
||||
"reason": "Sustained wearer response after the other speaker stops.",
|
||||
"needs_human_review": False,
|
||||
}
|
||||
expected = "Session_4/wearer_onset/00000024"
|
||||
decision = validate_decisions({"items": [item]}, [expected])[0]
|
||||
self.assertEqual(decision["uid"], expected)
|
||||
with self.assertRaisesRegex(ValueError, "duplicate"):
|
||||
validate_decisions({"items": [item, {**item, "uid": expected}]}, [expected])
|
||||
|
||||
def test_duplicate_rule_uids_are_disambiguated_with_provenance(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as temporary:
|
||||
path = Path(temporary) / "candidates.jsonl"
|
||||
base = {"uid": "Session_4/other_to_other/00003600", "task": "eot_response"}
|
||||
rows = [
|
||||
{**base, "previous_other_participant_ids": [6], "incoming_other_participant_ids": [4]},
|
||||
{**base, "previous_other_participant_ids": [3], "incoming_other_participant_ids": [6]},
|
||||
]
|
||||
path.write_text("".join(json.dumps(row) + "\n" for row in rows), encoding="utf-8")
|
||||
loaded = read_candidates(path)
|
||||
self.assertEqual(len({row["uid"] for row in loaded}), 2)
|
||||
self.assertTrue(all(row["uid_disambiguated_from"] == base["uid"] for row in loaded))
|
||||
|
||||
def test_transport_ids_are_short_unique_and_reversible(self) -> None:
|
||||
rows = [{"uid": "Session_4/other_to_other/00003600/a"},
|
||||
{"uid": "Session_4/other_to_other/00003600/b"}]
|
||||
transported, mapping = _transport_rows(rows)
|
||||
self.assertEqual([row["uid"] for row in transported], ["item-0000", "item-0001"])
|
||||
self.assertEqual(mapping["item-0001"], rows[1]["uid"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
@@ -50,17 +50,17 @@ class EOTINTReviewCandidateTests(unittest.TestCase):
|
||||
def derive(self, rows: list[Utterance]):
|
||||
return derive_protocol_candidates(rows, [clip()], session="Session_4")
|
||||
|
||||
def test_eot_response_is_anchored_by_wearer_onset(self) -> None:
|
||||
def test_every_wearer_onset_is_an_unresolved_entry_candidate(self) -> None:
|
||||
rows = [
|
||||
utterance("other", 4, 0.0, 1.0, "Your turn."),
|
||||
utterance("wearer", 2, 1.2, 2.0, "My response."),
|
||||
]
|
||||
event = next(row for row in self.derive(rows) if row.task == "eot_response")
|
||||
self.assertEqual(event.automatic_protocol_label, "positive")
|
||||
self.assertEqual(event.anchor_type, "other_offset")
|
||||
self.assertEqual(event.behavior_anchor_type, "wearer_onset")
|
||||
self.assertAlmostEqual(event.anchor_dense_seconds, 1.0)
|
||||
self.assertAlmostEqual(event.behavior_seconds or -1, 1.2)
|
||||
event = next(row for row in self.derive(rows) if row.candidate_source == "WEARER_ONSET")
|
||||
self.assertEqual(event.automatic_protocol_label, "unsure")
|
||||
self.assertEqual(event.rule_candidate_type, "ENTRY_CANDIDATE")
|
||||
self.assertEqual(event.anchor_type, "wearer_onset")
|
||||
self.assertAlmostEqual(event.event_time, 1.2)
|
||||
self.assertAlmostEqual(event.temporal_evidence["wearer_speech_duration_after_onset_seconds"], 0.8)
|
||||
|
||||
def test_same_other_resume_is_eot_midturn_hard_negative(self) -> None:
|
||||
rows = [
|
||||
@@ -70,11 +70,11 @@ class EOTINTReviewCandidateTests(unittest.TestCase):
|
||||
event = next(
|
||||
row
|
||||
for row in self.derive(rows)
|
||||
if row.task == "eot_response" and row.anchor_dense_seconds == 1.0
|
||||
if row.candidate_source == "SAME_SPEAKER_PAUSE" and row.anchor_dense_seconds == 1.0
|
||||
)
|
||||
self.assertEqual(event.automatic_protocol_label, "hard_negative")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "eot_midturn_pause")
|
||||
self.assertEqual(event.region_end_reason, "competing_other_onset")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "EOT_MIDTURN_PAUSE")
|
||||
self.assertAlmostEqual(event.temporal_evidence["resume_delay_seconds"], 0.3)
|
||||
|
||||
def test_other_to_other_transfer_is_eot_hard_negative(self) -> None:
|
||||
rows = [
|
||||
@@ -84,31 +84,44 @@ class EOTINTReviewCandidateTests(unittest.TestCase):
|
||||
event = next(
|
||||
row
|
||||
for row in self.derive(rows)
|
||||
if row.task == "eot_response" and row.anchor_dense_seconds == 1.0
|
||||
if row.candidate_source == "OTHER_TO_OTHER" and row.anchor_dense_seconds == 1.0
|
||||
)
|
||||
self.assertEqual(event.automatic_protocol_label, "hard_negative")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "eot_other_to_other")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "EOT_OTHER_TO_OTHER")
|
||||
self.assertNotEqual(
|
||||
event.temporal_evidence["previous_speaker_id"],
|
||||
event.temporal_evidence["incoming_speaker_id"],
|
||||
)
|
||||
|
||||
def test_int_yield_is_anchored_by_wearer_offset(self) -> None:
|
||||
def test_every_wearer_offset_is_an_unresolved_yield_candidate(self) -> None:
|
||||
rows = [
|
||||
utterance("wearer", 2, 0.0, 2.0, "Wearer is speaking."),
|
||||
utterance("other", 4, 1.0, 3.0, "I will take over now."),
|
||||
]
|
||||
event = next(row for row in self.derive(rows) if row.task == "int_yield")
|
||||
self.assertEqual(event.automatic_protocol_label, "positive")
|
||||
self.assertEqual(event.anchor_type, "other_onset")
|
||||
self.assertEqual(event.behavior_anchor_type, "wearer_offset")
|
||||
self.assertAlmostEqual(event.behavior_seconds or -1, 2.0)
|
||||
event = next(row for row in self.derive(rows) if row.candidate_source == "WEARER_OFFSET")
|
||||
self.assertEqual(event.automatic_protocol_label, "unsure")
|
||||
self.assertEqual(event.rule_candidate_type, "YIELD_CANDIDATE")
|
||||
self.assertEqual(event.anchor_type, "wearer_offset")
|
||||
self.assertAlmostEqual(event.event_time, 2.0)
|
||||
|
||||
def test_brief_feedback_is_int_backchannel_hard_negative(self) -> None:
|
||||
rows = [
|
||||
utterance("wearer", 2, 0.0, 3.0, "Wearer keeps the floor."),
|
||||
utterance("other", 4, 1.0, 1.4, "Yeah."),
|
||||
]
|
||||
event = next(row for row in self.derive(rows) if row.task == "int_yield")
|
||||
event = next(row for row in self.derive(rows) if row.candidate_source == "OTHER_BACKCHANNEL")
|
||||
self.assertEqual(event.automatic_protocol_label, "hard_negative")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "int_backchannel")
|
||||
self.assertEqual(event.region_end_reason, "competing_other_offset")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "INT_OTHER_BACKCHANNEL")
|
||||
self.assertTrue(event.temporal_evidence["wearer_active_through_region"])
|
||||
|
||||
def test_non_takeover_overlap_is_scanned_without_wearer_transition(self) -> None:
|
||||
rows = [
|
||||
utterance("wearer", 2, 0.0, 4.0, "Long wearer turn."),
|
||||
utterance("other", 4, 1.0, 2.0, "Long overlapping contribution."),
|
||||
]
|
||||
event = next(row for row in self.derive(rows) if row.candidate_source == "NON_TAKEOVER_OVERLAP")
|
||||
self.assertEqual(event.automatic_hard_negative_type, "INT_NON_TAKEOVER_OVERLAP")
|
||||
self.assertAlmostEqual(event.region_start_seconds, 1.0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
+43
-11
@@ -56,11 +56,23 @@ class FloorEventReviewTests(unittest.TestCase):
|
||||
"clip_stem": "00-00-000",
|
||||
"source_start_frame": 25,
|
||||
"dense_start_seconds": 1.2,
|
||||
"event_time": 1.2,
|
||||
"candidate_source": "WEARER_ONSET",
|
||||
"ai_verdict": "POSITIVE",
|
||||
"ai_event_type": "EOT_RESPONSE",
|
||||
"ai_subtype": None,
|
||||
}
|
||||
)
|
||||
+ "\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
(root / "easycom_session_timelines.json").write_text(
|
||||
json.dumps({"schema_version": 1, "sessions": {"Session_4": [
|
||||
{"participant_id": 4, "participant_role": "other", "start_seconds": 0.0, "end_seconds": 1.0},
|
||||
{"participant_id": 2, "participant_role": "wearer", "start_seconds": 1.2, "end_seconds": 2.0},
|
||||
]}}),
|
||||
encoding="utf-8",
|
||||
)
|
||||
return dataset, events
|
||||
|
||||
def test_candidate_detail_resolves_media_and_transcript(self) -> None:
|
||||
@@ -79,6 +91,7 @@ class FloorEventReviewTests(unittest.TestCase):
|
||||
self.assertEqual(detail["event_local_seconds"], 1.2)
|
||||
self.assertEqual(len(detail["transcript"]), 2)
|
||||
self.assertTrue(detail["transcript"][1]["is_anchor"])
|
||||
self.assertEqual(len(repository.session_timelines["Session_4"]), 2)
|
||||
|
||||
def test_review_store_writes_resumable_jsonl_and_csv(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as temporary:
|
||||
@@ -88,15 +101,12 @@ class FloorEventReviewTests(unittest.TestCase):
|
||||
output = root / "reviews" / "reviews.jsonl"
|
||||
store = ReviewStore(output, candidates)
|
||||
review = {
|
||||
"protocol_disposition": "positive",
|
||||
"hard_negative_type": "not_applicable",
|
||||
"validity": "valid",
|
||||
"onset_function": "backchannel",
|
||||
"context": "addressed_wearer",
|
||||
"offset_outcome": "not_applicable",
|
||||
"anchor_adjustment_bins": 0,
|
||||
"behavior_adjustment_bins": 0,
|
||||
"region_end_adjustment_bins": None,
|
||||
"human_action": "accept",
|
||||
"human_verdict": None,
|
||||
"human_event_type": None,
|
||||
"human_subtype": None,
|
||||
"exclude_reason": None,
|
||||
"boundary_correction_seconds": None,
|
||||
"confidence": "high",
|
||||
"reviewer": "r1",
|
||||
"notes": "clear acknowledgement",
|
||||
@@ -106,9 +116,10 @@ class FloorEventReviewTests(unittest.TestCase):
|
||||
self.assertTrue(output.with_suffix(".csv").is_file())
|
||||
resumed = ReviewStore(output, candidates)
|
||||
self.assertEqual(
|
||||
resumed.reviews[candidates[0].candidate_id]["review"]["onset_function"],
|
||||
"backchannel",
|
||||
resumed.reviews[candidates[0].candidate_id]["review"]["human_event_type"],
|
||||
"EOT_RESPONSE",
|
||||
)
|
||||
self.assertFalse(resumed.reviews[candidates[0].candidate_id]["review"]["relabeled"])
|
||||
|
||||
def test_directory_expansion_excludes_output(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as temporary:
|
||||
@@ -122,6 +133,27 @@ class FloorEventReviewTests(unittest.TestCase):
|
||||
[candidate.resolve()],
|
||||
)
|
||||
|
||||
def test_relabel_preserves_ai_and_records_human_decision(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as temporary:
|
||||
root = Path(temporary)
|
||||
_, events = self._fixture(root)
|
||||
candidates = load_candidates([events])
|
||||
store = ReviewStore(root / "reviews.jsonl", candidates)
|
||||
record = store.save(candidates[0].candidate_id, {
|
||||
"human_action": "relabel",
|
||||
"human_verdict": "HARD_NEGATIVE",
|
||||
"human_event_type": None,
|
||||
"human_subtype": "EOT_WEARER_BACKCHANNEL",
|
||||
"exclude_reason": None,
|
||||
"boundary_correction_seconds": None,
|
||||
"confidence": "medium",
|
||||
"reviewer": "r2",
|
||||
"notes": "brief acknowledgement",
|
||||
})
|
||||
self.assertEqual(record["ai_event_type"], "EOT_RESPONSE")
|
||||
self.assertEqual(record["review"]["human_subtype"], "EOT_WEARER_BACKCHANNEL")
|
||||
self.assertTrue(record["review"]["relabeled"])
|
||||
|
||||
def test_byte_ranges(self) -> None:
|
||||
self.assertEqual(parse_byte_range("bytes=2-5", 10), (2, 5))
|
||||
self.assertEqual(parse_byte_range("bytes=7-", 10), (7, 9))
|
||||
|
||||
Reference in New Issue
Block a user