feat: llm-filter 两级过滤（规则层+五类分类+去重+长文本保护），工作流单线程 v4
- 规则层（不调 LLM）：横线装饰/HTML 水印 token/URL/邮箱/单双 ASCII 字符直接删 - LLM 五类分类：garbage/overlay/noise 删，repeat/dialogue 留，未识别回退保留 - 按文本去重：相同文本只调一次 LLM（忽略空白/大小写），判定一致并省调用 - 长文本保护：≥min_keep_len 时 noise 不构成删除依据 - 真实任务 run_ac7f480a3ccb 验证：非规则误删 350→193（-45%），呻吟/对话保留 - ocr-subtitle 工作流 v4：pool 钉死单线程（1/1） - 回归夹具 testdata/ocr_srt_run_ac7f480a3ccb.srt（真实 1666 条 OCR 输出）
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@@ -1,14 +1,30 @@
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"""LLM 字幕过滤节点测试。
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覆盖 SRT 解析/序列化、±N 上下文窗口组装（纯文本无时间戳、目标标记）、
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LLM 调用（按 I/O 边界 mock urlopen）与删除判定、invoke 全链路与异常路径。
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覆盖：
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- SRT 解析/序列化（多行、末条无空行、重新编号）；
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- 确定性规则层（横线装饰、HTML/水印 token、URL、单双 ASCII 字符直接删，不调 LLM）；
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- LLM 五类分类判断（garbage/overlay/noise 删，repeat/dialogue 留，未识别回退保留）；
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- 文本去重（相同文本只调一次 LLM，上下文取首次出现，判定结果一致）；
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- 长文本保护（≥min_keep_len 时 noise 类不删，需明确垃圾类别）；
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- 真实任务 run_ac7f480a3ccb 留存数据回归（OCR 1666 条：垃圾删除、对话保留、时间轴单调、去重后 LLM 调用数）；
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- invoke 全链路与异常路径。
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"""
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import json
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import urllib.error
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from pathlib import Path
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from nodes.llm_filter import invoke, parse_srt, serialize_srt
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import pytest
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from nodes.llm_filter import (
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_dedup_key,
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_judge_category,
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_rule_verdict,
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_should_delete,
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invoke,
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parse_srt,
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serialize_srt,
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)
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from wov_sdk.models import InvokeRequest
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# 4 条字幕的 SRT：第 3 条为"答："开头的无意义杂项，模拟 OCR 噪声。
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@@ -19,6 +35,10 @@ _SRT = (
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"4\n00:00:13,000 --> 00:00:16,000\n第二句正常字幕\n"
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)
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# 真实任务 run_ac7f480a3ccb 的 OCR 输出（1666 条，2026-08 单线程 v4 跑完整 2 小时视频）。
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WORKSPACE = Path(__file__).resolve().parent.parent
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REAL_SRT = WORKSPACE / "testdata" / "ocr_srt_run_ac7f480a3ccb.srt"
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class FakeResponse:
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"""模拟 urllib 响应：read() 返回 LLM 兼容接口的 JSON 载荷。"""
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@@ -37,9 +57,9 @@ class FakeResponse:
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class FakeLLM:
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"""模拟 LLM 兼容接口：记录请求体，按策略返回"保留/删除"。
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"""模拟 LLM 兼容接口：记录请求体，按策略返回类别词。
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支持两种策略：contents（按队列顺序，用于单次直调 _judge_target 的
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支持两种策略：contents（按队列顺序，用于单次直调 _judge_category 的
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确定性测试）或 decision_fn（按请求体内容决策，用于并发 invoke 测试，
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保证任何线程执行顺序下判定结果都确定）。
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"""
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@@ -70,12 +90,17 @@ def _patch_llm(monkeypatch, contents: list[str] | None = None, decision_fn=None)
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def _decision_by_target(body) -> str:
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"""按目标字幕内容决策：含"答："判为删除，其余保留（与 _SRT 的噪声对应）。"""
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"""按目标字幕内容决策：含"答："判为 garbage，其余为 dialogue。"""
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target = next(
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line for line in body["messages"][1]["content"].splitlines()
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if line.startswith("【目标】")
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)
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return "删除" if "答：" in target else "保留"
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return "garbage" if "答：" in target else "dialogue"
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# ---------------------------------------------------------------------------
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# SRT 解析 / 序列化
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# ---------------------------------------------------------------------------
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def test_parse_srt_multiline_and_last_block() -> None:
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@@ -105,14 +130,49 @@ def test_serialize_srt_renumbers() -> None:
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)
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def test_judge_target_window_and_keep(monkeypatch) -> None:
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"""窗口只含纯文本（无时间戳）、目标带标记；模型答"保留"则返回 False。"""
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from nodes.llm_filter import _judge_target
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# ---------------------------------------------------------------------------
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# 确定性规则层
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# ---------------------------------------------------------------------------
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def test_rule_verdict_deletes_garbage_patterns() -> None:
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"""规则层直接删除：横线装饰、HTML/水印 token、URL/邮箱、单双 ASCII 字符。"""
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tokens = {"html", "background", "___"}
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for text in (
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"---", "------", "------------------", "--- ---", "___", "= = =",
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"…", "・", "HTML", "html", "Background", "background", "___",
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"https://example.com/x", "www.example.com", "a@b.com",
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"A", "V", "1", "DQ", "P4",
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):
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assert _rule_verdict(text, tokens) is True, text
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# 空文本/纯空白也删除。
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assert _rule_verdict(" ", tokens) is True
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def test_rule_verdict_passes_cjk_and_dialogue() -> None:
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"""规则层不误伤：CJK 单字、正常对话、内容性短句交给 LLM 判断（None）。"""
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tokens = {"html"}
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for text in ("嗯", "好", "谢谢你 松井小姐", "不这么做的话 可没法胜任患者的对象", "好舒服"):
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assert _rule_verdict(text, tokens) is None, text
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def test_rule_verdict_custom_overlay_tokens() -> None:
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"""overlay_tokens 参数化：自定义 token 同样直接删除。"""
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assert _rule_verdict("Cleaning", {"cleaning"}) is True
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assert _rule_verdict("Cleaning", set()) is None
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# ---------------------------------------------------------------------------
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# LLM 分类判断
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# ---------------------------------------------------------------------------
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def test_judge_category_window_and_dialogue(monkeypatch) -> None:
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"""窗口只含纯文本（无时间戳）、目标带标记；模型答 dialogue 则保留。"""
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entries = parse_srt(_SRT)
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fake = _patch_llm(monkeypatch, ["保留"])
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fake = _patch_llm(monkeypatch, ["dialogue"])
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# context_size=1，目标为第 2 条（index=1）：窗口 0..2 共 3 行，目标在中间。
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assert _judge_target(entries, 1, context_size=1, params={}) is False
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assert _judge_category(entries, 1, context_size=1, params={}) == "dialogue"
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body = fake.bodies[0]
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lines = body["messages"][1]["content"].splitlines()
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assert len(lines) == 3
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@@ -125,32 +185,71 @@ def test_judge_target_window_and_keep(monkeypatch) -> None:
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assert body["max_tokens"] == 16
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def test_judge_target_delete(monkeypatch) -> None:
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"""模型答"删除"时返回 True（判定该条无意义）。"""
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from nodes.llm_filter import _judge_target
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def test_judge_category_delete_classes(monkeypatch) -> None:
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"""模型答 garbage/overlay/noise 时返回对应类别（删除类）。"""
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entries = parse_srt(_SRT)
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for cat in ("garbage", "overlay", "noise"):
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_patch_llm(monkeypatch, [cat])
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assert _judge_category(entries, 2, context_size=10, params={}) == cat
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def test_judge_category_repeat_and_unknown_kept(monkeypatch) -> None:
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"""repeat/dialogue 返回保留类；未识别输出（空/乱码/旧式"保留"）回退 dialogue。"""
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entries = parse_srt(_SRT)
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for answer in ("repeat", "dialogue", "", "???", "保留"):
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_patch_llm(monkeypatch, [answer])
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got = _judge_category(entries, 2, context_size=10, params={})
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assert got in ("repeat", "dialogue"), (answer, got)
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# 旧式"删除"回答兼容为垃圾类。
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_patch_llm(monkeypatch, ["删除"])
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assert _judge_target(entries, 2, context_size=10, params={}) is True
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assert _judge_category(entries, 2, context_size=10, params={}) == "garbage"
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def test_judge_target_model_and_auth(monkeypatch) -> None:
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def test_judge_category_model_and_auth(monkeypatch) -> None:
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"""模型名从参数取；配置 API Key 时附带 Bearer 鉴权头。"""
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from nodes.llm_filter import _judge_target
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entries = parse_srt(_SRT)
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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fake = _patch_llm(monkeypatch, ["保留"])
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assert _judge_target(entries, 0, context_size=10, params={"model": "m/1"}) is False
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fake = _patch_llm(monkeypatch, ["dialogue"])
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assert _judge_category(entries, 0, context_size=10, params={"model": "m/1"}) == "dialogue"
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assert fake.bodies[0]["model"] == "m/1"
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assert fake.headers[0]["Authorization"] == "Bearer sk-test"
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# ---------------------------------------------------------------------------
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# 去重键与删除判定
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# ---------------------------------------------------------------------------
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def test_dedup_key_normalizes_whitespace_and_case() -> None:
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"""去重键：去掉全部空白并统一大小写，空格差异视为同一文本。"""
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assert _dedup_key("不这么做的话 可没法胜任患者的对象") == _dedup_key(
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"不这么做的话可没法胜任患者的对象"
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)
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assert _dedup_key("HTML") == _dedup_key("html")
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assert _dedup_key("你好 世界") != _dedup_key("你好世界2")
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def test_should_delete_long_text_noise_protected() -> None:
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"""长文本保护：≥min_keep_len 时 noise 不删，garbage/overlay 仍删；短文本三类都删。"""
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long_text = "不这么做的话 可没法胜任患者的对象"
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assert _should_delete("noise", long_text, min_keep_len=12) is False
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assert _should_delete("garbage", long_text, min_keep_len=12) is True
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assert _should_delete("overlay", long_text, min_keep_len=12) is True
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assert _should_delete("noise", "答：无意义杂项", min_keep_len=12) is True
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assert _should_delete("repeat", long_text, min_keep_len=12) is False
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assert _should_delete("dialogue", long_text, min_keep_len=12) is False
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# ---------------------------------------------------------------------------
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# invoke 全链路
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# ---------------------------------------------------------------------------
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def test_invoke_filters_and_renumbers(monkeypatch, tmp_path) -> None:
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"""全链路（并发）：按 LLM 判定删除无意义条，保留条重新编号输出。"""
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"""全链路（并发）：按 LLM 类别判定删除无意义条，保留条重新编号输出。"""
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srt = tmp_path / "in.srt"
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srt.write_text(_SRT, encoding="utf-8")
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# 内容决策：目标字幕含"答："判删除，其余保留（任何线程顺序下结果确定）。
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# 内容决策：目标字幕含"答："判 garbage，其余 dialogue（任何线程顺序下结果确定）。
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_patch_llm(monkeypatch, decision_fn=_decision_by_target)
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response = invoke(
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InvokeRequest(
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@@ -172,6 +271,79 @@ def test_invoke_filters_and_renumbers(monkeypatch, tmp_path) -> None:
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assert "00:00:13,000 --> 00:00:16,000\n第二句正常字幕\n" in out
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def test_invoke_rules_skip_llm(monkeypatch, tmp_path) -> None:
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"""规则层命中时直接删除且不调用 LLM：横线/HTML/单字符全部清理。"""
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srt = tmp_path / "in.srt"
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srt.write_text(
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"1\n00:00:01,000 --> 00:00:04,000\n---\n\n"
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"2\n00:00:05,000 --> 00:00:08,000\nHTML\n\n"
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"3\n00:00:09,000 --> 00:00:12,000\nV\n\n"
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"4\n00:00:13,000 --> 00:00:16,000\n正常对话\n",
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encoding="utf-8",
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)
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fake = _patch_llm(monkeypatch, ["dialogue"])
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response = invoke(
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InvokeRequest(
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run_id="r", node_instance_id="",
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inputs={"srt_uri": str(srt)},
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params={},
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output_dir=str(tmp_path / "out"),
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)
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)
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assert response.status == "completed", response.error
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assert response.outputs["kept"] == 1
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assert response.outputs["removed"] == 3
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assert len(fake.bodies) == 1 # 仅"正常对话"需 LLM，其余全走规则。
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def test_invoke_dedup_single_llm_call(monkeypatch, tmp_path) -> None:
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"""去重：相同文本（含空格变体）只调一次 LLM，判定结果一致。"""
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srt = tmp_path / "in.srt"
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srt.write_text(
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"1\n00:00:01,000 --> 00:00:04,000\n好舒服\n\n"
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"2\n00:00:05,000 --> 00:00:08,000\n好 舒 服\n\n"
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"3\n00:00:09,000 --> 00:00:12,000\n好舒服\n\n"
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"4\n00:00:13,000 --> 00:00:16,000\n别的台词\n",
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encoding="utf-8",
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)
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fake = _patch_llm(monkeypatch, decision_fn=lambda body: "repeat")
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response = invoke(
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InvokeRequest(
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run_id="r", node_instance_id="",
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inputs={"srt_uri": str(srt)},
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params={},
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output_dir=str(tmp_path / "out"),
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)
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)
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assert response.status == "completed", response.error
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# 2 个唯一文本（好舒服/别的台词）→ 恰好 2 次 LLM 调用。
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assert len(fake.bodies) == 2
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# repeat 类保留 → 4 条全部保留。
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assert response.outputs["kept"] == 4
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assert response.outputs["removed"] == 0
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def test_invoke_dedupe_disabled(monkeypatch, tmp_path) -> None:
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"""dedupe=0 关闭去重：每个条目都调一次 LLM。"""
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srt = tmp_path / "in.srt"
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srt.write_text(
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"1\n00:00:01,000 --> 00:00:04,000\n同一句\n\n"
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"2\n00:00:05,000 --> 00:00:08,000\n同一句\n",
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encoding="utf-8",
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)
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fake = _patch_llm(monkeypatch, decision_fn=lambda body: "dialogue")
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response = invoke(
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InvokeRequest(
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run_id="r", node_instance_id="",
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inputs={"srt_uri": str(srt)},
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params={"dedupe": "0"},
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output_dir=str(tmp_path / "out"),
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)
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)
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assert response.status == "completed", response.error
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assert len(fake.bodies) == 2
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def test_invoke_context_size_param(monkeypatch, tmp_path) -> None:
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"""context_size 参数生效：窗口大小=2×context_size+1（两端截断除外）。"""
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srt = tmp_path / "in.srt"
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@@ -195,6 +367,52 @@ def test_invoke_context_size_param(monkeypatch, tmp_path) -> None:
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assert len(window) == 3
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def test_invoke_overlay_tokens_param(monkeypatch, tmp_path) -> None:
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"""overlay_tokens 参数：自定义 token 走规则层删除，不调用 LLM。"""
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srt = tmp_path / "in.srt"
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srt.write_text(
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"1\n00:00:01,000 --> 00:00:04,000\nCleaning\n\n"
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"2\n00:00:05,000 --> 00:00:08,000\n正常对话\n",
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encoding="utf-8",
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)
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fake = _patch_llm(monkeypatch, decision_fn=lambda body: "dialogue")
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response = invoke(
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InvokeRequest(
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run_id="r", node_instance_id="",
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inputs={"srt_uri": str(srt)},
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params={"overlay_tokens": ["cleaning"]},
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output_dir=str(tmp_path / "out"),
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)
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)
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assert response.status == "completed", response.error
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assert response.outputs["removed"] == 1
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assert response.outputs["kept"] == 1
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assert len(fake.bodies) == 1 # 只判正常对话。
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def test_invoke_overlay_tokens_json_string(monkeypatch, tmp_path) -> None:
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"""overlay_tokens 以 JSON 字符串形式传入（工作流参数常见形态）同样生效。"""
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srt = tmp_path / "in.srt"
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srt.write_text(
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"1\n00:00:01,000 --> 00:00:04,000\nXXLogo\n\n"
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"2\n00:00:05,000 --> 00:00:08,000\n正常对话\n",
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encoding="utf-8",
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)
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fake = _patch_llm(monkeypatch, ["dialogue"])
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response = invoke(
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||||
InvokeRequest(
|
||||
run_id="r", node_instance_id="",
|
||||
inputs={"srt_uri": str(srt)},
|
||||
params={"overlay_tokens": '["xxlogo", "xx"]'},
|
||||
output_dir=str(tmp_path / "out"),
|
||||
)
|
||||
)
|
||||
assert response.status == "completed", response.error
|
||||
assert response.outputs["removed"] == 1
|
||||
assert response.outputs["kept"] == 1
|
||||
assert len(fake.bodies) == 1
|
||||
|
||||
|
||||
def test_invoke_missing_input(tmp_path) -> None:
|
||||
"""缺少 srt_uri 时返回失败。"""
|
||||
response = invoke(
|
||||
@@ -255,3 +473,79 @@ def test_invoke_empty_srt(monkeypatch, tmp_path) -> None:
|
||||
assert response.outputs["kept"] == 0
|
||||
assert response.outputs["removed"] == 0
|
||||
assert fake.bodies == []
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 真实任务留存数据回归（testdata/ocr_srt_run_ac7f480a3ccb.srt）
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_real_run_rules_and_dialogue_regression(monkeypatch, tmp_path) -> None:
|
||||
"""真实数据回归：规则层清理垃圾、对话保留、时间轴单调、去重生效。
|
||||
|
||||
夹具为 run_ac7f480a3ccb（单线程 v4 跑完整 2 小时视频）的 OCR 输出 1666 条。
|
||||
假 LLM 对非规则条目一律答 dialogue：验证规则层删掉全部垃圾（横线/水印/
|
||||
单字符），真实对话全部保留，且相同文本只调一次 LLM（去重）。
|
||||
"""
|
||||
srt = REAL_SRT
|
||||
if not srt.is_file():
|
||||
pytest.skip("缺少 testdata/ocr_srt_run_ac7f480a3ccb.srt，跳过回归测试")
|
||||
fake = _patch_llm(monkeypatch, decision_fn=lambda body: "dialogue")
|
||||
response = invoke(
|
||||
InvokeRequest(
|
||||
run_id="r", node_instance_id="",
|
||||
inputs={"srt_uri": str(srt)},
|
||||
params={},
|
||||
output_dir=str(tmp_path / "out"),
|
||||
)
|
||||
)
|
||||
assert response.status == "completed", response.error
|
||||
|
||||
entries = parse_srt(srt.read_text(encoding="utf-8"))
|
||||
out = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8")
|
||||
|
||||
# 规则层删除量 = 夹具中直接命中规则文本的条数（真实数据动态计算）。
|
||||
# 规则层删除量 = 夹具中直接命中规则文本的条数（与 invoke 默认 token 集一致）。
|
||||
from nodes.llm_filter import DEFAULT_OVERLAY_TOKENS
|
||||
|
||||
tokens = set(DEFAULT_OVERLAY_TOKENS)
|
||||
rule_removed = sum(
|
||||
1 for e in entries if _rule_verdict(e["text"], tokens) is True
|
||||
)
|
||||
assert response.outputs["removed"] == rule_removed
|
||||
assert response.outputs["kept"] == len(entries) - rule_removed
|
||||
|
||||
# 垃圾全部清出输出（横线、HTML、单字符都不再出现；只检查文本字段，
|
||||
# 排除 SRT 的序号行与时间轴行）。
|
||||
for entry in parse_srt(out):
|
||||
assert _rule_verdict(entry["text"], tokens) is not True, entry["text"]
|
||||
# 真实对话保留（修复前被误删的自我介绍、请求对话、呻吟内容行）。
|
||||
# 注意：片头标题卡（淫魔病院…）真实 LLM 判为 overlay（烧录标题），
|
||||
# 不在必保留列表内；本测试假 LLM 一律答 dialogue，仅验证机制正确。
|
||||
for kept_line in (
|
||||
"谢谢你 松井小姐",
|
||||
"我姓泷本 请多多指教",
|
||||
"不这么做的话 可没法胜任患者的对象",
|
||||
"你要看着北冈小姐的脸 你们俩相互看看嘛",
|
||||
"好舒服",
|
||||
):
|
||||
assert kept_line in out, kept_line
|
||||
|
||||
# 时间轴单调递增（输出顺序即时间顺序）。
|
||||
times = []
|
||||
for m in __import__("re").finditer(
|
||||
r"(\d{2}:\d{2}:\d{2},\d{3})\s*-->", out
|
||||
):
|
||||
t = m.group(1).replace(",", ".")
|
||||
h, mi, s = t.split(":")
|
||||
times.append(int(h) * 3600 + int(mi) * 60 + float(s))
|
||||
assert all(a < b for a, b in zip(times, times[1:]))
|
||||
|
||||
# 去重：LLM 调用数 == 非规则条目中的唯一文本数（远小于条目数）。
|
||||
unique_llm = len({
|
||||
_dedup_key(e["text"])
|
||||
for e in entries
|
||||
if _rule_verdict(e["text"], tokens) is None
|
||||
})
|
||||
assert len(fake.bodies) == unique_llm
|
||||
assert len(fake.bodies) < len(entries) # 去重确实省调用。
|
||||
|
||||
Reference in New Issue
Block a user