cat-shark
8a715a8064
按"测试规则"重写 tests/：一个模块一个目录、用例按数据→过程→验证三段书写、 不保留全局 conftest.py、测试过程只调用真实生产代码。 结构（73 个文件、30 个模块目录、477 用例）： - tests/nodes/ 15 个模块目录（srt/whisper/ass/ffmpeg/frame_extract/vlm/ subtitle_ocr/llm/llm_filter/subtitle_cleanup/subtitle_correction/ proper_nouns/adaptive_pool/vad_profiler/echo）； - tests/app/ 11 个模块目录（db/scheduler/batch/maintenance/registry/seed/ storage/config/logging/main/routers 三组 API）； - tests/sdk/test_models、tests/web/test_crop、tests/shared（公共设施）。 测试数据随模块目录入库（tests/**/data/），删除根级 testdata/；.gitignore 的 data/ 改为 /data/，否则会连带忽略 tests/**/data/ 导致测试数据无法入库。 顺带发现并修复三个真实缺陷： - nodes/srt.py：相邻条目缺少空行时把下一条时间轴吞进正文（静默错位）， 改为正文行遇时间戳行即报错； - src/wov_app/scheduler.py：_file_size 只捕获 OSError，含 \x00 的产物 URI 抛 ValueError 导致任务误判失败，改为同时捕获； - nodes/subtitle_correction.py：生产代码依赖测试包解析 SRT， 改用生产模块 nodes/srt.py。 真实模型/服务集成测试按外部状态跳过：新增 tests/shared/gpu_memory.py （运行时探测显存、CUDA OOM 转跳过）与 tests/shared/llm_service.py （无 Key / 余额 / 限流转跳过）。全量 477 passed。
249 lines
8.9 KiB
Python
249 lines
8.9 KiB
Python
"""nodes/subtitle_cleanup.py 的模块级测试（数据 → 测试过程 → 验证结果）。
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被测模块：`nodes/subtitle_cleanup.py`（幻觉整条删除 + 短呻吟过滤），被
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whisper（日语链路）与 llm-translate（中文链路）复用，纯函数可独立调用。
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每个用例构造真实 SRT 文本，调用真实清理函数，并解析输出验证时间轴与序号。
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"""
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from __future__ import annotations
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from nodes.subtitle_cleanup import (
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DEFAULT_MOAN_MAX_CHARS,
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HALLUCINATION_TOKENS,
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JAPANESE_HALLUCINATION_TOKENS,
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clean_japanese_hallucinations,
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clean_srt_text,
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remove_hallucination_entries,
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remove_short_moan_entries,
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)
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from tests.shared.srt_entries import parse_srt_entries
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def _srt(*cues: tuple[str, str, str]) -> str:
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"""把 (起始, 结束, 文本) 列表拼成标准 SRT 文本，供各用例作为输入数据。"""
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blocks = [
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f"{i}\n{start} --> {end}\n{text}\n"
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for i, (start, end, text) in enumerate(cues, 1)
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]
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return "\n".join(blocks)
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# ---------------------------------------------------------------------------
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# 幻觉整条删除（中文词表 / 日语词表）
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# ---------------------------------------------------------------------------
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def test_removes_long_hallucination_cue_entirely() -> None:
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"""展示时长达到阈值的寒暄幻觉整条删除（时间轴不残留空 cue）。"""
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# 数据：一条 20s 的"谢谢观看"（超过默认 15s 阈值）。
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text = _srt(
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("00:00:01,000", "00:00:02,000", "真实的对话"),
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("00:00:03,000", "00:00:23,000", "谢谢观看"),
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)
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# 测试过程
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cleaned = clean_srt_text(text)
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# 验证结果：只剩真实对话，序号重排为 1，幻想的行完全消失。
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entries = parse_srt_entries(cleaned)
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assert [e["text"] for e in entries] == ["真实的对话"]
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assert cleaned.startswith("1\n")
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assert "谢谢观看" not in cleaned
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def test_keeps_short_hallucination_when_inside_threshold() -> None:
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"""展示时长低于阈值的相同词可能是剧情真实内容，必须保留。"""
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# 数据：一条 2s 的"晚安"（剧情中真实互道晚安）。
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text = _srt(("00:00:01,000", "00:00:03,000", "晚安"))
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# 测试过程
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cleaned = clean_srt_text(text)
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# 验证结果：保留。
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["晚安"]
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def test_keeps_non_hallucination_long_cue() -> None:
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"""长时但不是幻觉词的内容必须保留（只按词表删除）。"""
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# 数据：一条 30s 的正常长台词。
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text = _srt(("00:00:01,000", "00:00:31,000", "这是一段很长的真实独白内容"))
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# 测试过程
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cleaned = clean_srt_text(text)
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# 验证结果
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assert "这是一段很长的真实独白内容" in cleaned
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def test_threshold_boundary_is_inclusive() -> None:
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"""阈值边界按"≥ 阈值"删除（严格等于阈值即删除）。"""
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# 数据：恰好 15s 的幻觉条目与 14.999s 的同类条目。
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text = _srt(
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("00:00:00,000", "00:00:15,000", "谢谢观看"),
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("00:00:16,000", "00:00:30,999", "感谢观看"),
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)
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# 测试过程
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cleaned = clean_srt_text(text, threshold_seconds=15.0)
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# 验证结果：15s 的被删除，14.999s 的保留。
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kept = [e["text"] for e in parse_srt_entries(cleaned)]
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assert kept == ["感谢观看"]
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def test_resequences_after_middle_removal() -> None:
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"""删除中间条目后剩余条目从 1 连续编号，保持合法 SRT。"""
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# 数据：三条，中间一条是长时幻觉。
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text = _srt(
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("00:00:01,000", "00:00:02,000", "第一条"),
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("00:00:03,000", "00:00:25,000", "谢谢观看"),
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("00:00:26,000", "00:00:27,000", "第三条"),
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)
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# 测试过程
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cleaned = clean_srt_text(text)
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# 验证结果：序号连续且内容为第一、三条。
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assert cleaned.splitlines()[0] == "1"
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assert "\n2\n" in cleaned
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["第一条", "第三条"]
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def test_japanese_hallucination_removed_and_short_kept() -> None:
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"""日语词表：长时"おやすみなさい"删除，短时保留。"""
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# 数据：一条 30s 日语幻觉 + 一条 3s 同词。
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text = _srt(
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("00:01:00,000", "00:01:30,000", "おやすみなさい"),
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("00:02:00,000", "00:02:03,000", "おやすみなさい"),
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)
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# 测试过程
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cleaned = clean_japanese_hallucinations(text)
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# 验证结果：只保留短的那条。
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entries = parse_srt_entries(cleaned)
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assert len(entries) == 1
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assert entries[0]["start"] == 120.0
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def test_custom_token_list_is_honored() -> None:
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"""自定义词表生效：只删除传入词命中的条目。"""
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# 数据：两个不同的长时条目。
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text = _srt(
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("00:00:01,000", "00:00:20,000", "自定义幻觉词"),
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("00:00:21,000", "00:00:40,000", "谢谢观看"),
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)
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# 测试过程：只传"自定义幻觉词"。
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cleaned = remove_hallucination_entries(text, ("自定义幻觉词",))
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# 验证结果：只删除自定义词，"谢谢观看"保留。
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["谢谢观看"]
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def test_default_tables_are_not_empty() -> None:
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"""两张默认词表都非空（防止重构时误清空导致清理失效）。"""
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# 数据：模块导出的词表常量。
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# 测试过程与验证结果
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assert len(HALLUCINATION_TOKENS) > 0
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assert len(JAPANESE_HALLUCINATION_TOKENS) > 0
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# ---------------------------------------------------------------------------
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# 短呻吟过滤（decode_full 去噪）
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# ---------------------------------------------------------------------------
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def test_removes_pure_moan_fragments() -> None:
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"""纯呻吟碎片（あ…/ん?/はぁ…）整条删除。"""
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# 数据：三条纯呻吟与一条真实短对话。
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text = _srt(
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("00:00:01,000", "00:00:02,000", "あ…"),
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("00:00:03,000", "00:00:04,000", "ん?"),
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("00:00:05,000", "00:00:06,000", "はぁ…"),
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("00:00:07,000", "00:00:09,000", "そこ、だめ"),
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)
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# 测试过程
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cleaned = remove_short_moan_entries(text)
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# 验证结果：只剩真实短对话。
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["そこ、だめ"]
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def test_keeps_real_short_dialogue() -> None:
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"""含真实假名（そ/や/ね）的短对话不命中判据，必须保留。"""
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# 数据：四条真实短对话。
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dialog = ["そこ", "やばい", "ねえ", "やだ"]
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# 测试过程
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cleaned = remove_short_moan_entries(_srt(*[
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(f"00:00:0{i},000", f"00:00:0{i + 1},000", text)
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for i, text in enumerate(dialog, 1)
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]))
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# 验证结果：全部保留。
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assert [e["text"] for e in parse_srt_entries(cleaned)] == dialog
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def test_moan_threshold_boundary() -> None:
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"""有效假名数超过阈值（默认 3）的纯呻吟串保留，等于阈值的删除。"""
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# 数据：3 个假名（删除）与 4 个假名（保留）。
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text = _srt(
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("00:00:01,000", "00:00:02,000", "あんあ"),
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("00:00:03,000", "00:00:04,000", "あんあん"),
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)
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# 测试过程
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cleaned = remove_short_moan_entries(text, max_chars=3)
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# 验证结果
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["あんあん"]
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def test_moan_default_max_chars_constant() -> None:
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"""默认阈值常量为 3（与文档约定一致，改动需同步文档）。"""
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# 数据：模块常量。
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# 测试过程与验证结果
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assert DEFAULT_MOAN_MAX_CHARS == 3
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def test_moan_filter_can_be_disabled() -> None:
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"""max_chars=0 时关闭过滤，输入原样返回。"""
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# 数据：一条纯呻吟。
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text = _srt(("00:00:01,000", "00:00:02,000", "あ…"))
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# 测试过程与验证结果
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assert remove_short_moan_entries(text, max_chars=0) == text
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def test_moan_removal_in_middle_resequences() -> None:
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"""删除中间呻吟后剩余条目序号连续。"""
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# 数据：真实对话、呻吟、真实对话。
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text = _srt(
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("00:00:01,000", "00:00:02,000", "行くよ"),
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("00:00:03,000", "00:00:04,000", "ん…"),
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("00:00:05,000", "00:00:06,000", "だめ"),
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)
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# 测试过程
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cleaned = remove_short_moan_entries(text)
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# 验证结果
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["行くよ", "だめ"]
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assert "\n2\n" in cleaned
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def test_multiline_moan_entry_removed_as_one_cue() -> None:
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"""多行呻吟条目整体作为一条 cue 删除（不残留半条）。"""
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# 数据：两行纯呻吟组成一条 cue。
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text = "1\n00:00:01,000 --> 00:00:03,000\nあ…\nん…\n\n2\n00:00:04,000 --> 00:00:05,000\nそこ\n"
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# 测试过程
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cleaned = remove_short_moan_entries(text)
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# 验证结果：只剩第二条并重编号。
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assert [e["text"] for e in parse_srt_entries(cleaned)] == ["そこ"]
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assert cleaned.startswith("1\n")
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