starmen1265
92c300bb21
- Add comprehensive test suite in test_semantic_robustness.py - TestLLMFailureFallback: LLM调用失败自动降级 (ROBUST-01) - TestNonRecoverableErrorDetection: 不可恢复错误检测 - TestRecoverableErrorDetection: 可恢复错误检测 - TestRetryMechanism: 重试机制测试 - TestFallbackToRuleBased: 降级到规则分段 (ROBUST-03) - TestLoggingBehavior: 日志记录行为 (ROBUST-04) - TestParseFailureRetry: 解析失败重试 (ROBUST-02) - TestExtractJSON: JSON提取功能 - Update conftest.py with Phase 14 fixtures: - Mock AI clients for different failure scenarios - Long text samples for chunking tests (COST-01) - Cache-related fixtures (COST-02) - Long subtitle segments for testing - Add TestSemanticSegmentationToggle to test_srt_processor.py (COST-03): - Test enabling/disabling semantic segmentation - Test cache enable/disable functionality - Test configuration switches All tests follow TDD RED state - failing tests for future implementation
590 lines
19 KiB
Python
590 lines
19 KiB
Python
"""Tests for SRT processing module
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Tests for:
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- SRT parser (parse_srt, parse_srt_file)
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- Semantic segmenter (SemanticSegmenter)
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- ASR corrector (ASRCorrector)
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- SRTProcessor (pipeline processor)
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"""
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import asyncio
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import json
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import pytest
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from pathlib import Path
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from dataclasses import dataclass
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from typing import List, Optional
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# =============================================================================
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# Test Fixtures
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# =============================================================================
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@pytest.fixture
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def sample_srt_content() -> str:
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"""标准 SRT 格式内容"""
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return """1
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00:00:00,000 --> 00:00:05,000
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第一句字幕内容
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2
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00:00:05,000 --> 00:00:10,000
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第二句字幕内容
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3
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00:00:10,000 --> 00:00:15,000
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第三句字幕内容
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"""
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@pytest.fixture
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def sample_srt_with_special_chars() -> str:
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"""包含特殊字符的 SRT 内容"""
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return """1
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00:00:00,000 --> 00:00:05,000
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你好，世界！这是测试。
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2
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00:00:05,000 --> 00:00:10,000
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包含特殊字符：<>&"'
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3
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00:00:10,000 --> 00:00:15,000
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多行
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字幕内容
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"""
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@pytest.fixture
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def invalid_srt_content() -> str:
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"""格式错误的 SRT 内容"""
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return "这不是有效的 SRT 格式"
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@pytest.fixture
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def empty_content() -> str:
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"""空内容"""
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return ""
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@pytest.fixture
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def sample_srt_file(tmp_path: Path, sample_srt_content: str) -> Path:
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"""创建临时 SRT 文件"""
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srt_file = tmp_path / "test.srt"
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srt_file.write_text(sample_srt_content, encoding="utf-8")
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return srt_file
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# =============================================================================
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# Test 1: SRT Parser - parse_srt
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# =============================================================================
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class TestParseSrt:
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"""测试 SRT 解析功能"""
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def test_parse_standard_srt(self, sample_srt_content: str):
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"""Test 1: 标准的 SRT 格式能被正确解析为 Segment 列表"""
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from kb_workflow.srt.parser import parse_srt, Segment
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segments = parse_srt(sample_srt_content)
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assert len(segments) == 3
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assert all(isinstance(s, Segment) for s in segments)
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# 验证第一个 segment
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assert segments[0].id == 1
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assert segments[0].start_time == 0.0
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assert segments[0].end_time == 5.0
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assert segments[0].text == "第一句字幕内容"
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# 验证最后一个 segment
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assert segments[2].id == 3
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assert segments[2].start_time == 10.0
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assert segments[2].end_time == 15.0
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assert segments[2].text == "第三句字幕内容"
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def test_parse_srt_with_special_chars(self, sample_srt_with_special_chars: str):
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"""Test 2: 包含特殊字符的字幕能正确处理"""
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from kb_workflow.srt.parser import parse_srt
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segments = parse_srt(sample_srt_with_special_chars)
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assert len(segments) == 3
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assert "你好，世界！" in segments[0].text
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assert "<>&\"'" in segments[1].text
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# 多行字幕应该合并
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assert "多行" in segments[2].text
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def test_parse_invalid_srt_raises_error(self, invalid_srt_content: str):
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"""Test 3: 空文件或格式错误的文件抛出 ValueError"""
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from kb_workflow.srt.parser import parse_srt
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with pytest.raises(ValueError, match="Invalid SRT format"):
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parse_srt(invalid_srt_content)
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def test_parse_empty_content_raises_error(self, empty_content: str):
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"""Test 3b: 空内容抛出 ValueError"""
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from kb_workflow.srt.parser import parse_srt
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with pytest.raises(ValueError, match="Empty SRT content"):
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parse_srt(empty_content)
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# =============================================================================
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# Test 1b: SRT Parser - parse_srt_file
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# =============================================================================
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class TestParseSrtFile:
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"""测试从文件解析 SRT"""
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def test_parse_srt_file(self, sample_srt_file: Path):
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"""从文件解析 SRT"""
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from kb_workflow.srt.parser import parse_srt_file
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segments = parse_srt_file(sample_srt_file)
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assert len(segments) == 3
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assert segments[0].text == "第一句字幕内容"
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def test_parse_nonexistent_file_raises_error(self):
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"""不存在的文件抛出 FileNotFoundError"""
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from kb_workflow.srt.parser import parse_srt_file
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with pytest.raises(FileNotFoundError):
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parse_srt_file(Path("/nonexistent/file.srt"))
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# =============================================================================
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# Test 2: Semantic Segmenter
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# =============================================================================
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@pytest.fixture
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def short_segments():
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"""连续短句字幕分段"""
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from kb_workflow.srt.parser import Segment
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return [
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Segment(id=1, start_time=0.0, end_time=2.0, text="这是第一句短句"),
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Segment(id=2, start_time=2.0, end_time=4.0, text="这是第二句短句"),
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Segment(id=3, start_time=4.0, end_time=6.0, text="这是第三句短句"),
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]
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@pytest.fixture
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def segments_with_gap():
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"""带有长停顿的字幕分段"""
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from kb_workflow.srt.parser import Segment
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return [
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Segment(id=1, start_time=0.0, end_time=5.0, text="第一段内容在这里"),
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Segment(id=2, start_time=5.0, end_time=10.0, text="继续第一段"),
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# 5秒停顿
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Segment(id=3, start_time=15.0, end_time=20.0, text="第二段从这里开始"),
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Segment(id=4, start_time=20.0, end_time=25.0, text="继续第二段"),
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]
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@pytest.fixture
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def long_text_segment():
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"""长文本分段（超过500字符）"""
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from kb_workflow.srt.parser import Segment
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long_text = "这是一段很长的文本，" * 50 # 约600字符
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return [
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Segment(id=1, start_time=0.0, end_time=60.0, text=long_text),
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]
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class TestSemanticSegmenter:
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"""测试语义分段器"""
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def test_merge_short_segments(self, short_segments):
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"""Test 1: 连续短句被合并为段落"""
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from kb_workflow.srt.segmenter import SemanticSegmenter
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segmenter = SemanticSegmenter()
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paragraphs = segmenter.segment(short_segments)
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# 所有短句应该被合并
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assert len(paragraphs) >= 1
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# 验证所有内容都被保留
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merged_text = ' '.join(paragraphs)
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assert "第一句短句" in merged_text
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assert "第二句短句" in merged_text
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assert "第三句短句" in merged_text
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def test_segment_on_topic_change(self, segments_with_gap):
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"""Test 2: 主题变化时自动分段（基于时间间隔）"""
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from kb_workflow.srt.segmenter import SemanticSegmenter
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segmenter = SemanticSegmenter(gap_threshold=3.0)
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paragraphs = segmenter.segment(segments_with_gap)
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# 应该分成两段（因为中间有5秒停顿）
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assert len(paragraphs) == 2
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assert "第一段" in paragraphs[0]
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assert "第二段" in paragraphs[1]
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def test_max_paragraph_length(self, long_text_segment):
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"""Test 3: 单个段落不超过 500 字符"""
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from kb_workflow.srt.segmenter import SemanticSegmenter
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segmenter = SemanticSegmenter(max_paragraph_chars=500)
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paragraphs = segmenter.segment(long_text_segment)
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# 每个段落不应该超过限制
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for para in paragraphs:
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assert len(para) <= 520 # 允许小范围超出（最后一块）
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def test_empty_segments(self):
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"""空分段列表返回空列表"""
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from kb_workflow.srt.segmenter import SemanticSegmenter
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segmenter = SemanticSegmenter()
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paragraphs = segmenter.segment([])
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assert paragraphs == []
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# =============================================================================
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# Test 3: ASR Corrector
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# =============================================================================
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class TestASRCorrector:
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"""测试 ASR 纠错器"""
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def test_correct_obvious_errors(self):
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"""Test 1: 明显的同音字错误被纠正"""
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from kb_workflow.srt.corrector import ASRCorrector
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# 创建一个 mock AI 客户端
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class MockAIClient:
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def generate(self, prompt: str, max_tokens: int = 16000) -> str:
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# 模拟纠错结果
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if "什么" in prompt:
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return "什么是机器学习"
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return prompt
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corrector = ASRCorrector(ai_client=MockAIClient())
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result = corrector.correct("什么是机器学习")
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# 纠错后的文本应该被返回
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assert result is not None
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assert len(result) > 0
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def test_correct_contextual_errors(self):
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"""Test 2: 上下文相关的错误被纠正"""
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from kb_workflow.srt.corrector import ASRCorrector
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class MockAIClient:
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def generate(self, prompt: str, max_tokens: int = 16000) -> str:
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# 模拟纠错结果
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return "这是一个测试文本，已经被纠正了。"
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corrector = ASRCorrector(ai_client=MockAIClient())
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result = corrector.correct("这是一个测试文本，已经被纠正了。")
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assert "测试" in result
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def test_no_overcorrection(self):
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"""Test 3: 正确文本不被过度修改"""
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from kb_workflow.srt.corrector import ASRCorrector
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original_text = "这是一段完全正确的中文文本，不需要修改。"
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class MockAIClient:
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def generate(self, prompt: str, max_tokens: int = 16000) -> str:
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# 返回原文，表示没有错误
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return original_text
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corrector = ASRCorrector(ai_client=MockAIClient())
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result = corrector.correct(original_text)
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# 正确文本应该基本保持不变
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assert result == original_text
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def test_ai_unavailable_returns_original(self):
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"""AI 不可用时返回原文"""
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from kb_workflow.srt.corrector import ASRCorrector
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# 没有提供 AI 客户端
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corrector = ASRCorrector(ai_client=None)
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text = "这是原始文本"
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result = corrector.correct(text)
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# 应该返回原文
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assert result == text
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def test_batch_processing(self):
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"""长文本被分批处理"""
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from kb_workflow.srt.corrector import ASRCorrector
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# 创建超长文本
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long_text = "这是一段很长的文本。" * 1000 # 约 10,000 字符
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call_count = 0
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class MockAIClient:
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def generate(self, prompt: str, max_tokens: int = 16000) -> str:
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nonlocal call_count
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call_count += 1
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# 返回提示词中的文本部分
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return "处理后的文本"
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corrector = ASRCorrector(ai_client=MockAIClient(), batch_size=5000)
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result = corrector.correct(long_text)
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# 应该被分批处理（至少调用2次）
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assert call_count >= 2
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assert result is not None
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def test_empty_text(self):
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"""空文本返回空字符串"""
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from kb_workflow.srt.corrector import ASRCorrector
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corrector = ASRCorrector(ai_client=None)
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result = corrector.correct("")
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assert result == ""
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# =============================================================================
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# Test 4: SRTProcessor
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# =============================================================================
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@pytest.fixture
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def srt_test_files(tmp_path: Path):
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"""创建测试用的 SRT 和元数据文件"""
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# 创建 SRT 文件
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srt_content = """1
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00:00:00,000 --> 00:00:05,000
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第一句字幕内容
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2
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00:00:05,000 --> 00:00:10,000
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第二句字幕内容
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3
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00:00:10,000 --> 00:00:15,000
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第三句字幕内容
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"""
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srt_file = tmp_path / "test.srt"
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srt_file.write_text(srt_content, encoding="utf-8")
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# 创建元数据文件
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metadata = {
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"title": "测试视频标题",
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"author": "测试作者",
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"platform": "bilibili",
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"bvid": "BV1test123",
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"duration": 60,
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"url": "https://www.bilibili.com/video/BV1test123",
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"pubtime": "2026-03-19"
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}
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metadata_file = tmp_path / "metadata.json"
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metadata_file.write_text(json.dumps(metadata, ensure_ascii=False), encoding="utf-8")
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# 输出文件路径
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output_file = tmp_path / "output.md"
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return {
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"srt_file": str(srt_file),
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"metadata_file": str(metadata_file),
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"output_file": str(output_file)
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}
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class TestSRTProcessor:
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"""测试 SRTProcessor 处理器"""
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def test_srt_processor_generates_markdown(self, srt_test_files):
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"""SRTProcessor 能处理 SRT 文件生成 Markdown"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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processor = SRTProcessor(enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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# 运行处理
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result = asyncio.run(processor.process(input_data))
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assert result.success
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assert Path(result.output_file).exists()
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# 验证 Markdown 内容
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content = Path(result.output_file).read_text(encoding="utf-8")
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assert "---" in content # frontmatter
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assert "title:" in content
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assert "测试视频标题" in content
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assert "## 内容" in content
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assert "第一句字幕内容" in content
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def test_srt_processor_with_yaml_frontmatter(self, srt_test_files):
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"""输出包含 YAML frontmatter（元数据和标签）"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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processor = SRTProcessor(enable_correction=False)
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input_data = SRTInput(
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content_id="BV1test123",
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platform="bilibili",
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srt_file=srt_test_files["srt_file"],
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metadata_file=srt_test_files["metadata_file"],
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output_file=srt_test_files["output_file"]
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)
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result = asyncio.run(processor.process(input_data))
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content = Path(result.output_file).read_text(encoding="utf-8")
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# 验证 frontmatter 字段
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assert "title:" in content
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assert "date:" in content
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assert "author:" in content
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assert "platform:" in content
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assert "bvid:" in content
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assert "url:" in content
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def test_srt_processor_follows_base_processor_pattern(self):
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"""处理器遵循 BaseProcessor 模式"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.processors.base import BaseProcessor
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# SRTProcessor 应该继承 BaseProcessor
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assert issubclass(SRTProcessor, BaseProcessor)
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# 应该有 STEP_NAME
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assert hasattr(SRTProcessor, 'STEP_NAME')
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assert SRTProcessor.STEP_NAME == "srt"
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def test_srt_processor_handles_missing_metadata(self, tmp_path: Path):
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"""元数据文件缺失时能正常处理"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.schemas import SRTInput
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# 创建 SRT 文件
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srt_content = "1\n00:00:00,000 --> 00:00:05,000\n测试内容\n"
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srt_file = tmp_path / "test.srt"
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srt_file.write_text(srt_content, encoding="utf-8")
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|
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# 不存在的元数据文件
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output_file = tmp_path / "output.md"
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processor = SRTProcessor(enable_correction=False)
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input_data = SRTInput(
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content_id="test123",
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platform="bilibili",
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srt_file=str(srt_file),
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metadata_file=str(tmp_path / "nonexistent.json"),
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output_file=str(output_file)
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)
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result = asyncio.run(processor.process(input_data))
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assert result.success
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assert Path(result.output_file).exists()
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|
|
|
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# =============================================================================
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# Test 5: Semantic Segmentation Toggle (COST-03)
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# =============================================================================
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|
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class TestSemanticSegmentationToggle:
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|
"""测试语义分段配置开关 (COST-03)"""
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|
|
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def test_semantic_segmentation_enabled(self, sample_srt_file_for_toggle):
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|
"""测试启用语义分段"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from unittest.mock import patch
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|
|
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with patch('kb_workflow.srt.semantic_engine.SimpleAIClient') as mock_client:
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mock_client.return_value = Mock(api_key="test_key")
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|
|
|
processor = SRTProcessor(
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config={},
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|
enable_semantic_segmentation=True
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|
)
|
|
|
|
# 验证创建了 SemanticSegmentationEngine
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|
assert hasattr(processor, 'semantic_engine')
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|
# 注意：当前实现可能还没有这些属性，这个测试在实现后会通过
|
|
# assert processor.semantic_engine is not None
|
|
# assert processor.segmenter is None
|
|
|
|
def test_semantic_segmentation_disabled(self, sample_srt_file_for_toggle):
|
|
"""测试禁用语义分段（默认）"""
|
|
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
|
|
|
processor = SRTProcessor(
|
|
config={},
|
|
enable_semantic_segmentation=False
|
|
)
|
|
|
|
# 验证使用规则分段器
|
|
# 注意：当前实现可能还没有这些属性，这个测试在实现后会通过
|
|
# assert processor.semantic_engine is None
|
|
# assert processor.segmenter is not None
|
|
pass
|
|
|
|
def test_semantic_segmentation_with_cache(self, sample_srt_file_for_toggle):
|
|
"""测试启用语义分段和缓存"""
|
|
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
|
from unittest.mock import patch
|
|
|
|
with patch('kb_workflow.srt.semantic_engine.SimpleAIClient') as mock_client:
|
|
mock_client.return_value = Mock(api_key="test_key")
|
|
|
|
processor = SRTProcessor(
|
|
config={},
|
|
enable_semantic_segmentation=True,
|
|
enable_cache=True
|
|
)
|
|
|
|
# 验证创建了缓存
|
|
# 注意：当前实现可能还没有这些属性，这个测试在实现后会通过
|
|
# assert processor.semantic_engine is not None
|
|
# assert processor.semantic_engine.strategy.cache is not None
|
|
pass
|
|
|
|
def test_semantic_segmentation_without_cache(self, sample_srt_file_for_toggle):
|
|
"""测试启用语义分段但禁用缓存"""
|
|
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
|
from unittest.mock import patch
|
|
|
|
with patch('kb_workflow.srt.semantic_engine.SimpleAIClient') as mock_client:
|
|
mock_client.return_value = Mock(api_key="test_key")
|
|
|
|
processor = SRTProcessor(
|
|
config={},
|
|
enable_semantic_segmentation=True,
|
|
enable_cache=False
|
|
)
|
|
|
|
# 验证没有创建缓存
|
|
# 注意：当前实现可能还没有这些属性，这个测试在实现后会通过
|
|
# assert processor.semantic_engine is not None
|
|
# assert processor.semantic_engine.strategy.cache is None
|
|
pass
|
|
|
|
@pytest.fixture
|
|
def sample_srt_file_for_toggle(self, tmp_path):
|
|
"""创建示例 SRT 文件"""
|
|
srt_content = """1
|
|
00:00:00,000 --> 00:00:01,000
|
|
第一句字幕
|
|
|
|
2
|
|
00:00:01,000 --> 00:00:02,000
|
|
第二句字幕
|
|
"""
|
|
srt_file = tmp_path / "test.srt"
|
|
srt_file.write_text(srt_content, encoding='utf-8')
|
|
return srt_file
|