"""Tests for SRT processing module Tests for: - SRT parser (parse_srt, parse_srt_file) - Semantic segmenter (SemanticSegmenter) - ASR corrector (ASRCorrector) - SRTProcessor (pipeline processor) """ import pytest from pathlib import Path from dataclasses import dataclass from typing import List, Optional # ============================================================================= # Test Fixtures # ============================================================================= @pytest.fixture def sample_srt_content() -> str: """标准 SRT 格式内容""" return """1 00:00:00,000 --> 00:00:05,000 第一句字幕内容 2 00:00:05,000 --> 00:00:10,000 第二句字幕内容 3 00:00:10,000 --> 00:00:15,000 第三句字幕内容 """ @pytest.fixture def sample_srt_with_special_chars() -> str: """包含特殊字符的 SRT 内容""" return """1 00:00:00,000 --> 00:00:05,000 你好，世界！这是测试。 2 00:00:05,000 --> 00:00:10,000 包含特殊字符：<>&"' 3 00:00:10,000 --> 00:00:15,000 多行 字幕内容 """ @pytest.fixture def invalid_srt_content() -> str: """格式错误的 SRT 内容""" return "这不是有效的 SRT 格式" @pytest.fixture def empty_content() -> str: """空内容""" return "" @pytest.fixture def sample_srt_file(tmp_path: Path, sample_srt_content: str) -> Path: """创建临时 SRT 文件""" srt_file = tmp_path / "test.srt" srt_file.write_text(sample_srt_content, encoding="utf-8") return srt_file # ============================================================================= # Test 1: SRT Parser - parse_srt # ============================================================================= class TestParseSrt: """测试 SRT 解析功能""" def test_parse_standard_srt(self, sample_srt_content: str): """Test 1: 标准的 SRT 格式能被正确解析为 Segment 列表""" from kb_workflow.srt.parser import parse_srt, Segment segments = parse_srt(sample_srt_content) assert len(segments) == 3 assert all(isinstance(s, Segment) for s in segments) # 验证第一个 segment assert segments[0].id == 1 assert segments[0].start_time == 0.0 assert segments[0].end_time == 5.0 assert segments[0].text == "第一句字幕内容" # 验证最后一个 segment assert segments[2].id == 3 assert segments[2].start_time == 10.0 assert segments[2].end_time == 15.0 assert segments[2].text == "第三句字幕内容" def test_parse_srt_with_special_chars(self, sample_srt_with_special_chars: str): """Test 2: 包含特殊字符的字幕能正确处理""" from kb_workflow.srt.parser import parse_srt segments = parse_srt(sample_srt_with_special_chars) assert len(segments) == 3 assert "你好，世界！" in segments[0].text assert "<>&\"'" in segments[1].text # 多行字幕应该合并 assert "多行" in segments[2].text def test_parse_invalid_srt_raises_error(self, invalid_srt_content: str): """Test 3: 空文件或格式错误的文件抛出 ValueError""" from kb_workflow.srt.parser import parse_srt with pytest.raises(ValueError, match="Invalid SRT format"): parse_srt(invalid_srt_content) def test_parse_empty_content_raises_error(self, empty_content: str): """Test 3b: 空内容抛出 ValueError""" from kb_workflow.srt.parser import parse_srt with pytest.raises(ValueError, match="Empty SRT content"): parse_srt(empty_content) # ============================================================================= # Test 1b: SRT Parser - parse_srt_file # ============================================================================= class TestParseSrtFile: """测试从文件解析 SRT""" def test_parse_srt_file(self, sample_srt_file: Path): """从文件解析 SRT""" from kb_workflow.srt.parser import parse_srt_file segments = parse_srt_file(sample_srt_file) assert len(segments) == 3 assert segments[0].text == "第一句字幕内容" def test_parse_nonexistent_file_raises_error(self): """不存在的文件抛出 FileNotFoundError""" from kb_workflow.srt.parser import parse_srt_file with pytest.raises(FileNotFoundError): parse_srt_file(Path("/nonexistent/file.srt")) # ============================================================================= # Test 2: Semantic Segmenter # ============================================================================= @pytest.fixture def short_segments(): """连续短句字幕分段""" from kb_workflow.srt.parser import Segment return [ Segment(id=1, start_time=0.0, end_time=2.0, text="这是第一句短句"), Segment(id=2, start_time=2.0, end_time=4.0, text="这是第二句短句"), Segment(id=3, start_time=4.0, end_time=6.0, text="这是第三句短句"), ] @pytest.fixture def segments_with_gap(): """带有长停顿的字幕分段""" from kb_workflow.srt.parser import Segment return [ Segment(id=1, start_time=0.0, end_time=5.0, text="第一段内容在这里"), Segment(id=2, start_time=5.0, end_time=10.0, text="继续第一段"), # 5秒停顿 Segment(id=3, start_time=15.0, end_time=20.0, text="第二段从这里开始"), Segment(id=4, start_time=20.0, end_time=25.0, text="继续第二段"), ] @pytest.fixture def long_text_segment(): """长文本分段（超过500字符）""" from kb_workflow.srt.parser import Segment long_text = "这是一段很长的文本，" * 50 # 约600字符 return [ Segment(id=1, start_time=0.0, end_time=60.0, text=long_text), ] class TestSemanticSegmenter: """测试语义分段器""" def test_merge_short_segments(self, short_segments): """Test 1: 连续短句被合并为段落""" from kb_workflow.srt.segmenter import SemanticSegmenter segmenter = SemanticSegmenter() paragraphs = segmenter.segment(short_segments) # 所有短句应该被合并 assert len(paragraphs) >= 1 # 验证所有内容都被保留 merged_text = ' '.join(paragraphs) assert "第一句短句" in merged_text assert "第二句短句" in merged_text assert "第三句短句" in merged_text def test_segment_on_topic_change(self, segments_with_gap): """Test 2: 主题变化时自动分段（基于时间间隔）""" from kb_workflow.srt.segmenter import SemanticSegmenter segmenter = SemanticSegmenter(gap_threshold=3.0) paragraphs = segmenter.segment(segments_with_gap) # 应该分成两段（因为中间有5秒停顿） assert len(paragraphs) == 2 assert "第一段" in paragraphs[0] assert "第二段" in paragraphs[1] def test_max_paragraph_length(self, long_text_segment): """Test 3: 单个段落不超过 500 字符""" from kb_workflow.srt.segmenter import SemanticSegmenter segmenter = SemanticSegmenter(max_paragraph_chars=500) paragraphs = segmenter.segment(long_text_segment) # 每个段落不应该超过限制 for para in paragraphs: assert len(para) <= 520 # 允许小范围超出（最后一块） def test_empty_segments(self): """空分段列表返回空列表""" from kb_workflow.srt.segmenter import SemanticSegmenter segmenter = SemanticSegmenter() paragraphs = segmenter.segment([]) assert paragraphs == []