040aada160
基于项目需求文档（PRD.md, FeatureSummary.md, DevelopmentPlan.md, UIDesign.md, User_Role_Interfaces.md）编写的 TDD 测试用例。 后端测试 (Python/pytest): - 单元测试: rule_engine, brief_parser, timestamp_alignment, video_auditor, validators - 集成测试: API Brief, Video, Review 端点 - AI 模块测试: ASR, OCR, Logo 检测服务 - 全局 fixtures 和 pytest 配置 前端测试 (TypeScript/Vitest): - 工具函数测试: utils.test.ts - 组件测试: Button, VideoPlayer, ViolationList - Hooks 测试: useVideoAudit, useVideoPlayer, useAppeal - MSW mock handlers 配置 E2E 测试 (Playwright): - 认证流程测试 - 视频上传流程测试 - 视频审核流程测试 - 申诉流程测试 所有测试当前使用 pytest.skip() / it.skip() 作为占位符， 遵循 TDD 红灯阶段 - 等待实现代码后运行。 验收标准覆盖: - ASR WER ≤ 10% - OCR 准确率 ≥ 95% - Logo F1 ≥ 0.85 - 时间戳误差 ≤ 0.5s - 频次统计准确率 ≥ 95% Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
328 lines
11 KiB
Python
328 lines
11 KiB
Python
"""
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ASR 服务单元测试
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TDD 测试用例 - 基于 DevelopmentPlan.md 的验收标准
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验收标准：
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- 字错率 (WER) ≤ 10%
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- 时间戳精度 ≤ 100ms
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"""
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import pytest
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from typing import Any
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# 导入待实现的模块（TDD 红灯阶段）
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# from app.services.ai.asr import ASRService, ASRResult, ASRSegment
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class TestASRService:
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"""ASR 服务测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_asr_service_initialization(self) -> None:
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"""测试 ASR 服务初始化"""
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# TODO: 实现 ASR 服务
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# service = ASRService()
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# assert service.is_ready()
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# assert service.model_name is not None
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pytest.skip("待实现：ASR 服务初始化")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_asr_transcribe_audio_file(self) -> None:
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"""测试音频文件转写"""
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# TODO: 实现音频转写
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# assert result.status == "success"
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# assert result.text is not None
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# assert len(result.text) > 0
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pytest.skip("待实现：音频转写")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_asr_output_format(self) -> None:
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"""测试 ASR 输出格式"""
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# TODO: 实现 ASR 服务
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# # 验证输出结构
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# assert hasattr(result, "text")
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# assert hasattr(result, "segments")
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# assert hasattr(result, "language")
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# assert hasattr(result, "duration_ms")
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#
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# # 验证 segment 结构
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# for segment in result.segments:
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# assert hasattr(segment, "text")
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# assert hasattr(segment, "start_ms")
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# assert hasattr(segment, "end_ms")
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# assert hasattr(segment, "confidence")
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# assert segment.end_ms >= segment.start_ms
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pytest.skip("待实现：ASR 输出格式")
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class TestASRAccuracy:
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"""ASR 准确率测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_word_error_rate_threshold(self) -> None:
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"""
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测试字错率阈值
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验收标准：WER ≤ 10%
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"""
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# TODO: 使用标注测试集验证
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# service = ASRService()
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# test_cases = load_asr_labeled_dataset()
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#
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# total_errors = 0
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# total_words = 0
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#
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# for case in test_cases:
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# result = service.transcribe(case["audio_path"])
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# wer = calculate_word_error_rate(
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# result.text,
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# case["ground_truth"]
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# )
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# total_errors += wer * len(case["ground_truth"])
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# total_words += len(case["ground_truth"])
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#
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# overall_wer = total_errors / total_words
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# assert overall_wer <= 0.10, f"WER {overall_wer:.2%} 超过阈值 10%"
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pytest.skip("待实现：WER 测试")
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@pytest.mark.ai
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@pytest.mark.unit
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@pytest.mark.parametrize("audio_type,expected_wer_threshold", [
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("clean_speech", 0.05), # 清晰语音 WER < 5%
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("background_music", 0.10), # 背景音乐 WER < 10%
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("multiple_speakers", 0.15), # 多人对话 WER < 15%
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("noisy_environment", 0.20), # 嘈杂环境 WER < 20%
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])
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def test_wer_by_audio_type(
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self,
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audio_type: str,
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expected_wer_threshold: float,
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) -> None:
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"""测试不同音频类型的 WER"""
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# TODO: 实现分类型 WER 测试
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# service = ASRService()
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# test_cases = load_asr_test_set_by_type(audio_type)
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#
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# wer = calculate_average_wer(service, test_cases)
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# assert wer <= expected_wer_threshold
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pytest.skip(f"待实现：{audio_type} WER 测试")
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class TestASRTimestamp:
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"""ASR 时间戳测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_monotonic_increase(self) -> None:
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"""测试时间戳单调递增"""
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# TODO: 实现时间戳验证
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# prev_end = 0
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# for segment in result.segments:
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# assert segment.start_ms >= prev_end, \
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# f"时间戳不是单调递增: {segment.start_ms} < {prev_end}"
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# prev_end = segment.end_ms
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pytest.skip("待实现：时间戳单调递增")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_precision(self) -> None:
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"""
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测试时间戳精度
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验收标准：精度 ≤ 100ms
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"""
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# TODO: 使用标注测试集验证
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# service = ASRService()
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# test_cases = load_timestamp_labeled_dataset()
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#
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# total_error = 0
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# total_segments = 0
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#
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# for case in test_cases:
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# result = service.transcribe(case["audio_path"])
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# for i, segment in enumerate(result.segments):
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# if i < len(case["ground_truth_timestamps"]):
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# gt = case["ground_truth_timestamps"][i]
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# start_error = abs(segment.start_ms - gt["start_ms"])
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# end_error = abs(segment.end_ms - gt["end_ms"])
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# total_error += (start_error + end_error) / 2
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# total_segments += 1
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#
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# avg_error = total_error / total_segments if total_segments > 0 else 0
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# assert avg_error <= 100, f"平均时间戳误差 {avg_error:.0f}ms 超过阈值 100ms"
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pytest.skip("待实现：时间戳精度测试")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_within_audio_duration(self) -> None:
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"""测试时间戳在音频时长范围内"""
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# TODO: 实现边界验证
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# for segment in result.segments:
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# assert segment.start_ms >= 0
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# assert segment.end_ms <= result.duration_ms
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pytest.skip("待实现：时间戳边界验证")
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class TestASRLanguage:
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"""ASR 语言处理测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_chinese_mandarin_recognition(self) -> None:
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"""测试普通话识别"""
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# TODO: 实现普通话测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/mandarin.wav")
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#
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# assert result.language == "zh-CN"
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# assert "你好" in result.text or len(result.text) > 0
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pytest.skip("待实现：普通话识别")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_mixed_language_handling(self) -> None:
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"""测试中英混合语音处理"""
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# TODO: 实现混合语言测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/mixed_cn_en.wav")
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#
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# # 应能识别中英文混合内容
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# assert result.status == "success"
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pytest.skip("待实现：中英混合识别")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_dialect_handling(self) -> None:
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"""测试方言处理"""
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# TODO: 实现方言测试
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# service = ASRService()
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#
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# # 方言可能降级处理或提示
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# result = service.transcribe("tests/fixtures/audio/cantonese.wav")
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#
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# if result.status == "success":
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# assert result.language in ["zh-CN", "zh-HK", "yue"]
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# else:
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# assert result.warning == "dialect_detected"
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pytest.skip("待实现：方言处理")
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class TestASRSpecialCases:
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"""ASR 特殊情况测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_silent_audio(self) -> None:
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"""测试静音音频"""
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# TODO: 实现静音测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/silent.wav")
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#
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# assert result.status == "success"
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# assert result.text == "" or result.segments == []
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pytest.skip("待实现：静音音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_very_short_audio(self) -> None:
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"""测试极短音频 (< 1秒)"""
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# TODO: 实现极短音频测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/short_500ms.wav")
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#
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# assert result.status == "success"
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pytest.skip("待实现：极短音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_long_audio(self) -> None:
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"""测试长音频 (> 5分钟)"""
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# TODO: 实现长音频测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/long_10min.wav")
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#
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# assert result.status == "success"
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# assert result.duration_ms >= 600000 # 10分钟
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pytest.skip("待实现：长音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_corrupted_audio_handling(self) -> None:
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"""测试损坏音频处理"""
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# TODO: 实现错误处理测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/corrupted.wav")
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#
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# assert result.status == "error"
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# assert "corrupted" in result.error_message.lower() or \
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# "invalid" in result.error_message.lower()
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pytest.skip("待实现：损坏音频处理")
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class TestASRPerformance:
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"""ASR 性能测试"""
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@pytest.mark.ai
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@pytest.mark.performance
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def test_transcription_speed(self) -> None:
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"""
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测试转写速度
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验收标准：实时率 ≤ 0.5 (转写时间 / 音频时长)
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"""
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# TODO: 实现性能测试
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# import time
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#
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# service = ASRService()
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#
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# # 60秒测试音频
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# start_time = time.time()
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# result = service.transcribe("tests/fixtures/audio/60s_sample.wav")
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# processing_time = time.time() - start_time
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#
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# audio_duration = result.duration_ms / 1000
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# real_time_factor = processing_time / audio_duration
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#
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# assert real_time_factor <= 0.5, \
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# f"实时率 {real_time_factor:.2f} 超过阈值 0.5"
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pytest.skip("待实现：转写速度测试")
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@pytest.mark.ai
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@pytest.mark.performance
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def test_concurrent_transcription(self) -> None:
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"""测试并发转写"""
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# TODO: 实现并发测试
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# import asyncio
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#
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# service = ASRService()
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#
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# async def transcribe_one(audio_path: str):
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# return await service.transcribe_async(audio_path)
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#
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# # 并发处理 5 个音频
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# tasks = [
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# transcribe_one(f"tests/fixtures/audio/sample_{i}.wav")
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# for i in range(5)
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# ]
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# results = await asyncio.gather(*tasks)
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#
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# assert all(r.status == "success" for r in results)
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pytest.skip("待实现：并发转写测试")
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