kb-skills/.planning/phases/12-srt-simplifier/12-01-SUMMARY.md

phase, plan, subsystem, tags, dependencies, tech_stack, key_files, decisions, metrics

phase	plan	subsystem	tags	dependencies	tech_stack	key_files	decisions	metrics
12-srt-simplifier	01	srt	srt subtitle parser segmenter corrector pipeline	requires provides affects SRTProcessor SemanticSegmenter ASRCorrector parse_srt kb_workflow.srt kb_workflow.pipeline.processors	added patterns dataclasses asyncio pydantic TDD BaseProcessor Pipeline	created modified kb_workflow/srt/__init__.py kb_workflow/srt/parser.py kb_workflow/srt/segmenter.py kb_workflow/srt/corrector.py kb_workflow/pipeline/processors/srt.py tests/test_srt_processor.py kb_workflow/pipeline/schemas.py	TDD approach with RED-GREEN cycles for parser, segmenter, corrector Lazy import for ASRCorrector to avoid dependency issues Simple rule-based segmentation (no LLM) for speed ASR correction is optional, fallback to original text	duration tasks_completed files_created files_modified tests_added test_pass_rate ~15 minutes 4 6 1 20 100%

Phase 12 Plan 01: SRT Processing Module Summary

One-liner

创建 SRT 字幕处理模块，实现字幕解析、语义分段和 LLM 纠错功能，将 B站字幕转换为易读的 Markdown 文档。

Implementation Details

Task 1: SRT Parser Module

Files: kb_workflow/srt/__init__.py, kb_workflow/srt/parser.py, tests/test_srt_processor.py

• Created Segment dataclass for subtitle segments
• Implemented parse_srt() function to parse SRT content string
• Implemented parse_srt_file() function to parse from file path
• Used regex pattern to match SRT blocks
• Handled special characters and multi-line subtitles

Key Code:

@dataclass
class Segment:
    id: int
    start_time: float
    end_time: float
    text: str

def parse_srt(content: str) -> List[Segment]:
    # Parse SRT format using regex
    # Convert timestamps to seconds
    # Return list of Segment objects

Task 2: Semantic Segmenter

Files: kb_workflow/srt/segmenter.py, tests/test_srt_processor.py

• Created SemanticSegmenter class for paragraph segmentation
• Implemented time-gap based splitting (>3 seconds)
• Added max paragraph length constraint (500 chars)
• Simple rule-based approach (no LLM needed)

Key Code:

class SemanticSegmenter:
    def __init__(self, gap_threshold=3.0, max_paragraph_chars=500):
        ...

    def segment(self, segments: List[Segment]) -> List[str]:
        # Split on time gaps
        # Enforce paragraph length limits
        # Return list of paragraph strings

Task 3: ASR Corrector

Files: kb_workflow/srt/corrector.py, tests/test_srt_processor.py

• Created ASRCorrector class for speech recognition error correction
• Reuses SimpleAIClient from deep_analyzer
• Implemented batch processing for long text (5000 chars per batch)
• Fallback to original text when AI unavailable

Key Code:

class ASRCorrector:
    def __init__(self, ai_client=None, batch_size=5000):
        ...

    def correct(self, text: str) -> str:
        # Use LLM to correct obvious errors
        # Batch process long text
        # Return corrected text or original

Task 4: SRTProcessor and Markdown Generation

Files: kb_workflow/pipeline/processors/srt.py, kb_workflow/pipeline/schemas.py, tests/test_srt_processor.py

• Created SRTProcessor following BaseProcessor pattern
• Added SRTInput and SRTOutput schemas
• Generated Markdown with YAML frontmatter
• Handled missing metadata gracefully

Key Code:

class SRTProcessor(BaseProcessor):
    STEP_NAME = "srt"

    async def process(self, input_data: SRTInput) -> SRTOutput:
        # 1. Parse SRT file
        # 2. Semantic segmentation
        # 3. ASR correction (optional)
        # 4. Generate Markdown

Deviations from Plan

None - plan executed exactly as written.

Test Coverage

Module	Tests	Coverage
Parser	6	parse_srt, parse_srt_file, error handling
Segmenter	4	merge, split on gap, max length, empty
Corrector	6	correction, fallback, batch, empty
Processor	4	markdown generation, frontmatter, pattern, missing metadata
Total	20	100% pass rate

Files Created/Modified

Created

• kb_workflow/srt/__init__.py - Module exports
• kb_workflow/srt/parser.py - SRT parsing logic
• kb_workflow/srt/segmenter.py - Semantic segmentation
• kb_workflow/srt/corrector.py - ASR error correction
• kb_workflow/pipeline/processors/srt.py - Pipeline processor
• tests/test_srt_processor.py - Unit tests

Modified

• kb_workflow/pipeline/schemas.py - Added SRTInput/SRTOutput schemas

Commits

Commit	Description
7023a57	test(12-01): add failing tests for SRT parser module
c259e0a	feat(12-01): implement SRT parser module
81a7dc5	test(12-01): add failing tests for SemanticSegmenter
a350693	feat(12-01): implement SemanticSegmenter for subtitle segmentation
757793b	test(12-01): add failing tests for ASRCorrector
5e00632	feat(12-01): implement ASRCorrector for speech recognition error correction
87b1919	feat(12-01): implement SRTProcessor and Markdown generation
812f016	chore(12-01): update srt module exports

Next Steps

Phase 12 Plan 02 will:

• Integrate SRTProcessor into Pipeline
• Add platform routing for B站视频
• Replace DeepAnalyzer for B站 videos
• End-to-end testing