Transcript Fixer

Two-phase correction pipeline: deterministic dictionary rules (instant, free) followed by AI-powered error detection. Corrections accumulate in ~/.transcript-fixer/corrections.db, improving accuracy over time.

Prerequisites

All scripts use PEP 723 inline metadata — uv run auto-installs dependencies. Requires uv (install guide).

Quick Start

# First time: Initialize database
uv run scripts/fix_transcription.py --init

# Phase 1: Dictionary corrections (instant, free)
uv run scripts/fix_transcription.py --input meeting.md --stage 1

After Stage 1, Claude reads the output and fixes remaining ASR errors natively (no API key needed):

1. Read Stage 1 output in ~200-line chunks using the Read tool
2. Identify ASR errors — homophones, garbled terms, broken sentences
3. Present corrections in a table for user review before applying
4. Save stable patterns to dictionary for future reuse

See references/example_session.md for a concrete input/output walkthrough.

Alternative: API batch processing (for automation without Claude Code):

export GLM_API_KEY="<api-key>"  # From https://open.bigmodel.cn/
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected

Core Workflow

Two-phase pipeline with persistent learning:

1. Initialize (once): uv run scripts/fix_transcription.py --init
2. Add domain corrections: --add "错误词" "正确词" --domain <domain>
3. Phase 1 — Dictionary: --input file.md --stage 1 (instant, free)
4. Phase 2 — AI Correction: Claude reads output and fixes errors natively, or --stage 3 with GLM_API_KEY for API mode
5. Save stable patterns: --add "错误词" "正确词" after each session
6. Review learned patterns: --review-learned and --approve high-confidence suggestions

Domains: general, embodied_ai, finance, medical, or custom (e.g., 火星加速器) Learning: Patterns appearing ≥3 times at ≥80% confidence auto-promote from AI to dictionary

After fixing, always save reusable corrections to dictionary. This is the skill's core value — see references/iteration_workflow.md for the complete checklist.

False Positive Prevention

Adding wrong dictionary rules silently corrupts future transcripts. Read references/false_positive_guide.md before adding any correction rule, especially for short words (≤2 chars) or common Chinese words that appear correctly in normal text.

Native AI Correction (Default Mode)

When running inside Claude Code, use Claude's own language understanding for Phase 2:

1. Run Stage 1 (dictionary): --input file.md --stage 1
2. Verify Stage 1 — diff original vs output. If dictionary introduced false positives, work from the original file
3. Read the full text in ~200-line chunks. Read the entire transcript before proposing corrections — later context often disambiguates earlier errors
4. Identify ASR errors:
   • Product/tool names: "close code" → "Claude Code", "get Hub" → "GitHub"
   • Technical terms: "Web coding" → "Vibe Coding", "happy pass" → "happy path"
   • Homophone errors: "上海文" → "上下文", "分值" → "分支"
   • English ASR garbling: "Pre top" → "prototype", "rapper" → "repo"
   • Broken sentences: "很大程。路上" → "很大程度上"
5. Present corrections in high/medium confidence tables with line numbers
6. Apply with sed on a copy, verify with diff, replace original
7. Generate word diff: uv run scripts/generate_word_diff.py original.md corrected.md diff.html
8. Save stable patterns to dictionary
9. Remove false positives if Stage 1 had any

Enhanced Capabilities (Native Mode Only)

• Intelligent paragraph breaks: Add \n\n at logical topic transitions
• Filler word reduction: "这个这个这个" → "这个"
• Interactive review: Corrections confirmed before applying
• Context-aware judgment: Full document context resolves ambiguous errors

When to Use API Mode Instead

Use GLM_API_KEY + Stage 3 for batch processing, standalone usage without Claude Code, or reproducible automated processing.

Legacy Fallback

When the script outputs [CLAUDE_FALLBACK] (GLM API error), switch to native mode automatically.

Utility Scripts

Timestamp repair:

uv run scripts/fix_transcript_timestamps.py meeting.txt --in-place

Split transcript into sections (rebase each to 00:00:00):

uv run scripts/split_transcript_sections.py meeting.txt \
  --first-section-name "课前聊天" \
  --section "正式上课::好，无缝切换嘛。" \
  --rebase-to-zero

Word-level diff (recommended for reviewing corrections):

uv run scripts/generate_word_diff.py original.md corrected.md output.html

Output Files

• *_stage1.md — Dictionary corrections applied
• *_corrected.txt — Final version (native mode) or *_stage2.md (API mode)
• *_对比.html — Visual diff (open in browser)

Database Operations

Read references/database_schema.md before any database operations.

sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM active_corrections;"
sqlite3 ~/.transcript-fixer/corrections.db "SELECT value FROM system_config WHERE key='schema_version';"

Stages

Stage	Description	Speed	Cost
1	Dictionary only	Instant	Free
1 + Native	Dictionary + Claude AI (default)	~1min	Free
3	Dictionary + API AI + diff report	~10s	API calls

Bundled Resources

Scripts:

• fix_transcription.py — Core CLI (dictionary, add, audit, learning)
• fix_transcript_enhanced.py — Enhanced wrapper for interactive use
• fix_transcript_timestamps.py — Timestamp normalization and repair
• generate_word_diff.py — Word-level diff HTML generation
• split_transcript_sections.py — Split transcript by marker phrases

References (load as needed):

• Safety: false_positive_guide.md (read before adding rules), database_schema.md (read before DB ops)
• Workflow: iteration_workflow.md, workflow_guide.md, example_session.md
• CLI: quick_reference.md, script_parameters.md
• Advanced: dictionary_guide.md, sql_queries.md, architecture.md, best_practices.md
• Operations: troubleshooting.md, installation_setup.md, glm_api_setup.md, team_collaboration.md

Troubleshooting

uv run scripts/fix_transcription.py --validate checks setup health. See references/troubleshooting.md for detailed resolution.

Next Step: Structure into Meeting Minutes

After correcting a transcript, if the content is from a meeting, lecture, or interview, suggest structuring it:

Transcript corrected: [N] errors fixed, saved to [output_path].

Want to turn this into structured meeting minutes with decisions and action items?

Options:
A) Yes — run /meeting-minutes-taker (Recommended for meetings/lectures)
B) Export as PDF — run /pdf-creator on the corrected text
C) No thanks — the corrected transcript is all I need