transcript-fixer

name	transcript-fixer
description	Corrects speech-to-text transcription errors in meeting notes, lectures, and interviews using dictionary rules and AI. Learns patterns to build personalized correction databases. Use when working with transcripts containing ASR/STT errors, homophones, or Chinese/English mixed content requiring cleanup.
zh_description	用于转录稿、fixer，支持文档、表格、演示和资料整理。
version	1.0.0
author	seaworld008
source	in-house
source_url
tags	["fixer", "transcript"]
created_at	2026-03-04
updated_at	2026-03-20
quality	4
complexity	intermediate

Transcript Fixer

Correct speech-to-text transcription errors through dictionary-based rules, AI-powered corrections, and automatic pattern detection. Build a personalized knowledge base that learns from each correction.

When to Use This Skill

• Correcting ASR/STT errors in meeting notes, lectures, or interviews
• Building domain-specific correction dictionaries
• Fixing Chinese/English homophone errors or technical terminology
• Collaborating on shared correction knowledge bases

Prerequisites

Python execution must use uv - never use system Python directly.

If uv is not installed:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Quick Start

Recommended: Use Enhanced Wrapper (auto-detects API key, opens HTML diff):

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

# Process transcript with enhanced UX
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected

The enhanced wrapper automatically:

• Detects GLM API key from shell configs (checks lines near ANTHROPIC_BASE_URL)
• Moves output files to specified directory
• Opens HTML visual diff in browser for immediate feedback

Alternative: Use Core Script Directly:

# 1. Set API key (if not auto-detected)
export GLM_API_KEY="<api-key>"  # From https://open.bigmodel.cn/

# 2. Add common corrections (5-10 terms)
uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general

# 3. Run full correction pipeline
uv run scripts/fix_transcription.py --input meeting.md --stage 3

# 4. Review learned patterns after 3-5 runs
uv run scripts/fix_transcription.py --review-learned

Output files: