Transcript Fixer
Correct speech-to-text errors with dictionary rules and AI analysis — fixing homophones, garbled technical terms, and mixed Chinese/English — while building a personalized correction database that learns from each fix.
ASR output is littered with the same recurring mistakes — wrong names, mangled jargon, homophones. This playbook fixes them and remembers the corrections so they stop coming back.
Who it's for: podcasters, researchers, transcribers, content teams
Example
"Clean up the errors in this auto-transcript" → Corrected text with technical terms and names fixed, plus a growing dictionary that learns your vocabulary
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add transcript-fixer# 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.
**What each phase is actually good at** (calibration, not a rule): the dictionary shines on *recurring* errors — product names, common homophones, anything you've corrected before — at zero cost and zero latency. But on a fresh database, on high-quality ASR (e.g. transcripts from a strong engine like Whisper, Otter, or Feishu / Tencent-Meeting), or in specialized domains (finance, medical, legal), the dictionary often matches almost nothing — the errors that remain are proper nouns and domain terms it has never seen. There, the AI pass does essentially all the real work. Treat Stage 1 as a cheap pre-filter for known repeats, not as the primary corrector, and don't be alarmed when it changes only a handful of lines on a clean transcript.
## Prerequisites
All scripts use PEP 723 inline metadata — `uv run` auto-installs dependencies. Requires `uv` ([install guide](https://docs.astral.sh/uv/getting-started/installation/)).
## Quick Start
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What This Does
Correct speech-to-text errors with dictionary rules and AI analysis — fixing homophones, garbled technical terms, and mixed Chinese/English — while building a personalized correction database that learns from each fix.
What's Inside
The template covers:
- Prerequisites
- Core Workflow
- False Positive Prevention
- Native AI Correction (Default Mode)
- Utility Scripts
- Output Files
Quick Start
Step 1: Create a Project Folder
Make a dedicated folder for this workflow and open it in Claude Code.
Step 2: Download the Template
Click Download above to save the template, then drop it into your project as CLAUDE.md (or paste it into your existing one).
Step 3: Start Working
Tell Claude what you need in plain language — it will follow the template's workflow automatically. For example:
Clean up the errors in this auto-transcript
Claude reads the template and runs the steps for you.