transcript-fixer

Original：🇺🇸 English

Translated

51 scriptsChecked / no sensitive code detected

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.

1installs

Sourcedaymade/claude-code-skills

Added on2026-02-04

NPX Install

npx skill4agent add daymade/claude-code-skills transcript-fixer

SKILL.md Content

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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:

bash

# 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):

bash

# 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:

bash

# 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:

```
*_stage1.md
```
- Dictionary corrections applied
```
*_stage2.md
```
- AI corrections applied (final version)
```
*_对比.html
```
- Visual diff (open in browser for best experience)

Generate word-level diff (recommended for reviewing corrections):

bash

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

This creates an HTML file showing word-by-word differences with clear highlighting:

🔴
```
japanese 3 pro
```
→ 🟢
```
Gemini 3 Pro
```
(complete word replacements)
Easy to spot exactly what changed without character-level noise

Example Session

Input transcript (

meeting.md

):

今天我们讨论了巨升智能的最新进展。
股价系统需要优化，目前性能不够好。

After Stage 1 (

meeting_stage1.md

):

今天我们讨论了具身智能的最新进展。  ← "巨升"→"具身" corrected
股价系统需要优化,目前性能不够好。  ← Unchanged (not in dictionary)

After Stage 2 (

meeting_stage2.md

):

今天我们讨论了具身智能的最新进展。
框架系统需要优化，目前性能不够好。  ← "股价"→"框架" corrected by AI

Learned pattern detected:

✓ Detected: "股价" → "框架" (confidence: 85%, count: 1)
  Run --review-learned after 2 more occurrences to approve

Core Workflow

Three-stage pipeline stores corrections in

~/.transcript-fixer/corrections.db

:

Initialize (first time):

uv run scripts/fix_transcription.py --init

Add domain corrections:

--add "错误词" "正确词" --domain <domain>

Process transcript:
```
--input file.md --stage 3
```
Review learned patterns:
```
--review-learned
```
and
```
--approve
```
high-confidence suggestions

Stages: Dictionary (instant, free) → AI via GLM API (parallel) → Full pipeline Domains:

general

,

embodied_ai

,

finance

,

medical

, or custom names including Chinese (e.g.,

火星加速器

,

具身智能

) Learning: Patterns appearing ≥3 times at ≥80% confidence move from AI to dictionary

See

references/workflow_guide.md

for detailed workflows,

references/script_parameters.md

for complete CLI reference, and

references/team_collaboration.md

for collaboration patterns.

Critical Workflow: Dictionary Iteration

MUST save corrections after each fix. This is the skill's core value.

After fixing errors manually, immediately save to dictionary:

bash

uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general

See

references/iteration_workflow.md

for complete iteration guide with checklist.

AI Fallback Strategy

When GLM API is unavailable (503, network issues), the script outputs

[CLAUDE_FALLBACK]

marker.

Claude Code should then:

Analyze the text directly for ASR errors
Fix using Edit tool
MUST save corrections to dictionary with
```
--add
```

Database Operations

MUST read
references/database_schema.md
before any database operations.

Quick reference:

bash

# View all corrections
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM active_corrections;"

# Check schema version
sqlite3 ~/.transcript-fixer/corrections.db "SELECT value FROM system_config WHERE key='schema_version';"

Stages

Stage	Description	Speed	Cost
1	Dictionary only	Instant	Free
2	AI only	~10s	API calls
3	Full pipeline	~10s	API calls

Bundled Resources

Scripts:

```
ensure_deps.py
```
- Initialize shared virtual environment (run once, optional)
```
fix_transcript_enhanced.py
```
- Enhanced wrapper (recommended for interactive use)
```
fix_transcription.py
```
- Core CLI (for automation)
```
generate_word_diff.py
```
- Generate word-level diff HTML for reviewing corrections
```
examples/bulk_import.py
```
- Bulk import example

References (load as needed):

Critical:
```
database_schema.md
```
(read before DB operations),
```
iteration_workflow.md
```
(dictionary iteration best practices)

Getting started:

installation_setup.md

,

glm_api_setup.md

,

workflow_guide.md

Daily use:

quick_reference.md

,

script_parameters.md

,

dictionary_guide.md

Advanced:

sql_queries.md

,

file_formats.md

,

architecture.md

,

best_practices.md

Operations:
```
troubleshooting.md
```
,
```
team_collaboration.md
```

Troubleshooting

Verify setup health with

uv run scripts/fix_transcription.py --validate

. Common issues:

Missing database → Run
```
--init
```
Missing API key →
```
export GLM_API_KEY="<key>"
```
(obtain from https://open.bigmodel.cn/)
Permission errors → Check
```
~/.transcript-fixer/
```
ownership

See

references/troubleshooting.md

for detailed error resolution and

references/glm_api_setup.md

for API configuration.

transcript-fixer

NPX Install

Tags

SKILL.md Content

Transcript Fixer

When to Use This Skill

Prerequisites

Quick Start

Example Session

Core Workflow

Critical Workflow: Dictionary Iteration

AI Fallback Strategy

Database Operations

Stages

Bundled Resources

Troubleshooting