Skill detail

Transcription Automation

Automates audio/video transcription, meeting notes, subtitle generation, and content processing with diarization and multiple output formats.

MatchDirectRecommended for Audio and Voice
Sourceclaude-office-skills/​skillsExternal source
Reported installs4,365Popularity signal only

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---
name: Transcription Automation
description: Automate audio/video transcription, meeting notes, subtitle generation, and content processing
version: 1.0.0
author: Claude Office Skills
category: multimedia
tags:
  - transcription
  - audio
  - video
  - meetings
  - subtitles
department: content
models:
  - claude-3-opus
  - claude-3-sonnet
  - gpt-4
  - whisper
mcp:
  server: media-mcp
  tools:
    - whisper_transcribe
    - assembly_ai
    - deepgram_api
    - subtitle_generate
capabilities:
  - Speech-to-text conversion
  - Meeting transcription
  - Subtitle generation
  - Speaker diarization
input:
  - Audio files
  - Video files
  - Live streams
  - Meeting recordings
output:
  - Text transcripts
  - SRT/VTT subtitles
  - Meeting notes
  - Searchable archives
languages:
  - en
  - zh
  - multi
related_skills:
  - youtube-automation
  - podcast-automation
  - meeting-notes
---

# Transcription Automation

Comprehensive skill for automating audio/video transcription and content processing.

## Core Workflows

### 1. Transcription Pipeline

```
TRANSCRIPTION FLOW:
┌─────────────────┐
│  Audio/Video    │
│     Input       │
└────────┬────────┘
         ▼
┌─────────────────┐
│  Pre-Processing │
│  - Convert      │
│  - Enhance      │
│  - Split        │
└────────┬────────┘
         ▼
┌─────────────────┐
│  Transcription  │
│  - STT Engine   │
│  - Diarization  │
└────────┬────────┘
         ▼
┌─────────────────┐
│ Post-Processing │
│  - Format       │
│  - Timestamps   │
│  - Speakers     │
└────────┬────────┘
         ▼
┌─────────────────┐
│     Output      │
│  - Text/SRT/VTT │
│  - Summary      │
└─────────────────┘
```

### 2. Transcription Configuration

```yaml
transcription_config:
  engine: whisper  # whisper, assembly_ai, deepgram
  
  audio_settings:
    sample_rate: 16000
    channels: mono
    format: wav
    
  transcription:
    language: auto  # or specific: en, zh, es
    model: large  # tiny, base, small, medium, large
    task: transcribe  # transcribe or translate
    
  features:
    speaker_diarization: true
    word_timestamps: true
    punctuation: true
    profanity_filter: false
    
  output:
    formats:
      - txt
      - srt
      - vtt
      - json
    include_confidence: true
    include_timestamps: true
```

## Meeting Transcription

### Meeting Notes Template

```yaml
meeting_transcript:
  metadata:
    title: "{{meeting_title}}"
    date: "{{date}}"
    duration: "{{duration}}"
    attendees: "{{speakers}}"
    
  output_template: |
    # {{title}}
    
    **Date:** {{date}}
    **Duration:** {{duration}}
    **Attendees:** {{attendees}}
    
    ## Summary
    {{ai_summary}}
    
    ## Key Points
    {{#each key_points}}
    - {{this}}
    {{/each}}
    
    ## Action Items
    {{#each action_items}}
    - [ ] {{task}} - @{{assignee}} - Due: {{due_date}}
    {{/each}}
    
    ## Full Transcript
    {{#each segments}}
    **[{{timestamp}}] {{speaker}}:** {{text}}
    
    {{/each}}
```

### Speaker Diarization

```yaml
diarization_config:
  min_speakers: 2
  max_speakers: 10
  
  speaker_labels:
    - name: "Speaker 1"
      voice_sample: "sample_1.wav"  # Optional
    - name: "Speaker 2"
      voice_sample: "sample_2.wav"
      
  output_format:
    speaker_prefix: true
    speaker_timestamps: true
    
  example_output: |
    [00:00:05] SPEAKER_1: Welcome everyone to today's meeting.
    [00:00:12] SPEAKER_2: Thanks for having us.
    [00:00:18] SPEAKER_1: Let's start with the agenda.
```

## Subtitle Generation

### SRT Format

```yaml
subtitle_config:
  format: srt
  
  timing:
    max_duration: 7  # seconds per subtitle
    min_gap: 0.1     # seconds between subtitles
    chars_per_line: 42
    max_lines: 2
    
  style:
    case: sentence  # sentence, upper, lower
    numbers: words  # words, digits
    
  example_output: |
    1
    00:00:05,000 --> 00:00:08,500
    Welcome to today's presentation
    about transcription automation.
    
    2
    00:00:09,000 --> 00:00:12,000
   
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