audio-transcriber
sickn33/agentic-awesome-skills
Transform audio recordings into professional Markdown documentation with intelligent summaries using speech-to-text.
What is audio-transcriber?
Transcribes audio and video files into Markdown using Whisper, with automatic speaker identification, meeting minutes generation, and subtitle export. Use this when you need to convert recordings to text, generate meeting notes, or create captions from audio content.
- Transcribe audio/video files (MP3, WAV, M4A, OGG, FLAC, WEBM) to text
- Automatically identify speakers and generate speaker labels (diarization)
- Generate executive summaries and meeting minutes from recordings
- Export subtitles in SRT and VTT formats
- Batch process multiple audio files simultaneously
- Detect language automatically and support multilingual transcription
How to install audio-transcriber
npx skills add https://github.com/sickn33/agentic-awesome-skills --skill audio-transcriber- Faster-Whisper (optimized transcription engine)
- ffmpeg (for audio format conversion)
- GitHub Copilot CLI environment
How to use audio-transcriber
- 1.Install the skill using: npx skills add https://github.com/sickn33/agentic-awesome-skills --skill audio-transcriber
- 2.Provide an audio or video file path (supports MP3, WAV, M4A, OGG, FLAC, WEBM)
- 3.The skill automatically detects language, identifies speakers, and generates Markdown output
- 4.Review the generated Markdown file with transcription, speaker labels, and summary
- 5.For batch processing, provide a glob pattern (e.g., recordings/*.mp3) to process multiple files at once
Use cases
- Convert meeting recordings into structured meeting minutes with action items
- Transcribe podcast or interview episodes into searchable Markdown documents
- Generate captions and subtitles for video content
- Create documentation from recorded walkthroughs or training sessions
- Batch transcribe multiple team standup or client call recordings
- Product managers and team leads managing meeting documentation
- Content creators and podcasters
- Technical writers documenting recorded sessions
- Remote teams needing searchable meeting records
- Anyone converting audio content to text-based formats
audio-transcriber FAQ
MP3, WAV, M4A, OGG, FLAC, and WEBM formats are supported. ffmpeg handles format conversion automatically.
Yes, the skill includes speaker identification (diarization) and labels speakers in the output.
Yes, batch mode processes multiple audio files simultaneously. Use glob patterns like recordings/*.mp3 to specify multiple files.
Processing time depends on file size and duration. Large files (87+ MB) may take 10-15 minutes. The skill shows progress and estimated time remaining.
The skill automatically detects the language of the audio and supports multilingual transcription.
Full instructions (SKILL.md)
Source of truth, from sickn33/agentic-awesome-skills.
name: audio-transcriber description: "Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration" category: content risk: safe source: community tags: "[audio, transcription, whisper, meeting-minutes, speech-to-text]" date_added: "2026-02-27"
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use
Invoke this skill when:
- User needs to transcribe audio/video files to text
- User wants meeting minutes automatically generated from recordings
- User requires speaker identification (diarization) in conversations
- User needs subtitles/captions (SRT, VTT formats)
- User wants executive summaries of long audio content
- User asks variations of "transcribe this audio", "convert audio to text", "generate meeting notes from recording"
- User has audio files in common formats (MP3, WAV, M4A, OGG, FLAC, WEBM)
Example Usage
Example 1: Basic Transcription
User Input:
copilot> transcribe audio to markdown: meeting-2026-02-02.mp3
Skill Output:
✅ Faster-Whisper detected (optimized)
✅ ffmpeg available (format conversion enabled)
📂 File: meeting-2026-02-02.mp3
📊 Size: 12.3 MB
⏱️ Duration: 00:45:32
🎙️ Processing...
[████████████████████] 100%
✅ Language detected: Portuguese (pt-BR)
👥 Speakers identified: 4
📝 Generating Markdown output...
✅ Transcription Complete!
📊 Results:
File: meeting-2026-02-02.md
Language: pt-BR
Duration: 00:45:32
Speakers: 4
Words: 6,842
Processing time: 127s
📝 Generated:
- meeting-2026-02-02.md (Markdown report)
🎯 Next steps:
1. Review meeting minutes and action items
2. Share report with participants
3. Track action items to completion
Example 3: Batch Processing
User Input:
copilot> transcreva estes áudios: recordings/*.mp3
Skill Output:
📦 Batch mode: 5 files found
1. team-standup.mp3
2. client-call.mp3
3. brainstorm-session.mp3
4. product-demo.mp3
5. retrospective.mp3
🎙️ Processing batch...
[1/5] team-standup.mp3 ✅ (2m 34s)
[2/5] client-call.mp3 ✅ (15m 12s)
[3/5] brainstorm-session.mp3 ✅ (8m 47s)
[4/5] product-demo.mp3 ✅ (22m 03s)
[5/5] retrospective.mp3 ✅ (11m 28s)
✅ Batch Complete!
📝 Generated 5 Markdown reports
⏱️ Total processing time: 6m 15s
Example 5: Large File Warning
User Input:
copilot> transcribe audio to markdown: conference-keynote.mp3
Skill Output:
✅ Faster-Whisper detected (optimized)
📂 File: conference-keynote.mp3
📊 Size: 87.2 MB
⏱️ Duration: 02:15:47
⚠️ Large file (87.2 MB) - processing may take several minutes
Continue? [Y/n]:
User: Y
🎙️ Processing... (this may take 10-15 minutes)
[████░░░░░░░░░░░░░░░░] 20% - Estimated time remaining: 12m
This skill is platform-agnostic and works in any terminal context where GitHub Copilot CLI is available. It does not depend on specific project configurations or external APIs, following the zero-configuration philosophy.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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