audio-transcriber

---
name: audio-transcriber
description: "Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off."
version: "0.0.1"
category: content-media
tags: ["audio", "transcription", "whisper", "meeting-minutes", "speech-to-text", "audio-transcriber", "transform", "recordings"]
complexity: advanced
risk: safe
tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"]
source: community
author: "sickn33"
date_added: "2026-04-15"
date_updated: "2026-04-25"
---

# Check for Faster-Whisper (preferred - 4-5x faster)

## Overview

This public intake copy packages `plugins/antigravity-awesome-skills-claude/skills/audio-transcriber` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Purpose, 📊 Metadata, 📋 Meeting Minutes, Limitations.

## When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

- 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"

## Operating Table

| Situation | Start here | Why it matters |
| --- | --- | --- |
| First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow |
| Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | `references/tools-comparison.md` | Starts with the smallest copied file that materially changes execution |
| Supporting context | `examples/basic-transcription.sh` | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts |

## Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Accept file path or URL from user:
2. Local file: meeting.mp3
3. URL: https://example.com/audio.mp3 (download to temp directory)
4. Verify file exists:
5. Extract metadata using ffprobe or file utilities:
6. Check file size (warn if large for cloud APIs):
7. Validate format (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):

### Imported Workflow Notes

#### Imported: Workflow

### Step 0: Discovery (Auto-detect Transcription Tools)

**Objective:** Identify available transcription engines without user configuration.

**Actions:**

Run detection commands to find installed tools:

```bash
# Check for Faster-Whisper (preferred - 4-5x faster)
if python3 -c "import faster_whisper" 2>/dev/null; then
    TRANSCRIBER="faster-whisper"
    echo "✅ Faster-Whisper detected (optimized)"
# Fallback to original Whisper
elif python3 -c "import whisper" 2>/dev/null; then
    TRANSCRIBER="whisper"
    echo "✅ OpenAI Whisper detected"
else
    TRANSCRIBER="none"
    echo "⚠️  No transcription tool found"
fi

# Check for ffmpeg (audio format conversion)
if command -v ffmpeg &>/dev/null; then
    echo "✅ ffmpeg available (format conversion enabled)"
else
    echo "ℹ️  ffmpeg not found (limited format support)"
fi
```

**If no transcriber found:**

Offer automatic installation using the provided script:

```bash
echo "⚠️  No transcription tool found"
echo ""
echo "🔧 Auto-install dependencies? (Recommended)"
read -p "Run installation script? [Y/n]: " AUTO_INSTALL

if [[ ! "$AUTO_INSTALL" =~ ^[Nn] ]]; then
    # Get skill directory (works for both repo and symlinked installations)
    SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

    # Run installation script
    if [[ -f "$SKILL_DIR/scripts/install-requirements.sh" ]]; then
        bash "$SKILL_DIR/scripts/install-requirements.sh"
    else
        echo "❌ Installation script not found"
        echo ""
        echo "📦 Manual installation:"
        echo "  pip install faster-whisper  # Recommended"
        echo "  pip install openai-whisper  # Alternative"
        echo "  brew install ffmpeg         # Optional (macOS)"
        exit 1
    fi

    # Verify installation succeeded
    if python3 -c "import faster_whisper" 2>/dev/null || python3 -c "import whisper" 2>/dev/null; then
        echo "✅ Installation successful! Proceeding with transcription..."
    else
        echo "❌ Installation failed. Please install manually."
        exit 1
    fi
else
    echo ""
    echo "📦 Manual installation required:"
    echo ""
    echo "Recommended (fastest):"
    echo "  pip install faster-whisper"
    echo ""
    echo "Alternative (original):"
    echo "  pip install openai-whisper"
    echo ""
    echo "Optional (format conversion):"
    echo "  brew install ffmpeg  # macOS"
    echo "  apt install ffmpeg   # Linux"
    echo ""
    exit 1
fi
```

This ensures users can install dependencies with one confirmation, or opt for manual installation if preferred.

**If transcriber found:**

Proceed to Step 0b (CLI Detection).


### Step 1: Validate Audio File

**Objective:** Verify file exists, check format, and extract metadata.

**Actions:**

1. **Accept file path or URL** from user:
   - Local file: `meeting.mp3`
   - URL: `https://example.com/audio.mp3` (download to temp directory)

2. **Verify file exists:**

```bash
if [[ ! -f "$AUDIO_FILE" ]]; then
    echo "❌ File not found: $AUDIO_FILE"
    exit 1
fi
```

3. **Extract metadata** using ffprobe or file utilities:

```bash
# Get file size
FILE_SIZE=$(du -h "$AUDIO_FILE" | cut -f1)

# Get duration and format using ffprobe
DURATION=$(ffprobe -v error -show_entries format=duration \
    -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)
FORMAT=$(ffprobe -v error -select_streams a:0 -show_entries \
    stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)

# Convert duration to HH:MM:SS
DURATION_HMS=$(date -u -r "$DURATION" +%H:%M:%S 2>/dev/null || echo "Unknown")
```

4. **Check file size** (warn if large for cloud APIs):

```bash
SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
if [[ $SIZE_MB -gt 25 ]]; then
    echo "⚠️  Large file ($FILE_SIZE) - processing may take several minutes"
fi
```

5. **Validate format** (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):

```bash
EXTENSION="${AUDIO_FILE##*.}"
SUPPORTED_FORMATS=("mp3" "wav" "m4a" "ogg" "flac" "webm" "mp4")

if [[ ! " ${SUPPORTED_FORMATS[@]} " =~ " ${EXTENSION,,} " ]]; then
    echo "⚠️  Unsupported format: $EXTENSION"
    if command -v ffmpeg &>/dev/null; then
        echo "🔄 Converting to WAV..."
        ffmpeg -i "$AUDIO_FILE" -ar 16000 "${AUDIO_FILE%.*}.wav" -y
        AUDIO_FILE="${AUDIO_FILE%.*}.wav"
    else
        echo "❌ Install ffmpeg to convert formats: brew install ffmpeg"
        exit 1
    fi
fi
```


### Step 3: Generate Markdown Output

**Objective:** Create structured Markdown with metadata, transcription, meeting minutes, and summary.

**Output Template:**

```markdown
# Audio Transcription Report

#### Imported: Purpose

This skill automates audio-to-text transcription with professional Markdown output, extracting rich technical metadata (speakers, timestamps, language, file size, duration) and generating structured meeting minutes and executive summaries. It uses Faster-Whisper or Whisper with zero configuration, working universally across projects without hardcoded paths or API keys.

Inspired by tools like Plaud, this skill transforms raw audio recordings into actionable documentation, making it ideal for meetings, interviews, lectures, and content analysis.

## Examples

### Example 1: Ask for the upstream workflow directly

```text
Use @audio-transcriber to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
```

**Explanation:** This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

### Example 2: Ask for a provenance-grounded review

```text
Review @audio-transcriber against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
```

**Explanation:** Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

### Example 3: Narrow the copied support files before execution

```text
Use @audio-transcriber for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
```

**Explanation:** This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

### Example 4: Build a reviewer packet

```text
Review @audio-transcriber using the copied upstream files plus provenance, then summarize any gaps before merge.
```

**Explanation:** This is useful when the PR is waiting for human review and you want a repeatable audit packet.

### Imported Usage Notes

#### Imported: Example Usage

### **Example 1: Basic Transcription**

**User Input:**
```bash
copilot> transcribe audio to markdown: meeting-2026-02-02.mp3
```

**Skill Output:**

```bash
✅ 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:**
```bash
copilot> transcreva estes áudios: recordings/*.mp3
```

**Skill Output:**

```bash
📦 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:**
```bash
copilot> transcribe audio to markdown: conference-keynote.mp3
```

**Skill Output:**

```bash
✅ 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`

```bash
🎙️  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.

## Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

- Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
- Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
- Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
- Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
- Treat generated examples as scaffolding; adapt them to the concrete task before execution.
- Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.



## Troubleshooting

### Problem: The operator skipped the imported context and answered too generically

**Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills-claude/skills/audio-transcriber`, fails to mention provenance, or does not use any copied source files at all.
**Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing.

### Problem: The imported workflow feels incomplete during review

**Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task.
**Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

### Problem: The task drifted into a different specialization

**Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
**Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.



## Related Skills

- `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@10-andruia-skill-smith-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.

## Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

| Resource family | What it gives the reviewer | Example path |
| --- | --- | --- |
| `references` | copied reference notes, guides, or background material from upstream | `references/tools-comparison.md` |
| `examples` | worked examples or reusable prompts copied from upstream | `examples/basic-transcription.sh` |
| `scripts` | upstream helper scripts that change execution or validation | `scripts/install-requirements.sh` |
| `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` |
| `assets` | supporting assets or schemas copied from the source package | `assets/n/a` |

- [tools-comparison.md](references/tools-comparison.md)
- [basic-transcription.sh](examples/basic-transcription.sh)
- [install-requirements.sh](scripts/install-requirements.sh)
- [transcribe.py](scripts/transcribe.py)
- [CHANGELOG.md](CHANGELOG.md)
- [README.md](README.md)

### Imported Reference Notes

#### Imported: 📊 Metadata

| Field | Value |
|-------|-------|
| **File Name** | {filename} |
| **File Size** | {file_size} |
| **Duration** | {duration_hms} |
| **Language** | {language} ({language_code}) |
| **Processed Date** | {process_date} |
| **Speakers Identified** | {num_speakers} |
| **Transcription Engine** | {engine} (model: {model}) |

#### Imported: 📋 Meeting Minutes

### Participants
- {speaker_1}
- {speaker_2}
- ...

### Topics Discussed
1. **{topic_1}** ({timestamp})
   - {key_point_1}
   - {key_point_2}

2. **{topic_2}** ({timestamp})
   - {key_point_1}

### Decisions Made
- ✅ {decision_1}
- ✅ {decision_2}

### Action Items
- [ ] **{action_1}** - Assigned to: {speaker} - Due: {date_if_mentioned}
- [ ] **{action_2}** - Assigned to: {speaker}


*Generated by audio-transcriber skill v1.0.0*
*Transcription engine: {engine} | Processing time: {elapsed_time}s*
```

**Implementation:**

Use Python or bash with AI model (Claude/GPT) for intelligent summarization:

```python
def generate_meeting_minutes(segments):
    """Extract topics, decisions, action items from transcription."""

    # Group segments by topic (simple clustering by timestamps)
    topics = cluster_by_topic(segments)

    # Identify action items (keywords: "should", "will", "need to", "action")
    action_items = extract_action_items(segments)

    # Identify decisions (keywords: "decided", "agreed", "approved")
    decisions = extract_decisions(segments)

    return {
        "topics": topics,
        "decisions": decisions,
        "action_items": action_items
    }

def generate_summary(segments, max_paragraphs=5):
    """Create executive summary using AI (Claude/GPT via API or local model)."""

    full_text = " ".join([s["text"] for s in segments])

    # Use Chain of Density approach (from prompt-engineer frameworks)
    summary_prompt = f"""
    Summarize the following transcription in {max_paragraphs} concise paragraphs.
    Focus on key topics, decisions, and action items.

    Transcription:
    {full_text}
    """

    # Call AI model (placeholder - user can integrate Claude API or use local model)
    summary = call_ai_model(summary_prompt)

    return summary
```

**Output file naming:**

```bash
# v1.1.0: Use timestamp para evitar sobrescrever
TIMESTAMP=$(date +%Y%m%d-%H%M%S)
TRANSCRIPT_FILE="transcript-${TIMESTAMP}.md"
ATA_FILE="ata-${TIMESTAMP}.md"

echo "$TRANSCRIPT_CONTENT" > "$TRANSCRIPT_FILE"
echo "✅ Transcript salvo: $TRANSCRIPT_FILE"

if [[ -n "$ATA_CONTENT" ]]; then
    echo "$ATA_CONTENT" > "$ATA_FILE"
    echo "✅ Ata salva: $ATA_FILE"
fi
```


#### **SCENARIO A: User Provided Custom Prompt**

**Workflow:**

1. **Display user's prompt:**
   ```
   📝 Prompt fornecido pelo usuário:
   ┌──────────────────────────────────┐
   │ [User's prompt preview]          │
   └──────────────────────────────────┘
   ```

2. **Automatically improve with prompt-engineer (if available):**
   ```bash
   🔧 Melhorando prompt com prompt-engineer...
   [Invokes: gh copilot -p "melhore este prompt: {user_prompt}"]
   ```

3. **Show both versions:**
   ```
   ✨ Versão melhorada:
   ┌──────────────────────────────────┐
   │ Role: Você é um documentador...  │
   │ Instructions: Transforme...      │
   │ Steps: 1) ... 2) ...             │
   │ End Goal: ...                    │
   └──────────────────────────────────┘

   📝 Versão original:
   ┌──────────────────────────────────┐
   │ [User's original prompt]         │
   └──────────────────────────────────┘
   ```

4. **Ask which to use:**
   ```bash
   💡 Usar versão melhorada? [s/n] (default: s):
   ```

5. **Process with selected prompt:**
   - If "s": use improved
   - If "n": use original


#### **LLM Processing (Both Scenarios)**

Once prompt is finalized:

```python
from rich.progress import Progress, SpinnerColumn, TextColumn

def process_with_llm(transcript, prompt, cli_tool='claude'):
    full_prompt = f"{prompt}\n\n---\n\nTranscrição:\n\n{transcript}"

    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        transient=True
    ) as progress:
        progress.add_task(
            description=f"🤖 Processando com {cli_tool}...",
            total=None
        )

        if cli_tool == 'claude':
            result = subprocess.run(
                ['claude', '-'],
                input=full_prompt,
                capture_output=True,
                text=True,
                timeout=300  # 5 minutes
            )
        elif cli_tool == 'gh-copilot':
            result = subprocess.run(
                ['gh', 'copilot', 'suggest', '-t', 'shell', full_prompt],
                capture_output=True,
                text=True,
                timeout=300
            )

    if result.returncode == 0:
        return result.stdout.strip()
    else:
        return None
```

**Progress output:**
```
🤖 Processando com claude... ⠋
[After completion:]
✅ Ata gerada com sucesso!
```


#### **Final Output**

**Success (both files):**
```bash
💾 Salvando arquivos...

✅ Arquivos criados:
  - transcript-20260203-023045.md  (transcript puro)
  - ata-20260203-023045.md         (processado com LLM)

🧹 Removidos arquivos temporários: metadata.json, transcription.json

✅ Concluído! Tempo total: 3m 45s
```

**Transcript only (user declined LLM):**
```bash
💾 Salvando arquivos...

✅ Arquivo criado:
  - transcript-20260203-023045.md

ℹ️  Ata não gerada (processamento LLM recusado pelo usuário)

🧹 Removidos arquivos temporários: metadata.json, transcription.json

✅ Concluído!
```


### Step 5: Display Results Summary

**Objective:** Show completion status and next steps.

**Output:**

```bash
echo ""
echo "✅ Transcription Complete!"
echo ""
echo "📊 Results:"
echo "  File: $OUTPUT_FILE"
echo "  Language: $LANGUAGE"
echo "  Duration: $DURATION_HMS"
echo "  Speakers: $NUM_SPEAKERS"
echo "  Words: $WORD_COUNT"
echo "  Processing time: ${ELAPSED_TIME}s"
echo ""
echo "📝 Generated:"
echo "  - $OUTPUT_FILE (Markdown report)"
[if alternative formats:]
echo "  - ${OUTPUT_FILE%.*}.srt (Subtitles)"
echo "  - ${OUTPUT_FILE%.*}.json (Structured data)"
echo ""
echo "🎯 Next steps:"
echo "  1. Review meeting minutes and action items"
echo "  2. Share report with participants"
echo "  3. Track action items to completion"
```

#### Imported: 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.