801c8fa475
- Add audio-transcriber skill (v1.2.0): Transform audio to Markdown with Whisper - Add youtube-summarizer skill (v1.2.0): Generate summaries from YouTube videos - Update prompt-engineer skill: Enhanced with 11 optimization frameworks - Update skill-creator skill: Improved automation workflow All skills are zero-config, cross-platform (Claude Code, Copilot CLI, Codex) and follow Quality Bar V4 standards. Source: https://github.com/ericgandrade/cli-ai-skills
251 lines
6.3 KiB
Bash
Executable File
251 lines
6.3 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Basic Audio Transcription Example
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# Demonstrates how to use the audio-transcriber skill manually
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set -euo pipefail
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# Configuration
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AUDIO_FILE="${1:-}"
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MODEL="${MODEL:-base}" # Options: tiny, base, small, medium, large
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OUTPUT_FORMAT="${OUTPUT_FORMAT:-markdown}" # Options: markdown, txt, srt, vtt, json
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# Helper functions
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error() {
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echo -e "${RED}❌ Error: $1${NC}" >&2
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exit 1
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}
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success() {
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echo -e "${GREEN}✅ $1${NC}"
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}
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info() {
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echo -e "${BLUE}ℹ️ $1${NC}"
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}
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warn() {
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echo -e "${YELLOW}⚠️ $1${NC}"
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}
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# Check if audio file is provided
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if [[ -z "$AUDIO_FILE" ]]; then
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error "Usage: $0 <audio_file>"
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fi
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# Verify file exists
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if [[ ! -f "$AUDIO_FILE" ]]; then
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error "File not found: $AUDIO_FILE"
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fi
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# Step 0: Discovery - Check for transcription tools
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info "Step 0: Discovering transcription tools..."
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TRANSCRIBER=""
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if python3 -c "import faster_whisper" 2>/dev/null; then
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TRANSCRIBER="faster-whisper"
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success "Faster-Whisper detected (optimized)"
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elif python3 -c "import whisper" 2>/dev/null; then
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TRANSCRIBER="whisper"
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success "OpenAI Whisper detected"
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else
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error "No transcription tool found. Install with: pip install faster-whisper"
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fi
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# Check for ffmpeg
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if command -v ffmpeg &>/dev/null; then
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success "ffmpeg available (format conversion enabled)"
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else
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warn "ffmpeg not found (limited format support)"
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fi
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# Step 1: Extract metadata
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info "Step 1: Extracting audio metadata..."
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FILE_SIZE=$(du -h "$AUDIO_FILE" | cut -f1)
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info "File size: $FILE_SIZE"
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# Get duration if ffprobe is available
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if command -v ffprobe &>/dev/null; then
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DURATION=$(ffprobe -v error -show_entries format=duration \
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-of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null || echo "0")
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# Convert to HH:MM:SS
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if command -v date &>/dev/null; then
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if [[ "$OSTYPE" == "darwin"* ]]; then
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# macOS
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DURATION_HMS=$(date -u -r "${DURATION%.*}" +%H:%M:%S 2>/dev/null || echo "Unknown")
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else
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# Linux
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DURATION_HMS=$(date -u -d @"${DURATION%.*}" +%H:%M:%S 2>/dev/null || echo "Unknown")
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fi
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else
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DURATION_HMS="Unknown"
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fi
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info "Duration: $DURATION_HMS"
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else
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warn "ffprobe not found - cannot extract duration"
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DURATION="0"
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DURATION_HMS="Unknown"
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fi
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# Check file size warning
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SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
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if [[ $SIZE_MB -gt 25 ]]; then
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warn "Large file ($FILE_SIZE) - processing may take several minutes"
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read -p "Continue? [Y/n]: " CONTINUE
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if [[ "$CONTINUE" =~ ^[Nn] ]]; then
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info "Transcription cancelled"
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exit 0
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fi
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fi
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# Step 2: Transcribe using Python
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info "Step 2: Transcribing audio..."
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OUTPUT_FILE="${AUDIO_FILE%.*}.md"
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TEMP_JSON="/tmp/transcription_$$.json"
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python3 << EOF
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import sys
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import json
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from datetime import datetime
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try:
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if "$TRANSCRIBER" == "faster-whisper":
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from faster_whisper import WhisperModel
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model = WhisperModel("$MODEL", device="cpu", compute_type="int8")
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segments, info = model.transcribe("$AUDIO_FILE", language=None, vad_filter=True)
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data = {
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"language": info.language,
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"language_probability": round(info.language_probability, 2),
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"duration": info.duration,
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"segments": []
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}
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for segment in segments:
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data["segments"].append({
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"start": round(segment.start, 2),
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"end": round(segment.end, 2),
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"text": segment.text.strip()
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})
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else:
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import whisper
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model = whisper.load_model("$MODEL")
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result = model.transcribe("$AUDIO_FILE")
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data = {
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"language": result["language"],
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"duration": result["segments"][-1]["end"] if result["segments"] else 0,
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"segments": result["segments"]
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}
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with open("$TEMP_JSON", "w") as f:
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json.dump(data, f)
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print(f"✅ Language detected: {data['language']}")
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print(f"📝 Transcribed {len(data['segments'])} segments")
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except Exception as e:
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print(f"❌ Error: {e}", file=sys.stderr)
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sys.exit(1)
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EOF
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# Check if transcription succeeded
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if [[ ! -f "$TEMP_JSON" ]]; then
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error "Transcription failed"
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fi
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# Step 3: Generate Markdown output
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info "Step 3: Generating Markdown report..."
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python3 << 'EOF'
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import json
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import sys
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from datetime import datetime
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# Load transcription data
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with open("${TEMP_JSON}") as f:
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data = json.load(f)
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# Prepare metadata
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filename = "${AUDIO_FILE}".split("/")[-1]
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file_size = "${FILE_SIZE}"
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duration_hms = "${DURATION_HMS}"
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language = data["language"]
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process_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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num_segments = len(data["segments"])
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# Generate Markdown
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markdown = f"""# Audio Transcription Report
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## 📊 Metadata
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| Field | Value |
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|-------|-------|
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| **File Name** | {filename} |
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| **File Size** | {file_size} |
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| **Duration** | {duration_hms} |
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| **Language** | {language.upper()} |
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| **Processed Date** | {process_date} |
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| **Segments** | {num_segments} |
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| **Transcription Engine** | ${TRANSCRIBER} (model: ${MODEL}) |
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---
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## 🎙️ Full Transcription
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"""
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# Add transcription with timestamps
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for seg in data["segments"]:
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start_time = f"{int(seg['start'] // 60):02d}:{int(seg['start'] % 60):02d}"
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end_time = f"{int(seg['end'] // 60):02d}:{int(seg['end'] % 60):02d}"
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markdown += f"**[{start_time} → {end_time}]** \n{seg['text']}\n\n"
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markdown += """---
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## 📝 Summary
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*Automatic summary generation requires AI integration (Claude/GPT).*
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*For now, review the full transcription above.*
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---
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*Generated by audio-transcriber skill example script*
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*Transcription engine: ${TRANSCRIBER} | Model: ${MODEL}*
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"""
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# Write to file
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with open("${OUTPUT_FILE}", "w") as f:
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f.write(markdown)
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print(f"✅ Markdown report saved: ${OUTPUT_FILE}")
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EOF
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# Clean up
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rm -f "$TEMP_JSON"
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# Step 4: Display summary
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success "Transcription complete!"
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echo ""
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echo "📊 Results:"
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echo " Output file: $OUTPUT_FILE"
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echo " Transcription engine: $TRANSCRIBER"
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echo " Model: $MODEL"
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echo ""
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info "Next steps:"
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echo " 1. Review the transcription: cat $OUTPUT_FILE"
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echo " 2. Edit if needed: vim $OUTPUT_FILE"
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echo " 3. Share with team or archive"
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EOF
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