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