transcribe-video

Video Transcription Skill

Generate subtitles and transcripts from $ARGUMENTS (a video or audio file path, optionally followed by a language code like en-US or es-ES) using AWS Transcribe.

Outputs .srt, .vtt, and .txt files next to the source file.

Process

1. Verify prerequisites - check ffmpeg and aws CLI are installed and configured
2. Extract audio from the video as MP3 using ffmpeg
3. Create temporary S3 bucket, upload audio
4. Run AWS Transcribe job with SRT and VTT subtitle output
5. Download results and generate plain text transcript
6. Clean up all AWS resources - delete S3 bucket, Transcribe job, and temp files. No recurring costs.

Prerequisites

• ffmpeg installed (brew install ffmpeg)
• aws CLI installed and configured with valid credentials (brew install awscli && aws configure)
• AWS credentials need permissions for: s3:* (create/delete buckets), transcribe:* (start/delete jobs)

Step-by-Step

Step 1: Extract audio

ffmpeg -i "input.mp4" -vn -acodec mp3 -q:a 2 "/tmp/transcribe-audio.mp3" -y

Step 2: Create temp S3 bucket and upload

BUCKET="tmp-transcribe-$(date +%s)"
aws s3 mb "s3://$BUCKET" --region us-east-1
aws s3 cp "/tmp/transcribe-audio.mp3" "s3://$BUCKET/audio.mp3"

Step 3: Start transcription job

JOB_NAME="tmp-job-$(date +%s)"
aws transcribe start-transcription-job \
  --transcription-job-name "$JOB_NAME" \
  --language-code en-US \
  --media-format mp3 \
  --media "MediaFileUri=s3://$BUCKET/audio.mp3" \
  --subtitles "Formats=srt,vtt" \
  --output-bucket-name "$BUCKET" \
  --region us-east-1

Language codes: en-US, es-ES, fr-FR, de-DE, pt-BR, ja-JP, zh-CN, it-IT, ko-KR, etc. Default to en-US if not specified.

Step 4: Poll until complete

while true; do
  STATUS=$(aws transcribe get-transcription-job \
    --transcription-job-name "$JOB_NAME" \
    --region us-east-1 \
    --query 'TranscriptionJob.TranscriptionJobStatus' \
    --output text)
  if [ "$STATUS" = "COMPLETED" ] || [ "$STATUS" = "FAILED" ]; then break; fi
  sleep 5
done

Step 5: Download subtitle files

Save .srt and .vtt next to the original file:

aws s3 cp "s3://$BUCKET/$JOB_NAME.srt" "/path/to/input.srt"
aws s3 cp "s3://$BUCKET/$JOB_NAME.vtt" "/path/to/input.vtt"

Step 6: Generate plain text transcript

Download the JSON result and extract the full transcript text:

aws s3 cp "s3://$BUCKET/$JOB_NAME.json" "/tmp/transcribe-result.json"

Then use a tool to extract the .results.transcripts[0].transcript field from the JSON and save it as a .txt file next to the original.

Step 7: Clean up everything

IMPORTANT: Always clean up to avoid recurring S3 storage costs.

# Delete S3 bucket and all contents
aws s3 rb "s3://$BUCKET" --force --region us-east-1

# Delete the transcription job
aws transcribe delete-transcription-job --transcription-job-name "$JOB_NAME" --region us-east-1

# Delete temp audio file
rm -f "/tmp/transcribe-audio.mp3" "/tmp/transcribe-result.json"

Real-World Results (Reference)

From actual transcription runs:

| Video | Duration | Audio Size | Transcribe Time | Subtitle Segments | |-------|----------|------------|-----------------|-------------------| | X/Twitter clip | 2:40 | 2.5 MB | ~20 seconds | 83 | | Screen recording | 18:45 | 11.4 MB | ~60 seconds | 500+ |

Key Insights

1. AWS Transcribe is fast - even 19-minute videos complete in about a minute
2. Short-form content (tweets, reels) transcribes almost instantly
3. Cost is negligible - AWS Transcribe charges ~$0.024/min, so a 19-min video costs ~$0.46
4. Cleanup is critical - always delete the S3 bucket to avoid storage charges
5. SRT is most compatible - works with most video players and editors; VTT is better for web

Output Files

original-video.mp4
original-video.srt          # Subtitles with timestamps (most compatible)
original-video.vtt          # Web-optimized subtitles (for HTML5 <track>)
original-video.txt          # Plain text transcript (no timestamps)

After Transcription

1. Verify all output files exist: ls -lh /path/to/original-video.{srt,vtt,txt}
2. Report the number of subtitle segments and total duration
3. Confirm all AWS resources have been cleaned up (no S3 buckets, no Transcribe jobs remaining)