akrindev/batch-processor

Batch Processor

This skill enables batch processing of multiple videos for maximum efficiency.

When to Use

• Processing multiple YouTube videos at once
• Batch converting podcast episodes to shorts
• Repurposing entire video libraries
• Creating content at scale
• Processing seasonal content (holiday, event videos)

Available Scripts

scripts/batch_process.py

Process multiple videos in batch.

Usage:

python skills/batch-processor/scripts/batch_process.py --input <json_file> [options]

Input JSON Format:

[
  {
    "source": "https://youtube.com/watch?v=VIDEO1",
    "source_type": "youtube",
    "num_clips": 5,
    "platform": "tiktok"
  },
  {
    "source": "./videos/video2.mp4",
    "source_type": "file",
    "num_clips": 3,
    "platform": "shorts"
  }
]

Options:

• --input: JSON file with video list
• --output-dir: Output directory (default: ./batch_output/)
• --parallel: Number of parallel processes (default: 1)
• --transcription-model: Transcription model (auto, whisper, gemini)

Example:

python skills/batch-processor/scripts/batch_process.py --input videos.json --parallel 2

scripts/batch_from_urls.py

Download and process multiple YouTube URLs.

Usage:

python skills/batch-processor/scripts/batch_from_urls.py --urls <file> [options]

URLs File Format:

https://youtube.com/watch?v=VIDEO1
https://youtube.com/watch?v=VIDEO2
https://youtube.com/watch?v=VIDEO3

Options:

• --urls: File with YouTube URLs (one per line)
• --num-clips: Clips per video (default: 5)
• --platform: Target platform (default: tiktok)
• All other autocut options

Example:

python skills/batch-processor/scripts/batch_from_urls.py --urls urls.txt --num-clips 5 --platform shorts

Output

Directory Structure

batch_output/
  2024-01-30/
    video1/
      video1_tiktok_001.mp4
      video1_tiktok_002.mp4
      report.json
    video2/
      video2_shorts_001.mp4
      video2_shorts_002.mp4
      report.json
    batch_report.json

Batch Report

{
  "batch_id": "batch_20240130_120000",
  "total_videos": 10,
  "successful": 8,
  "failed": 2,
  "total_clips": 40,
  "output_dir": "./batch_output/2024-01-30/",
  "processing_time": 1800.5,
  "results": [
    {
      "source": "https://youtube.com/watch?v=...",
      "status": "success",
      "clips": 5,
      "output_dir": "batch_output/2024-01-30/video1/"
    },
    {
      "source": "https://youtube.com/watch?v=...",
      "status": "failed",
      "error": "Transcription failed"
    }
  ]
}

Performance

• Sequential processing: 1 video at a time
• Parallel processing: 2-4 videos simultaneously (based on CPU cores)
• Estimated time: (video duration × 2) per video

Error Handling

• Individual failures: Continues with next video
• Partial success: Reports successful vs failed
• Retry logic: Retries failed videos once
• Progress tracking: Shows progress during processing

Tips

• Use parallel processing for large batches
• Process overnight for big libraries
• Check batch report for failed videos
• Retry failed videos individually
• Monitor disk space for large batches

References

• Parallel processing in Python
• Batch workflow optimization