Custom Scripts for Batch Post-Production: Rename, Format, Optimize 100 Videos in Batch

As soon as you handle dozens or hundreds of videos per week, manual clicking in editing software becomes a bottleneck.

The Python + FFmpeg combination allows you to build a batch video processing pipeline that renames, reformats, subtitles, resizes, and compresses your files without human intervention.

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Rename Your Files for Multi-Platform 📁

The Problem

Files named VID_20260123_142305.mp4 tell you nothing about:

• The target platform
• The content
• The publication date

The Solution: Intelligent Naming Convention

A first level of automation consists of imposing a logical naming convention:

Integrating the target platform, a timestamp, and a readable slug.

The Python Architecture

In Python, you can:

Step 1: Browse a source folder

Step 2: Read a CSV or table

Read a CSV or table (Airtable, Notion) containing metadata:

• Platform
• Title
• Publication date

Step 3: Rename according to the pattern

Rename each file in the form:

• TIKTOK_2026-01-23_my-hook-title.mp4
• YT_SHORTS_2026-01-23_topic-keyword.mp4

The Code

import os
import csv
from pathlib import Path

def rename_videos_from_csv(source_dir, csv_path):
    """
    Renames videos according to CSV metadata.
    CSV format: filename,platform,slug,date
    """
    with open(csv_path, 'r') as f:
        reader = csv.DictReader(f)
        for row in reader:
            old_path = Path(source_dir) / row['filename']
            new_name = f"{row['platform']}_{row['date']}_{row['slug']}.mp4"
            new_path = Path(source_dir) / new_name
            
            if old_path.exists():
                old_path.rename(new_path)
                print(f"✓ Renamed: {old_path.name}→{new_path.name}")

The Impact

This simple renaming aligns your filesystem with your editorial strategy and facilitates the rest of the pipeline:

• Filter by prefix for each platform
• Archive by date
• Automate upload

💡 Pro Tip

Add an MD5 hash to the filename to detect duplicates.

This prevents reprocessing the same video twice if it's already in the pipeline.

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Add Captions in Batch 📝

The Problem

Manually adding subtitles to 100 videos = 100 hours of work.

The Automated Solution

Batch generation and injection of subtitles.

The Two Approaches

Approach 1: OCR or Speech-to-Text

Via an external service to produce subtitle files (SRT, VTT).

Approach 2: Direct addition to the video

With FFmpeg, by burning text into the image or adding a separate subtitle track.

The Complete Pipeline

A Python script can:

Step 1: Transcription

Send each audio/video file to a transcription service.

Step 2: SRT Retrieval

Retrieve the generated SRT.

Step 3: Integration with FFmpeg

Call FFmpeg on the command line to integrate subtitles, respecting the resolution and target format.

The Code

import subprocess

def burn_subtitles(video_path, srt_path, output_path):
    """
    Burns subtitles into the video with FFmpeg.
    """
    cmd = [
        'ffmpeg',
        '-i', video_path,
        '-vf', f"subtitles={srt_path}:force_style='FontSize=24,PrimaryColour=&HFFFFFF&'",
        '-c:a', 'copy',
        output_path
    ]
    subprocess.run(cmd, check=True)
    print(f"✓ Subtitles burned: {output_path}")

The Result

You get a batch of videos ready for TikTok, Reels, or Shorts with integrated captions, without going through a manual editor for each one.

⚠️ Warning

Burning subtitles into the video = irreversible.

Always keep a copy of the video without subtitles to be able to adjust the style or language later.

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Resize and Compress for TikTok, Reels, Shorts 📱

The Context

The three main platforms share the same vertical format (9:16), but weight and codec constraints remain crucial for:

• Upload speed
• Playback fluidity

FFmpeg is designed for this type of large-scale processing.

Compression Profiles

You can configure a series of profiles:

Vertical 9:16 Profile

scale=1080:1920 with cropping if necessary, for TikTok, Reels, Shorts.

Social Compression Profile

Adjusted video bitrate (for example ~4–8 Mbps depending on duration), H.264 codec and AAC audio to find the right balance between quality and upload speed.

The Batch Pipeline

On the script side, a simple Python loop can:

Step 1: Browse all .mp4 files in a folder

Step 2: Apply the FFmpeg command

Apply the corresponding FFmpeg command to each profile, generating suffixed files:

• _tiktok.mp4
• _reels.mp4
• _shorts.mp4

The Code

def process_vertical_video(input_path, output_path, bitrate='6M'):
    """
    Resizes to 9:16 and compresses for social networks.
    """
    cmd = [
        'ffmpeg',
        '-i', input_path,
        '-vf', 'scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920',
        '-c:v', 'libx264',
        '-b:v', bitrate,
        '-c:a', 'aac',
        '-b:a', '128k',
        '-movflags', '+faststart',
        output_path
    ]
    subprocess.run(cmd, check=True)
    print(f"✓ Processed: {output_path}")

# Batch processing
source_dir = Path('./raw_videos')
output_dir = Path('./processed_videos')
output_dir.mkdir(exist_ok=True)

for video in source_dir.glob('*.mp4'):
    output_path = output_dir / f"{video.stem}_vertical.mp4"
    process_vertical_video(str(video), str(output_path))

💡 Pro Tip

Use the -movflags +faststart flag to optimize streaming.

This allows the video to start playing before being fully downloaded. Crucial for mobile user experience.

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Conclusion: From Craft to Automated Factory

By combining these operations — renaming, subtitles, resize, compression — in a single script, you move from a craft flow to a true automated post-production factory.

The difference between a creator who scales and a creator who stagnates?

Post-production automation.

Act now.