How to Edit and Modify Video Metadata Tags

Every video file is a container, and that container holds more than just picture and sound. The metadata layer stores descriptive tags like title, artist, album, genre, description, copyright, and creation date. MP4 and MOV files use an atom/box structure inherited from Apple's QuickTime format, where each piece of metadata lives in a named atom. MP4 containers can hold over 50 distinct tag types across iTunes-style atoms and custom user data atoms. MKV files use a different approach: Matroska's tag system supports hierarchical tagging with nested elements for tracks, chapters, and editions.

Beyond basic descriptive tags, video containers can store chapter markers (timed navigation points), embedded artwork, subtitle track metadata, encoding parameters, and even GPS coordinates from mobile recordings. YouTube, Vimeo, and social platforms read embedded metadata when you upload a file. They pull the title, description, and sometimes thumbnail data directly from these tags as initial suggestions.

Understanding which container format you're working with determines which tools and tag schemas apply. MP4 and M4V use iTunes-style atoms. MKV uses Matroska tags. MOV shares the atom structure with MP4 but may include QuickTime-specific extensions. AVI and WMV have their own legacy tag systems, though these formats are increasingly uncommon in modern workflows. Production teams that need video files to stay tagged, organized, and findable can use Fast.io workspaces with collaboration and AI-powered metadata extraction layered on top.

FFmpeg is the most versatile command-line tool for video metadata editing. It supports virtually every container format and can read, write, and strip metadata without re-encoding the video stream. The key flag is -metadata, which accepts key-value pairs.

Basic Tag Editing

To set the title and artist on an MP4 file without re-encoding:

ffmpeg -i input.mp4 -metadata title="Project Demo" -metadata artist="Jane Smith" -c copy output.mp4

The -c copy flag copies all streams (video, audio, subtitles) directly, so the operation finishes in seconds regardless of file size. You can chain as many -metadata flags as needed in a single command.

Common MP4 Tags

FFmpeg recognizes standard tags including title, artist, album, genre, date, description, comment, copyright, encoder, and language. For MP4 files specifically, adding -movflags use_metadata_tags enables custom key-value pairs beyond the standard set:

ffmpeg -i input.mp4 -metadata project_id="PRJ-2026-042" -movflags use_metadata_tags -c copy output.mp4

Stripping All Metadata

To remove every metadata tag from a file, use -map_metadata -1:

ffmpeg -i input.mp4 -map_metadata -1 -c copy clean.mp4

This clears global metadata while preserving stream-level technical information (codec, resolution, frame rate) that players need.

Clearing a Single Tag

Set the tag to an empty string to remove it without touching other metadata:

ffmpeg -i input.mp4 -metadata title="" -c copy output.mp4

Preserving Existing Tags

When you only want to add or overwrite specific tags while keeping everything else, use -map_metadata 0 alongside your new values:

ffmpeg -i input.mp4 -map_metadata 0 -metadata title="Updated Title" -c copy output.mp4

This copies all existing metadata from the input, then overwrites only the tags you specify.

AtomicParsley is a lightweight command-line tool built specifically for reading and writing metadata in MPEG-4 containers (MP4, M4V, M4A). Where FFmpeg treats metadata as one part of a larger toolkit, AtomicParsley focuses entirely on the atom structure of MP4 files and supports iTunes-style tags that FFmpeg handles less reliably.

Installation

On macOS with Homebrew: brew install atomicparsley. On Ubuntu/Debian: apt install atomicparsley. On Windows, download the binary from the GitHub releases page at github.com/wez/atomicparsley.

Reading Tags

To display all metadata tags in a file:

AtomicParsley input.mp4 -t

For a full atom tree showing the internal structure:

AtomicParsley input.mp4 -T

Writing Tags

AtomicParsley uses named flags for common iTunes-style tags:

AtomicParsley input.mp4 --title "Project Demo" --artist "Jane Smith" --year "2026" --comment "Internal review copy" --overWrite

The --overWrite flag modifies the file in place rather than creating a temp file with a modified name.

Artwork Embedding

One area where AtomicParsley excels over FFmpeg is embedding cover artwork into MP4 files:

AtomicParsley input.mp4 --artwork cover.jpg --overWrite

This is particularly useful for video podcasts, training videos, and media libraries where thumbnail images need to travel with the file.

When to Choose AtomicParsley Over FFmpeg

Use AtomicParsley when you need reliable iTunes-style tagging, artwork embedding, or you're working with files destined for Apple ecosystem playback. For everything else, including format conversion and batch operations across mixed containers, FFmpeg is the more flexible choice.

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MKV files use a different metadata system than MP4, and MKVToolNix is the standard toolset for working with Matroska containers. It includes both a GUI (MKVToolNix GUI) and command-line tools (mkvpropedit, mkvmerge, mkvinfo).

Editing Tags with mkvpropedit

The mkvpropedit command modifies MKV metadata in place without remuxing:

mkvpropedit input.mkv --edit info --set "title=Project Demo"

You can edit track-level properties too. For example, setting the language of the first audio track:

mkvpropedit input.mkv --edit track:a1 --set language=eng

Adding Chapter Markers

MKVToolNix has strong chapter support. Create a chapters file in XML or simple text format, then apply it:

mkvmerge -o output.mkv --chapters chapters.xml input.mkv

A simple chapters text file looks like this:

CHAPTER01=00:00:00.000
CHAPTER01NAME=Introduction
CHAPTER02=00:05:30.000
CHAPTER02NAME=Setup Guide
CHAPTER03=00:12:15.000
CHAPTER03NAME=Advanced Configuration

Using the GUI

MKVToolNix GUI includes a Header

Editor tab where you can click through segment info, track headers, and chapter data visually. Open a file, navigate to the tag you want, edit the value, and save. This is the fast option for one-off edits where writing command-line flags would be slower.

Installation

Available on all platforms: brew install mkvtoolnix (macOS), apt install mkvtoolnix mkvtoolnix-gui (Debian/Ubuntu), or download installers from mkvtoolnix.download for Windows.

Chapter markers turn a flat video timeline into navigable sections. Viewers can jump between topics, and platforms like YouTube can use chapters for enhanced scrubbing. Both MP4 and MKV support embedded chapters, and FFmpeg can write them using a metadata file.

Creating an FFmpeg Metadata File

First, export any existing metadata from your video:

ffmpeg -i input.mp4 -f ffmetadata metadata.txt

Then add chapter entries to the metadata file. Each chapter needs a start time, end time (in microseconds or as a timebase fraction), and a title:

[CHAPTER]
TIMEBASE=1/1000
START=0
END=330000
title=Introduction

[CHAPTER]
TIMEBASE=1/1000
START=330000
END=735000
title=Setup and Installation

[CHAPTER]
TIMEBASE=1/1000
START=735000
END=1200000
title=Configuration

Applying Chapters to the Video

Write the chapter metadata back into the file:

ffmpeg -i input.mp4 -i metadata.txt -map_metadata 1 -c copy output.mp4

The -map_metadata 1 flag tells FFmpeg to use the metadata from the second input (the text file) instead of the original file's metadata. Combined with -c copy, no re-encoding happens.

Preserving Chapters During Conversion

When converting between formats, use -map_chapters 0 to carry chapter data forward:

ffmpeg -i input.mkv -map 0:v -map 0:a -map_chapters 0 -c:v copy -c:a copy output.mp4

When you need to edit metadata across dozens or hundreds of video files, scripting is the practical path. Both Python and Node.js have libraries that wrap FFmpeg or work with container formats directly.

Python with subprocess and FFmpeg

The most reliable approach in Python is calling FFmpeg through subprocess. This gives you full access to every FFmpeg flag without depending on wrapper libraries that may lag behind FFmpeg releases:

import subprocess
import json

def read_metadata(filepath):
    result = subprocess.run(
        ["ffprobe", "-v", "quiet", "-print_format", "json",
         "-show_format", filepath],
        capture_output=True, text=True
    )
    return json.loads(result.stdout)["format"]["tags"]

def set_metadata(filepath, output, tags):
    cmd = ["ffmpeg", "-i", filepath]
    for key, value in tags.items():
        cmd.extend(["-metadata", f"{key}={value}"])
    cmd.extend(["-c", "copy", output])
    subprocess.run(cmd, check=True)

Python with Mutagen for MP4

The Mutagen library provides native Python access to MP4 tags without requiring FFmpeg. It works well for iTunes-style metadata but is limited to audio-oriented containers:

from mutagen.mp4 import MP4

video = MP4("input.mp4")
video["\xa9nam"] = ["Project Demo"]  # title
video["\xa9ART"] = ["Jane Smith"]    # artist
video["\xa9day"] = ["2026"]          # year
video.save()

Mutagen's MP4 support covers the tag types that overlap between audio and video, but it won't handle video-specific atoms or chapter markers. For full video metadata control, FFmpeg remains the better choice.

Node.js with fluent-ffmpeg

For JavaScript projects, the fluent-ffmpeg package provides a chainable API:

const ffmpeg = require("fluent-ffmpeg");

ffmpeg("input.mp4")
  .outputOptions([
    "-metadata", "title=Project Demo",
    "-metadata", "artist=Jane Smith",
    "-c", "copy"
  ])
  .save("output.mp4")
  .on("end", () => console.log("Metadata updated"));

Batch Processing Pattern

A practical batch script reads a CSV or JSON file mapping filenames to their intended tags, then loops through each entry. This approach works well for video libraries where you need to apply consistent project names, copyright notices, or client codes across an entire delivery folder.

For teams managing large volumes of video files, Fast.io Metadata Views can extract and organize metadata from uploaded files into a sortable, filterable grid. Describe the fields you want in natural language, and the AI builds a typed schema, matches files in your workspace, and populates a queryable spreadsheet. This is particularly useful when you need to audit existing metadata across a video library before deciding what to change.