How do I handle batch transcoding for a back catalog of videos?

A good way to handle batch transcoding for a back catalog is to treat it like an ingestion pipeline, not a one-off script. The key is to standardize, automate, and make the process resumable.

1) Define your target outputs first

Before transcoding anything, decide:

• Delivery formats: e.g. H.264 MP4 for widest compatibility, H.265 for storage savings, AV1 for newer platforms
• Resolutions: keep originals? produce 1080p/720p/480p?
• Audio specs: AAC stereo, 48 kHz, target bitrate
• Container: usually MP4 for broad support
• Quality policy: constant quality vs fixed bitrate
• Subtitles/metadata: whether to preserve embedded tracks, chapters, thumbnails, etc.

If you don’t define these up front, you’ll end up redoing work later.

2) Inventory and normalize the source library

Scan the back catalog and build a manifest with:

• file path
• duration
• codec
• resolution
• audio streams
• subtitle tracks
• file size
• checksum
• any known bad/corrupt files

Tools:

• ffprobe for media inspection
• a database or JSON/CSV manifest to track status

Example:

ffprobe -v error -show_format -show_streams -of json input.mp4

3) Use an ffmpeg-based workflow

For most back catalogs, ffmpeg is the practical choice.

Typical example for a widely compatible output:

ffmpeg -i input.mkv \
  -c:v libx264 -preset medium -crf 20 \
  -c:a aac -b:a 160k \
  -movflags +faststart \
  output.mp4

If you need multiple renditions, generate them from the same source rather than chaining transcodes.

4) Make it resumable and idempotent

Batch jobs fail. Assume they will.

Design the pipeline so that:

• completed files are skipped on rerun
• partially written outputs are written to temp files first
• each job has a status: pending / running / done / failed / retry
• failures are logged with enough detail to debug later

Common pattern:

1. transcode to output.tmp
2. verify file
3. rename to final name only if successful

5) Parallelize carefully

You can speed up transcoding by processing multiple files in parallel, but don’t oversubscribe CPU, disk, or GPU.

Guidelines:

• CPU encoding: run a limited number of concurrent jobs, often 1 per core group or fewer
• GPU encoding: watch GPU session limits and quality tradeoffs
• avoid too many simultaneous reads from the same disk
• separate I/O-heavy and CPU-heavy workloads if possible

If using GNU Parallel:

find /source -name '*.mkv' | parallel -j 4 'ffmpeg -i {} -c:v libx264 -crf 20 -c:a aac {/}.mp4'

6) Preserve what matters

If your back catalog includes:

• multiple audio tracks
• subtitles
• chapters
• artwork
• timecode metadata

then decide whether to:

• copy them through
• extract them separately
• standardize them
• discard them

Example to copy subtitles and chapters when possible:

ffmpeg -i input.mkv -c:v libx264 -crf 20 -c:a aac -c:s mov_text output.mp4

7) Add validation

Don’t trust a successful exit code alone.

Validate:

• output file exists and is non-zero size
• duration matches source within tolerance
• file plays
• codec/profile matches your target
• no decode errors in logs

At minimum:

ffprobe output.mp4

For higher assurance, compare duration and maybe sample hashes or frame checksums for spot checks.

8) Plan storage and retention

Back catalogs often multiply storage needs.

Common approaches:

• keep original masters untouched
• store transcoded derivatives separately
• use lifecycle rules for temp files and logs
• estimate storage before starting

If disk space is tight, transcode in waves and archive originals afterward, but only after you’ve verified outputs.

9) Track jobs with a manifest

A simple CSV/SQLite/Postgres table helps a lot.

Fields:

• source path
• output path
• settings preset
• hash
• job status
• error message
• timestamps
• retries

This lets you:

• resume after interruption
• re-run only failed items
• compare different encoding settings later

10) Consider orchestration tools for larger libraries

If the catalog is large enough to be a project, use a workflow system:

• simple shell scripts for small jobs
• Python with subprocess + SQLite for medium
• Airflow, Prefect, Luigi, or Argo for large-scale pipelines
• a job queue if processing continuously

Practical recommendation

For most back catalogs, start with:

• ffprobe inventory
• a manifest in SQLite or CSV
• ffmpeg transcodes with temp files
• limited parallelism
• validation and resumability

That gives you a pipeline you can rerun safely.

If you want, I can also give you:

1. a sample ffmpeg batch script,
2. a Python transcoding pipeline with resume support, or
3. recommendations for choosing codecs/bitrates for your specific library.