One video at a time is fine for getting started. But once VidNo becomes part of your routine, you accumulate recordings faster than you process them. Batch processing lets you queue up a week's worth of coding sessions and let VidNo handle them overnight.

How Batch Processing Works

VidNo's batch mode processes multiple recordings sequentially through the same pipeline. Each recording gets its own independent analysis, script, and rendering pass -- they are not merged or mixed.

# Add recordings to the queue
vidno queue add ~/recordings/monday-api.mp4
vidno queue add ~/recordings/tuesday-frontend.mp4
vidno queue add ~/recordings/wednesday-tests.mp4
vidno queue add ~/recordings/thursday-deploy.mp4
vidno queue add ~/recordings/friday-refactor.mp4

# Process the entire queue
vidno queue process

VidNo processes each recording completely before starting the next. This avoids GPU memory contention and ensures each video gets the full pipeline's attention.

Queue Management

# View the current queue
vidno queue list
# 1. monday-api.mp4        queued    (est. 6 min)
# 2. tuesday-frontend.mp4  queued    (est. 8 min)
# 3. wednesday-tests.mp4   queued    (est. 4 min)
# 4. thursday-deploy.mp4   queued    (est. 7 min)
# 5. friday-refactor.mp4   queued    (est. 5 min)
# Total estimated time: 30 minutes

# Reorder the queue
vidno queue move 3 1  # Move tests recording to first position

# Remove an item
vidno queue remove 4

# Clear the entire queue
vidno queue clear

Watch Directory Mode

For continuous workflows, VidNo can watch a directory and automatically process new recordings:

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# Start watching a directory
vidno watch ~/recordings/

# VidNo now monitors for new .mp4/.mkv/.webm files
# When one appears, it's automatically queued and processed

This is useful for teams where developers drop recordings into a shared folder. Combine it with a dedicated processing machine (a workstation with a good GPU sitting under a desk) and you have an automated content pipeline.

Per-Recording Configuration

Different recordings might need different settings. You can attach configuration to individual queue items:

# This recording needs a specific git repo
vidno queue add recording.mp4 --repo ~/projects/api-service

# This one should only produce the full tutorial
vidno queue add recording.mp4 --format full

# This one uses a different voice profile
vidno queue add recording.mp4 --voice presentation-voice

Resource Management

Batch processing is GPU-intensive. Some considerations:

  • GPU memory: VidNo uses 8-10 GB of VRAM during voice synthesis. If your GPU has 12 GB, do not run other GPU-intensive applications during batch processing.
  • CPU: Frame analysis and FFmpeg encoding are CPU-bound. On a 4-core system, batch processing will saturate all cores. On 8+ cores, other work is feasible alongside.
  • Disk space: Each recording generates 2-3x its file size in intermediate data. For a 5-recording batch totaling 8 GB, reserve at least 25 GB of free disk space.
  • Power: If running overnight on a laptop, keep it plugged in. GPU processing at full load drains batteries in under an hour.

Notifications

# Get notified when batch completes
vidno queue process --notify

# This sends a desktop notification on completion
# Or configure webhook notifications:
vidno config set notify-webhook https://hooks.slack.com/xxx

Error Handling

If a recording fails to process (corrupted file, unsupported format, etc.), VidNo skips it and moves to the next item in the queue. Failed items are logged:

vidno queue history
# 1. monday-api.mp4        completed  6m 12s
# 2. tuesday-frontend.mp4  completed  7m 45s
# 3. wednesday-tests.mp4   FAILED     corrupt file
# 4. thursday-deploy.mp4   completed  6m 58s
# 5. friday-refactor.mp4   completed  5m 23s

A failed recording does not block the rest of the queue. You can re-queue it after fixing the issue.

Batch processing makes VidNo practical for content at scale. Whether you are a solo creator banking a month of videos or a team producing daily content, the queue system handles the volume. For team-specific workflows, see VidNo for teams.