---

name: data-preparation description: "Routes VITS dataset preparation, filelist cleaning, and monotonic-alignment build tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data preparation

Use this route for custom corpus setup, filelist cleaning, and build-time prerequisites that must happen before training or inference.

Use this route when

• You need to clean raw text filelists into the repo's .cleaned format.
• You need to match the LJ Speech or VCTK filelist layouts.
• You need to verify dataset sampling rates or speaker-id columns.
• You need to build the monotonic-alignment extension before importing models.
• You need to diagnose phonemizer, espeak, or filelist parsing issues.

Do not use this route when

• You are actually launching training; use training.
• You are generating audio from a checkpoint; use inference.
• You only need a quick environment check; use ../../scripts/check_install.py from the root skill.

Read first

• ../../references/configuration.md for the filelist formats and config-field mapping.
• ../../references/workflows.md for the data-preparation command flow.
• ../../references/troubleshooting.md for cross-cutting failures.
• references/troubleshooting.md in this sub-skill for filelist- and cleaner-specific failures.

Bundled helpers

• ../../scripts/preprocess_text.py — clean one or more filelists.
• ../../scripts/build_monotonic_align.py — build the nested extension layout expected by monotonic_align.
• ../../scripts/check_install.py — confirm the repo imports before you move to training or inference.

Common workflow

1. Confirm the dataset layout and audio sampling rate.
2. Decide whether the repo's provided cleaned filelists are already enough.
3. Run ../../scripts/preprocess_text.py only for custom raw filelists.
4. Build monotonic_align with ../../scripts/build_monotonic_align.py before any model import.
5. Re-run ../../scripts/check_install.py when the data or build step changes.

Ownership boundaries

• Include text normalization, filelist column mapping, and dataset-layout validation here.
• Exclude training launch, checkpoints, and synthesis outputs; route those to sibling sub-skills.