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name: data-preparation description: "Guides Tacotron LJ Speech and Blizzard data layouts, spectrogram preprocessing, train.txt metadata contracts, and custom dataset preprocessors." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data preparation

Use this route for dataset layout checks, preprocess.py command construction, custom preprocessor design, spectrogram metadata validation, and failures before training. Read references/data-formats.md before creating data.

Workflow

1. Put the selected dataset in a stable base directory. LJ Speech requires LJSpeech-1.1/metadata.csv and LJSpeech-1.1/wavs/*.wav; Blizzard expects the documented book directories, sentence_index.txt, lab/, and wav/.
2. Build the preprocessing command with the bundled helper, choosing an explicit dataset and output directory. Review the printed command before intentionally executing the repository's matching entry point.
3. Confirm that the output contains time-major linear and mel .npy arrays and train.txt rows. Use the bundled validator before training.
4. For a new corpus, implement the four-field tuple contract and register a dataset branch only after a tiny fixture validates its paths, dtypes, frame counts, and text.

Command roots and external audio boundary

Run bundled dry-runs from the skill root and execute the repository preprocessor only from the checkout root:

SKILL_ROOT=/path/to/tacotron-skill
CHECKOUT_ROOT=/path/to/tacotron-checkout
cd "$SKILL_ROOT" && python sub-skills/data-preparation/scripts/build_preprocess_command.py --checkout-root "$CHECKOUT_ROOT" --base-dir /data/tacotron --dataset ljspeech --output training
cd "$SKILL_ROOT" && python sub-skills/data-preparation/scripts/validate_training_metadata.py --data-dir /data/tacotron/training

Preprocessing itself needs downloaded LJ Speech or Blizzard audio, readable WAV decoding through the pinned librosa/SciPy stack, writable storage, and enough disk. The fixture creates only local layout/array metadata plus a placeholder PCM WAV; it does not run preprocessing, validate spectrograms, or train.

The bundled scripts/build_preprocess_command.py builds the documented CLI without reading a dataset. The bundled scripts/create_tiny_ljspeech_fixture.py creates a no-network layout fixture. scripts/validate_training_metadata.py checks rows and arrays without starting TensorFlow training. Read references/workflows.md for preprocessing and custom-corpus recipes and references/troubleshooting.md for path, audio, and shape failures.