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name: text-normalization description: "Guides Tacotron text cleaning, number expansion, symbol IDs, ARPAbet pronunciation syntax, and CMUDict integration for training and synthesis." disable-model-invocation: true metadata: disco-role: operating license: MIT

Text normalization

Use this route when the task asks how Tacotron converts text into model input, why pronunciation differs, how numbers or abbreviations are expanded, or how to select cleaners for English and non-English text.

Workflow

1. Choose the cleaner pipeline before changing data: english_cleaners expands numbers/abbreviations and transliterates; transliteration_cleaners only transliterates/lowercases/collapses whitespace; basic_cleaners preserves non-ASCII characters but requires a matching symbol vocabulary.
2. Convert with text.text_to_sequence(text, cleaner_names). It appends the ~ EOS id and accepts ARPAbet tokens in braces, for example Turn left on {HH AW1 S S T AH0 N} Street..
3. Keep the cleaner names and symbol set consistent between preprocessing, training, evaluation, and serving. Use scripts/text_pipeline_check.py for a safe local check.
4. If use_cmudict=True, provide the expected CMUDict file beside the preprocessed metadata and understand that training randomly substitutes pronunciations; see the references for ambiguity behavior.

Command roots and boundary

Use the generated helper from the skill root, and use the source checkout as cwd for the real cleaner. These paths exist in the target checkout; replace the assignments when using a different copy.

SKILL_ROOT=/path/to/tacotron-skill
CHECKOUT_ROOT=/path/to/tacotron-checkout
cd "$SKILL_ROOT" && python sub-skills/text-normalization/scripts/text_pipeline_check.py
cd "$CHECKOUT_ROOT" && python -c "from text import text_to_sequence; print(text_to_sequence('A test.', ['english_cleaners']))"

The first command checks routing syntax only; it does not import the checkout, prove the repository cleaners, download CMUDict, or synthesize audio. The checkout command requires the legacy Unidecode/inflect dependencies.

Read references/api-reference.md for exact functions and symbol contracts, references/workflows.md for language/pronunciation recipes, and references/troubleshooting.md for invalid cleaners, dropped symbols, and CMUDict failures. The bundled scripts/text_pipeline_check.py is a safe smoke helper; it does not train or synthesize audio.