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Pulse STT - `age_detection` removed from the pre-recorded HTTP API

The age_detection query parameter and the corresponding top-level age field in the response have been removed from the Pulse STT pre-recorded HTTP API (POST /waves/v1/pulse/get_text). Gender detection (gender_detection / gender) and emotion detection (emotion_detection / emotions) are unaffected.

Specs and reference docs updated:

  • fern/apis/waves/openapi/pulse-stt-openapi.yaml - age_detection query param dropped; age response field and example value removed.
  • fern/products/waves/pages/v4.0.0/api-references/pulse-stt.mdx (+ versions mirror) - cURL/Python/JavaScript samples for both raw-bytes and audio-URL methods no longer pass age_detection.
  • fern/products/waves/pages/v4.0.0/speech-to-text/pre-recorded/code-examples.mdx (+ versions mirror) - Python end-to-end sample no longer requests or prints age.
  • fern/products/waves/pages/v4.0.0/speech-to-text/features/age-and-gender-detection.mdx (+ versions mirror) - page retitled to Gender detection and trimmed to gender-only content. The file path is unchanged so existing /features/age-and-gender-detection links keep resolving.
  • fern/products/waves/pages/v4.0.0/integrations/n8n.mdx, speech-to-text/overview.mdx, speech-to-text/pre-recorded/features.mdx, speech-to-text/model-cards/pulse.mdx, and the STT benchmarks metrics-overview.mdx - surrounding tables, accordions, and feature cards updated to drop age references.
  • fern/products/waves/versions/v4.0.0.yml - sidebar entry retitled to Gender Detection.

If your code passes age_detection=true or reads response.age, drop both - the parameter is now ignored and the field will not be returned. No other Pulse STT request shape or response field changes.

→ Gender detection


Pulse STT - recommend `itn_normalize` over `numerals` for new integrations

The numerals query parameter on the Pulse STT WebSocket API still works and continues to behave as documented. For new integrations we now recommend itn_normalize=true instead - it covers digits as well as dates, currencies, phone numbers, and other spoken-form entities, and gives more consistent results across languages.

Existing code that uses numerals does not need to change.

→ Inverse Text Normalization