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.yamlage_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