Advanced capabilities - Cartesia Docs

Sonic handles most transcripts as-is. The capabilities below cover cases where you want finer control over mixed-language speech and how written forms are spoken.

Hinglish

Sonic supports code-switching between Hindi and English (Hinglish) in a single generation. Pass the transcript in conventional written form — Devanagari, Latin script, or a mix — and the model switches languages naturally mid-sentence.

from cartesia import Cartesia

client = Cartesia(api_key="your-api-key")
response = client.tts.generate(
    model_id="sonic-preview",
    transcript="आपका order confirm हो गया है। Delivery expected by Friday.",
    voice="a0e99841-438c-4a64-b679-ae501e7d6091",
    language="hi",
)
audio = response.read()

Romanized Hindi and Indic text

Sonic reads Hindi and other Indic languages written in Latin script — Hinglish and other transliterated text — and follows romanized transcripts materially better in Sonic 3.6, available on sonic-preview now. Write romanized text the way it’s naturally typed, and keep English words in their standard spelling:

Aapka order confirm ho gaya hai. Delivery kal shaam tak hogi.

Set the language field to the language of the transcript (hi for Hinglish) even when the text is romanized. Quality varies with how scripts are mixed in the transcript:

Script mix	Example	`language`	`normalization`
Pure Devanagari	`आपका ऑर्डर आ गया है।`	`hi`	`hi-IN`
Pure English	`Your order has arrived.`	`en` or `hi`	`en-IN`
Pure romanized Hindi	`Aapka order aa gaya hai.`	`hi`	`en-IN`
Romanized Hindi + English	`Aapka order confirm ho gaya hai.`	`hi`	`en-IN`
Devanagari + English loanwords	`आपका order confirm हो गया है।`	`hi`	`hi-IN`
Devanagari + romanized Hindi	`आपका order aa gaya hai.`	Not supported	—
All three scripts	`आपका order confirm ho gaya है।`	Not supported	—

For pure English transcripts, either en or hi works as the language: the more Indian words the sentence carries — names, places, product terms — the more the hi setting pronounces them correctly.

Experiment with these settings to find what sounds best for your content. Both language and normalization are set per generation, so you can vary them transcript by transcript rather than picking one combination for your whole integration.

Worked examples

Accent and reading conventions are independent controls: locale picks the voice’s accent, normalization picks how dates, times, and numbers are read.

Hindi voice, English digit reading

An OTP or confirmation code inside a Hindi transcript should read digit-by-digit the English way: 4821 as “four eight two one” rather than as a Hindi number.

{
  "model_id": "sonic-preview",
  "transcript": "आपका OTP 4821 है।",
  "voice": "a0e99841-438c-4a64-b679-ae501e7d6091",
  "locale": "hi",
  "normalization": "en-IN",
  "output_format": { "container": "mp3", "sample_rate": 44100, "bit_rate": 128000 }
}

Romanized Hindi with English read-outs

The highest-traffic combination: a Hinglish transcript spoken with a Hindi-Indian accent while dates, times, and digits follow English-Indian conventions.

{
  "model_id": "sonic-preview",
  "transcript": "Aapka order 14/08/2026 ko deliver hoga, confirmation code 4821 hai.",
  "voice": "a0e99841-438c-4a64-b679-ae501e7d6091",
  "locale": "hi-IN",
  "normalization": "en-IN",
  "output_format": { "container": "mp3", "sample_rate": 44100, "bit_rate": 128000 }
}

Normalizers

Normalizers control how Sonic expands written forms — numbers, currency, dates, phone numbers — into spoken words. By default, Sonic applies locale-aware normalization automatically, so $19.99 is spoken as “nineteen dollars and ninety-nine cents”.

response = client.tts.generate(
    model_id="sonic-preview",
    transcript="Your total is $19.99, due 04/20/2025.",
    voice="a0e99841-438c-4a64-b679-ae501e7d6091",
)
# Spoken: "Your total is nineteen dollars and ninety-nine cents, due April twentieth, twenty twenty-five."

See Prompting tips for the written forms Sonic normalizes today, and pre-normalization as a fallback for edge cases.

Documentation for configuring individual normalizers is coming soon.

Regional reading conventions

Today, en-GB and en-US produce identical normalization output: English and Hindi are excluded from the locale-aware normalization engine, so all English regional variants inherit the same reading conventions. The locale and normalization fields make regional differentiation expressible. If British-specific read-outs matter for your product today, pre-normalize the forms that differ (dates, currency) and see turning normalization off.

Turning normalization off

Setting normalization to "off" skips the automatic normalizer — and only the automatic normalizer. Everything else still applies:

SSML tags and generation controls
Pronunciation dictionaries
Transcript buffering
Input validation: potentially malicious character sequences may be blocked for security and stability reasons

Turn it off when you pre-normalize text yourself, need a custom read-out for a symbol (for example, # as “number”), or need all-caps words spoken as words rather than spelled out letter by letter. Normalization is all-or-nothing per request — there is no per-span control. If most of a transcript should be normalized but one span shouldn’t, write out that span as it should be spoken (along with any other written forms in the transcript that would have needed normalizing) and send the request with normalization off.

Normalizer worked examples

Pre-normalized text with the normalizer off

For “teleprompter” transcripts you’ve already written out the way they should be spoken, custom symbol read-outs, or all-caps words that should be spoken as words:

{
  "model_id": "sonic-preview",
  "transcript": "Your total is nineteen dollars and ninety-nine cents.",
  "voice": "a0e99841-438c-4a64-b679-ae501e7d6091",
  "locale": "en",
  "normalization": "off",
  "output_format": { "container": "mp3", "sample_rate": 44100, "bit_rate": 128000 }
}

Only the automatic normalizer is skipped — see turning normalization off for exactly what still applies.

British accent with US reading conventions

The decoupling pattern itself: pick the accent with locale, pick the reading conventions with normalization.

{
  "model_id": "sonic-preview",
  "transcript": "Your appointment is on 03/04/2026.",
  "voice": "62ae83ad-4f6a-430b-af41-a9bede9286ca",
  "locale": "en-GB",
  "normalization": "en-US",
  "output_format": { "container": "mp3", "sample_rate": 44100, "bit_rate": 128000 }
}

This reads with a British accent and US conventions (03/04/2026 as March fourth). Note that today the reverse isn’t distinguishable — see regional reading conventions: en-GB and en-US normalization output is currently identical, so this pattern matters as regional differentiation lands rather than changing read-outs today.

Different read-out conventions inside one transcript

A single request applies one normalization value to the whole transcript — there is no per-span control, because detecting language switches inside a transcript would add latency to every request. If one clause needs Hindi read-outs and another needs English read-outs, split the transcript and send two requests with different normalization values and the same voice, so the accent stays consistent, then concatenate the audio:

Request 1

{
  "model_id": "sonic-preview",
  "transcript": "Aapki appointment 15 tareekh ko hai.",
  "voice": "a0e99841-438c-4a64-b679-ae501e7d6091",
  "locale": "hi-IN",
  "normalization": "hi-IN",
  "output_format": { "container": "raw", "encoding": "pcm_s16le", "sample_rate": 44100 }
}

Request 2

{
  "model_id": "sonic-preview",
  "transcript": "Please arrive by 2:30 PM on 08/15/2026.",
  "voice": "a0e99841-438c-4a64-b679-ae501e7d6091",
  "locale": "hi-IN",
  "normalization": "en-IN",
  "output_format": { "container": "raw", "encoding": "pcm_s16le", "sample_rate": 44100 }
}

On the WebSocket API, raw PCM chunks from consecutive generations can be concatenated directly.

​Hinglish

​Romanized Hindi and Indic text

​Worked examples

​Hindi voice, English digit reading

​Romanized Hindi with English read-outs

​Normalizers

​Regional reading conventions

​Turning normalization off

​Normalizer worked examples

​Pre-normalized text with the normalizer off

​British accent with US reading conventions

​Different read-out conventions inside one transcript

Hinglish

Romanized Hindi and Indic text

Worked examples

Hindi voice, English digit reading

Romanized Hindi with English read-outs

Normalizers

Regional reading conventions

Turning normalization off

Normalizer worked examples

Pre-normalized text with the normalizer off

British accent with US reading conventions

Different read-out conventions inside one transcript