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Sonic-3 provides rich controls for the speed, volume, and emotion of generated speech. These controls are available on play.cartesia.ai using the UI controls, or by passing in a generation_config parameter, or by using SSML tags within the transcript itself.
Sonic-3 interprets these parameters as guidance instead of as strict adjustments to ensure natural speech, so we recommend testing them against your content to ensure the output matches your expectations.

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Speed and Volume Controls

You can guide the speed and volume of a TTS generation with the generation_config.speed and generation_config.volume parameters. These values are roughly a multiplier on the default speed and volume, eg, 1.5 will generate audio at 1.5x the default speed.
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generation_config.speed
number
The speed of the generation, ranging from 0.6 to 1.5.
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generation_config.volume
number
The volume of the generation, ranging from 0.5 to 2.0.
You can also specify these inside the transcript itself, using SSML, like: <speed ratio="1.5"/> I like to speak quickly because it makes me sound smart. <volume level="1.5"/> And I can be loud, too!

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Emotion Controls Beta

By default, the model attempts to interpret the emotional subtext present in the provided transcript. You can guide the emotion of a TTS generation, like a director providing guidance to an actor, using the generation_config.emotion parameter.
Emotion tags are good to push the model to be more emotive, but they only work when the emotion is consistent with transcript. For instance `I’m so excited!` is unlikely to work well.
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generation_config.emotion
string
The emotional guidance for a generation, one of the emotions below.
The primary emotions, for which we have the most data and produce the best results, are: neutral, angry, excited, content, sad, and scared. The complete list of available emotions is: happy, excited, enthusiastic, elated, euphoric, triumphant, amazed, surprised, flirtatious, joking/comedic, curious, content, peaceful, serene, calm, grateful, affectionate, trust, sympathetic, anticipation, mysterious, angry, mad, outraged, frustrated, agitated, threatened, disgusted, contempt, envious, sarcastic, ironic, sad, dejected, melancholic, disappointed, hurt, guilty, bored, tired, rejected, nostalgic, wistful, apologetic, hesitant, insecure, confused, resigned, anxious, panicked, alarmed, scared, neutral, proud, confident, distant, skeptical, contemplative, determined. The Voices with the best emotional response are:
  • Leo (id: 0834f3df-e650-4766-a20c-5a93a43aa6e3)
  • Jace (id: 6776173b-fd72-460d-89b3-d85812ee518d)
  • Kyle (id: c961b81c-a935-4c17-bfb3-ba2239de8c2f)
  • Gavin (id: f4a3a8e4-694c-4c45-9ca0-27caf97901b5)
  • Maya (id: cbaf8084-f009-4838-a096-07ee2e6612b1)
  • Tessa (id: 6ccbfb76-1fc6-48f7-b71d-91ac6298247b)
  • Dana (id: cc00e582-ed66-4004-8336-0175b85c85f6)
  • Marian (id: 26403c37-80c1-4a1a-8692-540551ca2ae5)
View the full list of emotive Voices on our Voice Library with voices tagged ‘Emotive’. You can also use SSML tags for emotions, for example: <emotion value="angry" /> How dare you speak to me like I'm just a robot!

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Nonverbalisms

Insert [laughter]in your transcript to make the model laugh. In the future we plan to add more non-speech verbalisms like sighs and coughs.