Sold by: Cartesia
Deployed on AWS
Cartesia Sonic delivers natural AI Voices in 40+ languages including accent localization and controls for emotional expressiveness, all at 2-4x lower latency than alternatives with industry leading reliability.
Overview
Cartesia is the leading Voice AI foundation model research and development company powering the next generation of Voice AI applications. The team pioneered State Space Models during their PhDs at Stanford and commercialized the architecture in real-time speech synthesis.
Highlights
- Sonic's support for 40+ language with accent localization and multilingual voices reaches customers around the world.
- Full control over emotional expressiveness, speed, volume and more, all at 2-4x lower latencies than alternatives.
- Achieve accurate pronunciation for complex phone numbers, addresses, and IDs every invocation.
Details
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Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.001/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.037/request |
Dimensions summary
This listing bills on usage, with two separate dimensions. The first charges by the hour for running model inference in batch mode on the ml.m5.4xlarge instance type. Your cost here scales with how many hours the instance runs. The second dimension charges per inference request processed. Your cost here scales with the number of requests you make. The two dimensions are independent and bill on their own units. You can incur charges from one or both depending on how you run the text-to-speech model.
Top-of-mind questions for buyers
This dimension charges per hour that the ml.m5.4xlarge instance runs the model in batch mode. Batch mode processes inputs in grouped jobs rather than one at a time. Charges accrue for each running hour. A stopped or terminated instance stops accruing these software charges, though underlying AWS infrastructure fees may still apply.
The two dimensions bill independently and can both appear on one invoice. The instance dimension meters running hours of the ml.m5.4xlarge instance. The inference dimension meters each request processed. Running-time cost dominates for continuous batch jobs, while request volume drives cost when you process many separate inference calls.
This listing runs model inference on a dedicated AWS SageMaker instance you provision. The vendor also supports on-premise and private cloud deployment for the broader product, available under separate arrangements. For those options, contact the vendor to confirm fit with your environment and requirements.
www.cartesia.ai
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Improved voices across all locales Added new voices Improved multilingual support
Additional details
Inputs
- Summary
The response streaming endpoint takes in a JSON object as the input that specifies the transcript, voice, language, and output format for the generation.
- Input MIME type
- application/json
{
"context_id": "0",
"transcript": "The detective burst through the door. 'We've got maybe five minutes before they realize we're here, so listen carefully and listen well: <speed ratio='1.5'/> the artifact is hidden beneath the old courthouse, exactly three feet below the cornerstone, and <volume ratio='0.5'/>whatever you do, DO NOT touch it with your bare hands!' She paused, catching her breath. 'Now... here's the important part... <speed ratio='0.6'/>you need to... very slowly... very carefully... wrap it in the copper wire first... then the silk cloth... then seal it in the lead box.' <volume ratio='2.0'/> Footsteps echoed in the hallway. 'GO GO GO! They're coming up the stairs RIGHT NOW!'",
"language": "en",
"output_format": {
"container": "raw",
"sample_rate": 44100,
"encoding": "pcm"
}
"voice_id": {
"mode": "id",
"id": "bf0a246a-8642-498a-9950-80c35e9276b5"
},
}
Do not use batch mode.
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
context_id | A unique ID provided by the client to identify the request. It can be any string value and helps with tracking or debugging | - | Yes |
transcript | The text that will be converted into speech. You can include additional controls (e.g., emotion, speed, volume) as supported by Sonic 3 models: https://docs.cartesia.ai/build-with-cartesia/sonic-3/volume-speed-emotion | - | Yes |
language | The language code of the transcript text. Supported codes include: ar, bg, bn, cs, da, de, el, en, es, fi, fr, gu, he, hi, hr, hu, id, it, ja, ka, kn, ko, ml, mr, ms, nl, no, pa, pl, pt, ro, ru, sk, sv, ta, te, th, tl, tr, uk, vi, zh, en-US, de-DE, es-MX, fr-FR, hi-IN, it-IT, ja-JP, nl-NL, pt-BR | If not provided, the transcript language will be automatically detected | No |
output_format | Must match the raw option from the Cartesia TTS SSE API: https://docs.cartesia.ai/api-reference/tts/sse#body-output-format. Only raw is supported | - | Yes |
voice | Matches the voice field from the Cartesia TTS SSE API: https://docs.cartesia.ai/api-reference/tts/sse#body-voice. Only mode = id is supported. Example: { "mode": "id", "id": "voice_123" } | - | Yes |
generation_config | Optional configuration object matching the API schema: https://docs.cartesia.ai/api-reference/tts/sse#body-generation-config | - | No |
add_timestamps | Whether to include word-level timestamps in the output: https://docs.cartesia.ai/api-reference/tts/sse#body-add-timestamps | - | No |
add_phoneme_timestamps | Whether to include phoneme-level timestamps in the output: https://docs.cartesia.ai/api-reference/tts/sse#body-add-phoneme-timestamps | - | No |
use_normalized_timestamps | Whether timestamps should be normalized (0–1 range): https://docs.cartesia.ai/api-reference/tts/sse#body-use-normalized-timestamps | - | No |
pronunciation_map | Optional map of word-to-pronunciation pairs that customizes how specific words are spoken. Keys are the words as they appear in the transcript; values are the desired pronunciations. Use this to override default pronunciations for domain-specific terms, proper nouns, acronyms, or any word the model mispronounces: https://docs.cartesia.ai/build-with-cartesia/capability-guides/custom-pronunciations Example: {"Cartesia": "kar-TEE-zhuh", "API": "A-P-I"} | 16 KB max size | No |
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