
Overview
Introducing SSFM (Speech Synthesis Foundation Model), Typecast's cutting-edge AI voice model that represents the next generation of speech synthesis. Built on an advanced large language model, SSFM is 14 times more powerful than its predecessor in understanding language nuances. Trained on a speech dataset 75 times larger than before, it creates incredibly natural and expressive synthetic voices. One of its most impressive features is Voice Cloning - SSFM can replicate any voice with just a 10-second sample, perfectly capturing the speaker's unique style. The model offers complete control over voiceovers, allowing easy adjustments to emotions and speaking pace. Looking ahead, SSFM will support over 30 languages and enable emotion control through natural language prompts. Experience the future of voice technology at https://typecast.ai/Â and discover what SSFM can do for you.
Highlights
- Naturalness
- Expressiveness
- Controllability
Details
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Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $100.00 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $100.00 |
Vendor refund policy
Refund Policy Summary
AWS Marketplace purchases are eligible for refunds. Software products can be fully refunded within 7 days if not functioning as described. Cancellations must be made 7 days before billing. Products with heavy usage, custom implementations, or terms violations are ineligible. Submit requests via AWS Marketplace or help@typecast.ai . Requests are reviewed within 5 business days and processed within 10 business days. Contact help@typecast.ai with questions. Policy updates appear on our AWS Marketplace listing.
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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.
Version release notes
Initial version
Additional details
Inputs
- Summary
The model accepts input in JSON format.
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
text | A single string containing the input text to be processed by the model. This text serves as the basis for generating the audio output.
| Type: FreeText | Yes |
Resources
Vendor resources
Support
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