Sold by: Sigmodata
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Chatterbox Turbo text-to-speech as a SageMaker model package. Send JSON with an input string and receive WAV or AIFF audio bytes. Deploy on a GPU instance for production latency; CPU instances are supported but slow.
Overview
Image
Python predict
Image
POST /invocations
This SageMaker model package converts text to natural speech using Resemble AI's Chatterbox Turbo (MIT license). Deploy a real-time endpoint or run SageMaker batch transform. Send JSON with an input or text field and optional voice, language, and response_format. The response is raw audio (audio/wav or audio/aiff bytes).
GPU is strongly recommended. Deploy on ml.g4dn.xlarge (NVIDIA T4) or larger g4dn/g5 types for faster-than-realtime English synthesis. The same CUDA image falls back to CPU when no GPU is present, but synthesis is much slower than realtime on CPU. Real-time payloads are limited to 6 MB.
This listing is the SageMaker path. For a self-hosted HTTPS web UI, admin console, bundled batch folder processing, and zero-shot cloning uploads, use the Text-to-Speech Server AMI.
Common use cases include IVR and telephony prompts, accessibility narration, content read-aloud, and private voice generation pipelines where text and audio must stay inside your own AWS account.
Model and training data The model is Chatterbox Turbo from Resemble AI, served with weights and bundled reference voices baked into the image. Sigmodata did not train the base checkpoint; it is distributed under the MIT license. English uses the Turbo checkpoint; other languages use the bundled multilingual model when requested via the language field.
Measured performance
- On ml.g4dn.xlarge (NVIDIA T4), measured after engine warmup: a one-line English sentence is about 2.5 s wall clock for 5 s of audio (faster than realtime). A short phrase is about 0.8 s; a typical IVR paragraph about 5 s for 11 s of audio.
- First endpoint startup includes model load from the container image; plan for several minutes on a new GPU instance before /ping returns 200.
- The GPU serializes synthesis; parallel requests queue (MaxConcurrentTransforms=1).
Known limitations
- Real-time invocations accept up to 6 MB. Longer inputs should use batch transform (SingleRecord).
- One synthesis runs on the GPU at a time per instance.
- Zero-shot cloning from an uploaded reference clip is not exposed on this SageMaker endpoint; use the Text-to-Speech Server AMI for that workflow.
- CPU-only instances work but are slower than realtime for typical prompts.
We welcome your feedback at aws-support@sigmodata.com . Sample notebook: https://www.sigmodata.com/products/text-to-speech-model/getting-started.ipynb
Highlights
- Synthesize natural speech on a SageMaker endpoint from JSON text input
- Same CUDA image as the Text-to-Speech Server AMI, with bundled Turbo weights
- Real-time endpoints and batch transform on GPU instances (ml.g4dn.xlarge recommended)
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.xlarge instance type, batch mode | $0.20 |
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.20 |
ml.g4dn.2xlarge Inference (Real-Time) | Model inference on the ml.g4dn.2xlarge instance type, real-time mode | $0.20 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $0.20 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $0.20 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $0.20 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.20 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.20 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.20 |
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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
First release.
- Chatterbox Turbo English synthesis
- JSON input with voice and response_format
- Real-time endpoints and SageMaker batch transform
- GPU recommended (ml.g4dn.xlarge)
Additional details
Inputs
- Summary
JSON object with input or text (required), plus optional voice, language, response_format (wav or aiff), temperature, and top_k. Max 6 MB per real-time request.
- Input MIME type
- application/json, text/plain, text/csv
{"input": "Hello from SageMaker.", "voice": "default", "response_format": "wav"}
{"input": "Hello from SageMaker.", "voice": "default", "response_format": "wav"}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
input | Text to synthesize (text is accepted as an alias) | - | Yes |
voice | Bundled speaker id (default is default) | - | No |
response_format | Output codec, wav or aiff (default wav) | - | No |
language | Language code for multilingual synthesis (default en) | - | No |
Resources
Vendor resources
Support
Vendor support
Support contact: Email: aws-support@sigmodata.com Support URL: https://www.sigmodata.com
Support description: Support via email for deployment, configuration, and troubleshooting.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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