> For clean Markdown of any page, append `.md` to the page URL.
> For a complete documentation index, see https://docs.sarvam.ai/llms.txt.
> For full documentation content in one file, see https://docs.sarvam.ai/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.sarvam.ai/_mcp/server.

# Deploy Speech-to-Text (Saaras v3)

> Deploy the Saaras v3 speech-to-text model on Amazon SageMaker — real-time and streaming endpoints — from your AWS Marketplace subscription using boto3.

Deploy **Saaras v3** as a SageMaker endpoint in your own account. This guide covers a real-time endpoint end to end with `boto3`, then streaming for longer audio. The full, runnable notebook lives in [`sarvamai/amazon-sagemaker-examples`](https://github.com/sarvamai/amazon-sagemaker-examples/tree/main/speech-to-text/saaras-v3.1).

> **Note**
>
> **Model identifier.** In API requests you pass **`model: saaras:v3`**. The Marketplace package is versioned **`saaras:v3.1`** — that's the package revision you subscribe to, *not* the value you send in a request. See the [API reference](/api/self-hosted/sagemaker/api-saaras).

> **Warning**
>
> Saaras v3 self-hosted supports **real-time** (audio up to 30 seconds) and **streaming** (WebSocket, for longer/continuous audio). **Batch transform is not currently supported** for Saaras v3 — use the streaming endpoint for long recordings.

## Prerequisites

Complete [Get started on SageMaker](/api/self-hosted/sagemaker/get-started) first: subscribe to the listing, copy your **model package ARN**, and have an **execution role ARN** ready. Read the endpoint's [API reference](/api/self-hosted/sagemaker/api-saaras) carefully before you integrate — the request contract is enforced by the container.

## 1. Configure

**`Configuration`**

```python Configuration
import boto3

region       = boto3.Session().region_name
role         = "arn:aws:iam::<account>:role/<your-sagemaker-execution-role>"
# Copy this from your Marketplace subscription, for your region:
model_package_arn = "arn:aws:sagemaker:<region>:<vendor>:model-package/<saaras-package-id>"

realtime_instance = "ml.g6e.xlarge"   # 1x NVIDIA L40S
endpoint_name     = "saaras-stt"

sm      = boto3.client("sagemaker", region_name=region)
runtime = boto3.client("sagemaker-runtime", region_name=region)
```

## 2. Deploy a real-time endpoint

#### Create the model

Point a SageMaker model at the Marketplace package. Keep **network isolation on** so the container has no internet egress.

```python
sm.create_model(
    ModelName=endpoint_name,
    PrimaryContainer={"ModelPackageName": model_package_arn},
    ExecutionRoleArn=role,
    EnableNetworkIsolation=True,
)
```

#### Create the endpoint config

```python
sm.create_endpoint_config(
    EndpointConfigName=endpoint_name,
    ProductionVariants=[{
        "VariantName": "AllTraffic",
        "ModelName": endpoint_name,
        "InstanceType": realtime_instance,
        "InitialInstanceCount": 1,
    }],
)
```

#### Create the endpoint

```python
sm.create_endpoint(EndpointName=endpoint_name, EndpointConfigName=endpoint_name)
sm.get_waiter("endpoint_in_service").wait(EndpointName=endpoint_name)
```

Provisioning a GPU endpoint typically takes **10–15 minutes**. It's ready when its status is `InService`.

## 3. Invoke it

Send audio as `multipart/form-data` (the only accepted content type — see the [API reference](/api/self-hosted/sagemaker/api-saaras)). Choose an output mode with the `mode` field (`transcribe`, `translate`, `verbatim`, `translit`, `codemix`).

**`Invoke`**

```python Invoke
# build_multipart() assembles the form fields + audio file into a multipart body;
# see the reference notebook for the full helper.
boundary = "----sarvam-boundary"
with open("call.wav", "rb") as f:
    body = build_multipart(
        fields={"model": "saaras:v3", "mode": "transcribe", "with_timestamps": "true"},
        file_bytes=f.read(),
        boundary=boundary,
    )

resp = runtime.invoke_endpoint(
    EndpointName=endpoint_name,
    ContentType=f"multipart/form-data; boundary={boundary}",
    Accept="application/json",
    Body=body,
)
print(resp["Body"].read().decode())
```

See the full request and response schema in the [Speech-to-Text API reference](/api/self-hosted/sagemaker/api-saaras).

> **Warning**
>
> The real-time REST path accepts audio up to **30 seconds**. For longer or continuous audio, use the **streaming** endpoint below — batch transform is not supported for Saaras v3.

## Streaming (live audio)

For continuous, low-latency transcription, deploy the model and invoke the **bidirectional streaming** operation `InvokeEndpointWithBidirectionalStream` — a two-way SigV4 HTTP/2 stream on port 8443. The message protocol and parameters (`language-code`, `sample_rate`, `vad_signals`) are documented in the [API reference](/api/self-hosted/sagemaker/api-saaras#streaming-invokeendpointwithbidirectionalstream).

## Clean up

GPU endpoints bill by the hour. Delete resources you're not using:

**`Cleanup`**

```python Cleanup
sm.delete_endpoint(EndpointName=endpoint_name)
sm.delete_endpoint_config(EndpointConfigName=endpoint_name)
sm.delete_model(ModelName=endpoint_name)
```

#### [Prefer infrastructure-as-code?](/api/self-hosted/sagemaker/terraform)

Deploy the same endpoint with Terraform — IAM role, model, endpoint config, and endpoint in one `apply`.

> Deploy the Saaras v3 speech-to-text model on Amazon SageMaker — real-time and streaming endpoints — from your AWS Marketplace subscription using boto3.