> ## Documentation Index
> Fetch the complete documentation index at: https://rimelabs-docs-coda-websocket-reference.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Baseten

> Deploy Rime TTS on Baseten's serverless GPU infrastructure.

export const IntegrationHeader = ({partnerLogo, partnerAlt = "Partner logo", rimeLogo = "/images/rime_logo_new.svg", rimeAlt = "Rime logo", rimeHeight = "40px", partnerHeight = "40px"}) => <div style={{
  width: "100%",
  display: "flex",
  flexDirection: "row",
  alignItems: "center",
  justifyContent: "center",
  gap: "12px",
  flexWrap: "nowrap",
  marginBottom: "24px"
}}>
    <img src={rimeLogo} style={{
  height: rimeHeight,
  display: "inline-block"
}} alt={rimeAlt} />
    <span style={{
  fontSize: "24px",
  color: "#666",
  display: "inline-block"
}}>
      +
    </span>
    <img src={partnerLogo} style={{
  height: partnerHeight,
  display: "inline-block"
}} alt={partnerAlt} />
  </div>;

<IntegrationHeader partnerLogo="/images/baseten.svg" partnerAlt="Baseten logo" />

[Baseten](https://www.baseten.co/) is a platform for deploying and serving AI models on autoscaling GPU infrastructure. Rime's text-to-speech engine images run on Baseten as single-container deployments: the engine validates its own license and serves TTS directly, with no separate router.

### Get started

Ready-to-use deploy configs for Rime's models live in the [`rimelabs/rime-baseten-deploy`](https://github.com/rimelabs/rime-baseten-deploy) repository. Each model directory contains a Baseten/[Truss](https://truss.baseten.co/) `config.yaml` plus a README with full deploy, secrets, and invocation steps.

| Model                                    | Directory                                                                                 |
| ---------------------------------------- | ----------------------------------------------------------------------------------------- |
| Rime Coda v1: flagship expressive TTS    | [`rime-coda-v1/`](https://github.com/rimelabs/rime-baseten-deploy/tree/main/rime-coda-v1) |
| Rime Mist v3: low-latency conversational | [`rime-mist-v3/`](https://github.com/rimelabs/rime-baseten-deploy/tree/main/rime-mist-v3) |

## Prerequisites

Before cloning the deployment repository, contact [help@rime.ai](mailto:help@rime.ai) for engine-image and license access. Your Baseten workspace needs these secrets:

* `gcp_rime_service_account`
* `rime_license`
* `rime_api_key`

To deploy:

1. Clone the repo and `cd` into the model directory you want.
2. Set the three required secrets in your Baseten workspace. The per-model README describes each value.
3. Push the config with the [Truss CLI](https://truss.baseten.co/):

```bash theme={null}
truss push .
```

4. Set the autoscaling concurrency target (each replica handles \~10 concurrent requests before a new one is added). See the model README for the exact Management API call.

### Invoke

Once deployed, send inference requests to your model's sync endpoint. The Rime API key is baked in via the `rime_api_key` secret, so callers authenticate to Baseten only:

```python theme={null}
import requests

resp = requests.post(
    "https://model-<model-id>.api.baseten.co/environments/production/sync",
    headers={
        "Authorization": f"Api-Key {BASETEN_API_KEY}",
        "Accept": "audio/webm;codecs=opus",   # or audio/pcm, audio/mpeg
    },
    json={
        "text": "Natural-sounding, accurately-pronouncing AI TTS built for high-stakes enterprise conversations.",
        "speaker": "luna",
        "lang": "en",
    },
)
resp.raise_for_status()
with open("output.webm", "wb") as f:
    f.write(resp.content)
```

For full setup instructions, including secrets and autoscaling, see the [`rime-baseten-deploy`](https://github.com/rimelabs/rime-baseten-deploy) README for your chosen model.