Baseten

Deploy Rime TTS on Baseten’s serverless GPU infrastructure.

Baseten 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 repository. Each model directory contains a Baseten/Truss config.yaml plus a README with full deploy, secrets, and invocation steps.

Model	Directory
Rime Coda v1: flagship expressive TTS	rime-coda-v1/
Rime Mist v3: low-latency conversational	rime-mist-v3/

Prerequisites

Before cloning the deployment repository, contact [e-mail] 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:

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:

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 README for your chosen model.