OpenAI logoWhisper V3 Turbo

A 809M-parameter Whisper v3 model with an 8x faster relative speed vs Whisper Large.

Deploy Whisper V3 Turbo behind an API endpoint in seconds.

Deploy model

Example usage

For a full list of supported parameters, see the TensorRT-LLM Engine Builder documentation for Whisper.

Input
1import requests
2import os
3
4# Model ID for production deployment
5model_id = ""
6# Read secrets from environment variables
7baseten_api_key = os.environ["BASETEN_API_KEY"]
8
9# Call model endpoint
10resp = requests.post(
11    f"https://model-{model_id}.api.baseten.co/production/predict",
12    headers={"Authorization": f"Api-Key {baseten_api_key}"},
13    json={
14      "url": "https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg10.wav",
15    }
16)
17
18print(resp.content.decode("utf-8"))
JSON output
1{
2    "segments": [
3        {
4            "start": 0,
5            "end": 9.8,
6            "text": "Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal."
7        }
8    ],
9    "language_code": "en"
10}

Deploy any model in just a few commands

Avoid getting tangled in complex deployment processes. Deploy best-in-class open-source models and take advantage of optimized serving for your own models.

$

truss init -- example stable-diffusion-2-1-base ./my-sd-truss

$

cd ./my-sd-truss

$

export BASETEN_API_KEY=MdNmOCXc.YBtEZD0WFOYKso2A6NEQkRqTe

$

truss push

INFO

Serializing Stable Diffusion 2.1 truss.

INFO

Making contact with Baseten 👋 👽

INFO

🚀 Uploading model to Baseten 🚀

Upload progress: 0% | | 0.00G/2.39G