OpenAI logoWhisperX

Audio diarization model

Deploy WhisperX behind an API endpoint in seconds.

Example usage

1import requests
2import os
3
4# Replace the empty string with your model id below
5model_id = ""
6baseten_api_key = os.environ["BASETEN_API_KEY"]
7
8data = {
9    "audio_file": "https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg10.wav"
10}
11
12
13# Call model endpoint
14res = requests.post(
15    f"https://model-{model_id}.api.baseten.co/environments/production/predict",
16    headers={"Authorization": f"Api-Key {baseten_api_key}"},
17    json=data
18)
19
20# Print the output of the model
21print(res.json())
JSON output
1[
2    {
3        "start": 0,
4        "end": 9.8,
5        "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.",
6        "speaker": "SPEAKER_01"
7    }
8]

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