OpenAI logoFaster Whisper V2

An implementation of the Whisper audio transcription model optimized for faster inference.

Deploy Faster Whisper V2 behind an API endpoint in seconds.

Deploy model

Example usage

The model accepts a single URL to an audio file, such as a .mp3 or .wav. The audio file should contain clearly audible speech. This example transcribes a ten-second snippet of a recitation of the Gettysburg address.

The JSON output includes the auto-detected language, transcription segments with timestamps, and the complete transcribed text.

Input
1import requests
2import os
3
4# Replace the empty string with your model id below
5model_id = ""
6
7data = {
8  "url": "https://cdn.baseten.co/docs/production/Gettysburg.mp3"
9}
10
11
12# Call model endpoint
13res = requests.post(
14    f"https://model-{model_id}.api.baseten.co/production/predict",
15    headers={"Authorization": f"Api-Key {baseten_api_key}"},
16    json=data
17)
18
19# Print the output of the model
20print(res.json())
JSON output
1{
2    "language": "en",
3    "language_probability": 0.99365234375,
4    "duration": 11.52,
5    "segments": [
6        {
7            "text": "Four score and seven years ago, our fathers brought forth upon this continent a new nation",
8            "start": 0,
9            "end": 6.48
10        },
11        {
12            "text": "conceived in liberty and dedicated to the proposition that all men are created equal.",
13            "start": 6.48,
14            "end": 11
15        }
16    ]
17}

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 πŸ‘‹ πŸ‘½

INFO

πŸš€ Uploading model to Baseten πŸš€

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