openai/whisper-small.en
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labelled data without the need for fine-tuning. It is a Transformer based encoder-decoder model, trained on either English-only or multilingual data, and is available in five configurations of varying model sizes. The models were trained on the tasks of speech recognition and speech translation, predicting transcriptions in the same or different languages as the audio.
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labelled data without the need for fine-tuning. It is a Transformer based encoder-decoder model, trained on either English-only or multilingual data, and is available in five configurations of varying model sizes. The models were trained on the tasks of speech recognition and speech translation, predicting transcriptions in the same or different languages as the audio.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-H "Authorization: bearer $(deepctl auth token)" \
-F audio=@my_voice.mp3 \
'https://api.deepinfra.com/v1/inference/openai/whisper-small.en'
which will give you back something similar to:
{
"text": "",
"segments": [
{
"id": 0,
"text": "Hello",
"start": 0.0,
"end": 1.0
},
{
"id": 1,
"text": "World",
"start": 4.0,
"end": 5.0
}
],
"language": "en",
"input_length_ms": 0,
"request_id": null,
"inference_status": {
"status": "unknown",
"runtime_ms": 0,
"cost": 0.0,
"tokens_generated": 0,
"tokens_input": 0
}
}
Input fields
condition_on_previous_textboolean
provide the previous output of the model as a prompt for the next window
Default value: true
temperature_increment_on_fallbacknumber
temperature to increase when falling back when the decoding fails to meet either of the thresholds below
Default value: 0.2
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
