Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labeled data without fine-tuning. It's a Transformer based encoder-decoder model, trained on English-only or multilingual data, predicting transcriptions in the same or different language as the audio. Whisper checkpoints come in five configurations of varying model sizes.
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labeled data without fine-tuning. It's a Transformer based encoder-decoder model, trained on English-only or multilingual data, predicting transcriptions in the same or different language as the audio. Whisper checkpoints come in five configurations of varying model sizes.
You can use cURL or any other http client to run inferences:
curl -X POST \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-F audio=@my_voice.mp3 \
'https://api.deepinfra.com/v1/inference/openai/whisper-tiny.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
}
}
condition_on_previous_text
booleanprovide the previous output of the model as a prompt for the next window
Default value: true
temperature_increment_on_fallback
numbertemperature to increase when falling back when the decoding fails to meet either of the thresholds below
Default value: 0.2
webhook
fileThe webhook to call when inference is done, by default you will get the output in the response of your inference request