Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labeled data and demonstrates strong abilities to generalize to various datasets and domains without fine-tuning. The model is based on a Transformer encoder-decoder architecture.
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labeled data and demonstrates strong abilities to generalize to various datasets and domains without fine-tuning. The model is based on a Transformer encoder-decoder architecture.
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-medium'
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
}
}
language
stringlanguage that the audio is in; uses detected language if None; use two letter language code (ISO 639-1) (e.g. en, de, ja)
webhook
fileThe webhook to call when inference is done, by default you will get the output in the response of your inference request