| from typing import Dict, List, Any | |
| import torch as torch | |
| from transformers import pipeline, WhisperProcessor | |
| from scipy.io.wavfile import read | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| self.pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model="openai/whisper-large", | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| processor = WhisperProcessor.from_pretrained("openai/whisper-large") | |
| self.pipe.model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="nl", task="transcribe") | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """ | |
| data args: | |
| inputs (:obj: `str`) | |
| date (:obj: `str`) | |
| Return: | |
| A :obj:`list` | `dict`: will be serialized and returned | |
| """ | |
| #print request | |
| print("request") | |
| print(data.inputs) | |
| # audio_data = read(io.BytesIO(data)) | |
| # get inputs, inputs in request body is possible equal to wav or mp3 file | |
| inputs = data.pop("inputs", data) | |
| print("here comes text") | |
| print(self.pipe(inputs)) | |
| text = self.pipe(inputs)["text"] | |
| print(text) | |
| return text | |