| from typing import Dict, List, Any | |
| from transformers import pipeline | |
| import torch | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| self.pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=path, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| self.pipe.model.config.forced_decoder_ids = self.pipe.tokenizer.get_decoder_prompt_ids(language="turkish", task="transcribe") | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| inputs = data.pop("inputs",data) | |
| prediction = self.pipe(inputs, return_timestamps=False, generate_kwargs={"language": "turkish", "task": "transcribe"}) | |
| return prediction | |