| import librosa |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| from typing import Optional |
| import torch |
|
|
|
|
| class ASRService: |
| def __init__(self): |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" |
| self.processor, self.model = self._load_model() |
|
|
| def _load_model(self): |
| """Ładowanie modelu Whisper""" |
| model_name = "openai/whisper-large-v3-turbo" |
| processor = WhisperProcessor.from_pretrained(model_name) |
| model = WhisperForConditionalGeneration.from_pretrained(model_name).to(self.device) |
| model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="pl", task="transcribe") |
| return processor, model |
|
|
| def transcribe(self, audio_path: str) -> str: |
| """Transkrypcja audio na tekst""" |
| sample, sr = librosa.load(audio_path, sr=16000) |
| input_features = self.processor( |
| sample, |
| sampling_rate=sr, |
| return_tensors="pt" |
| ).input_features.to(self.device) |
| |
| predicted_ids = self.model.generate(input_features) |
| return self.processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] |
|
|
| |
| ASR = ASRService() |
