| import numpy as np | |
| import librosa | |
| import io | |
| from .init import pipe | |
| TASK = "transcribe" | |
| BATCH_SIZE = 8 | |
| class A2T: | |
| def __init__(self, mic): | |
| self.mic = mic | |
| def __generate_text(self, inputs, task: str = None): | |
| if inputs is None: | |
| raise Exception("Inputs is None") | |
| transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] | |
| return transcribed_text | |
| def __preprocces(self, raw: bytes): | |
| print(f"Raw type : {type(raw)}") | |
| chunk = io.BytesIO(raw) | |
| audio, sample_rate = librosa.load(chunk, sr=16000) | |
| print(f"Sample rate : {sample_rate}") | |
| return audio | |
| def predict(self): | |
| try: | |
| if self.mic is not None: | |
| raw = self.mic | |
| audio = self.__preprocces(raw=raw) | |
| print(f"audio type : {type(audio)} \n shape : {audio.shape} \n audio max value : {np.max(audio)}") | |
| else: | |
| raise Exception("please provide audio") | |
| if isinstance(audio , np.ndarray): | |
| return self.__generate_text(inputs=audio, task=TASK) | |
| else: | |
| raise Exception("Audio is not np array") | |
| except Exception as e: | |
| return f"Oops some kinda error : {e}" |