| import librosa | |
| import numpy as np | |
| from .init import pipe | |
| TASK = "transcribe" | |
| BATCH_SIZE = 8 | |
| LIMIT = 60 | |
| SAMPLING_RATE = 16000 | |
| class A2T: | |
| def __init__(self, mic): | |
| self.mic = mic | |
| def __transcribe(self, inputs, task: str = None, lang: str = "english"): | |
| if inputs is None: | |
| print("Inputs None") | |
| transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": lang}, return_timestamps=True)["text"] | |
| return transcribed_text | |
| def __preprocces(self, raw: np.ndarray, sampling_rate: int): | |
| if sampling_rate != SAMPLING_RATE: | |
| raw = librosa.resample(raw, orig_sr=sampling_rate, target_sr=SAMPLING_RATE) | |
| print(f"Sampling rate : {raw} max chunk : {np.max(raw)}") | |
| chunk = raw.astype(np.float32, order='C') / 32768.0 | |
| print(f"Chunk : {chunk} max chunk : {np.max(chunk)}") | |
| if len(chunk.shape) > 1: | |
| chunk = librosa.to_mono(chunk.T) | |
| chunk = chunk[:SAMPLING_RATE*LIMIT] | |
| print(f"Chunk cut : {chunk} max chunk : {np.max(chunk)}") | |
| return chunk | |
| def predict(self): | |
| try: | |
| if self.mic is not None: | |
| raw = self.mic.get_array_of_samples() | |
| chunk = np.array(raw, dtype=np.int16) | |
| sampling_rate = self.mic.frame_rate | |
| audio = self.__preprocces(raw=chunk, sampling_rate=sampling_rate) | |
| print(f"audio : {audio} \n shape : {audio.shape} \n max : {np.max(audio)} \n shape of chunk : {chunk.shape} \n sampling rate : {sampling_rate} \n max chunk : {np.max(chunk)} \n chunk : {chunk}") | |
| else: | |
| raise Exception("please provide audio") | |
| if isinstance(audio , np.ndarray): | |
| return self.__transcribe(inputs=audio, task=TASK) | |
| else: | |
| raise Exception("Audio is not np array") | |
| except Exception as e: | |
| return f"Oops some kinda error : {e}" | |