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