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audio_test / pipeline.py

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Create pipeline.py

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	from typing import Any, List, Tuple
	import numpy as np

	class PreTrainedPipeline():
	def __init__(self, path=""):
	# IMPLEMENT_THIS
	# Preload all the elements you are going to need at inference.
	# For instance your model, processors, tokenizer that might be needed.
	# This function is only called once, so do all the heavy processing I/O here"""

	self.sampling_rate = # IMPLEMENT THIS

	raise NotImplementedError(
	"Please implement PreTrainedPipeline __init__ function"
	)

	def __call__(self, inputs: np.array) -> Tuple[np.array, int, List[str]]:
	"""
	Args:
	inputs (:obj:`np.array`):
	The raw waveform of audio received. By default sampled at `self.sampling_rate`.
	The shape of this array is `T`, where `T` is the time axis
	Return:
	A :obj:`tuple` containing:
	- :obj:`np.array`:
	The return shape of the array must be `C'`x`T'`
	- a :obj:`int`: the sampling rate as an int in Hz.
	- a :obj:`List[str]`: the annotation for each out channel.
	This can be the name of the instruments for audio source separation
	or some annotation for speech enhancement. The length must be `C'`.
	"""
	# IMPLEMENT_THIS
	raise NotImplementedError(
	"Please implement PreTrainedPipeline __call__ function"
	)