39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
try:
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import librosa
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except ImportError: # pragma: no-cover
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raise ImportError('You want to use `librosa` plugins which are not installed yet,' # pragma: no-cover
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' install it with `pip install librosa`.')
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import numpy as np
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class Speed(object):
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def __init__(self, max_amount=0.3, speed_min=1, speed_max=1):
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self.speed_max = speed_max if speed_max else 1
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self.speed_min = speed_min if speed_min else 1
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# noinspection PyTypeChecker
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self.max_amount = min(max(0, max_amount), 1)
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def __repr__(self):
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return f'{self.__class__.__name__}({self.__dict__})'
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def __call__(self, x):
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if self.speed_min == 1 and self.speed_max == 1:
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return x
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start = int(np.random.randint(low=0, high=x.shape[-1], size=1))
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width = np.random.uniform(low=0, high=self.max_amount, size=1) * x.shape[-1]
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end = int(width + start)
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end = min(end, x.shape[-1])
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try:
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speed_factor = float(np.random.uniform(low=self.speed_min, high=self.speed_max, size=1))
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aug_data = librosa.effects.time_stretch(y=x[start:end], rate=speed_factor)
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x_aug = np.concatenate((x[:start], aug_data, x[end:]), axis=0)[:x.shape[-1]]
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if speed_factor > 1:
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embedding = np.zeros_like(x)
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embedding[:x_aug.shape[0]] = x_aug
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x_aug = embedding
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return x_aug
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except ValueError:
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return x
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