92 lines
2.1 KiB
Python
92 lines
2.1 KiB
Python
import librosa
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import torch
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from scipy.signal import butter, lfilter
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from ml_lib.modules.utils import AutoPad
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def butter_lowpass(cutoff, sr, order=5):
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nyq = 0.5 * sr
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normal_cutoff = cutoff / nyq
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b, a = butter(order, normal_cutoff, btype='low', analog=False)
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return b, a
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def butter_lowpass_filter(data, cutoff, sr, order=5):
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b, a = butter_lowpass(cutoff, sr, order=order)
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y = lfilter(b, a, data)
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return y
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class MFCC(object):
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def __init__(self, **kwargs):
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self.__dict__.update(kwargs)
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def __call__(self, y):
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mfcc = librosa.feature.mfcc(y, **self.__dict__)
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return mfcc
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class NormalizeLocal(object):
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def __init__(self):
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self.cache: torch.Tensor
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pass
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def __call__(self, x: torch.Tensor):
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mean = x.mean()
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std = x.std()
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x = x.__sub__(mean).__div__(std)
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x[torch.isnan(x)] = 0
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x[torch.isinf(x)] = 0
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return x
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class NormalizeMelband(object):
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def __init__(self):
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self.cache: torch.Tensor
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pass
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def __call__(self, x: torch.Tensor):
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mean = x.mean(-1).unsqueeze(-1)
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std = x.std(-1).unsqueeze(-1)
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x = x.__sub__(mean).__div__(std)
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x[torch.isnan(x)] = 0
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x[torch.isinf(x)] = 0
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return x
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class AutoPadTransform(object):
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def __init__(self, **kwargs):
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self.__dict__.update(kwargs)
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self.padder = AutoPad()
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def __call__(self, y):
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if not torch.is_tensor(y):
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y = torch.as_tensor(y)
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return self.padder(y)
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def __repr__(self):
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return 'AutoPadTransform()'
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class Melspectogram(object):
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def __init__(self, **kwargs):
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self.__dict__.update(kwargs)
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def __call__(self, y):
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mel = librosa.feature.melspectrogram(y, **self.__dict__)
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mel = librosa.amplitude_to_db(mel, ref=np.max)
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return mel
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def __repr__(self):
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return f'MelSpectogram({self.__dict__})'
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class LowPass(object):
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def __init__(self, sr=16000):
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self.sr = sr
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def __call__(self, x):
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return butter_lowpass_filter(x, 1000, 1) |