41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
import numpy as np
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from tensorflow.keras.utils import Sequence
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def lpc2rc(lpc):
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#print("shape is = ", lpc.shape)
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order = lpc.shape[-1]
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rc = 0*lpc
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for i in range(order, 0, -1):
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rc[:,:,i-1] = lpc[:,:,-1]
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ki = rc[:,:,i-1:i].repeat(i-1, axis=2)
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lpc = (lpc[:,:,:-1] - ki*lpc[:,:,-2::-1])/(1-ki*ki)
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return rc
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class LPCNetLoader(Sequence):
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def __init__(self, data, features, periods, batch_size, lpc_out=False):
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self.batch_size = batch_size
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self.nb_batches = np.minimum(np.minimum(data.shape[0], features.shape[0]), periods.shape[0])//self.batch_size
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self.data = data[:self.nb_batches*self.batch_size, :]
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self.features = features[:self.nb_batches*self.batch_size, :]
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self.periods = periods[:self.nb_batches*self.batch_size, :]
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self.lpc_out = lpc_out
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self.on_epoch_end()
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def on_epoch_end(self):
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self.indices = np.arange(self.nb_batches*self.batch_size)
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np.random.shuffle(self.indices)
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def __getitem__(self, index):
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data = self.data[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
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in_data = data[: , :, :3]
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out_data = data[: , :, 3:4]
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features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :-16]
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periods = self.periods[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
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outputs = [out_data]
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if self.lpc_out:
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lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 2:-2, -16:]
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outputs.append(lpc2rc(lpc))
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return ([in_data, features, periods], outputs)
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def __len__(self):
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return self.nb_batches
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