ref: fe7b54c0e8ffa051c885704c6d601d1a649f307a
dir: /dnn/training_tf2/dataloader.py/
import numpy as np
from tensorflow.keras.utils import Sequence
def lpc2rc(lpc):
#print("shape is = ", lpc.shape)
order = lpc.shape[-1]
rc = 0*lpc
for i in range(order, 0, -1):
rc[:,:,i-1] = lpc[:,:,-1]
ki = rc[:,:,i-1:i].repeat(i-1, axis=2)
lpc = (lpc[:,:,:-1] - ki*lpc[:,:,-2::-1])/(1-ki*ki)
return rc
class LPCNetLoader(Sequence):
def __init__(self, data, features, periods, batch_size, lpc_out=False):
self.batch_size = batch_size
self.nb_batches = np.minimum(np.minimum(data.shape[0], features.shape[0]), periods.shape[0])//self.batch_size
self.data = data[:self.nb_batches*self.batch_size, :]
self.features = features[:self.nb_batches*self.batch_size, :]
self.periods = periods[:self.nb_batches*self.batch_size, :]
self.lpc_out = lpc_out
self.on_epoch_end()
def on_epoch_end(self):
self.indices = np.arange(self.nb_batches*self.batch_size)
np.random.shuffle(self.indices)
def __getitem__(self, index):
data = self.data[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
in_data = data[: , :, :3]
out_data = data[: , :, 3:4]
features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :-16]
periods = self.periods[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
outputs = [out_data]
if self.lpc_out:
lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 2:-2, -16:]
outputs.append(lpc2rc(lpc))
return ([in_data, features, periods], outputs)
def __len__(self):
return self.nb_batches