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import torch
import numpy as np
import fargan
class FARGANDataset(torch.utils.data.Dataset):
def __init__(self,
feature_file,
signal_file,
frame_size=160,
sequence_length=15,
lookahead=1,
nb_used_features=20,
nb_features=36):
self.frame_size = frame_size
self.sequence_length = sequence_length
self.lookahead = lookahead
self.nb_features = nb_features
self.nb_used_features = nb_used_features
pcm_chunk_size = self.frame_size*self.sequence_length
self.data = np.memmap(signal_file, dtype='int16', mode='r')
#self.data = self.data[1::2]
self.nb_sequences = len(self.data)//(pcm_chunk_size)-4
self.data = self.data[(4-self.lookahead)*self.frame_size:]
self.data = self.data[:self.nb_sequences*pcm_chunk_size]
#self.data = np.reshape(self.data, (self.nb_sequences, pcm_chunk_size))
sizeof = self.data.strides[-1]
self.data = np.lib.stride_tricks.as_strided(self.data, shape=(self.nb_sequences, pcm_chunk_size*2),
strides=(pcm_chunk_size*sizeof, sizeof))
self.features = np.reshape(np.memmap(feature_file, dtype='float32', mode='r'), (-1, nb_features))
sizeof = self.features.strides[-1]
self.features = np.lib.stride_tricks.as_strided(self.features, shape=(self.nb_sequences, self.sequence_length*2+4, nb_features),
strides=(self.sequence_length*self.nb_features*sizeof, self.nb_features*sizeof, sizeof))
self.periods = np.round(50*self.features[:,:,self.nb_used_features-2]+100).astype('int')
self.lpc = self.features[:, :, self.nb_used_features:]
self.features = self.features[:, :, :self.nb_used_features]
print("lpc_size:", self.lpc.shape)
def __len__(self):
return self.nb_sequences
def __getitem__(self, index):
features = self.features[index, :, :].copy()
if self.lookahead != 0:
lpc = self.lpc[index, 4-self.lookahead:-self.lookahead, :].copy()
else:
lpc = self.lpc[index, 4:, :].copy()
data = self.data[index, :].copy().astype(np.float32) / 2**15
periods = self.periods[index, :].copy()
lpc = lpc*(.92**np.arange(1,17))
#lpc=lpc[None,:,:]
#lpc = fargan.interp_lpc(lpc, 4)
#lpc=lpc[0,:,:]
return features, periods, data, lpc
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