ref: c41afe41f0fb13b80ca79e987a21ea411e1545a7
dir: /dnn/train_lpcnet.py/
#!/usr/bin/python3 import lpcnet import sys import numpy as np from keras.optimizers import Adam from ulaw import ulaw2lin, lin2ulaw nb_epochs = 10 batch_size = 32 model = lpcnet.new_wavernn_model() model.compile(optimizer=Adam(0.001), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) model.summary() pcmfile = sys.argv[1] chunk_size = int(sys.argv[2]) data = np.fromfile(pcmfile, dtype='int16') #data = data[:100000000] data = data/32768 nb_frames = (len(data)-1)//chunk_size in_data = data[:nb_frames*chunk_size] #out_data = data[1:1+nb_frames*chunk_size]//256 + 128 out_data = lin2ulaw(data[1:1+nb_frames*chunk_size]) + 128 in_data = np.reshape(in_data, (nb_frames, chunk_size, 1)) out_data = np.reshape(out_data, (nb_frames, chunk_size, 1)) model.fit(in_data, out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.2)