initial commit

dnn/train_lpcnet.py

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+#!/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)