ref: 61c6391c210b4f17a1415e545676e56c50aab4e4
dir: /dnn/train_lpcnet.py/
#!/usr/bin/python3
import lpcnet
import sys
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from ulaw import ulaw2lin, lin2ulaw
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.44
set_session(tf.Session(config=config))
nb_epochs = 40
batch_size = 64
model = lpcnet.new_wavernn_model()
model.compile(optimizer=Adam(0.0008), 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))
checkpoint = ModelCheckpoint('wavernn1f_{epoch:02d}.h5')
#model.load_weights('wavernn1c_01.h5')
model.compile(optimizer=Adam(0.002, amsgrad=True, decay=1e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit(in_data, out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])