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42 lines
1.4 KiB
Python
Executable file
42 lines
1.4 KiB
Python
Executable file
#!/usr/bin/python3
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import lpcnet
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import sys
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import numpy as np
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from keras.optimizers import Adam
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from keras.callbacks import ModelCheckpoint
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from ulaw import ulaw2lin, lin2ulaw
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import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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config = tf.ConfigProto()
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config.gpu_options.per_process_gpu_memory_fraction = 0.44
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set_session(tf.Session(config=config))
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nb_epochs = 40
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batch_size = 64
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model = lpcnet.new_wavernn_model()
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model.compile(optimizer=Adam(0.0008), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.summary()
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pcmfile = sys.argv[1]
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chunk_size = int(sys.argv[2])
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data = np.fromfile(pcmfile, dtype='int16')
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#data = data[:100000000]
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data = data/32768
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nb_frames = (len(data)-1)//chunk_size
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in_data = data[:nb_frames*chunk_size]
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#out_data = data[1:1+nb_frames*chunk_size]//256 + 128
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out_data = lin2ulaw(data[1:1+nb_frames*chunk_size]) + 128
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in_data = np.reshape(in_data, (nb_frames, chunk_size, 1))
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out_data = np.reshape(out_data, (nb_frames, chunk_size, 1))
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checkpoint = ModelCheckpoint('wavernn1f_{epoch:02d}.h5')
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#model.load_weights('wavernn1c_01.h5')
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model.compile(optimizer=Adam(0.002, amsgrad=True, decay=1e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.fit(in_data, out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
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