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70 lines
2.4 KiB
Python
Executable file
70 lines
2.4 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 keras.backend as K
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import h5py
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from adadiff import Adadiff
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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, enc, dec = lpcnet.new_wavernn_model()
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model.compile(optimizer=Adadiff(), 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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feature_file = sys.argv[2]
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frame_size = 160
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nb_features = 54
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nb_used_features = lpcnet.nb_used_features
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feature_chunk_size = 15
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pcm_chunk_size = frame_size*feature_chunk_size
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data = np.fromfile(pcmfile, dtype='int8')
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nb_frames = len(data)//pcm_chunk_size
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features = np.fromfile(feature_file, dtype='float32')
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data = data[:nb_frames*pcm_chunk_size]
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features = features[:nb_frames*feature_chunk_size*nb_features]
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in_data = np.concatenate([data[0:1], data[:-1]])/16.;
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features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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pitch = 1.*data
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pitch[:320] = 0
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for i in range(2, nb_frames*feature_chunk_size):
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period = int(50*features[i,36]+100)
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period = period - 4
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pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
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in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
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in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
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out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
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out_data = (out_data.astype('int16')+128).astype('uint8')
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features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
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features = features[:, :, :nb_used_features]
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#in_data = np.concatenate([in_data, in_pitch], axis=-1)
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#with h5py.File('in_data.h5', 'w') as f:
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# f.create_dataset('data', data=in_data[:50000, :, :])
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# f.create_dataset('feat', data=features[:50000, :, :])
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checkpoint = ModelCheckpoint('lpcnet3b_{epoch:02d}.h5')
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#model.load_weights('wavernn1c_01.h5')
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model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.fit([in_data, features], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
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