Initial commit

added libraries:
opus
flac
libsndfile

updated:
libvorbis
libogg
openal

- Everything works as expected for now. Bare in mind libsndfile needed the check for whether or not it could find the xiph libraries removed in order for this to work.

Engine/lib/opus/scripts/dump_rnn.py

@@ -0,0 +1,57 @@
+#!/usr/bin/python
+
+from __future__ import print_function
+
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.layers import GRU
+from keras.models import load_model
+from keras import backend as K
+
+import numpy as np
+
+def printVector(f, vector, name):
+    v = np.reshape(vector, (-1));
+    #print('static const float ', name, '[', len(v), '] = \n', file=f)
+    f.write('static const opus_int16 {}[{}] = {{\n   '.format(name, len(v)))
+    for i in range(0, len(v)):
+        f.write('{}'.format(int(round(8192*v[i]))))
+        if (i!=len(v)-1):
+            f.write(',')
+        else:
+            break;
+        if (i%8==7):
+            f.write("\n   ")
+        else:
+            f.write(" ")
+    #print(v, file=f)
+    f.write('\n};\n\n')
+    return;
+
+def binary_crossentrop2(y_true, y_pred):
+        return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
+
+
+model = load_model("weights.hdf5", custom_objects={'binary_crossentrop2': binary_crossentrop2})
+
+weights = model.get_weights()
+
+f = open('rnn_weights.c', 'w')
+
+f.write('/*This file is automatically generated from a Keras model*/\n\n')
+f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
+
+printVector(f, weights[0], 'layer0_weights')
+printVector(f, weights[1], 'layer0_bias')
+printVector(f, weights[2], 'layer1_weights')
+printVector(f, weights[3], 'layer1_recur_weights')
+printVector(f, weights[4], 'layer1_bias')
+printVector(f, weights[5], 'layer2_weights')
+printVector(f, weights[6], 'layer2_bias')
+
+f.write('const DenseLayer layer0 = {\n   layer0_bias,\n   layer0_weights,\n   25, 16, 0\n};\n\n')
+f.write('const GRULayer layer1 = {\n   layer1_bias,\n   layer1_weights,\n   layer1_recur_weights,\n   16, 12\n};\n\n')
+f.write('const DenseLayer layer2 = {\n   layer2_bias,\n   layer2_weights,\n   12, 2, 1\n};\n\n')
+
+f.close()

Engine/lib/opus/scripts/rnn_train.py

@@ -0,0 +1,67 @@
+#!/usr/bin/python
+
+from __future__ import print_function
+
+from keras.models import Sequential
+from keras.models import Model
+from keras.layers import Input
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.layers import GRU
+from keras.layers import SimpleRNN
+from keras.layers import Dropout
+from keras import losses
+import h5py
+
+from keras import backend as K
+import numpy as np
+
+def binary_crossentrop2(y_true, y_pred):
+    return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
+
+print('Build model...')
+#model = Sequential()
+#model.add(Dense(16, activation='tanh', input_shape=(None, 25)))
+#model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True))
+#model.add(Dense(2, activation='sigmoid'))
+
+main_input = Input(shape=(None, 25), name='main_input')
+x = Dense(16, activation='tanh')(main_input)
+x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
+x = Dense(2, activation='sigmoid')(x)
+model = Model(inputs=main_input, outputs=x)
+
+batch_size = 64
+
+print('Loading data...')
+with h5py.File('features.h5', 'r') as hf:
+    all_data = hf['features'][:]
+print('done.')
+
+window_size = 1500
+
+nb_sequences = len(all_data)/window_size
+print(nb_sequences, ' sequences')
+x_train = all_data[:nb_sequences*window_size, :-2]
+x_train = np.reshape(x_train, (nb_sequences, window_size, 25))
+
+y_train = np.copy(all_data[:nb_sequences*window_size, -2:])
+y_train = np.reshape(y_train, (nb_sequences, window_size, 2))
+
+all_data = 0;
+x_train = x_train.astype('float32')
+y_train = y_train.astype('float32')
+
+print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
+
+# try using different optimizers and different optimizer configs
+model.compile(loss=binary_crossentrop2,
+              optimizer='adam',
+              metrics=['binary_accuracy'])
+
+print('Train...')
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=200,
+          validation_data=(x_train, y_train))
+model.save("newweights.hdf5")