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/training/rnn_dump.py

@@ -0,0 +1,66 @@
+#!/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.models import load_model
+from keras import backend as K
+import sys
+
+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_int8 {}[{}] = {{\n   '.format(name, len(v)))
+    for i in range(0, len(v)):
+        f.write('{}'.format(max(-128,min(127,int(round(128*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(sys.argv[1], custom_objects={'binary_crossentrop2': binary_crossentrop2})
+main_input = Input(shape=(None, 25), name='main_input')
+x = Dense(32, activation='tanh')(main_input)
+x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
+x = Dense(2, activation='sigmoid')(x)
+model = Model(inputs=main_input, outputs=x)
+model.load_weights(sys.argv[1])
+
+weights = model.get_weights()
+
+f = open(sys.argv[2], '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, 32, 0\n};\n\n')
+f.write('const GRULayer layer1 = {\n   layer1_bias,\n   layer1_weights,\n   layer1_recur_weights,\n   32, 24\n};\n\n')
+f.write('const DenseLayer layer2 = {\n   layer2_bias,\n   layer2_weights,\n   24, 2, 1\n};\n\n')
+
+f.close()

Engine/lib/opus/training/rnn_train.py

@@ -0,0 +1,177 @@
+#!/usr/bin/python3
+
+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 CuDNNGRU
+from keras.layers import SimpleRNN
+from keras.layers import Dropout
+from keras import losses
+import h5py
+from keras.optimizers import Adam
+
+from keras.constraints import Constraint
+from keras import backend as K
+import numpy as np
+
+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))
+
+def binary_crossentrop2(y_true, y_pred):
+    return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_true, y_pred), axis=-1)
+
+def binary_accuracy2(y_true, y_pred):
+    return K.mean(K.cast(K.equal(y_true, K.round(y_pred)), 'float32') + K.cast(K.equal(y_true, 0.5), 'float32'), axis=-1)
+
+def quant_model(model):
+    weights = model.get_weights()
+    for k in range(len(weights)):
+        weights[k] = np.maximum(-128, np.minimum(127, np.round(128*weights[k])*0.0078125))
+    model.set_weights(weights)
+
+class WeightClip(Constraint):
+    '''Clips the weights incident to each hidden unit to be inside a range
+    '''
+    def __init__(self, c=2):
+        self.c = c
+
+    def __call__(self, p):
+        return K.clip(p, -self.c, self.c)
+
+    def get_config(self):
+        return {'name': self.__class__.__name__,
+            'c': self.c}
+
+reg = 0.000001
+constraint = WeightClip(.998)
+
+print('Build model...')
+
+main_input = Input(shape=(None, 25), name='main_input')
+x = Dense(32, activation='tanh', kernel_constraint=constraint, bias_constraint=constraint)(main_input)
+#x = CuDNNGRU(24, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x)
+x = GRU(24, recurrent_activation='sigmoid', activation='tanh', return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x)
+x = Dense(2, activation='sigmoid', kernel_constraint=constraint, bias_constraint=constraint)(x)
+model = Model(inputs=main_input, outputs=x)
+
+batch_size = 2048
+
+print('Loading data...')
+with h5py.File('features10b.h5', 'r') as hf:
+    all_data = hf['data'][:]
+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))
+
+print("Marking ignores")
+for s in y_train:
+    for e in s:
+        if (e[1] >= 1):
+            break
+        e[0] = 0.5
+
+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)
+
+model.load_weights('newweights10a1b_ep206.hdf5')
+
+#weights = model.get_weights()
+#for k in range(len(weights)):
+#    weights[k] = np.round(128*weights[k])*0.0078125
+#model.set_weights(weights)
+
+# try using different optimizers and different optimizer configs
+model.compile(loss=binary_crossentrop2,
+              optimizer=Adam(0.0001),
+              metrics=[binary_accuracy2])
+
+print('Train...')
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=10, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep10.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=50, initial_epoch=10)
+model.save("newweights10a1c_ep50.hdf5")
+
+model.compile(loss=binary_crossentrop2,
+              optimizer=Adam(0.0001),
+              metrics=[binary_accuracy2])
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=100, initial_epoch=50)
+model.save("newweights10a1c_ep100.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=150, initial_epoch=100)
+model.save("newweights10a1c_ep150.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=200, initial_epoch=150)
+model.save("newweights10a1c_ep200.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=201, initial_epoch=200)
+model.save("newweights10a1c_ep201.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=202, initial_epoch=201, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep202.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=203, initial_epoch=202, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep203.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=204, initial_epoch=203, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep204.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=205, initial_epoch=204, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep205.hdf5")
+
+quant_model(model)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=206, initial_epoch=205, validation_data=(x_train, y_train))
+model.save("newweights10a1c_ep206.hdf5")
+

Engine/lib/opus/training/txt2hdf5.py

@@ -0,0 +1,12 @@
+#!/usr/bin/python
+
+from __future__ import print_function
+
+import numpy as np
+import h5py
+import sys
+
+data = np.loadtxt(sys.argv[1], dtype='float32')
+h5f = h5py.File(sys.argv[2], 'w');
+h5f.create_dataset('data', data=data)
+h5f.close()