Adds end-to-end LPC training

Making LPC computation and prediction differentiable

dnn/training_tf2/difflpc.py

@@ -0,0 +1,27 @@
+"""
+Tensorflow model (differentiable lpc) to learn the lpcs from the features
+"""
+
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense, Concatenate, Lambda, Conv1D, Multiply, Layer, LeakyReLU
+from tensorflow.keras import backend as K
+from tf_funcs import diff_rc2lpc
+
+frame_size = 160
+lpcoeffs_N = 16
+
+def difflpc(nb_used_features = 20, training=False):
+    feat = Input(shape=(None, nb_used_features)) # BFCC
+    padding = 'valid' if training else 'same'
+    L1 = Conv1D(100, 3, padding=padding, activation='tanh', name='f2rc_conv1')
+    L2 = Conv1D(75, 3, padding=padding, activation='tanh', name='f2rc_conv2')
+    L3 = Dense(50, activation='tanh',name = 'f2rc_dense3')
+    L4 = Dense(lpcoeffs_N, activation='tanh',name = "f2rc_dense4_outp_rc")
+    rc = L4(L3(L2(L1(feat))))
+    # Differentiable RC 2 LPC
+    lpcoeffs = diff_rc2lpc(name = "rc2lpc")(rc)
+
+    model = Model(feat,lpcoeffs,name = 'f2lpc')
+    model.nb_used_features = nb_used_features
+    model.frame_size = frame_size
+    return model