Adds end-to-end LPC training

Making LPC computation and prediction differentiable

dnn/training_tf2/lossfuncs.py

@@ -0,0 +1,85 @@
+"""
+Custom Loss functions and metrics for training/analysis
+"""
+
+from tf_funcs import *
+import tensorflow as tf
+
+# The following loss functions all expect the lpcnet model to output the lpc prediction
+
+# Computing the excitation by subtracting the lpc prediction from the target, followed by minimizing the cross entropy
+def res_from_sigloss():
+    def loss(y_true,y_pred):
+        p = y_pred[:,:,0:1]
+        model_out = y_pred[:,:,1:]
+        e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+        e_gt = tf.round(e_gt)
+        e_gt = tf.cast(e_gt,'int32')
+        sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,model_out)
+        return sparse_cel
+    return loss
+
+# Interpolated and Compensated Loss (In case of end to end lpcnet)
+# Interpolates between adjacent embeddings based on the fractional value of the excitation computed (similar to the embedding interpolation)
+# Also adds a probability compensation (to account for matching cross entropy in the linear domain), weighted by gamma
+def interp_mulaw(gamma = 1):
+    def loss(y_true,y_pred):
+        p = y_pred[:,:,0:1]
+        model_out = y_pred[:,:,1:]
+        e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+        prob_compensation = tf.squeeze((K.abs(e_gt - 128)/128.0)*K.log(256.0))
+        alpha = e_gt - tf.math.floor(e_gt)
+        alpha = tf.tile(alpha,[1,1,256])
+        e_gt = tf.cast(e_gt,'int32')
+        e_gt = tf.clip_by_value(e_gt,0,254) 
+        interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1)
+        sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab)
+        loss_mod = sparse_cel + gamma*prob_compensation
+        return loss_mod
+    return loss
+
+# Same as above, except a metric
+def metric_oginterploss(y_true,y_pred):
+    p = y_pred[:,:,0:1]
+    model_out = y_pred[:,:,1:]
+    e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+    prob_compensation = tf.squeeze((K.abs(e_gt - 128)/128.0)*K.log(256.0))
+    alpha = e_gt - tf.math.floor(e_gt)
+    alpha = tf.tile(alpha,[1,1,256])
+    e_gt = tf.cast(e_gt,'int32')
+    e_gt = tf.clip_by_value(e_gt,0,254) 
+    interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1)
+    sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab)
+    loss_mod = sparse_cel + prob_compensation
+    return loss_mod
+
+# Interpolated cross entropy loss metric
+def metric_icel(y_true, y_pred):
+    p = y_pred[:,:,0:1]
+    model_out = y_pred[:,:,1:]
+    e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+    alpha = e_gt - tf.math.floor(e_gt)
+    alpha = tf.tile(alpha,[1,1,256])
+    e_gt = tf.cast(e_gt,'int32')
+    e_gt = tf.clip_by_value(e_gt,0,254) #Check direction
+    interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1)
+    sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab)
+    return sparse_cel
+
+# Non-interpolated (rounded) cross entropy loss metric
+def metric_cel(y_true, y_pred):
+    p = y_pred[:,:,0:1]
+    model_out = y_pred[:,:,1:]
+    e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+    e_gt = tf.round(e_gt)
+    e_gt = tf.cast(e_gt,'int32')
+    e_gt = tf.clip_by_value(e_gt,0,255) 
+    sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,model_out)
+    return sparse_cel
+
+# Variance metric of the output excitation
+def metric_exc_sd(y_true,y_pred):
+    p = y_pred[:,:,0:1]
+    e_gt = tf_l2u(tf_u2l(y_true) - tf_u2l(p))
+    sd_egt = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)(e_gt,128)
+    return sd_egt