Arbitrary 16x1 sparseness

dnn/lpcnet.py

@@ -5,6 +5,7 @@ from keras.models import Model
 from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
 from keras import backend as K
 from keras.initializers import Initializer
+from keras.callbacks import Callback
 from mdense import MDense
 import numpy as np
 import h5py
@@ -17,6 +18,49 @@ embed_size = 128
 pcm_levels = 2**pcm_bits
 nb_used_features = 38
 
+class Sparsify(Callback):
+    def __init__(self, t_start, t_end, interval, density):
+        super(Sparsify, self).__init__()
+        self.batch = 0
+        self.t_start = t_start
+        self.t_end = t_end
+        self.interval = interval
+        self.final_density = density
+
+    def on_batch_end(self, batch, logs=None):
+        #print("batch number", self.batch)
+        self.batch += 1
+        if self.batch < self.t_start or ((self.batch-self.t_start) % self.interval != 0 and self.batch < self.t_end):
+            #print("don't constrain");
+            pass
+        else:
+            #print("constrain");
+            layer = self.model.get_layer('cu_dnngru_1')
+            w = layer.get_weights()
+            p = w[1]
+            nb = p.shape[1]//p.shape[0]
+            N = p.shape[0]
+            #print("nb = ", nb, ", N = ", N);
+            #print(p.shape)
+            density = self.final_density
+            if self.batch < self.t_end:
+                r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
+                density = 1 - (1-self.final_density)*(1 - r*r*r)
+            #print ("density = ", density)
+            for k in range(nb):
+                A = p[:, k*N:(k+1)*N]
+                L=np.reshape(A, (N, N//16, 16))
+                S=np.sum(L*L, axis=-1)
+                SS=np.sort(np.reshape(S, (-1,)))
+                thresh = SS[round(N*N//16*(1-density))]
+                mask = (S>=thresh).astype('float32');
+                mask = np.repeat(mask, 16, axis=1)
+                p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask
+                #print(thresh, np.mean(mask))
+            w[1] = p
+            layer.set_weights(w)
+            
+
 class PCMInit(Initializer):
     def __init__(self, gain=.1, seed=None):
         self.gain = gain

dnn/test_wavenet_audio.py

@@ -42,7 +42,7 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
 
 
 
-model.load_weights('wavenet5e3_60.h5')
+model.load_weights('wavenet5p0_30.h5')
 
 order = 16
 

dnn/train_wavenet_audio.py

@@ -68,6 +68,7 @@ features = features[:nb_frames*feature_chunk_size*nb_features]
 
 in_data = np.concatenate([data[0:1], data[:-1]]);
 noise = np.concatenate([np.zeros((len(data)*1//5)), np.random.randint(-3, 3, len(data)*1//5), np.random.randint(-2, 2, len(data)*1//5), np.random.randint(-1, 1, len(data)*2//5)])
+#noise = np.round(np.concatenate([np.zeros((len(data)*1//5)), np.random.laplace(0, 1.2, len(data)*1//5), np.random.laplace(0, .77, len(data)*1//5), np.random.laplace(0, .33, len(data)*1//5), np.random.randint(-1, 1, len(data)*1//5)]))
 in_data = in_data + noise
 in_data = np.clip(in_data, 0, 255)
 
@@ -118,8 +119,8 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
 in_data = np.concatenate([in_data, pred], axis=-1)
 
 # dump models to disk as we go
-checkpoint = ModelCheckpoint('wavenet5d0_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('wavenet5p0_{epoch:02d}.h5')
 
 #model.load_weights('wavenet4f2_30.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
-model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=60, validation_split=0.2, callbacks=[checkpoint])
+model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=60, validation_split=0.2, callbacks=[checkpoint, lpcnet.Sparsify(1000, 20000, 200, 0.25)])