shithub: opus

--- a/dnn/dump_lpcnet.py

+++ b/dnn/dump_lpcnet.py

@@ -67,11 +67,19 @@

     A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))

     A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))

     printVector(f, diag, name + '_diag')

+    idx = np.zeros((0,), dtype='int')

     for i in range(3*N//16):

+        pos = idx.shape[0]

+        idx = np.append(idx, -1)

+        nb_nonzero = 0

         for j in range(N):

-            W = np.concatenate([W, A[j, i*16:(i+1)*16]])

+            if np.sum(np.abs(A[j, i*16:(i+1)*16])) > 1e-10:

+                nb_nonzero = nb_nonzero + 1

+                idx = np.append(idx, j)

+                W = np.concatenate([W, A[j, i*16:(i+1)*16]])

+        idx[pos] = nb_nonzero

     printVector(f, W, name)

-    idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)

+    #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)

     printVector(f, idx, name + '_idx', dtype='int')

     return;

--- a/dnn/lpcnet.py

+++ b/dnn/lpcnet.py

@@ -57,7 +57,7 @@

             pass

         else:

             #print("constrain");

-            layer = self.model.get_layer('cu_dnngru_1')

+            layer = self.model.get_layer('gru_a')

             w = layer.get_weights()

             p = w[1]

             nb = p.shape[1]//p.shape[0]

@@ -72,6 +72,7 @@

             for k in range(nb):

                 A = p[:, k*N:(k+1)*N]

                 A = A - np.diag(np.diag(A))

+                A = np.transpose(A, (1, 0))

                 L=np.reshape(A, (N, N//16, 16))

                 S=np.sum(L*L, axis=-1)

                 SS=np.sort(np.reshape(S, (-1,)))

@@ -79,6 +80,7 @@

                 mask = (S>=thresh).astype('float32');

                 mask = np.repeat(mask, 16, axis=1)

                 mask = np.minimum(1, mask + np.diag(np.ones((N,))))

+                mask = np.transpose(mask, (1, 0))

                 p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask

                 #print(thresh, np.mean(mask))

             w[1] = p

--- a/dnn/train_lpcnet.py

+++ b/dnn/train_lpcnet.py

@@ -139,7 +139,7 @@

 in_data = np.concatenate([in_data, pred], axis=-1)

 # dump models to disk as we go

-checkpoint = ModelCheckpoint('lpcnet9_384_10_G16_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('lpcnet9b_384_10_G16_{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'])

--

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