shithub: opus

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wip 8x4 sparseness

--- a/dnn/training_tf2/dump_lpcnet.py

+++ b/dnn/training_tf2/dump_lpcnet.py

@@ -66,15 +66,17 @@

     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):

+    for i in range(3*N//8):

         pos = idx.shape[0]

         idx = np.append(idx, -1)

         nb_nonzero = 0

-        for j in range(N):

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

+        for j in range(N//4):

+            block = A[j*4:(j+1)*4, i*8:(i+1)*8]

+            if np.sum(np.abs(block)) > 1e-10:

                 nb_nonzero = nb_nonzero + 1

                 idx = np.append(idx, j)

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

+                vblock = block.transpose((1,0)).reshape((-1,))

+                W = np.concatenate([W, vblock])

         idx[pos] = nb_nonzero

     printVector(f, W, name)

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

--- a/dnn/training_tf2/lpcnet.py

+++ b/dnn/training_tf2/lpcnet.py

@@ -74,12 +74,14 @@

                 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))

+                L=np.reshape(A, (N//4, 4, N//8, 8))

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

+                S=np.sum(S, axis=1)

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

-                thresh = SS[round(N*N//16*(1-density))]

+                thresh = SS[round(N*N//32*(1-density))]

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

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

+                mask = np.repeat(mask, 4, axis=0)

+                mask = np.repeat(mask, 8, 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

--- a/dnn/training_tf2/train_lpcnet.py

+++ b/dnn/training_tf2/train_lpcnet.py

@@ -102,7 +102,7 @@

 del in_exc

 # dump models to disk as we go

-checkpoint = ModelCheckpoint('lpcnet32c_384_10_G16_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('lpcnet32v_384_10_G16_{epoch:02d}.h5')

 #Set this to True to adapt an existing model (e.g. on new data)

 adaptation = False

@@ -120,5 +120,5 @@

     decay = 5e-5

 model.compile(optimizer=Adam(lr, decay=decay, beta_2=0.99), loss='sparse_categorical_crossentropy')

-model.save_weights('lpcnet32c_384_10_G16_00.h5');

+model.save_weights('lpcnet32v_384_10_G16_00.h5');

 model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])

-- 

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