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Use a single u-law embedding

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This commit is contained in:
Jean-Marc Valin 2019-01-21 16:52:57 -05:00
parent dc082d7c1c
commit b84a06dd08
4 changed files with 14 additions and 18 deletions
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5
dnn/train_lpcnet.py
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@ -91,14 +91,15 @@ features[:,:,18:36] = 0
periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')
in_data = np.concatenate([sig, pred], axis=-1)
in_data = np.concatenate([sig, pred, in_exc], axis=-1)
del sig
del pred
del in_exc
# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet20g_384_10_G16_{epoch:02d}.h5')
#model.load_weights('lpcnet9b_384_10_G16_01.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy')
model.fit([in_data, in_exc, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])