shithub: opus

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training

--- a/dnn/train_lpcnet.py

+++ b/dnn/train_lpcnet.py

@@ -103,7 +103,7 @@

 del in_exc

 # dump models to disk as we go

-checkpoint = ModelCheckpoint('lpcnet24g_384_10_G16_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('lpcnet30_384_10_G16_{epoch:02d}.h5')

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

 adaptation = False

@@ -121,4 +121,5 @@

     decay = 5e-5

 model.compile(optimizer=Adam(lr, amsgrad=True, decay=decay), loss='sparse_categorical_crossentropy')

+model.save_weights('lpcnet30_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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