shithub: opus

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Making it easier to adapt (or not) a model

--- a/dnn/train_lpcnet.py

+++ b/dnn/train_lpcnet.py

@@ -105,6 +105,20 @@

 # dump models to disk as we go

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

-model.load_weights('lpcnet24c_384_10_G16_120.h5')

-model.compile(optimizer=Adam(0.0001, amsgrad=True), loss='sparse_categorical_crossentropy')

-model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))])

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

+adaptation = False

+

+if adaptation:

+    #Adapting from an existing model

+    model.load_weights('lpcnet24c_384_10_G16_120.h5')

+    sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))

+    lr = 0.0001

+    decay = 0

+else:

+    #Training from scratch

+    sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))

+    lr = 0.001

+    decay = 5e-5

+

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

+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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