From 219fbff4e6adee83c3b43a745c413259477df8f9 Mon Sep 17 00:00:00 2001
From: Jean-Marc Valin <jmvalin@jmvalin.ca>
Date: Sun, 24 Mar 2019 03:48:26 -0400
Subject: [PATCH] Making it easier to adapt (or not) a model
---
dnn/train_lpcnet.py | 20 +++++++++++++++++---
1 file changed, 17 insertions(+), 3 deletions(-)
diff --git a/dnn/train_lpcnet.py b/dnn/train_lpcnet.py
index 8c82f1970..0b5e0ba25 100755
--- a/dnn/train_lpcnet.py
+++ b/dnn/train_lpcnet.py
@@ -105,6 +105,20 @@ del in_exc
# 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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