shithub: opus

Info  •  Files  •  Log  •  Branches

Minor update to training scripts

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

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

@@ -37,17 +37,17 @@

 import h5py

 import tensorflow as tf

-gpus = tf.config.experimental.list_physical_devices('GPU')

-if gpus:

-  try:

-    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])

-  except RuntimeError as e:

-    print(e)

+#gpus = tf.config.experimental.list_physical_devices('GPU')

+#if gpus:

+#  try:

+#    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])

+#  except RuntimeError as e:

+#    print(e)

 nb_epochs = 120

 # Try reducing batch_size if you run out of memory on your GPU

-batch_size = 64

+batch_size = 128

 model, _, _ = lpcnet.new_lpcnet_model(training=True)

@@ -102,15 +102,14 @@

 del in_exc

 # dump models to disk as we go

-checkpoint = ModelCheckpoint('lpcnet32y_384_10_G16_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('lpcnet33_384_{epoch:02d}.h5')

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

 adaptation = False

-model.load_weights('lpcnet32v_384_10_G16_00.h5')

 if adaptation:

     #Adapting from an existing model

-    model.load_weights('lpcnet32v_384_10_G16_100.h5')

+    model.load_weights('lpcnet32v_384_100.h5')

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

     lr = 0.0001

     decay = 0

@@ -121,5 +120,5 @@

     decay = 5e-5

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

-model.save_weights('lpcnet32y_384_10_G16_00.h5');

+model.save_weights('lpcnet33_384_00.h5');

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

-- 

⑨