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Adds end-to-end LPC training

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Making LPC computation and prediction differentiable
This commit is contained in:
Krishna Subramani 2021-07-29 03:36:13 -04:00 • committed by Jean-Marc Valin
parent cba0ecd483
commit c1532559a2
11 changed files with 357 additions and 17 deletions
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11
dnn/training_tf2/dump_lpcnet.py
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@ -35,6 +35,9 @@ from mdense import MDense
import h5py
import re
# Flag for dumping e2e (differentiable lpc) network weights
flag_e2e = False
max_rnn_neurons = 1
max_conv_inputs = 1
max_mdense_tmp = 1
@ -237,7 +240,7 @@ with h5py.File(filename, "r") as f:
units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = flag_e2e)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()
@ -288,6 +291,12 @@ for i, layer in enumerate(model.layers):
if layer.dump_layer(f, hf):
layer_list.append(layer.name)
if flag_e2e:
print("-- Weight Dumping for the Differentiable LPC Block --")
for i, layer in enumerate(model.get_layer("f2lpc").layers):
if layer.dump_layer(f, hf):
layer_list.append(layer.name)
dump_sparse_gru(model.get_layer('gru_a'), f, hf)
hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))