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Using Burg cepstrum for feature prediction

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This commit is contained in:
Jean-Marc Valin 2022-02-04 22:04:23 -05:00
parent b93dbfc0bc
commit 2e18f0d160
8 changed files with 50 additions and 15 deletions
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5
dnn/training_tf2/lpcnet_plc.py
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@ -62,8 +62,8 @@ class WeightClip(Constraint):
constraint = WeightClip(0.992)
def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, batch_size=128, training=False, adaptation=False, quantize=False, cond_size=128):
feat = Input(shape=(None, nb_used_features), batch_size=batch_size)
def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, nb_burg_features=36, batch_size=128, training=False, adaptation=False, quantize=False, cond_size=128):
feat = Input(shape=(None, nb_used_features+nb_burg_features), batch_size=batch_size)
lost = Input(shape=(None, 1), batch_size=batch_size)
fdense1 = Dense(cond_size, activation='tanh', name='plc_dense1')
@ -96,5 +96,6 @@ def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, batch_size=128, tra
model.rnn_units = rnn_units
model.cond_size = cond_size
model.nb_used_features = nb_used_features
model.nb_burg_features = nb_burg_features
return model