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Freeze LPCs when quantizing e2e models

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This commit is contained in:
Jean-Marc Valin 2021-10-16 17:20:24 -04:00
parent 8cdc8081d8
commit 054d984bf3
1 changed files with 6 additions and 0 deletions
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6
dnn/training_tf2/lpcnet.py
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@ -249,6 +249,12 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
fdense1 = Dense(128, activation='tanh', name='feature_dense1')
fdense2 = Dense(128, activation='tanh', name='feature_dense2')
if flag_e2e and quantize:
fconv1.trainable = False
fconv2.trainable = False
fdense1.trainable = False
fdense2.trainable = False
cfeat = fdense2(fdense1(cfeat))
if not flag_e2e: