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moving hyper-parameters to new_lpcnet_model() arguments

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more cleaning up too
This commit is contained in:
Jean-Marc Valin 2018-10-24 14:57:34 -04:00
parent 5ff58fa117
commit db7569c3da
3 changed files with 11 additions and 15 deletions
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5
dnn/train_lpcnet.py
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@ -28,8 +28,7 @@ nb_epochs = 40
# Try reducing batch_size if you run out of memory on your GPU
batch_size = 64
# Note we are creating a LPCNet model
model, _, _ = lpcnet.new_wavernn_model()
model, _, _ = lpcnet.new_lpcnet_model()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
@ -40,7 +39,7 @@ pred_file = sys.argv[3] # LPC predictor samples. Not used at present, see bel
pcm_file = sys.argv[4] # 16 bit unsigned short PCM samples
frame_size = 160
nb_features = 55
nb_used_features = lpcnet.nb_used_features
nb_used_features = model.nb_used_features
feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size