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Managing to actually use sparse matrices

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Now 2x real-time!
This commit is contained in:
Jean-Marc Valin 2018-11-28 20:20:17 -05:00
parent 4de3e53a73
commit d961d009a0
3 changed files with 14 additions and 4 deletions
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2
dnn/train_lpcnet.py
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@ -139,7 +139,7 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
in_data = np.concatenate([in_data, pred], axis=-1)
# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet9_384_10_G16_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('lpcnet9b_384_10_G16_{epoch:02d}.h5')
#model.load_weights('wavenet4f2_30.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])