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This commit is contained in:
Jean-Marc Valin 2018-10-16 04:05:52 -04:00
parent fa1d2824fa
commit fb1d4fdec2
2 changed files with 5 additions and 4 deletions
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2
dnn/lpcnet.py
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@ -11,7 +11,7 @@ import numpy as np
import h5py
import sys
rnn_units1=256
rnn_units1=512
rnn_units2=32
pcm_bits = 8
embed_size = 128

7
dnn/train_wavenet_audio.py
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@ -111,6 +111,7 @@ in_exc = in_exc.astype('uint8')
features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features]
features[:,:,18:36] = 0
pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
pred = pred.astype('uint8')
@ -119,8 +120,8 @@ 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('wavenet5p0_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('lpcnet5_512_10_G32np_{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'])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=60, validation_split=0.2, callbacks=[checkpoint, lpcnet.Sparsify(1000, 20000, 200, 0.25)])
model.compile(optimizer=Adam(0.0005, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=60, validation_split=0.2, callbacks=[checkpoint, lpcnet.Sparsify(1000, 20000, 200, 0.1)])