shithub: opus

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excitation model

--- a/dnn/lpcnet.py

+++ b/dnn/lpcnet.py

@@ -9,9 +9,9 @@

 import h5py

 import sys

-rnn_units=256

+rnn_units=64

 pcm_bits = 8

-pcm_levels = 1+2**pcm_bits

+pcm_levels = 2**pcm_bits

 def new_wavernn_model():

     pcm = Input(shape=(None, 1))

--- a/dnn/train_lpcnet.py

+++ b/dnn/train_lpcnet.py

@@ -6,6 +6,7 @@

 from keras.optimizers import Adam

 from keras.callbacks import ModelCheckpoint

 from ulaw import ulaw2lin, lin2ulaw

+import keras.backend as K

 import tensorflow as tf

 from keras.backend.tensorflow_backend import set_session

@@ -21,22 +22,30 @@

 model.summary()

 pcmfile = sys.argv[1]

-chunk_size = int(sys.argv[2])

+feature_file = sys.argv[2]

+nb_features = 54

+nb_used_features = 38

+feature_chunk_size = 15

+pcm_chunk_size = 160*feature_chunk_size

-data = np.fromfile(pcmfile, dtype='int16')

-#data = data[:100000000]

-data = data/32768

-nb_frames = (len(data)-1)//chunk_size

+data = np.fromfile(pcmfile, dtype='int8')

+nb_frames = len(data)//pcm_chunk_size

-in_data = data[:nb_frames*chunk_size]

-#out_data = data[1:1+nb_frames*chunk_size]//256 + 128

-out_data = lin2ulaw(data[1:1+nb_frames*chunk_size]) + 128

+features = np.fromfile(feature_file, dtype='float32')

-in_data = np.reshape(in_data, (nb_frames, chunk_size, 1))

-out_data = np.reshape(out_data, (nb_frames, chunk_size, 1))

+data = data[:nb_frames*pcm_chunk_size]

+features = features[:nb_frames*feature_chunk_size*nb_features]

-checkpoint = ModelCheckpoint('wavernn1f_{epoch:02d}.h5')

+in_data = np.concatenate([data[0:1], data[:-1]])/16.;

+in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))

+out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))

+out_data = (out_data.astype('int16')+128).astype('uint8')

+features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))

+features = features[:, :, :nb_used_features]

+

+checkpoint = ModelCheckpoint('lpcnet1b_{epoch:02d}.h5')

+

 #model.load_weights('wavernn1c_01.h5')

-model.compile(optimizer=Adam(0.002, amsgrad=True, decay=1e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

+model.compile(optimizer=Adam(0.002, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

 model.fit(in_data, out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])

-- 

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