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Predicting pre-emphasized audio

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This commit is contained in:
Jean-Marc Valin 2018-08-16 13:58:33 -04:00
parent 3d20cdaed4
commit 785a2b2e84
3 changed files with 11 additions and 11 deletions
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6
dnn/lpcnet.py
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@ -10,7 +10,7 @@ import numpy as np
import h5py
import sys
rnn_units=64
rnn_units=512
pcm_bits = 8
pcm_levels = 2**pcm_bits
nb_used_features = 38
@ -41,7 +41,7 @@ class PCMInit(Initializer):
}
def new_wavernn_model():
pcm = Input(shape=(None, 2))
pcm = Input(shape=(None, 1))
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
dec_feat = Input(shape=(None, 32))
@ -61,7 +61,7 @@ def new_wavernn_model():
cpitch = pitch
embed = Embedding(256, 128, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, 128*2))(embed(pcm))
cpcm = Reshape((-1, 128))(embed(pcm))
cfeat = fconv2(fconv1(feat))

10
dnn/test_wavenet_audio.py
View file

@ -23,18 +23,18 @@ batch_size = 64
model, enc, dec = lpcnet.new_wavernn_model()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
#model.summary()
pcmfile = sys.argv[1]
feature_file = sys.argv[2]
frame_size = 160
nb_features = 54
nb_features = 55
nb_used_features = wavenet.nb_used_features
feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size
data = np.fromfile(pcmfile, dtype='int16')
data = np.minimum(127, lin2ulaw(data[80:]/32768.))
data = np.minimum(127, lin2ulaw(data/32768.))
nb_frames = len(data)//pcm_chunk_size
features = np.fromfile(feature_file, dtype='float32')
@ -66,7 +66,7 @@ in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
model.load_weights('wavenet3g_30.h5')
model.load_weights('wavenet3h12_30.h5')
order = 16
@ -92,7 +92,7 @@ for c in range(1, nb_frames):
#fexc[0, 0, 0] = in_data[f*frame_size + i, 0]
#print(cfeat.shape)
p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state])
#p = np.maximum(p-0.003, 0)
p = np.maximum(p-0.0003, 0)
p = p/(1e-5 + np.sum(p))
#print(np.sum(p))
iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128

6
dnn/train_wavenet_audio.py
View file

@ -30,7 +30,7 @@ feature_file = sys.argv[2]
pred_file = sys.argv[3]
pcm_file = sys.argv[4]
frame_size = 160
nb_features = 54
nb_features = 55
nb_used_features = wavenet.nb_used_features
feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size
@ -72,7 +72,7 @@ features = features[:, :, :nb_used_features]
pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
pred = (pred.astype('int16')+128).astype('uint8')
in_data = np.concatenate([in_data, pred], axis=-1)
#in_data = np.concatenate([in_data, pred], axis=-1)
#in_data = np.concatenate([in_data, in_pitch], axis=-1)
@ -80,7 +80,7 @@ in_data = np.concatenate([in_data, pred], axis=-1)
# f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[:50000, :, :])
checkpoint = ModelCheckpoint('wavenet3h9_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('wavenet3h13_{epoch:02d}.h5')
#model.load_weights('wavernn1c_01.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])