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decodes something...

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This commit is contained in:
Jean-Marc Valin 2018-07-11 01:30:30 -04:00
parent 638252a965
commit 5d8a1313d6
2 changed files with 19 additions and 10 deletions
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7
dnn/lpcnet.py
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@ -9,7 +9,7 @@ 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 = 37
@ -20,6 +20,7 @@ def new_wavernn_model():
pitch = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
dec_feat = Input(shape=(None, 32))
dec_state = Input(shape=(rnn_units,))
conv1 = Conv1D(16, 7, padding='causal')
pconv1 = Conv1D(16, 5, padding='same')
@ -48,8 +49,8 @@ def new_wavernn_model():
encoder = Model(feat, cfeat)
dec_rnn_in = Concatenate()([cpcm, cpitch, dec_feat])
dec_gru_out, state = rnn(dec_rnn_in)
dec_gru_out, state = rnn(dec_rnn_in, initial_state=dec_state)
dec_ulaw_prob = md(dec_gru_out)
decoder = Model([pcm, pitch, dec_feat], [dec_ulaw_prob, state])
decoder = Model([pcm, pitch, dec_feat, dec_state], [dec_ulaw_prob, state])
return model, encoder, decoder

22
dnn/test_lpcnet.py
View file

@ -21,7 +21,7 @@ batch_size = 64
model, enc, dec = lpcnet.new_wavernn_model()
model.compile(optimizer=Adadiff(), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
#model.summary()
pcmfile = sys.argv[1]
feature_file = sys.argv[2]
@ -47,14 +47,15 @@ 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('lpcnet1h_30.h5')
model.load_weights('lpcnet1i_30.h5')
order = 16
pcm = 0.*out_data
exc = 0.*out_data
exc = out_data-0
pitch = np.zeros((1, 1, 1), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='float32')
fexc = np.zeros((1, 1, 1), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16')
state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
for c in range(1, nb_frames):
cfeat = enc.predict(features[c:c+1, :, :nb_used_features])
@ -68,7 +69,14 @@ for c in range(1, nb_frames):
period = period - 4
for i in range(frame_size):
pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
#p, state = dec.predict([
pcm[f*frame_size + i, 0] = gain*out_data[f*frame_size + i, 0] - sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
print(pcm[f*frame_size + i, 0])
fexc[0, 0, 0] = exc[f*frame_size + i - 1]
#print(cfeat.shape)
p, state = dec.predict([fexc, pitch, cfeat[:, fr:fr+1, :], state])
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
exc[f*frame_size + i] = iexc[0, 0, 0]/16.
#out_data[f*frame_size + i, 0] = iexc[0, 0, 0]
pcm[f*frame_size + i, 0] = gain*iexc[0, 0, 0] - sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])