eden-emu/opus
15
0
Fork 0
mirror of https://github.com/xiph/opus.git synced 2025-05-19 18:08:29 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

second RNN

Browse source
This commit is contained in:
Jean-Marc Valin 2018-10-10 01:41:58 -04:00
parent 3698977292
commit 495f8ea5f3
3 changed files with 18 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

21
dnn/lpcnet.py
View file

@ -10,7 +10,8 @@ import numpy as np
import h5py import h5py
import sys import sys
rnn_units=128 rnn_units1=128
rnn_units2=32
pcm_bits = 8 pcm_bits = 8
embed_size = 128 embed_size = 128
pcm_levels = 2**pcm_bits pcm_levels = 2**pcm_bits
@ -47,7 +48,8 @@ def new_wavernn_model():
feat = Input(shape=(None, nb_used_features)) feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1)) pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 128)) dec_feat = Input(shape=(None, 128))
dec_state = Input(shape=(rnn_units,)) dec_state1 = Input(shape=(rnn_units1,))
dec_state2 = Input(shape=(rnn_units2,))
fconv1 = Conv1D(128, 3, padding='same', activation='tanh') fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
fconv2 = Conv1D(102, 3, padding='same', activation='tanh') fconv2 = Conv1D(102, 3, padding='same', activation='tanh')
@ -70,18 +72,21 @@ def new_wavernn_model():
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1)) rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True) rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True)
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True)
rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)]) rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax') md = MDense(pcm_levels, activation='softmax')
gru_out, state = rnn(rnn_in) gru_out1, _ = rnn(rnn_in)
ulaw_prob = md(gru_out) gru_out2, _ = rnn2(gru_out1)
ulaw_prob = md(gru_out2)
model = Model([pcm, exc, feat, pitch], ulaw_prob) model = Model([pcm, exc, feat, pitch], ulaw_prob)
encoder = Model([feat, pitch], cfeat) encoder = Model([feat, pitch], cfeat)
dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat]) dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat])
dec_gru_out, state = rnn(dec_rnn_in, initial_state=dec_state) dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
dec_ulaw_prob = md(dec_gru_out) dec_gru_out2, state2 = rnn2(dec_gru_out1, initial_state=dec_state2)
dec_ulaw_prob = md(dec_gru_out2)
decoder = Model([pcm, exc, dec_feat, dec_state], [dec_ulaw_prob, state]) decoder = Model([pcm, exc, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
return model, encoder, decoder return model, encoder, decoder

7
dnn/test_wavenet_audio.py
View file

@ -59,14 +59,15 @@ 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 = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
model.load_weights('wavenet4f2_30.h5') model.load_weights('wavenet5d0_19.h5')
order = 16 order = 16
pcm = 0.*out_data pcm = 0.*out_data
fexc = np.zeros((1, 1, 2), dtype='float32') fexc = np.zeros((1, 1, 2), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16') iexc = np.zeros((1, 1, 1), dtype='int16')
state = np.zeros((1, lpcnet.rnn_units), dtype='float32') state1 = np.zeros((1, lpcnet.rnn_units1), dtype='float32')
state2 = np.zeros((1, lpcnet.rnn_units2), dtype='float32')
for c in range(1, nb_frames): for c in range(1, nb_frames):
cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]]) cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
for fr in range(1, feature_chunk_size): for fr in range(1, feature_chunk_size):
@ -82,7 +83,7 @@ for c in range(1, nb_frames):
pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0]) pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
fexc[0, 0, 1] = lin2ulaw(pred) fexc[0, 0, 1] = lin2ulaw(pred)
p, state = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state]) p, state1, state2 = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state1, state2])
#p = p*p #p = p*p
#p = p/(1e-18 + np.sum(p)) #p = p/(1e-18 + np.sum(p))
p = np.maximum(p-0.001, 0).astype('float64') p = np.maximum(p-0.001, 0).astype('float64')

2
dnn/train_wavenet_audio.py
View file

@ -86,7 +86,7 @@ periods = (50*features[:,:,36:37]+100).astype('int16')
in_data = np.concatenate([in_data, pred], axis=-1) in_data = np.concatenate([in_data, pred], axis=-1)
checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5') checkpoint = ModelCheckpoint('wavenet5d0_{epoch:02d}.h5')
#model.load_weights('wavenet4f2_30.h5') #model.load_weights('wavenet4f2_30.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])