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remove unused/dead code

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This commit is contained in:
Jean-Marc Valin 2018-10-09 12:27:02 -04:00
parent a9835c4e5f
commit 03fa20d532
2 changed files with 0 additions and 20 deletions
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11
dnn/lpcnet.py
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@ -44,25 +44,14 @@ class PCMInit(Initializer):
def new_wavernn_model(): def new_wavernn_model():
pcm = Input(shape=(None, 2)) pcm = Input(shape=(None, 2))
exc = Input(shape=(None, 1)) exc = Input(shape=(None, 1))
pitch = Input(shape=(None, 1))
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_state = Input(shape=(rnn_units,))
conv1 = Conv1D(16, 7, padding='causal', activation='tanh')
pconv1 = Conv1D(16, 5, padding='same', activation='tanh')
pconv2 = Conv1D(16, 5, padding='same', activation='tanh')
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')
if False:
cpcm = conv1(pcm)
cpitch = pconv2(pconv1(pitch))
else:
cpcm = pcm
cpitch = pitch
embed = Embedding(256, embed_size, embeddings_initializer=PCMInit()) embed = Embedding(256, embed_size, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, embed_size*2))(embed(pcm)) cpcm = Reshape((-1, embed_size*2))(embed(pcm))
embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit()) embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit())

9
dnn/train_wavenet_audio.py
View file

@ -58,14 +58,11 @@ upred = upred[:nb_frames*pcm_chunk_size]
pred_in = ulaw2lin(in_data) pred_in = ulaw2lin(in_data)
for i in range(2, nb_frames*feature_chunk_size): for i in range(2, nb_frames*feature_chunk_size):
upred[i*frame_size:(i+1)*frame_size] = 0 upred[i*frame_size:(i+1)*frame_size] = 0
#if i % 100000 == 0:
# print(i)
for k in range(16): for k in range(16):
upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \ upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k] pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
pred = lin2ulaw(upred) pred = lin2ulaw(upred)
#pred = pred + np.random.randint(-1, 1, len(data))
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1)) in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
@ -89,12 +86,6 @@ 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)
#in_data = np.concatenate([in_data, in_pitch], axis=-1)
#with h5py.File('in_data.h5', 'w') as f:
# f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[:50000, :, :])
checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5') checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5')
#model.load_weights('wavenet4f2_30.h5') #model.load_weights('wavenet4f2_30.h5')