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Add input embedding

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This commit is contained in:
Jean-Marc Valin 2018-07-27 16:33:01 -04:00
parent 1837dad072
commit 2aba2a9c49
2 changed files with 30 additions and 3 deletions
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5
dnn/train_wavenet_audio.py
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@ -31,7 +31,7 @@ feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size
data = np.fromfile(pcmfile, dtype='int16')
data = np.minimum(127, lin2ulaw(data[160:]/32768.))
data = np.minimum(127, lin2ulaw(data[80:]/32768.))
nb_frames = len(data)//pcm_chunk_size
features = np.fromfile(feature_file, dtype='float32')
@ -39,7 +39,7 @@ features = np.fromfile(feature_file, dtype='float32')
data = data[:nb_frames*pcm_chunk_size]
features = features[:nb_frames*feature_chunk_size*nb_features]
in_data = np.concatenate([data[0:1], data[:-1]])/16.;
in_data = np.concatenate([data[0:1], data[:-1]]);
features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
pitch = 1.*data
@ -51,6 +51,7 @@ for i in range(2, nb_frames*feature_chunk_size):
in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
in_data = (in_data.astype('int16')+128).astype('uint8')
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))

28
dnn/wavenet.py
View file

@ -4,6 +4,7 @@ import math
from keras.models import Model
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Add, Multiply, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from keras.initializers import VarianceScaling
from mdense import MDense
import numpy as np
@ -17,6 +18,30 @@ pcm_bits = 8
pcm_levels = 2**pcm_bits
nb_used_features = 38
class PCMInit(Initializer):
def __init__(self, gain=.1, seed=None):
self.gain = gain
self.seed = seed
def __call__(self, shape, dtype=None):
num_rows = 1
for dim in shape[:-1]:
num_rows *= dim
num_cols = shape[-1]
flat_shape = (num_rows, num_cols)
if self.seed is not None:
np.random.seed(self.seed)
a = np.random.uniform(-1.7321, 1.7321, flat_shape)
#a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
#a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
return self.gain * a
def get_config(self):
return {
'gain': self.gain,
'seed': self.seed
}
def new_wavenet_model(fftnet=False):
pcm = Input(shape=(None, 1))
@ -34,7 +59,8 @@ def new_wavenet_model(fftnet=False):
activation='tanh'
rfeat = rep(cfeat)
#tmp = Concatenate()([pcm, rfeat])
tmp = pcm
embed = Embedding(256, units, embeddings_initializer=PCMInit())
tmp = Reshape((-1, units))(embed(pcm))
init = VarianceScaling(scale=1.5,mode='fan_avg',distribution='uniform')
for k in range(10):
res = tmp