audio-domain synthesis

This commit is contained in:
Jean-Marc Valin 2018-07-31 18:37:27 -04:00
parent 4cf2b2705a
commit 70789e6f43
4 changed files with 138 additions and 3 deletions

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, Multiply, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from mdense import MDense
import numpy as np
import h5py
@ -14,6 +15,30 @@ 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_wavernn_model():
pcm = Input(shape=(None, 1))
@ -35,6 +60,10 @@ def new_wavernn_model():
cpcm = pcm
cpitch = pitch
embed = Embedding(256, 128, embeddings_initializer=PCMInit())
cpcm = Reshape((-1, 128))(embed(pcm))
cfeat = fconv2(fconv1(feat))
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))

103
dnn/test_wavenet_audio.py Executable file
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@ -0,0 +1,103 @@
#!/usr/bin/python3
import wavenet
import lpcnet
import sys
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from ulaw import ulaw2lin, lin2ulaw
import keras.backend as K
import h5py
#import tensorflow as tf
#from keras.backend.tensorflow_backend import set_session
#config = tf.ConfigProto()
#config.gpu_options.per_process_gpu_memory_fraction = 0.44
#set_session(tf.Session(config=config))
nb_epochs = 40
batch_size = 64
#model = wavenet.new_wavenet_model(fftnet=True)
model, enc, dec = lpcnet.new_wavernn_model()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
pcmfile = sys.argv[1]
feature_file = sys.argv[2]
frame_size = 160
nb_features = 54
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.))
nb_frames = len(data)//pcm_chunk_size
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]]);
features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
pitch = 1.*data
pitch[:320] = 0
for i in range(2, nb_frames*feature_chunk_size):
period = int(50*features[i,36]+100)
period = period - 4
pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
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))
features = features[:, :, :nb_used_features]
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('wavenet3e_30.h5')
order = 16
pcm = 0.*out_data
exc = out_data-0
pitch = 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])
for fr in range(1, feature_chunk_size):
f = c*feature_chunk_size + fr
a = features[c, fr, nb_used_features:]
#print(a)
gain = 1.;
period = int(50*features[c, fr, 36]+100)
period = period - 4
for i in range(frame_size):
pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
fexc[0, 0, 0] = iexc + 128
#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 = 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] = 32768*ulaw2lin(iexc[0, 0, 0]*1.0)
print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])

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@ -1,6 +1,7 @@
#!/usr/bin/python3
import wavenet
import lpcnet
import sys
import numpy as np
from keras.optimizers import Adam
@ -18,7 +19,9 @@ import h5py
nb_epochs = 40
batch_size = 64
model = wavenet.new_wavenet_model(fftnet=True)
#model = wavenet.new_wavenet_model(fftnet=True)
model, _, _ = lpcnet.new_wavernn_model()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
@ -64,7 +67,7 @@ features = features[:, :, :nb_used_features]
# f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[:50000, :, :])
checkpoint = ModelCheckpoint('wavenet3c_{epoch:02d}.h5')
checkpoint = ModelCheckpoint('wavenet3e_{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'])

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@ -5,7 +5,7 @@ import math
def ulaw2lin(u):
s = np.sign(u)
u = np.abs(u)
return s*(np.exp(u/128*math.log(256))-1)/255
return s*(np.exp(u/128.*math.log(256))-1)/255
def lin2ulaw(x):