ref: 171b1ba0ce0411695c8c7b285a8fe636ece3e2c2
dir: /dnn/lpcnet.py/
#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
import math
from keras.models import Model
from keras.layers import Input, GRU, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from keras.callbacks import Callback
from mdense import MDense
import numpy as np
import h5py
import sys
frame_size = 160
pcm_bits = 8
embed_size = 128
pcm_levels = 2**pcm_bits
class Sparsify(Callback):
def __init__(self, t_start, t_end, interval, density):
super(Sparsify, self).__init__()
self.batch = 0
self.t_start = t_start
self.t_end = t_end
self.interval = interval
self.final_density = density
def on_batch_end(self, batch, logs=None):
#print("batch number", self.batch)
self.batch += 1
if self.batch < self.t_start or ((self.batch-self.t_start) % self.interval != 0 and self.batch < self.t_end):
#print("don't constrain");
pass
else:
#print("constrain");
layer = self.model.get_layer('gru_a')
w = layer.get_weights()
p = w[1]
nb = p.shape[1]//p.shape[0]
N = p.shape[0]
#print("nb = ", nb, ", N = ", N);
#print(p.shape)
#print ("density = ", density)
for k in range(nb):
density = self.final_density[k]
if self.batch < self.t_end:
r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
density = 1 - (1-self.final_density[k])*(1 - r*r*r)
A = p[:, k*N:(k+1)*N]
A = A - np.diag(np.diag(A))
A = np.transpose(A, (1, 0))
L=np.reshape(A, (N, N//16, 16))
S=np.sum(L*L, axis=-1)
SS=np.sort(np.reshape(S, (-1,)))
thresh = SS[round(N*N//16*(1-density))]
mask = (S>=thresh).astype('float32');
mask = np.repeat(mask, 16, axis=1)
mask = np.minimum(1, mask + np.diag(np.ones((N,))))
mask = np.transpose(mask, (1, 0))
p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask
#print(thresh, np.mean(mask))
w[1] = p
layer.set_weights(w)
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_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, training=False, use_gpu=True, adaptation=False):
pcm = Input(shape=(None, 3))
feat = Input(shape=(None, nb_used_features))
pitch = Input(shape=(None, 1))
dec_feat = Input(shape=(None, 128))
dec_state1 = Input(shape=(rnn_units1,))
dec_state2 = Input(shape=(rnn_units2,))
padding = 'valid' if training else 'same'
fconv1 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv1')
fconv2 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv2')
embed = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_sig')
cpcm = Reshape((-1, embed_size*3))(embed(pcm))
pembed = Embedding(256, 64, name='embed_pitch')
cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
cfeat = fconv2(fconv1(cat_feat))
fdense1 = Dense(128, activation='tanh', name='feature_dense1')
fdense2 = Dense(128, activation='tanh', name='feature_dense2')
cfeat = fdense2(fdense1(cfeat))
rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1))
if use_gpu:
rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a')
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b')
else:
rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a')
rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b')
rnn_in = Concatenate()([cpcm, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax', name='dual_fc')
gru_out1, _ = rnn(rnn_in)
gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
ulaw_prob = md(gru_out2)
if adaptation:
rnn.trainable=False
rnn2.trainable=False
md.trainable=False
embed.Trainable=False
model = Model([pcm, feat, pitch], ulaw_prob)
model.rnn_units1 = rnn_units1
model.rnn_units2 = rnn_units2
model.nb_used_features = nb_used_features
model.frame_size = frame_size
encoder = Model([feat, pitch], cfeat)
dec_rnn_in = Concatenate()([cpcm, dec_feat])
dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]), initial_state=dec_state2)
dec_ulaw_prob = md(dec_gru_out2)
decoder = Model([pcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
return model, encoder, decoder