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RDO-VAE work in progress

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Jean-Marc Valin 2022-09-08 03:12:02 -04:00
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dnn/training_tf2/decode_rdovae.py Normal file
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#!/usr/bin/python3
'''Copyright (c) 2021-2022 Amazon
Copyright (c) 2018-2019 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.
'''
# Train an LPCNet model
import argparse
#from plc_loader import PLCLoader
parser = argparse.ArgumentParser(description='Train a PLC model')
parser.add_argument('bits', metavar='<bits file>', help='binary features file (int16)')
parser.add_argument('output', metavar='<output>', help='output features')
parser.add_argument('--model', metavar='<model>', default='rdovae', help='PLC model python definition (without .py)')
group1 = parser.add_mutually_exclusive_group()
group1.add_argument('--weights', metavar='<input weights>', help='model weights')
parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
parser.add_argument('--batch-size', metavar='<batch size>', default=1, type=int, help='batch size to use (default 128)')
parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')
args = parser.parse_args()
import importlib
rdovae = importlib.import_module(args.model)
import sys
import numpy as np
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
import tensorflow.keras.backend as K
import h5py
import tensorflow as tf
# Try reducing batch_size if you run out of memory on your GPU
batch_size = args.batch_size
model, encoder, decoder = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)
model.load_weights(args.weights)
lpc_order = 16
bits_file = args.bits
sequence_size = args.seq_length
# u for unquantised, load 16 bit PCM samples and convert to mu-law
bits = np.memmap(bits_file + "-bits.s16", dtype='int16', mode='r')
nb_sequences = len(bits)//(20*sequence_size)//batch_size*batch_size
bits = bits[:nb_sequences*sequence_size*20]
bits = np.reshape(bits, (nb_sequences, sequence_size//4, 20*4))
print(bits.shape)
quant = np.memmap(bits_file + "-quant.f32", dtype='float32', mode='r')
state = np.memmap(bits_file + "-state.f32", dtype='float32', mode='r')
quant = np.reshape(quant, (nb_sequences, sequence_size//4, 6*20*4))
state = np.reshape(state, (nb_sequences, sequence_size//2, 16))
state = state[:,-1,:]
print("shapes are:")
print(bits.shape)
print(quant.shape)
print(state.shape)
features = decoder.predict([bits, quant, state], batch_size=batch_size)
features.astype('float32').tofile(args.output)

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dnn/training_tf2/encode_rdovae.py Normal file
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#!/usr/bin/python3
'''Copyright (c) 2021-2022 Amazon
Copyright (c) 2018-2019 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.
'''
# Train an LPCNet model
import argparse
#from plc_loader import PLCLoader
parser = argparse.ArgumentParser(description='Train a PLC model')
parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
parser.add_argument('--model', metavar='<model>', default='rdovae', help='PLC model python definition (without .py)')
group1 = parser.add_mutually_exclusive_group()
group1.add_argument('--weights', metavar='<input weights>', help='model weights')
parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
parser.add_argument('--batch-size', metavar='<batch size>', default=1, type=int, help='batch size to use (default 128)')
parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')
args = parser.parse_args()
import importlib
rdovae = importlib.import_module(args.model)
import sys
import numpy as np
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
import tensorflow.keras.backend as K
import h5py
import tensorflow as tf
# Try reducing batch_size if you run out of memory on your GPU
batch_size = args.batch_size
model, encoder, decoder = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)
model.load_weights(args.weights)
lpc_order = 16
feature_file = args.features
nb_features = model.nb_used_features + lpc_order
nb_used_features = model.nb_used_features
sequence_size = args.seq_length
# u for unquantised, load 16 bit PCM samples and convert to mu-law
features = np.memmap(feature_file, dtype='float32', mode='r')
nb_sequences = len(features)//(nb_features*sequence_size)//batch_size*batch_size
features = features[:nb_sequences*sequence_size*nb_features]
features = np.reshape(features, (nb_sequences, sequence_size, nb_features))
print(features.shape)
features = features[:, :, :nb_used_features]
#features = np.random.randn(73600, 1000, 17)
lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1))
quant_id = np.round(10*np.log(lambda_val/.0007)).astype('int16')
quant_id = quant_id[:,:,0]
bits, quant_embed_dec, gru_state_dec = encoder.predict([features, quant_id, lambda_val], batch_size=batch_size)
(gru_state_dec).astype('float32').tofile(args.output + "-state.f32")
#quant_out, _, _, model_bits, _ = model.predict([features, quant_id, lambda_val], batch_size=batch_size)
#dist = rdovae.feat_dist_loss(features, quant_out)
#rate = rdovae.sq1_rate_loss(features, model_bits)
#rate2 = rdovae.sq_rate_metric(features, model_bits)
#print(dist, rate, rate2)
print("shapes are:")
print(bits.shape)
print(quant_embed_dec.shape)
print(gru_state_dec.shape)
features.astype('float32').tofile(args.output + "-input.f32")
#quant_out.astype('float32').tofile(args.output + "-enc_dec.f32")
np.round(bits).astype('int16').tofile(args.output + "-bits.s16")
quant_embed_dec.astype('float32').tofile(args.output + "-quant.f32")
gru_state_dec = gru_state_dec[:,-1,:]
dec_out = decoder([bits, quant_embed_dec, gru_state_dec])
dec_out.numpy().astype('float32').tofile(args.output + "-dec_out.f32")

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dnn/training_tf2/rdovae.py Normal file
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#!/usr/bin/python3
'''Copyright (c) 2022 Amazon
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
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise, AveragePooling1D, RepeatVector
from tensorflow.compat.v1.keras.layers import CuDNNGRU
from tensorflow.keras import backend as K
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.initializers import Initializer
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.regularizers import l1
import numpy as np
import h5py
from uniform_noise import UniformNoise
class WeightClip(Constraint):
'''Clips the weights incident to each hidden unit to be inside a range
'''
def __init__(self, c=2):
self.c = c
def __call__(self, p):
# Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of
# saturation when implementing dot products with SSSE3 or AVX2.
return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1))
#return K.clip(p, -self.c, self.c)
def get_config(self):
return {'name': self.__class__.__name__,
'c': self.c}
constraint = WeightClip(0.496)
def soft_quantize(x):
#x = 4*x
#x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
#x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
#x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
return x
def noise_quantize(x):
return soft_quantize(x + (K.random_uniform((128, 16, 80))-.5) )
def hard_quantize(x):
x = soft_quantize(x)
quantized = tf.round(x)
return x + tf.stop_gradient(quantized - x)
def apply_dead_zone(x):
d = x[1]*.05
x = x[0]
y = x - d*tf.math.tanh(x/(.1+d))
return y
def rate_loss(y_true,y_pred):
log2_e = 1.4427
n = y_pred.shape[-1]
C = n - log2_e*np.math.log(np.math.gamma(n))
k = K.sum(K.abs(y_pred), axis=-1)
p = 1.5
#rate = C + (n-1)*log2_e*tf.math.log((k**p + (n/5)**p)**(1/p))
rate = C + (n-1)*log2_e*tf.math.log(k + .112*n**2/(n/1.8+k) )
return K.mean(rate)
eps=1e-6
def safelog2(x):
log2_e = 1.4427
return log2_e*tf.math.log(eps+x)
def feat_dist_loss(y_true,y_pred):
ceps = y_pred[:,:,:18] - y_true[:,:,:18]
pitch = 2*(y_pred[:,:,18:19] - y_true[:,:,18:19])/(y_true[:,:,18:19] + 2)
corr = y_pred[:,:,19:] - y_true[:,:,19:]
pitch_weight = K.square(K.maximum(0., y_true[:,:,19:]+.5))
return K.mean(K.square(ceps) + 10*(1/18.)*K.abs(pitch)*pitch_weight + (1/18.)*K.square(corr))
def sq1_rate_loss(y_true,y_pred):
lambda_val = y_pred[:,:,-1]
y_pred = y_pred[:,:,:-1]
log2_e = 1.4427
n = y_pred.shape[-1]//3
r = (y_pred[:,:,2*n:])
p0 = (y_pred[:,:,n:2*n])
p0 = 1-r**(.5+.5*p0)
y_pred = y_pred[:,:,:n]
y_pred = soft_quantize(y_pred)
y0 = K.maximum(0., 1. - K.abs(y_pred))**2
rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1))
rate = -safelog2(-.5*tf.math.log(r)*r**K.abs(y_pred))
rate = -safelog2((1-r)/(1+r)*r**K.abs(y_pred))
#rate = -safelog2(- tf.math.sinh(.5*tf.math.log(r))* r**K.abs(y_pred) - tf.math.cosh(K.maximum(0., .5 - K.abs(y_pred))*tf.math.log(r)) + 1)
rate = lambda_val*K.sum(rate, axis=-1)
return K.mean(rate)
def sq2_rate_loss(y_true,y_pred):
lambda_val = y_pred[:,:,-1]
y_pred = y_pred[:,:,:-1]
log2_e = 1.4427
n = y_pred.shape[-1]//3
r = y_pred[:,:,2*n:]
p0 = y_pred[:,:,n:2*n]
p0 = 1-r**(.5+.5*p0)
#theta = K.minimum(1., .5 + 0*p0 - 0.04*tf.math.log(r))
#p0 = 1-r**theta
y_pred = tf.round(y_pred[:,:,:n])
y0 = K.maximum(0., 1. - K.abs(y_pred))**2
rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1))
rate = lambda_val*K.sum(rate, axis=-1)
return K.mean(rate)
def sq_rate_metric(y_true,y_pred):
lambda_val = y_pred[:,:,-1]
y_pred = y_pred[:,:,:-1]
log2_e = 1.4427
n = y_pred.shape[-1]//3
r = y_pred[:,:,2*n:]
p0 = y_pred[:,:,n:2*n]
p0 = 1-r**(.5+.5*p0)
#theta = K.minimum(1., .5 + 0*p0 - 0.04*tf.math.log(r))
#p0 = 1-r**theta
y_pred = tf.round(y_pred[:,:,:n])
y0 = K.maximum(0., 1. - K.abs(y_pred))**2
rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1))
rate = K.sum(rate, axis=-1)
return K.mean(rate)
def pvq_quant_search(x, k):
x = x/tf.reduce_sum(tf.abs(x), axis=-1, keepdims=True)
kx = k*x
y = tf.round(kx)
newk = k
for j in range(10):
#print("y = ", y)
#print("iteration ", j)
abs_y = tf.abs(y)
abs_kx = tf.abs(kx)
kk=tf.reduce_sum(abs_y, axis=-1)
#print("sums = ", kk)
plus = 1.0001*tf.reduce_min((abs_y+.5)/(abs_kx+1e-15), axis=-1)
minus = .9999*tf.reduce_max((abs_y-.5)/(abs_kx+1e-15), axis=-1)
#print("plus = ", plus)
#print("minus = ", minus)
factor = tf.where(kk>k, minus, plus)
factor = tf.where(kk==k, tf.ones_like(factor), factor)
#print("scale = ", factor)
factor = tf.expand_dims(factor, axis=-1)
#newk = newk * (k/kk)**.2
newk = newk*factor
kx = newk*x
#print("newk = ", newk)
#print("unquantized = ", newk*x)
y = tf.round(kx)
#print(y)
return y
def pvq_quantize(x, k):
x = x/(1e-15+tf.norm(x, axis=-1,keepdims=True))
quantized = pvq_quant_search(x, k)
quantized = quantized/(1e-15+tf.norm(quantized, axis=-1,keepdims=True))
return x + tf.stop_gradient(quantized - x)
def var_repeat(x):
return RepeatVector(K.shape(x[1])[1])(x[0])
nb_state_dim = 24
def new_rdovae_encoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):
feat = Input(shape=(None, nb_used_features), batch_size=batch_size)
quant_id = Input(shape=(None,), batch_size=batch_size)
lambda_val = Input(shape=(None, 1), batch_size=batch_size)
qembedding = Embedding(nb_quant, 6*nb_bits, name='quant_embed', embeddings_initializer='zeros')
quant_embed = qembedding(quant_id)
quant_embed_bunched = AveragePooling1D(pool_size=bunch//2, strides=bunch//2, padding="valid")(quant_embed)
quant_scale = Activation('softplus')(Lambda(lambda x: x[:,:,:nb_bits], name='quant_scale_embed')(quant_embed_bunched))
enc_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense1')
enc_dense2 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense2')
enc_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense3')
enc_dense4 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense4')
enc_dense5 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense5')
enc_dense6 = CuDNNGRU(cond_size, return_sequences=True, return_state=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense6')
enc_dense7 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense7')
enc_dense8 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense8')
#bits_dense = Dense(nb_bits, activation='linear', name='bits_dense')
bits_dense = Conv1D(nb_bits, 4, padding='causal', activation='linear', name='bits_dense')
zero_out = Lambda(lambda x: 0*x)
inputs = Concatenate()([Reshape((-1, 2*nb_used_features))(feat), tf.stop_gradient(quant_embed), lambda_val])
#inputs = Concatenate()([feat, tf.stop_gradient(quant_embed), lambda_val])
d1 = enc_dense1(inputs)
d2 = enc_dense2(d1)
d3 = enc_dense3(d2)
d4 = enc_dense4(d3)
d5 = enc_dense5(d4)
d6, gru_state = enc_dense6(d5)
d7 = enc_dense7(d6)
d8 = enc_dense8(d7)
enc_out = bits_dense(Concatenate()([d1, d2, d3, d4, d5, d6, d7, d8]))
enc_out = Lambda(lambda x: x[:, bunch//2-1::bunch//2])(enc_out)
bits = Multiply()([enc_out, quant_scale])
global_dense1 = Dense(128, activation='tanh', name='gdense1')
global_dense2 = Dense(nb_state_dim, activation='tanh', name='gdense2')
global_bits = global_dense2(global_dense1(d6))
encoder = Model([feat, quant_id, lambda_val], [bits, quant_embed_bunched, global_bits], name='encoder')
return encoder
def new_rdovae_decoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):
bits_input = Input(shape=(None, nb_bits), batch_size=batch_size)
quant_embed_input = Input(shape=(None, 6*nb_bits), batch_size=batch_size)
gru_state_input = Input(shape=(nb_state_dim,), batch_size=batch_size)
dec_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense1')
dec_dense2 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='dec_dense2')
dec_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense3')
dec_dense4 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense4')
dec_dense5 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense5')
dec_dense6 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense6')
dec_dense7 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense7')
dec_dense8 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense8')
dec_final = Dense(bunch*nb_used_features, activation='linear', name='dec_final')
div = Lambda(lambda x: x[0]/x[1])
time_reverse = Lambda(lambda x: K.reverse(x, 1))
#time_reverse = Lambda(lambda x: x)
quant_scale_dec = Activation('softplus')(Lambda(lambda x: x[:,:,:nb_bits], name='quant_scale_embed_dec')(quant_embed_input))
#gru_state_rep = RepeatVector(64//bunch)(gru_state_input)
gru_state_rep = Lambda(var_repeat, output_shape=(None, nb_state_dim)) ([gru_state_input, bits_input])
dec_inputs = Concatenate()([div([bits_input,quant_scale_dec]), tf.stop_gradient(quant_embed_input), gru_state_rep])
dec1 = dec_dense1(time_reverse(dec_inputs))
dec2 = dec_dense2(dec1)
dec3 = dec_dense3(dec2)
dec4 = dec_dense4(dec3)
dec5 = dec_dense5(dec4)
dec6 = dec_dense6(dec5)
dec7 = dec_dense7(dec6)
dec8 = dec_dense8(dec7)
output = Reshape((-1, nb_used_features))(dec_final(Concatenate()([dec1, dec2, dec3, dec4, dec5, dec6, dec7, dec8])))
decoder = Model([bits_input, quant_embed_input, gru_state_input], time_reverse(output), name='decoder')
decoder.nb_bits = nb_bits
decoder.bunch = bunch
return decoder
def new_split_decoder(decoder):
nb_bits = decoder.nb_bits
bunch = decoder.bunch
bits_input = Input(shape=(None, nb_bits))
quant_embed_input = Input(shape=(None, 6*nb_bits))
gru_state_input = Input(shape=(None,nb_state_dim))
range_select = Lambda(lambda x: x[0][:,x[1]:x[2],:])
elem_select = Lambda(lambda x: x[0][:,x[1],:])
points = [0, 64, 128, 192, 256]
outputs = []
for i in range(len(points)-1):
begin = points[i]//bunch
end = points[i+1]//bunch
state = elem_select([gru_state_input, 2*end-1])
bits = range_select([bits_input, begin, end])
embed = range_select([quant_embed_input, begin, end])
outputs.append(decoder([bits, embed, state]))
output = Concatenate(axis=1)(outputs)
split = Model([bits_input, quant_embed_input, gru_state_input], output, name="split")
return split
def new_rdovae_model(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):
feat = Input(shape=(None, nb_used_features), batch_size=batch_size)
quant_id = Input(shape=(None,), batch_size=batch_size)
lambda_val = Input(shape=(None, 1), batch_size=batch_size)
lambda_bunched = AveragePooling1D(pool_size=bunch//2, strides=bunch//2, padding="valid")(lambda_val)
encoder = new_rdovae_encoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2)
ze, quant_embed_dec, gru_state_dec = encoder([feat, quant_id, lambda_val])
decoder = new_rdovae_decoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2)
split_decoder = new_split_decoder(decoder)
dead_zone = Activation('softplus')(Lambda(lambda x: x[:,:,nb_bits:2*nb_bits], name='dead_zone_embed')(quant_embed_dec))
soft_distr_embed = Activation('sigmoid')(Lambda(lambda x: x[:,:,2*nb_bits:4*nb_bits], name='soft_distr_embed')(quant_embed_dec))
hard_distr_embed = Activation('sigmoid')(Lambda(lambda x: x[:,:,4*nb_bits:], name='hard_distr_embed')(quant_embed_dec))
noisequant = UniformNoise()
hardquant = Lambda(hard_quantize)
dzone = Lambda(apply_dead_zone)
dze = dzone([ze,dead_zone])
gru_state_dec = Lambda(lambda x: pvq_quantize(x, 30))(gru_state_dec)
combined_output = split_decoder([hardquant(dze), tf.stop_gradient(quant_embed_dec), gru_state_dec])
ndze = noisequant(dze)
unquantized_output = split_decoder([ndze, quant_embed_dec, gru_state_dec])
unquantized_output_dec = split_decoder([tf.stop_gradient(ndze), tf.stop_gradient(quant_embed_dec), gru_state_dec])
e2 = Concatenate(name="hard_bits")([dze, hard_distr_embed, lambda_bunched])
e = Concatenate(name="soft_bits")([dze, soft_distr_embed, lambda_bunched])
model = Model([feat, quant_id, lambda_val], [combined_output, unquantized_output, unquantized_output_dec, e, e2], name="end2end")
model.nb_used_features = nb_used_features
return model, encoder, decoder

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dnn/training_tf2/train_rdovae.py Normal file
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#!/usr/bin/python3
'''Copyright (c) 2021-2022 Amazon
Copyright (c) 2018-2019 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.
'''
# Train an LPCNet model
import tensorflow as tf
strategy = tf.distribute.MultiWorkerMirroredStrategy()
import argparse
#from plc_loader import PLCLoader
parser = argparse.ArgumentParser(description='Train a quantization model')
parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
parser.add_argument('--model', metavar='<model>', default='rdovae', help='PLC model python definition (without .py)')
group1 = parser.add_mutually_exclusive_group()
group1.add_argument('--quantize', metavar='<input weights>', help='quantize model')
group1.add_argument('--retrain', metavar='<input weights>', help='continue training model')
parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
parser.add_argument('--epochs', metavar='<epochs>', default=120, type=int, help='number of epochs to train for (default 120)')
parser.add_argument('--batch-size', metavar='<batch size>', default=128, type=int, help='batch size to use (default 128)')
parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')
parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate')
parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')
parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')
args = parser.parse_args()
import importlib
rdovae = importlib.import_module(args.model)
import sys
import numpy as np
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
import tensorflow.keras.backend as K
import h5py
#gpus = tf.config.experimental.list_physical_devices('GPU')
#if gpus:
# try:
# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
# except RuntimeError as e:
# print(e)
nb_epochs = args.epochs
# Try reducing batch_size if you run out of memory on your GPU
batch_size = args.batch_size
quantize = args.quantize is not None
retrain = args.retrain is not None
if quantize:
lr = 0.00003
decay = 0
input_model = args.quantize
else:
lr = 0.001
decay = 2.5e-5
if args.lr is not None:
lr = args.lr
if args.decay is not None:
decay = args.decay
if retrain:
input_model = args.retrain
opt = Adam(lr, decay=decay, beta_2=0.99)
with strategy.scope():
model, encoder, decoder = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)
model.compile(optimizer=opt, loss=[rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.sq1_rate_loss, rdovae.sq2_rate_loss], loss_weights=[0.5, 0.5, 0., 1., .1], metrics={'split':'mse', 'hard_bits':rdovae.sq_rate_metric})
model.summary()
lpc_order = 16
feature_file = args.features
nb_features = model.nb_used_features + lpc_order
nb_used_features = model.nb_used_features
sequence_size = args.seq_length
# u for unquantised, load 16 bit PCM samples and convert to mu-law
features = np.memmap(feature_file, dtype='float32', mode='r')
nb_sequences = len(features)//(nb_features*sequence_size)//batch_size*batch_size
features = features[:nb_sequences*sequence_size*nb_features]
features = np.reshape(features, (nb_sequences, sequence_size, nb_features))
print(features.shape)
features = features[:, :, :nb_used_features]
#lambda_val = np.random.uniform(.0007, .002, (features.shape[0], features.shape[1], 1))
lambda_val = np.repeat(np.random.uniform(.0007, .002, (features.shape[0], 1, 1)), features.shape[1]//2, axis=1)
#lambda_val = 0*lambda_val + .001
quant_id = np.round(10*np.log(lambda_val/.0007)).astype('int16')
quant_id = quant_id[:,:,0]
# dump models to disk as we go
checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.cond_size, '{epoch:02d}'))
if args.retrain is not None:
model.load_weights(args.retrain)
if quantize or retrain:
#Adapting from an existing model
model.load_weights(input_model)
model.save_weights('{}_{}_initial.h5'.format(args.output, args.cond_size))
callbacks = [checkpoint]
#callbacks = []
if args.logdir is not None:
logdir = '{}/{}_{}_logs'.format(args.logdir, args.output, args.cond_size)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
callbacks.append(tensorboard_callback)
model.fit([features, quant_id, lambda_val], [features, features, features, features, features], batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks)

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dnn/training_tf2/uniform_noise.py Normal file
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains the UniformNoise layer."""
import tensorflow.compat.v2 as tf
from tensorflow.keras import backend
from tensorflow.keras.layers import Layer
class UniformNoise(Layer):
"""Apply additive zero-centered uniform noise.
This is useful to mitigate overfitting
(you could see it as a form of random data augmentation).
Gaussian Noise (GS) is a natural choice as corruption process
for real valued inputs.
As it is a regularization layer, it is only active at training time.
Args:
stddev: Float, standard deviation of the noise distribution.
seed: Integer, optional random seed to enable deterministic behavior.
Call arguments:
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in
training mode (adding noise) or in inference mode (doing nothing).
Input shape:
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape:
Same shape as input.
"""
def __init__(self, stddev=0.5, seed=None, **kwargs):
super().__init__(**kwargs)
self.supports_masking = True
self.stddev = stddev
def call(self, inputs, training=None):
def noised():
return inputs + backend.random_uniform(
shape=tf.shape(inputs),
minval=-self.stddev,
maxval=self.stddev,
dtype=inputs.dtype,
)
return backend.in_train_phase(noised, inputs, training=training)
def get_config(self):
config = {"stddev": self.stddev}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape