add some code

managed_components/78__esp-opus/dnn/training_tf2/lpcnet_plc.py

@@ -0,0 +1,101 @@
+#!/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.
+'''
+
+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
+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
+import numpy as np
+
+def quant_regularizer(x):
+    Q = 128
+    Q_1 = 1./Q
+    #return .01 * tf.reduce_mean(1 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x))))
+    return .01 * tf.reduce_mean(K.sqrt(K.sqrt(1.0001 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x))))))
+
+
+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(0.992)
+
+def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, nb_burg_features=36, batch_size=128, training=False, adaptation=False, quantize=False, cond_size=128):
+    feat = Input(shape=(None, nb_used_features+nb_burg_features), batch_size=batch_size)
+    lost = Input(shape=(None, 1), batch_size=batch_size)
+
+    fdense1 = Dense(cond_size, activation='tanh', name='plc_dense1')
+
+    cfeat = Concatenate()([feat, lost])
+    cfeat = fdense1(cfeat)
+    #cfeat = Conv1D(cond_size, 3, padding='causal', activation='tanh', name='plc_conv1')(cfeat)
+
+    quant = quant_regularizer if quantize else None
+
+    if training:
+        rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru1', stateful=True,
+              kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
+        rnn2 = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru2', stateful=True,
+              kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
+    else:
+        rnn = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru1', stateful=True,
+              kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
+        rnn2 = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru2', stateful=True,
+              kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
+
+    gru_out1, _ = rnn(cfeat)
+    gru_out1 = GaussianNoise(.005)(gru_out1)
+    gru_out2, _ = rnn2(gru_out1)
+
+    out_dense = Dense(nb_used_features, activation='linear', name='plc_out')
+    plc_out = out_dense(gru_out2)
+
+    model = Model([feat, lost], plc_out)
+    model.rnn_units = rnn_units
+    model.cond_size = cond_size
+    model.nb_used_features = nb_used_features
+    model.nb_burg_features = nb_burg_features
+
+    return model