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diff --git a/dnn/training_tf2/dump_lpcnet.py b/dnn/training_tf2/dump_lpcnet.py
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+++ b/dnn/training_tf2/dump_lpcnet.py
@@ -0,0 +1,388 @@
+#!/usr/bin/python3
+'''Copyright (c) 2017-2018 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 os
+import io
+import lpcnet
+import sys
+import numpy as np
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding
+from ulaw import ulaw2lin, lin2ulaw
+from mdense import MDense
+from diffembed import diff_Embed
+from parameters import get_parameter
+import h5py
+import re
+import argparse
+
+
+# no cuda devices needed
+os.environ['CUDA_VISIBLE_DEVICES'] = ""
+
+# Flag for dumping e2e (differentiable lpc) network weights
+flag_e2e = False
+
+
+max_rnn_neurons = 1
+max_conv_inputs = 1
+max_mdense_tmp = 1
+
+def printVector(f, vector, name, dtype='float', dotp=False):
+    global array_list
+    if dotp:
+        vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
+        vector = vector.transpose((2, 0, 3, 1))
+    v = np.reshape(vector, (-1));
+    #print('static const float ', name, '[', len(v), '] = \n', file=f)
+    if name not in array_list:
+        array_list.append(name)
+    f.write('#ifndef USE_WEIGHTS_FILE\n')
+    f.write('#define WEIGHTS_{}_DEFINED\n'.format(name))
+    f.write('#define WEIGHTS_{}_TYPE WEIGHT_TYPE_{}\n'.format(name, dtype))
+    f.write('static const {} {}[{}] = {{\n   '.format(dtype, name, len(v)))
+    for i in range(0, len(v)):
+        f.write('{}'.format(v[i]))
+        if (i!=len(v)-1):
+            f.write(',')
+        else:
+            break;
+        if (i%8==7):
+            f.write("\n   ")
+        else:
+            f.write(" ")
+    #print(v, file=f)
+    f.write('\n};\n')
+    f.write('#endif\n\n')
+    return;
+
+def printSparseVector(f, A, name, have_diag=True):
+    N = A.shape[0]
+    M = A.shape[1]
+    W = np.zeros((0,), dtype='int')
+    W0 = np.zeros((0,))
+    if have_diag:
+        diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
+        A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
+        A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
+        A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
+        printVector(f, diag, name + '_diag')
+    AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
+    idx = np.zeros((0,), dtype='int')
+    for i in range(M//8):
+        pos = idx.shape[0]
+        idx = np.append(idx, -1)
+        nb_nonzero = 0
+        for j in range(N//4):
+            block = A[j*4:(j+1)*4, i*8:(i+1)*8]
+            qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
+            if np.sum(np.abs(block)) > 1e-10:
+                nb_nonzero = nb_nonzero + 1
+                idx = np.append(idx, j*4)
+                vblock = qblock.transpose((1,0)).reshape((-1,))
+                W0 = np.concatenate([W0, block.reshape((-1,))])
+                W = np.concatenate([W, vblock])
+        idx[pos] = nb_nonzero
+    f.write('#ifdef DOT_PROD\n')
+    printVector(f, W, name, dtype='qweight')
+    f.write('#else /*DOT_PROD*/\n')
+    printVector(f, W0, name, dtype='qweight')
+    f.write('#endif /*DOT_PROD*/\n')
+    #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
+    printVector(f, idx, name + '_idx', dtype='int')
+    return AQ
+
+def dump_layer_ignore(self, f, hf):
+    print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
+    return False
+Layer.dump_layer = dump_layer_ignore
+
+def dump_sparse_gru(self, f, hf):
+    global max_rnn_neurons
+    name = 'sparse_' + self.name
+    print("printing layer " + name + " of type sparse " + self.__class__.__name__)
+    weights = self.get_weights()
+    qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
+    printVector(f, weights[-1], name + '_bias')
+    subias = weights[-1].copy()
+    subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
+    printVector(f, subias, name + '_subias')
+    if hasattr(self, 'activation'):
+        activation = self.activation.__name__.upper()
+    else:
+        activation = 'TANH'
+    if hasattr(self, 'reset_after') and not self.reset_after:
+        reset_after = 0
+    else:
+        reset_after = 1
+    neurons = weights[0].shape[1]//3
+    max_rnn_neurons = max(max_rnn_neurons, neurons)
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    model_struct.write('  SparseGRULayer {};\n'.format(name));
+    model_init.write('  if (sparse_gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_recurrent_weights_diag", "{}_recurrent_weights", "{}_recurrent_weights_idx",  {}, ACTIVATION_{}, {})) return 1;\n'
+            .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
+    return True
+
+def dump_grub(self, f, hf, gru_a_size):
+    global max_rnn_neurons
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)
+
+    f.write('#ifdef DOT_PROD\n')
+    qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
+    printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
+    f.write('#else /*DOT_PROD*/\n')
+    printVector(f, weights[1], name + '_recurrent_weights')
+    f.write('#endif /*DOT_PROD*/\n')
+
+    printVector(f, weights[-1], name + '_bias')
+    subias = weights[-1].copy()
+    subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
+    subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
+    printVector(f, subias, name + '_subias')
+    if hasattr(self, 'activation'):
+        activation = self.activation.__name__.upper()
+    else:
+        activation = 'TANH'
+    if hasattr(self, 'reset_after') and not self.reset_after:
+        reset_after = 0
+    else:
+        reset_after = 1
+    neurons = weights[0].shape[1]//3
+    max_rnn_neurons = max(max_rnn_neurons, neurons)
+    model_struct.write('  GRULayer {};\n'.format(name));
+    model_init.write('  if (gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_weights", "{}_weights_idx", "{}_recurrent_weights", {}, {}, ACTIVATION_{}, {})) return 1;\n'
+            .format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))
+    return True
+
+def dump_gru_layer_dummy(self, f, hf):
+    name = self.name
+    weights = self.get_weights()
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    return True;
+
+GRU.dump_layer = dump_gru_layer_dummy
+
+def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
+    printVector(f, weights, name + '_weights')
+    printVector(f, bias, name + '_bias')
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
+    model_struct.write('  DenseLayer {};\n'.format(name));
+    model_init.write('  if (dense_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, ACTIVATION_{})) return 1;\n'
+            .format(name, name, name, weights.shape[0], weights.shape[1], activation))
+
+def dump_dense_layer(self, f, hf):
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    activation = self.activation.__name__.upper()
+    dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
+    return False
+
+Dense.dump_layer = dump_dense_layer
+
+def dump_mdense_layer(self, f, hf):
+    global max_mdense_tmp
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    printVector(f, np.transpose(weights[0], (0, 2, 1)), name + '_weights')
+    printVector(f, np.transpose(weights[1], (1, 0)), name + '_bias')
+    printVector(f, np.transpose(weights[2], (1, 0)), name + '_factor')
+    activation = self.activation.__name__.upper()
+    max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
+    model_struct.write('  MDenseLayer {};\n'.format(name));
+    model_init.write('  if (mdense_init(&model->{}, arrays, "{}_bias",  "{}_weights",  "{}_factor",  {}, {}, {}, ACTIVATION_{})) return 1;\n'
+            .format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
+    return False
+MDense.dump_layer = dump_mdense_layer
+
+def dump_conv1d_layer(self, f, hf):
+    global max_conv_inputs
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    printVector(f, weights[0], name + '_weights')
+    printVector(f, weights[-1], name + '_bias')
+    activation = self.activation.__name__.upper()
+    max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
+    hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
+    hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
+    model_struct.write('  Conv1DLayer {};\n'.format(name));
+    model_init.write('  if (conv1d_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, {}, ACTIVATION_{})) return 1;\n'
+            .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
+    return True
+Conv1D.dump_layer = dump_conv1d_layer
+
+
+def dump_embedding_layer_impl(name, weights, f, hf):
+    printVector(f, weights, name + '_weights')
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
+    model_struct.write('  EmbeddingLayer {};\n'.format(name));
+    model_init.write('  if (embedding_init(&model->{}, arrays, "{}_weights", {}, {})) return 1;\n'
+            .format(name, name, weights.shape[0], weights.shape[1]))
+
+def dump_embedding_layer(self, f, hf):
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()[0]
+    dump_embedding_layer_impl(name, weights, f, hf)
+    return False
+Embedding.dump_layer = dump_embedding_layer
+diff_Embed.dump_layer = dump_embedding_layer
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('model_file', type=str, help='model weight h5 file')
+    parser.add_argument('--nnet-header', type=str, help='name of c header file for dumped model', default='nnet_data.h')
+    parser.add_argument('--nnet-source', type=str, help='name of c source file for dumped model', default='nnet_data.c')
+    parser.add_argument('--lpc-gamma', type=float, help='LPC weighting factor. If not specified I will attempt to read it from the model file with 1 as default', default=None)
+    parser.add_argument('--lookahead', type=float, help='Features lookahead. If not specified I will attempt to read it from the model file with 2 as default', default=None)
+
+    args = parser.parse_args()
+
+    filename = args.model_file
+    with h5py.File(filename, "r") as f:
+        units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
+        units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
+        cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
+        e2e = 'rc2lpc' in f['model_weights']
+
+    model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size)
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+    #model.summary()
+
+    model.load_weights(filename, by_name=True)
+
+    cfile = args.nnet_source
+    hfile = args.nnet_header
+
+    f = open(cfile, 'w')
+    hf = open(hfile, 'w')
+    model_struct = io.StringIO()
+    model_init = io.StringIO()
+    model_struct.write('typedef struct {\n')
+    model_init.write('#ifndef DUMP_BINARY_WEIGHTS\n')
+    model_init.write('int init_lpcnet_model(LPCNetModel *model, const WeightArray *arrays) {\n')
+    array_list = []
+
+    f.write('/*This file is automatically generated from a Keras model*/\n')
+    f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
+    f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile))
+
+    hf.write('/*This file is automatically generated from a Keras model*/\n\n')
+    hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "nnet.h"\n\n')
+
+    if e2e:
+        hf.write('/* This is an end-to-end model */\n')
+        hf.write('#define END2END\n\n')
+    else:
+        hf.write('/* This is *not* an end-to-end model */\n')
+        hf.write('/* #define END2END */\n\n')
+
+    # LPC weighting factor
+    if type(args.lpc_gamma) == type(None):
+        lpc_gamma = get_parameter(model, 'lpc_gamma', 1)
+    else:
+        lpc_gamma = args.lpc_gamma
+
+    hf.write('/* LPC weighting factor */\n')
+    hf.write('#define LPC_GAMMA ' + str(lpc_gamma) +'f\n\n')
+
+    # look-ahead
+    if type(args.lookahead) == type(None):
+        lookahead = get_parameter(model, 'lookahead', 2)
+    else:
+        lookahead = args.lookahead
+
+    hf.write('/* Features look-ahead */\n')
+    hf.write('#define FEATURES_DELAY ' + str(lookahead) +'\n\n')
+
+    embed_size = lpcnet.embed_size
+
+    E = model.get_layer('embed_sig').get_weights()[0]
+    W = model.get_layer('gru_a').get_weights()[0][:embed_size,:]
+    dump_embedding_layer_impl('gru_a_embed_sig', np.dot(E, W), f, hf)
+    W = model.get_layer('gru_a').get_weights()[0][embed_size:2*embed_size,:]
+    dump_embedding_layer_impl('gru_a_embed_pred', np.dot(E, W), f, hf)
+    W = model.get_layer('gru_a').get_weights()[0][2*embed_size:3*embed_size,:]
+    dump_embedding_layer_impl('gru_a_embed_exc', np.dot(E, W), f, hf)
+    W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:]
+    #FIXME: dump only half the biases
+    b = model.get_layer('gru_a').get_weights()[2]
+    dump_dense_layer_impl('gru_a_dense_feature', W, b[:len(b)//2], 'LINEAR', f, hf)
+
+    W = model.get_layer('gru_b').get_weights()[0][model.rnn_units1:,:]
+    b = model.get_layer('gru_b').get_weights()[2]
+    # Set biases to zero because they'll be included in the GRU input part
+    # (we need regular and SU biases)
+    dump_dense_layer_impl('gru_b_dense_feature', W, 0*b[:len(b)//2], 'LINEAR', f, hf)
+    dump_grub(model.get_layer('gru_b'), f, hf, model.rnn_units1)
+
+    layer_list = []
+    for i, layer in enumerate(model.layers):
+        if layer.dump_layer(f, hf):
+            layer_list.append(layer.name)
+
+    dump_sparse_gru(model.get_layer('gru_a'), f, hf)
+
+    f.write('#ifndef USE_WEIGHTS_FILE\n')
+    f.write('const WeightArray lpcnet_arrays[] = {\n')
+    for name in array_list:
+        f.write('#ifdef WEIGHTS_{}_DEFINED\n'.format(name))
+        f.write('  {{"{}", WEIGHTS_{}_TYPE, sizeof({}), {}}},\n'.format(name, name, name, name))
+        f.write('#endif\n')
+    f.write('  {NULL, 0, 0, NULL}\n};\n')
+    f.write('#endif\n')
+
+    model_init.write('  return 0;\n}\n')
+    model_init.write('#endif\n')
+    f.write(model_init.getvalue())
+
+    hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
+    hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
+    hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))
+
+
+    hf.write('typedef struct {\n')
+    for i, name in enumerate(layer_list):
+        hf.write('  float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper()))
+    hf.write('} NNetState;\n\n')
+
+    model_struct.write('} LPCNetModel;\n\n')
+    hf.write(model_struct.getvalue())
+    hf.write('int init_lpcnet_model(LPCNetModel *model, const WeightArray *arrays);\n\n')
+    hf.write('\n\n#endif\n')
+
+    f.close()
+    hf.close()