diff --git a/dnn/dump_data.c b/dnn/dump_data.c
index 0ed47abc..a97cec36 100644
--- a/dnn/dump_data.c
+++ b/dnn/dump_data.c
@@ -75,7 +75,7 @@ void compute_noise(int *noise, float noise_std) {
 }
 
 
-void write_audio(LPCNetEncState *st, const short *pcm, const int *noise, FILE *file, int nframes) {
+void write_audio(LPCNetEncState *st, const short *pcm, const int *noise, FILE *file, int nframes, int e2e) {
   int i, k;
   for (k=0;k<nframes;k++) {
   unsigned char data[4*FRAME_SIZE];
@@ -92,11 +92,11 @@ void write_audio(LPCNetEncState *st, const short *pcm, const int *noise, FILE *f
     /* Excitation in. */
     data[4*i+2] = st->exc_mem;
     /* Excitation out. */
-#ifdef END2END
-    data[4*i+3] = lin2ulaw(pcm[k*FRAME_SIZE+i]);
-#else
-    data[4*i+3] = e;
-#endif
+    if (e2e) {
+      data[4*i+3] = lin2ulaw(pcm[k*FRAME_SIZE+i]);
+    } else {
+      data[4*i+3] = e;
+    }
     /* Simulate error on excitation. */
     e += noise[k*FRAME_SIZE+i];
     e = IMIN(255, IMAX(0, e));
@@ -118,6 +118,8 @@ static short float2short(float x)
 
 int main(int argc, char **argv) {
   int i;
+  char *argv0;
+  int e2e=0;
   int count=0;
   static const float a_hp[2] = {-1.99599, 0.99600};
   static const float b_hp[2] = {-2, 1};
@@ -148,6 +150,12 @@ int main(int argc, char **argv) {
   int quantize = 0;
   srand(getpid());
   st = lpcnet_encoder_create();
+  argv0=argv[0];
+  if (argc > 2 && strcmp(argv[1], "-end2end")==0) {
+      e2e = 1;
+      argv++;
+      argc--;
+  }
   if (argc == 5 && strcmp(argv[1], "-train")==0) training = 1;
   if (argc == 5 && strcmp(argv[1], "-qtrain")==0) {
       training = 1;
@@ -168,8 +176,8 @@ int main(int argc, char **argv) {
       decode = 1;
   }
   if (training == -1) {
-    fprintf(stderr, "usage: %s -train <speech> <features out> <pcm out>\n", argv[0]);
-    fprintf(stderr, "  or   %s -test <speech> <features out>\n", argv[0]);
+    fprintf(stderr, "usage: %s -train <speech> <features out> <pcm out>\n", argv0);
+    fprintf(stderr, "  or   %s -test <speech> <features out>\n", argv0);
     return 1;
   }
   f1 = fopen(argv[2], "r");
@@ -273,7 +281,7 @@ int main(int argc, char **argv) {
     
     if (!quantize) {
       process_single_frame(st, ffeat);
-      if (fpcm) write_audio(st, pcm, &noisebuf[st->pcount*FRAME_SIZE], fpcm, 1);
+      if (fpcm) write_audio(st, pcm, &noisebuf[st->pcount*FRAME_SIZE], fpcm, 1, e2e);
     }
     st->pcount++;
     /* Running on groups of 4 frames. */
@@ -281,7 +289,7 @@ int main(int argc, char **argv) {
       if (quantize) {
         unsigned char buf[8];
         process_superframe(st, buf, ffeat, encode, quantize);
-        if (fpcm) write_audio(st, pcmbuf, noisebuf, fpcm, 4);
+        if (fpcm) write_audio(st, pcmbuf, noisebuf, fpcm, 4, e2e);
       }
       st->pcount = 0;
     }
diff --git a/dnn/training_tf2/dump_lpcnet.py b/dnn/training_tf2/dump_lpcnet.py
index bfd09946..768e41b3 100755
--- a/dnn/training_tf2/dump_lpcnet.py
+++ b/dnn/training_tf2/dump_lpcnet.py
@@ -250,6 +250,7 @@ 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 = flag_e2e, cond_size=cond_size)
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
@@ -276,6 +277,13 @@ f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\
 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')
+
 embed_size = lpcnet.embed_size
 
 E = model.get_layer('embed_sig').get_weights()[0]