Make RDOVAE encoder use LinearLayer directly

autogen.bat

@@ -10,8 +10,6 @@ for /F "tokens=4 delims= " %%A in ('findstr "download_model.sh" autogen.sh') do
 REM Remove trailing ")" character from the model variable
 set "model=%model:~0,-1%"
 
-cd dnn
-call download_model.bat %model%
-cd ..
+call dnn\download_model.bat %model%
 
 echo Updating build configuration files, please wait....

autogen.sh

@@ -9,7 +9,7 @@ set -e
 srcdir=`dirname $0`
 test -n "$srcdir" && cd "$srcdir"
 
-(cd dnn; ./download_model.sh 2ddc476)
+dnn/download_model.sh eb72d29
 
 echo "Updating build configuration files, please wait...."
 

dnn/download_model.bat

@@ -7,5 +7,3 @@ if not exist %model% (
 )
 
 tar -xvzf %model%
-move .\src\*.c .
-move .\src\*.h .

dnn/download_model.sh

@@ -8,6 +8,4 @@ if [ ! -f $model ]; then
         wget https://media.xiph.org/lpcnet/data/$model
 fi
 tar xvof $model
-touch src/nnet_data.[ch]
-touch src/plc_data.[ch]
-mv src/*.[ch] .
+touch *_data.[ch]

dnn/dred_rdovae_enc.c

@@ -46,50 +46,49 @@ void dred_rdovae_encode_dframe(
     float buffer[ENC_DENSE1_OUT_SIZE + ENC_DENSE2_OUT_SIZE + ENC_DENSE3_OUT_SIZE + ENC_DENSE4_OUT_SIZE + ENC_DENSE5_OUT_SIZE + ENC_DENSE6_OUT_SIZE + ENC_DENSE7_OUT_SIZE + ENC_DENSE8_OUT_SIZE + GDENSE1_OUT_SIZE];
     int output_index = 0;
     int input_index = 0;
-    float zero_vector[1024] = {0};
 
     /* run encoder stack and concatenate output in buffer*/
-    _lpcnet_compute_dense(&model->enc_dense1, &buffer[output_index], input);
+    compute_generic_dense(&model->enc_dense1, &buffer[output_index], input, ACTIVATION_TANH);
     input_index = output_index;
     output_index += ENC_DENSE1_OUT_SIZE;
 
-    compute_gruB(&model->enc_dense2, zero_vector, enc_state->dense2_state, &buffer[input_index]);
+    compute_generic_gru(&model->enc_dense2_input, &model->enc_dense2_recurrent, enc_state->dense2_state, &buffer[input_index]);
     OPUS_COPY(&buffer[output_index], enc_state->dense2_state, ENC_DENSE2_OUT_SIZE);
     input_index = output_index;
     output_index += ENC_DENSE2_OUT_SIZE;
 
-    _lpcnet_compute_dense(&model->enc_dense3, &buffer[output_index], &buffer[input_index]);
+    compute_generic_dense(&model->enc_dense3, &buffer[output_index], &buffer[input_index], ACTIVATION_TANH);
     input_index = output_index;
     output_index += ENC_DENSE3_OUT_SIZE;
 
-    compute_gruB(&model->enc_dense4, zero_vector, enc_state->dense4_state, &buffer[input_index]);
+    compute_generic_gru(&model->enc_dense4_input, &model->enc_dense4_recurrent, enc_state->dense4_state, &buffer[input_index]);
     OPUS_COPY(&buffer[output_index], enc_state->dense4_state, ENC_DENSE4_OUT_SIZE);
     input_index = output_index;
     output_index += ENC_DENSE4_OUT_SIZE;
 
-    _lpcnet_compute_dense(&model->enc_dense5, &buffer[output_index], &buffer[input_index]);
+    compute_generic_dense(&model->enc_dense5, &buffer[output_index], &buffer[input_index], ACTIVATION_TANH);
     input_index = output_index;
     output_index += ENC_DENSE5_OUT_SIZE;
 
-    compute_gruB(&model->enc_dense6, zero_vector, enc_state->dense6_state, &buffer[input_index]);
+    compute_generic_gru(&model->enc_dense6_input, &model->enc_dense6_recurrent, enc_state->dense6_state, &buffer[input_index]);
     OPUS_COPY(&buffer[output_index], enc_state->dense6_state, ENC_DENSE6_OUT_SIZE);
     input_index = output_index;
     output_index += ENC_DENSE6_OUT_SIZE;
 
-    _lpcnet_compute_dense(&model->enc_dense7, &buffer[output_index], &buffer[input_index]);
+    compute_generic_dense(&model->enc_dense7, &buffer[output_index], &buffer[input_index], ACTIVATION_TANH);
     input_index = output_index;
     output_index += ENC_DENSE7_OUT_SIZE;
 
-    _lpcnet_compute_dense(&model->enc_dense8, &buffer[output_index], &buffer[input_index]);
+    compute_generic_dense(&model->enc_dense8, &buffer[output_index], &buffer[input_index], ACTIVATION_TANH);
     output_index += ENC_DENSE8_OUT_SIZE;
 
     /* compute latents from concatenated input buffer */
-    compute_conv1d(&model->bits_dense, latents, enc_state->bits_dense_state, buffer);
+    compute_generic_conv1d(&model->bits_dense, latents, enc_state->bits_dense_state, buffer, BITS_DENSE_IN_SIZE, ACTIVATION_LINEAR);
 
 
     /* next, calculate initial state */
-    _lpcnet_compute_dense(&model->gdense1, &buffer[output_index], buffer);
+    compute_generic_dense(&model->gdense1, &buffer[output_index], buffer, ACTIVATION_TANH);
     input_index = output_index;
-    _lpcnet_compute_dense(&model->gdense2, initial_state, &buffer[input_index]);
+    compute_generic_dense(&model->gdense2, initial_state, &buffer[input_index], ACTIVATION_TANH);
 
 }

dnn/nnet.h

@@ -161,7 +161,7 @@ int sample_from_pdf(const float *pdf, int N, float exp_boost, float pdf_floor);
 
 extern const WeightArray lpcnet_arrays[];
 extern const WeightArray lpcnet_plc_arrays[];
-extern const WeightArray rdovae_enc_arrays[];
+extern const WeightArray rdovaeenc_arrays[];
 extern const WeightArray rdovae_dec_arrays[];
 
 int linear_init(LinearLayer *layer, const WeightArray *arrays,

dnn/write_lpcnet_weights.c

@@ -72,7 +72,7 @@ int main(void)
   FILE *fout = fopen("weights_blob.bin", "w");
   write_weights(lpcnet_arrays, fout);
   write_weights(lpcnet_plc_arrays, fout);
-  write_weights(rdovae_enc_arrays, fout);
+  write_weights(rdovaeenc_arrays, fout);
   write_weights(rdovae_dec_arrays, fout);
   fclose(fout);
   return 0;

silk/dred_encoder.c

@@ -69,7 +69,7 @@ void dred_encoder_init(DREDEnc* enc, opus_int32 Fs, int channels)
     enc->Fs = Fs;
     enc->channels = channels;
 #ifndef USE_WEIGHTS_FILE
-    init_rdovaeenc(&enc->model, rdovae_enc_arrays);
+    init_rdovaeenc(&enc->model, rdovaeenc_arrays);
 #endif
     dred_encoder_reset(enc);
 }