shithub: opus

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Make RDOVAE encoder use LinearLayer directly

--- a/autogen.bat

+++ b/autogen.bat

@@ -10,8 +10,6 @@

 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....

--- a/autogen.sh

+++ b/autogen.sh

@@ -9,7 +9,7 @@

 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...."

--- a/dnn/download_model.bat

+++ b/dnn/download_model.bat

@@ -7,5 +7,3 @@

 )

 tar -xvzf %model%

-move .\src\*.c .

-move .\src\*.h .

--- a/dnn/download_model.sh

+++ b/dnn/download_model.sh

@@ -8,6 +8,4 @@

         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]

--- a/dnn/dred_rdovae_enc.c

+++ b/dnn/dred_rdovae_enc.c

@@ -46,50 +46,49 @@

     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);

 }

--- a/dnn/nnet.h

+++ b/dnn/nnet.h

@@ -161,7 +161,7 @@

 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,

--- a/dnn/write_lpcnet_weights.c

+++ b/dnn/write_lpcnet_weights.c

@@ -72,7 +72,7 @@

   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;

--- a/silk/dred_encoder.c

+++ b/silk/dred_encoder.c

@@ -69,7 +69,7 @@

     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);

 }

-- 

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