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Using PyTorch model (same architecture for now)

--- a/autogen.sh

+++ b/autogen.sh

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

 srcdir=`dirname $0`

 test -n "$srcdir" && cd "$srcdir"

-dnn/download_model.sh 88477f4

+dnn/download_model.sh e699263

 echo "Updating build configuration files, please wait...."

--- a/dnn/lpcnet_plc.c

+++ b/dnn/lpcnet_plc.c

@@ -62,7 +62,7 @@

   lpcnet_encoder_init(&st->enc);

   st->loaded = 0;

 #ifndef USE_WEIGHTS_FILE

-  ret = init_plc_model(&st->model, lpcnet_plc_arrays);

+  ret = init_plcmodel(&st->model, plcmodel_arrays);

   if (ret == 0) st->loaded = 1;

 #else

   ret = 0;

@@ -76,7 +76,7 @@

   WeightArray *list;

   int ret;

   parse_weights(&list, data, len);

-  ret = init_plc_model(&st->model, list);

+  ret = init_plcmodel(&st->model, list);

   opus_free(list);

   if (ret == 0) {

     ret = lpcnet_encoder_load_model(&st->enc, data, len);

@@ -108,14 +108,14 @@

 static void compute_plc_pred(LPCNetPLCState *st, float *out, const float *in) {

-  float zeros[3*PLC_MAX_RNN_NEURONS] = {0};

-  float dense_out[PLC_DENSE1_OUT_SIZE];

+  float tmp[PLC_DENSE_IN_OUT_SIZE];

+  PLCModel *model = &st->model;

   PLCNetState *net = &st->plc_net;

   celt_assert(st->loaded);

-  _lpcnet_compute_dense(&st->model.plc_dense1, dense_out, in, st->arch);

-  compute_gruB(&st->model.plc_gru1, zeros, net->plc_gru1_state, dense_out, st->arch);

-  compute_gruB(&st->model.plc_gru2, zeros, net->plc_gru2_state, net->plc_gru1_state, st->arch);

-  _lpcnet_compute_dense(&st->model.plc_out, out, net->plc_gru2_state, st->arch);

+  compute_generic_dense(&model->plc_dense_in, tmp, in, ACTIVATION_TANH, 0);

+  compute_generic_gru(&model->plc_gru1_input, &model->plc_gru1_recurrent, net->gru1_state, tmp, 0);

+  compute_generic_gru(&model->plc_gru2_input, &model->plc_gru2_recurrent, net->gru2_state, net->gru1_state, 0);

+  compute_generic_dense(&model->plc_dense_out, out, net->gru2_state, ACTIVATION_LINEAR, 0);

 }

 static int get_fec_or_pred(LPCNetPLCState *st, float *out) {

--- a/dnn/lpcnet_private.h

+++ b/dnn/lpcnet_private.h

@@ -41,6 +41,11 @@

   float burg_cepstrum[2*NB_BANDS];

 };

+typedef struct {

+  float gru1_state[PLC_GRU1_STATE_SIZE];

+  float gru2_state[PLC_GRU2_STATE_SIZE];

+} PLCNetState;

+

 #define PLC_BUF_SIZE ((CONT_VECTORS+5)*FRAME_SIZE)

 struct LPCNetPLCState {

   PLCModel model;

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -64,9 +64,9 @@

 }

 #ifdef ENABLE_OSCE

-#define MAX_RNN_NEURONS_ALL IMAX(IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS), DRED_MAX_RNN_NEURONS), OSCE_MAX_RNN_NEURONS)

+#define MAX_RNN_NEURONS_ALL IMAX(IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS), OSCE_MAX_RNN_NEURONS)

 #else

-#define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS), DRED_MAX_RNN_NEURONS)

+#define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS)

 #endif

 void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch)

--- a/dnn/nnet.h

+++ b/dnn/nnet.h

@@ -142,7 +142,7 @@

 extern const WeightArray lpcnet_arrays[];

-extern const WeightArray lpcnet_plc_arrays[];

+extern const WeightArray plcmodel_arrays[];

 extern const WeightArray rdovaeenc_arrays[];

 extern const WeightArray rdovaedec_arrays[];

 extern const WeightArray fwgan_arrays[];

--- a/dnn/torch/plc/plc_dataset.py

+++ b/dnn/torch/plc/plc_dataset.py

@@ -40,6 +40,10 @@

         lost_offset = np.random.randint(0, high=self.lost.shape[0]-self.sequence_length)

         lost = self.lost[lost_offset:lost_offset+self.sequence_length]

+        #randomly add a few 10-ms losses so that the model learns to handle them

+        lost = lost * (np.random.rand(lost.shape[-1]) > .02).astype('float32')

+        #randomly break long consecutive losses so we don't try too hard to predict them

+        lost = 1 - ((1-lost) * (np.random.rand(lost.shape[-1]) > .1).astype('float32'))

         lost = np.reshape(lost, (features.shape[0], 1))

         lost_mask = np.tile(lost, (1,features.shape[-1]))

         in_features = features*lost_mask

--- a/dnn/write_lpcnet_weights.c

+++ b/dnn/write_lpcnet_weights.c

@@ -81,7 +81,7 @@

   FILE *fout = fopen("weights_blob.bin", "w");

   write_weights(pitchdnn_arrays, fout);

   write_weights(fargan_arrays, fout);

-  write_weights(lpcnet_plc_arrays, fout);

+  write_weights(plcmodel_arrays, fout);

   write_weights(rdovaeenc_arrays, fout);

   write_weights(rdovaedec_arrays, fout);

 #ifdef ENABLE_OSCE

-- 

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