shithub: opus

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Starting to actually test this -- fix a few OOB reads

--- a/dnn/dump_lpcnet.py

+++ b/dnn/dump_lpcnet.py

@@ -116,7 +116,7 @@

     activation = self.activation.__name__.upper()

     max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])

     f.write('const MDenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}_factor,\n   {}, {}, {}, ACTIVATION_{}\n}};\n\n'

-            .format(name, name, name, name, weights[0].shape[0], weights[0].shape[1], weights[0].shape[2], activation))

+            .format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))

     hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))

     hf.write('extern const MDenseLayer {};\n\n'.format(name));

     return False

--- a/dnn/lpcnet.c

+++ b/dnn/lpcnet.c

@@ -25,6 +25,7 @@

 */

 #include <math.h>

+#include <stdio.h>

 #include "nnet_data.h"

 #include "nnet.h"

 #include "common.h"

@@ -150,3 +151,21 @@

         lpcnet->last_exc = exc;

     }

 }

+

+#if 1

+#define FRAME_SIZE 160

+int main(int argc, char **argv) {

+    LPCNetState *net;

+    net = lpcnet_create();

+    while (1) {

+        float features[NB_FEATURES];

+        short pcm[FRAME_SIZE];

+        fread(features, sizeof(features[0]), NB_FEATURES, stdin);

+        if (feof(stdin)) break;

+        lpcnet_synthesize(net, pcm, features, FRAME_SIZE);

+        fwrite(pcm, sizeof(pcm[0]), FRAME_SIZE, stdout);

+    }

+    lpcnet_destroy(net);

+    return 0;

+}

+#endif

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -217,13 +217,13 @@

    celt_assert(input != output);

    celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS);

    RNN_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));

-   RNN_COPY(tmp, input, layer->nb_inputs);

+   RNN_COPY(&tmp[layer->nb_inputs*(layer->kernel_size-1)], input, layer->nb_inputs);

    M = layer->nb_inputs*layer->kernel_size;

    N = layer->nb_neurons;

    stride = N;

    for (i=0;i<N;i++)

       output[i] = layer->bias[i];

-   gemm_accum(output, layer->input_weights, N, M, stride, input);

+   gemm_accum(output, layer->input_weights, N, M, stride, tmp);

    compute_activation(output, output, N, layer->activation);

    RNN_COPY(mem, &tmp[layer->nb_inputs], layer->nb_inputs*(layer->kernel_size-1));

 }

-- 

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