diff --git a/dnn/dump_lpcnet.py b/dnn/dump_lpcnet.py
index 8b0d25c2..936074c7 100755
--- a/dnn/dump_lpcnet.py
+++ b/dnn/dump_lpcnet.py
@@ -115,7 +115,7 @@ def dump_embedding_layer(self, f, hf):
 Embedding.dump_layer = dump_embedding_layer
 
 
-model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640, use_gpu=False)
+model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=384, use_gpu=False)
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
 #model.summary()
 
diff --git a/dnn/nnet.c b/dnn/nnet.c
index 1e9333cb..9c567687 100644
--- a/dnn/nnet.c
+++ b/dnn/nnet.c
@@ -210,11 +210,9 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
    int stride;
    float tmp[MAX_CONV_INPUTS];
    celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS);
-   M = layer->nb_inputs;
-   N = layer->nb_neurons;
    RNN_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));
    RNN_COPY(tmp, input, layer->nb_inputs);
-   M = layer->nb_inputs;
+   M = layer->nb_inputs*layer->kernel_size;
    N = layer->nb_neurons;
    stride = N;
    for (i=0;i<N;i++)