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Plumbing for the frame rate network

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This commit is contained in:
Jean-Marc Valin 2018-11-25 17:20:24 -05:00
parent 70fdf47471
commit 7119eaf33b
3 changed files with 58 additions and 2 deletions
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4
dnn/dump_lpcnet.py
View file

@ -187,10 +187,10 @@ hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))
hf.write('struct RNNState {\n')
hf.write('typedef struct {\n')
for i, name in enumerate(layer_list):
hf.write(' float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper()))
hf.write('};\n')
hf.write('} LPCNetState;\n')
hf.write('\n\n#endif\n')

52
dnn/lpcnet.c Normal file
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@ -0,0 +1,52 @@
/* Copyright (c) 2018 Mozilla */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "nnet_data.h"
#include "nnet.h"
#include "common.h"
#include "arch.h"
#define NB_FEATURES 38
#define FRAME_INPUT_SIZE (NB_FEATURES + EMBED_PITCH_OUT_SIZE)
void run_frame_network(LPCNetState *net, float *out, const float *features, int pitch)
{
int i;
float in[FRAME_INPUT_SIZE];
float conv1_out[FEATURE_CONV1_OUT_SIZE];
float conv2_out[FEATURE_CONV2_OUT_SIZE];
float dense1_out[FEATURE_DENSE1_OUT_SIZE];
RNN_COPY(in, features, NB_FEATURES);
compute_embedding(&embed_pitch, &in[NB_FEATURES], pitch);
compute_conv1d(&feature_conv1, conv1_out, net->feature_conv1_state, in);
compute_conv1d(&feature_conv2, conv2_out, net->feature_conv2_state, conv1_out);
celt_assert(FRAME_INPUT_SIZE == FEATURE_CONV2_OUT_SIZE);
for (i=0;i<FEATURE_CONV2_OUT_SIZE;i++) conv2_out[i] += in[i];
compute_dense(&feature_dense1, dense1_out, conv2_out);
compute_dense(&feature_dense2, out, dense1_out);
}

4
dnn/nnet.c
View file

@ -122,6 +122,7 @@ void compute_dense(const DenseLayer *layer, float *output, const float *input)
M = layer->nb_inputs;
N = layer->nb_neurons;
stride = N;
celt_assert(input != output);
for (i=0;i<N;i++)
output[i] = layer->bias[i];
gemm_accum(output, layer->input_weights, N, M, stride, input);
@ -134,6 +135,7 @@ void compute_mdense(const MDenseLayer *layer, float *output, const float *input)
int N, M, C;
int stride;
float tmp[MAX_MDENSE_TMP];
celt_assert(input != output);
M = layer->nb_inputs;
N = layer->nb_neurons;
C = layer->nb_channels;
@ -163,6 +165,7 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
float r[MAX_RNN_NEURONS];
float h[MAX_RNN_NEURONS];
celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
celt_assert(input != state);
M = gru->nb_inputs;
N = gru->nb_neurons;
stride = 3*N;
@ -210,6 +213,7 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
int N, M;
int stride;
float tmp[MAX_CONV_INPUTS];
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);