Unverified Commit 5e7af838 authored by Gregor Richards's avatar Gregor Richards Committed by Jean-Marc Valin
Browse files

Neural network model files

Extending the neural network dumper to dump to a simple text file
format, and adding reader functions to read a neural network description
from a FILE *.
parent f30741be
......@@ -22,6 +22,7 @@ librnnoise_la_SOURCES = \
src/denoise.c \
src/rnn.c \
src/rnn_data.c \
src/rnn_reader.c \
src/pitch.c \
src/kiss_fft.c \
src/celt_lpc.c
......
......@@ -28,6 +28,9 @@
#ifndef RNNOISE_H
#define RNNOISE_H 1
#include <stdio.h>
#ifndef RNNOISE_EXPORT
# if defined(WIN32)
# if defined(RNNOISE_BUILD) && defined(DLL_EXPORT)
......@@ -42,7 +45,6 @@
# endif
#endif
typedef struct DenoiseState DenoiseState;
typedef struct RNNModel RNNModel;
......@@ -56,4 +58,8 @@ RNNOISE_EXPORT void rnnoise_destroy(DenoiseState *st);
RNNOISE_EXPORT float rnnoise_process_frame(DenoiseState *st, float *out, const float *in);
RNNOISE_EXPORT RNNModel *rnnoise_model_from_file(FILE *f);
RNNOISE_EXPORT void rnnoise_model_free(RNNModel *model);
#endif
/* Copyright (c) 2018 Gregor Richards */
/*
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.
*/
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <stdio.h>
#include <stdlib.h>
#include <sys/types.h>
#include "rnn.h"
#include "rnn_data.h"
#include "rnnoise.h"
/* Although these values are the same as in rnn.h, we make them separate to
* avoid accidentally burning internal values into a file format */
#define F_ACTIVATION_TANH 0
#define F_ACTIVATION_SIGMOID 1
#define F_ACTIVATION_RELU 2
RNNModel *rnnoise_model_from_file(FILE *f)
{
int i, in;
if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1)
return NULL;
RNNModel *ret = calloc(1, sizeof(RNNModel));
if (!ret)
return NULL;
#define ALLOC_LAYER(type, name) \
type *name; \
name = calloc(1, sizeof(type)); \
if (!name) { \
rnnoise_model_free(ret); \
return NULL; \
} \
ret->name = name
ALLOC_LAYER(DenseLayer, input_dense);
ALLOC_LAYER(GRULayer, vad_gru);
ALLOC_LAYER(GRULayer, noise_gru);
ALLOC_LAYER(GRULayer, denoise_gru);
ALLOC_LAYER(DenseLayer, denoise_output);
ALLOC_LAYER(DenseLayer, vad_output);
#define INPUT_VAL(name) do { \
if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \
rnnoise_model_free(ret); \
return NULL; \
} \
name = in; \
} while (0)
#define INPUT_ACTIVATION(name) do { \
int activation; \
INPUT_VAL(activation); \
switch (activation) { \
case F_ACTIVATION_SIGMOID: \
name = ACTIVATION_SIGMOID; \
break; \
case F_ACTIVATION_RELU: \
name = ACTIVATION_RELU; \
break; \
default: \
name = ACTIVATION_TANH; \
} \
} while (0)
#define INPUT_ARRAY(name, len) do { \
rnn_weight *values = malloc((len) * sizeof(rnn_weight)); \
if (!values) { \
rnnoise_model_free(ret); \
return NULL; \
} \
name = values; \
for (i = 0; i < (len); i++) { \
if (fscanf(f, "%d", &in) != 1) { \
rnnoise_model_free(ret); \
return NULL; \
} \
values[i] = in; \
} \
} while (0)
#define INPUT_DENSE(name) do { \
INPUT_VAL(name->nb_inputs); \
INPUT_VAL(name->nb_neurons); \
ret->name ## _size = name->nb_neurons; \
INPUT_ACTIVATION(name->activation); \
INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
INPUT_ARRAY(name->bias, name->nb_neurons); \
} while (0)
#define INPUT_GRU(name) do { \
INPUT_VAL(name->nb_inputs); \
INPUT_VAL(name->nb_neurons); \
ret->name ## _size = name->nb_neurons; \
INPUT_ACTIVATION(name->activation); \
INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons * 3); \
INPUT_ARRAY(name->recurrent_weights, name->nb_neurons * name->nb_neurons * 3); \
INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
} while (0)
INPUT_DENSE(input_dense);
INPUT_GRU(vad_gru);
INPUT_GRU(noise_gru);
INPUT_GRU(denoise_gru);
INPUT_DENSE(denoise_output);
INPUT_DENSE(vad_output);
return ret;
}
void rnnoise_model_free(RNNModel *model)
{
#define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0)
#define FREE_DENSE(name) do { \
if (model->name) { \
free((void *) model->name->input_weights); \
free((void *) model->name->bias); \
free((void *) model->name); \
} \
} while (0)
#define FREE_GRU(name) do { \
if (model->name) { \
free((void *) model->name->input_weights); \
free((void *) model->name->recurrent_weights); \
free((void *) model->name->bias); \
free((void *) model->name); \
} \
} while (0)
if (!model)
return;
FREE_DENSE(input_dense);
FREE_GRU(vad_gru);
FREE_GRU(noise_gru);
FREE_GRU(denoise_gru);
FREE_DENSE(denoise_output);
FREE_DENSE(vad_output);
free(model);
}
......@@ -12,32 +12,45 @@ import sys
import re
import numpy as np
def printVector(f, vector, name):
def printVector(f, ft, vector, name):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const rnn_weight {}[{}] = {{\n '.format(name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(min(127, int(round(256*v[i])))))
ft.write('{}'.format(min(127, int(round(256*v[i])))))
if (i!=len(v)-1):
f.write(',')
else:
break;
ft.write(" ")
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
#print(v, file=f)
f.write('\n};\n\n')
ft.write("\n")
return;
def printLayer(f, layer):
def printLayer(f, ft, layer):
weights = layer.get_weights()
printVector(f, weights[0], layer.name + '_weights')
activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if len(weights) > 2:
printVector(f, weights[1], layer.name + '_recurrent_weights')
printVector(f, weights[-1], layer.name + '_bias')
ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1]/3))
else:
ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1]))
if activation == 'SIGMOID':
ft.write('1\n')
elif activation == 'RELU':
ft.write('2\n')
else:
ft.write('0\n')
printVector(f, ft, weights[0], layer.name + '_weights')
if len(weights) > 2:
printVector(f, ft, weights[1], layer.name + '_recurrent_weights')
printVector(f, ft, weights[-1], layer.name + '_bias')
name = layer.name
activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if len(weights) > 2:
f.write('static const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]/3, activation))
......@@ -67,18 +80,20 @@ model = load_model(sys.argv[1], custom_objects={'msse': mean_squared_sqrt_error,
weights = model.get_weights()
f = open(sys.argv[2], 'w')
ft = open(sys.argv[3], 'w')
f.write('/*This file is automatically generated from a Keras model*/\n\n')
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "rnn.h"\n\n')
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "rnn.h"\n#include "rnn_data.h"\n\n')
ft.write('rnnoise-nu model file version 1\n')
layer_list = []
for i, layer in enumerate(model.layers):
if len(layer.get_weights()) > 0:
printLayer(f, layer)
printLayer(f, ft, layer)
if len(layer.get_weights()) > 2:
layer_list.append(layer.name)
f.write('const struct RNNModel rnnoise_model_{} = {{\n'.format(sys.argv[3]))
f.write('const struct RNNModel rnnoise_model_{} = {{\n'.format(sys.argv[4]))
for i, layer in enumerate(model.layers):
if len(layer.get_weights()) > 0:
structLayer(f, layer)
......
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