1 /* Copyright (c) 2018 Gregor Richards */
2 /*
3 Redistribution and use in source and binary forms, with or without
4 modification, are permitted provided that the following conditions
5 are met:
6
7 - Redistributions of source code must retain the above copyright
8 notice, this list of conditions and the following disclaimer.
9
10 - Redistributions in binary form must reproduce the above copyright
11 notice, this list of conditions and the following disclaimer in the
12 documentation and/or other materials provided with the distribution.
13
14 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
15 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
16 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
17 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
18 CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
19 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
20 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
21 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
22 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
23 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
24 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
25 */
26
27 #ifdef HAVE_CONFIG_H
28 #include "config.h"
29 #endif
30
31 #include <stdio.h>
32 #include <stdlib.h>
33 #include <sys/types.h>
34
35 #include "rnn.h"
36 #include "rnn_data.h"
37 #include "rnnoise.h"
38
39 /* Although these values are the same as in rnn.h, we make them separate to
40 * avoid accidentally burning internal values into a file format */
41 #define F_ACTIVATION_TANH 0
42 #define F_ACTIVATION_SIGMOID 1
43 #define F_ACTIVATION_RELU 2
44
rnnoise_model_from_file(FILE * f)45 RNNModel *rnnoise_model_from_file(FILE *f)
46 {
47 int i, in;
48
49 if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1)
50 return NULL;
51
52 RNNModel *ret = calloc(1, sizeof(RNNModel));
53 if (!ret)
54 return NULL;
55
56 #define ALLOC_LAYER(type, name) \
57 type *name; \
58 name = calloc(1, sizeof(type)); \
59 if (!name) { \
60 rnnoise_model_free(ret); \
61 return NULL; \
62 } \
63 ret->name = name
64
65 ALLOC_LAYER(DenseLayer, input_dense);
66 ALLOC_LAYER(GRULayer, vad_gru);
67 ALLOC_LAYER(GRULayer, noise_gru);
68 ALLOC_LAYER(GRULayer, denoise_gru);
69 ALLOC_LAYER(DenseLayer, denoise_output);
70 ALLOC_LAYER(DenseLayer, vad_output);
71
72 #define INPUT_VAL(name) do { \
73 if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \
74 rnnoise_model_free(ret); \
75 return NULL; \
76 } \
77 name = in; \
78 } while (0)
79
80 #define INPUT_ACTIVATION(name) do { \
81 int activation; \
82 INPUT_VAL(activation); \
83 switch (activation) { \
84 case F_ACTIVATION_SIGMOID: \
85 name = ACTIVATION_SIGMOID; \
86 break; \
87 case F_ACTIVATION_RELU: \
88 name = ACTIVATION_RELU; \
89 break; \
90 default: \
91 name = ACTIVATION_TANH; \
92 } \
93 } while (0)
94
95 #define INPUT_ARRAY(name, len) do { \
96 rnn_weight *values = malloc((len) * sizeof(rnn_weight)); \
97 if (!values) { \
98 rnnoise_model_free(ret); \
99 return NULL; \
100 } \
101 name = values; \
102 for (i = 0; i < (len); i++) { \
103 if (fscanf(f, "%d", &in) != 1) { \
104 rnnoise_model_free(ret); \
105 return NULL; \
106 } \
107 values[i] = in; \
108 } \
109 } while (0)
110
111 #define INPUT_DENSE(name) do { \
112 INPUT_VAL(name->nb_inputs); \
113 INPUT_VAL(name->nb_neurons); \
114 ret->name ## _size = name->nb_neurons; \
115 INPUT_ACTIVATION(name->activation); \
116 INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
117 INPUT_ARRAY(name->bias, name->nb_neurons); \
118 } while (0)
119
120 #define INPUT_GRU(name) do { \
121 INPUT_VAL(name->nb_inputs); \
122 INPUT_VAL(name->nb_neurons); \
123 ret->name ## _size = name->nb_neurons; \
124 INPUT_ACTIVATION(name->activation); \
125 INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons * 3); \
126 INPUT_ARRAY(name->recurrent_weights, name->nb_neurons * name->nb_neurons * 3); \
127 INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
128 } while (0)
129
130 INPUT_DENSE(input_dense);
131 INPUT_GRU(vad_gru);
132 INPUT_GRU(noise_gru);
133 INPUT_GRU(denoise_gru);
134 INPUT_DENSE(denoise_output);
135 INPUT_DENSE(vad_output);
136
137 return ret;
138 }
139
rnnoise_model_free(RNNModel * model)140 void rnnoise_model_free(RNNModel *model)
141 {
142 #define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0)
143 #define FREE_DENSE(name) do { \
144 if (model->name) { \
145 free((void *) model->name->input_weights); \
146 free((void *) model->name->bias); \
147 free((void *) model->name); \
148 } \
149 } while (0)
150 #define FREE_GRU(name) do { \
151 if (model->name) { \
152 free((void *) model->name->input_weights); \
153 free((void *) model->name->recurrent_weights); \
154 free((void *) model->name->bias); \
155 free((void *) model->name); \
156 } \
157 } while (0)
158
159 if (!model)
160 return;
161 FREE_DENSE(input_dense);
162 FREE_GRU(vad_gru);
163 FREE_GRU(noise_gru);
164 FREE_GRU(denoise_gru);
165 FREE_DENSE(denoise_output);
166 FREE_DENSE(vad_output);
167 free(model);
168 }
169