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-nu.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 INPUT_ACTIVATION(name->activation); \
115 INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
116 INPUT_ARRAY(name->bias, name->nb_neurons); \
117 } while (0)
118
119 #define INPUT_GRU(name) do { \
120 INPUT_VAL(name->nb_inputs); \
121 INPUT_VAL(name->nb_neurons); \
122 INPUT_ACTIVATION(name->activation); \
123 INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons * 3); \
124 INPUT_ARRAY(name->recurrent_weights, name->nb_neurons * name->nb_neurons * 3); \
125 INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
126 } while (0)
127
128 INPUT_DENSE(input_dense);
129 INPUT_GRU(vad_gru);
130 INPUT_GRU(noise_gru);
131 INPUT_GRU(denoise_gru);
132 INPUT_DENSE(denoise_output);
133 INPUT_DENSE(vad_output);
134
135 return ret;
136 }
137
rnnoise_model_free(RNNModel * model)138 void rnnoise_model_free(RNNModel *model)
139 {
140 #define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0)
141 #define FREE_DENSE(name) do { \
142 if (model->name) { \
143 free((void *) model->name->input_weights); \
144 free((void *) model->name->bias); \
145 free((void *) model->name); \
146 } \
147 } while (0)
148 #define FREE_GRU(name) do { \
149 if (model->name) { \
150 free((void *) model->name->input_weights); \
151 free((void *) model->name->recurrent_weights); \
152 free((void *) model->name->bias); \
153 free((void *) model->name); \
154 } \
155 } while (0)
156
157 if (!model)
158 return;
159 FREE_DENSE(input_dense);
160 FREE_GRU(vad_gru);
161 FREE_GRU(noise_gru);
162 FREE_GRU(denoise_gru);
163 FREE_DENSE(denoise_output);
164 FREE_DENSE(vad_output);
165 free(model);
166 }
167