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Diffstat (limited to 'src/rnn_reader.c')
| -rw-r--r-- | src/rnn_reader.c | 168 |
1 files changed, 168 insertions, 0 deletions
diff --git a/src/rnn_reader.c b/src/rnn_reader.c new file mode 100644 index 0000000..2a031db --- /dev/null +++ b/src/rnn_reader.c @@ -0,0 +1,168 @@ +/* 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); +} |