Merge pull request #6 from shiguredo/feature/rnn-model-from-string

`rnnoise_model_from_string`関数の追加
shiguredo · Jan 20, 2022 · e121724 · e121724
2 parents add3412 + e71093d
commit e121724
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diff --git a/include/rnnoise.h b/include/rnnoise.h
@@ -102,6 +102,13 @@ RNNOISE_EXPORT float rnnoise_process_frame(DenoiseState* st,
  */
 RNNOISE_EXPORT RNNModel* rnnoise_model_from_file(FILE* f);
 
+  /**
+ * Load a model from a string
+ *
+ * It must be deallocated with rnnoise_model_free()
+ */
+RNNOISE_EXPORT RNNModel* rnnoise_model_from_string(const char* s);
+
 /**
  * Free a custom model
  *

diff --git a/src/rnn_reader.c b/src/rnn_reader.c
@@ -142,6 +142,116 @@ RNNModel* rnnoise_model_from_file(FILE* f) {
   return ret;
 }
 
+RNNModel* rnnoise_model_from_string(const char* s) {
+  int i, in;
+  int n;
+
+  if (sscanf(s, "rnnoise-nu model file version %d\n%n", &in, &n) != 1 || in != 1)
+    return NULL;
+  s += n;
+
+  RNNModel* ret = calloc(1, sizeof(RNNModel));
+  if (!ret)
+    return NULL;
+
+#undef ALLOC_LAYER
+#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);
+
+#undef INPUT_VAL
+#define INPUT_VAL(name)                                            \
+  do {                                                             \
+    if (sscanf(s, "%d%n", &in, &n) != 1 || in < 0 || in > 128) {   \
+      rnnoise_model_free(ret);                                     \
+      return NULL;                                                 \
+    }                                                              \
+    s += n;                                                        \
+    name = in;                                                     \
+  } while (0)
+
+#undef INPUT_ACTIVATION
+#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)
+
+#undef INPUT_ARRAY
+#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 (sscanf(s, "%d%n", &in, &n) != 1) {                 \
+        rnnoise_model_free(ret);                             \
+        return NULL;                                         \
+      }                                                      \
+      s += n;                                                \
+      values[i] = in;                                        \
+    }                                                        \
+  } while (0)
+
+#undef INPUT_DENSE
+#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)
+
+#undef INPUT_GRU
+#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 {                  \