rnnoise Namespace Reference

Functions
float	TansigApproximated (float x)
float	SigmoidApproximated (const float x)
float	RectifiedLinearUnit (const float x)

Variables
const int8_t	kInputDenseWeights [kInputLayerWeights]
const int8_t	kInputDenseBias [kInputLayerOutputSize]
const int8_t	kHiddenGruWeights [kHiddenLayerWeights]
const int8_t	kHiddenGruRecurrentWeights [kHiddenLayerWeights]
const int8_t	kHiddenGruBias [kHiddenLayerBiases]
const int8_t	kOutputDenseWeights [kOutputLayerWeights]
const int8_t	kOutputDenseBias [kOutputLayerOutputSize] = {-50}
const float	kWeightsScale = 1.f / 256.f
const size_t	kInputLayerInputSize = 42
const size_t	kInputLayerOutputSize = 24
const size_t	kInputLayerWeights = kInputLayerInputSize * kInputLayerOutputSize
const size_t	kHiddenLayerOutputSize = 24
const size_t	kHiddenLayerWeights
const size_t	kHiddenLayerBiases = 3 * kHiddenLayerOutputSize
const size_t	kOutputLayerOutputSize = 1
const size_t	kOutputLayerWeights

Function Documentation

RectifiedLinearUnit()

float rnnoise::RectifiedLinearUnit ( const float  x )

inline

SigmoidApproximated()

float rnnoise::SigmoidApproximated ( const float  x )

inline

TansigApproximated()

float rnnoise::TansigApproximated ( float  x )

inline

Variable Documentation

kHiddenGruBias

const int8_t rnnoise::kHiddenGruBias

Initial value:

= {
    124, 125, -57, -126, 53,  123, 127,  -75, 68,  102, -2, 116,
    124, 127, 124, 125,  126, 123, -16,  48,  125, 126, 78, 85,
    11,  126, -30, -30,  -64, -3,  -105, -29, -17, 69,  63, 2,
    -32, -10, -62, 113,  -52, 112, -109, 112, 7,   -40, 73, 53,
    62,  6,   -2,  0,    0,   100, -16,  26,  -24, 56,  26, -10,
    -33, 41,  70,  109,  -29, 127, 34,   -66, 49,  53,  27, 62}

kHiddenGruRecurrentWeights

const int8_t rnnoise::kHiddenGruRecurrentWeights

kHiddenGruWeights

const int8_t rnnoise::kHiddenGruWeights

kHiddenLayerBiases

const size_t rnnoise::kHiddenLayerBiases = 3 * kHiddenLayerOutputSize

kHiddenLayerOutputSize

const size_t rnnoise::kHiddenLayerOutputSize = 24

kHiddenLayerWeights

const size_t rnnoise::kHiddenLayerWeights

Initial value:

=
    3 * kInputLayerOutputSize * kHiddenLayerOutputSize

kInputDenseBias

const int8_t rnnoise::kInputDenseBias

Initial value:

= {
    38,  -6,  127,  127, 127,  -43, -127, 78,  127, 5,   127, 123,
    127, 127, -128, -76, -126, 28,  127,  125, -30, 127, -89, -20}

kInputDenseWeights

const int8_t rnnoise::kInputDenseWeights

kInputLayerInputSize

const size_t rnnoise::kInputLayerInputSize = 42

kInputLayerOutputSize

const size_t rnnoise::kInputLayerOutputSize = 24

kInputLayerWeights

const size_t rnnoise::kInputLayerWeights = kInputLayerInputSize * kInputLayerOutputSize

kOutputDenseBias

const int8_t rnnoise::kOutputDenseBias = {-50}

kOutputDenseWeights

const int8_t rnnoise::kOutputDenseWeights

Initial value:

= {
    127,  127,  127, 127,  127,  20,  127,  -126, -126, -54, 14,  125,
    -126, -126, 127, -125, -126, 127, -127, -127, -57,  -30, 127, 80}

kOutputLayerOutputSize

const size_t rnnoise::kOutputLayerOutputSize = 1

kOutputLayerWeights

const size_t rnnoise::kOutputLayerWeights

Initial value:

=
    kHiddenLayerOutputSize * kOutputLayerOutputSize

kWeightsScale

const float rnnoise::kWeightsScale = 1.f / 256.f