AnotherFoxGuy/webrtc-audio-processing
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Bump to WebRTC M120 release

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Some API deprecation -- ExperimentalAgc and ExperimentalNs are gone.
We're continuing to carry iSAC even though it's gone upstream, but maybe
we'll want to drop that soon.
This commit is contained in:
Arun Raghavan
2023-12-12 10:42:58 -05:00
parent 9a202fb8c2
commit c6abf6cd3f
479 changed files with 20900 additions and 11996 deletions
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103
webrtc/modules/audio_processing/agc2/rnn_vad/rnn.h
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@ -18,106 +18,33 @@
#include <vector>
#include "api/array_view.h"
#include "api/function_view.h"
#include "modules/audio_processing/agc2/cpu_features.h"
#include "modules/audio_processing/agc2/rnn_vad/common.h"
#include "rtc_base/system/arch.h"
#include "modules/audio_processing/agc2/rnn_vad/rnn_fc.h"
#include "modules/audio_processing/agc2/rnn_vad/rnn_gru.h"
namespace webrtc {
namespace rnn_vad {
// Maximum number of units for a fully-connected layer. This value is used to
// over-allocate space for fully-connected layers output vectors (implemented as
// std::array). The value should equal the number of units of the largest
// fully-connected layer.
constexpr size_t kFullyConnectedLayersMaxUnits = 24;
// Maximum number of units for a recurrent layer. This value is used to
// over-allocate space for recurrent layers state vectors (implemented as
// std::array). The value should equal the number of units of the largest
// recurrent layer.
constexpr size_t kRecurrentLayersMaxUnits = 24;
// Fully-connected layer.
class FullyConnectedLayer {
// Recurrent network with hard-coded architecture and weights for voice activity
// detection.
class RnnVad {
public:
FullyConnectedLayer(size_t input_size,
size_t output_size,
rtc::ArrayView<const int8_t> bias,
rtc::ArrayView<const int8_t> weights,
rtc::FunctionView<float(float)> activation_function,
Optimization optimization);
FullyConnectedLayer(const FullyConnectedLayer&) = delete;
FullyConnectedLayer& operator=(const FullyConnectedLayer&) = delete;
~FullyConnectedLayer();
size_t input_size() const { return input_size_; }
size_t output_size() const { return output_size_; }
Optimization optimization() const { return optimization_; }
rtc::ArrayView<const float> GetOutput() const;
// Computes the fully-connected layer output.
void ComputeOutput(rtc::ArrayView<const float> input);
private:
const size_t input_size_;
const size_t output_size_;
const std::vector<float> bias_;
const std::vector<float> weights_;
rtc::FunctionView<float(float)> activation_function_;
// The output vector of a recurrent layer has length equal to |output_size_|.
// However, for efficiency, over-allocation is used.
std::array<float, kFullyConnectedLayersMaxUnits> output_;
const Optimization optimization_;
};
// Recurrent layer with gated recurrent units (GRUs) with sigmoid and ReLU as
// activation functions for the update/reset and output gates respectively.
class GatedRecurrentLayer {
public:
GatedRecurrentLayer(size_t input_size,
size_t output_size,
rtc::ArrayView<const int8_t> bias,
rtc::ArrayView<const int8_t> weights,
rtc::ArrayView<const int8_t> recurrent_weights,
Optimization optimization);
GatedRecurrentLayer(const GatedRecurrentLayer&) = delete;
GatedRecurrentLayer& operator=(const GatedRecurrentLayer&) = delete;
~GatedRecurrentLayer();
size_t input_size() const { return input_size_; }
size_t output_size() const { return output_size_; }
Optimization optimization() const { return optimization_; }
rtc::ArrayView<const float> GetOutput() const;
explicit RnnVad(const AvailableCpuFeatures& cpu_features);
RnnVad(const RnnVad&) = delete;
RnnVad& operator=(const RnnVad&) = delete;
~RnnVad();
void Reset();
// Computes the recurrent layer output and updates the status.
void ComputeOutput(rtc::ArrayView<const float> input);
private:
const size_t input_size_;
const size_t output_size_;
const std::vector<float> bias_;
const std::vector<float> weights_;
const std::vector<float> recurrent_weights_;
// The state vector of a recurrent layer has length equal to |output_size_|.
// However, to avoid dynamic allocation, over-allocation is used.
std::array<float, kRecurrentLayersMaxUnits> state_;
const Optimization optimization_;
};
// Recurrent network based VAD.
class RnnBasedVad {
public:
RnnBasedVad();
RnnBasedVad(const RnnBasedVad&) = delete;
RnnBasedVad& operator=(const RnnBasedVad&) = delete;
~RnnBasedVad();
void Reset();
// Compute and returns the probability of voice (range: [0.0, 1.0]).
// Observes `feature_vector` and `is_silence`, updates the RNN and returns the
// current voice probability.
float ComputeVadProbability(
rtc::ArrayView<const float, kFeatureVectorSize> feature_vector,
bool is_silence);
private:
FullyConnectedLayer input_layer_;
GatedRecurrentLayer hidden_layer_;
FullyConnectedLayer output_layer_;
FullyConnectedLayer input_;
GatedRecurrentLayer hidden_;
FullyConnectedLayer output_;
};
} // namespace rnn_vad