rnn.h - mozsearch

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/*

 *  Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.

 *  Use of this source code is governed by a BSD-style license

 *  that can be found in the LICENSE file in the root of the source

 *  tree. An additional intellectual property rights grant can be found

 *  in the file PATENTS.  All contributing project authors may

 *  be found in the AUTHORS file in the root of the source tree.

*/

#ifndef MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_H_

#define MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_H_

#include <stddef.h>

#include <sys/types.h>

#include <array>

#include <vector>

#include "api/array_view.h"

#include "modules/audio_processing/agc2/cpu_features.h"

#include "modules/audio_processing/agc2/rnn_vad/common.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 {

// Recurrent network with hard-coded architecture and weights for voice activity

// detection.

class RnnVad {

 public:

  explicit RnnVad(const AvailableCpuFeatures& cpu_features);

  RnnVad(const RnnVad&) = delete;

  RnnVad& operator=(const RnnVad&) = delete;

  ~RnnVad();

  void Reset();

  // 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_;

  GatedRecurrentLayer hidden_;

  FullyConnectedLayer output_;

};

}  // namespace rnn_vad

}  // namespace webrtc

#endif  // MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_H_