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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.
*/
#include "modules/audio_processing/agc2/vad_with_level.h"
#include <algorithm>
#include <array>
#include <cmath>
#include "api/array_view.h"
#include "common_audio/include/audio_util.h"
#include "common_audio/resampler/include/push_resampler.h"
#include "modules/audio_processing/agc2/agc2_common.h"
#include "modules/audio_processing/agc2/rnn_vad/common.h"
#include "modules/audio_processing/agc2/rnn_vad/features_extraction.h"
#include "modules/audio_processing/agc2/rnn_vad/rnn.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace {
using VoiceActivityDetector = VadLevelAnalyzer::VoiceActivityDetector;
// Default VAD that combines a resampler and the RNN VAD.
// Computes the speech probability on the first channel.
class Vad : public VoiceActivityDetector {
public:
explicit Vad(const AvailableCpuFeatures& cpu_features)
: features_extractor_(cpu_features), rnn_vad_(cpu_features) {}
Vad(const Vad&) = delete;
Vad& operator=(const Vad&) = delete;
~Vad() = default;
void Reset() override { rnn_vad_.Reset(); }
float ComputeProbability(AudioFrameView<const float> frame) override {
// The source number of channels is 1, because we always use the 1st
// channel.
resampler_.InitializeIfNeeded(
/*sample_rate_hz=*/static_cast<int>(frame.samples_per_channel() * 100),
rnn_vad::kSampleRate24kHz,
/*num_channels=*/1);
std::array<float, rnn_vad::kFrameSize10ms24kHz> work_frame;
// Feed the 1st channel to the resampler.
resampler_.Resample(frame.channel(0).data(), frame.samples_per_channel(),
work_frame.data(), rnn_vad::kFrameSize10ms24kHz);
std::array<float, rnn_vad::kFeatureVectorSize> feature_vector;
const bool is_silence = features_extractor_.CheckSilenceComputeFeatures(
work_frame, feature_vector);
return rnn_vad_.ComputeVadProbability(feature_vector, is_silence);
}
private:
PushResampler<float> resampler_;
rnn_vad::FeaturesExtractor features_extractor_;
rnn_vad::RnnVad rnn_vad_;
};
} // namespace
VadLevelAnalyzer::VadLevelAnalyzer(int vad_reset_period_ms,
const AvailableCpuFeatures& cpu_features)
: VadLevelAnalyzer(vad_reset_period_ms,
std::make_unique<Vad>(cpu_features)) {}
VadLevelAnalyzer::VadLevelAnalyzer(int vad_reset_period_ms,
std::unique_ptr<VoiceActivityDetector> vad)
: vad_(std::move(vad)),
vad_reset_period_frames_(
rtc::CheckedDivExact(vad_reset_period_ms, kFrameDurationMs)),
time_to_vad_reset_(vad_reset_period_frames_) {
RTC_DCHECK(vad_);
RTC_DCHECK_GT(vad_reset_period_frames_, 1);
}
VadLevelAnalyzer::~VadLevelAnalyzer() = default;
VadLevelAnalyzer::Result VadLevelAnalyzer::AnalyzeFrame(
AudioFrameView<const float> frame) {
// Periodically reset the VAD.
time_to_vad_reset_--;
if (time_to_vad_reset_ <= 0) {
vad_->Reset();
time_to_vad_reset_ = vad_reset_period_frames_;
}
// Compute levels.
float peak = 0.0f;
float rms = 0.0f;
for (const auto& x : frame.channel(0)) {
peak = std::max(std::fabs(x), peak);
rms += x * x;
}
return {vad_->ComputeProbability(frame),
FloatS16ToDbfs(std::sqrt(rms / frame.samples_per_channel())),
FloatS16ToDbfs(peak)};
}
} // namespace webrtc
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