530781d03f
Refactoring CL to improve names and allow to inject a VAD into `VadLevelAnalyzer` (new name for `VadWithLevel`). The injectable VAD is needed to inject a mock VAD and write better unit tests as new features are going to be added to the class. Bug: webrtc:7494 Change-Id: Ic0cea1e86a19a82533bd40fa04c061be3c44f068 Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/185180 Commit-Queue: Alessio Bazzica <alessiob@webrtc.org> Reviewed-by: Minyue Li <minyue@webrtc.org> Cr-Commit-Position: refs/heads/master@{#32195}
89 lines
2.9 KiB
C++
89 lines
2.9 KiB
C++
/*
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* Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "modules/audio_processing/agc2/vad_with_level.h"
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#include <algorithm>
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#include <array>
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#include <cmath>
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#include "api/array_view.h"
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#include "common_audio/include/audio_util.h"
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#include "common_audio/resampler/include/push_resampler.h"
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#include "modules/audio_processing/agc2/rnn_vad/common.h"
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#include "modules/audio_processing/agc2/rnn_vad/features_extraction.h"
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#include "modules/audio_processing/agc2/rnn_vad/rnn.h"
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#include "rtc_base/checks.h"
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namespace webrtc {
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namespace {
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using VoiceActivityDetector = VadLevelAnalyzer::VoiceActivityDetector;
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// Default VAD that combines a resampler and the RNN VAD.
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// Computes the speech probability on the first channel.
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class Vad : public VoiceActivityDetector {
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public:
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Vad() = default;
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Vad(const Vad&) = delete;
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Vad& operator=(const Vad&) = delete;
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~Vad() = default;
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float ComputeProbability(AudioFrameView<const float> frame) override {
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// The source number of channels is 1, because we always use the 1st
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// channel.
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resampler_.InitializeIfNeeded(
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/*sample_rate_hz=*/static_cast<int>(frame.samples_per_channel() * 100),
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rnn_vad::kSampleRate24kHz,
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/*num_channels=*/1);
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std::array<float, rnn_vad::kFrameSize10ms24kHz> work_frame;
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// Feed the 1st channel to the resampler.
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resampler_.Resample(frame.channel(0).data(), frame.samples_per_channel(),
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work_frame.data(), rnn_vad::kFrameSize10ms24kHz);
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std::array<float, rnn_vad::kFeatureVectorSize> feature_vector;
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const bool is_silence = features_extractor_.CheckSilenceComputeFeatures(
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work_frame, feature_vector);
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return rnn_vad_.ComputeVadProbability(feature_vector, is_silence);
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}
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private:
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PushResampler<float> resampler_;
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rnn_vad::FeaturesExtractor features_extractor_;
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rnn_vad::RnnBasedVad rnn_vad_;
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};
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} // namespace
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VadLevelAnalyzer::VadLevelAnalyzer() : vad_(std::make_unique<Vad>()) {}
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VadLevelAnalyzer::VadLevelAnalyzer(std::unique_ptr<VoiceActivityDetector> vad)
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: vad_(std::move(vad)) {
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RTC_DCHECK(vad_);
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}
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VadLevelAnalyzer::~VadLevelAnalyzer() = default;
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VadLevelAnalyzer::Result VadLevelAnalyzer::AnalyzeFrame(
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AudioFrameView<const float> frame) {
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float peak = 0.f;
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float rms = 0.f;
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for (const auto& x : frame.channel(0)) {
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peak = std::max(std::fabs(x), peak);
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rms += x * x;
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}
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return {vad_->ComputeProbability(frame),
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FloatS16ToDbfs(std::sqrt(rms / frame.samples_per_channel())),
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FloatS16ToDbfs(peak)};
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}
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} // namespace webrtc
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