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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 <array>
#include <memory>
#include <string>
#include <vector>
#include "common_audio/resampler/push_sinc_resampler.h"
#include "modules/audio_processing/agc2/cpu_features.h"
#include "modules/audio_processing/agc2/rnn_vad/features_extraction.h"
#include "modules/audio_processing/agc2/rnn_vad/rnn.h"
#include "modules/audio_processing/agc2/rnn_vad/test_utils.h"
#include "modules/audio_processing/test/performance_timer.h"
#include "rtc_base/checks.h"
#include "rtc_base/logging.h"
#include "test/gtest.h"
#include "third_party/rnnoise/src/rnn_activations.h"
#include "third_party/rnnoise/src/rnn_vad_weights.h"
namespace webrtc {
namespace rnn_vad {
namespace {
constexpr int kFrameSize10ms48kHz = 480;
void DumpPerfStats(int num_samples,
int sample_rate,
double average_us,
double standard_deviation) {
float audio_track_length_ms =
1e3f * static_cast<float>(num_samples) / static_cast<float>(sample_rate);
float average_ms = static_cast<float>(average_us) / 1e3f;
float speed = audio_track_length_ms / average_ms;
RTC_LOG(LS_INFO) << "track duration (ms): " << audio_track_length_ms;
RTC_LOG(LS_INFO) << "average processing time (ms): " << average_ms << " +/- "
<< (standard_deviation / 1e3);
RTC_LOG(LS_INFO) << "speed: " << speed << "x";
}
// When the RNN VAD model is updated and the expected output changes, set the
// constant below to true in order to write new expected output binary files.
constexpr bool kWriteComputedOutputToFile = false;
// Avoids that one forgets to set `kWriteComputedOutputToFile` back to false
// when the expected output files are re-exported.
TEST(RnnVadTest, CheckWriteComputedOutputIsFalse) {
ASSERT_FALSE(kWriteComputedOutputToFile)
<< "Cannot land if kWriteComputedOutput is true.";
}
class RnnVadProbabilityParametrization
: public ::testing::TestWithParam<AvailableCpuFeatures> {};
// Checks that the computed VAD probability for a test input sequence sampled at
// 48 kHz is within tolerance.
TEST_P(RnnVadProbabilityParametrization, RnnVadProbabilityWithinTolerance) {
// Init resampler, feature extractor and RNN.
PushSincResampler decimator(kFrameSize10ms48kHz, kFrameSize10ms24kHz);
const AvailableCpuFeatures cpu_features = GetParam();
FeaturesExtractor features_extractor(cpu_features);
RnnVad rnn_vad(cpu_features);
// Init input samples and expected output readers.
std::unique_ptr<FileReader> samples_reader = CreatePcmSamplesReader();
std::unique_ptr<FileReader> expected_vad_prob_reader = CreateVadProbsReader();
// Input length. The last incomplete frame is ignored.
const int num_frames = samples_reader->size() / kFrameSize10ms48kHz;
// Init buffers.
std::vector<float> samples_48k(kFrameSize10ms48kHz);
std::vector<float> samples_24k(kFrameSize10ms24kHz);
std::vector<float> feature_vector(kFeatureVectorSize);
std::vector<float> computed_vad_prob(num_frames);
std::vector<float> expected_vad_prob(num_frames);
// Read expected output.
ASSERT_TRUE(expected_vad_prob_reader->ReadChunk(expected_vad_prob));
// Compute VAD probabilities on the downsampled input.
float cumulative_error = 0.f;
for (int i = 0; i < num_frames; ++i) {
ASSERT_TRUE(samples_reader->ReadChunk(samples_48k));
decimator.Resample(samples_48k.data(), samples_48k.size(),
samples_24k.data(), samples_24k.size());
bool is_silence = features_extractor.CheckSilenceComputeFeatures(
{samples_24k.data(), kFrameSize10ms24kHz},
{feature_vector.data(), kFeatureVectorSize});
computed_vad_prob[i] = rnn_vad.ComputeVadProbability(
{feature_vector.data(), kFeatureVectorSize}, is_silence);
EXPECT_NEAR(computed_vad_prob[i], expected_vad_prob[i], 1e-3f);
cumulative_error += std::abs(computed_vad_prob[i] - expected_vad_prob[i]);
}
// Check average error.
EXPECT_LT(cumulative_error / num_frames, 1e-4f);
if (kWriteComputedOutputToFile) {
FileWriter vad_prob_writer("new_vad_prob.dat");
vad_prob_writer.WriteChunk(computed_vad_prob);
}
}
// Performance test for the RNN VAD (pre-fetching and downsampling are
// excluded). Keep disabled and only enable locally to measure performance as
// follows:
// - on desktop: run the this unit test adding "--logs";
// - on android: run the this unit test adding "--logcat-output-file".
TEST_P(RnnVadProbabilityParametrization, DISABLED_RnnVadPerformance) {
// PCM samples reader and buffers.
std::unique_ptr<FileReader> samples_reader = CreatePcmSamplesReader();
// The last incomplete frame is ignored.
const int num_frames = samples_reader->size() / kFrameSize10ms48kHz;
std::array<float, kFrameSize10ms48kHz> samples;
// Pre-fetch and decimate samples.
PushSincResampler decimator(kFrameSize10ms48kHz, kFrameSize10ms24kHz);
std::vector<float> prefetched_decimated_samples;
prefetched_decimated_samples.resize(num_frames * kFrameSize10ms24kHz);
for (int i = 0; i < num_frames; ++i) {
ASSERT_TRUE(samples_reader->ReadChunk(samples));
decimator.Resample(samples.data(), samples.size(),
&prefetched_decimated_samples[i * kFrameSize10ms24kHz],
kFrameSize10ms24kHz);
}
// Initialize.
const AvailableCpuFeatures cpu_features = GetParam();
FeaturesExtractor features_extractor(cpu_features);
std::array<float, kFeatureVectorSize> feature_vector;
RnnVad rnn_vad(cpu_features);
constexpr int number_of_tests = 100;
::webrtc::test::PerformanceTimer perf_timer(number_of_tests);
for (int k = 0; k < number_of_tests; ++k) {
features_extractor.Reset();
rnn_vad.Reset();
// Process frames.
perf_timer.StartTimer();
for (int i = 0; i < num_frames; ++i) {
bool is_silence = features_extractor.CheckSilenceComputeFeatures(
{&prefetched_decimated_samples[i * kFrameSize10ms24kHz],
kFrameSize10ms24kHz},
feature_vector);
rnn_vad.ComputeVadProbability(feature_vector, is_silence);
}
perf_timer.StopTimer();
}
DumpPerfStats(num_frames * kFrameSize10ms24kHz, kSampleRate24kHz,
perf_timer.GetDurationAverage(),
perf_timer.GetDurationStandardDeviation());
}
// Finds the relevant CPU features combinations to test.
std::vector<AvailableCpuFeatures> GetCpuFeaturesToTest() {
std::vector<AvailableCpuFeatures> v;
v.push_back(NoAvailableCpuFeatures());
AvailableCpuFeatures available = GetAvailableCpuFeatures();
if (available.avx2 && available.sse2) {
v.push_back({/*sse2=*/true, /*avx2=*/true, /*neon=*/false});
}
if (available.sse2) {
v.push_back({/*sse2=*/true, /*avx2=*/false, /*neon=*/false});
}
if (available.neon) {
v.push_back({/*sse2=*/false, /*avx2=*/false, /*neon=*/true});
}
return v;
}
INSTANTIATE_TEST_SUITE_P(
RnnVadTest,
RnnVadProbabilityParametrization,
::testing::ValuesIn(GetCpuFeaturesToTest()),
[](const ::testing::TestParamInfo<AvailableCpuFeatures>& info) {
return info.param.ToString();
});
} // namespace
} // namespace rnn_vad
} // namespace webrtc