Revert "AGC2 RNN VAD: Recurrent Neural Network impl"
This reverts commit 2491cb73820fe82923b848dfcab6772b4b0addb0.
Reason for revert: broke internal build
Original change's description:
> AGC2 RNN VAD: Recurrent Neural Network impl
>
> RNN implementation for the AGC2 VAD that includes a fully connected
> layer and a gated recurrent unit layer.
>
> Bug: webrtc:9076
> Change-Id: Ibb8b0b4e9213f09eb9dbe118bbdc94d7e8e4f91b
> Reviewed-on: https://webrtc-review.googlesource.com/72060
> Reviewed-by: Patrik Höglund <phoglund@webrtc.org>
> Reviewed-by: Alex Loiko <aleloi@webrtc.org>
> Reviewed-by: Ivo Creusen <ivoc@webrtc.org>
> Commit-Queue: Alessio Bazzica <alessiob@webrtc.org>
> Cr-Commit-Position: refs/heads/master@{#23101}
TBR=phoglund@webrtc.org,alessiob@webrtc.org,aleloi@webrtc.org,ivoc@webrtc.org
Change-Id: Ic311c4b7d79094e959d3a2c4a53c398f34c954e2
No-Presubmit: true
No-Tree-Checks: true
No-Try: true
Bug: webrtc:9076
Reviewed-on: https://webrtc-review.googlesource.com/74200
Reviewed-by: Sam Zackrisson <saza@webrtc.org>
Commit-Queue: Sam Zackrisson <saza@webrtc.org>
Cr-Commit-Position: refs/heads/master@{#23103}diff --git a/modules/audio_processing/agc2/rnn_vad/BUILD.gn b/modules/audio_processing/agc2/rnn_vad/BUILD.gn
index 395e522..e05dcab 100644
--- a/modules/audio_processing/agc2/rnn_vad/BUILD.gn
+++ b/modules/audio_processing/agc2/rnn_vad/BUILD.gn
@@ -25,15 +25,12 @@
"pitch_search_internal.cc",
"pitch_search_internal.h",
"ring_buffer.h",
- "rnn.cc",
- "rnn.h",
"sequence_buffer.h",
"symmetric_matrix_buffer.h",
]
deps = [
"../../../../api:array_view",
"../../../../rtc_base:checks",
- "//third_party/rnnoise:rnn_vad",
]
}
@@ -56,8 +53,6 @@
unittest_resources = [
"../../../../resources/audio_processing/agc2/rnn_vad/pitch_buf_24k.dat",
"../../../../resources/audio_processing/agc2/rnn_vad/pitch_lp_res.dat",
- "../../../../resources/audio_processing/agc2/rnn_vad/sil_features.dat",
- "../../../../resources/audio_processing/agc2/rnn_vad/vad_prob.dat",
]
if (is_ios) {
@@ -77,7 +72,6 @@
"pitch_search_internal_unittest.cc",
"pitch_search_unittest.cc",
"ring_buffer_unittest.cc",
- "rnn_unittest.cc",
"sequence_buffer_unittest.cc",
"symmetric_matrix_buffer_unittest.cc",
]
@@ -85,9 +79,7 @@
":lib",
":lib_test",
"../../../../api:array_view",
- "../../../../rtc_base:checks",
"../../../../test:test_support",
- "//third_party/rnnoise:rnn_vad",
]
data = unittest_resources
if (is_ios) {
diff --git a/modules/audio_processing/agc2/rnn_vad/DEPS b/modules/audio_processing/agc2/rnn_vad/DEPS
deleted file mode 100644
index 773c2d7..0000000
--- a/modules/audio_processing/agc2/rnn_vad/DEPS
+++ /dev/null
@@ -1,3 +0,0 @@
-include_rules = [
- "+third_party/rnnoise",
-]
diff --git a/modules/audio_processing/agc2/rnn_vad/common.h b/modules/audio_processing/agc2/rnn_vad/common.h
index 3af0719..252bf84 100644
--- a/modules/audio_processing/agc2/rnn_vad/common.h
+++ b/modules/audio_processing/agc2/rnn_vad/common.h
@@ -43,8 +43,6 @@
constexpr size_t kMinPitch48kHz = kMinPitch24kHz * 2;
constexpr size_t kMaxPitch48kHz = kMaxPitch24kHz * 2;
-constexpr size_t kFeatureVectorSize = 42;
-
} // namespace rnn_vad
} // namespace webrtc
diff --git a/modules/audio_processing/agc2/rnn_vad/rnn.cc b/modules/audio_processing/agc2/rnn_vad/rnn.cc
deleted file mode 100644
index f88fb75..0000000
--- a/modules/audio_processing/agc2/rnn_vad/rnn.cc
+++ /dev/null
@@ -1,227 +0,0 @@
-/*
- * 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/rnn_vad/rnn.h"
-
-#include <algorithm>
-#include <array>
-#include <cmath>
-
-#include "rtc_base/checks.h"
-#include "third_party/rnnoise/src/rnn_activations.h"
-#include "third_party/rnnoise/src/rnn_vad_weights.h"
-
-namespace webrtc {
-namespace rnn_vad {
-
-using rnnoise::kWeightsScale;
-
-using rnnoise::kInputLayerInputSize;
-static_assert(kFeatureVectorSize == kInputLayerInputSize, "");
-using rnnoise::kInputDenseWeights;
-using rnnoise::kInputDenseBias;
-using rnnoise::kInputLayerOutputSize;
-static_assert(kInputLayerOutputSize <= kFullyConnectedLayersMaxUnits,
- "Increase kFullyConnectedLayersMaxUnits.");
-
-using rnnoise::kHiddenGruRecurrentWeights;
-using rnnoise::kHiddenGruWeights;
-using rnnoise::kHiddenGruBias;
-using rnnoise::kHiddenLayerOutputSize;
-static_assert(kHiddenLayerOutputSize <= kRecurrentLayersMaxUnits,
- "Increase kRecurrentLayersMaxUnits.");
-
-using rnnoise::kOutputDenseWeights;
-using rnnoise::kOutputDenseBias;
-using rnnoise::kOutputLayerOutputSize;
-static_assert(kOutputLayerOutputSize <= kFullyConnectedLayersMaxUnits,
- "Increase kFullyConnectedLayersMaxUnits.");
-
-using rnnoise::RectifiedLinearUnit;
-using rnnoise::SigmoidApproximated;
-using rnnoise::TansigApproximated;
-
-FullyConnectedLayer::FullyConnectedLayer(
- const size_t input_size,
- const size_t output_size,
- const rtc::ArrayView<const int8_t> bias,
- const rtc::ArrayView<const int8_t> weights,
- float (*const activation_function)(float))
- : input_size_(input_size),
- output_size_(output_size),
- bias_(bias),
- weights_(weights),
- activation_function_(activation_function) {
- RTC_DCHECK_LE(output_size_, kFullyConnectedLayersMaxUnits)
- << "Static over-allocation of fully-connected layers output vectors is "
- "not sufficient.";
- RTC_DCHECK_EQ(output_size_, bias_.size())
- << "Mismatching output size and bias terms array size.";
- RTC_DCHECK_EQ(input_size_ * output_size_, weights_.size())
- << "Mismatching input-output size and weight coefficients array size.";
-}
-
-FullyConnectedLayer::~FullyConnectedLayer() = default;
-
-rtc::ArrayView<const float> FullyConnectedLayer::GetOutput() const {
- return rtc::ArrayView<const float>(output_.data(), output_size_);
-}
-
-void FullyConnectedLayer::ComputeOutput(rtc::ArrayView<const float> input) {
- // TODO(bugs.chromium.org/9076): Optimize using SSE/AVX fused multiply-add
- // operations.
- for (size_t o = 0; o < output_size_; ++o) {
- output_[o] = bias_[o];
- // TODO(bugs.chromium.org/9076): Benchmark how different layouts for
- // |weights_| change the performance across different platforms.
- for (size_t i = 0; i < input_size_; ++i) {
- output_[o] += input[i] * weights_[i * output_size_ + o];
- }
- output_[o] = (*activation_function_)(kWeightsScale * output_[o]);
- }
-}
-
-GatedRecurrentLayer::GatedRecurrentLayer(
- const size_t input_size,
- const size_t output_size,
- const rtc::ArrayView<const int8_t> bias,
- const rtc::ArrayView<const int8_t> weights,
- const rtc::ArrayView<const int8_t> recurrent_weights,
- float (*const activation_function)(float))
- : input_size_(input_size),
- output_size_(output_size),
- bias_(bias),
- weights_(weights),
- recurrent_weights_(recurrent_weights),
- activation_function_(activation_function) {
- RTC_DCHECK_LE(output_size_, kRecurrentLayersMaxUnits)
- << "Static over-allocation of recurrent layers state vectors is not "
- << "sufficient.";
- RTC_DCHECK_EQ(3 * output_size_, bias_.size())
- << "Mismatching output size and bias terms array size.";
- RTC_DCHECK_EQ(3 * input_size_ * output_size_, weights_.size())
- << "Mismatching input-output size and weight coefficients array size.";
- RTC_DCHECK_EQ(3 * input_size_ * output_size_, recurrent_weights_.size())
- << "Mismatching input-output size and recurrent weight coefficients array"
- << " size.";
- Reset();
-}
-
-GatedRecurrentLayer::~GatedRecurrentLayer() = default;
-
-rtc::ArrayView<const float> GatedRecurrentLayer::GetOutput() const {
- return rtc::ArrayView<const float>(state_.data(), output_size_);
-}
-
-void GatedRecurrentLayer::Reset() {
- state_.fill(0.f);
-}
-
-void GatedRecurrentLayer::ComputeOutput(rtc::ArrayView<const float> input) {
- // TODO(bugs.chromium.org/9076): Optimize using SSE/AVX fused multiply-add
- // operations.
- // Stride and offset used to read parameter arrays.
- const size_t stride = 3 * output_size_;
- size_t offset = 0;
-
- // Compute update gates.
- std::array<float, kRecurrentLayersMaxUnits> update;
- for (size_t o = 0; o < output_size_; ++o) {
- update[o] = bias_[o];
- // TODO(bugs.chromium.org/9076): Benchmark how different layouts for
- // |weights_| and |recurrent_weights_| change the performance across
- // different platforms.
- for (size_t i = 0; i < input_size_; ++i) { // Add input.
- update[o] += input[i] * weights_[i * stride + o];
- }
- for (size_t s = 0; s < output_size_; ++s) {
- update[o] += state_[s] * recurrent_weights_[s * stride + o];
- } // Add state.
- update[o] = SigmoidApproximated(kWeightsScale * update[o]);
- }
-
- // Compute reset gates.
- offset += output_size_;
- std::array<float, kRecurrentLayersMaxUnits> reset;
- for (size_t o = 0; o < output_size_; ++o) {
- reset[o] = bias_[offset + o];
- for (size_t i = 0; i < input_size_; ++i) { // Add input.
- reset[o] += input[i] * weights_[offset + i * stride + o];
- }
- for (size_t s = 0; s < output_size_; ++s) { // Add state.
- reset[o] += state_[s] * recurrent_weights_[offset + s * stride + o];
- }
- reset[o] = SigmoidApproximated(kWeightsScale * reset[o]);
- }
-
- // Compute output.
- offset += output_size_;
- std::array<float, kRecurrentLayersMaxUnits> output;
- for (size_t o = 0; o < output_size_; ++o) {
- output[o] = bias_[offset + o];
- for (size_t i = 0; i < input_size_; ++i) { // Add input.
- output[o] += input[i] * weights_[offset + i * stride + o];
- }
- for (size_t s = 0; s < output_size_;
- ++s) { // Add state through reset gates.
- output[o] +=
- state_[s] * recurrent_weights_[offset + s * stride + o] * reset[s];
- }
- output[o] = (*activation_function_)(kWeightsScale * output[o]);
- // Update output through the update gates.
- output[o] = update[o] * state_[o] + (1.f - update[o]) * output[o];
- }
-
- // Update the state. Not done in the previous loop since that would pollute
- // the current state and lead to incorrect output values.
- std::copy(output.begin(), output.end(), state_.begin());
-}
-
-RnnBasedVad::RnnBasedVad()
- : input_layer_(kInputLayerInputSize,
- kInputLayerOutputSize,
- kInputDenseBias,
- kInputDenseWeights,
- TansigApproximated),
- hidden_layer_(kInputLayerOutputSize,
- kHiddenLayerOutputSize,
- kHiddenGruBias,
- kHiddenGruWeights,
- kHiddenGruRecurrentWeights,
- RectifiedLinearUnit),
- output_layer_(kHiddenLayerOutputSize,
- kOutputLayerOutputSize,
- kOutputDenseBias,
- kOutputDenseWeights,
- SigmoidApproximated) {
- // Input-output chaining size checks.
- RTC_DCHECK_EQ(input_layer_.output_size(), hidden_layer_.input_size())
- << "The input and the hidden layers sizes do not match.";
- RTC_DCHECK_EQ(hidden_layer_.output_size(), output_layer_.input_size())
- << "The hidden and the output layers sizes do not match.";
-}
-
-RnnBasedVad::~RnnBasedVad() = default;
-
-void RnnBasedVad::Reset() {
- hidden_layer_.Reset();
-}
-
-void RnnBasedVad::ComputeVadProbability(
- rtc::ArrayView<const float, kFeatureVectorSize> feature_vector) {
- input_layer_.ComputeOutput(feature_vector);
- hidden_layer_.ComputeOutput(input_layer_.GetOutput());
- output_layer_.ComputeOutput(hidden_layer_.GetOutput());
- const auto vad_output = output_layer_.GetOutput();
- vad_probability_ = vad_output[0];
-}
-
-} // namespace rnn_vad
-} // namespace webrtc
diff --git a/modules/audio_processing/agc2/rnn_vad/rnn.h b/modules/audio_processing/agc2/rnn_vad/rnn.h
deleted file mode 100644
index 81ab87e..0000000
--- a/modules/audio_processing/agc2/rnn_vad/rnn.h
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * 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 <array>
-
-#include "api/array_view.h"
-#include "modules/audio_processing/agc2/rnn_vad/common.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 {
- public:
- FullyConnectedLayer(const size_t input_size,
- const size_t output_size,
- const rtc::ArrayView<const int8_t> bias,
- const rtc::ArrayView<const int8_t> weights,
- float (*const activation_function)(float));
- 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_; }
- 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 rtc::ArrayView<const int8_t> bias_;
- const rtc::ArrayView<const int8_t> weights_;
- float (*const activation_function_)(float);
- // 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_;
-};
-
-// Recurrent layer with gated recurrent units (GRUs).
-class GatedRecurrentLayer {
- public:
- GatedRecurrentLayer(const size_t input_size,
- const size_t output_size,
- const rtc::ArrayView<const int8_t> bias,
- const rtc::ArrayView<const int8_t> weights,
- const rtc::ArrayView<const int8_t> recurrent_weights,
- float (*const activation_function)(float));
- 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_; }
- rtc::ArrayView<const float> GetOutput() const;
- 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 rtc::ArrayView<const int8_t> bias_;
- const rtc::ArrayView<const int8_t> weights_;
- const rtc::ArrayView<const int8_t> recurrent_weights_;
- float (*const activation_function_)(float);
- // 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_;
-};
-
-// Recurrent network based VAD.
-class RnnBasedVad {
- public:
- RnnBasedVad();
- RnnBasedVad(const RnnBasedVad&) = delete;
- RnnBasedVad& operator=(const RnnBasedVad&) = delete;
- ~RnnBasedVad();
- float vad_probability() const { return vad_probability_; }
- void Reset();
- // Compute and returns the probability of voice (range: [0.0, 1.0]).
- void ComputeVadProbability(
- rtc::ArrayView<const float, kFeatureVectorSize> feature_vector);
-
- private:
- FullyConnectedLayer input_layer_;
- GatedRecurrentLayer hidden_layer_;
- FullyConnectedLayer output_layer_;
- float vad_probability_;
-};
-
-} // namespace rnn_vad
-} // namespace webrtc
-
-#endif // MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_H_
diff --git a/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc b/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
deleted file mode 100644
index d774c6d..0000000
--- a/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
+++ /dev/null
@@ -1,180 +0,0 @@
-/*
- * 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 <algorithm>
-#include <array>
-#include <vector>
-
-#include "modules/audio_processing/agc2/rnn_vad/rnn.h"
-#include "modules/audio_processing/agc2/rnn_vad/test_utils.h"
-#include "rtc_base/checks.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 test {
-
-using rnnoise::RectifiedLinearUnit;
-using rnnoise::SigmoidApproximated;
-
-namespace {
-
-void TestFullyConnectedLayer(FullyConnectedLayer* fc,
- rtc::ArrayView<const float> input_vector,
- const float expected_output) {
- RTC_CHECK(fc);
- fc->ComputeOutput(input_vector);
- const auto output = fc->GetOutput();
- EXPECT_NEAR(expected_output, output[0], 3e-6f);
-}
-
-void TestGatedRecurrentLayer(
- GatedRecurrentLayer* gru,
- rtc::ArrayView<const float> input_sequence,
- rtc::ArrayView<const float> expected_output_sequence) {
- RTC_CHECK(gru);
- auto gru_output_view = gru->GetOutput();
- const size_t input_sequence_length =
- rtc::CheckedDivExact(input_sequence.size(), gru->input_size());
- const size_t output_sequence_length =
- rtc::CheckedDivExact(expected_output_sequence.size(), gru->output_size());
- ASSERT_EQ(input_sequence_length, output_sequence_length)
- << "The test data length is invalid.";
- // Feed the GRU layer and check the output at every step.
- gru->Reset();
- for (size_t i = 0; i < input_sequence_length; ++i) {
- SCOPED_TRACE(i);
- gru->ComputeOutput(
- input_sequence.subview(i * gru->input_size(), gru->input_size()));
- const auto expected_output = expected_output_sequence.subview(
- i * gru->output_size(), gru->output_size());
- ExpectNearAbsolute(expected_output, gru_output_view, 3e-6f);
- }
-}
-
-} // namespace
-
-// Bit-exactness check for fully connected layers.
-TEST(RnnVadTest, CheckFullyConnectedLayerOutput) {
- const std::array<int8_t, 1> bias = {-50};
- const std::array<int8_t, 24> weights = {
- 127, 127, 127, 127, 127, 20, 127, -126, -126, -54, 14, 125,
- -126, -126, 127, -125, -126, 127, -127, -127, -57, -30, 127, 80};
- FullyConnectedLayer fc(24, 1, bias, weights, SigmoidApproximated);
- // Test on different inputs.
- {
- const std::array<float, 24> input_vector = {
- 0.f, 0.f, 0.f, 0.f, 0.f,
- 0.f, 0.215833917f, 0.290601075f, 0.238759011f, 0.244751841f,
- 0.f, 0.0461241305f, 0.106401242f, 0.223070428f, 0.630603909f,
- 0.690453172f, 0.f, 0.387645692f, 0.166913897f, 0.f,
- 0.0327451192f, 0.f, 0.136149868f, 0.446351469f};
- TestFullyConnectedLayer(&fc, {input_vector}, 0.436567038f);
- }
- {
- const std::array<float, 24> input_vector = {
- 0.592162728f, 0.529089332f, 1.18205106f,
- 1.21736848f, 0.f, 0.470851123f,
- 0.130675942f, 0.320903003f, 0.305496395f,
- 0.0571633279f, 1.57001138f, 0.0182026215f,
- 0.0977443159f, 0.347477973f, 0.493206412f,
- 0.9688586f, 0.0320267938f, 0.244722098f,
- 0.312745273f, 0.f, 0.00650715502f,
- 0.312553257f, 1.62619662f, 0.782880902f};
- TestFullyConnectedLayer(&fc, {input_vector}, 0.874741316f);
- }
- {
- const std::array<float, 24> input_vector = {
- 0.395022154f, 0.333681047f, 0.76302278f,
- 0.965480626f, 0.f, 0.941198349f,
- 0.0892967582f, 0.745046318f, 0.635769248f,
- 0.238564298f, 0.970656633f, 0.014159563f,
- 0.094203949f, 0.446816623f, 0.640755892f,
- 1.20532358f, 0.0254284926f, 0.283327013f,
- 0.726210058f, 0.0550272502f, 0.000344108557f,
- 0.369803518f, 1.56680179f, 0.997883797f};
- TestFullyConnectedLayer(&fc, {input_vector}, 0.672785878f);
- }
-}
-
-TEST(RnnVadTest, CheckGatedRecurrentLayer) {
- const std::array<int8_t, 12> bias = {96, -99, -81, -114, 49, 119,
- -118, 68, -76, 91, 121, 125};
- const std::array<int8_t, 60> weights = {
- 124, 9, 1, 116, -66, -21, -118, -110, 104, 75, -23, -51,
- -72, -111, 47, 93, 77, -98, 41, -8, 40, -23, -43, -107,
- 9, -73, 30, -32, -2, 64, -26, 91, -48, -24, -28, -104,
- 74, -46, 116, 15, 32, 52, -126, -38, -121, 12, -16, 110,
- -95, 66, -103, -35, -38, 3, -126, -61, 28, 98, -117, -43};
- const std::array<int8_t, 60> recurrent_weights = {
- -3, 87, 50, 51, -22, 27, -39, 62, 31, -83, -52, -48,
- -6, 83, -19, 104, 105, 48, 23, 68, 23, 40, 7, -120,
- 64, -62, 117, 85, -51, -43, 54, -105, 120, 56, -128, -107,
- 39, 50, -17, -47, -117, 14, 108, 12, -7, -72, 103, -87,
- -66, 82, 84, 100, -98, 102, -49, 44, 122, 106, -20, -69};
- GatedRecurrentLayer gru(5, 4, bias, weights, recurrent_weights,
- RectifiedLinearUnit);
- // Test on different inputs.
- {
- const std::array<float, 20> input_sequence = {
- 0.89395463f, 0.93224651f, 0.55788344f, 0.32341808f, 0.93355054f,
- 0.13475326f, 0.97370994f, 0.14253306f, 0.93710381f, 0.76093364f,
- 0.65780413f, 0.41657975f, 0.49403164f, 0.46843281f, 0.75138855f,
- 0.24517593f, 0.47657707f, 0.57064998f, 0.435184f, 0.19319285f};
- const std::array<float, 16> expected_output_sequence = {
- 0.0239123f, 0.5773077f, 0.f, 0.f,
- 0.01282811f, 0.64330572f, 0.f, 0.04863098f,
- 0.00781069f, 0.75267816f, 0.f, 0.02579715f,
- 0.00471378f, 0.59162533f, 0.11087593f, 0.01334511f};
- TestGatedRecurrentLayer(&gru, input_sequence, expected_output_sequence);
- }
-}
-
-// TODO(bugs.webrtc.org/9076): Remove when the issue is fixed.
-// Bit-exactness test checking that precomputed frame-wise features lead to the
-// expected VAD probabilities.
-TEST(RnnVadTest, RnnBitExactness) {
- // Init.
- auto features_reader = CreateSilenceFlagsFeatureMatrixReader();
- auto vad_probs_reader = CreateVadProbsReader();
- ASSERT_EQ(features_reader.second, vad_probs_reader.second);
- const size_t num_frames = features_reader.second;
- // Frame-wise buffers.
- float expected_vad_probability;
- float is_silence;
- std::array<float, kFeatureVectorSize> features;
-
- // Compute VAD probability using the precomputed features.
- RnnBasedVad vad;
- for (size_t i = 0; i < num_frames; ++i) {
- SCOPED_TRACE(i);
- // Read frame data.
- RTC_CHECK(vad_probs_reader.first->ReadValue(&expected_vad_probability));
- // The features file also includes a silence flag for each frame.
- RTC_CHECK(features_reader.first->ReadValue(&is_silence));
- RTC_CHECK(
- features_reader.first->ReadChunk({features.data(), features.size()}));
- // Skip silent frames.
- ASSERT_TRUE(is_silence == 0.f || is_silence == 1.f);
- if (is_silence == 1.f) {
- ASSERT_EQ(expected_vad_probability, 0.f);
- continue;
- }
- // Compute and check VAD probability.
- vad.ComputeVadProbability({features.data(), features.size()});
- EXPECT_NEAR(expected_vad_probability, vad.vad_probability(), 3e-6f);
- }
-}
-
-} // namespace test
-} // namespace rnn_vad
-} // namespace webrtc
diff --git a/modules/audio_processing/agc2/rnn_vad/test_utils.cc b/modules/audio_processing/agc2/rnn_vad/test_utils.cc
index ff91ef7..c6cf21e 100644
--- a/modules/audio_processing/agc2/rnn_vad/test_utils.cc
+++ b/modules/audio_processing/agc2/rnn_vad/test_utils.cc
@@ -53,21 +53,6 @@
rtc::CheckedDivExact(ptr->data_length(), 2 + num_lp_residual_coeffs)};
}
-ReaderPairType CreateSilenceFlagsFeatureMatrixReader() {
- auto ptr = rtc::MakeUnique<BinaryFileReader<float>>(
- test::ResourcePath("audio_processing/agc2/rnn_vad/sil_features", "dat"),
- 42);
- // Features (42) and silence flag.
- return {std::move(ptr),
- rtc::CheckedDivExact(ptr->data_length(), static_cast<size_t>(43))};
-}
-
-ReaderPairType CreateVadProbsReader() {
- auto ptr = rtc::MakeUnique<BinaryFileReader<float>>(
- test::ResourcePath("audio_processing/agc2/rnn_vad/vad_prob", "dat"));
- return {std::move(ptr), ptr->data_length()};
-}
-
} // namespace test
} // namespace rnn_vad
} // namespace webrtc
diff --git a/modules/audio_processing/agc2/rnn_vad/test_utils.h b/modules/audio_processing/agc2/rnn_vad/test_utils.h
index 92d3706..3f580ab 100644
--- a/modules/audio_processing/agc2/rnn_vad/test_utils.h
+++ b/modules/audio_processing/agc2/rnn_vad/test_utils.h
@@ -95,12 +95,6 @@
// and gain values.
std::pair<std::unique_ptr<BinaryFileReader<float>>, const size_t>
CreateLpResidualAndPitchPeriodGainReader();
-// Instance a reader for the silence flags and the feature matrix.
-std::pair<std::unique_ptr<BinaryFileReader<float>>, const size_t>
-CreateSilenceFlagsFeatureMatrixReader();
-// Instance a reader for the VAD probabilities.
-std::pair<std::unique_ptr<BinaryFileReader<float>>, const size_t>
-CreateVadProbsReader();
} // namespace test
} // namespace rnn_vad
diff --git a/resources/audio_processing/agc2/rnn_vad/sil_features.dat.sha1 b/resources/audio_processing/agc2/rnn_vad/sil_features.dat.sha1
deleted file mode 100644
index bc591e9..0000000
--- a/resources/audio_processing/agc2/rnn_vad/sil_features.dat.sha1
+++ /dev/null
@@ -1 +0,0 @@
-e0a92782c2903be9da10385d924d34e8bf212d5e
\ No newline at end of file
diff --git a/resources/audio_processing/agc2/rnn_vad/vad_prob.dat.sha1 b/resources/audio_processing/agc2/rnn_vad/vad_prob.dat.sha1
deleted file mode 100644
index 1aa3bd0..0000000
--- a/resources/audio_processing/agc2/rnn_vad/vad_prob.dat.sha1
+++ /dev/null
@@ -1 +0,0 @@
-05735ede0b457318e307d12f5acfd11bbbbd0afd
\ No newline at end of file
diff --git a/tools_webrtc/libs/generate_licenses.py b/tools_webrtc/libs/generate_licenses.py
index df7ad82..9bbe752 100755
--- a/tools_webrtc/libs/generate_licenses.py
+++ b/tools_webrtc/libs/generate_licenses.py
@@ -44,7 +44,6 @@
'openmax_dl': ['third_party/openmax_dl/LICENSE'],
'opus': ['third_party/opus/src/COPYING'],
'protobuf': ['third_party/protobuf/LICENSE'],
- 'rnnoise': ['third_party/rnnoise/COPYING'],
'usrsctp': ['third_party/usrsctp/LICENSE'],
'webrtc': ['LICENSE', 'LICENSE_THIRD_PARTY'],
'zlib': ['third_party/zlib/LICENSE'],