Implement the initial version of TensorsToAudioCalculator that supports ifft and inverse hann windowing.
PiperOrigin-RevId: 484605092
This commit is contained in:
parent
c5c639d634
commit
e16be2e8fa
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@ -109,6 +109,56 @@ cc_test(
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],
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],
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)
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)
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mediapipe_proto_library(
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name = "tensors_to_audio_calculator_proto",
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srcs = ["tensors_to_audio_calculator.proto"],
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visibility = [
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"//mediapipe/framework:mediapipe_internal",
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],
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deps = [
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"//mediapipe/framework:calculator_options_proto",
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"//mediapipe/framework:calculator_proto",
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],
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)
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cc_library(
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name = "tensors_to_audio_calculator",
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srcs = ["tensors_to_audio_calculator.cc"],
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visibility = [
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"//mediapipe/framework:mediapipe_internal",
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],
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deps = [
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":tensors_to_audio_calculator_cc_proto",
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"//mediapipe/framework:calculator_framework",
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"//mediapipe/framework/api2:node",
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"//mediapipe/framework/formats:matrix",
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"//mediapipe/framework/formats:tensor",
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"//mediapipe/framework/port:ret_check",
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"@com_google_absl//absl/algorithm:container",
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"@com_google_absl//absl/status",
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"@com_google_audio_tools//audio/dsp:window_functions",
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"@pffft",
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],
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alwayslink = 1,
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)
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cc_test(
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name = "tensors_to_audio_calculator_test",
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srcs = ["tensors_to_audio_calculator_test.cc"],
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deps = [
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":audio_to_tensor_calculator",
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":audio_to_tensor_calculator_cc_proto",
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":tensors_to_audio_calculator",
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":tensors_to_audio_calculator_cc_proto",
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"//mediapipe/framework:calculator_framework",
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"//mediapipe/framework/formats:matrix",
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"//mediapipe/framework/port:gtest_main",
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"//mediapipe/framework/port:parse_text_proto",
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"@com_google_absl//absl/status",
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"@com_google_absl//absl/strings",
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],
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)
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mediapipe_proto_library(
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mediapipe_proto_library(
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name = "feedback_tensors_calculator_proto",
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name = "feedback_tensors_calculator_proto",
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srcs = ["feedback_tensors_calculator.proto"],
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srcs = ["feedback_tensors_calculator.proto"],
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@ -133,7 +133,7 @@ bool IsValidFftSize(int size) {
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// invocation. In the non-streaming mode, the vector contains all of the
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// invocation. In the non-streaming mode, the vector contains all of the
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// output timestamps for an input audio buffer.
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// output timestamps for an input audio buffer.
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// DC_AND_NYQUIST - std::pair<float, float> @Optional.
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// DC_AND_NYQUIST - std::pair<float, float> @Optional.
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// A pair of dc component and nyquest component. Only can be connected when
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// A pair of dc component and nyquist component. Only can be connected when
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// the calculator performs fft (the fft_size is set in the calculator
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// the calculator performs fft (the fft_size is set in the calculator
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// options).
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// options).
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//
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//
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197
mediapipe/calculators/tensor/tensors_to_audio_calculator.cc
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197
mediapipe/calculators/tensor/tensors_to_audio_calculator.cc
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@ -0,0 +1,197 @@
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// Copyright 2022 The MediaPipe Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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#include <new>
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#include <utility>
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#include <vector>
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#include "absl/algorithm/container.h"
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#include "absl/status/status.h"
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#include "audio/dsp/window_functions.h"
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#include "mediapipe/calculators/tensor/tensors_to_audio_calculator.pb.h"
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#include "mediapipe/framework/api2/node.h"
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#include "mediapipe/framework/calculator_framework.h"
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#include "mediapipe/framework/formats/matrix.h"
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#include "mediapipe/framework/formats/tensor.h"
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#include "mediapipe/framework/port/ret_check.h"
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#include "pffft.h"
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namespace mediapipe {
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namespace api2 {
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namespace {
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std::vector<float> HannWindow(int window_size, bool sqrt_hann) {
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std::vector<float> hann_window(window_size);
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audio_dsp::HannWindow().GetPeriodicSamples(window_size, &hann_window);
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if (sqrt_hann) {
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absl::c_transform(hann_window, hann_window.begin(),
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[](double x) { return std::sqrt(x); });
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}
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return hann_window;
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}
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// Note that the InvHannWindow function may only work for 50% overlapping case.
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std::vector<float> InvHannWindow(int window_size, bool sqrt_hann) {
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std::vector<float> window = HannWindow(window_size, sqrt_hann);
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std::vector<float> inv_window(window.size());
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if (sqrt_hann) {
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absl::c_copy(window, inv_window.begin());
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} else {
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const int kHalfWindowSize = window.size() / 2;
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absl::c_transform(window, inv_window.begin(),
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[](double x) { return x * x; });
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for (int i = 0; i < kHalfWindowSize; ++i) {
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double sum = inv_window[i] + inv_window[kHalfWindowSize + i];
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inv_window[i] = window[i] / sum;
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inv_window[kHalfWindowSize + i] = window[kHalfWindowSize + i] / sum;
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}
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}
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return inv_window;
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}
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// PFFFT only supports transforms for inputs of length N of the form
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// N = (2^a)*(3^b)*(5^c) where b >=0 and c >= 0 and a >= 5 for the real FFT.
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bool IsValidFftSize(int size) {
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if (size <= 0) {
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return false;
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}
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constexpr int kFactors[] = {2, 3, 5};
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int factorization[] = {0, 0, 0};
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int n = static_cast<int>(size);
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for (int i = 0; i < 3; ++i) {
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while (n % kFactors[i] == 0) {
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n = n / kFactors[i];
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++factorization[i];
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}
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}
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return factorization[0] >= 5 && n == 1;
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}
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} // namespace
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// Converts 2D MediaPipe float Tensors to audio buffers.
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// The calculator will perform ifft on the complex DFT and apply the window
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// function (Inverse Hann) afterwards. The input 2D MediaPipe Tensor must
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// have the DFT real parts in its first row and the DFT imagery parts in its
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// second row. A valid "fft_size" must be set in the CalculatorOptions.
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//
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// Inputs:
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// TENSORS - std::vector<Tensor>
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// Vector containing a single Tensor that represents the audio's complex DFT
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// results.
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// DC_AND_NYQUIST - std::pair<float, float>
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// A pair of dc component and nyquist component.
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//
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// Outputs:
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// AUDIO - mediapipe::Matrix
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// The audio data represented as mediapipe::Matrix.
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//
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// Example:
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// node {
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// calculator: "TensorsToAudioCalculator"
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// input_stream: "TENSORS:tensors"
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// input_stream: "DC_AND_NYQUIST:dc_and_nyquist"
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// output_stream: "AUDIO:audio"
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// options {
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// [mediapipe.AudioToTensorCalculatorOptions.ext] {
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// fft_size: 256
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// }
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// }
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// }
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class TensorsToAudioCalculator : public Node {
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public:
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static constexpr Input<std::vector<Tensor>> kTensorsIn{"TENSORS"};
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static constexpr Input<std::pair<float, float>> kDcAndNyquistIn{
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"DC_AND_NYQUIST"};
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static constexpr Output<Matrix> kAudioOut{"AUDIO"};
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MEDIAPIPE_NODE_CONTRACT(kTensorsIn, kDcAndNyquistIn, kAudioOut);
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absl::Status Open(CalculatorContext* cc) override;
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absl::Status Process(CalculatorContext* cc) override;
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absl::Status Close(CalculatorContext* cc) override;
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private:
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// The internal state of the FFT library.
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PFFFT_Setup* fft_state_ = nullptr;
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int fft_size_ = 0;
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float inverse_fft_size_ = 0;
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std::vector<float, Eigen::aligned_allocator<float>> input_dft_;
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std::vector<float> inv_fft_window_;
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std::vector<float, Eigen::aligned_allocator<float>> fft_input_buffer_;
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// pffft requires memory to work with to avoid using the stack.
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std::vector<float, Eigen::aligned_allocator<float>> fft_workplace_;
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std::vector<float, Eigen::aligned_allocator<float>> fft_output_;
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};
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absl::Status TensorsToAudioCalculator::Open(CalculatorContext* cc) {
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const auto& options =
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cc->Options<mediapipe::TensorsToAudioCalculatorOptions>();
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RET_CHECK(options.has_fft_size()) << "FFT size must be specified.";
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RET_CHECK(IsValidFftSize(options.fft_size()))
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<< "FFT size must be of the form fft_size = (2^a)*(3^b)*(5^c) where b "
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">=0 and c >= 0 and a >= 5, the requested fft size is "
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<< options.fft_size();
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fft_size_ = options.fft_size();
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inverse_fft_size_ = 1.0f / fft_size_;
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fft_state_ = pffft_new_setup(fft_size_, PFFFT_REAL);
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input_dft_.resize(fft_size_);
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inv_fft_window_ = InvHannWindow(fft_size_, /* sqrt_hann = */ false);
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fft_input_buffer_.resize(fft_size_);
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fft_workplace_.resize(fft_size_);
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fft_output_.resize(fft_size_);
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return absl::OkStatus();
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}
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absl::Status TensorsToAudioCalculator::Process(CalculatorContext* cc) {
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if (kTensorsIn(cc).IsEmpty() || kDcAndNyquistIn(cc).IsEmpty()) {
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return absl::OkStatus();
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}
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const auto& input_tensors = *kTensorsIn(cc);
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RET_CHECK_EQ(input_tensors.size(), 1);
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RET_CHECK(input_tensors[0].element_type() == Tensor::ElementType::kFloat32);
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auto view = input_tensors[0].GetCpuReadView();
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// DC's real part.
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input_dft_[0] = kDcAndNyquistIn(cc)->first;
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// Nyquist's real part is the penultimate element of the tensor buffer.
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// pffft ignores the Nyquist's imagery part. No need to fetch the last value
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// from the tensor buffer.
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input_dft_[1] = *(view.buffer<float>() + (fft_size_ - 2));
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std::memcpy(input_dft_.data() + 2, view.buffer<float>(),
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(fft_size_ - 2) * sizeof(float));
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pffft_transform_ordered(fft_state_, input_dft_.data(), fft_output_.data(),
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fft_workplace_.data(), PFFFT_BACKWARD);
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// Applies the inverse window function.
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std::transform(
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fft_output_.begin(), fft_output_.end(), inv_fft_window_.begin(),
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fft_output_.begin(),
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[this](float a, float b) { return a * b * inverse_fft_size_; });
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Matrix matrix = Eigen::Map<Matrix>(fft_output_.data(), 1, fft_output_.size());
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kAudioOut(cc).Send(std::move(matrix));
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return absl::OkStatus();
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}
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absl::Status TensorsToAudioCalculator::Close(CalculatorContext* cc) {
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if (fft_state_) {
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pffft_destroy_setup(fft_state_);
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}
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return absl::OkStatus();
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}
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MEDIAPIPE_REGISTER_NODE(TensorsToAudioCalculator);
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} // namespace api2
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} // namespace mediapipe
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@ -0,0 +1,29 @@
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// Copyright 2022 The MediaPipe Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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syntax = "proto2";
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package mediapipe;
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import "mediapipe/framework/calculator.proto";
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message TensorsToAudioCalculatorOptions {
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extend mediapipe.CalculatorOptions {
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optional TensorsToAudioCalculatorOptions ext = 484297136;
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}
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// Size of the fft in number of bins. If set, the calculator will do ifft
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// on the input tensor.
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optional int64 fft_size = 1;
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}
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149
mediapipe/calculators/tensor/tensors_to_audio_calculator_test.cc
Normal file
149
mediapipe/calculators/tensor/tensors_to_audio_calculator_test.cc
Normal file
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@ -0,0 +1,149 @@
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// Copyright 2022 The MediaPipe Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <algorithm>
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#include <new>
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#include <string>
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#include <vector>
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#include "absl/status/status.h"
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#include "absl/strings/substitute.h"
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#include "mediapipe/calculators/tensor/audio_to_tensor_calculator.pb.h"
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#include "mediapipe/calculators/tensor/tensors_to_audio_calculator.pb.h"
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#include "mediapipe/framework/calculator_framework.h"
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#include "mediapipe/framework/formats/matrix.h"
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#include "mediapipe/framework/port/gtest.h"
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#include "mediapipe/framework/port/parse_text_proto.h"
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#include "mediapipe/framework/port/status_matchers.h"
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namespace mediapipe {
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namespace {
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class TensorsToAudioCalculatorFftTest : public ::testing::Test {
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protected:
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// Creates an audio matrix containing a single sample of 1.0 at a specified
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// offset.
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Matrix CreateImpulseSignalData(int64 num_samples, int impulse_offset_idx) {
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Matrix impulse = Matrix::Zero(1, num_samples);
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impulse(0, impulse_offset_idx) = 1.0;
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return impulse;
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}
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void ConfigGraph(int num_samples, double sample_rate, int fft_size) {
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graph_config_ = ParseTextProtoOrDie<CalculatorGraphConfig>(
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absl::Substitute(R"(
|
||||||
|
input_stream: "audio_in"
|
||||||
|
input_stream: "sample_rate"
|
||||||
|
output_stream: "audio_out"
|
||||||
|
node {
|
||||||
|
calculator: "AudioToTensorCalculator"
|
||||||
|
input_stream: "AUDIO:audio_in"
|
||||||
|
input_stream: "SAMPLE_RATE:sample_rate"
|
||||||
|
output_stream: "TENSORS:tensors"
|
||||||
|
output_stream: "DC_AND_NYQUIST:dc_and_nyquist"
|
||||||
|
options {
|
||||||
|
[mediapipe.AudioToTensorCalculatorOptions.ext] {
|
||||||
|
num_channels: 1
|
||||||
|
num_samples: $0
|
||||||
|
num_overlapping_samples: 0
|
||||||
|
target_sample_rate: $1
|
||||||
|
fft_size: $2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
node {
|
||||||
|
calculator: "TensorsToAudioCalculator"
|
||||||
|
input_stream: "TENSORS:tensors"
|
||||||
|
input_stream: "DC_AND_NYQUIST:dc_and_nyquist"
|
||||||
|
output_stream: "AUDIO:audio_out"
|
||||||
|
options {
|
||||||
|
[mediapipe.TensorsToAudioCalculatorOptions.ext] {
|
||||||
|
fft_size: $2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
)",
|
||||||
|
/*$0=*/num_samples,
|
||||||
|
/*$1=*/sample_rate,
|
||||||
|
/*$2=*/fft_size));
|
||||||
|
tool::AddVectorSink("audio_out", &graph_config_, &audio_out_packets_);
|
||||||
|
}
|
||||||
|
|
||||||
|
void RunGraph(const Matrix& input_data, double sample_rate) {
|
||||||
|
MP_ASSERT_OK(graph_.Initialize(graph_config_));
|
||||||
|
MP_ASSERT_OK(graph_.StartRun({}));
|
||||||
|
MP_ASSERT_OK(graph_.AddPacketToInputStream(
|
||||||
|
"sample_rate", MakePacket<double>(sample_rate).At(Timestamp(0))));
|
||||||
|
MP_ASSERT_OK(graph_.AddPacketToInputStream(
|
||||||
|
"audio_in", MakePacket<Matrix>(input_data).At(Timestamp(0))));
|
||||||
|
MP_ASSERT_OK(graph_.CloseAllInputStreams());
|
||||||
|
MP_ASSERT_OK(graph_.WaitUntilDone());
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<Packet> audio_out_packets_;
|
||||||
|
CalculatorGraphConfig graph_config_;
|
||||||
|
CalculatorGraph graph_;
|
||||||
|
};
|
||||||
|
|
||||||
|
TEST_F(TensorsToAudioCalculatorFftTest, TestInvalidFftSize) {
|
||||||
|
ConfigGraph(320, 16000, 103);
|
||||||
|
MP_ASSERT_OK(graph_.Initialize(graph_config_));
|
||||||
|
MP_ASSERT_OK(graph_.StartRun({}));
|
||||||
|
auto status = graph_.WaitUntilIdle();
|
||||||
|
EXPECT_EQ(status.code(), absl::StatusCode::kInternal);
|
||||||
|
EXPECT_THAT(status.message(),
|
||||||
|
::testing::HasSubstr("FFT size must be of the form"));
|
||||||
|
}
|
||||||
|
|
||||||
|
TEST_F(TensorsToAudioCalculatorFftTest, TestImpulseSignalAtTheCenter) {
|
||||||
|
constexpr int sample_size = 320;
|
||||||
|
constexpr double sample_rate = 16000;
|
||||||
|
ConfigGraph(sample_size, sample_rate, 320);
|
||||||
|
|
||||||
|
Matrix impulse_data = CreateImpulseSignalData(sample_size, sample_size / 2);
|
||||||
|
RunGraph(impulse_data, sample_rate);
|
||||||
|
ASSERT_EQ(1, audio_out_packets_.size());
|
||||||
|
MP_ASSERT_OK(audio_out_packets_[0].ValidateAsType<Matrix>());
|
||||||
|
// The impulse signal at the center is not affected by the window function.
|
||||||
|
EXPECT_EQ(audio_out_packets_[0].Get<Matrix>(), impulse_data);
|
||||||
|
}
|
||||||
|
|
||||||
|
TEST_F(TensorsToAudioCalculatorFftTest, TestWindowedImpulseSignal) {
|
||||||
|
constexpr int sample_size = 320;
|
||||||
|
constexpr double sample_rate = 16000;
|
||||||
|
ConfigGraph(sample_size, sample_rate, 320);
|
||||||
|
Matrix impulse_data = CreateImpulseSignalData(sample_size, sample_size / 4);
|
||||||
|
RunGraph(impulse_data, sample_rate);
|
||||||
|
ASSERT_EQ(1, audio_out_packets_.size());
|
||||||
|
MP_ASSERT_OK(audio_out_packets_[0].ValidateAsType<Matrix>());
|
||||||
|
// As the impulse signal sits at the 1/4 of the hann window, the inverse
|
||||||
|
// window function reduces it by half.
|
||||||
|
EXPECT_EQ(audio_out_packets_[0].Get<Matrix>(), impulse_data / 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
TEST_F(TensorsToAudioCalculatorFftTest, TestImpulseSignalAtBeginning) {
|
||||||
|
constexpr int sample_size = 320;
|
||||||
|
constexpr double sample_rate = 16000;
|
||||||
|
ConfigGraph(sample_size, sample_rate, 320);
|
||||||
|
Matrix impulse_data = CreateImpulseSignalData(sample_size, 0);
|
||||||
|
RunGraph(impulse_data, sample_rate);
|
||||||
|
ASSERT_EQ(1, audio_out_packets_.size());
|
||||||
|
MP_ASSERT_OK(audio_out_packets_[0].ValidateAsType<Matrix>());
|
||||||
|
// As the impulse signal sits at the beginning of the hann window, the inverse
|
||||||
|
// window function completely removes it.
|
||||||
|
EXPECT_EQ(audio_out_packets_[0].Get<Matrix>(), Matrix::Zero(1, sample_size));
|
||||||
|
}
|
||||||
|
|
||||||
|
} // namespace
|
||||||
|
} // namespace mediapipe
|
Loading…
Reference in New Issue
Block a user