mirror of https://github.com/alibaba/MNN.git
94 lines
3.2 KiB
C++
94 lines
3.2 KiB
C++
//
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// CPUStft.cpp
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// MNN
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//
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// Created by MNN on 2024/11/26.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifdef MNN_BUILD_AUDIO
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#ifndef M_PI
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#define M_PI 3.141592654
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#endif
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#include <algorithm>
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#include "backend/cpu/CPUStft.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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#include "core/Concurrency.h"
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#include "core/TensorUtils.hpp"
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#include "core/Macro.h"
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#include "compute/CommonOptFunction.h"
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namespace MNN {
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static void MNNDftAbs(const float* input, const float* window, float* output, float* buffer, int nfft) {
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for (int i = 0; i < nfft; ++i) {
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buffer[i] = input[i] * window[i];
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}
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for (int k = 0; k < nfft / 2 + 1; ++k) {
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float real_sum = 0.f, imag_sum = 0.f;
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for (int n = 0; n < nfft; ++n) {
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float angle = 2 * M_PI * k * n / nfft;
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real_sum += buffer[n] * std::cos(angle);
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imag_sum -= buffer[n] * std::sin(angle);
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}
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output[k] = std::sqrt(real_sum * real_sum + imag_sum * imag_sum);
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}
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}
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CPUStft::CPUStft(Backend* backend, int nfft, int hop_length, bool abs)
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: Execution(backend), mNfft(nfft), mHopLength(hop_length), mAbs(abs) {
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// nothing to do
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}
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ErrorCode CPUStft::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto cpuBn = static_cast<CPUBackend*>(backend());
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mTmpFrames.buffer().dim[0].extent = cpuBn->threadNumber();
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mTmpFrames.buffer().dim[1].extent = mNfft;
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TensorUtils::getDescribe(&mTmpFrames)->dimensionFormat = MNN_DATA_FORMAT_NHWC;
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mTmpFrames.buffer().dimensions = 2;
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mTmpFrames.buffer().type = inputs[0]->getType();
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backend()->onAcquireBuffer(&mTmpFrames, Backend::DYNAMIC);
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backend()->onReleaseBuffer(&mTmpFrames, Backend::DYNAMIC);
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return NO_ERROR;
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}
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ErrorCode CPUStft::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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const float* sample = inputs[0]->host<float>();
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const float* window = inputs[1]->host<float>();
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float* buffer = mTmpFrames.host<float>();
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float* output = outputs[0]->host<float>();
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auto outputShape = outputs[0]->shape();
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int frames = outputShape[0];
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int col = outputShape[1];
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auto cpuBn = static_cast<CPUBackend*>(backend());
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int threadNum = cpuBn->threadNumber();
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// div frames to threadNum
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int threadNumber = std::min(threadNum, frames);
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int sizeDivide = frames / threadNumber;
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MNN_CONCURRENCY_BEGIN(tId, threadNumber) {
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int number = sizeDivide;
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if (tId == threadNumber - 1) {
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number = frames - tId * sizeDivide;
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}
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for (int i = tId * sizeDivide; i < tId * sizeDivide + number; ++i) {
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MNNDftAbs(sample + i * mHopLength, window, output + i * col, buffer + tId * mNfft, mNfft);
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}
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};
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MNN_CONCURRENCY_END();
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return NO_ERROR;
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}
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class CPUStftCreator : public CPUBackend::Creator {
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public:
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virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
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const MNN::Op* op, Backend* backend) const {
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auto stft = op->main_as_StftParam();
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return new CPUStft(backend, stft->n_fft(), stft->hop_length(), stft->abs());
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}
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};
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REGISTER_CPU_OP_CREATOR_AUDIO(CPUStftCreator, OpType_Stft);
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} // namespace MNN
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#endif // MNN_BUILD_AUDIO
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