File size: 3,583 Bytes
819e690 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | // RNNoise AX SDK：用 AX Engine 替换原版 compute_rnn（网络推理），
// 其余信号处理（biquad/FFT/pitch/合成）沿用原版 C 实现。
#include "rnnoise_ax.hpp"
#include "model_runner.hpp"
#include <cstring>
#include <mutex>
#include <stdexcept>
#include <vector>
extern "C" {
#include "denoise.h"
#include "rnn.h"
}
namespace {
// 单实例模型 runner（compute_rnn 无状态上下文可用，采用全局注册方式；
// 同一时刻只允许一个 RNNoiseAX 实例执行推理）。
std::mutex g_runner_mu;
ModelRunner* g_ax_runner = nullptr;
extern "C" void rnnoise_ax_set_runner(ModelRunner* runner) {
std::lock_guard<std::mutex> lk(g_runner_mu);
g_ax_runner = runner;
}
extern "C" void compute_rnn(const RNNoise* model, RNNState* rnn,
float* gains, float* vad,
const float* input, int arch) {
(void)model;
(void)arch;
ModelRunner* runner = nullptr;
{
std::lock_guard<std::mutex> lk(g_runner_mu);
runner = g_ax_runner;
}
if (runner == nullptr) {
throw std::runtime_error("compute_rnn: AX runner 未初始化");
}
std::vector<std::vector<float>> feeds = {
std::vector<float>(input, input + NB_FEATURES),
std::vector<float>(rnn->conv1_state,
rnn->conv1_state + CONV1_STATE_SIZE),
std::vector<float>(rnn->conv2_state,
rnn->conv2_state + CONV2_STATE_SIZE),
std::vector<float>(rnn->gru1_state,
rnn->gru1_state + GRU1_OUT_SIZE),
std::vector<float>(rnn->gru2_state,
rnn->gru2_state + GRU2_OUT_SIZE),
std::vector<float>(rnn->gru3_state,
rnn->gru3_state + GRU3_OUT_SIZE),
};
std::vector<std::vector<float>> outs = runner->Run(feeds);
std::memcpy(gains, outs[0].data(), NB_BANDS * sizeof(float));
*vad = outs[1][0];
std::memcpy(rnn->conv1_state, outs[2].data(),
CONV1_STATE_SIZE * sizeof(float));
std::memcpy(rnn->conv2_state, outs[3].data(),
CONV2_STATE_SIZE * sizeof(float));
std::memcpy(rnn->gru1_state, outs[4].data(),
GRU1_OUT_SIZE * sizeof(float));
std::memcpy(rnn->gru2_state, outs[5].data(),
GRU2_OUT_SIZE * sizeof(float));
std::memcpy(rnn->gru3_state, outs[6].data(),
GRU3_OUT_SIZE * sizeof(float));
}
} // namespace
struct RNNoiseAX::Impl {
std::unique_ptr<ModelRunner> runner;
DenoiseState* state = nullptr;
explicit Impl(const std::string& model_path)
: runner(new ModelRunner(model_path)),
state(rnnoise_create(nullptr)) {
if (state == nullptr) {
throw std::runtime_error("rnnoise_create 失败");
}
rnnoise_ax_set_runner(runner.get());
}
~Impl() {
rnnoise_ax_set_runner(nullptr);
if (state) {
rnnoise_destroy(state);
}
}
};
RNNoiseAX::RNNoiseAX(const std::string& model_path)
: impl_(new Impl(model_path)) {}
RNNoiseAX::~RNNoiseAX() = default;
void RNNoiseAX::Reset() {
if (!impl_) {
return;
}
rnnoise_ax_set_runner(nullptr);
rnnoise_destroy(impl_->state);
impl_->state = rnnoise_create(nullptr);
if (impl_->state == nullptr) {
throw std::runtime_error("rnnoise_create 失败");
}
rnnoise_ax_set_runner(impl_->runner.get());
}
float RNNoiseAX::ProcessFrame(float* out, const float* in) {
return rnnoise_process_frame(impl_->state, out, in);
}
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