File size: 1,949 Bytes
37c4768 | 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 | #include "diarizen_segmenter.h"
#include <cmath>
#include <cstring>
#include <algorithm>
#include <numeric>
// AX Engine runtime API placeholder.
// When AX_RUNTIME_ROOT is provided, include the real headers:
// #include "ax_engine.h"
namespace diarizen {
struct DiarizenSegmenter::Impl {
std::string cnn_path;
std::string backend_path;
// void* cnn_handle = nullptr; // AX engine handle
};
DiarizenSegmenter::DiarizenSegmenter(
const std::string& cnn_model_path,
const std::string& backend_onnx_path)
: impl_(std::make_unique<Impl>())
{
impl_->cnn_path = cnn_model_path;
impl_->backend_path = backend_onnx_path;
// TODO: Load CNN axmodel via AX Engine API
// TODO: Load backend ONNX via ONNX Runtime C++ API
}
DiarizenSegmenter::~DiarizenSegmenter() = default;
SegmentResult DiarizenSegmenter::run(const float* audio, int num_samples) {
SegmentResult result;
result.log_probs.resize(result.num_frames * result.num_classes, 0.0f);
// Preprocessing: LayerNorm on CPU
if (num_samples != 64000) {
// Input must be exactly 64000 samples (4s @ 16kHz)
return result;
}
float mean = 0.0f;
for (int i = 0; i < num_samples; ++i) mean += audio[i];
mean /= num_samples;
float var = 0.0f;
for (int i = 0; i < num_samples; ++i) {
float d = audio[i] - mean;
var += d * d;
}
var = var / num_samples + 1e-5f;
float inv_std = 1.0f / std::sqrt(var);
std::vector<float> normalized(num_samples);
for (int i = 0; i < num_samples; ++i) {
normalized[i] = (audio[i] - mean) * inv_std;
}
// TODO: Run CNN NPU inference
// TODO: Run backend ONNX inference
// Placeholder: fill with uniform log(1/11) = -2.398
float uniform_log_prob = std::log(1.0f / result.num_classes);
std::fill(result.log_probs.begin(), result.log_probs.end(), uniform_log_prob);
return result;
}
} // namespace diarizen
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