80 lines
2.0 KiB
C++
80 lines
2.0 KiB
C++
// sherpa/csrc/features.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/features.h"
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#include <algorithm>
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#include <memory>
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#include <vector>
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namespace sherpa_onnx {
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FeatureExtractor::FeatureExtractor() {
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opts_.frame_opts.dither = 0;
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opts_.frame_opts.snip_edges = false;
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opts_.frame_opts.samp_freq = 16000;
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// cache 100 seconds of feature frames, which is more than enough
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// for real needs
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opts_.frame_opts.max_feature_vectors = 100 * 100;
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opts_.mel_opts.num_bins = 80; // feature dim
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fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
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}
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FeatureExtractor::FeatureExtractor(const knf::FbankOptions &opts)
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: opts_(opts) {
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fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
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}
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void FeatureExtractor::AcceptWaveform(float sampling_rate,
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const float *waveform, int32_t n) {
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std::lock_guard<std::mutex> lock(mutex_);
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fbank_->AcceptWaveform(sampling_rate, waveform, n);
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}
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void FeatureExtractor::InputFinished() {
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std::lock_guard<std::mutex> lock(mutex_);
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fbank_->InputFinished();
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}
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int32_t FeatureExtractor::NumFramesReady() const {
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std::lock_guard<std::mutex> lock(mutex_);
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return fbank_->NumFramesReady();
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}
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bool FeatureExtractor::IsLastFrame(int32_t frame) const {
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std::lock_guard<std::mutex> lock(mutex_);
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return fbank_->IsLastFrame(frame);
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}
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std::vector<float> FeatureExtractor::GetFrames(int32_t frame_index,
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int32_t n) const {
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if (frame_index + n > NumFramesReady()) {
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fprintf(stderr, "%d + %d > %d\n", frame_index, n, NumFramesReady());
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exit(-1);
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}
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std::lock_guard<std::mutex> lock(mutex_);
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int32_t feature_dim = fbank_->Dim();
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std::vector<float> features(feature_dim * n);
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float *p = features.data();
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for (int32_t i = 0; i != n; ++i) {
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const float *f = fbank_->GetFrame(i + frame_index);
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std::copy(f, f + feature_dim, p);
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p += feature_dim;
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}
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return features;
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}
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void FeatureExtractor::Reset() {
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fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
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}
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} // namespace sherpa_onnx
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