features.cc
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// sherpa-onnx/csrc/features.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/features.h"
#include <algorithm>
#include <memory>
#include <mutex> // NOLINT
#include <sstream>
#include <vector>
#include "kaldi-native-fbank/csrc/online-feature.h"
namespace sherpa_onnx {
void FeatureExtractorConfig::Register(ParseOptions *po) {
po->Register("sample-rate", &sampling_rate,
"Sampling rate of the input waveform. Must match the one "
"expected by the model.");
po->Register("feat-dim", &feature_dim,
"Feature dimension. Must match the one expected by the model.");
}
std::string FeatureExtractorConfig::ToString() const {
std::ostringstream os;
os << "FeatureExtractorConfig(";
os << "sampling_rate=" << sampling_rate << ", ";
os << "feature_dim=" << feature_dim << ", ";
os << "max_feature_vectors=" << max_feature_vectors << ")";
return os.str();
}
class FeatureExtractor::Impl {
public:
explicit Impl(const FeatureExtractorConfig &config) {
opts_.frame_opts.dither = 0;
opts_.frame_opts.snip_edges = false;
opts_.frame_opts.samp_freq = config.sampling_rate;
opts_.frame_opts.max_feature_vectors = config.max_feature_vectors;
opts_.mel_opts.num_bins = config.feature_dim;
fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
}
void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n) {
std::lock_guard<std::mutex> lock(mutex_);
fbank_->AcceptWaveform(sampling_rate, waveform, n);
}
void InputFinished() {
std::lock_guard<std::mutex> lock(mutex_);
fbank_->InputFinished();
}
int32_t NumFramesReady() const {
std::lock_guard<std::mutex> lock(mutex_);
return fbank_->NumFramesReady();
}
bool IsLastFrame(int32_t frame) const {
std::lock_guard<std::mutex> lock(mutex_);
return fbank_->IsLastFrame(frame);
}
std::vector<float> GetFrames(int32_t frame_index, int32_t n) const {
if (frame_index + n > NumFramesReady()) {
fprintf(stderr, "%d + %d > %d\n", frame_index, n, NumFramesReady());
exit(-1);
}
std::lock_guard<std::mutex> lock(mutex_);
int32_t feature_dim = fbank_->Dim();
std::vector<float> features(feature_dim * n);
float *p = features.data();
for (int32_t i = 0; i != n; ++i) {
const float *f = fbank_->GetFrame(i + frame_index);
std::copy(f, f + feature_dim, p);
p += feature_dim;
}
return features;
}
void Reset() {
std::lock_guard<std::mutex> lock(mutex_);
fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
}
int32_t FeatureDim() const { return opts_.mel_opts.num_bins; }
private:
std::unique_ptr<knf::OnlineFbank> fbank_;
knf::FbankOptions opts_;
mutable std::mutex mutex_;
};
FeatureExtractor::FeatureExtractor(const FeatureExtractorConfig &config /*={}*/)
: impl_(std::make_unique<Impl>(config)) {}
FeatureExtractor::~FeatureExtractor() = default;
void FeatureExtractor::AcceptWaveform(float sampling_rate,
const float *waveform, int32_t n) {
impl_->AcceptWaveform(sampling_rate, waveform, n);
}
void FeatureExtractor::InputFinished() { impl_->InputFinished(); }
int32_t FeatureExtractor::NumFramesReady() const {
return impl_->NumFramesReady();
}
bool FeatureExtractor::IsLastFrame(int32_t frame) const {
return impl_->IsLastFrame(frame);
}
std::vector<float> FeatureExtractor::GetFrames(int32_t frame_index,
int32_t n) const {
return impl_->GetFrames(frame_index, n);
}
void FeatureExtractor::Reset() { impl_->Reset(); }
int32_t FeatureExtractor::FeatureDim() const { return impl_->FeatureDim(); }
} // namespace sherpa_onnx