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Refactor feature extractor (#26)

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This commit is contained in:
Fangjun Kuang
2023-02-19 09:57:56 +08:00
committed by GitHub
parent cb8f85ff83
commit 710edaa6f9
3 changed files with 105 additions and 67 deletions
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124
sherpa-onnx/csrc/features.cc
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@@ -6,74 +6,108 @@
#include <algorithm>
#include <memory>
#include <mutex> // NOLINT
#include <vector>
#include "kaldi-native-fbank/csrc/online-feature.h"
namespace sherpa_onnx {
FeatureExtractor::FeatureExtractor() {
opts_.frame_opts.dither = 0;
opts_.frame_opts.snip_edges = false;
opts_.frame_opts.samp_freq = 16000;
class FeatureExtractor::Impl {
public:
Impl(int32_t sampling_rate, int32_t feature_dim) {
opts_.frame_opts.dither = 0;
opts_.frame_opts.snip_edges = false;
opts_.frame_opts.samp_freq = sampling_rate;
// cache 100 seconds of feature frames, which is more than enough
// for real needs
opts_.frame_opts.max_feature_vectors = 100 * 100;
// cache 100 seconds of feature frames, which is more than enough
// for real needs
opts_.frame_opts.max_feature_vectors = 100 * 100;
opts_.mel_opts.num_bins = 80; // feature dim
opts_.mel_opts.num_bins = feature_dim;
fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
}
fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
}
FeatureExtractor::FeatureExtractor(const knf::FbankOptions &opts)
: opts_(opts) {
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() { 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(int32_t sampling_rate /*=16000*/,
int32_t feature_dim /*=80*/)
: impl_(std::make_unique<Impl>(sampling_rate, feature_dim)) {}
FeatureExtractor::~FeatureExtractor() = default;
void FeatureExtractor::AcceptWaveform(float sampling_rate,
const float *waveform, int32_t n) {
std::lock_guard<std::mutex> lock(mutex_);
fbank_->AcceptWaveform(sampling_rate, waveform, n);
impl_->AcceptWaveform(sampling_rate, waveform, n);
}
void FeatureExtractor::InputFinished() {
std::lock_guard<std::mutex> lock(mutex_);
fbank_->InputFinished();
}
void FeatureExtractor::InputFinished() { impl_->InputFinished(); }
int32_t FeatureExtractor::NumFramesReady() const {
std::lock_guard<std::mutex> lock(mutex_);
return fbank_->NumFramesReady();
return impl_->NumFramesReady();
}
bool FeatureExtractor::IsLastFrame(int32_t frame) const {
std::lock_guard<std::mutex> lock(mutex_);
return fbank_->IsLastFrame(frame);
return impl_->IsLastFrame(frame);
}
std::vector<float> FeatureExtractor::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;
return impl_->GetFrames(frame_index, n);
}
void FeatureExtractor::Reset() {
fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
}
void FeatureExtractor::Reset() { impl_->Reset(); }
int32_t FeatureExtractor::FeatureDim() const { return impl_->FeatureDim(); }
} // namespace sherpa_onnx