Add online LSTM transducer model (#25)

sherpa-onnx/csrc/features.cc

@@ -0,0 +1,79 @@
+// sherpa/csrc/features.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/features.h"
+
+#include <algorithm>
+#include <memory>
+#include <vector>
+
+namespace sherpa_onnx {
+
+FeatureExtractor::FeatureExtractor() {
+  opts_.frame_opts.dither = 0;
+  opts_.frame_opts.snip_edges = false;
+  opts_.frame_opts.samp_freq = 16000;
+
+  // 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
+
+  fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+}
+
+FeatureExtractor::FeatureExtractor(const knf::FbankOptions &opts)
+    : opts_(opts) {
+  fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+}
+
+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);
+}
+
+void FeatureExtractor::InputFinished() {
+  std::lock_guard<std::mutex> lock(mutex_);
+  fbank_->InputFinished();
+}
+
+int32_t FeatureExtractor::NumFramesReady() const {
+  std::lock_guard<std::mutex> lock(mutex_);
+  return fbank_->NumFramesReady();
+}
+
+bool FeatureExtractor::IsLastFrame(int32_t frame) const {
+  std::lock_guard<std::mutex> lock(mutex_);
+  return fbank_->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;
+}
+
+void FeatureExtractor::Reset() {
+  fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+}
+
+}  // namespace sherpa_onnx