Add online stream. (#28)

@@ -3,6 +3,7 @@ include_directories(${CMAKE_SOURCE_DIR})
 add_executable(sherpa-onnx
   features.cc
   online-lstm-transducer-model.cc
+  online-stream.cc
   online-transducer-greedy-search-decoder.cc
   online-transducer-model-config.cc
   online-transducer-model.cc

@@ -11,16 +11,12 @@
 namespace sherpa_onnx {
 
 struct FeatureExtractorConfig {
-  int32_t sampling_rate = 16000;
+  float sampling_rate = 16000;
   int32_t feature_dim = 80;
 };
 
 class FeatureExtractor {
  public:
-  /**
-   * @param sampling_rate  Sampling rate of the data used to train the model.
-   * @param feature_dim    Dimension of the features used to train the model.
-   */
   explicit FeatureExtractor(const FeatureExtractorConfig &config = {});
   ~FeatureExtractor();
 
@@ -32,16 +28,19 @@ class FeatureExtractor {
    */
   void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n);
 
-  // InputFinished() tells the class you won't be providing any
-  // more waveform.  This will help flush out the last frame or two
-  // of features, in the case where snip-edges == false; it also
-  // affects the return value of IsLastFrame().
+  /**
+   * InputFinished() tells the class you won't be providing any
+   * more waveform.  This will help flush out the last frame or two
+   * of features, in the case where snip-edges == false; it also
+   * affects the return value of IsLastFrame().
+   */
   void InputFinished();
 
   int32_t NumFramesReady() const;
 
-  // Note: IsLastFrame() will only ever return true if you have called
-  // InputFinished() (and this frame is the last frame).
+  /** Note: IsLastFrame() will only ever return true if you have called
+   * InputFinished() (and this frame is the last frame).
+   */
   bool IsLastFrame(int32_t frame) const;
 
   /** Get n frames starting from the given frame index.

+// sherpa-onnx/csrc/online-stream.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#include "sherpa-onnx/csrc/online-stream.h"
+
+#include <memory>
+#include <vector>
+
+#include "sherpa-onnx/csrc/features.h"
+
+namespace sherpa_onnx {
+
+class OnlineStream::Impl {
+ public:
+  explicit Impl(const FeatureExtractorConfig &config)
+      : feat_extractor_(config) {}
+
+  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n) {
+    feat_extractor_.AcceptWaveform(sampling_rate, waveform, n);
+  }
+
+  void InputFinished() { feat_extractor_.InputFinished(); }
+
+  int32_t NumFramesReady() const { return feat_extractor_.NumFramesReady(); }
+
+  bool IsLastFrame(int32_t frame) const {
+    return feat_extractor_.IsLastFrame(frame);
+  }
+
+  std::vector<float> GetFrames(int32_t frame_index, int32_t n) const {
+    return feat_extractor_.GetFrames(frame_index, n);
+  }
+
+  void Reset() { feat_extractor_.Reset(); }
+
+  int32_t &GetNumProcessedFrames() { return num_processed_frames_; }
+
+  void SetResult(const OnlineTransducerDecoderResult &r) { result_ = r; }
+
+  const OnlineTransducerDecoderResult &GetResult() const { return result_; }
+
+  int32_t FeatureDim() const { return feat_extractor_.FeatureDim(); }
+
+ private:
+  FeatureExtractor feat_extractor_;
+  int32_t num_processed_frames_ = 0;  // before subsampling
+  OnlineTransducerDecoderResult result_;
+};
+
+OnlineStream::OnlineStream(const FeatureExtractorConfig &config /*= {}*/)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+OnlineStream::~OnlineStream() = default;
+
+void OnlineStream::AcceptWaveform(float sampling_rate, const float *waveform,
+                                  int32_t n) {
+  impl_->AcceptWaveform(sampling_rate, waveform, n);
+}
+
+void OnlineStream::InputFinished() { impl_->InputFinished(); }
+
+int32_t OnlineStream::NumFramesReady() const { return impl_->NumFramesReady(); }
+
+bool OnlineStream::IsLastFrame(int32_t frame) const {
+  return impl_->IsLastFrame(frame);
+}
+
+std::vector<float> OnlineStream::GetFrames(int32_t frame_index,
+                                           int32_t n) const {
+  return impl_->GetFrames(frame_index, n);
+}
+
+void OnlineStream::Reset() { impl_->Reset(); }
+
+int32_t OnlineStream::FeatureDim() const { return impl_->FeatureDim(); }
+
+int32_t &OnlineStream::GetNumProcessedFrames() {
+  return impl_->GetNumProcessedFrames();
+}
+
+void OnlineStream::SetResult(const OnlineTransducerDecoderResult &r) {
+  impl_->SetResult(r);
+}
+
+const OnlineTransducerDecoderResult &OnlineStream::GetResult() const {
+  return impl_->GetResult();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/online-stream.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_ONLINE_STREAM_H_
+#define SHERPA_ONNX_CSRC_ONLINE_STREAM_H_
+
+#include <memory>
+#include <vector>
+
+#include "sherpa-onnx/csrc/features.h"
+#include "sherpa-onnx/csrc/online-transducer-decoder.h"
+
+namespace sherpa_onnx {
+
+class OnlineStream {
+ public:
+  explicit OnlineStream(const FeatureExtractorConfig &config = {});
+  ~OnlineStream();
+
+  /**
+     @param sampling_rate The sampling_rate of the input waveform. Should match
+                          the one expected by the feature extractor.
+     @param waveform Pointer to a 1-D array of size n
+     @param n Number of entries in waveform
+   */
+  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n);
+
+  /**
+   * InputFinished() tells the class you won't be providing any
+   * more waveform.  This will help flush out the last frame or two
+   * of features, in the case where snip-edges == false; it also
+   * affects the return value of IsLastFrame().
+   */
+  void InputFinished();
+
+  int32_t NumFramesReady() const;
+
+  /** Note: IsLastFrame() will only ever return true if you have called
+   * InputFinished() (and this frame is the last frame).
+   */
+  bool IsLastFrame(int32_t frame) const;
+
+  /** Get n frames starting from the given frame index.
+   *
+   * @param frame_index  The starting frame index
+   * @param n  Number of frames to get.
+   * @return Return a 2-D tensor of shape (n, feature_dim).
+   *         which is flattened into a 1-D vector (flattened in in row major)
+   */
+  std::vector<float> GetFrames(int32_t frame_index, int32_t n) const;
+
+  void Reset();
+
+  int32_t FeatureDim() const;
+
+  // Return a reference to the number of processed frames so far.
+  // Initially, it is 0. It is always less than NumFramesReady().
+  //
+  // The returned reference is valid as long as this object is alive.
+  int32_t &GetNumProcessedFrames();
+
+  void SetResult(const OnlineTransducerDecoderResult &r);
+  const OnlineTransducerDecoderResult &GetResult() const;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_STREAM_H_

@@ -8,8 +8,7 @@
 #include <string>
 #include <vector>
 
-#include "kaldi-native-fbank/csrc/online-feature.h"
-#include "sherpa-onnx/csrc/features.h"
+#include "sherpa-onnx/csrc/online-stream.h"
 #include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
 #include "sherpa-onnx/csrc/online-transducer-model-config.h"
 #include "sherpa-onnx/csrc/online-transducer-model.h"
@@ -64,7 +63,7 @@ for a list of pre-trained models to download.
 
   std::vector<Ort::Value> states = model->GetEncoderInitStates();
 
-  int32_t expected_sampling_rate = 16000;
+  float expected_sampling_rate = 16000;
 
   bool is_ok = false;
   std::vector<float> samples =
@@ -75,7 +74,7 @@ for a list of pre-trained models to download.
     return -1;
   }
 
-  float duration = samples.size() / static_cast<float>(expected_sampling_rate);
+  float duration = samples.size() / expected_sampling_rate;
 
   fprintf(stderr, "wav filename: %s\n", wav_filename.c_str());
   fprintf(stderr, "wav duration (s): %.3f\n", duration);
@@ -83,32 +82,33 @@ for a list of pre-trained models to download.
   auto begin = std::chrono::steady_clock::now();
   fprintf(stderr, "Started\n");
 
-  sherpa_onnx::FeatureExtractor feat_extractor;
-  feat_extractor.AcceptWaveform(expected_sampling_rate, samples.data(),
-                                samples.size());
+  sherpa_onnx::OnlineStream stream;
+  stream.AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
 
   std::vector<float> tail_paddings(
       static_cast<int>(0.2 * expected_sampling_rate));
-  feat_extractor.AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
-                                tail_paddings.size());
-  feat_extractor.InputFinished();
+  stream.AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
+                        tail_paddings.size());
+  stream.InputFinished();
 
-  int32_t num_frames = feat_extractor.NumFramesReady();
-  int32_t feature_dim = feat_extractor.FeatureDim();
+  int32_t num_frames = stream.NumFramesReady();
+  int32_t feature_dim = stream.FeatureDim();
 
   std::array<int64_t, 3> x_shape{1, chunk_size, feature_dim};
 
   sherpa_onnx::OnlineTransducerGreedySearchDecoder decoder(model.get());
   std::vector<sherpa_onnx::OnlineTransducerDecoderResult> result = {
       decoder.GetEmptyResult()};
-
-  for (int32_t start = 0; start + chunk_size < num_frames;
-       start += chunk_shift) {
-    std::vector<float> features = feat_extractor.GetFrames(start, chunk_size);
+  while (stream.NumFramesReady() - stream.GetNumProcessedFrames() >
+         chunk_size) {
+    std::vector<float> features =
+        stream.GetFrames(stream.GetNumProcessedFrames(), chunk_size);
+    stream.GetNumProcessedFrames() += chunk_shift;
 
     Ort::Value x =
         Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
                                  x_shape.data(), x_shape.size());
+
     auto pair = model->RunEncoder(std::move(x), states);
     states = std::move(pair.second);
     decoder.Decode(std::move(pair.first), &result);
@@ -116,8 +116,8 @@ for a list of pre-trained models to download.
   decoder.StripLeadingBlanks(&result[0]);
   const auto &hyp = result[0].tokens;
   std::string text;
-  for (size_t i = model->ContextSize(); i != hyp.size(); ++i) {
-    text += sym[hyp[i]];
+  for (auto t : hyp) {
+    text += sym[t];
   }
 
   fprintf(stderr, "Done!\n");