Add OnlineGenericBaseFeature.

kaldifeat/csrc/feature-common.h

@@ -62,6 +62,10 @@ class OfflineFeatureTpl {
   int32_t Dim() const { return computer_.Dim(); }
   const Options &GetOptions() const { return computer_.GetOptions(); }
 
+  const FrameExtractionOptions &GetFrameOptions() const {
+    return GetOptions().frame_opts;
+  }
+
   // Copy constructor.
   OfflineFeatureTpl(const OfflineFeatureTpl<F> &) = delete;
   OfflineFeatureTpl<F> &operator=(const OfflineFeatureTpl<F> &) = delete;

kaldifeat/csrc/feature-window.cc

@@ -161,19 +161,20 @@ torch::Tensor Dither(const torch::Tensor &wave, float dither_value) {
 #if 1
   return wave + rand_gauss * dither_value;
 #else
-  // use in-place version of wave and change its to pointer type
+  // use in-place version of wave and change it to pointer type
   wave_->add_(rand_gauss, dither_value);
 #endif
 }
 
 torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
-  using namespace torch::indexing;  // It imports: Slice, None  // NOLINT
   if (preemph_coeff == 0.0f) return wave;
 
   KALDIFEAT_ASSERT(preemph_coeff >= 0.0f && preemph_coeff <= 1.0f);
 
   torch::Tensor ans = torch::empty_like(wave);
 
+  using torch::indexing::None;
+  using torch::indexing::Slice;
   // right = wave[:, 1:]
   torch::Tensor right = wave.index({"...", Slice(1, None, None)});
 
@@ -188,4 +189,58 @@ torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
   return ans;
 }
 
+torch::Tensor ExtractWindow(int64_t sample_offset, const torch::Tensor &wave,
+                            int32_t f, const FrameExtractionOptions &opts) {
+  KALDIFEAT_ASSERT(sample_offset >= 0 && wave.numel() != 0);
+
+  int32_t frame_length = opts.WindowSize();
+  int64_t num_samples = sample_offset + wave.numel();
+  int64_t start_sample = FirstSampleOfFrame(f, opts);
+  int64_t end_sample = start_sample + frame_length;
+
+  if (opts.snip_edges) {
+    KALDIFEAT_ASSERT(start_sample >= sample_offset &&
+                     end_sample <= num_samples);
+  } else {
+    KALDIFEAT_ASSERT(sample_offset == 0 || start_sample >= sample_offset);
+  }
+
+  // wave_start and wave_end are start and end indexes into 'wave', for the
+  // piece of wave that we're trying to extract.
+  int32_t wave_start = static_cast<int32_t>(start_sample - sample_offset);
+  int32_t wave_end = wave_start + frame_length;
+
+  if (wave_start >= 0 && wave_end <= wave.numel()) {
+    // the normal case -- no edge effects to consider.
+    // return wave[wave_start:wave_end]
+    return wave.index({torch::indexing::Slice(wave_start, wave_end)});
+  } else {
+    torch::Tensor window = torch::empty({frame_length}, torch::kFloat);
+    auto p_window = window.accessor<float, 1>();
+    auto p_wave = wave.accessor<float, 1>();
+
+    // Deal with any end effects by reflection, if needed.  This code will only
+    // be reached for about two frames per utterance, so we don't concern
+    // ourselves excessively with efficiency.
+    int32_t wave_dim = wave.numel();
+    for (int32_t s = 0; s != frame_length; ++s) {
+      int32_t s_in_wave = s + wave_start;
+      while (s_in_wave < 0 || s_in_wave >= wave_dim) {
+        // reflect around the beginning or end of the wave.
+        // e.g. -1 -> 0, -2 -> 1.
+        // dim -> dim - 1, dim + 1 -> dim - 2.
+        // the code supports repeated reflections, although this
+        // would only be needed in pathological cases.
+        if (s_in_wave < 0) {
+          s_in_wave = -s_in_wave - 1;
+        } else {
+          s_in_wave = 2 * wave_dim - 1 - s_in_wave;
+        }
+      }
+
+      p_window[s] = p_wave[s_in_wave];
+    }
+  }
+}
+
 }  // namespace kaldifeat

kaldifeat/csrc/feature-window.h

@@ -44,7 +44,11 @@ struct FrameExtractionOptions {
   bool snip_edges = true;
   // bool allow_downsample = false;
   // bool allow_upsample = false;
-  // int32_t max_feature_vectors = -1;
+
+  // Used for streaming feature extraction. It indicates the number
+  // of feature frames to keep in the recycling vector. -1 means to
+  // keep all feature frames.
+  int32_t max_feature_vectors = -1;
 
   int32_t WindowShift() const {
     return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
@@ -71,7 +75,7 @@ struct FrameExtractionOptions {
     KALDIFEAT_PRINT(snip_edges);
     // KALDIFEAT_PRINT(allow_downsample);
     // KALDIFEAT_PRINT(allow_upsample);
-    // KALDIFEAT_PRINT(max_feature_vectors);
+    KALDIFEAT_PRINT(max_feature_vectors);
 #undef KALDIFEAT_PRINT
     return os.str();
   }
@@ -100,11 +104,11 @@ class FeatureWindowFunction {
 
       @param [in] flush   True if we are asserting that this number of samples
    is 'all there is', false if we expecting more data to possibly come in.  This
-   only makes a difference to the answer if opts.snips_edges
-             == false.  For offline feature extraction you always want flush ==
-             true.  In an online-decoding context, once you know (or decide)
-   that no more data is coming in, you'd call it with flush == true at the end
-   to flush out any remaining data.
+   only makes a difference to the answer
+   if opts.snips_edges== false.  For offline feature extraction you always want
+   flush == true.  In an online-decoding context, once you know (or decide) that
+   no more data is coming in, you'd call it with flush == true at the end to
+   flush out any remaining data.
 */
 int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
                   bool flush = true);
@@ -133,6 +137,29 @@ torch::Tensor Dither(const torch::Tensor &wave, float dither_value);
 
 torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave);
 
+/*
+  ExtractWindow() extracts "frame_length" samples from the given waveform.
+  Note: This function only extracts "frame_length" samples
+  from the input waveform, without any further processing.
+
+  @param [in] sample_offset  If 'wave' is not the entire waveform, but
+                   part of it to the left has been discarded, then the
+                   number of samples prior to 'wave' that we have
+                   already discarded.  Set this to zero if you are
+                   processing the entire waveform in one piece, or
+                   if you get 'no matching function' compilation
+                   errors when updating the code.
+  @param [in] wave  The waveform
+  @param [in] f     The frame index to be extracted, with
+                    0 <= f < NumFrames(sample_offset + wave.numel(), opts, true)
+  @param [in] opts  The options class to be used
+  @return  Return a tensor containing "frame_length" samples extracted from
+           `wave`, without any further processing. Its shape is
+           (1, frame_length).
+*/
+torch::Tensor ExtractWindow(int64_t sample_offset, const torch::Tensor &wave,
+                            int32_t f, const FrameExtractionOptions &opts);
+
 }  // namespace kaldifeat
 
 #endif  // KALDIFEAT_CSRC_FEATURE_WINDOW_H_

kaldifeat/csrc/online-feature-itf.h

@@ -19,6 +19,10 @@ class OnlineFeatureInterface {
   virtual ~OnlineFeatureInterface() = default;
 
   virtual int32_t Dim() const = 0;  /// returns the feature dimension.
+                                    //
+  // Returns frame shift in seconds.  Helps to estimate duration from frame
+  // counts.
+  virtual float FrameShiftInSeconds() const = 0;
 
   /// Returns the total number of frames, since the start of the utterance, that
   /// are now available.  In an online-decoding context, this will likely
@@ -64,10 +68,6 @@ class OnlineFeatureInterface {
     return torch::cat(features, /*dim*/ 0);
   }
 
-  // Returns frame shift in seconds.  Helps to estimate duration from frame
-  // counts.
-  virtual float FrameShiftInSeconds() const = 0;
-
   /// This would be called from the application, when you get more wave data.
   /// Note: the sampling_rate is typically only provided so the code can assert
   /// that it matches the sampling rate expected in the options.

kaldifeat/csrc/online-feature.cc

@@ -6,6 +6,7 @@
 
 #include "kaldifeat/csrc/online-feature.h"
 
+#include "kaldifeat/csrc/feature-window.h"
 #include "kaldifeat/csrc/log.h"
 
 namespace kaldifeat {
@@ -40,4 +41,97 @@ int32_t RecyclingVector::Size() const {
   return first_available_index_ + static_cast<int32_t>(items_.size());
 }
 
+template <class C>
+OnlineGenericBaseFeature<C>::OnlineGenericBaseFeature(
+    const typename C::Options &opts)
+    : computer_(opts),
+      window_function_(opts.frame_opts, opts.device),
+      features_(opts.frame_opts.max_feature_vectors),
+      input_finished_(false),
+      waveform_offset_(0) {
+  // Casting to uint32_t, an unsigned type, means that -1 would be treated
+  // as `very large`.
+  KALDIFEAT_ASSERT(static_cast<uint32_t>(opts.frame_opts.max_feature_vectors) >
+                   200);
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::AcceptWaveform(
+    float sampling_rate, const torch::Tensor &original_waveform) {
+  if (original_waveform.numel() == 0) return;  // Nothing to do.
+
+  KALDIFEAT_ASSERT(original_waveform.dim() == 1);
+
+  if (input_finished_)
+    KALDIFEAT_ERR << "AcceptWaveform called after InputFinished() was called.";
+
+  if (waveform_remainder_.numel() == 0) {
+    waveform_remainder_ = original_waveform;
+  } else {
+    waveform_remainder_ =
+        torch::cat({waveform_remainder_, original_waveform}, /*dim*/ 0);
+  }
+
+  ComputeFeatures();
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::InputFinished() {
+  input_finished_ = true;
+  ComputeFeatures();
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::ComputeFeatures() {
+  const FrameExtractionOptions &frame_opts = computer_.GetFrameOptions();
+
+  int64_t num_samples_total = waveform_offset_ + waveform_remainder_.numel();
+  int32_t num_frames_old = features_.Size();
+  int32_t num_frames_new =
+      NumFrames(num_samples_total, frame_opts, input_finished_);
+
+  KALDIFEAT_ASSERT(num_frames_new >= num_frames_old);
+
+  // note: this online feature-extraction code does not support VTLN.
+  float vtln_warp = 1.0;
+
+  for (int32_t frame = num_frames_old; frame < num_frames_new; ++frame) {
+    torch::Tensor window =
+        ExtractWindow(waveform_offset_, waveform_remainder_, frame, frame_opts);
+
+    // TODO(fangjun): We can compute all feature frames at once
+    torch::Tensor this_feature =
+        computer_.ComputeFeatures(window.unsqueeze(0), vtln_warp);
+    features_.PushBack(this_feature);
+  }
+
+  // OK, we will now discard any portion of the signal that will not be
+  // necessary to compute frames in the future.
+  int64_t first_sample_of_next_frame =
+      FirstSampleOfFrame(num_frames_new, frame_opts);
+  int32_t samples_to_discard = first_sample_of_next_frame - waveform_offset_;
+  if (samples_to_discard > 0) {
+    // discard the leftmost part of the waveform that we no longer need.
+    int32_t new_num_samples = waveform_remainder_.numel() - samples_to_discard;
+    if (new_num_samples <= 0) {
+      // odd, but we'll try to handle it.
+      waveform_offset_ += waveform_remainder_.numel();
+      waveform_remainder_.resize_({0});
+    } else {
+      using torch::indexing::None;
+      using torch::indexing::Slice;
+
+      waveform_remainder_ =
+          waveform_remainder_.index({Slice(samples_to_discard, None)});
+
+      waveform_offset_ += samples_to_discard;
+    }
+  }
+}
+
+// instantiate the templates defined here for MFCC, PLP and filterbank classes.
+template class OnlineGenericBaseFeature<Mfcc>;
+template class OnlineGenericBaseFeature<Plp>;
+template class OnlineGenericBaseFeature<Fbank>;
+
 }  // namespace kaldifeat

kaldifeat/csrc/online-feature.h

@@ -9,6 +9,10 @@
 
 #include <deque>
 
+#include "kaldifeat/csrc/feature-fbank.h"
+#include "kaldifeat/csrc/feature-mfcc.h"
+#include "kaldifeat/csrc/feature-plp.h"
+#include "kaldifeat/csrc/feature-window.h"
 #include "kaldifeat/csrc/online-feature-itf.h"
 
 namespace kaldifeat {
@@ -44,6 +48,80 @@ class RecyclingVector {
   int32_t first_available_index_;
 };
 
+/// This is a templated class for online feature extraction;
+/// it's templated on a class like MfccComputer or PlpComputer
+/// that does the basic feature extraction.
+template <class C>
+class OnlineGenericBaseFeature : public OnlineFeatureInterface {
+ public:
+  // Constructor from options class
+  explicit OnlineGenericBaseFeature(const typename C::Options &opts);
+
+  int32_t Dim() const override { return computer_.Dim(); }
+
+  float FrameShiftInSeconds() const override {
+    return computer_.GetFrameOptions().frame_shift_ms / 1000.0f;
+  }
+
+  int32_t NumFramesReady() const override { return features_.Size(); }
+
+  // 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 override {
+    return input_finished_ && frame == NumFramesReady() - 1;
+  }
+
+  torch::Tensor GetFrame(int32_t frame) override { return features_.At(frame); }
+
+  // This would be called from the application, when you get
+  // more wave data.  Note: the sampling_rate is only provided so
+  // the code can assert that it matches the sampling rate
+  // expected in the options.
+  void AcceptWaveform(float sampling_rate,
+                      const torch::Tensor &waveform) override;
+
+  // 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() override;
+
+ private:
+  // This function computes any additional feature frames that it is possible to
+  // compute from 'waveform_remainder_', which at this point may contain more
+  // than just a remainder-sized quantity (because AcceptWaveform() appends to
+  // waveform_remainder_ before calling this function).  It adds these feature
+  // frames to features_, and shifts off any now-unneeded samples of input from
+  // waveform_remainder_ while incrementing waveform_offset_ by the same amount.
+  void ComputeFeatures();
+
+  C computer_;  // class that does the MFCC or PLP or filterbank computation
+
+  FeatureWindowFunction window_function_;
+
+  // features_ is the Mfcc or Plp or Fbank features that we have already
+  // computed.
+
+  RecyclingVector features_;
+
+  // True if the user has called "InputFinished()"
+  bool input_finished_;
+
+  // waveform_offset_ is the number of samples of waveform that we have
+  // already discarded, i.e. that were prior to 'waveform_remainder_'.
+  int64_t waveform_offset_;
+
+  // waveform_remainder_ is a short piece of waveform that we may need to keep
+  // after extracting all the whole frames we can (whatever length of feature
+  // will be required for the next phase of computation).
+  // It is a 1-D tensor
+  torch::Tensor waveform_remainder_;
+};
+
+using OnlineMfcc = OnlineGenericBaseFeature<Mfcc>;
+using OnlinePlp = OnlineGenericBaseFeature<Plp>;
+using OnlineFbank = OnlineGenericBaseFeature<Fbank>;
+
 }  // namespace kaldifeat
 
 #endif  // KALDIFEAT_CSRC_ONLINE_FEATURE_H_

kaldifeat/python/csrc/feature-window.cc

@@ -25,6 +25,7 @@ static void PybindFrameExtractionOptions(py::module &m) {
       .def_readwrite("round_to_power_of_two", &PyClass::round_to_power_of_two)
       .def_readwrite("blackman_coeff", &PyClass::blackman_coeff)
       .def_readwrite("snip_edges", &PyClass::snip_edges)
+      .def_readwrite("max_feature_vectors", &PyClass::max_feature_vectors)
       .def("as_dict",
            [](const PyClass &self) -> py::dict { return AsDict(self); })
       .def_static("from_dict",
@@ -35,8 +36,6 @@ static void PybindFrameExtractionOptions(py::module &m) {
       .def_readwrite("allow_downsample",
                      &PyClass::allow_downsample)
       .def_readwrite("allow_upsample", &PyClass::allow_upsample)
-      .def_readwrite("max_feature_vectors",
-                     &PyClass::max_feature_vectors)
 #endif
       .def("__str__",
            [](const PyClass &self) -> std::string { return self.ToString(); })

kaldifeat/python/csrc/utils.cc

@@ -30,6 +30,7 @@ FrameExtractionOptions FrameExtractionOptionsFromDict(py::dict dict) {
   FROM_DICT(bool_, round_to_power_of_two);
   FROM_DICT(float_, blackman_coeff);
   FROM_DICT(bool_, snip_edges);
+  FROM_DICT(int_, max_feature_vectors);
 
   return opts;
 }
@@ -47,6 +48,7 @@ py::dict AsDict(const FrameExtractionOptions &opts) {
   AS_DICT(round_to_power_of_two);
   AS_DICT(blackman_coeff);
   AS_DICT(snip_edges);
+  AS_DICT(max_feature_vectors);
 
   return dict;
 }