make streaming pipeline work

speechx/examples/feat/streaming_feat_main.cc

@@ -1,17 +1,34 @@
 // todo refactor, repalce with gtest
 
+#include "base/log.h"
+#include "base/flags.h"
 #include "frontend/linear_spectrogram.h"
 #include "frontend/normalizer.h"
 #include "frontend/feature_extractor_interface.h"
+#include "frontend/raw_audio.h"
 #include "kaldi/util/table-types.h"
-#include "base/log.h"
-#include "base/flags.h"
 #include "kaldi/feat/wave-reader.h"
 #include "kaldi/util/kaldi-io.h"
 
 DEFINE_string(wav_rspecifier, "", "test wav path");
 DEFINE_string(feature_wspecifier, "", "test wav ark");
-DEFINE_string(cmvn_path, "./cmvn.ark", "test wav ark");
+DEFINE_string(feature_check_wspecifier, "", "test wav ark");
+DEFINE_string(cmvn_write_path, "./cmvn.ark", "test wav ark");
+
+
+std::vector<float> mean_{-13730251.531853663, -12982852.199316509, -13673844.299583456, -13089406.559646806, -12673095.524938712, -12823859.223276224, -13590267.158903603, -14257618.467152044, -14374605.116185192, -14490009.21822485, -14849827.158924166, -15354435.470563512, -15834149.206532761, -16172971.985514281, -16348740.496746974, -16423536.699409386, -16556246.263649225, -16744088.772748645, -16916184.08510357, -17054034.840031497, -17165612.509455364, -17255955.470915023, -17322572.527648456, -17408943.862033736, -17521554.799865916, -17620623.254924215, -17699792.395918526, -17723364.411134344, -17741483.4433254, -17747426.888704527, -17733315.928209435, -17748780.160905756, -17808336.883775543, -17895918.671983004, -18009812.59173023, -18098188.66548325, -18195798.958462656, -18293617.62980999, -18397432.92077201, -18505834.787318766, -18585451.8100908, -18652438.235649142, -18700960.306275308, -18734944.58792185, -18737426.313365128, -18735347.165987637, -18738813.444170244, -18737086.848890636, -18731576.2474336, -18717405.44095871, -18703089.25545657, -18691014.546456724, -18692460.568905357, -18702119.628629155, -18727710.621126678, -18761582.72034647, -18806745.835547544, -18850674.8692112, -18884431.510951452, -18919999.992506847, -18939303.799078144, -18952946.273760635, -18980289.22996379, -19011610.17803294, -19040948.61805145, -19061021.429847397, -19112055.53768819, -19149667.414264943, -19201127.05091321, -19270250.82564605, -19334606.883057203, -19390513.336589377, -19444176.259208687, -19502755.000038862, -19544333.014549147, -19612668.183176614, -19681902.19006569, -19771969.951249883, -19873329.723376893, -19996752.59235844, -20110031.131400537, -20231658.612529557, -20319378.894054495, -20378534.45718066, -20413332.089584175, -20438147.844177883, -20443710.248040095, -20465457.02238927, -20488610.969337028, -20516295.16424432, -20541423.795738827, -20553192.874953747, -20573605.50701977, -20577871.61936797, -20571807.008916274, -20556242.38912231, -20542199.30819195, -20521239.063551214, -20519150.80004532, -20527204.80248933, -20536933.769257784, -20543470.522332076, -20549700.089992985, -20551525.24958494, -20554873.406493705, -20564277.65794227, -20572211.740052115, -20574305.69550465, -20575494.450104576, -20567092.577932164, -20549302.929608088, -20545445.11878376, -20546625.326603737, -20549190.03499401, -20554824.947828256, -20568341.378989458, -20577582.331383612, -20577980.519402675, -20566603.03458152, -20560131.592262644, -20552166.469060015, -20549063.06763577, -20544490.562339947, -20539817.82346569, -20528747.715731595, -20518026.24576161, -20510977.844974525, -20506874.36087992, -20506731.11977665, -20510482.133420516, -20507760.92101862, -20494644.834457114, -20480107.89304893, -20461312.091867123, -20442941.75080173, -20426123.02834838, -20424607.675283, -20426810.369107097, -20434024.50097819, -20437404.75544205, -20447688.63916367, -20460893.335563846, -20482922.735127095, -20503610.119434915, -20527062.76448319, -20557830.035128627, -20593274.72068722, -20632528.452965066, -20673637.471334763, -20733106.97143075, -20842921.0447562, -21054357.83621519, -21416569.534189366, -21978460.272811692, -22753170.052172784, -23671344.10563395, -24613499.293358143, -25406477.12230188, -25884377.82156489, -26049040.62791664, -26996879.104431007};
+std::vector<float> variance_{213747175.10846674, 188395815.34302503, 212706429.10966414, 199109025.81461075, 189235901.23864496, 194901336.53253657, 217481594.29306737, 238689869.12327808, 243977501.24115244, 248479623.6431067, 259766741.47116545, 275516766.7790273, 291271202.3691234, 302693239.8220509, 308627358.3997694, 311143911.38788426, 315446105.07731867, 321705430.9341829, 327458907.4659941, 332245072.43223983, 336251717.5935284, 339694069.7639722, 342188204.4322228, 345587110.31313115, 349903086.2875232, 353660214.20643026, 356700344.5270885, 357665362.3529641, 358493352.05658793, 358857951.620328, 358375239.52774596, 358899733.6342954, 361051818.3511561, 364361716.05025816, 368750322.3771452, 372047800.6462831, 375655861.1349018, 379358519.1980013, 383327605.3935181, 387458599.282341, 390434692.3406868, 392994486.35057056, 394874418.04603153, 396230525.79763395, 396365592.0414835, 396334819.8242737, 396488353.19250053, 396438877.00744957, 396197980.4459586, 395590921.6672991, 395001107.62072515, 394528291.7318225, 394593110.424006, 395018405.59353715, 396110577.5415993, 397506704.0371068, 399400197.4657644, 401243568.2468382, 402687134.7805103, 404136047.2872507, 404883170.001883, 405522253.219517, 406660365.3626476, 407919346.0991902, 409045348.5384909, 409759588.7889818, 411974821.8564483, 413489718.78201455, 415535392.56684107, 418466481.97674364, 421104678.35678065, 423405392.5200779, 425550570.40798235, 427929423.9579701, 429585274.253478, 432368493.55181056, 435193587.13513297, 438886855.20476013, 443058876.8633751, 448181232.5093362, 452883835.6332396, 458056721.77926534, 461816531.22735566, 464363620.1970998, 465886343.5057493, 466928872.0651, 467180536.42647296, 468111848.70714295, 469138695.3071312, 470378429.6930793, 471517958.7132626, 472109050.4262365, 473087417.0177867, 473381322.04648733, 473220195.85483915, 472666071.8998819, 472124669.87879956, 471298571.411737, 471251033.2902761, 471672676.43128747, 472177147.2193172, 472572361.7711908, 472968783.7751127, 473156295.4164052, 473398034.82676554, 473897703.5203811, 474328271.33112127, 474452670.98002136, 474549003.99284613, 474252887.13567275, 473557462.909069, 473483385.85193115, 473609738.04855174, 473746944.82085115, 474016729.91696435, 474617321.94138587, 475045097.237122, 475125402.586558, 474664112.9824912, 474426247.5800283, 474104075.42796475, 473978219.7273978, 473773171.7798875, 473578534.69508696, 473102924.16904145, 472651240.5232615, 472374383.1810912, 472209479.6956096, 472202298.8921673, 472370090.76781124, 472220933.99374026, 471625467.37106377, 470994646.51883453, 470182428.9637543, 469348211.5939578, 468570387.4467277, 468540442.7225135, 468672018.90414184, 468994346.9533251, 469138757.58201426, 469553915.95710236, 470134523.38582784, 471082421.62055486, 471962316.51804745, 472939745.1708408, 474250621.5944825, 475773933.43199486, 477465399.71087736, 479218782.61382693, 481752299.7930922, 486608947.8984568, 496119403.2067917, 512730085.5704984, 539048915.2641417, 576285298.3548826, 621610270.2240586, 669308196.4436442, 710656993.5957186, 736344437.3725077, 745481288.0241544, 801121432.9925804};
+int count_ = 912592;
+
+void WriteMatrix() {
+  kaldi::Matrix<double> cmvn_stats(2, mean_.size()+ 1); 
+  for (size_t idx = 0; idx < mean_.size(); ++idx) {
+    cmvn_stats(0, idx) = mean_[idx];
+    cmvn_stats(1, idx) = variance_[idx];
+  }
+  cmvn_stats(0, mean_.size()) = count_;
+  kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, true);
+}
 
 int main(int argc, char* argv[]) {
   gflags::ParseCommandLineFlags(&argc, &argv, false);
@@ -19,33 +36,69 @@ int main(int argc, char* argv[]) {
   
   kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(FLAGS_wav_rspecifier);
   kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
+  WriteMatrix();
 
   // test feature linear_spectorgram: wave --> decibel_normalizer --> hanning window -->linear_spectrogram --> cmvn
-  // --> feature_cache
   int32 num_done = 0, num_err = 0;
+  std::unique_ptr<ppspeech::FeatureExtractorInterface> data_source(new ppspeech::RawDataSource());
+
   ppspeech::LinearSpectrogramOptions opt;
   opt.frame_opts.frame_length_ms = 20;
   opt.frame_opts.frame_shift_ms = 10;
   ppspeech::DecibelNormalizerOptions db_norm_opt;
   std::unique_ptr<ppspeech::FeatureExtractorInterface> base_feature_extractor(
-      new ppspeech::DecibelNormalizer(db_norm_opt));
+    new ppspeech::DecibelNormalizer(db_norm_opt, std::move(data_source)));
 
-  std::shared_ptr<ppspeech::FeatureExtractorInterface> linear_spectrogram(
-      new ppspeech::LinearSpectrogram(opt, base_feature_extractor));
+  std::unique_ptr<ppspeech::FeatureExtractorInterface> linear_spectrogram(
+    new ppspeech::LinearSpectrogram(opt, std::move(base_feature_extractor)));
 
-  std::shared_ptr<ppspeech::FeatureExtractorInterface> cmvn(
-      new ppspeech::CMVN(FLAGS_cmvn_path, linear_spectrogram);
-  ppspeech::FeatureCache(cmvn);
+  ppspeech::CMVN cmvn(FLAGS_cmvn_write_path, std::move(linear_spectrogram));
 
   float streaming_chunk = 0.36;
   int sample_rate = 16000;
   int chunk_sample_size = streaming_chunk * sample_rate;
-  // thread 1 feed feature
 
   for (; !wav_reader.Done(); wav_reader.Next()) {
     std::string utt = wav_reader.Key();
     const kaldi::WaveData &wave_data = wav_reader.Value();
 
+    int32 this_channel = 0;
+    kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(), this_channel);
+    int tot_samples = waveform.Dim(); 
+    int sample_offset = 0;
+    std::vector<kaldi::Vector<BaseFloat>> feats;
+    int feature_rows = 0;
+    while (sample_offset < tot_samples) {
+      int cur_chunk_size = std::min(chunk_sample_size, tot_samples - sample_offset);
+      kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
+      for (int i = 0; i < cur_chunk_size; ++i) {
+        wav_chunk(i) = waveform(sample_offset + i); 
+      }
+      kaldi::Vector<BaseFloat> features;
+      cmvn.AcceptWaveform(wav_chunk);
+      cmvn.Read(&features);
+
+      std::cout << wav_chunk(0) << std::endl;
+      std::cout << features(0) << std::endl;
+
+      feats.push_back(features);
+      sample_offset += cur_chunk_size;
+      feature_rows += features.Dim() / cmvn.Dim();
+    }
+
+    int cur_idx = 0;
+    kaldi::Matrix<kaldi::BaseFloat> features(feature_rows, cmvn.Dim()); 
+    for (auto feat : feats) {
+      int num_rows = feat.Dim() / cmvn.Dim();
+      for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
+          for (int col_idx = 0; col_idx < cmvn.Dim(); ++col_idx) {
+              features(cur_idx, col_idx) = feat(row_idx*cmvn.Dim() + col_idx);
+          }
+          ++cur_idx;
+      }
+    }
+    feat_writer.Write(utt, features);
+
     if (num_done % 50 == 0 && num_done != 0)
     KALDI_VLOG(2) << "Processed " << num_done << " utterances";
     num_done++;

speechx/speechx/base/common.h

@@ -15,22 +15,23 @@
 #pragma once
 
 #include <deque>
-#include <fstream>
 #include <iostream>
 #include <istream>
+#include <fstream>
 #include <map>
 #include <memory>
-#include <mutex>
 #include <ostream>
 #include <set>
 #include <sstream>
 #include <stack>
 #include <string>
+#include <vector>
 #include <unordered_map>
 #include <unordered_set>
-#include <vector>
+#include <mutex>
+#include <condition_variable>
 
-#include "base/basic_types.h"
-#include "base/flags.h"
 #include "base/log.h"
+#include "base/flags.h"
+#include "base/basic_types.h"
 #include "base/macros.h"

speechx/speechx/frontend/CMakeLists.txt

@@ -2,7 +2,8 @@ project(frontend)
 
 add_library(frontend STATIC
   normalizer.cc
-  linear_spectrogram.cc  
+  linear_spectrogram.cc
+  raw_audio.cc
 )
 
 target_link_libraries(frontend PUBLIC kaldi-matrix)

speechx/speechx/frontend/feature_extractor_interface.h

@@ -21,9 +21,8 @@ namespace ppspeech {
 
 class FeatureExtractorInterface {
   public:
-    virtual void AcceptWaveform(
-        const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
-    virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat) = 0;
+    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
+    virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat) = 0;
     virtual size_t Dim() const = 0;
 };
 

speechx/speechx/frontend/linear_spectrogram.cc

@@ -25,153 +25,146 @@ using kaldi::VectorBase;
 using kaldi::Matrix;
 using std::vector;
 
-// todo remove later
+//todo remove later
 void CopyVector2StdVector_(const VectorBase<BaseFloat>& input,
-                           vector<BaseFloat>* output) {
-    if (input.Dim() == 0) return;
-    output->resize(input.Dim());
-    for (size_t idx = 0; idx < input.Dim(); ++idx) {
-        (*output)[idx] = input(idx);
-    }
+                          vector<BaseFloat>* output) {
+  if (input.Dim() == 0) return;
+  output->resize(input.Dim());
+  for (size_t idx = 0; idx < input.Dim(); ++idx) {
+    (*output)[idx] = input(idx);
+  }
 }
 
 void CopyStdVector2Vector_(const vector<BaseFloat>& input,
-                           Vector<BaseFloat>* output) {
-    if (input.empty()) return;
-    output->Resize(input.size());
-    for (size_t idx = 0; idx < input.size(); ++idx) {
-        (*output)(idx) = input[idx];
-    }
+                          Vector<BaseFloat>* output) {
+  if (input.empty()) return;
+  output->Resize(input.size());
+  for (size_t idx = 0; idx < input.size(); ++idx) {
+    (*output)(idx) = input[idx];
+  }
 }
 
 LinearSpectrogram::LinearSpectrogram(
     const LinearSpectrogramOptions& opts,
     std::unique_ptr<FeatureExtractorInterface> base_extractor) {
-    opts_ = opts;
-    base_extractor_ = std::move(base_extractor);
-    int32 window_size = opts.frame_opts.WindowSize();
-    int32 window_shift = opts.frame_opts.WindowShift();
-    fft_points_ = window_size;
-    hanning_window_.resize(window_size);
-
-    double a = M_2PI / (window_size - 1);
-    hanning_window_energy_ = 0;
-    for (int i = 0; i < window_size; ++i) {
-        hanning_window_[i] = 0.5 - 0.5 * cos(a * i);
-        hanning_window_energy_ += hanning_window_[i] * hanning_window_[i];
-    }
-
-    dim_ = fft_points_ / 2 + 1;  // the dimension is Fs/2 Hz
+  opts_ = opts;
+  base_extractor_ = std::move(base_extractor);
+  int32 window_size = opts.frame_opts.WindowSize();
+  int32 window_shift = opts.frame_opts.WindowShift();
+  fft_points_ = window_size;
+  chunk_sample_size_ = 
+      static_cast<int32>(opts.streaming_chunk * opts.frame_opts.samp_freq);
+  hanning_window_.resize(window_size);
+
+  double a = M_2PI / (window_size - 1);
+  hanning_window_energy_ = 0;
+  for (int i = 0; i < window_size; ++i) {
+    hanning_window_[i] = 0.5 - 0.5 * cos(a * i);
+    hanning_window_energy_ += hanning_window_[i] * hanning_window_[i];
+  }
+
+  dim_ = fft_points_ / 2 + 1;  // the dimension is Fs/2 Hz
 }
 
 void LinearSpectrogram::AcceptWaveform(const VectorBase<BaseFloat>& input) {
     base_extractor_->AcceptWaveform(input);
 }
 
+void LinearSpectrogram::Read(Vector<BaseFloat>* feat) {
+  Vector<BaseFloat> input_feats(chunk_sample_size_);
+  base_extractor_->Read(&input_feats);
+  vector<BaseFloat> input_feats_vec(input_feats.Dim());
+  CopyVector2StdVector_(input_feats, &input_feats_vec);
+  //for (int idx = 0; idx < input_feats.Dim(); ++idx) {
+  //  input_feats_vec[idx] = input_feats(idx); 
+  //}
+  vector<vector<BaseFloat>> result;
+  Compute(input_feats_vec, result);
+  int32 feat_size = 0;
+  if (result.size() != 0) {
+    feat_size = result.size() * result[0].size();
+  }
+  feat->Resize(feat_size);
+  for (size_t idx = 0; idx < feat_size; ++idx) {
+    (*feat)(idx) = result[idx / dim_][idx % dim_];
+  }
+  return;
+}
+
 void LinearSpectrogram::Hanning(vector<float>* data) const {
-    CHECK_GE(data->size(), hanning_window_.size());
+  CHECK_GE(data->size(), hanning_window_.size());
 
-    for (size_t i = 0; i < hanning_window_.size(); ++i) {
-        data->at(i) *= hanning_window_[i];
-    }
+  for (size_t i = 0; i < hanning_window_.size(); ++i) {
+      data->at(i) *= hanning_window_[i];
+  }
 }
 
 bool LinearSpectrogram::NumpyFft(vector<BaseFloat>* v,
                                  vector<BaseFloat>* real,
                                  vector<BaseFloat>* img) const {
-    Vector<BaseFloat> v_tmp;
-    CopyStdVector2Vector_(*v, &v_tmp);
-    RealFft(&v_tmp, true);
-    CopyVector2StdVector_(v_tmp, v);
-    real->push_back(v->at(0));
-    img->push_back(0);
-    for (int i = 1; i < v->size() / 2; i++) {
-        real->push_back(v->at(2 * i));
-        img->push_back(v->at(2 * i + 1));
-    }
-    real->push_back(v->at(1));
-    img->push_back(0);
-
-    return true;
-}
-
-// todo remove later
-void LinearSpectrogram::ReadFeats(Matrix<BaseFloat>* feats) {
-    Vector<BaseFloat> tmp;
-    waveform_.Resize(base_extractor_->Dim());
-    Compute(tmp, &waveform_);
-    vector<vector<BaseFloat>> result;
-    vector<BaseFloat> feats_vec;
-    CopyVector2StdVector_(waveform_, &feats_vec);
-    Compute(feats_vec, result);
-    feats->Resize(result.size(), result[0].size());
-    for (int row_idx = 0; row_idx < result.size(); ++row_idx) {
-        for (int col_idx = 0; col_idx < result[0].size(); ++col_idx) {
-            (*feats)(row_idx, col_idx) = result[row_idx][col_idx];
-        }
-    }
-    waveform_.Resize(0);
-}
-
-void LinearSpectrogram::Read(VectorBase<BaseFloat>* feat) {
-    // todo
-    return;
-}
-
-// only for test, remove later
-// todo: compute the feature frame by frame.
-void LinearSpectrogram::Compute(const VectorBase<kaldi::BaseFloat>& input,
-                                VectorBase<kaldi::BaseFloat>* feature) {
-    base_extractor_->Read(feature);
+  Vector<BaseFloat> v_tmp;
+  CopyStdVector2Vector_(*v, &v_tmp);
+  RealFft(&v_tmp, true);
+  CopyVector2StdVector_(v_tmp, v);
+  real->push_back(v->at(0));
+  img->push_back(0);
+  for (int i = 1; i < v->size() / 2; i++) {
+    real->push_back(v->at(2 * i));
+    img->push_back(v->at(2 * i + 1));
+  }
+  real->push_back(v->at(1));
+  img->push_back(0);
+
+  return true;
 }
 
 // Compute spectrogram feat, only for test, remove later
 // todo: refactor later (SmileGoat)
 bool LinearSpectrogram::Compute(const vector<float>& wave,
                                 vector<vector<float>>& feat) {
-    int num_samples = wave.size();
-    const int& frame_length = opts_.frame_opts.WindowSize();
-    const int& sample_rate = opts_.frame_opts.samp_freq;
-    const int& frame_shift = opts_.frame_opts.WindowShift();
-    const int& fft_points = fft_points_;
-    const float scale = hanning_window_energy_ * sample_rate;
-
-    if (num_samples < frame_length) {
-        return true;
-    }
-
-    int num_frames = 1 + ((num_samples - frame_length) / frame_shift);
-    feat.resize(num_frames);
-    vector<float> fft_real((fft_points_ / 2 + 1), 0);
-    vector<float> fft_img((fft_points_ / 2 + 1), 0);
-    vector<float> v(frame_length, 0);
-    vector<float> power((fft_points / 2 + 1));
-
-    for (int i = 0; i < num_frames; ++i) {
-        vector<float> data(wave.data() + i * frame_shift,
-                           wave.data() + i * frame_shift + frame_length);
-        Hanning(&data);
-        fft_img.clear();
-        fft_real.clear();
-        v.assign(data.begin(), data.end());
-        NumpyFft(&v, &fft_real, &fft_img);
-
-        feat[i].resize(fft_points / 2 + 1);  // the last dimension is Fs/2 Hz
-        for (int j = 0; j < (fft_points / 2 + 1); ++j) {
-            power[j] = fft_real[j] * fft_real[j] + fft_img[j] * fft_img[j];
-            feat[i][j] = power[j];
-
-            if (j == 0 || j == feat[0].size() - 1) {
-                feat[i][j] /= scale;
-            } else {
-                feat[i][j] *= (2.0 / scale);
-            }
-
-            // log added eps=1e-14
-            feat[i][j] = std::log(feat[i][j] + 1e-14);
-        }
+  int num_samples = wave.size();
+  const int& frame_length = opts_.frame_opts.WindowSize();
+  const int& sample_rate = opts_.frame_opts.samp_freq;
+  const int& frame_shift = opts_.frame_opts.WindowShift();
+  const int& fft_points = fft_points_;
+  const float scale = hanning_window_energy_ * sample_rate;
+
+  if (num_samples < frame_length) {
+          return true;
+  }
+
+  int num_frames = 1 + ((num_samples - frame_length) / frame_shift);
+  feat.resize(num_frames);
+  vector<float> fft_real((fft_points_ / 2 + 1), 0);
+  vector<float> fft_img((fft_points_ / 2 + 1), 0);
+  vector<float> v(frame_length, 0);
+  vector<float> power((fft_points / 2 + 1));
+
+  for (int i = 0; i < num_frames; ++i) {
+    vector<float> data(wave.data() + i * frame_shift,
+                       wave.data() + i * frame_shift + frame_length);
+    Hanning(&data);
+    fft_img.clear();
+    fft_real.clear();
+    v.assign(data.begin(), data.end());
+    NumpyFft(&v, &fft_real, &fft_img);
+
+    feat[i].resize(fft_points / 2 + 1);  // the last dimension is Fs/2 Hz
+    for (int j = 0; j < (fft_points / 2 + 1); ++j) {
+      power[j] = fft_real[j] * fft_real[j] + fft_img[j] * fft_img[j];
+      feat[i][j] = power[j];
+
+      if (j == 0 || j == feat[0].size() - 1) {
+          feat[i][j] /= scale;
+      } else {
+          feat[i][j] *= (2.0 / scale);
+      }
+
+      // log added eps=1e-14
+      feat[i][j] = std::log(feat[i][j] + 1e-14);
     }
-    return true;
+  }
+  return true;
 }
 
 }  // namespace ppspeech

speechx/speechx/frontend/linear_spectrogram.h

@@ -1,45 +1,35 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 
 #pragma once
 
-#include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
 #include "kaldi/feat/feature-window.h"
+#include "base/common.h"
 
 namespace ppspeech {
 
 struct LinearSpectrogramOptions {
     kaldi::FrameExtractionOptions frame_opts;
-    LinearSpectrogramOptions() : frame_opts() {}
-
-    void Register(kaldi::OptionsItf* opts) { frame_opts.Register(opts); }
+    kaldi::BaseFloat streaming_chunk;
+    LinearSpectrogramOptions():
+        streaming_chunk(0.36),
+        frame_opts() {}
+
+    void Register(kaldi::OptionsItf* opts) {
+        opts->Register("streaming-chunk", &streaming_chunk, "streaming chunk size");
+        frame_opts.Register(opts);
+    }
 };
 
 class LinearSpectrogram : public FeatureExtractorInterface {
   public:
-    explicit LinearSpectrogram(
-        const LinearSpectrogramOptions& opts,
-        std::unique_ptr<FeatureExtractorInterface> base_extractor);
-    virtual void AcceptWaveform(
-        const kaldi::VectorBase<kaldi::BaseFloat>& input);
-    virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
+    explicit LinearSpectrogram(const LinearSpectrogramOptions& opts,
+                               std::unique_ptr<FeatureExtractorInterface> base_extractor);
+    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
     void ReadFeats(kaldi::Matrix<kaldi::BaseFloat>* feats);
 
-  private:
+  private: 
     void Hanning(std::vector<kaldi::BaseFloat>* data) const;
     bool Compute(const std::vector<kaldi::BaseFloat>& wave,
                  std::vector<std::vector<kaldi::BaseFloat>>& feat);
@@ -54,8 +44,9 @@ class LinearSpectrogram : public FeatureExtractorInterface {
     std::vector<kaldi::BaseFloat> hanning_window_;
     kaldi::BaseFloat hanning_window_energy_;
     LinearSpectrogramOptions opts_;
-    kaldi::Vector<kaldi::BaseFloat> waveform_;  // remove later, todo(SmileGoat)
+    kaldi::Vector<kaldi::BaseFloat> waveform_; // remove later, todo(SmileGoat)
     std::unique_ptr<FeatureExtractorInterface> base_extractor_;
+    int chunk_sample_size_;
     DISALLOW_COPY_AND_ASSIGN(LinearSpectrogram);
 };
 

speechx/speechx/frontend/normalizer.cc

@@ -1,17 +1,3 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 
 #include "frontend/normalizer.h"
 #include "kaldi/feat/cmvn.h"
@@ -24,175 +10,176 @@ using kaldi::VectorBase;
 using kaldi::BaseFloat;
 using std::vector;
 using kaldi::SubVector;
+using std::unique_ptr;
 
-DecibelNormalizer::DecibelNormalizer(const DecibelNormalizerOptions& opts) {
-    opts_ = opts;
-    dim_ = 0;
+DecibelNormalizer::DecibelNormalizer(const DecibelNormalizerOptions& opts,
+    std::unique_ptr<FeatureExtractorInterface> base_extractor) {
+  base_extractor_ = std::move(base_extractor);
+  opts_ = opts;
+  dim_ = 0;
 }
-
-void DecibelNormalizer::AcceptWaveform(
-    const kaldi::VectorBase<BaseFloat>& input) {
-    dim_ = input.Dim();
-    waveform_.Resize(input.Dim());
-    waveform_.CopyFromVec(input);
+                                    
+void DecibelNormalizer::AcceptWaveform(const kaldi::VectorBase<BaseFloat>& input) {
+  //dim_ = input.Dim();
+  //waveform_.Resize(input.Dim());
+  //waveform_.CopyFromVec(input);
+  base_extractor_->AcceptWaveform(input);
 }
 
-void DecibelNormalizer::Read(kaldi::VectorBase<BaseFloat>* feat) {
-    if (waveform_.Dim() == 0) return;
-    Compute(waveform_, feat);
+void DecibelNormalizer::Read(kaldi::Vector<BaseFloat>* feat) {
+  // if (waveform_.Dim() == 0) return;
+  base_extractor_->Read(feat);
+  Compute(feat);
 }
 
-// todo remove later
+//todo remove later
 void CopyVector2StdVector(const kaldi::VectorBase<BaseFloat>& input,
                           vector<BaseFloat>* output) {
-    if (input.Dim() == 0) return;
-    output->resize(input.Dim());
-    for (size_t idx = 0; idx < input.Dim(); ++idx) {
-        (*output)[idx] = input(idx);
-    }
+  if (input.Dim() == 0) return;
+  output->resize(input.Dim());
+  for (size_t idx = 0; idx < input.Dim(); ++idx) {
+    (*output)[idx] = input(idx);
+  }
 }
 
 void CopyStdVector2Vector(const vector<BaseFloat>& input,
                           VectorBase<BaseFloat>* output) {
-    if (input.empty()) return;
-    assert(input.size() == output->Dim());
-    for (size_t idx = 0; idx < input.size(); ++idx) {
-        (*output)(idx) = input[idx];
-    }
+  if (input.empty()) return;
+  assert(input.size() == output->Dim());
+  for (size_t idx = 0; idx < input.size(); ++idx) {
+    (*output)(idx) = input[idx];
+  }
 }
 
-bool DecibelNormalizer::Compute(const VectorBase<BaseFloat>& input,
-                                VectorBase<BaseFloat>* feat) const {
-    // calculate db rms
-    BaseFloat rms_db = 0.0;
-    BaseFloat mean_square = 0.0;
-    BaseFloat gain = 0.0;
-    BaseFloat wave_float_normlization = 1.0f / (std::pow(2, 16 - 1));
-
-    vector<BaseFloat> samples;
-    samples.resize(input.Dim());
-    for (int32 i = 0; i < samples.size(); ++i) {
-        samples[i] = input(i);
-    }
-
-    // square
-    for (auto& d : samples) {
-        if (opts_.convert_int_float) {
-            d = d * wave_float_normlization;
-        }
-        mean_square += d * d;
+bool DecibelNormalizer::Compute(VectorBase<BaseFloat>* feat) const {
+  // calculate db rms
+  BaseFloat rms_db = 0.0;
+  BaseFloat mean_square = 0.0;
+  BaseFloat gain = 0.0;
+  BaseFloat wave_float_normlization = 1.0f / (std::pow(2, 16 - 1));
+
+  vector<BaseFloat> samples;
+  samples.resize(feat->Dim());
+  for (size_t i = 0; i < samples.size(); ++i) {
+    samples[i] = (*feat)(i);
+  }
+  
+  // square
+  for (auto &d : samples) {
+    if (opts_.convert_int_float) {
+    d = d * wave_float_normlization;
     }
-
-    // mean
-    mean_square /= samples.size();
-    rms_db = 10 * std::log10(mean_square);
-    gain = opts_.target_db - rms_db;
-
-    if (gain > opts_.max_gain_db) {
-        LOG(ERROR)
-            << "Unable to normalize segment to " << opts_.target_db << "dB,"
-            << "because the the probable gain have exceeds opts_.max_gain_db"
-            << opts_.max_gain_db << "dB.";
-        return false;
-    }
-
-    // Note that this is an in-place transformation.
-    for (auto& item : samples) {
-        // python item *= 10.0 ** (gain / 20.0)
-        item *= std::pow(10.0, gain / 20.0);
-    }
-
-    CopyStdVector2Vector(samples, feat);
-    return true;
+    mean_square += d * d;
+  }
+
+  // mean
+  mean_square /= samples.size();
+  rms_db = 10 * std::log10(mean_square);
+  gain = opts_.target_db - rms_db;
+
+  if (gain > opts_.max_gain_db) {
+    LOG(ERROR) << "Unable to normalize segment to " << opts_.target_db << "dB,"
+                << "because the the probable gain have exceeds opts_.max_gain_db" 
+                <<  opts_.max_gain_db << "dB.";
+    return false;
+  }
+
+  // Note that this is an in-place transformation.
+  for (auto &item : samples) {
+    // python item *= 10.0 ** (gain / 20.0)
+    item *= std::pow(10.0, gain / 20.0);
+  }
+  
+  CopyStdVector2Vector(samples, feat);
+  return true;
 }
 
-CMVN::CMVN(std::string cmvn_file) : var_norm_(true) {
+CMVN::CMVN(std::string cmvn_file, 
+           unique_ptr<FeatureExtractorInterface> base_extractor) 
+    : var_norm_(true) {
+    base_extractor_ = std::move(base_extractor);
     bool binary;
     kaldi::Input ki(cmvn_file, &binary);
     stats_.Read(ki.Stream(), binary);
+    dim_ = stats_.NumCols() - 1;
 }
 
 void CMVN::AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) {
+    base_extractor_->AcceptWaveform(input);
     return;
 }
 
-void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) { return; }
+void CMVN::Read(kaldi::Vector<BaseFloat>* feat) {
+    base_extractor_->Read(feat);
+    Compute(feat);
+    return;
+}
 
 // feats contain num_frames feature.
-void CMVN::ApplyCMVN(bool var_norm, VectorBase<BaseFloat>* feats) {
-    KALDI_ASSERT(feats != NULL);
-    int32 dim = stats_.NumCols() - 1;
-    if (stats_.NumRows() > 2 || stats_.NumRows() < 1 ||
-        feats->Dim() % dim != 0) {
-        KALDI_ERR << "Dim mismatch: cmvn " << stats_.NumRows() << 'x'
-                  << stats_.NumCols() << ", feats " << feats->Dim() << 'x';
+void CMVN::Compute(VectorBase<BaseFloat>* feats) const {
+  KALDI_ASSERT(feats != NULL); 
+  int32 dim = stats_.NumCols() - 1;
+  if (stats_.NumRows() > 2 || stats_.NumRows() < 1 || feats->Dim() % dim != 0) {
+    KALDI_ERR << "Dim mismatch: cmvn "
+              << stats_.NumRows() << 'x' << stats_.NumCols()
+              << ", feats " << feats->Dim() << 'x';
+  }
+  if (stats_.NumRows() == 1 && var_norm_) {
+    KALDI_ERR << "You requested variance normalization but no variance stats_ "
+              << "are supplied.";
+  }
+
+  double count = stats_(0, dim);
+  // Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
+  // computing an offset and representing it as stats_, we use a count of one.
+  if (count < 1.0)
+    KALDI_ERR << "Insufficient stats_ for cepstral mean and variance normalization: "
+              << "count = " << count;
+
+  if (!var_norm_) {
+    Vector<BaseFloat> offset(feats->Dim());
+    SubVector<double> mean_stats(stats_.RowData(0), dim);
+    Vector<double> mean_stats_apply(feats->Dim());
+    //fill the datat of mean_stats in mean_stats_appy whose dim is equal with the dim of feature.
+    //the dim of feats = dim * num_frames;
+    for (int32 idx = 0; idx < feats->Dim() / dim; ++idx) {
+      SubVector<double> stats_tmp(mean_stats_apply.Data() + dim*idx, dim);
+      stats_tmp.CopyFromVec(mean_stats);
     }
-    if (stats_.NumRows() == 1 && var_norm) {
-        KALDI_ERR
-            << "You requested variance normalization but no variance stats_ "
-            << "are supplied.";
-    }
-
-    double count = stats_(0, dim);
-    // Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
-    // computing an offset and representing it as stats_, we use a count of one.
-    if (count < 1.0)
-        KALDI_ERR << "Insufficient stats_ for cepstral mean and variance "
-                     "normalization: "
-                  << "count = " << count;
-
-    if (!var_norm) {
-        Vector<BaseFloat> offset(feats->Dim());
-        SubVector<double> mean_stats(stats_.RowData(0), dim);
-        Vector<double> mean_stats_apply(feats->Dim());
-        // fill the datat of mean_stats in mean_stats_appy whose dim is equal
-        // with the dim of feature.
-        // the dim of feats = dim * num_frames;
-        for (int32 idx = 0; idx < feats->Dim() / dim; ++idx) {
-            SubVector<double> stats_tmp(mean_stats_apply.Data() + dim * idx,
-                                        dim);
-            stats_tmp.CopyFromVec(mean_stats);
-        }
-        offset.AddVec(-1.0 / count, mean_stats_apply);
-        feats->AddVec(1.0, offset);
-        return;
+    offset.AddVec(-1.0 / count, mean_stats_apply);
+    feats->AddVec(1.0, offset);
+    return;
+  }
+  // norm(0, d) = mean offset;
+  // norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
+  kaldi::Matrix<BaseFloat> norm(2, feats->Dim());
+  for (int32 d = 0; d < dim; d++) {
+    double mean, offset, scale;
+    mean = stats_(0, d)/count;
+    double var = (stats_(1, d)/count) - mean*mean,
+        floor = 1.0e-20;
+    if (var < floor) {
+      KALDI_WARN << "Flooring cepstral variance from " << var << " to "
+                 << floor;
+      var = floor;
     }
-    // norm(0, d) = mean offset;
-    // norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
-    kaldi::Matrix<BaseFloat> norm(2, feats->Dim());
-    for (int32 d = 0; d < dim; d++) {
-        double mean, offset, scale;
-        mean = stats_(0, d) / count;
-        double var = (stats_(1, d) / count) - mean * mean, floor = 1.0e-20;
-        if (var < floor) {
-            KALDI_WARN << "Flooring cepstral variance from " << var << " to "
-                       << floor;
-            var = floor;
-        }
-        scale = 1.0 / sqrt(var);
-        if (scale != scale || 1 / scale == 0.0)
-            KALDI_ERR
-                << "NaN or infinity in cepstral mean/variance computation";
-        offset = -(mean * scale);
-        for (int32 d_skip = d; d_skip < feats->Dim();) {
-            norm(0, d_skip) = offset;
-            norm(1, d_skip) = scale;
-            d_skip = d_skip + dim;
-        }
+    scale = 1.0 / sqrt(var);
+    if (scale != scale || 1/scale == 0.0)
+      KALDI_ERR << "NaN or infinity in cepstral mean/variance computation";
+    offset = -(mean*scale);
+    for (int32 d_skip = d; d_skip < feats->Dim();) {
+      norm(0, d_skip) = offset;
+      norm(1, d_skip) = scale;
+      d_skip = d_skip + dim;
     }
-    // Apply the normalization.
-    feats->MulElements(norm.Row(1));
-    feats->AddVec(1.0, norm.Row(0));
+  }
+  // Apply the normalization.
+  feats->MulElements(norm.Row(1));
+  feats->AddVec(1.0, norm.Row(0));
 }
 
-void CMVN::ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats) {
-    ApplyCmvn(stats_, var_norm, feats);
+void CMVN::ApplyCMVN(kaldi::MatrixBase<BaseFloat>* feats) {
+  ApplyCmvn(stats_, var_norm_, feats);
 }
 
-bool CMVN::Compute(const VectorBase<BaseFloat>& input,
-                   VectorBase<BaseFloat>* feat) const {
-    return false;
-}
-
-
-}  // namespace ppspeech
+} // namespace ppspeech

speechx/speechx/frontend/normalizer.h

@@ -1,56 +1,40 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
 
 #pragma once
 
 #include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
-#include "kaldi/matrix/kaldi-matrix.h"
 #include "kaldi/util/options-itf.h"
+#include "kaldi/matrix/kaldi-matrix.h"
 
 namespace ppspeech {
 
 struct DecibelNormalizerOptions {
-    float target_db;
-    float max_gain_db;
-    bool convert_int_float;
-    DecibelNormalizerOptions()
-        : target_db(-20), max_gain_db(300.0), convert_int_float(false) {}
+  float target_db;
+  float max_gain_db;
+  bool convert_int_float;
+  DecibelNormalizerOptions() :
+    target_db(-20),
+    max_gain_db(300.0),
+    convert_int_float(false){}
 
     void Register(kaldi::OptionsItf* opts) {
-        opts->Register(
-            "target-db", &target_db, "target db for db normalization");
-        opts->Register(
-            "max-gain-db", &max_gain_db, "max gain db for db normalization");
-        opts->Register("convert-int-float",
-                       &convert_int_float,
-                       "if convert int samples to float");
+      opts->Register("target-db", &target_db, "target db for db normalization");
+      opts->Register("max-gain-db", &max_gain_db, "max gain db for db normalization");
+      opts->Register("convert-int-float", &convert_int_float, "if convert int samples to float");
     }
 };
 
 class DecibelNormalizer : public FeatureExtractorInterface {
   public:
-    explicit DecibelNormalizer(const DecibelNormalizerOptions& opts);
-    virtual void AcceptWaveform(
-        const kaldi::VectorBase<kaldi::BaseFloat>& input);
-    virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
+    explicit DecibelNormalizer(
+        const DecibelNormalizerOptions& opts,
+        std::unique_ptr<FeatureExtractorInterface> base_extractor);
+    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
-    bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
-                 kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
 
   private:
+    bool Compute(kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
     DecibelNormalizerOptions opts_;
     size_t dim_;
     std::unique_ptr<FeatureExtractorInterface> base_extractor_;
@@ -60,20 +44,18 @@ class DecibelNormalizer : public FeatureExtractorInterface {
 
 class CMVN : public FeatureExtractorInterface {
   public:
-    explicit CMVN(std::string cmvn_file);
-    virtual void AcceptWaveform(
-        const kaldi::VectorBase<kaldi::BaseFloat>& input);
-    virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
-    virtual size_t Dim() const { return stats_.NumCols() - 1; }
-    bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
-                 kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
-    // for test
-    void ApplyCMVN(bool var_norm, kaldi::VectorBase<BaseFloat>* feats);
-    void ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats);
+    explicit CMVN(
+        std::string cmvn_file,
+        std::unique_ptr<FeatureExtractorInterface> base_extractor);
+    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat);
+    virtual size_t Dim() const { return dim_; }
 
   private:
+    void Compute(kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
+    void ApplyCMVN(kaldi::MatrixBase<BaseFloat>* feats);
     kaldi::Matrix<double> stats_;
-    std::shared_ptr<FeatureExtractorInterface> base_extractor_;
+    std::unique_ptr<FeatureExtractorInterface> base_extractor_;
     size_t dim_;
     bool var_norm_;
 };