Refactor feature extractor (#26)

sherpa-onnx/csrc/features.cc

@@ -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() {
+class FeatureExtractor::Impl {
-  opts_.frame_opts.dither = 0;
+ public:
-  opts_.frame_opts.snip_edges = false;
+  Impl(int32_t sampling_rate, int32_t feature_dim) {
-  opts_.frame_opts.samp_freq = 16000;
+    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
+    // cache 100 seconds of feature frames, which is more than enough
-  // for real needs
+    // for real needs
-  opts_.frame_opts.max_feature_vectors = 100 * 100;
+    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)
+  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n) {
-    : opts_(opts) {
+    std::lock_guard<std::mutex> lock(mutex_);
-  fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+    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_);
+  impl_->AcceptWaveform(sampling_rate, waveform, n);
-  fbank_->AcceptWaveform(sampling_rate, waveform, n);
 }
 
-void FeatureExtractor::InputFinished() {
+void FeatureExtractor::InputFinished() { impl_->InputFinished(); }
-  std::lock_guard<std::mutex> lock(mutex_);
-  fbank_->InputFinished();
-}
 
 int32_t FeatureExtractor::NumFramesReady() const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  return impl_->NumFramesReady();
-  return fbank_->NumFramesReady();
 }
 
 bool FeatureExtractor::IsLastFrame(int32_t frame) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  return impl_->IsLastFrame(frame);
-  return fbank_->IsLastFrame(frame);
 }
 
 std::vector<float> FeatureExtractor::GetFrames(int32_t frame_index,
                                                int32_t n) const {
-  if (frame_index + n > NumFramesReady()) {
+  return impl_->GetFrames(frame_index, n);
-    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() {
+void FeatureExtractor::Reset() { impl_->Reset(); }
-  fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
+
-}
+int32_t FeatureExtractor::FeatureDim() const { return impl_->FeatureDim(); }
 
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/features.h

@@ -6,17 +6,19 @@
 #define SHERPA_ONNX_CSRC_FEATURES_H_
 
 #include <memory>
-#include <mutex>  // NOLINT
 #include <vector>
 
-#include "kaldi-native-fbank/csrc/online-feature.h"
-
 namespace sherpa_onnx {
 
 class FeatureExtractor {
  public:
-  FeatureExtractor();
+  /**
-  explicit FeatureExtractor(const knf::FbankOptions &fbank_opts);
+   * @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(int32_t sampling_rate = 16000,
+                            int32_t feature_dim = 80);
+  ~FeatureExtractor();
 
   /**
      @param sampling_rate The sampling_rate of the input waveform. Should match
@@ -48,12 +50,13 @@ class FeatureExtractor {
   std::vector<float> GetFrames(int32_t frame_index, int32_t n) const;
 
   void Reset();
-  int32_t FeatureDim() const { return opts_.mel_opts.num_bins; }
+
+  /// Return feature dim of this extractor
+  int32_t FeatureDim() const;
 
  private:
-  std::unique_ptr<knf::OnlineFbank> fbank_;
+  class Impl;
-  knf::FbankOptions opts_;
+  std::unique_ptr<Impl> impl_;
-  mutable std::mutex mutex_;
 };
 
 }  // namespace sherpa_onnx

sherpa-onnx/csrc/sherpa-onnx.cc

@@ -2,8 +2,9 @@
 //
 // Copyright (c)  2022-2023  Xiaomi Corporation
 
+#include <stdio.h>
+
 #include <chrono>  // NOLINT
-#include <iostream>
 #include <string>
 #include <vector>
 
@@ -30,14 +31,14 @@ Please refer to
 https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
 for a list of pre-trained models to download.
 )usage";
-    std::cerr << usage << "\n";
+    fprintf(stderr, "%s\n", usage);
 
     return 0;
   }
 
   std::string tokens = argv[1];
   sherpa_onnx::OnlineTransducerModelConfig config;
-  config.debug = true;
+  config.debug = false;
   config.encoder_filename = argv[2];
   config.decoder_filename = argv[3];
   config.joiner_filename = argv[4];
@@ -47,7 +48,7 @@ for a list of pre-trained models to download.
   if (argc == 7) {
     config.num_threads = atoi(argv[6]);
   }
-  std::cout << config.ToString().c_str() << "\n";
+  fprintf(stderr, "%s\n", config.ToString().c_str());
 
   auto model = sherpa_onnx::OnlineTransducerModel::Create(config);
 
@@ -72,17 +73,17 @@ for a list of pre-trained models to download.
       sherpa_onnx::ReadWave(wav_filename, expected_sampling_rate, &is_ok);
 
   if (!is_ok) {
-    std::cerr << "Failed to read " << wav_filename << "\n";
+    fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
     return -1;
   }
 
-  const float duration = samples.size() / expected_sampling_rate;
+  float duration = samples.size() / static_cast<float>(expected_sampling_rate);
 
-  std::cout << "wav filename: " << wav_filename << "\n";
+  fprintf(stderr, "wav filename: %s\n", wav_filename.c_str());
-  std::cout << "wav duration (s): " << duration << "\n";
+  fprintf(stderr, "wav duration (s): %.3f\n", duration);
 
   auto begin = std::chrono::steady_clock::now();
-  std::cout << "Started!\n";
+  fprintf(stderr, "Started\n");
 
   sherpa_onnx::FeatureExtractor feat_extractor;
   feat_extractor.AcceptWaveform(expected_sampling_rate, samples.data(),
@@ -115,10 +116,10 @@ for a list of pre-trained models to download.
     text += sym[hyp[i]];
   }
 
-  std::cout << "Done!\n";
+  fprintf(stderr, "Done!\n");
 
-  std::cout << "Recognition result for " << wav_filename << "\n"
+  fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
-            << text << "\n";
+          text.c_str());
 
   auto end = std::chrono::steady_clock::now();
   float elapsed_seconds =
@@ -126,7 +127,7 @@ for a list of pre-trained models to download.
           .count() /
       1000.;
 
-  std::cout << "num threads: " << config.num_threads << "\n";
+  fprintf(stderr, "num threads: %d\n", config.num_threads);
 
   fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
   float rtf = elapsed_seconds / duration;