Support resampling (#77)

@@ -78,8 +78,6 @@ def get_args():
 
 
 def main():
-    sample_rate = 16000
-
     args = get_args()
     assert_file_exists(args.encoder)
     assert_file_exists(args.decoder)
@@ -95,12 +93,16 @@ def main():
         decoder=args.decoder,
         joiner=args.joiner,
         num_threads=args.num_threads,
-        sample_rate=sample_rate,
+        sample_rate=16000,
         feature_dim=80,
         decoding_method=args.decoding_method,
     )
     with wave.open(args.wave_filename) as f:
-        assert f.getframerate() == sample_rate, f.getframerate()
+        # If the wave file has a different sampling rate from the one
+        # expected by the model (16 kHz in our case), we will do
+        # resampling inside sherpa-onnx
+        wave_file_sample_rate = f.getframerate()
+
         assert f.getnchannels() == 1, f.getnchannels()
         assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
         num_samples = f.getnframes()
@@ -110,17 +112,17 @@ def main():
 
         samples_float32 = samples_float32 / 32768
 
-    duration = len(samples_float32) / sample_rate
+    duration = len(samples_float32) / wave_file_sample_rate
 
     start_time = time.time()
     print("Started!")
 
     stream = recognizer.create_stream()
 
-    stream.accept_waveform(sample_rate, samples_float32)
+    stream.accept_waveform(wave_file_sample_rate, samples_float32)
 
-    tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
-    stream.accept_waveform(sample_rate, tail_paddings)
+    tail_paddings = np.zeros(int(0.2 * wave_file_sample_rate), dtype=np.float32)
+    stream.accept_waveform(wave_file_sample_rate, tail_paddings)
 
     stream.input_finished()
 

@@ -100,7 +100,9 @@ def main():
     recognizer = create_recognizer()
     print("Started! Please speak")
 
-    sample_rate = 16000
+    # The model is using 16 kHz, we use 48 kHz here to demonstrate that
+    # sherpa-onnx will do resampling inside.
+    sample_rate = 48000
     samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
     last_result = ""
     stream = recognizer.create_stream()

@@ -92,9 +92,12 @@ def create_recognizer():
 
 
 def main():
-    print("Started! Please speak")
     recognizer = create_recognizer()
-    sample_rate = 16000
+    print("Started! Please speak")
+
+    # The model is using 16 kHz, we use 48 kHz here to demonstrate that
+    # sherpa-onnx will do resampling inside.
+    sample_rate = 48000
     samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
     last_result = ""
     stream = recognizer.create_stream()

@@ -115,8 +115,9 @@ void DestoryOnlineStream(SherpaOnnxOnlineStream *stream);
 /// decoding.
 ///
 /// @param stream  A pointer returned by CreateOnlineStream().
-/// @param sample_rate  Sampler rate of the input samples. It has to be 16 kHz
-///                     for models from icefall.
+/// @param sample_rate  Sample rate of the input samples. If it is different
+///                     from config.feat_config.sample_rate, we will do
+///                     resampling inside sherpa-onnx.
 /// @param samples A pointer to a 1-D array containing audio samples.
 ///                The range of samples has to be normalized to [-1, 1].
 /// @param n  Number of elements in the samples array.

@@ -11,6 +11,8 @@
 #include <vector>
 
 #include "kaldi-native-fbank/csrc/online-feature.h"
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/resample.h"
 
 namespace sherpa_onnx {
 
@@ -50,6 +52,46 @@ class FeatureExtractor::Impl {
 
   void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n) {
     std::lock_guard<std::mutex> lock(mutex_);
+
+    if (resampler_) {
+      if (sampling_rate != resampler_->GetInputSamplingRate()) {
+        SHERPA_ONNX_LOGE(
+            "You changed the input sampling rate!! Expected: %d, given: "
+            "%d",
+            resampler_->GetInputSamplingRate(), sampling_rate);
+        exit(-1);
+      }
+
+      std::vector<float> samples;
+      resampler_->Resample(waveform, n, false, &samples);
+      fbank_->AcceptWaveform(opts_.frame_opts.samp_freq, samples.data(),
+                             samples.size());
+      return;
+    }
+
+    if (sampling_rate != opts_.frame_opts.samp_freq) {
+      SHERPA_ONNX_LOGE(
+          "Creating a resampler:\n"
+          "   in_sample_rate: %d\n"
+          "   output_sample_rate: %d\n",
+          sampling_rate, static_cast<int32_t>(opts_.frame_opts.samp_freq));
+
+      float min_freq =
+          std::min<int32_t>(sampling_rate, opts_.frame_opts.samp_freq);
+      float lowpass_cutoff = 0.99 * 0.5 * min_freq;
+
+      int32_t lowpass_filter_width = 6;
+      resampler_ = std::make_unique<LinearResample>(
+          sampling_rate, opts_.frame_opts.samp_freq, lowpass_cutoff,
+          lowpass_filter_width);
+
+      std::vector<float> samples;
+      resampler_->Resample(waveform, n, false, &samples);
+      fbank_->AcceptWaveform(opts_.frame_opts.samp_freq, samples.data(),
+                             samples.size());
+      return;
+    }
+
     fbank_->AcceptWaveform(sampling_rate, waveform, n);
   }
 
@@ -100,6 +142,7 @@ class FeatureExtractor::Impl {
   std::unique_ptr<knf::OnlineFbank> fbank_;
   knf::FbankOptions opts_;
   mutable std::mutex mutex_;
+  std::unique_ptr<LinearResample> resampler_;
 };
 
 FeatureExtractor::FeatureExtractor(const FeatureExtractorConfig &config /*={}*/)

@@ -29,9 +29,11 @@ class FeatureExtractor {
   ~FeatureExtractor();
 
   /**
-     @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 sampling_rate The sampling_rate of the input waveform. If it does
+                          not equal to  config.sampling_rate, we will do
+                          resampling inside.
+     @param waveform Pointer to a 1-D array of size n. It must be normalized to
+                     the range [-1, 1].
      @param n Number of entries in waveform
    */
   void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n);

@@ -16,7 +16,7 @@ class OnlineStream::Impl {
   explicit Impl(const FeatureExtractorConfig &config)
       : feat_extractor_(config) {}
 
-  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n) {
+  void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n) {
     feat_extractor_.AcceptWaveform(sampling_rate, waveform, n);
   }
 
@@ -67,7 +67,7 @@ OnlineStream::OnlineStream(const FeatureExtractorConfig &config /*= {}*/)
 
 OnlineStream::~OnlineStream() = default;
 
-void OnlineStream::AcceptWaveform(float sampling_rate, const float *waveform,
+void OnlineStream::AcceptWaveform(int32_t sampling_rate, const float *waveform,
                                   int32_t n) {
   impl_->AcceptWaveform(sampling_rate, waveform, n);
 }

@@ -20,12 +20,14 @@ class OnlineStream {
   ~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 sampling_rate The sampling_rate of the input waveform. If it does
+                          not equal to  config.sampling_rate, we will do
+                          resampling inside.
+     @param waveform Pointer to a 1-D array of size n. It must be normalized to
+                     the range [-1, 1].
      @param n Number of entries in waveform
    */
-  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n);
+  void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n);
 
   /**
    * InputFinished() tells the class you won't be providing any

@@ -76,6 +76,7 @@ OnlineTransducerModifiedBeamSearchDecoder::GetEmptyResult() const {
   std::vector<int64_t> blanks(context_size, blank_id);
   Hypotheses blank_hyp({{blanks, 0}});
   r.hyps = std::move(blank_hyp);
+  r.tokens = std::move(blanks);
   return r;
 }
 

@@ -8,13 +8,27 @@
 
 namespace sherpa_onnx {
 
+constexpr const char *kAcceptWaveformUsage = R"(
+Process audio samples.
+
+Args:
+  sample_rate:
+    Sample rate of the input samples. If it is different from the one
+    expected by the model, we will do resampling inside.
+  waveform:
+    A 1-D float32 tensor containing audio samples. It must be normalized
+    to the range [-1, 1].
+)";
+
 void PybindOnlineStream(py::module *m) {
   using PyClass = OnlineStream;
   py::class_<PyClass>(*m, "OnlineStream")
-      .def("accept_waveform",
-           [](PyClass &self, float sample_rate, py::array_t<float> waveform) {
-             self.AcceptWaveform(sample_rate, waveform.data(), waveform.size());
-           })
+      .def(
+          "accept_waveform",
+          [](PyClass &self, float sample_rate, py::array_t<float> waveform) {
+            self.AcceptWaveform(sample_rate, waveform.data(), waveform.size());
+          },
+          py::arg("sample_rate"), py::arg("waveform"), kAcceptWaveformUsage)
       .def("input_finished", &PyClass::InputFinished);
 }