add test for the default parameters.

kaldifeat/csrc/feature-common-inl.h

@@ -20,15 +20,14 @@ torch::Tensor OfflineFeatureTpl<F>::ComputeFeatures(const torch::Tensor &wave,
 
   const FrameExtractionOptions &frame_opts = computer_.GetFrameOptions();
 
-  // TODO(fangjun): avoid clone
+  torch::Tensor strided_input = GetStrided(wave, frame_opts);
-  torch::Tensor strided_input = GetStrided(wave, frame_opts).clone();
 
   if (frame_opts.dither != 0)
     strided_input = Dither(strided_input, frame_opts.dither);
 
   if (frame_opts.remove_dc_offset) {
     torch::Tensor row_means = strided_input.mean(1).unsqueeze(1);
-    strided_input -= row_means;
+    strided_input = strided_input - row_means;
   }
 
   bool use_raw_log_energy = computer_.NeedRawLogEnergy();

kaldifeat/csrc/test_kaldifeat.cc

@@ -36,7 +36,34 @@ static void TestPad() {
   std::cout << b << "\n";
 }
 
+static void TestGetStrided() {
+  // 0 1 2 3 4 5
+  //
+  //
+  // 0 1 2 3
+  // 2 3 4 5
+
+  torch::Tensor a = torch::arange(0, 6).to(torch::kFloat);
+  torch::Tensor b = a.as_strided({2, 4}, {2, 1});
+  // b = b.clone();
+  std::cout << a << "\n";
+  std::cout << b << "\n";
+  std::cout << b.mean(1).unsqueeze(1) << "\n";
+  b = b - b.mean(1).unsqueeze(1);
+  std::cout << a << "\n";
+  std::cout << b << "\n";
+}
+
+static void TestDither() {
+  torch::Tensor a = torch::arange(0, 6).reshape({2, 3}).to(torch::kFloat);
+  torch::Tensor b = torch::arange(0, 6).reshape({2, 3}).to(torch::kFloat) * 0.1;
+  std::cout << a << "\n";
+  std::cout << b << "\n";
+  std::cout << (a + b * 2) << "\n";
+}
+
 int main() {
-  TestPad();
+  // TestDither();
+  TestGetStrided();
   return 0;
 }

kaldifeat/python/csrc/kaldifeat.cc

@@ -14,28 +14,29 @@ namespace kaldifeat {
 PYBIND11_MODULE(_kaldifeat, m) {
   m.doc() = "Python wrapper for kaldifeat";
 
-  m.def("test", [](const torch::Tensor &tensor) -> torch::Tensor {
+  // It verifies that the reimplementation produces the same output
-    FbankOptions fbank_opts;
+  // as kaldi using default paremters with dither disabled.
-    fbank_opts.frame_opts.dither = 0.0f;
+  m.def("test_default_parameters",
+        [](const torch::Tensor &tensor) -> std::pair<torch::Tensor, double> {
+          FbankOptions fbank_opts;
+          fbank_opts.frame_opts.dither = 0.0f;
 
-    Fbank fbank(fbank_opts);
+          Fbank fbank(fbank_opts);
-    float vtln_warp = 1.0f;
+          float vtln_warp = 1.0f;
 
-    std::chrono::steady_clock::time_point begin =
+          std::chrono::steady_clock::time_point begin =
-        std::chrono::steady_clock::now();
+              std::chrono::steady_clock::now();
 
-    torch::Tensor ans = fbank.ComputeFeatures(tensor, vtln_warp);
+          torch::Tensor ans = fbank.ComputeFeatures(tensor, vtln_warp);
-    std::chrono::steady_clock::time_point end =
+          std::chrono::steady_clock::time_point end =
-        std::chrono::steady_clock::now();
+              std::chrono::steady_clock::now();
-    std::cout << "Time difference = "
+          double elapsed_seconds =
-              << std::chrono::duration_cast<std::chrono::microseconds>(end -
+              std::chrono::duration_cast<std::chrono::microseconds>(end - begin)
-                                                                       begin)
+                  .count() /
-                         .count() /
+              1000000.;
-                     1000000.
-              << "[s]" << std::endl;
 
-    return ans;
+          return std::make_pair(ans, elapsed_seconds);
-  });
+        });
 }
 
 }  // namespace kaldifeat

kaldifeat/python/tests/test.py

@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
-
-import sys
-
-sys.path.insert(0, '/root/fangjun/open-source/kaldifeat/build/lib')
-
-import torch
-import numpy as np
-import soundfile as sf
-import _kaldifeat
-
-
-def main():
-    # sox -n -r 16000 -b 16 abc.wav synth 1 sine 100
-    filename = '/root/fangjun/open-source/kaldi/src/featbin/abc.wav'
-    with sf.SoundFile(filename) as sf_desc:
-        sampling_rate = sf_desc.samplerate
-        assert sampling_rate == 16000
-        a = sf_desc.read(dtype=np.float32, always_2d=False)
-        a *= 32768
-        tensor = torch.from_numpy(a)
-        ans = _kaldifeat.test(tensor)
-        #  torch.set_printoptions(profile="full")
-        #  print(ans.shape)
-        #  print(ans)
-
-
-if __name__ == '__main__':
-    main()

kaldifeat/python/tests/test_data/README.md

@@ -0,0 +1,23 @@
+
+# File descriptions
+
+## abc.wav
+
+It is generated by
+
+```bash
+sox -n -r 16000 -b 16 abc.wav synth 320 sine 300
+```
+
+## abc.scp and abc.txt
+
+It is generated by
+
+```
+echo "1 abc.wav" > abc.scp
+kaldi/src/featbin$ ./compute-fbank-feats --dither=0 scp:abc.scp ark,t:abc.txt
+```
+
+## abc.diff
+
+It's the change that measures only the feature computation time, excluding the I/O time.

kaldifeat/python/tests/test_data/abc.diff

@@ -0,0 +1,30 @@
+diff --git a/src/featbin/compute-fbank-feats.cc b/src/featbin/compute-fbank-feats.cc
+index e52b30baf..63735c985 100644
+--- a/src/featbin/compute-fbank-feats.cc
++++ b/src/featbin/compute-fbank-feats.cc
+@@ -18,6 +18,8 @@
+ // See the Apache 2 License for the specific language governing permissions and
+ // limitations under the License.
+ 
++#include <chrono>
++
+ #include "base/kaldi-common.h"
+ #include "feat/feature-fbank.h"
+ #include "feat/wave-reader.h"
+@@ -145,8 +147,16 @@ int main(int argc, char *argv[]) {
+       SubVector<BaseFloat> waveform(wave_data.Data(), this_chan);
+       Matrix<BaseFloat> features;
+       try {
++        std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
+         fbank.ComputeFeatures(waveform, wave_data.SampFreq(),
+                               vtln_warp_local, &features);
++        std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
++        std::cout << "Time difference = "
++                  << std::chrono::duration_cast<std::chrono::microseconds>(
++                         end - begin)
++                             .count() /
++                         1000000.
++                  << "[s]" << std::endl;
+       } catch (...) {
+         KALDI_WARN << "Failed to compute features for utterance " << utt;
+         continue;

kaldifeat/python/tests/test_data/abc.scp

@@ -0,0 +1 @@
+1 abc.wav

kaldifeat/python/tests/test_data/run.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+#
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+KALDI_ROOT=/root/fangjun/open-source/kaldi
+export PATH=${KALDI_ROOT}/src/featbin:$PATH
+
+sox -n -r 16000 -b 16 abc.wav synth 320 sine 300
+echo "1 abc.wav" > abc.scp
+compute-fbank-feats --dither=0 scp:abc.scp ark,t:abc.txt
+
+# Output
+#
+# compute-fbank-feats --dither=0 scp:abc.scp ark,t:abc.txt
+# Time difference = 0.304916[s]
+# LOG (compute-fbank-feats[5.5.880~4-3e446]:main():compute-fbank-feats.cc:195)  Done 1 out of 1 utterances.

kaldifeat/python/tests/test_default_parameters.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+#
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+from pathlib import Path
+cur_dir = Path(__file__).resolve().parent
+kaldi_feat_dir = cur_dir.parent.parent.parent
+
+import sys
+sys.path.insert(0, f'{kaldi_feat_dir}/build/lib')
+
+import torch
+import numpy as np
+import soundfile as sf
+import _kaldifeat
+
+
+def read_ark_txt() -> torch.Tensor:
+    test_data_dir = cur_dir / 'test_data'
+    filename = test_data_dir / 'abc.txt'
+    features = []
+    with open(filename) as f:
+        for line in f:
+            if '[' in line: continue
+            line = line.strip('').split()
+            data = [float(d) for d in line if d != ']']
+            features.append(data)
+    ans = torch.tensor(features)
+    return ans
+
+
+def main():
+    test_data_dir = cur_dir / 'test_data'
+    filename = test_data_dir / 'abc.wav'
+    with sf.SoundFile(filename) as sf_desc:
+        sampling_rate = sf_desc.samplerate
+        assert sampling_rate == 16000
+        data = sf_desc.read(dtype=np.float32, always_2d=False)
+    data *= 32768
+    tensor = torch.from_numpy(data)
+    ans, elapsed_seconds = _kaldifeat.test_default_parameters(tensor)
+    expected = read_ark_txt()
+    assert torch.allclose(ans, expected, rtol=1e-3)
+    print('elapsed seconds:', elapsed_seconds)
+
+
+if __name__ == '__main__':
+    main()