First working version.

It produces the same output as kaldi's `compute-fbank-feats`
using default parameters with `--dither=0`.

kaldifeat/CMakeLists.txt

@@ -1 +1,2 @@
 add_subdirectory(csrc)
+add_subdirectory(python)

kaldifeat/csrc/feature-common-inl.h

@@ -18,10 +18,10 @@ torch::Tensor OfflineFeatureTpl<F>::ComputeFeatures(const torch::Tensor &wave,
   int32_t rows_out = NumFrames(wave.sizes()[0], computer_.GetFrameOptions());
   int32_t cols_out = computer_.Dim();
 
-  const FrameExtractionOptions &frame_opts =
-      computer_.GetFrameOptions().frame_opts;
+  const FrameExtractionOptions &frame_opts = computer_.GetFrameOptions();
 
-  torch::Tensor strided_input = GetStrided(wave, frame_opts);
+  // TODO(fangjun): avoid clone
+  torch::Tensor strided_input = GetStrided(wave, frame_opts).clone();
 
   if (frame_opts.dither != 0)
     strided_input = Dither(strided_input, frame_opts.dither);
@@ -62,24 +62,21 @@ torch::Tensor OfflineFeatureTpl<F>::ComputeFeatures(const torch::Tensor &wave,
                                        torch::indexing::None)}) =
         right - frame_opts.preemph_coeff * current;
 
-    strided_input.index({"...", 0}) *= frame_opts.preemph_coeff;
+    strided_input.index({"...", 0}) *= 1 - frame_opts.preemph_coeff;
   }
 
   strided_input = feature_window_function_.Apply(strided_input);
 
-#if 0
-  Vector<BaseFloat> window;  // windowed waveform.
-  bool use_raw_log_energy = computer_.NeedRawLogEnergy();
-  for (int32 r = 0; r < rows_out; r++) {  // r is frame index.
-    BaseFloat raw_log_energy = 0.0;
-    ExtractWindow(0, wave, r, computer_.GetFrameOptions(),
-                  feature_window_function_, &window,
-                  (use_raw_log_energy ? &raw_log_energy : NULL));
+  int32_t padding = frame_opts.PaddedWindowSize() - strided_input.sizes()[1];
 
-    SubVector<BaseFloat> output_row(*output, r);
-    computer_.Compute(raw_log_energy, vtln_warp, &window, &output_row);
+  if (padding > 0) {
+    strided_input = torch::nn::functional::pad(
+        strided_input, torch::nn::functional::PadFuncOptions({0, padding})
+                           .mode(torch::kConstant)
+                           .value(0));
   }
-#endif
+
+  return computer_.Compute(log_energy_pre_window, vtln_warp, strided_input);
 }
 
 }  // namespace kaldifeat

kaldifeat/csrc/feature-fbank.cc

@@ -79,6 +79,7 @@ torch::Tensor FbankComputer::Compute(torch::Tensor signal_raw_log_energy,
   SubVector<float> mel_energies(*feature, mel_offset, opts_.mel_opts.num_bins);
 #endif
 
+  // TODO(fangjun): remove the last column of spectrum
   torch::Tensor mel_energies = mel_banks.Compute(spectrum);
   if (opts_.use_log_fbank) {
     // Avoid log of zero (which should be prevented anyway by dithering).

kaldifeat/csrc/mel-computations.cc

@@ -131,7 +131,9 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
                   << " and vtln-high " << vtln_high << ", versus "
                   << "low-freq " << low_freq << " and high-freq " << high_freq;
 
-  bins_mat_ = torch::zeros({num_bins, num_fft_bins}, torch::kFloat);
+  // TODO(fangjun): remove the last column of the power spectrum
+  // and set the number of columns to num_fft_bins instead of num_fft_bins + 1
+  bins_mat_ = torch::zeros({num_bins, num_fft_bins + 1}, torch::kFloat);
   int32_t stride = bins_mat_.strides()[0];
 
   for (int32_t bin = 0; bin < num_bins; ++bin) {

kaldifeat/csrc/mel-computations.h

@@ -63,6 +63,9 @@ class MelBanks {
 
   torch::Tensor Compute(const torch::Tensor &spectrum) const;
 
+  // for debug only
+  const torch::Tensor &GetBinsMat() const { return bins_mat_; }
+
  private:
   // A 2-D matrix of shape [num_bins, num_fft_bins]
   torch::Tensor bins_mat_;

kaldifeat/csrc/test_kaldifeat.cc

@@ -26,12 +26,17 @@ static void TestPreemph() {
   std::cout << d << "\n";
 }
 
-int main() {
+static void TestPad() {
   torch::Tensor a = torch::arange(0, 6).reshape({2, 3}).to(torch::kFloat);
-  torch::Tensor b = torch::arange(1, 4).to(torch::kFloat).unsqueeze(0);
+  torch::Tensor b = torch::nn::functional::pad(
+      a, torch::nn::functional::PadFuncOptions({0, 3})
+             .mode(torch::kConstant)
+             .value(0));
   std::cout << a << "\n";
   std::cout << b << "\n";
-  std::cout << a * b << "\n";
+}
 
+int main() {
+  TestPad();
   return 0;
 }

kaldifeat/python/CMakeLists.txt

@@ -0,0 +1 @@
+add_subdirectory(csrc)

kaldifeat/python/csrc/CMakeLists.txt

@@ -0,0 +1,4 @@
+add_definitions(-DTORCH_API_INCLUDE_EXTENSION_H)
+pybind11_add_module(_kaldifeat kaldifeat.cc)
+target_link_libraries(_kaldifeat PRIVATE kaldifeat_core)
+target_link_libraries(_kaldifeat PRIVATE ${TORCH_DIR}/lib/libtorch_python.so)

kaldifeat/python/csrc/kaldifeat.cc

@@ -0,0 +1,28 @@
+// kaldifeat/python/csrc/kaldifeat.cc
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+#include "kaldifeat/python/csrc/kaldifeat.h"
+
+#include "kaldifeat/csrc/feature-fbank.h"
+#include "torch/torch.h"
+
+namespace kaldifeat {
+
+PYBIND11_MODULE(_kaldifeat, m) {
+  m.doc() = "Python wrapper for kaldifeat";
+
+  m.def("test", [](const torch::Tensor &tensor) -> torch::Tensor {
+    std::cout << "size: " << tensor.sizes() << "\n";
+    FbankOptions fbank_opts;
+    fbank_opts.frame_opts.dither = 0.0f;
+
+    Fbank fbank(fbank_opts);
+    float vtln_warp = 1.0f;
+
+    torch::Tensor ans = fbank.ComputeFeatures(tensor, vtln_warp);
+    return ans;
+  });
+}
+
+}  // namespace kaldifeat

kaldifeat/python/csrc/kaldifeat.h

@@ -0,0 +1,11 @@
+// kaldifeat/python/csrc/kaldifeat.h
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+#ifndef KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
+#define KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
+
+#include "pybind11/pybind11.h"
+namespace py = pybind11;
+
+#endif  // KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_

kaldifeat/python/tests/test.py

@@ -0,0 +1,31 @@
+#!/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()