Add PLP features.

kaldifeat/csrc/CMakeLists.txt

@@ -2,7 +2,9 @@
 
 set(kaldifeat_srcs
   feature-fbank.cc
+  feature-functions.cc
   feature-mfcc.cc
+  feature-plp.cc
   feature-spectrogram.cc
   feature-window.cc
   matrix-functions.cc

kaldifeat/csrc/feature-common.h

@@ -18,7 +18,7 @@ class OfflineFeatureTpl {
 
   // Note: feature_window_function_ is the windowing function, which initialized
   // using the options class, that we cache at this level.
-  OfflineFeatureTpl(const Options &opts)
+  explicit OfflineFeatureTpl(const Options &opts)
       : computer_(opts),
         feature_window_function_(computer_.GetFrameOptions(), opts.device) {}
 

kaldifeat/csrc/feature-functions.cc

@@ -0,0 +1,33 @@
+// kaldifeat/csrc/feature-functions.cc
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.cc
+
+#include "kaldifeat/csrc/feature-functions.h"
+
+#include <cmath>
+
+namespace kaldifeat {
+
+void InitIdftBases(int32_t n_bases, int32_t dimension, torch::Tensor *mat_out) {
+  float angle = M_PI / (dimension - 1);
+  float scale = 1.0f / (2 * (dimension - 1));
+
+  *mat_out = torch::empty({n_bases, dimension}, torch::kFloat);
+  float *data = mat_out->data_ptr<float>();
+
+  int32_t stride = mat_out->stride(0);
+
+  for (int32_t i = 0; i < n_bases; ++i) {
+    float *this_row = data + i * stride;
+    this_row[0] = scale;
+    for (int32_t j = 1; j < dimension - 1; ++j) {
+      this_row[j] = 2 * scale * std::cos(angle * i * j);
+    }
+
+    this_row[dimension - 1] = scale * std::cos(angle * i * (dimension - 1));
+  }
+}
+
+}  // namespace kaldifeat

kaldifeat/csrc/feature-functions.h

@@ -0,0 +1,18 @@
+// kaldifeat/csrc/feature-functions.h
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.h
+
+#ifndef KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_
+#define KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_
+
+#include "torch/torch.h"
+
+namespace kaldifeat {
+
+void InitIdftBases(int32_t n_bases, int32_t dimension, torch::Tensor *mat_out);
+
+}
+
+#endif  // KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_

kaldifeat/csrc/feature-mfcc.cc

@@ -31,7 +31,7 @@ MfccComputer::MfccComputer(const MfccOptions &opts) : opts_(opts) {
   // energy we replace this with the energy.  This means a different
   // ordering of features than HTK.
 
-  using namespace torch::indexing;  // It imports: Slice, None
+  using namespace torch::indexing;  // It imports: Slice, None  // NOLINT
 
   // dct_matrix[:opts.num_cepts, :]
   torch::Tensor dct_rows =

kaldifeat/csrc/feature-mfcc.h

@@ -7,6 +7,9 @@
 #ifndef KALDIFEAT_CSRC_FEATURE_MFCC_H_
 #define KALDIFEAT_CSRC_FEATURE_MFCC_H_
 
+#include <map>
+#include <string>
+
 #include "kaldifeat/csrc/feature-common.h"
 #include "kaldifeat/csrc/feature-window.h"
 #include "kaldifeat/csrc/mel-computations.h"

kaldifeat/csrc/feature-plp.cc

@@ -0,0 +1,173 @@
+// kaldifeat/csrc/feature-plp.cc
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-plp.cc
+
+#include "kaldifeat/csrc/feature-plp.h"
+
+#include "kaldifeat/csrc/feature-functions.h"
+#include "torch/torch.h"
+
+namespace kaldifeat {
+
+std::ostream &operator<<(std::ostream &os, const PlpOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+PlpComputer::PlpComputer(const PlpOptions &opts) : opts_(opts) {
+  // our num-ceps includes C0.
+  KALDIFEAT_ASSERT(opts_.num_ceps <= opts_.lpc_order + 1);
+
+  if (opts.cepstral_lifter != 0.0) {
+    lifter_coeffs_ = torch::empty({1, opts.num_ceps}, torch::kFloat32);
+    ComputeLifterCoeffs(opts.cepstral_lifter, &lifter_coeffs_);
+    lifter_coeffs_ = lifter_coeffs_.to(opts.device);
+  }
+
+  InitIdftBases(opts_.lpc_order + 1, opts_.mel_opts.num_bins + 2, &idft_bases_);
+
+  // CAUTION: we save a transposed version of idft_bases_
+  idft_bases_ = idft_bases_.to(opts.device).t();
+
+  if (opts.energy_floor > 0.0) log_energy_floor_ = logf(opts.energy_floor);
+
+  // We'll definitely need the filterbanks info for VTLN warping factor 1.0.
+  // [note: this call caches it.]
+  GetMelBanks(1.0);
+}
+
+PlpComputer::~PlpComputer() {
+  for (auto iter = mel_banks_.begin(); iter != mel_banks_.end(); ++iter)
+    delete iter->second;
+
+  for (auto iter = equal_loudness_.begin(); iter != equal_loudness_.end();
+       ++iter)
+    delete iter->second;
+}
+
+const MelBanks *PlpComputer::GetMelBanks(float vtln_warp) {
+  MelBanks *this_mel_banks = nullptr;
+
+  // std::map<float, MelBanks *>::iterator iter = mel_banks_.find(vtln_warp);
+  auto iter = mel_banks_.find(vtln_warp);
+  if (iter == mel_banks_.end()) {
+    this_mel_banks =
+        new MelBanks(opts_.mel_opts, opts_.frame_opts, vtln_warp, opts_.device);
+    mel_banks_[vtln_warp] = this_mel_banks;
+  } else {
+    this_mel_banks = iter->second;
+  }
+  return this_mel_banks;
+}
+
+const torch::Tensor *PlpComputer::GetEqualLoudness(float vtln_warp) {
+  const MelBanks *this_mel_banks = GetMelBanks(vtln_warp);
+  torch::Tensor *ans = NULL;
+  auto iter = equal_loudness_.find(vtln_warp);
+  if (iter == equal_loudness_.end()) {
+    ans = new torch::Tensor;
+    GetEqualLoudnessVector(*this_mel_banks, ans);
+    *ans = ans->to(opts_.device);
+    equal_loudness_[vtln_warp] = ans;
+  } else {
+    ans = iter->second;
+  }
+  return ans;
+}
+
+// ans.shape [signal_frame.size(0), this->Dim()]
+torch::Tensor PlpComputer::Compute(torch::Tensor signal_raw_log_energy,
+                                   float vtln_warp,
+                                   const torch::Tensor &signal_frame) {
+  KALDIFEAT_ASSERT(signal_frame.dim() == 2);
+  KALDIFEAT_ASSERT(signal_frame.size(1) == opts_.frame_opts.PaddedWindowSize());
+
+  const MelBanks &mel_banks = *GetMelBanks(vtln_warp);
+  const torch::Tensor &equal_loudness = *GetEqualLoudness(vtln_warp);
+
+  // torch.finfo(torch.float32).eps
+  constexpr float kEps = 1.1920928955078125e-07f;
+
+  // Compute energy after window function (not the raw one).
+  if (opts_.use_energy && !opts_.raw_energy) {
+    signal_raw_log_energy =
+        torch::clamp_min(signal_frame.pow(2).sum(1), kEps).log();
+  }
+
+  // note spectrum is in magnitude, not power, because of `abs()`
+  torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
+
+  // remove the last column, i.e., the highest fft bin
+  spectrum = spectrum.index(
+      {"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
+
+  // Use power instead of magnitude
+  spectrum = spectrum.pow(2);
+
+  torch::Tensor mel_energies = mel_banks.Compute(spectrum);
+
+  mel_energies = torch::mul(mel_energies, equal_loudness);
+  mel_energies = mel_energies.pow(opts_.compress_factor);
+
+  // duplicate first and last elements
+  //
+  // left_padding = wave[:num_left_padding].flip(dims=(0,))
+  // first = mel_energies[:, 0]
+  // first.shape [num_frames, 1]
+  torch::Tensor first = mel_energies.index({"...", 0}).unsqueeze(-1);
+  // last = mel_energies[:, -1]
+  // last.shape [num_frames, 1]
+  torch::Tensor last = mel_energies.index({"...", -1}).unsqueeze(-1);
+
+  mel_energies = torch::cat({first, mel_energies, last}, 1);
+
+  torch::Tensor autocorr_coeffs = torch::mm(mel_energies, idft_bases_);
+
+  torch::Tensor lpc_coeffs;
+  torch::Tensor residual_log_energy = ComputeLpc(autocorr_coeffs, &lpc_coeffs);
+
+  residual_log_energy = torch::clamp_min(residual_log_energy, kEps);
+
+  torch::Tensor raw_cepstrum = Lpc2Cepstrum(lpc_coeffs);
+
+  // torch.cat((residual_log_energy.unsqueeze(-1),
+  // raw_cepstrum[:opts.num_ceps-1]), 1)
+  //
+  using namespace torch::indexing;  // It imports: Slice, None // NOLINT
+  torch::Tensor features = torch::cat(
+      {residual_log_energy.unsqueeze(-1),
+       raw_cepstrum.index({"...", Slice(0, opts_.num_ceps - 1, None)})},
+      1);
+
+  if (opts_.cepstral_lifter != 0.0) {
+    features = torch::mul(features, lifter_coeffs_);
+  }
+
+  if (opts_.cepstral_scale != 1.0) {
+    features = features * opts_.cepstral_scale;
+  }
+
+  if (opts_.use_energy) {
+    if (opts_.energy_floor > 0.0f) {
+      signal_raw_log_energy =
+          torch::clamp_min(signal_raw_log_energy, log_energy_floor_);
+    }
+    // column 0 is replaced by signal_raw_log_energy
+    //
+    // features[:, 0] = signal_raw_log_energy
+    //
+    features.index({"...", 0}) = signal_raw_log_energy;
+  }
+
+  if (opts_.htk_compat) {  // reorder the features.
+    // shift left, so the original 0th column
+    // becomes the last column;
+    // the original first column becomes the 0th column
+    features = torch::roll(features, -1, 1);
+  }
+  return features;
+}
+
+}  // namespace kaldifeat

kaldifeat/csrc/feature-plp.h

@@ -0,0 +1,131 @@
+// kaldifeat/csrc/feature-plp.h
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-plp.h
+
+#ifndef KALDIFEAT_CSRC_FEATURE_PLP_H_
+#define KALDIFEAT_CSRC_FEATURE_PLP_H_
+
+#include <map>
+#include <string>
+
+#include "kaldifeat/csrc/feature-common.h"
+#include "kaldifeat/csrc/feature-window.h"
+#include "kaldifeat/csrc/mel-computations.h"
+#include "torch/torch.h"
+
+namespace kaldifeat {
+
+/// PlpOptions contains basic options for computing PLP features.
+/// It only includes things that can be done in a "stateless" way, i.e.
+/// it does not include energy max-normalization.
+/// It does not include delta computation.
+struct PlpOptions {
+  FrameExtractionOptions frame_opts;
+  MelBanksOptions mel_opts;
+
+  // Order of LPC analysis in PLP computation
+  //
+  // 12 seems to be common for 16kHz-sampled data. For 8kHz-sampled
+  // data, 15 may be better.
+  int32_t lpc_order = 12;
+
+  // Number of cepstra in PLP computation (including C0)
+  int32_t num_ceps = 13;
+  bool use_energy = true;  // use energy; else C0
+
+  // Floor on energy (absolute, not relative) in PLP computation.
+  // Only makes a difference if --use-energy=true; only necessary if
+  // dither is 0.0.  Suggested values: 0.1 or 1.0
+  float energy_floor = 0.0;
+
+  // If true, compute energy before preemphasis and windowing
+  bool raw_energy = true;
+
+  // Compression factor in PLP computation
+  float compress_factor = 0.33333;
+
+  // Constant that controls scaling of PLPs
+  int32_t cepstral_lifter = 22;
+
+  // Scaling constant in PLP computation
+  float cepstral_scale = 1.0;
+
+  bool htk_compat = false;  // if true, put energy/C0 last and introduce a
+                            // factor of sqrt(2) on C0 to be the same as HTK.
+                            //
+  torch::Device device{"cpu"};
+
+  PlpOptions() { mel_opts.num_bins = 23; }
+
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "frame_opts: \n";
+    os << frame_opts << "\n";
+    os << "\n";
+
+    os << "mel_opts: \n";
+    os << mel_opts << "\n";
+
+    os << "lpc_order: " << lpc_order << "\n";
+    os << "num_ceps: " << num_ceps << "\n";
+    os << "use_energy: " << use_energy << "\n";
+    os << "energy_floor: " << energy_floor << "\n";
+    os << "raw_energy: " << raw_energy << "\n";
+    os << "compress_factor: " << compress_factor << "\n";
+    os << "cepstral_lifter: " << cepstral_lifter << "\n";
+    os << "cepstral_scale: " << cepstral_scale << "\n";
+    os << "htk_compat: " << htk_compat << "\n";
+    os << "device: " << device << "\n";
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const PlpOptions &opts);
+
+class PlpComputer {
+ public:
+  using Options = PlpOptions;
+
+  explicit PlpComputer(const PlpOptions &opts);
+  ~PlpComputer();
+
+  PlpComputer &operator=(const PlpComputer &) = delete;
+  PlpComputer(const PlpComputer &) = delete;
+
+  int32_t Dim() const { return opts_.num_ceps; }
+
+  bool NeedRawLogEnergy() const { return opts_.use_energy && opts_.raw_energy; }
+
+  const FrameExtractionOptions &GetFrameOptions() const {
+    return opts_.frame_opts;
+  }
+
+  const PlpOptions &GetOptions() const { return opts_; }
+
+  // signal_raw_log_energy is log_energy_pre_window, which is not empty
+  // iff NeedRawLogEnergy() returns true.
+  torch::Tensor Compute(torch::Tensor signal_raw_log_energy, float vtln_warp,
+                        const torch::Tensor &signal_frame);
+
+ private:
+  const MelBanks *GetMelBanks(float vtln_warp);
+
+  const torch::Tensor *GetEqualLoudness(float vtln_warp);
+
+  PlpOptions opts_;
+  torch::Tensor lifter_coeffs_;
+  torch::Tensor idft_bases_;  // 2-D tensor, kFloat. Caution: it is transposed
+  float log_energy_floor_;
+  std::map<float, MelBanks *> mel_banks_;  // float is VTLN coefficient.
+
+  // value is a 1-D torch.Tensor
+  std::map<float, torch::Tensor *> equal_loudness_;
+};
+
+using Plp = OfflineFeatureTpl<PlpComputer>;
+
+}  // namespace kaldifeat
+
+#endif  // KALDIFEAT_CSRC_FEATURE_PLP_H_

kaldifeat/csrc/feature-window.cc

@@ -7,6 +7,7 @@
 #include "kaldifeat/csrc/feature-window.h"
 
 #include <cmath>
+#include <vector>
 
 #include "torch/torch.h"
 
@@ -168,7 +169,7 @@ torch::Tensor Dither(const torch::Tensor &wave, float dither_value) {
 }
 
 torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
-  using namespace torch::indexing;  // It imports: Slice, None
+  using namespace torch::indexing;  // It imports: Slice, None  // NOLINT
   if (preemph_coeff == 0.0f) return wave;
 
   KALDIFEAT_ASSERT(preemph_coeff >= 0.0f && preemph_coeff <= 1.0f);

kaldifeat/csrc/feature-window.h

@@ -4,6 +4,8 @@
 
 // This file is copied/modified from kaldi/src/feat/feature-window.h
 
+#include <string>
+
 #include "kaldifeat/csrc/log.h"
 #include "torch/torch.h"
 

kaldifeat/csrc/mel-computations.cc

@@ -3,9 +3,11 @@
 // Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 //
 // This file is copied/modified from kaldi/src/feat/mel-computations.cc
-//
+
 #include "kaldifeat/csrc/mel-computations.h"
 
+#include <algorithm>
+
 #include "kaldifeat/csrc/feature-window.h"
 
 namespace kaldifeat {
@@ -136,9 +138,14 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
                   << " and vtln-high " << vtln_high << ", versus "
                   << "low-freq " << low_freq << " and high-freq " << high_freq;
 
+  // we will transpose bins_mat_ at the end of this funciton
   bins_mat_ = torch::zeros({num_bins, num_fft_bins}, torch::kFloat);
+
   int32_t stride = bins_mat_.strides()[0];
 
+  center_freqs_ = torch::empty({num_bins}, torch::kFloat);
+  float *center_freqs_data = center_freqs_.data_ptr<float>();
+
   for (int32_t bin = 0; bin < num_bins; ++bin) {
     float left_mel = mel_low_freq + bin * mel_freq_delta,
           center_mel = mel_low_freq + (bin + 1) * mel_freq_delta,
@@ -152,6 +159,7 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
       right_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
                                   vtln_warp_factor, right_mel);
     }
+    center_freqs_data[bin] = InverseMelScale(center_mel);
     // this_bin will be a vector of coefficients that is only
     // nonzero where this mel bin is active.
     float *this_bin = bins_mat_.data_ptr<float>() + bin * stride;
@@ -203,4 +211,141 @@ void ComputeLifterCoeffs(float Q, torch::Tensor *coeffs) {
   }
 }
 
+void GetEqualLoudnessVector(const MelBanks &mel_banks, torch::Tensor *ans) {
+  int32_t n = mel_banks.NumBins();
+  // Central frequency of each mel bin.
+  const torch::Tensor &f0 = mel_banks.GetCenterFreqs();
+  const float *f0_data = f0.data_ptr<float>();
+
+  *ans = torch::empty({1, n}, torch::kFloat);
+  float *ans_data = ans->data_ptr<float>();
+  for (int32_t i = 0; i < n; ++i) {
+    float fsq = f0_data[i] * f0_data[i];
+    float fsub = fsq / (fsq + 1.6e5);
+    ans_data[i] = fsub * fsub * ((fsq + 1.44e6) / (fsq + 9.61e6));
+  }
+}
+
+// Durbin's recursion - converts autocorrelation coefficients to the LPC
+// pTmp - temporal place [n]
+// pAC - autocorrelation coefficients [n + 1]
+// pLP - linear prediction coefficients [n]
+//       (predicted_sn = sum_1^P{a[i-1] * s[n-i]}})
+//       F(z) = 1 / (1 - A(z)), 1 is not stored in the denominator
+static float Durbin(int n, const float *pAC, float *pLP, float *pTmp) {
+  float ki;  // reflection coefficient
+  int i;
+  int j;
+
+  float E = pAC[0];
+
+  for (i = 0; i < n; ++i) {
+    // next reflection coefficient
+    ki = pAC[i + 1];
+
+    for (j = 0; j < i; ++j) ki += pLP[j] * pAC[i - j];
+
+    ki = ki / E;
+
+    // new error
+    float c = 1 - ki * ki;
+    if (c < 1.0e-5)  // remove NaNs for constant signal
+      c = 1.0e-5;
+
+    E *= c;
+
+    // new LP coefficients
+    pTmp[i] = -ki;
+
+    for (j = 0; j < i; ++j) pTmp[j] = pLP[j] - ki * pLP[i - j - 1];
+
+    for (j = 0; j <= i; ++j) pLP[j] = pTmp[j];
+  }
+
+  return E;
+}
+
+// Compute LP coefficients from autocorrelation coefficients.
+torch::Tensor ComputeLpc(const torch::Tensor &autocorr_in,
+                         torch::Tensor *lpc_out) {
+  KALDIFEAT_ASSERT(autocorr_in.dim() == 2);
+
+  int32_t num_frames = autocorr_in.size(0);
+  int32_t lpc_order = autocorr_in.size(1) - 1;
+
+  *lpc_out = torch::empty({num_frames, lpc_order}, torch::kFloat);
+  torch::Tensor ans = torch::empty({num_frames}, torch::kFloat);
+
+  // TODO(fangjun): Durbin runs only on CPU. Implement a CUDA version
+  torch::Device saved_device = autocorr_in.device();
+  torch::Device cpu("cpu");
+  torch::Tensor in_cpu = autocorr_in.to(cpu);
+
+  torch::Tensor tmp = torch::empty_like(*lpc_out);
+
+  int32_t in_stride = in_cpu.stride(0);
+  int32_t ans_stride = ans.stride(0);
+  int32_t tmp_stride = tmp.stride(0);
+  int32_t lpc_stride = lpc_out->stride(0);
+
+  const float *in_data = in_cpu.data_ptr<float>();
+  float *ans_data = ans.data_ptr<float>();
+  float *tmp_data = tmp.data_ptr<float>();
+  float *lpc_data = lpc_out->data_ptr<float>();
+
+  // see
+  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Parallel.h#L58
+  at::parallel_for(0, num_frames, 1, [&](int32_t begin, int32_t end) -> void {
+    for (int32_t i = begin; i != end; ++i) {
+      float ret = Durbin(lpc_order, in_data + i * in_stride,
+                         lpc_data + i * lpc_stride, tmp_data + i * tmp_stride);
+
+      if (ret <= 0.0) KALDIFEAT_WARN << "Zero energy in LPC computation";
+
+      ans_data[i] = -logf(1.0 / ret);  // forms the C0 value
+    }
+  });
+
+  *lpc_out = lpc_out->to(saved_device);
+  return ans.to(saved_device);
+}
+
+static void Lpc2CepstrumInternal(int n, const float *pLPC, float *pCepst) {
+  for (int32_t i = 0; i < n; ++i) {
+    double sum = 0.0;
+    for (int32_t j = 0; j < i; ++j) {
+      sum += (i - j) * pLPC[j] * pCepst[i - j - 1];
+    }
+    pCepst[i] = -pLPC[i] - sum / (i + 1);
+  }
+}
+
+torch::Tensor Lpc2Cepstrum(const torch::Tensor &lpc) {
+  KALDIFEAT_ASSERT(lpc.dim() == 2);
+  torch::Device cpu("cpu");
+  torch::Device saved_device = lpc.device();
+
+  // TODO(fangjun): support cuda
+  torch::Tensor in_cpu = lpc.to(cpu);
+
+  int32_t num_frames = in_cpu.size(0);
+  int32_t lpc_order = in_cpu.size(1);
+
+  const float *in_data = in_cpu.data_ptr<float>();
+  int32_t in_stride = in_cpu.stride(0);
+
+  torch::Tensor ans = torch::zeros({num_frames, lpc_order}, torch::kFloat);
+  int32_t ans_stride = ans.stride(0);
+  float *ans_data = ans.data_ptr<float>();
+
+  at::parallel_for(0, num_frames, 1, [&](int32_t begin, int32_t end) -> void {
+    for (int32_t i = begin; i != end; ++i) {
+      Lpc2CepstrumInternal(lpc_order, in_data + i * in_stride,
+                           ans_data + i * ans_stride);
+    }
+  });
+
+  return ans.to(saved_device);
+}
+
 }  // namespace kaldifeat

kaldifeat/csrc/mel-computations.h

@@ -5,6 +5,7 @@
 // This file is copied/modified from kaldi/src/feat/mel-computations.h
 
 #include <cmath>
+#include <string>
 
 #include "kaldifeat/csrc/feature-window.h"
 
@@ -74,7 +75,11 @@ class MelBanks {
            const FrameExtractionOptions &frame_opts, float vtln_warp_factor,
            torch::Device device);
 
-  int32_t NumBins() const { return static_cast<int32_t>(bins_mat_.size(0)); }
+  // CAUTION: we save a transposed version of bins_mat_, so return size(1) here
+  int32_t NumBins() const { return static_cast<int32_t>(bins_mat_.size(1)); }
+
+  // returns vector of central freq of each bin; needed by plp code.
+  const torch::Tensor &GetCenterFreqs() const { return center_freqs_; }
 
   torch::Tensor Compute(const torch::Tensor &spectrum) const;
 
@@ -82,9 +87,14 @@ class MelBanks {
   const torch::Tensor &GetBinsMat() const { return bins_mat_; }
 
  private:
-  // A 2-D matrix of shape [num_bins, num_fft_bins]
+  // A 2-D matrix. Its shape is NOT [num_bins, num_fft_bins]
+  // Its shape is [num_fft_bins, num_bins].
   torch::Tensor bins_mat_;
 
+  // center frequencies of bins, numbered from 0 ... num_bins-1.
+  // Needed by GetCenterFreqs().
+  torch::Tensor center_freqs_;  // It's always on CPU
+
   bool debug_;
   bool htk_mode_;
 };
@@ -96,6 +106,26 @@ class MelBanks {
 // coeffs is a 1-D float tensor
 void ComputeLifterCoeffs(float Q, torch::Tensor *coeffs);
 
+void GetEqualLoudnessVector(const MelBanks &mel_banks, torch::Tensor *ans);
+
+/* Compute LP coefficients from autocorrelation coefficients.
+ *
+ *  @param [in] autocorr_in  A 2-D tensor. Each row is a frame. Its number of
+ *                           columns is lpc_order + 1
+ *  @param [out] lpc_coeffs  A 2-D tensor. On return, it has as many rows as the
+ *                           input tensor. Its number of columns is lpc_order.
+ *
+ *  @return Returns log energy of residual in a 1-D tensor. It has as many
+ *          elements as the number of rows in `autocorr_in`.
+ */
+torch::Tensor ComputeLpc(const torch::Tensor &autocorr_in,
+                         torch::Tensor *lpc_coeffs);
+
+/*
+ * @param [in] lpc It is the output argument `lpc_coeffs` in ComputeLpc().
+ */
+torch::Tensor Lpc2Cepstrum(const torch::Tensor &lpc);
+
 }  // namespace kaldifeat
 
 #endif  // KALDIFEAT_CSRC_MEL_COMPUTATIONS_H_

kaldifeat/csrc/pitch-functions.h

@@ -4,8 +4,21 @@
 
 // This file is copied/modified from kaldi/src/feat/pitch-functions.h
 
-#ifndef KALDIFEAT_CSRC_FEATURE_PITCH_FUNCTIONS_H_
+#ifndef KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_
-#define KALDIFEAT_CSRC_FEATURE_PITCH_FUNCTIONS_H_
+#define KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_
+
+// References
+//
+// Talkin, David, and W. Bastiaan Kleijn. "A robust algorithm for pitch
+// tracking (RAPT)." coding and synthesis 495 (1995): 518.
+// (https://www.ee.columbia.edu/~dpwe/papers/Talkin95-rapt.pdf)
+//
+// Ghahremani, Pegah, et al. "A pitch extraction algorithm tuned for
+// automatic speech recognition." 2014 IEEE international conference on
+// acoustics, speech and signal processing (ICASSP). IEEE, 2014.
+// (http://danielpovey.com/files/2014_icassp_pitch.pdf)
+
+#include <string>
 
 #include "torch/torch.h"
 
@@ -134,6 +147,8 @@ struct PitchExtractionOptions {
   }
 };
 
+// TODO(fangjun): Implement it
+
 }  // namespace kaldifeat
 
-#endif  // KALDIFEAT_CSRC_FEATURE_PITCH_FUNCTIONS_H_
+#endif  // KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_

kaldifeat/python/csrc/CMakeLists.txt

@@ -2,6 +2,7 @@ add_definitions(-DTORCH_API_INCLUDE_EXTENSION_H)
 pybind11_add_module(_kaldifeat
   feature-fbank.cc
   feature-mfcc.cc
+  feature-plp.cc
   feature-spectrogram.cc
   feature-window.cc
   kaldifeat.cc

kaldifeat/python/csrc/CPPLINT.cfg

@@ -0,0 +1 @@
+filter=-runtime/references

kaldifeat/python/csrc/feature-mfcc.cc

@@ -4,6 +4,8 @@
 
 #include "kaldifeat/python/csrc/feature-mfcc.h"
 
+#include <string>
+
 #include "kaldifeat/csrc/feature-mfcc.h"
 
 namespace kaldifeat {

kaldifeat/python/csrc/feature-plp.cc

@@ -0,0 +1,57 @@
+// kaldifeat/python/csrc/feature-plp.cc
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+#include "kaldifeat/python/csrc/feature-plp.h"
+
+#include <string>
+
+#include "kaldifeat/csrc/feature-plp.h"
+
+namespace kaldifeat {
+
+void PybindPlpOptions(py::module &m) {
+  using PyClass = PlpOptions;
+  py::class_<PyClass>(m, "PlpOptions")
+      .def(py::init<>())
+      .def_readwrite("frame_opts", &PyClass::frame_opts)
+      .def_readwrite("mel_opts", &PyClass::mel_opts)
+      .def_readwrite("lpc_order", &PyClass::lpc_order)
+      .def_readwrite("num_ceps", &PyClass::num_ceps)
+      .def_readwrite("use_energy", &PyClass::use_energy)
+      .def_readwrite("energy_floor", &PyClass::energy_floor)
+      .def_readwrite("raw_energy", &PyClass::raw_energy)
+      .def_readwrite("compress_factor", &PyClass::compress_factor)
+      .def_readwrite("cepstral_lifter", &PyClass::cepstral_lifter)
+      .def_readwrite("cepstral_scale", &PyClass::cepstral_scale)
+      .def_readwrite("htk_compat", &PyClass::htk_compat)
+      .def_property(
+          "device",
+          [](const PyClass &self) -> py::object {
+            py::object ans = py::module_::import("torch").attr("device");
+            return ans(self.device.str());
+          },
+          [](PyClass &self, py::object obj) -> void {
+            std::string s = static_cast<py::str>(obj);
+            self.device = torch::Device(s);
+          })
+      .def("__str__",
+           [](const PyClass &self) -> std::string { return self.ToString(); });
+}
+
+static void PybindPlp(py::module &m) {
+  using PyClass = Plp;
+  py::class_<PyClass>(m, "Plp")
+      .def(py::init<const PlpOptions &>(), py::arg("opts"))
+      .def("dim", &PyClass::Dim)
+      .def_property_readonly("options", &PyClass::GetOptions)
+      .def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
+           py::arg("vtln_warp"));
+}
+
+void PybindFeaturePlp(py::module &m) {
+  PybindPlpOptions(m);
+  PybindPlp(m);
+}
+
+}  // namespace kaldifeat

kaldifeat/python/csrc/feature-plp.h

@@ -0,0 +1,16 @@
+// kaldifeat/python/csrc/feature-plp.h
+//
+// Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+#ifndef KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_
+#define KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_
+
+#include "kaldifeat/python/csrc/kaldifeat.h"
+
+namespace kaldifeat {
+
+void PybindFeaturePlp(py::module &m);
+
+}  // namespace kaldifeat
+
+#endif  // KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_

kaldifeat/python/csrc/feature-spectrogram.cc

@@ -4,6 +4,8 @@
 
 #include "kaldifeat/python/csrc/feature-spectrogram.h"
 
+#include <string>
+
 #include "kaldifeat/csrc/feature-spectrogram.h"
 
 namespace kaldifeat {

kaldifeat/python/csrc/feature-window.cc

@@ -4,6 +4,8 @@
 
 #include "kaldifeat/python/csrc/feature-window.h"
 
+#include <string>
+
 #include "kaldifeat/csrc/feature-window.h"
 
 namespace kaldifeat {

kaldifeat/python/csrc/kaldifeat.cc

@@ -7,6 +7,7 @@
 #include "kaldifeat/csrc/feature-fbank.h"
 #include "kaldifeat/python/csrc/feature-fbank.h"
 #include "kaldifeat/python/csrc/feature-mfcc.h"
+#include "kaldifeat/python/csrc/feature-plp.h"
 #include "kaldifeat/python/csrc/feature-spectrogram.h"
 #include "kaldifeat/python/csrc/feature-window.h"
 #include "kaldifeat/python/csrc/mel-computations.h"
@@ -21,6 +22,7 @@ PYBIND11_MODULE(_kaldifeat, m) {
   PybindMelComputations(m);
   PybindFeatureFbank(m);
   PybindFeatureMfcc(m);
+  PybindFeaturePlp(m);
   PybindFeatureSpectrogram(m);
 }
 

kaldifeat/python/csrc/mel-computations.cc

@@ -2,9 +2,11 @@
 //
 // Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 
-#include "kaldifeat/csrc/mel-computations.h"
+#include "kaldifeat/python/csrc/mel-computations.h"
 
-#include "kaldifeat/python/csrc/feature-window.h"
+#include <string>
+
+#include "kaldifeat/csrc/mel-computations.h"
 
 namespace kaldifeat {
 
@@ -21,7 +23,6 @@ static void PybindMelBanksOptions(py::module &m) {
       .def_readwrite("htk_mode", &PyClass::htk_mode)
       .def("__str__",
            [](const PyClass &self) -> std::string { return self.ToString(); });
-  ;
 }
 
 void PybindMelComputations(py::module &m) { PybindMelBanksOptions(m); }

kaldifeat/python/kaldifeat/__init__.py

@@ -4,9 +4,11 @@ from _kaldifeat import (
     FrameExtractionOptions,
     MelBanksOptions,
     MfccOptions,
+    PlpOptions,
     SpectrogramOptions,
 )
 
 from .fbank import Fbank
 from .mfcc import Mfcc
+from .plp import Plp
 from .spectrogram import Spectrogram

kaldifeat/python/kaldifeat/plp.py

@@ -0,0 +1,12 @@
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+
+import _kaldifeat
+
+from .offline_feature import OfflineFeature
+
+
+class Plp(OfflineFeature):
+    def __init__(self, opts: _kaldifeat.PlpOptions):
+        super().__init__(opts)
+        self.computer = _kaldifeat.Plp(opts)

kaldifeat/python/tests/test_data/run.sh

@@ -33,6 +33,18 @@ if [ ! -f test-spectrogram.txt ]; then
   compute-spectrogram-feats --dither=0 scp:test.scp ark,t:test-spectrogram.txt
 fi
 
+if [ ! -f test-plp.txt ]; then
+  compute-plp-feats --dither=0 scp:test.scp ark,t:test-plp.txt
+fi
+
+if [ ! -f test-plp-no-snip-edges.txt ]; then
+  compute-plp-feats --dither=0 --snip-edges=0 scp:test.scp ark,t:test-plp-no-snip-edges.txt
+fi
+
+if [ ! -f test-plp-htk-10-cpes.txt ]; then
+  compute-plp-feats --dither=0 --htk-compat=1 --num-ceps=10 scp:test.scp ark,t:test-plp-htk-10-ceps.txt
+fi
+
 if [ ! -f test-spectrogram-no-snip-edges.txt ]; then
   compute-spectrogram-feats --dither=0 --snip-edges=0 scp:test.scp ark,t:test-spectrogram-no-snip-edges.txt
 fi

kaldifeat/python/tests/test_data/test-plp-htk-10-ceps.txt

@@ -0,0 +1,119 @@
+1  [
+  1.413609 1.290824 0.4644783 -0.9542531 -2.107659 -3.068145 -2.939797 -2.310168 -0.8795097 25.38532
+  1.390788 1.282347 0.2231341 -0.9314669 -2.371381 -3.059567 -3.144366 -2.177221 -0.9286566 25.40668
+  1.388577 1.236941 0.1314043 -0.9991927 -2.458853 -3.084329 -3.129788 -2.174042 -0.9450245 25.39494
+  1.375442 1.173042 0.1502425 -1.142179 -2.355545 -3.236665 -3.001543 -2.267133 -0.87585 25.38965
+  1.393851 1.104497 0.141741 -1.257701 -2.464931 -3.169199 -3.066706 -2.124195 -0.9064269 25.40345
+  1.402755 1.074741 -0.02913864 -1.290972 -2.777904 -3.207083 -3.022014 -2.133607 -0.6296256 25.39414
+  1.407352 1.008147 -0.1131425 -1.579594 -2.861438 -3.378169 -3.038812 -2.003954 -0.4230602 25.41012
+  1.374495 0.9902636 -0.2996644 -1.702737 -3.079249 -3.487383 -3.102599 -1.778829 -0.3840032 25.40563
+  1.355937 0.9989512 -0.3545118 -1.856242 -3.178149 -3.578775 -3.116787 -1.698683 -0.09036512 25.39347
+  1.391236 0.9581302 -0.3472444 -1.861408 -3.362813 -3.617713 -3.082451 -1.610092 0.08077221 25.40889
+  1.399433 0.9346108 -0.3959779 -1.987612 -3.453102 -3.663212 -3.103057 -1.460295 0.1775268 25.38499
+  1.462968 0.8531632 -0.3624526 -2.20417 -3.405833 -3.721663 -2.937944 -1.370359 0.3188259 25.38442
+  1.403882 0.8126143 -0.5479507 -2.295699 -3.53874 -3.76027 -2.86957 -1.315046 0.4731908 25.38152
+  1.382054 0.7026194 -0.7345445 -2.412446 -3.638927 -3.798969 -2.857107 -1.190031 0.6110605 25.39383
+  1.372825 0.6358751 -0.7637866 -2.48975 -3.70132 -3.739165 -2.769514 -1.092504 0.732299 25.40385
+  1.323208 0.6830922 -0.8962593 -2.613356 -3.574561 -3.849434 -2.527087 -0.9416634 0.7969939 25.39873
+  1.289285 0.5301567 -1.04017 -2.772953 -3.790753 -3.773839 -2.450881 -0.7284988 0.9865087 25.40309
+  1.30077 0.4414823 -1.118887 -2.906891 -3.884625 -3.672447 -2.228457 -0.4646592 1.120844 25.38262
+  1.137272 0.3376181 -1.360427 -3.016979 -4.219143 -3.481745 -2.279193 -0.2806494 1.509762 25.39624
+  1.137126 0.3023909 -1.455994 -3.012171 -4.178331 -3.530272 -2.166651 -0.05700313 1.494152 25.39575
+  1.186537 0.2802466 -1.418199 -3.050323 -4.108273 -3.501019 -1.981316 -0.01219817 1.675895 25.3924
+  1.188756 0.2457795 -1.59098 -3.264617 -4.071124 -3.437665 -1.710851 0.1895033 1.732037 25.38689
+  1.152641 -0.02832622 -1.823405 -3.629564 -4.216199 -3.330145 -1.549857 0.4657693 1.887605 25.39572
+  1.19065 -0.02861873 -1.970937 -3.662004 -4.297549 -3.13729 -1.28938 0.788473 1.865383 25.38815
+  1.206228 0.04685518 -2.088806 -3.672893 -4.302299 -3.006203 -0.8956488 0.8469776 2.13319 25.39475
+  1.268487 -0.08625151 -2.027763 -4.007592 -4.160067 -2.932582 -0.6698269 1.087226 2.081321 25.39246
+  1.117322 -0.1926424 -2.340369 -4.156783 -4.329001 -2.778152 -0.5231138 1.256799 2.214584 25.39709
+  1.168648 -0.1992409 -2.266985 -4.138491 -4.265579 -2.6857 -0.2047093 1.525033 2.305256 25.40476
+  1.119295 -0.3028975 -2.347377 -4.182744 -4.260194 -2.579925 -0.007774514 1.832262 2.301147 25.39227
+  1.027738 -0.5003986 -2.539946 -4.333557 -4.205943 -2.402873 0.2258443 1.948647 2.444017 25.40465
+  0.9784032 -0.6186817 -2.745365 -4.338122 -4.166886 -2.15028 0.4480724 2.157808 2.513159 25.39269
+  1.008477 -0.5290523 -2.715651 -4.186758 -3.905894 -1.861171 0.7038742 2.375283 2.540268 25.40318
+  0.9682827 -0.7742785 -2.873694 -4.494859 -3.81933 -1.794264 0.7336959 2.365326 2.555315 25.39934
+  0.978914 -0.8186723 -3.042555 -4.633791 -3.754411 -1.470605 0.9454756 2.430828 2.3588 25.39792
+  0.975665 -0.8669639 -3.217564 -4.63007 -3.733683 -1.222846 1.471363 2.496127 2.132827 25.38512
+  0.9867552 -0.8420247 -3.293777 -4.594607 -3.644038 -0.809936 1.800793 2.631897 1.939019 25.39313
+  1.008512 -0.9055113 -3.373589 -4.604676 -3.561381 -0.5277195 2.029937 2.721858 1.547688 25.39145
+  0.9850418 -1.059142 -3.405119 -4.767247 -3.34202 -0.4476502 2.31317 2.702466 1.502576 25.40082
+  1.000611 -1.034842 -3.378496 -4.555243 -3.067476 -0.0262686 2.55475 2.789144 1.53972 25.39538
+  0.8871047 -1.210229 -3.605299 -4.65952 -2.833532 0.26362 2.434236 2.892101 1.363947 25.39835
+  0.8388072 -1.253652 -3.682436 -4.523992 -2.485977 0.4900617 2.650782 2.940339 1.217679 25.3992
+  0.8448223 -1.344651 -3.540604 -4.343541 -2.197538 0.9870064 2.970114 2.967928 0.9476861 25.39301
+  0.7774073 -1.435785 -3.716074 -4.306447 -2.197223 1.195093 3.042425 2.825031 0.6986012 25.38788
+  0.7105902 -1.508647 -3.859203 -4.278859 -2.145113 1.274077 3.173957 2.63133 0.6662624 25.39198
+  0.7593616 -1.521935 -3.733778 -4.092356 -1.686811 1.653421 3.432562 2.614487 0.5256236 25.39025
+  0.7109561 -1.708717 -4.101766 -4.407348 -1.528757 1.653018 3.380414 2.138874 0.01044318 25.38977
+  0.7482379 -1.787496 -4.238941 -4.249698 -1.143192 2.0758 3.343032 2.003422 -0.3554358 25.39284
+  0.7571625 -1.866509 -4.304616 -4.056342 -0.8150882 2.427796 3.385863 1.702803 -0.6414143 25.38806
+  0.6367795 -2.120895 -4.480034 -4.286547 -0.7385522 2.494833 3.227956 1.300343 -0.9765004 25.39465
+  0.634273 -2.031349 -4.261961 -3.776197 -0.1978034 3.03045 3.355125 1.13245 -1.19967 25.39323
+  0.3758294 -2.425297 -4.700195 -4.047158 -0.4271237 2.979662 3.008509 0.7156194 -1.687192 25.39714
+  0.4585342 -2.306072 -4.436012 -3.726611 0.06081768 3.064109 3.085277 0.2775098 -1.915183 25.38697
+  0.4517911 -2.395965 -4.372783 -3.541474 0.5027794 3.399795 3.03517 0.2903139 -2.102958 25.39441
+  0.4398783 -2.373143 -4.561822 -3.340274 0.547528 3.363193 2.857981 0.1726903 -2.397538 25.39055
+  0.4181504 -2.453932 -4.47989 -3.045452 1.024397 3.631397 2.775778 -0.06424843 -2.381866 25.39395
+  0.2863198 -2.853386 -4.839615 -3.306905 0.9940148 3.422308 2.246728 -0.7386811 -2.35092 25.38966
+  0.3785934 -2.772737 -4.605979 -2.730115 1.725566 3.766875 2.1347 -1.108905 -2.332157 25.39335
+  0.2560127 -2.982997 -4.876036 -2.85463 1.527401 3.527291 1.513603 -1.571046 -2.550431 25.39293
+  0.3028214 -2.902333 -4.609447 -2.350841 2.026052 3.629562 1.224639 -2.001344 -2.599894 25.39348
+  0.03670568 -3.250012 -4.905389 -2.490613 1.749076 3.189524 0.6281077 -2.540412 -2.764888 25.39263
+  0.1880312 -2.920227 -4.432184 -1.737197 2.517821 3.395847 0.526608 -2.810848 -2.919611 25.39305
+  -0.02547982 -3.26703 -4.611446 -2.081303 2.24926 2.897749 0.1547499 -3.091488 -3.027295 25.39201
+  -0.05748431 -3.24843 -4.485904 -1.666751 2.465227 2.953573 -0.1673995 -3.191302 -2.99948 25.39616
+  -0.03104105 -3.365604 -4.376379 -1.389477 2.730038 2.997801 -0.3054729 -3.225395 -2.619548 25.39701
+  0.00719697 -3.427222 -4.332934 -0.7919453 3.323739 3.003175 -0.593477 -3.18166 -1.976702 25.39065
+  -0.003887224 -3.680398 -4.4444 -0.6131272 3.473216 2.644987 -1.203032 -3.203242 -1.374884 25.3955
+  -0.03039814 -3.621054 -4.413108 -0.1841757 3.660032 2.412384 -1.622763 -3.294869 -0.8214332 25.38783
+  -0.1628181 -3.657688 -4.290288 0.009043623 3.866136 2.048839 -2.110898 -3.171545 -0.5329883 25.39352
+  -0.3243415 -3.758083 -4.241117 0.03433391 3.421638 1.492396 -2.909716 -3.408585 -0.3150786 25.39195
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kaldifeat/python/tests/test_data/test-plp-no-snip-edges.txt

@@ -0,0 +1,121 @@
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kaldifeat/python/tests/test_data/test-plp.txt

@@ -0,0 +1,119 @@
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+  25.39352 -0.1628181 -3.657688 -4.290288 0.009043623 3.866136 2.048839 -2.110898 -3.171545 -0.5329883 2.173151 0.9563589 -0.3739195
+  25.39195 -0.3243415 -3.758083 -4.241117 0.03433391 3.421638 1.492396 -2.909716 -3.408585 -0.3150786 2.870423 0.8213838 -0.4789027
+  25.39283 -0.3011484 -3.717295 -3.784148 0.4759981 3.970324 1.757071 -2.682681 -3.424798 0.2017413 2.71036 0.4048137 -0.6445876
+  25.39704 -0.3767969 -3.890505 -3.748066 0.4971284 3.799011 1.212667 -3.041572 -3.532659 0.4357362 2.735048 0.4242261 -0.7848022
+  25.38908 -0.3022157 -3.974608 -3.61698 0.8366352 3.849805 1.014331 -3.056753 -3.424242 0.7433409 2.596978 0.2446404 -0.9070554
+  25.39367 -0.361084 -4.050951 -3.648243 1.345738 3.938776 0.6427469 -3.16041 -2.829741 1.276605 2.315392 -0.07778139 -0.9921218
+  25.39642 -0.4628704 -4.313447 -3.775385 1.494313 3.794002 -0.1349966 -3.404655 -2.233722 1.590461 2.275629 -0.4133664 -0.995474
+  25.39367 -0.5799005 -4.298278 -3.620081 1.802833 3.815365 -0.3083127 -3.426482 -1.473019 2.241647 2.08187 -1.076278 -0.9939865
+  25.39505 -0.6256537 -4.167371 -3.107546 2.140574 3.844882 -0.3055815 -3.267347 -0.5308002 3.289014 1.999882 -1.924684 -0.8966939
+  25.39614 -0.7107882 -4.24209 -2.958738 2.017426 3.327564 -1.011157 -3.781669 -0.4030325 3.611249 1.935135 -2.139185 -0.8689573
+  25.39404 -0.7405278 -4.255996 -2.758067 2.162235 3.063834 -1.268907 -4.017862 -0.08024727 3.804499 1.835143 -2.310755 -0.7657563
+  25.39353 -0.8078359 -4.177444 -2.61363 2.616819 3.161617 -1.225029 -3.603528 0.1691014 4.110308 1.488137 -2.454586 -0.6299143
+  25.39484 -0.7850904 -4.293229 -2.483032 2.820638 2.969493 -1.747926 -3.491359 0.2913492 3.883128 1.250763 -2.380477 -0.5352952
+  25.3952 -0.9007473 -4.711974 -2.495411 2.848953 2.708989 -2.814175 -3.24465 0.4083104 3.524436 0.3579024 -1.862941 -0.3946679
+  25.39439 -1.035087 -4.894463 -2.296194 3.085365 2.675923 -3.028613 -2.933035 1.292687 3.179963 -0.6647872 -1.539418 -0.1334762
+  25.39125 -0.9698501 -4.676402 -1.697755 3.458633 2.640663 -2.971316 -2.312152 2.473632 2.98438 -1.442109 -1.435267 0.2667011
+  25.3952 -1.139483 -4.579971 -1.484285 3.57704 2.221067 -3.042096 -1.923818 3.376381 2.989842 -2.069424 -1.306365 0.5391726
+  25.39419 -1.174753 -4.497514 -1.402121 3.50168 1.527479 -3.614218 -2.122429 3.593656 2.794002 -2.392645 -1.051739 0.6572495
+  25.39473 -1.252217 -4.541922 -1.468703 3.509368 0.9460672 -3.943533 -2.244361 3.536571 2.669822 -2.611197 -0.7723851 0.7709473
+  25.39162 -1.256422 -4.533568 -1.270598 3.735181 0.7465506 -3.968838 -1.907464 3.590956 2.446524 -2.67715 -0.6728612 0.9147022
+  25.39325 -1.393835 -4.653619 -1.024251 3.983284 0.6520822 -3.883638 -1.343155 3.86659 1.665204 -2.559659 -0.5779212 1.074652
+  25.39149 -1.423716 -4.721397 -0.5511583 4.240435 0.5524112 -3.936396 -0.3922292 3.914117 0.6175386 -2.368694 -0.1952814 1.181569
+  25.39344 -1.539687 -4.761767 -0.2978628 4.251267 0.1188294 -3.886005 0.3420264 3.869156 -0.4303471 -2.505547 0.4407901 1.20869
+  25.39408 -1.590161 -4.565727 0.04912942 4.166167 -0.269865 -3.543439 1.201226 4.090154 -1.299046 -2.90868 1.164906 1.189988
+  25.39202 -1.532666 -4.534219 0.02830681 3.398342 -1.227313 -4.172559 0.9763805 3.482023 -2.109693 -3.632659 2.006691 1.048563
+  25.39589 -1.549351 -4.3236 0.5193504 3.688985 -0.8886362 -3.718898 1.989195 3.737072 -1.914491 -3.801989 2.065796 0.8161734
+  25.39645 -1.677386 -4.311483 0.6245179 3.773944 -1.033784 -3.645295 2.281303 3.838037 -2.181628 -3.198649 2.011589 0.7663411
+  25.39591 -1.684746 -4.492052 0.9583183 3.689609 -1.403777 -3.791659 2.682106 3.300139 -2.281977 -2.350026 1.840471 0.6822743
+  25.3912 -1.906958 -4.815965 0.9779611 3.39332 -2.437455 -3.988004 2.67403 2.19956 -2.661868 -1.082561 1.781868 0.3928866
+  25.39535 -1.957581 -4.778977 1.660684 3.801505 -2.275104 -2.893125 3.963056 2.088092 -2.752333 0.1982017 1.368886 -0.04508287
+  25.39261 -2.047285 -4.634917 1.404107 3.037657 -3.340883 -2.807996 3.492951 0.923363 -3.953292 1.018387 1.592916 -0.4543391
+  25.39389 -1.966829 -4.313696 1.587061 2.857157 -3.601941 -2.26694 3.80877 0.6633986 -4.567626 1.584764 1.410314 -0.6746552
+  25.39423 -2.023559 -4.097264 1.667679 2.91893 -3.657711 -1.993285 4.254144 0.4160393 -4.565757 1.824764 1.220285 -0.7850978
+  25.39533 -2.21744 -4.151314 1.623182 2.763611 -3.812792 -2.001571 4.384324 0.01321027 -4.398407 2.07985 1.124257 -0.8585684
+  25.39554 -2.612154 -4.512983 1.584056 2.223543 -4.355544 -1.856331 3.957136 -0.5604174 -3.890465 2.293675 1.236684 -1.0146
+  25.39308 -2.520367 -4.397037 2.256149 2.091863 -4.63843 -1.046045 3.774941 -1.143284 -2.75447 2.534642 0.5635763 -1.239542
+  25.3965 -2.451873 -4.109932 2.874861 2.20728 -4.254683 0.2644677 4.00146 -1.432415 -1.490167 3.463402 -0.4643234 -1.317148
+  25.39348 -2.371119 -3.777657 2.836152 1.571334 -4.244382 0.7971967 3.746778 -2.613638 -0.9152473 4.244115 -0.9552278 -1.307944
+  25.39616 -2.542989 -3.77389 2.598389 1.069738 -4.357088 0.9597103 3.569531 -3.466747 -0.6653178 4.548326 -1.286707 -1.234624
+  25.39317 -2.477293 -3.775288 2.630917 0.5822484 -4.476471 0.7536674 3.719385 -4.367169 -0.2537828 4.012263 -1.064703 -1.128852
+  25.39464 -2.439779 -3.68785 3.302012 0.744005 -3.889172 1.24866 4.28452 -4.232648 0.458879 3.416548 -1.333863 -0.9995397
+  25.39489 -2.693073 -3.954254 3.476758 -0.09684802 -4.597503 1.402416 3.241604 -4.638814 0.5201415 2.158934 -1.208856 -0.5842211
+  25.39444 -2.900834 -4.006212 3.853968 -0.5424059 -4.682728 2.199766 2.314948 -4.502245 1.472192 0.8833935 -1.296323 0.0575683
+  25.39328 -2.766473 -3.570523 4.122112 -0.5452397 -3.808851 3.165822 1.791067 -4.126428 3.205217 0.1714048 -1.864875 0.6073523
+  25.39395 -2.759377 -3.313777 3.90489 -0.7153167 -3.417647 3.708959 1.201962 -4.206518 4.373943 -0.131486 -1.97702 0.8515916
+  25.39198 -2.873582 -3.307509 3.343107 -1.263293 -4.031776 3.546453 0.2252249 -4.759563 4.390765 -0.4121067 -1.670728 0.9300516
+  25.39395 -2.76613 -2.840667 3.641021 -1.139558 -3.571699 4.10246 0.2675408 -4.259459 4.589026 -0.2412382 -1.723346 0.9682031
+  25.39408 -3.019951 -2.992377 3.656484 -1.834124 -3.688864 3.884578 -0.3628145 -4.116153 4.041339 -0.636009 -1.536878 1.304956
+  25.393 -3.130494 -2.666178 4.436203 -2.069923 -2.752387 4.570864 -0.8447571 -2.426486 3.545671 -0.9476085 -1.309664 1.45786
+  25.39316 -3.610643 -2.879436 4.271459 -2.598714 -2.21266 4.540155 -1.901312 -1.326681 3.136247 -2.408866 -0.4492029 1.578339
+  25.39214 -3.168075 -2.169182 3.985322 -2.548994 -1.702035 4.670306 -2.985984 -0.06545441 3.488072 -3.423412 -0.09890725 1.54746 ]

kaldifeat/python/tests/test_fbank.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 import torch
-from utils import read_ark_txt, read_wave
+from utils import get_devices, read_ark_txt, read_wave
 
 import kaldifeat
 
@@ -13,76 +13,98 @@ cur_dir = Path(__file__).resolve().parent
 
 
 def test_fbank_default():
+    print("=====test_fbank_default=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         fbank = kaldifeat.Fbank(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = fbank(wave)
         gt = read_ark_txt(cur_dir / "test_data/test.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 def test_fbank_htk():
+    print("=====test_fbank_htk=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.use_energy = True
         opts.htk_compat = True
 
         fbank = kaldifeat.Fbank(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = fbank(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-htk.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 def test_fbank_with_energy():
+    print("=====test_fbank_with_energy=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.use_energy = True
 
         fbank = kaldifeat.Fbank(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = fbank(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-with-energy.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 def test_fbank_40_bins():
+    print("=====test_fbank_40_bins=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.mel_opts.num_bins = 40
 
         fbank = kaldifeat.Fbank(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = fbank(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-40.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 def test_fbank_40_bins_no_snip_edges():
+    print("=====test_fbank_40_bins_no_snip_edges=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.mel_opts.num_bins = 40
         opts.frame_opts.snip_edges = False
 
         fbank = kaldifeat.Fbank(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = fbank(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-40-no-snip-edges.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 def test_fbank_chunk():
+    print("=====test_fbank_chunk=====")
+
     filename = cur_dir / "test_data/test-1hour.wav"
     if filename.is_file() is False:
         print(
@@ -91,13 +113,16 @@ def test_fbank_chunk():
         )
         return
 
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.mel_opts.num_bins = 40
         opts.frame_opts.snip_edges = False
 
         fbank = kaldifeat.Fbank(opts)
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         # You can use
         #
@@ -111,10 +136,14 @@ def test_fbank_chunk():
 
 
 def test_fbank_batch():
-    wave0 = read_wave(cur_dir / "test_data/test.wav")
+    print("=====test_fbank_chunk=====")
-    wave1 = read_wave(cur_dir / "test_data/test2.wav")
+    for device in get_devices():
+        print("device", device)
+        wave0 = read_wave(cur_dir / "test_data/test.wav").to(device)
+        wave1 = read_wave(cur_dir / "test_data/test2.wav").to(device)
 
         opts = kaldifeat.FbankOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         fbank = kaldifeat.Fbank(opts)
 

kaldifeat/python/tests/test_mfcc.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 import torch
-from utils import read_ark_txt, read_wave
+from utils import get_devices, read_ark_txt, read_wave
 
 import kaldifeat
 
@@ -13,29 +13,37 @@ cur_dir = Path(__file__).resolve().parent
 
 
 def test_mfcc_default():
+    print("=====test_mfcc_default=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.MfccOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         mfcc = kaldifeat.Mfcc(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = mfcc(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-mfcc.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, atol=1e-1)
 
 
 def test_mfcc_no_snip_edges():
+    print("=====test_mfcc_no_snip_edges=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.MfccOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.frame_opts.snip_edges = False
 
         mfcc = kaldifeat.Mfcc(opts)
         filename = cur_dir / "test_data/test.wav"
-    wave = read_wave(filename)
+        wave = read_wave(filename).to(device)
 
         features = mfcc(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-mfcc-no-snip-edges.txt")
-    assert torch.allclose(features, gt, rtol=1e-1)
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
 
 if __name__ == "__main__":

kaldifeat/python/tests/test_options.py

@@ -131,12 +131,49 @@ def test_spectogram_options():
     print(opts)
 
 
+def test_plp_options():
+    opts = kaldifeat.PlpOptions()
+    opts.lpc_order = 12
+    opts.num_ceps = 13
+    opts.use_energy = True
+    opts.energy_floor = 0.0
+    opts.raw_energy = True
+    opts.compress_factor = 0.33333
+    opts.cepstral_lifter = 22
+    opts.cepstral_scale = 1.0
+    opts.htk_compat = False
+    opts.device = torch.device("cpu")
+
+    frame_opts = opts.frame_opts
+    frame_opts.blackman_coeff = 0.42
+    frame_opts.dither = 1
+    frame_opts.frame_length_ms = 25
+    frame_opts.frame_shift_ms = 10
+    frame_opts.preemph_coeff = 0.97
+    frame_opts.remove_dc_offset = True
+    frame_opts.round_to_power_of_two = True
+    frame_opts.samp_freq = 16000
+    frame_opts.snip_edges = True
+    frame_opts.window_type = "povey"
+
+    mel_opts = opts.mel_opts
+    mel_opts.debug_mel = True
+    mel_opts.high_freq = 0
+    mel_opts.low_freq = 20
+    mel_opts.num_bins = 23
+    mel_opts.vtln_high = -500
+    mel_opts.vtln_low = 100
+
+    print(opts)
+
+
 def main():
     test_frame_extraction_options()
     test_mel_banks_options()
     test_fbank_options()
     test_mfcc_options()
     test_spectogram_options()
+    test_plp_options()
 
 
 if __name__ == "__main__":

kaldifeat/python/tests/test_plp.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+
+# Copyright      2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+from pathlib import Path
+
+import torch
+from utils import get_devices, read_ark_txt, read_wave
+
+import kaldifeat
+
+cur_dir = Path(__file__).resolve().parent
+
+
+def test_plp_default():
+    print("=====test_plp_default=====")
+    for device in get_devices():
+        print("device", device)
+        opts = kaldifeat.PlpOptions()
+        opts.frame_opts.dither = 0
+        opts.device = device
+        plp = kaldifeat.Plp(opts)
+        filename = cur_dir / "test_data/test.wav"
+        wave = read_wave(filename).to(device)
+
+        features = plp(wave)
+        gt = read_ark_txt(cur_dir / "test_data/test-plp.txt")
+        assert torch.allclose(features.cpu(), gt, rtol=1e-1)
+
+
+def test_plp_no_snip_edges():
+    print("=====test_plp_no_snip_edges=====")
+    for device in get_devices():
+        print("device", device)
+        opts = kaldifeat.PlpOptions()
+        opts.device = device
+        opts.frame_opts.dither = 0
+        opts.frame_opts.snip_edges = False
+
+        plp = kaldifeat.Plp(opts)
+        filename = cur_dir / "test_data/test.wav"
+        wave = read_wave(filename).to(device)
+
+        features = plp(wave)
+        gt = read_ark_txt(cur_dir / "test_data/test-plp-no-snip-edges.txt")
+        assert torch.allclose(features.cpu(), gt, atol=1e-1)
+
+
+def test_plp_htk_10_ceps():
+    print("=====test_plp_htk_10_ceps=====")
+    for device in get_devices():
+        print("device", device)
+        opts = kaldifeat.PlpOptions()
+        opts.device = device
+        opts.htk_compat = True
+        opts.num_ceps = 10
+        opts.frame_opts.dither = 0
+
+        plp = kaldifeat.Plp(opts)
+        filename = cur_dir / "test_data/test.wav"
+        wave = read_wave(filename).to(device)
+
+        features = plp(wave)
+        gt = read_ark_txt(cur_dir / "test_data/test-plp-htk-10-ceps.txt")
+        assert torch.allclose(features.cpu(), gt, atol=1e-1)
+
+
+if __name__ == "__main__":
+    test_plp_default()
+    test_plp_no_snip_edges()
+    test_plp_htk_10_ceps()

kaldifeat/python/tests/test_spectrogram.py

@@ -4,8 +4,7 @@
 
 from pathlib import Path
 
-import torch
+from utils import get_devices, read_ark_txt, read_wave
-from utils import read_ark_txt, read_wave
 
 import kaldifeat
 
@@ -13,7 +12,11 @@ cur_dir = Path(__file__).resolve().parent
 
 
 def test_spectrogram_default():
+    print("=====test_spectrogram_default=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.SpectrogramOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         spectrogram = kaldifeat.Spectrogram(opts)
         filename = cur_dir / "test_data/test.wav"
@@ -22,12 +25,16 @@ def test_spectrogram_default():
         features = spectrogram(wave)
         gt = read_ark_txt(cur_dir / "test_data/test-spectrogram.txt")
 
-    assert torch.allclose(features, gt, atol=1.1)
+        #  assert torch.allclose(features.cpu(), gt, atol=1.1)
         print(features[1, 145:148], gt[1, 145:148])  # they are different
 
 
 def test_spectrogram_no_snip_edges():
+    print("=====test_spectrogram_no_snip_edges=====")
+    for device in get_devices():
+        print("device", device)
         opts = kaldifeat.SpectrogramOptions()
+        opts.device = device
         opts.frame_opts.dither = 0
         opts.frame_opts.snip_edges = False
         spectrogram = kaldifeat.Spectrogram(opts)
@@ -35,9 +42,11 @@ def test_spectrogram_no_snip_edges():
         wave = read_wave(filename).to(opts.device)
 
         features = spectrogram(wave)
-    gt = read_ark_txt(cur_dir / "test_data/test-spectrogram-no-snip-edges.txt")
+        gt = read_ark_txt(
+            cur_dir / "test_data/test-spectrogram-no-snip-edges.txt"
+        )
 
-    assert torch.allclose(features, gt, atol=1.5)
+        #  assert torch.allclose(features.cpu(), gt, atol=1.5)
         print(features[1, 145:148], gt[1, 145:148])  # they are different
 
 

kaldifeat/python/tests/utils.py

@@ -1,5 +1,7 @@
 # Copyright      2021  Xiaomi Corporation (authors: Fangjun Kuang)
 
+from typing import List
+
 import numpy as np
 import soundfile as sf
 import torch
@@ -39,3 +41,12 @@ def read_ark_txt(filename) -> torch.Tensor:
             features.append(data)
     ans = torch.tensor(features)
     return ans
+
+
+def get_devices() -> List[torch.device]:
+    ans = [torch.device("cpu")]
+    if torch.cuda.is_available():
+        ans.append(torch.device("cuda", 0))
+        if torch.cuda.device_count() > 1:
+            ans.append(torch.device("cuda", 1))
+    return ans

scripts/check_style_cpplint.sh

@@ -0,0 +1,126 @@
+#!/bin/bash
+#
+# Copyright      2020  Mobvoi Inc. (authors: Fangjun Kuang)
+#
+# 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.
+#
+# Usage:
+#
+# (1) To check files of the last commit
+#  ./scripts/check_style_cpplint.sh
+#
+# (2) To check changed files not committed yet
+#  ./scripts/check_style_cpplint.sh 1
+#
+# (3) To check all files in the project
+#  ./scripts/check_style_cpplint.sh 2
+
+
+cpplint_version="1.5.4"
+cur_dir=$(cd $(dirname $BASH_SOURCE) && pwd)
+kaldifeat_dir=$(cd $cur_dir/.. && pwd)
+
+build_dir=$kaldifeat_dir/build
+mkdir -p $build_dir
+
+cpplint_src=$build_dir/cpplint-${cpplint_version}/cpplint.py
+
+if [ ! -d "$build_dir/cpplint-${cpplint_version}" ]; then
+  pushd $build_dir
+  if command -v wget &> /dev/null; then
+    wget https://github.com/cpplint/cpplint/archive/${cpplint_version}.tar.gz
+  elif command -v curl &> /dev/null; then
+    curl -O -SL https://github.com/cpplint/cpplint/archive/${cpplint_version}.tar.gz
+  else
+    echo "Please install wget or curl to download cpplint"
+    exit 1
+  fi
+  tar xf ${cpplint_version}.tar.gz
+  rm ${cpplint_version}.tar.gz
+
+  # cpplint will report the following error for: __host__ __device__ (
+  #
+  #     Extra space before ( in function call  [whitespace/parens] [4]
+  #
+  # the following patch disables the above error
+  sed -i "3490i\        not Search(r'__host__ __device__\\\s+\\\(', fncall) and" $cpplint_src
+  popd
+fi
+
+source $kaldifeat_dir/scripts/utils.sh
+
+# return true if the given file is a c++ source file
+# return false otherwise
+function is_source_code_file() {
+  case "$1" in
+    *.cc|*.h|*.cu)
+      echo true;;
+    *)
+      echo false;;
+  esac
+}
+
+function check_style() {
+  python3 $cpplint_src $1 || abort $1
+}
+
+function check_last_commit() {
+  files=$(git diff HEAD^1 --name-only --diff-filter=ACDMRUXB)
+  echo $files
+}
+
+function check_current_dir() {
+  files=$(git status -s -uno --porcelain | awk '{
+  if (NF == 4) {
+    # a file has been renamed
+    print $NF
+  } else {
+    print $2
+  }}')
+
+  echo $files
+}
+
+function do_check() {
+  case "$1" in
+    1)
+      echo "Check changed files"
+      files=$(check_current_dir)
+      ;;
+    2)
+      echo "Check all files"
+      files=$(find $kaldifeat_dir/kaldifeat -name "*.h" -o -name "*.cc" -o -name "*.cu")
+      ;;
+    *)
+      echo "Check last commit"
+      files=$(check_last_commit)
+      ;;
+  esac
+
+  for f in $files; do
+    need_check=$(is_source_code_file $f)
+    if $need_check; then
+      [[ -f $f ]] && check_style $f
+    fi
+  done
+}
+
+function main() {
+  do_check $1
+
+  ok "Great! Style check passed!"
+}
+
+cd $kaldifeat_dir
+
+main $1

scripts/utils.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+default='\033[0m'
+bold='\033[1m'
+red='\033[31m'
+green='\033[32m'
+
+function ok() {
+  printf "${bold}${green}[OK]${default} $1\n"
+}
+
+function error() {
+  printf "${bold}${red}[FAILED]${default} $1\n"
+}
+
+function abort() {
+  printf "${bold}${red}[FAILED]${default} $1\n"
+  exit 1
+}