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120 lines
3.3 KiB
C++
120 lines
3.3 KiB
C++
// kaldifeat/csrc/feature-mfcc.h
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//
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// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
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// This file is copied/modified from kaldi/src/feat/feature-mfcc.h
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#ifndef KALDIFEAT_CSRC_FEATURE_MFCC_H_
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#define KALDIFEAT_CSRC_FEATURE_MFCC_H_
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#include <map>
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#include <string>
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#include "kaldifeat/csrc/feature-common.h"
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#include "kaldifeat/csrc/feature-window.h"
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#include "kaldifeat/csrc/mel-computations.h"
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#include "torch/torch.h"
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namespace kaldifeat {
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/// MfccOptions contains basic options for computing MFCC features.
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// (this class is copied from kaldi)
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struct MfccOptions {
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FrameExtractionOptions frame_opts;
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MelBanksOptions mel_opts;
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// Number of cepstra in MFCC computation (including C0)
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int32_t num_ceps = 13;
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// Use energy (not C0) in MFCC computation
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bool use_energy = true;
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// Floor on energy (absolute, not relative) in MFCC
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// computation. Only makes a difference if use_energy=true;
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// only necessary if dither=0.0.
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// Suggested values: 0.1 or 1.0
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float energy_floor = 0.0;
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// If true, compute energy before preemphasis and windowing
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bool raw_energy = true;
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// Constant that controls scaling of MFCCs
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float cepstral_lifter = 22.0;
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// If true, put energy or C0 last and use a factor of
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// sqrt(2) on C0.
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// Warning: not sufficient to get HTK compatible features
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// (need to change other parameters)
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bool htk_compat = false;
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torch::Device device{"cpu"};
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MfccOptions() { mel_opts.num_bins = 23; }
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std::string ToString() const {
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std::ostringstream os;
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os << "frame_opts: \n";
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os << frame_opts << "\n";
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os << "\n";
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os << "mel_opts: \n";
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os << mel_opts << "\n";
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os << "num_ceps: " << num_ceps << "\n";
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os << "use_energy: " << use_energy << "\n";
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os << "energy_floor: " << energy_floor << "\n";
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os << "raw_energy: " << raw_energy << "\n";
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os << "cepstral_lifter: " << cepstral_lifter << "\n";
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os << "htk_compat: " << htk_compat << "\n";
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os << "device: " << device << "\n";
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return os.str();
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}
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};
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std::ostream &operator<<(std::ostream &os, const MfccOptions &opts);
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class MfccComputer {
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public:
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using Options = MfccOptions;
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explicit MfccComputer(const MfccOptions &opts);
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~MfccComputer();
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MfccComputer &operator=(const MfccComputer &) = delete;
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MfccComputer(const MfccComputer &) = delete;
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int32_t Dim() const { return opts_.num_ceps; }
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bool NeedRawLogEnergy() const { return opts_.use_energy && opts_.raw_energy; }
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const FrameExtractionOptions &GetFrameOptions() const {
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return opts_.frame_opts;
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}
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const MfccOptions &GetOptions() const { return opts_; }
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// signal_raw_log_energy is log_energy_pre_window, which is not empty
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// iff NeedRawLogEnergy() returns true.
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torch::Tensor Compute(torch::Tensor signal_raw_log_energy, float vtln_warp,
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const torch::Tensor &signal_frame);
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private:
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const MelBanks *GetMelBanks(float vtln_warp);
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MfccOptions opts_;
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torch::Tensor lifter_coeffs_; // 1-D tensor
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// Note we save a transposed version of dct_matrix_
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// dct_matrix_.rows is num_mel_bins
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// dct_matrix_.cols is num_ceps
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torch::Tensor dct_matrix_; // matrix we right-multiply by to perform DCT.
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float log_energy_floor_;
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std::map<float, MelBanks *> mel_banks_; // float is VTLN coefficient.
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};
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using Mfcc = OfflineFeatureTpl<MfccComputer>;
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} // namespace kaldifeat
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#endif // KALDIFEAT_CSRC_FEATURE_MFCC_H_
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