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108 lines
3.1 KiB
C++
108 lines
3.1 KiB
C++
// kaldifeat/csrc/feature-fbank.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-fbank.h
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#ifndef KALDIFEAT_CSRC_FEATURE_FBANK_H_
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#define KALDIFEAT_CSRC_FEATURE_FBANK_H_
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#include <map>
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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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struct FbankOptions {
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FrameExtractionOptions frame_opts;
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MelBanksOptions mel_opts;
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// append an extra dimension with energy to the filter banks
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bool use_energy = false;
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float energy_floor = 0.0f; // active iff use_energy==true
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// If true, compute log_energy before preemphasis and windowing
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// If false, compute log_energy after preemphasis ans windowing
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bool raw_energy = true; // active iff use_energy==true
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// If true, put energy last (if using energy)
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// If false, put energy first
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bool htk_compat = false; // active iff use_energy==true
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// if true (default), produce log-filterbank, else linear
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bool use_log_fbank = true;
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// if true (default), use power in filterbank
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// analysis, else magnitude.
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bool use_power = true;
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torch::Device device;
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FbankOptions() : device("cpu") { 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 << "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 << "htk_compat: " << htk_compat << "\n";
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os << "use_log_fbank: " << use_log_fbank << "\n";
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os << "use_power: " << use_power << "\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 FbankOptions &opts);
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class FbankComputer {
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public:
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using Options = FbankOptions;
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explicit FbankComputer(const FbankOptions &opts);
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~FbankComputer();
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FbankComputer &operator=(const FbankComputer &) = delete;
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FbankComputer(const FbankComputer &) = delete;
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int32_t Dim() const {
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return opts_.mel_opts.num_bins + (opts_.use_energy ? 1 : 0);
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}
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// if true, compute log_energy_pre_window but after dithering and dc removal
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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 FbankOptions &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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FbankOptions opts_;
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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 Fbank = OfflineFeatureTpl<FbankComputer>;
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} // namespace kaldifeat
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#endif // KALDIFEAT_CSRC_FEATURE_FBANK_H_
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