// kaldi-native-fbank/csrc/feature-window.h
//
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
// This file is copied/modified from kaldi/src/feat/feature-window.h
#ifndef KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_
#define KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_
#include <sstream>
#include <string>
#include <vector>
#include "base/log.h"
namespace knf {
inline int32_t RoundUpToNearestPowerOfTwo(int32_t n) {
// copied from kaldi/src/base/kaldi-math.cc
CHECK_GT(n, 0);
n--;
n |= n >> 1;
n |= n >> 2;
n |= n >> 4;
n |= n >> 8;
n |= n >> 16;
return n + 1;
}
struct FrameExtractionOptions {
float samp_freq = 16000;
float frame_shift_ms = 10.0f; // in milliseconds.
float frame_length_ms = 25.0f; // in milliseconds.
float dither = 1.0f; // Amount of dithering, 0.0 means no dither.
float preemph_coeff = 0.97f; // Preemphasis coefficient.
bool remove_dc_offset = true; // Subtract mean of wave before FFT.
std::string window_type = "povey"; // e.g. Hamming window
// May be "hamming", "rectangular", "povey", "hanning", "sine", "blackman"
// "povey" is a window I made to be similar to Hamming but to go to zero at
// the edges, it's pow((0.5 - 0.5*cos(n/N*2*pi)), 0.85) I just don't think
// the
// Hamming window makes sense as a windowing function.
bool round_to_power_of_two = true;
float blackman_coeff = 0.42f;
bool snip_edges = true;
// bool allow_downsample = false;
// bool allow_upsample = false;
// Used for streaming feature extraction. It indicates the number
// of feature frames to keep in the recycling vector. -1 means to
// keep all feature frames.
int32_t max_feature_vectors = -1;
int32_t WindowShift() const {
return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
}
int32_t WindowSize() const {
return static_cast<int32_t>(samp_freq * 0.001f * frame_length_ms);
}
int32_t PaddedWindowSize() const {
return (round_to_power_of_two ? RoundUpToNearestPowerOfTwo(WindowSize())
: WindowSize());
}
std::string ToString() const {
std::ostringstream os;
#define KNF_PRINT(x) os << #x << ": " << x << "\n"
KNF_PRINT(samp_freq);
KNF_PRINT(frame_shift_ms);
KNF_PRINT(frame_length_ms);
KNF_PRINT(dither);
KNF_PRINT(preemph_coeff);
KNF_PRINT(remove_dc_offset);
KNF_PRINT(window_type);
KNF_PRINT(round_to_power_of_two);
KNF_PRINT(blackman_coeff);
KNF_PRINT(snip_edges);
// KNF_PRINT(allow_downsample);
// KNF_PRINT(allow_upsample);
KNF_PRINT(max_feature_vectors);
#undef KNF_PRINT
return os.str();
}
};
std::ostream &operator<<(std::ostream &os, const FrameExtractionOptions &opts);
class FeatureWindowFunction {
public:
FeatureWindowFunction() = default;
explicit FeatureWindowFunction(const FrameExtractionOptions &opts);
/**
* @param wave Pointer to a 1-D array of shape [window_size].
* It is modified in-place: wave[i] = wave[i] * window_[i].
* @param
*/
void Apply(float *wave) const;
private:
std::vector<float> window_; // of size opts.WindowSize()
};
int64_t FirstSampleOfFrame(int32_t frame, const FrameExtractionOptions &opts);
/**
This function returns the number of frames that we can extract from a wave
file with the given number of samples in it (assumed to have the same
sampling rate as specified in 'opts').
@param [in] num_samples The number of samples in the wave file.
@param [in] opts The frame-extraction options class
@param [in] flush True if we are asserting that this number of samples
is 'all there is', false if we expecting more data to possibly come in. This
only makes a difference to the answer
if opts.snips_edges== false. For offline feature extraction you always want
flush == true. In an online-decoding context, once you know (or decide) that
no more data is coming in, you'd call it with flush == true at the end to
flush out any remaining data.
*/
int32_t NumFrames(int64_t num_samples,
const FrameExtractionOptions &opts,
bool flush = true);
/*
ExtractWindow() extracts a windowed frame of waveform (possibly with a
power-of-two, padded size, depending on the config), including all the
processing done by ProcessWindow().
@param [in] sample_offset If 'wave' is not the entire waveform, but
part of it to the left has been discarded, then the
number of samples prior to 'wave' that we have
already discarded. Set this to zero if you are
processing the entire waveform in one piece, or
if you get 'no matching function' compilation
errors when updating the code.
@param [in] wave The waveform
@param [in] f The frame index to be extracted, with
0 <= f < NumFrames(sample_offset + wave.Dim(), opts, true)
@param [in] opts The options class to be used
@param [in] window_function The windowing function, as derived from the
options class.
@param [out] window The windowed, possibly-padded waveform to be
extracted. Will be resized as needed.
@param [out] log_energy_pre_window If non-NULL, the log-energy of
the signal prior to pre-emphasis and multiplying by
the windowing function will be written to here.
*/
void ExtractWindow(int64_t sample_offset,
const std::vector<float> &wave,
int32_t f,
const FrameExtractionOptions &opts,
const FeatureWindowFunction &window_function,
std::vector<float> *window,
float *log_energy_pre_window = nullptr);
/**
This function does all the windowing steps after actually
extracting the windowed signal: depending on the
configuration, it does dithering, dc offset removal,
preemphasis, and multiplication by the windowing function.
@param [in] opts The options class to be used
@param [in] window_function The windowing function-- should have
been initialized using 'opts'.
@param [in,out] window A vector of size opts.WindowSize(). Note:
it will typically be a sub-vector of a larger vector of size
opts.PaddedWindowSize(), with the remaining samples zero,
as the FFT code is more efficient if it operates on data with
power-of-two size.
@param [out] log_energy_pre_window If non-NULL, then after dithering and
DC offset removal, this function will write to this pointer the log of
the total energy (i.e. sum-squared) of the frame.
*/
void ProcessWindow(const FrameExtractionOptions &opts,
const FeatureWindowFunction &window_function,
float *window,
float *log_energy_pre_window = nullptr);
// Compute the inner product of two vectors
float InnerProduct(const float *a, const float *b, int32_t n);
} // namespace knf
#endif // KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_