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588 lines
19 KiB
C++
588 lines
19 KiB
C++
#include <opencv2/core.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/dnn.hpp>
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#include <iostream>
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#include <vector>
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#include <string>
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#include <unordered_map>
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#include <cmath>
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#include <random>
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#include <numeric>
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using namespace cv;
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using namespace std;
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class FilterbankFeatures {
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// Initializes pre-processing class. Default values are the values used by the Jasper
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// architecture for pre-processing. For more details, refer to the paper here:
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// https://arxiv.org/abs/1904.03288
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private:
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int sample_rate = 16000;
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double window_size = 0.02;
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double window_stride = 0.01;
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int win_length = static_cast<int>(sample_rate * window_size); // Number of samples in window
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int hop_length = static_cast<int>(sample_rate * window_stride); // Number of steps to advance between frames
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int n_fft = 512; // Size of window for STFT
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// Parameters for filterbanks calculation
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int n_filt = 64;
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double lowfreq = 0.;
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double highfreq = sample_rate / 2;
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public:
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// Mel filterbanks preparation
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double hz_to_mel(double frequencies)
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{
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//Converts frequencies from hz to mel scale
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// Fill in the linear scale
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double f_min = 0.0;
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double f_sp = 200.0 / 3;
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double mels = (frequencies - f_min) / f_sp;
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// Fill in the log-scale part
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double min_log_hz = 1000.0; // beginning of log region (Hz)
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double min_log_mel = (min_log_hz - f_min) / f_sp; // same (Mels)
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double logstep = std::log(6.4) / 27.0; // step size for log region
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if (frequencies >= min_log_hz)
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{
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mels = min_log_mel + std::log(frequencies / min_log_hz) / logstep;
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}
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return mels;
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}
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vector<double> mel_to_hz(vector<double>& mels)
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{
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// Converts frequencies from mel to hz scale
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// Fill in the linear scale
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double f_min = 0.0;
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double f_sp = 200.0 / 3;
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vector<double> freqs;
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for (size_t i = 0; i < mels.size(); i++)
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{
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freqs.push_back(f_min + f_sp * mels[i]);
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}
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// And now the nonlinear scale
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double min_log_hz = 1000.0; // beginning of log region (Hz)
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double min_log_mel = (min_log_hz - f_min) / f_sp; // same (Mels)
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double logstep = std::log(6.4) / 27.0; // step size for log region
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for(size_t i = 0; i < mels.size(); i++)
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{
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if (mels[i] >= min_log_mel)
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{
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freqs[i] = min_log_hz * exp(logstep * (mels[i] - min_log_mel));
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}
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}
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return freqs;
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}
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vector<double> mel_frequencies(int n_mels, double fmin, double fmax)
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{
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// Calculates n mel frequencies between 2 frequencies
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double min_mel = hz_to_mel(fmin);
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double max_mel = hz_to_mel(fmax);
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vector<double> mels;
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double step = (max_mel - min_mel) / (n_mels - 1);
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for(double i = min_mel; i < max_mel; i += step)
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{
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mels.push_back(i);
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}
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mels.push_back(max_mel);
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vector<double> res = mel_to_hz(mels);
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return res;
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}
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vector<vector<double>> mel(int n_mels, double fmin, double fmax)
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{
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// Generates mel filterbank matrix
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double num = 1 + n_fft / 2;
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vector<vector<double>> weights(n_mels, vector<double>(static_cast<int>(num), 0.));
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// Center freqs of each FFT bin
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vector<double> fftfreqs;
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double step = (sample_rate / 2) / (num - 1);
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for(double i = 0; i <= sample_rate / 2; i += step)
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{
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fftfreqs.push_back(i);
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}
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// 'Center freqs' of mel bands - uniformly spaced between limits
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vector<double> mel_f = mel_frequencies(n_mels + 2, fmin, fmax);
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vector<double> fdiff;
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for(size_t i = 1; i < mel_f.size(); ++i)
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{
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fdiff.push_back(mel_f[i]- mel_f[i - 1]);
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}
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vector<vector<double>> ramps(mel_f.size(), vector<double>(fftfreqs.size()));
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for (size_t i = 0; i < mel_f.size(); ++i)
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{
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for (size_t j = 0; j < fftfreqs.size(); ++j)
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{
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ramps[i][j] = mel_f[i] - fftfreqs[j];
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}
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}
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double lower, upper, enorm;
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for (int i = 0; i < n_mels; ++i)
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{
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// using Slaney-style mel which is scaled to be approx constant energy per channel
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enorm = 2./(mel_f[i + 2] - mel_f[i]);
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for (int j = 0; j < static_cast<int>(num); ++j)
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{
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// lower and upper slopes for all bins
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lower = (-1) * ramps[i][j] / fdiff[i];
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upper = ramps[i + 2][j] / fdiff[i + 1];
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weights[i][j] = max(0., min(lower, upper)) * enorm;
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}
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}
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return weights;
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}
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// STFT preparation
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vector<double> pad_window_center(vector<double>&data, int size)
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{
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// Pad the window out to n_fft size
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int n = static_cast<int>(data.size());
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int lpad = static_cast<int>((size - n) / 2);
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vector<double> pad_array;
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for(int i = 0; i < lpad; ++i)
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{
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pad_array.push_back(0.);
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}
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for(size_t i = 0; i < data.size(); ++i)
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{
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pad_array.push_back(data[i]);
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}
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for(int i = 0; i < lpad; ++i)
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{
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pad_array.push_back(0.);
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}
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return pad_array;
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}
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vector<vector<double>> frame(vector<double>& x)
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{
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// Slices a data array into overlapping frames.
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int n_frames = static_cast<int>(1 + (x.size() - n_fft) / hop_length);
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vector<vector<double>> new_x(n_fft, vector<double>(n_frames));
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for (int i = 0; i < n_fft; ++i)
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{
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for (int j = 0; j < n_frames; ++j)
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{
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new_x[i][j] = x[i + j * hop_length];
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}
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}
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return new_x;
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}
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vector<double> hanning()
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{
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// https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
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vector<double> window_tensor;
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for (int j = 1 - win_length; j < win_length; j+=2)
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{
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window_tensor.push_back(1 - (0.5 * (1 - cos(CV_PI * j / (win_length - 1)))));
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}
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return window_tensor;
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}
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vector<vector<double>> stft_power(vector<double>& y)
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{
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// Short Time Fourier Transform. The STFT represents a signal in the time-frequency
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// domain by computing discrete Fourier transforms (DFT) over short overlapping windows.
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// https://en.wikipedia.org/wiki/Short-time_Fourier_transform
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// Pad the time series so that frames are centered
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vector<double> new_y;
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int num = int(n_fft / 2);
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for (int i = 0; i < num; ++i)
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{
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new_y.push_back(y[num - i]);
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}
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for (size_t i = 0; i < y.size(); ++i)
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{
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new_y.push_back(y[i]);
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}
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for (size_t i = y.size() - 2; i >= y.size() - num - 1; --i)
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{
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new_y.push_back(y[i]);
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}
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// Compute a window function
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vector<double> window_tensor = hanning();
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// Pad the window out to n_fft size
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vector<double> fft_window = pad_window_center(window_tensor, n_fft);
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// Window the time series
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vector<vector<double>> y_frames = frame(new_y);
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// Multiply on fft_window
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for (size_t i = 0; i < y_frames.size(); ++i)
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{
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for (size_t j = 0; j < y_frames[0].size(); ++j)
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{
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y_frames[i][j] *= fft_window[i];
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}
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}
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// Transpose frames for computing stft
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vector<vector<double>> y_frames_transpose(y_frames[0].size(), vector<double>(y_frames.size()));
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for (size_t i = 0; i < y_frames[0].size(); ++i)
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{
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for (size_t j = 0; j < y_frames.size(); ++j)
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{
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y_frames_transpose[i][j] = y_frames[j][i];
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}
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}
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// Short Time Fourier Transform
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// and get power of spectrum
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vector<vector<double>> spectrum_power(y_frames_transpose[0].size() / 2 + 1 );
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for (size_t i = 0; i < y_frames_transpose.size(); ++i)
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{
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Mat dstMat;
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dft(y_frames_transpose[i], dstMat, DFT_COMPLEX_OUTPUT);
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// we need only the first part of the spectrum, the second part is symmetrical
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for (int j = 0; j < static_cast<int>(y_frames_transpose[0].size()) / 2 + 1; ++j)
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{
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double power_re = dstMat.at<double>(2 * j) * dstMat.at<double>(2 * j);
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double power_im = dstMat.at<double>(2 * j + 1) * dstMat.at<double>(2 * j + 1);
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spectrum_power[j].push_back(power_re + power_im);
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}
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}
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return spectrum_power;
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}
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Mat calculate_features(vector<double>& x)
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{
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// Calculates filterbank features matrix.
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// Do preemphasis
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std::default_random_engine generator;
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std::normal_distribution<double> normal_distr(0, 1);
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double dither = 1e-5;
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for(size_t i = 0; i < x.size(); ++i)
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{
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x[i] += dither * static_cast<double>(normal_distr(generator));
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}
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double preemph = 0.97;
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for (size_t i = x.size() - 1; i > 0; --i)
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{
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x[i] -= preemph * x[i-1];
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}
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// Calculate Short Time Fourier Transform and get power of spectrum
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auto spectrum_power = stft_power(x);
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vector<vector<double>> filterbanks = mel(n_filt, lowfreq, highfreq);
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// Calculate log of multiplication of filterbanks matrix on spectrum_power matrix
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vector<vector<double>> x_stft(filterbanks.size(), vector<double>(spectrum_power[0].size(), 0));
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for (size_t i = 0; i < filterbanks.size(); ++i)
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{
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for (size_t j = 0; j < filterbanks[0].size(); ++j)
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{
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for (size_t k = 0; k < spectrum_power[0].size(); ++k)
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{
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x_stft[i][k] += filterbanks[i][j] * spectrum_power[j][k];
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}
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}
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for (size_t k = 0; k < spectrum_power[0].size(); ++k)
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{
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x_stft[i][k] = std::log(x_stft[i][k] + 1e-20);
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}
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}
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// normalize data
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auto elments_num = x_stft[0].size();
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for(size_t i = 0; i < x_stft.size(); ++i)
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{
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double x_mean = std::accumulate(x_stft[i].begin(), x_stft[i].end(), 0.) / elments_num; // arithmetic mean
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double x_std = 0; // standard deviation
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for(size_t j = 0; j < elments_num; ++j)
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{
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double subtract = x_stft[i][j] - x_mean;
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x_std += subtract * subtract;
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}
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x_std /= elments_num;
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x_std = sqrt(x_std) + 1e-10; // make sure x_std is not zero
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for(size_t j = 0; j < elments_num; ++j)
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{
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x_stft[i][j] = (x_stft[i][j] - x_mean) / x_std; // standard score
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}
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}
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Mat calculate_features(static_cast<int>(x_stft.size()), static_cast<int>(x_stft[0].size()), CV_32F);
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for(int i = 0; i < calculate_features.size[0]; ++i)
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{
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for(int j = 0; j < calculate_features.size[1]; ++j)
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{
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calculate_features.at<float>(i, j) = static_cast<float>(x_stft[i][j]);
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}
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}
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return calculate_features;
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}
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};
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class Decoder {
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// Used for decoding the output of jasper model
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private:
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unordered_map<int, char> labels_map = fillMap();
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int blank_id = 28;
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public:
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unordered_map<int, char> fillMap()
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{
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vector<char> labels={' ','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p'
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,'q','r','s','t','u','v','w','x','y','z','\''};
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unordered_map<int, char> map;
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for(int i = 0; i < static_cast<int>(labels.size()); ++i)
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{
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map[i] = labels[i];
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}
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return map;
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}
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string decode(Mat& x)
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{
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// Takes output of Jasper model and performs ctc decoding algorithm to
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// remove duplicates and special symbol. Returns prediction
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vector<int> prediction;
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for(int i = 0; i < x.size[1]; ++i)
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{
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double maxEl = -1e10;
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int ind = 0;
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for(int j = 0; j < x.size[2]; ++j)
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{
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if (maxEl <= x.at<float>(0, i, j))
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{
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maxEl = x.at<float>(0, i, j);
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ind = j;
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}
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}
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prediction.push_back(ind);
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}
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// CTC decoding procedure
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vector<double> decoded_prediction = {};
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int previous = blank_id;
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for(int i = 0; i < static_cast<int>(prediction.size()); ++i)
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{
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if (( prediction[i] != previous || previous == blank_id) && prediction[i] != blank_id)
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{
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decoded_prediction.push_back(prediction[i]);
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}
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previous = prediction[i];
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}
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string hypotheses = {};
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for(size_t i = 0; i < decoded_prediction.size(); ++i)
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{
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auto it = labels_map.find(static_cast<char>(decoded_prediction[i]));
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if (it != labels_map.end())
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hypotheses.push_back(it->second);
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}
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return hypotheses;
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}
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};
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static string predict(Mat& features, dnn::Net net, Decoder decoder)
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{
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// Passes the features through the Jasper model and decodes the output to english transcripts.
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// expand 2d features matrix to 3d
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vector<int> sizes = {1, static_cast<int>(features.size[0]),
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static_cast<int>(features.size[1])};
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features = features.reshape(0, sizes);
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// make prediction
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net.setInput(features);
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Mat output = net.forward();
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// decode output to transcript
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auto prediction = decoder.decode(output);
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return prediction;
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}
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static int readAudioFile(vector<double>& inputAudio, string file, int audioStream)
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{
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VideoCapture cap;
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int samplingRate = 16000;
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vector<int> params { CAP_PROP_AUDIO_STREAM, audioStream,
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CAP_PROP_VIDEO_STREAM, -1,
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CAP_PROP_AUDIO_DATA_DEPTH, CV_32F,
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CAP_PROP_AUDIO_SAMPLES_PER_SECOND, samplingRate
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};
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cap.open(file, CAP_ANY, params);
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if (!cap.isOpened())
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{
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cerr << "Error : Can't read audio file: '" << file << "' with audioStream = " << audioStream << endl;
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return -1;
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}
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const int audioBaseIndex = (int)cap.get(CAP_PROP_AUDIO_BASE_INDEX);
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vector<double> frameVec;
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Mat frame;
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for (;;)
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{
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if (cap.grab())
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{
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cap.retrieve(frame, audioBaseIndex);
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frameVec = frame;
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inputAudio.insert(inputAudio.end(), frameVec.begin(), frameVec.end());
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}
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else
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{
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break;
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}
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}
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return samplingRate;
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}
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static int readAudioMicrophone(vector<double>& inputAudio, int microTime)
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{
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VideoCapture cap;
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int samplingRate = 16000;
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vector<int> params { CAP_PROP_AUDIO_STREAM, 0,
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CAP_PROP_VIDEO_STREAM, -1,
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CAP_PROP_AUDIO_DATA_DEPTH, CV_32F,
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CAP_PROP_AUDIO_SAMPLES_PER_SECOND, samplingRate
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};
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cap.open(0, CAP_ANY, params);
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if (!cap.isOpened())
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{
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cerr << "Error: Can't open microphone" << endl;
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return -1;
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}
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const int audioBaseIndex = (int)cap.get(CAP_PROP_AUDIO_BASE_INDEX);
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vector<double> frameVec;
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Mat frame;
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if (microTime <= 0)
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{
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cerr << "Error: Duration of audio chunk must be > 0" << endl;
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return -1;
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}
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size_t sizeOfData = static_cast<size_t>(microTime * samplingRate);
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while (inputAudio.size() < sizeOfData)
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{
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if (cap.grab())
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{
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cap.retrieve(frame, audioBaseIndex);
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frameVec = frame;
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inputAudio.insert(inputAudio.end(), frameVec.begin(), frameVec.end());
|
|
}
|
|
else
|
|
{
|
|
cerr << "Error: Grab error" << endl;
|
|
break;
|
|
}
|
|
}
|
|
return samplingRate;
|
|
}
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
const String keys =
|
|
"{help h usage ? | | This script runs Jasper Speech recognition model }"
|
|
"{input_file i | | Path to input audio file. If not specified, microphone input will be used }"
|
|
"{audio_duration t | 15 | Duration of audio chunk to be captured from microphone }"
|
|
"{audio_stream a | 0 | CAP_PROP_AUDIO_STREAM value }"
|
|
"{show_spectrogram s | false | Show a spectrogram of the input audio: true / false / 1 / 0 }"
|
|
"{model m | jasper.onnx | Path to the onnx file of Jasper. You can download the converted onnx model "
|
|
"from https://drive.google.com/drive/folders/1wLtxyao4ItAg8tt4Sb63zt6qXzhcQoR6?usp=sharing}"
|
|
"{backend b | dnn::DNN_BACKEND_DEFAULT | Select a computation backend: "
|
|
"dnn::DNN_BACKEND_DEFAULT, "
|
|
"dnn::DNN_BACKEND_INFERENCE_ENGINE, "
|
|
"dnn::DNN_BACKEND_OPENCV }"
|
|
"{target t | dnn::DNN_TARGET_CPU | Select a target device: "
|
|
"dnn::DNN_TARGET_CPU, "
|
|
"dnn::DNN_TARGET_OPENCL, "
|
|
"dnn::DNN_TARGET_OPENCL_FP16 }"
|
|
;
|
|
CommandLineParser parser(argc, argv, keys);
|
|
if (parser.has("help"))
|
|
{
|
|
parser.printMessage();
|
|
return 0;
|
|
}
|
|
|
|
// Load Network
|
|
dnn::Net net = dnn::readNetFromONNX(parser.get<std::string>("model"));
|
|
net.setPreferableBackend(parser.get<int>("backend"));
|
|
net.setPreferableTarget(parser.get<int>("target"));
|
|
|
|
// Get audio
|
|
vector<double>inputAudio = {};
|
|
int samplingRate = 0;
|
|
if (parser.has("input_file"))
|
|
{
|
|
string audio = samples::findFile(parser.get<std::string>("input_file"));
|
|
samplingRate = readAudioFile(inputAudio, audio, parser.get<int>("audio_stream"));
|
|
}
|
|
else
|
|
{
|
|
samplingRate = readAudioMicrophone(inputAudio, parser.get<int>("audio_duration"));
|
|
}
|
|
|
|
if ((inputAudio.size() == 0) || samplingRate <= 0)
|
|
{
|
|
cerr << "Error: problems with audio reading, check input arguments" << endl;
|
|
return -1;
|
|
}
|
|
|
|
if (inputAudio.size() / samplingRate < 6)
|
|
{
|
|
cout << "Warning: For predictable network performance duration of audio must exceed 6 sec."
|
|
" Audio will be extended with zero samples" << endl;
|
|
for(int i = static_cast<int>(inputAudio.size()) - 1; i < samplingRate * 6; ++i)
|
|
{
|
|
inputAudio.push_back(0);
|
|
}
|
|
}
|
|
|
|
// Calculate features
|
|
FilterbankFeatures filter;
|
|
auto calculated_features = filter.calculate_features(inputAudio);
|
|
|
|
// Show spectogram if required
|
|
if (parser.get<bool>("show_spectrogram") == true)
|
|
{
|
|
Mat spectogram;
|
|
normalize(calculated_features, spectogram, 0, 255, NORM_MINMAX, CV_8U);
|
|
applyColorMap(spectogram, spectogram, COLORMAP_INFERNO);
|
|
imshow("spectogram", spectogram);
|
|
waitKey(0);
|
|
}
|
|
|
|
Decoder decoder;
|
|
string prediction = predict(calculated_features, net, decoder);
|
|
for( auto &transcript: prediction)
|
|
{
|
|
cout << transcript;
|
|
}
|
|
|
|
return 0;
|
|
}
|