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Diffstat (limited to 'python/pyarmnn/examples/speech_recognition/preprocess.py')
-rw-r--r-- | python/pyarmnn/examples/speech_recognition/preprocess.py | 260 |
1 files changed, 260 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/speech_recognition/preprocess.py b/python/pyarmnn/examples/speech_recognition/preprocess.py new file mode 100644 index 0000000000..553ddba5de --- /dev/null +++ b/python/pyarmnn/examples/speech_recognition/preprocess.py @@ -0,0 +1,260 @@ +# Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +# SPDX-License-Identifier: MIT + +"""Class used to extract the Mel-frequency cepstral coefficients from a given audio frame.""" + +import numpy as np + + +class MFCCParams: + def __init__(self, sampling_freq, num_fbank_bins, + mel_lo_freq, mel_hi_freq, num_mfcc_feats, frame_len, use_htk_method, n_FFT): + self.sampling_freq = sampling_freq + self.num_fbank_bins = num_fbank_bins + self.mel_lo_freq = mel_lo_freq + self.mel_hi_freq = mel_hi_freq + self.num_mfcc_feats = num_mfcc_feats + self.frame_len = frame_len + self.use_htk_method = use_htk_method + self.n_FFT = n_FFT + + +class MFCC: + + def __init__(self, mfcc_params): + self.mfcc_params = mfcc_params + self.FREQ_STEP = 200.0 / 3 + self.MIN_LOG_HZ = 1000.0 + self.MIN_LOG_MEL = self.MIN_LOG_HZ / self.FREQ_STEP + self.LOG_STEP = 1.8562979903656 / 27.0 + self.__frame_len_padded = int(2 ** (np.ceil((np.log(self.mfcc_params.frame_len) / np.log(2.0))))) + self.__filter_bank_initialised = False + self.__frame = np.zeros(self.__frame_len_padded) + self.__buffer = np.zeros(self.__frame_len_padded) + self.__filter_bank_filter_first = np.zeros(self.mfcc_params.num_fbank_bins) + self.__filter_bank_filter_last = np.zeros(self.mfcc_params.num_fbank_bins) + self.__mel_energies = np.zeros(self.mfcc_params.num_fbank_bins) + self.__dct_matrix = self.create_dct_matrix(self.mfcc_params.num_fbank_bins, self.mfcc_params.num_mfcc_feats) + self.__mel_filter_bank = self.create_mel_filter_bank() + self.__np_mel_bank = np.zeros([self.mfcc_params.num_fbank_bins, int(self.mfcc_params.n_FFT / 2) + 1]) + + for i in range(self.mfcc_params.num_fbank_bins): + k = 0 + for j in range(int(self.__filter_bank_filter_first[i]), int(self.__filter_bank_filter_last[i]) + 1): + self.__np_mel_bank[i, j] = self.__mel_filter_bank[i][k] + k += 1 + + def mel_scale(self, freq, use_htk_method): + """ + Gets the mel scale for a particular sample frequency. + + Args: + freq: The sampling frequency. + use_htk_method: Boolean to set whether to use HTK method or not. + + Returns: + the mel scale + """ + if use_htk_method: + return 1127.0 * np.log(1.0 + freq / 700.0) + else: + mel = freq / self.FREQ_STEP + + if freq >= self.MIN_LOG_HZ: + mel = self.MIN_LOG_MEL + np.log(freq / self.MIN_LOG_HZ) / self.LOG_STEP + return mel + + def inv_mel_scale(self, mel_freq, use_htk_method): + """ + Gets the sample frequency for a particular mel. + + Args: + mel_freq: The mel frequency. + use_htk_method: Boolean to set whether to use HTK method or not. + + Returns: + the sample frequency + """ + if use_htk_method: + return 700.0 * (np.exp(mel_freq / 1127.0) - 1.0) + else: + freq = self.FREQ_STEP * mel_freq + + if mel_freq >= self.MIN_LOG_MEL: + freq = self.MIN_LOG_HZ * np.exp(self.LOG_STEP * (mel_freq - self.MIN_LOG_MEL)) + return freq + + def mfcc_compute(self, audio_data): + """ + Extracts the MFCC for a single frame. + + Args: + audio_data: The audio data to process. + + Returns: + the MFCC features + """ + if len(audio_data) != self.mfcc_params.frame_len: + raise ValueError( + f"audio_data buffer size {len(audio_data)} does not match the frame length {self.mfcc_params.frame_len}") + + audio_data = np.array(audio_data) + spec = np.abs(np.fft.rfft(np.hanning(self.mfcc_params.n_FFT + 1)[0:self.mfcc_params.n_FFT] * audio_data, + self.mfcc_params.n_FFT)) ** 2 + mel_energy = np.dot(self.__np_mel_bank.astype(np.float32), + np.transpose(spec).astype(np.float32)) + + mel_energy += 1e-10 + log_mel_energy = 10.0 * np.log10(mel_energy) + top_db = 80.0 + + log_mel_energy = np.maximum(log_mel_energy, log_mel_energy.max() - top_db) + + mfcc_feats = np.dot(self.__dct_matrix, log_mel_energy) + + return mfcc_feats + + def create_dct_matrix(self, num_fbank_bins, num_mfcc_feats): + """ + Creates the Discrete Cosine Transform matrix to be used in the compute function. + + Args: + num_fbank_bins: The number of filter bank bins + num_mfcc_feats: the number of MFCC features + + Returns: + the DCT matrix + """ + dct_m = np.zeros(num_fbank_bins * num_mfcc_feats) + for k in range(num_mfcc_feats): + for n in range(num_fbank_bins): + if k == 0: + dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (4 * num_fbank_bins)) * np.cos( + (np.pi / num_fbank_bins) * (n + 0.5) * k) + else: + dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (2 * num_fbank_bins)) * np.cos( + (np.pi / num_fbank_bins) * (n + 0.5) * k) + + dct_m = np.reshape(dct_m, [self.mfcc_params.num_mfcc_feats, self.mfcc_params.num_fbank_bins]) + return dct_m + + def create_mel_filter_bank(self): + """ + Creates the Mel filter bank. + + Returns: + the mel filter bank + """ + num_fft_bins = int(self.__frame_len_padded / 2) + fft_bin_width = self.mfcc_params.sampling_freq / self.__frame_len_padded + + mel_low_freq = self.mel_scale(self.mfcc_params.mel_lo_freq, False) + mel_high_freq = self.mel_scale(self.mfcc_params.mel_hi_freq, False) + mel_freq_delta = (mel_high_freq - mel_low_freq) / (self.mfcc_params.num_fbank_bins + 1) + + this_bin = np.zeros(num_fft_bins) + mel_fbank = [0] * self.mfcc_params.num_fbank_bins + + for bin_num in range(self.mfcc_params.num_fbank_bins): + left_mel = mel_low_freq + bin_num * mel_freq_delta + center_mel = mel_low_freq + (bin_num + 1) * mel_freq_delta + right_mel = mel_low_freq + (bin_num + 2) * mel_freq_delta + first_index = last_index = -1 + + for i in range(num_fft_bins): + freq = (fft_bin_width * i) + mel = self.mel_scale(freq, False) + this_bin[i] = 0.0 + + if (mel > left_mel) and (mel < right_mel): + if mel <= center_mel: + weight = (mel - left_mel) / (center_mel - left_mel) + else: + weight = (right_mel - mel) / (right_mel - center_mel) + + enorm = 2.0 / (self.inv_mel_scale(right_mel, False) - self.inv_mel_scale(left_mel, False)) + weight *= enorm + this_bin[i] = weight + + if first_index == -1: + first_index = i + last_index = i + + self.__filter_bank_filter_first[bin_num] = first_index + self.__filter_bank_filter_last[bin_num] = last_index + mel_fbank[bin_num] = np.zeros(last_index - first_index + 1) + j = 0 + + for i in range(first_index, last_index + 1): + mel_fbank[bin_num][j] = this_bin[i] + j += 1 + + return mel_fbank + + +class Preprocessor: + + def __init__(self, mfcc, model_input_size, stride): + self.model_input_size = model_input_size + self.stride = stride + + # Savitzky - Golay differential filters + self.__savgol_order1_coeffs = np.array([6.66666667e-02, 5.00000000e-02, 3.33333333e-02, + 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, + -3.33333333e-02, -5.00000000e-02, -6.66666667e-02]) + + self.savgol_order2_coeffs = np.array([0.06060606, 0.01515152, -0.01731602, + -0.03679654, -0.04329004, -0.03679654, + -0.01731602, 0.01515152, 0.06060606]) + + self.__mfcc_calc = mfcc + + def __normalize(self, values): + """ + Normalize values to mean 0 and std 1 + """ + ret_val = (values - np.mean(values)) / np.std(values) + return ret_val + + def __get_features(self, features, mfcc_instance, audio_data): + idx = 0 + while len(features) < self.model_input_size * mfcc_instance.mfcc_params.num_mfcc_feats: + features.extend(mfcc_instance.mfcc_compute(audio_data[idx:idx + int(mfcc_instance.mfcc_params.frame_len)])) + idx += self.stride + + def extract_features(self, audio_data): + """ + Extracts the MFCC features, and calculates each features first and second order derivative. + The matrix returned should be sized appropriately for input to the model, based + on the model info specified in the MFCC instance. + + Args: + mfcc_instance: The instance of MFCC used for this calculation + audio_data: the audio data to be used for this calculation + Returns: + the derived MFCC feature vector, sized appropriately for inference + """ + + num_samples_per_inference = ((self.model_input_size - 1) + * self.stride) + self.__mfcc_calc.mfcc_params.frame_len + if len(audio_data) < num_samples_per_inference: + raise ValueError("audio_data size for feature extraction is smaller than " + "the expected number of samples needed for inference") + + features = [] + self.__get_features(features, self.__mfcc_calc, np.asarray(audio_data)) + features = np.reshape(np.array(features), (self.model_input_size, self.__mfcc_calc.mfcc_params.num_mfcc_feats)) + + mfcc_delta_np = np.zeros_like(features) + mfcc_delta2_np = np.zeros_like(features) + + for i in range(features.shape[1]): + idelta = np.convolve(features[:, i], self.__savgol_order1_coeffs, 'same') + mfcc_delta_np[:, i] = (idelta) + ideltadelta = np.convolve(features[:, i], self.savgol_order2_coeffs, 'same') + mfcc_delta2_np[:, i] = (ideltadelta) + + features = np.concatenate((self.__normalize(features), self.__normalize(mfcc_delta_np), + self.__normalize(mfcc_delta2_np)), axis=1) + + return np.float32(features) |